Search results for: geophysical database referenced navigation

Authors: Krupali Mukeshkumar, Jinesh Shah

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Background: Bilateral Sagittal Split Osteotomy (BSSO), used for the correction of mandibular deformities, is a common oral and maxillofacial surgical procedure. Inferior alveolar nerve dysfunction is commonly reported post-operatively by patients as paresthesia or anesthesia. The current literature lacks a consensus on the incidence of inferior alveolar nerve dysfunction as patients are not routinely assessed pre and post-operatively with an objective assessment. The range of incidence varies from 9% to 85% of patients, with some authors arguing that 100% of patients experience nerve dysfunction immediately post-surgery. Systematic reviews have shown a difference between incidence rates at different follow-up periods using objective and subjective methods. Aim: To identify the incidence of inferior alveolar nerve dysfunction following BSSO. Gold standard: Nerve dysfunction incidence rates similar or lower than current literature of 83% day one post-operatively and 18.4% at one year follow up. Setting: A retrospective cross-sectional audit of patients treated between 2017-2019 at the Royal Stoke University Hospital, Maxillofacial and Orthodontic departments. Sample: All patients who underwent a BSSO (with or without le fort one osteotomy) between 2017–2019 were identified from the database. Patients with pre-existing neurosensory disturbance, those who had a genioplasty at the same time and those with no follow-up were excluded. The sample consisted of 121 patients, 37 males and 84 females between the ages of 17-50 years at the time of surgery. Methods: Clinical records of 121 cases were reviewed to assess the age, sex, type of mandibular osteotomy, status of the nerve during the surgical procedure, type of bony split and incidence of nerve dysfunction at follow-up appointments. The surgical procedure was carried out by three Maxillo-facial surgeons and follow-up appointments were carried out in the Orthodontic and Oral and Maxillo-facial departments. Results: 120 patients were treated to correct the mandibular facial deformity and 1 patient was treated for sleep apnoea. Seventeen patients had a mandibular setback and 104 patients had mandibular advancement. 68 patients reported inferior alveolar nerve dysfunction at one week following their surgery. Seventy-six patients had temporary paresthesia present between 2 weeks and 12 months post-surgery. 13 patients had persistent nerve dysfunction at 12 months, of which 1 had a bad bony split during the BSSO. The incidence of nerve dysfunction postoperatively was 6.6% after 1 day, 56.1% at 1 week, 62.8% at 2 weeks, 59.5% between 3-6 weeks, 43.0% between 8-16 weeks and 10.7% at 1 year. Conclusions: The results of this audit show a similar incidence rate to the research gold standard at the one-year follow-up. Future Recommendations: No changes to surgical procedure or technique are indicated, but a need for improved documentation and a standardized approach for assessment of post-operative nerve dysfunction would be beneficial.

Keywords: bilateral sagittal split osteotomy, inferior alveolar nerve, mandible, nerve dysfunction

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Authors: Akbar Gharbali, Ali Abbasian Ardekani, Afshin Mohammadi

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Introduction: Thyroid nodules have an incidence of 33-68% in the general population. More than 5-15% of these nodules are malignant. Early detection and treatment of thyroid nodules increase the cure rate and provide optimal treatment. Between the medical imaging methods, Ultrasound is the chosen imaging technique for assessment of thyroid nodules. The confirming of the diagnosis usually demands repeated fine-needle aspiration biopsy (FNAB). So, current management has morbidity and non-zero mortality. Objective: To explore diagnostic potential of automatic texture analysis (TA) methods in differentiation benign and malignant thyroid nodules by ultrasound imaging in order to help for reliable diagnosis and monitoring of the thyroid nodules in their early stages with no need biopsy. Material and Methods: The thyroid US image database consists of 70 patients (26 benign and 44 malignant) which were reported by Radiologist and proven by the biopsy. Two slices per patient were loaded in Mazda Software version 4.6 for automatic texture analysis. Regions of interests (ROIs) were defined within the abnormal part of the thyroid nodules ultrasound images. Gray levels within an ROI normalized according to three normalization schemes: N1: default or original gray levels, N2: +/- 3 Sigma or dynamic intensity limited to µ+/- 3σ, and N3: present intensity limited to 1% - 99%. Up to 270 multiscale texture features parameters per ROIs per each normalization schemes were computed from well-known statistical methods employed in Mazda software. From the statistical point of view, all calculated texture features parameters are not useful for texture analysis. So, the features based on maximum Fisher coefficient and the minimum probability of classification error and average correlation coefficients (POE+ACC) eliminated to 10 best and most effective features per normalization schemes. We analyze this feature under two standardization states (standard (S) and non-standard (NS)) with Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA). The 1NN classifier was performed to distinguish between benign and malignant tumors. The confusion matrix and Receiver operating characteristic (ROC) curve analysis were used for the formulation of more reliable criteria of the performance of employed texture analysis methods. Results: The results demonstrated the influence of the normalization schemes and reduction methods on the effectiveness of the obtained features as a descriptor on discrimination power and classification results. The selected subset features under 1%-99% normalization, POE+ACC reduction and NDA texture analysis yielded a high discrimination performance with the area under the ROC curve (Az) of 0.9722, in distinguishing Benign from Malignant Thyroid Nodules which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Conclusions: Our results indicate computer-aided diagnosis is a reliable method, and can provide useful information to help radiologists in the detection and classification of benign and malignant thyroid nodules.

Keywords: ultrasound imaging, thyroid nodules, computer aided diagnosis, texture analysis, PCA, LDA, NDA

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Authors: Maria Reznakova, Michala Strnadova, Lukas Reznak

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Successful achievement of strategic goals requires an effective performance management system, i.e. determining the appropriate indicators measuring the rate of goal achievement. Assuming that the goal of the owners is to grow the assets they invested in, it is vital to identify the key performance indicators, which contribute to value creation. These indicators are known as value drivers. Based on the undertaken literature search, a value driver is defined as any factor that affects the value of an enterprise. The important factors are then monitored by both financial and non-financial indicators. Financial performance indicators are most useful in strategic management, since they indicate whether a company's strategy implementation and execution are contributing to bottom line improvement. Non-financial indicators are mainly used for short-term decisions. The identification of value drivers, however, is problematic for companies which are not publicly traded. Therefore financial ratios continue to be used to measure the performance of companies, despite their considerable criticism. The main drawback of such indicators is the fact that they are calculated based on accounting data, while accounting rules may differ considerably across different environments. For successful enterprise performance management it is vital to avoid factors that may reduce (or even destroy) its value. Among the known factors reducing the enterprise value are the lack of capital, lack of strategic management system and poor quality of production. In order to gain further insight into the topic, the paper presents results of the research identifying factors that adversely affect the performance of mechanical engineering enterprises in the Czech Republic. The research methodology focuses on both the qualitative and the quantitative aspect of the topic. The qualitative data were obtained from a questionnaire survey of the enterprises senior management, while the quantitative financial data were obtained from the Analysis Major Database for European Sources (AMADEUS). The questionnaire prompted managers to list factors which negatively affect business performance of their enterprises. The range of potential factors was based on a secondary research – analysis of previously undertaken questionnaire surveys and research of studies published in the scientific literature. The results of the survey were evaluated both in general, by average scores, and by detailed sub-analyses of additional criteria. These include the company specific characteristics, such as its size and ownership structure. The evaluation also included a comparison of the managers’ opinions and the performance of their enterprises – measured by return on equity and return on assets ratios. The comparisons were tested by a series of non-parametric tests of statistical significance. The results of the analyses show that the factors most detrimental to the enterprise performance include the incompetence of responsible employees and the disregard to the customers‘ requirements.

Keywords: business value, financial ratios, performance measurement, value drivers

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Siti Khairunniza Bejo

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Conventional methods for nutrient soil mapping are based on laboratory tests of samples that are obtained from surveys. The time and cost involved in gathering and analyzing soil samples are the reasons that researchers use Predictive Soil Mapping (PSM). PSM can be defined as the development of a numerical or statistical model of the relationship among environmental variables and soil properties, which is then applied to a geographic database to create a predictive map. Kriging is a group of geostatistical techniques to spatially interpolate point values at an unobserved location from observations of values at nearby locations. The main problem with using kriging as an interpolator is that it is excessively data-dependent and requires a large number of closely spaced data points. Hence, there is a need to minimize the number of data points without sacrificing the accuracy of the results. In this paper, an Artificial Neural Networks (ANN) scheme was used to predict macronutrient values at un-sampled points. ANN has become a popular tool for prediction as it eliminates certain difficulties in soil property prediction, such as non-linear relationships and non-normality. Back-propagation multilayer feed-forward network structures were used to predict nitrogen, phosphorous and potassium values in the soil of the study area. A limited number of samples were used in the training, validation and testing phases of ANN (pattern reconstruction structures) to classify soil properties and the trained network was used for prediction. The soil analysis results of samples collected from the soil survey of block C of Sawah Sempadan, Tanjung Karang rice irrigation project at Selangor of Malaysia were used. Soil maps were produced by the Kriging method using 236 samples (or values) that were a combination of actual values (obtained from real samples) and virtual values (neural network predicted values). For each macronutrient element, three types of maps were generated with 118 actual and 118 virtual values, 59 actual and 177 virtual values, and 30 actual and 206 virtual values, respectively. To evaluate the performance of the proposed method, for each macronutrient element, a base map using 236 actual samples and test maps using 118, 59 and 30 actual samples respectively produced by the Kriging method. A set of parameters was defined to measure the similarity of the maps that were generated with the proposed method, termed the sample reduction method. The results show that the maps that were generated through the sample reduction method were more accurate than the corresponding base maps produced through a smaller number of real samples. For example, nitrogen maps that were produced from 118, 59 and 30 real samples have 78%, 62%, 41% similarity, respectively with the base map (236 samples) and the sample reduction method increased similarity to 87%, 77%, 71%, respectively. Hence, this method can reduce the number of real samples and substitute ANN predictive samples to achieve the specified level of accuracy.

Keywords: artificial neural network, kriging, macro nutrient, pattern recognition, precision farming, soil mapping

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Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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Authors: Jack Nash, Chris Simpson, Holly Hurn, Ronel Terblanche, Alan Mistlin

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There is an increasing incidence of mild traumatic brain injury (mTBI) cases throughout sport and with this, a growing interest from governing bodies to ensure these are managed appropriately and player welfare is prioritised. The Berlin consensus statement on concussion in sport recommends a multidisciplinary approach when managing those patients who do not have full resolution of mTBI symptoms. There are as of yet no standardised guideline to follow in the treatment of complex cases mTBI in athletes. The aim of this project was to analyse the outcomes, both clinical and vocational, of all patients admitted to the mild Traumatic Brain Injury (mTBI) service at the UK’s Defence Military Rehabilitation Centre Headley Court between 1st June 2008 and 1st February 2017, as a result of a sport induced injury, and evaluate potential predictive indicators of outcome. Patients were identified from a database maintained by the mTBI service. Clinical and occupational outcomes were ascertained from medical and occupational employment records, recorded prospectively, at time of discharge from the mTBI service. Outcomes were graded based on the vocational independence scale (VIS) and clinical documentation at discharge. Predictive indicators including referral time, age at time of injury, previous mental health diagnosis and a financial claim in place at time of entry to service were assessed using logistic regression. 45 Patients were treated for sport-related mTBI during this time frame. Clinically 96% of patients had full resolution of their mTBI symptoms after input from the mTBI service. 51% of patients returned to work at their previous vocational level, 4% had ongoing mTBI symptoms, 22% had ongoing physical rehabilitation needs, 11% required mental health input and 11% required further vestibular rehabilitation. Neither age, time to referral, pre-existing mental health condition nor compensation seeking had a significant impact on either vocational or clinical outcome in this population. The vast majority of patients reviewed in the mTBI clinic had persistent symptoms which could not be managed in primary care. A consultant-led, multidisciplinary approach to the diagnosis and management of mTBI has resulted in excellent clinical outcomes in these complex cases. High levels of symptom resolution suggest that this referral and treatment pathway is successful and is a model which could be replicated in other organisations with consultant led input. Further understanding of both predictive and individual factors would allow clinicians to focus treatments on those who are most likely to develop long-term complications following mTBI. A consultant-led, multidisciplinary service ensures a large number of patients will have complete resolution of mTBI symptoms after sport-related mTBI. Further research is now required to ascertain the key predictive indicators of outcome following sport-related mTBI.

Keywords: brain injury, concussion, neurology, rehabilitation, sports injury

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Authors: Takahiro Nishimura, Kouki Doi, Hiroshi Fujimoto

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Touch-sensitive screens that serve as displays and input devices have been adopted in many portable terminals such as smartphones and personal media players, and the market of touch-sensitive screens has expanded greatly. One of the advantages of touch-sensitive screen is the flexibility in the graphical user interface (GUI) design, and it is imperative to design an appropriate GUI to realize an easy-to-use interface. Moreover, it is important to evaluate the relationship between pointing performance and GUI design. There is much knowledge regarding easy-to-use GUI designs for portable terminals with touch-sensitive screens, and most have focused on GUI design approaches for women or children with small hands. In contrast, GUI design approaches for users with large hands have not received sufficient attention. In this study, to obtain knowledge that contributes to the establishment of individualized easy-to-use GUI design guidelines, we conducted experiments to investigate the relationship between thumb length and pointing performance on portable terminals with touch-sensitive screens. In this study, fourteen college students who participated in the experiment were divided into two groups based on the length of their thumbs. Specifically, we categorized the participants into two groups, thumbs longer than 64.2 mm into L (Long) group, and thumbs longer than 57.4 mm but shorter than 64.2 mm into A (Average) group, based on Japanese anthropometric database. They took part in this study under the authorization of Waseda University’s ‘Ethics Review Committee on Research with Human Subjects’. We created an application for the experimental task and implemented it on the projected capacitive touch-sensitive screen portable terminal (iPod touch (4th generation)). The display size was 3.5 inch and 960 × 640 - pixel resolution at 326 ppi (pixels per inch). This terminal was selected as the experimental device, because of its wide use and market share. The operational procedure of the application is as follows. First, the participants placed their thumb on the start position. Then, one cross-shaped target in a 10 × 7 array of 70 positions appeared at random. The participants pointed the target with their thumb as accurately and as fast as possible. Then, they returned their thumb to the start position and waited. The operation ended when this procedure had been repeated until all 70 targets had each been pointed at once by the participants. We adopted the evaluation indices for absolute error, variable error, and pointing time to investigate pointing performance when using the portable terminal. The results showed that pointing performance varied with thumb length. In particular, on the lower right side of the screen, the performance of L group with long thumb was low. Further, we presented an approach for designing easy-to- use button GUI for users with long thumbs. The contributions of this study include revelation of the relationship between pointing performance and user’s thumb length when using a portable terminal in terms of accuracy, precision, and speed of pointing. We hope that these findings contribute to an easy-to-use GUI design for users with large hands.

Keywords: pointing performance, portable terminal, thumb length, touch-sensitive screen

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Authors: Aleksandra Zieminska-Stolarska, Ireneusz Zbicinski

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Introduction: The objective of the present work was to develop and validate a 3D CFD numerical model for simulating flow through 17 kilometers long dam reservoir of a complex bathymetry. In contrast to flowing waters, dam reservoirs were not emphasized in the early years of water quality modeling, as this issue has never been the major focus of urban development. Starting in the 1970s, however, it was recognized that natural and man-made lakes are equal, if not more important than estuaries and rivers from a recreational standpoint. The Sulejow Reservoir (Central Poland) was selected as the study area as representative of many lowland dam reservoirs and due availability of a large database of the ecological, hydrological and morphological parameters of the lake. Method: 3D, 2-phase and 1-phase CFD models were analysed to determine hydrodynamics in the Sulejow Reservoir. Development of 3D, 2-phase CFD model of flow requires a construction of mesh with millions of elements and overcome serious convergence problems. As 1-phase CFD model of flow in relation to 2-phase CFD model excludes from the simulations the dynamics of waves only, which should not change significantly water flow pattern for the case of lowland, dam reservoirs. In 1-phase CFD model, the phases (water-air) are separated by a plate which allows calculations of one phase (water) flow only. As the wind affects velocity of flow, to take into account the effect of the wind on hydrodynamics in 1-phase CFD model, the plate must move with speed and direction equal to the speed and direction of the upper water layer. To determine the velocity at which the plate will move on the water surface and interacts with the underlying layers of water and apply this value in 1-phase CFD model, the 2D, 2-phase model was elaborated. Result: Model was verified on the basis of the extensive flow measurements (StreamPro ADCP, USA). Excellent agreement (an average error less than 10%) between computed and measured velocity profiles was found. As a result of work, the following main conclusions can be presented: •The results indicate that the flow field in the Sulejow Reservoir is transient in nature, with swirl flows in the lower part of the lake. Recirculating zones, with the size of even half kilometer, may increase water retention time in this region •The results of simulations confirm the pronounced effect of the wind on the development of the water circulation zones in the reservoir which might affect the accumulation of nutrients in the epilimnion layer and result e.g. in the algae bloom. Conclusion: The resulting model is accurate and the methodology develop in the frame of this work can be applied to all types of storage reservoir configurations, characteristics, and hydrodynamics conditions. Large recirculating zones in the lake which increase water retention time and might affect the accumulation of nutrients were detected. Accurate CFD model of hydrodynamics in large water body could help in the development of forecast of water quality, especially in terms of eutrophication and water management of the big water bodies.

Keywords: CFD, mathematical modelling, dam reservoirs, hydrodynamics

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Authors: Papali Maqalika

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Lesotho farmers produce significant quantities of Merino wool of a quality competitive on the global market and make a substantial impact on the economy of Lesotho. However, even with the economic contribution, the production and quality information and trends of this fibre has been recognised nor documented. This is a sombre shortcoming as Lesotho wool is unknown on international markets. The situation is worsened by the fact that Lesotho wool is auction together with South African wool, trading and benchmarking Lesotho wool are difficult not to mention attempts to advance its production and quality. Based on the information above, available data on Lesotho wool for 10 years were collected and analysed for trends to used in benchmarking where applicable. The fibre properties analysed include fibre diameter (fineness), vegetable matter and yield, application and price. These were selected because they are fundamental in determining fibre quality and price. Production of wool in Lesotho has increased slightly over the ten years covered by this study. It also became apparent that production and quality trends of Lesotho wool are greatly influenced by the farming practices, breed of sheep and climatic conditions. Greater adoption of the merino sheep breed, sheds/barns and sheep coats are suggested as ways to reduce mortality rate (due to extremely cold temperatures), to reduce the vegetable matter on the fibre thus improving the quality and increase yield per sheep and production as a whole. Some farming practices such as the lack of barns, supplementary feeding and veterinary care present constraints in wool production. The districts in the Highlands region were found to have the highest production of mostly wool, this being ascribed to better pastures, climatic, social and other conditions conducive to wool production. The production of Lesotho wool and its quality can be improved further, possibly because of the interventions the Ministry of Agriculture introduced through the Small Agricultural and Development Project (SADP) and other appropriate initiatives by the National Wool and Mohair Growers Association (NWMGA). The challenge however, remains the lack of direct involvement of the wool growers (farmers) in decisions making and policy development, this potentially influences and may lead to the reluctance to adopt the strategies. In some cases, the wool growers do not receive the benefits associated with the interventions immediately. Based on these discoveries; it is recommended that the relevant educators and researchers in wool and textile science, as well as the local wool farmers in Lesotho, be represented in policy and other decision making forums relating to these interventions. In this way, educational campaigns and training workshops will be demand driven with a better chance of adoption and success. This is because the direct beneficiaries will have been involved at inception and they will have a sense of ownership as well as intent to see them through successfully.

Keywords: lesotho wool, wool quality, wool production, lesotho economy, global market, apparel wool, database, textile science, exports, animal farming practices, intimate apparel, interventions

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Authors: Ankan Roy, Niharika, Samir Kumar Patra

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Anomalous nexus of complex topological assemblies and spatiotemporal epigenetic choreography at chromosomal territory may forms the most sophisticated regulatory layer of gene expression in cancer. Colon cancer is one of the leading malignant neoplasms of the lower gastrointestinal tract worldwide. There is still a paucity of information about the complex molecular mechanisms of colonic cancerogenesis. Bioinformatics prediction and analysis helps to identify essential genes and significant pathways for monitoring and conquering this deadly disease. The present study investigates and explores potential hub genes as biomarkers and effective therapeutic targets for colon cancer treatment. Colon cancer patient sample containing gene expression profile datasets, such as GSE44076, GSE20916, and GSE37364 were downloaded from Gene Expression Omnibus (GEO) database and thoroughly screened using the GEO2R tool and Funrich software to find out common 2 differentially expressed genes (DEGs). Other approaches, including Gene Ontology (GO) and KEGG pathway analysis, Protein-Protein Interaction (PPI) network construction and hub gene investigation, Overall Survival (OS) analysis, gene correlation analysis, methylation pattern analysis, and hub gene-Transcription factors regulatory network construction, were performed and validated using various bioinformatics tool. Initially, we identified 166 DEGs, including 68 up-regulated and 98 down-regulated genes. Up-regulated genes are mainly associated with the Cytokine-cytokine receptor interaction, IL17 signaling pathway, ECM-receptor interaction, Focal adhesion and PI3K-Akt pathway. Downregulated genes are enriched in metabolic pathways, retinol metabolism, Steroid hormone biosynthesis, and bile secretion. From the protein-protein interaction network, thirty hub genes with high connectivity are selected using the MCODE and cytoHubba plugin. Survival analysis, expression validation, correlation analysis, and methylation pattern analysis were further verified using TCGA data. Finally, we predicted COL1A1, COL1A2, COL4A1, SPP1, SPARC, and THBS2 as potential master regulators in colonic cancerogenesis. Moreover, our experimental data highlights that disruption of lipid raft and RAS/MAPK signaling cascade affects this gene hub at mRNA level. We identified COL1A1, COL1A2, COL4A1, SPP1, SPARC, and THBS2 as determinant hub genes in colon cancer progression. They can be considered as biomarkers for diagnosis and promising therapeutic targets in colon cancer treatment. Additionally, our experimental data advertise that signaling pathway act as connecting link between membrane hub and gene hub.

Keywords: hub genes, colon cancer, DNA methylation, epigenetic engineering, bioinformatic predictions

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Authors: Felice Elefant, Akanksha Bhatnaghar, Keegan Krick, Elizabeth Heller

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Context: The severity of Alzheimer’s Disease (AD) progression involves an interplay of genetics, age, and environmental factors orchestrated by histone acetyltransferase (HAT) mediated neuroepigenetic mechanisms. While disruption of Tip60 HAT action in neural gene control is implicated in AD, alternative mechanisms underlying Tip60 function remain unexplored. Altered RNA splicing has recently been highlighted as a widespread hallmark in the AD transcriptome that is implicated in the disease. Research Aim: The aim of this study was to identify a novel RNA binding/splicing function for Tip60 in human hippocampus and impaired in brains from AD fly models and AD patients. Methodology/Analysis: The authors used RNA immunoprecipitation using RNA isolated from 200 pooled wild type Drosophila brains for each of the 3 biological replicates. To identify Tip60’s RNA targets, they performed genome sequencing (DNB-SequencingTM technology, BGI genomics) on 3 replicates for Input RNA and RNA IPs by Tip60. Findings: The authors' transcriptomic analysis of RNA bound to Tip60 by Tip60-RNA immunoprecipitation (RIP) revealed Tip60 RNA targets enriched for critical neuronal processes implicated in AD. Remarkably, 79% of Tip60’s RNA targets overlap with its chromatin gene targets, supporting a model by which Tip60 orchestrates bi-level transcriptional regulation at both the chromatin and RNA level, a function unprecedented for any HAT to date. Since RNA splicing occurs co-transcriptionally and splicing defects are implicated in AD, the authors investigated whether Tip60-RNA targeting modulates splicing decisions and if this function is altered in AD. Replicate multivariate analysis of transcript splicing (rMATS) analysis of RNA-Seq data sets from wild-type and AD fly brains revealed a multitude of mammalian-like AS defects. Strikingly, over half of these altered RNAs were bonafide Tip60-RNA targets enriched for in the AD-gene curated database, with some AS alterations prevented against by increasing Tip60 in fly brain. Importantly, human orthologs of several Tip60-modulated spliced genes in Drosophila are well characterized aberrantly spliced genes in human AD brains, implicating disruption of Tip60’s splicing function in AD pathogenesis. Theoretical Importance: The authors' findings support a novel RNA interaction and splicing regulatory function for Tip60 that may underlie AS impairments that hallmark AD etiology. Data Collection: The authors collected data from RNA immunoprecipitation experiments using RNA isolated from 200 pooled wild type Drosophila brains for each of the 3 biological replicates. They also performed genome sequencing (DNBSequencingTM technology, BGI genomics) on 3 replicates for Input RNA and RNA IPs by Tip60. Questions: The question addressed by this study was whether Tip60 has a novel RNA binding/splicing function in human hippocampus and whether this function is impaired in brains from AD fly models and AD patients. Conclusions: The authors' findings support a novel RNA interaction and splicing regulatory function for Tip60 that may underlie AS impairments that hallmark AD etiology.

Keywords: Alzheimer's disease, cognition, aging, neuroepigenetics

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Authors: Stefanie M. Vanneste, Stijn Goeminne

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This paper studies the effect of political characteristics of 308 Flemish municipalities on their budget balance in the period 1995-2011. All local governments experience the same economic and financial setting, however some governments have high budget balances, while others have low budget balances. The aim of this paper is to explain the differences in municipal budget balances by a number of economic, socio-demographic and political variables. The economic and socio-demographic variables will be used as control variables, while the focus of this paper will be on the political variables. We test four hypotheses resulting from the literature, namely (i) the partisan hypothesis tests if left wing governments have lower budget balances, (ii) the fragmentation hypothesis stating that more fragmented governments have lower budget balances, (iii) the hypothesis regarding the power of the government, higher powered governments would resolve in higher budget balances, and (iv) the opportunistic budget cycle to test whether politicians manipulate the economic situation before elections in order to maximize their reelection possibilities and therefore have lower budget balances before elections. The contributions of our paper to the existing literature are multiple. First, we use the whole array of political variables and not just a selection of them. Second, we are dealing with a homogeneous database with the same budget and election rules, making it easier to focus on the political factors without having to control for the impact of differences in the political systems. Third, our research extends the existing literature on Flemish municipalities as this is the first dynamic research on local budget balances. We use a dynamic panel data model. Because of the two lagged dependent variables as explanatory variables, we employ the system GMM (Generalized Method of Moments) estimator. This is the best possible estimator as we are dealing with political panel data that is rather persistent. Our empirical results show that the effect of the ideological position and the power of the coalition are of less importance to explain the budget balance. The political fragmentation of the government on the other hand has a negative and significant effect on the budget balance. The more parties in a coalition the worse the budget balance is ceteris paribus. Our results also provide evidence of an opportunistic budget cycle, the budget balances are lower in pre-election years relative to the other years to try and increase the incumbents reelection possibilities. An additional finding is that the incremental effect of the budget balance is very important and should not be ignored like is being done in a lot of empirical research. The coefficients of the lagged dependent variables are always positive and very significant. This proves that the budget balance is subject to incrementalism. It is not possible to change the entire policy from one year to another so the actions taken in recent past years still have an impact on the current budget balance. Only a relatively small amount of research concerning the budget balance takes this considerable incremental effect into account. Our findings survive several robustness checks.

Keywords: budget balance, fragmentation, ideology, incrementalism, municipalities, opportunistic budget cycle, panel data, political characteristics, power, system GMM

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Authors: Giulia Bonaldo, Alberto Vaccheri, Ottavio D'Annibali, Domenico Motola

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The Human Papilloma Virus (HPV) was shown to be the cause of different types of carcinomas, first of all of the cervical intraepithelial neoplasia. Since the early 80s to today, thanks first to the preventive screening campaigns (pap-test) and following to the introduction of HPV vaccines on the market; the number of new cases of cervical cancer has decreased significantly. The HPV vaccines currently approved are three: Cervarix® (HPV2 - virus type: 16 and 18), Gardasil® (HPV4 - 6, 11, 16, 18) and Gardasil 9® (HPV9 - 6, 11, 16, 18, 31, 33, 45, 52, 58), which all protect against the two high-risk HPVs (6, 11) that are mainly involved in cervical cancers. Despite the remarkable effectiveness of these vaccines has been demonstrated, in the recent years, there have been many complaints about their risk-benefit profile due to Adverse Events Following Immunization (AEFI). The purpose of this study is to provide a support about the ongoing discussion on the safety profile of HPV vaccines based on real life data deriving from spontaneous reports of suspected AEFIs collected in the Vaccine Adverse Events Reporting System (VAERS). VAERS is a freely-available national vaccine safety surveillance database of AEFI, co-administered by the Centers for Disease Control and Prevention (CDC) and Food and Drug Administration (FDA). We collected all the reports between January 2007 to December 2017 related to the HPV vaccines with a brand name (HPV2, HPV4, HPV9) or without (HPVX). A disproportionality analysis using Reporting Odds Ratio (ROR) with 95% confidence interval and p value ≤ 0.05 was performed. Over the 10-year period, 54889 reports of AEFI related to HPV vaccines reported in VAERS, corresponding to 224863 vaccine-event pairs, were retrieved. The highest number of reports was related to Gardasil (n = 42244), followed by Gardasil 9 (7212) and Cervarix (3904). The brand name of the HPV vaccine was not reported in 1529 cases. The two events more frequently reported and statistically significant for each vaccine were: dizziness (n = 5053) ROR = 1.28 (CI95% 1.24 – 1.31) and syncope (4808) ROR = 1.21 (1.17 – 1.25) for Gardasil. For Gardasil 9, injection site pain (305) ROR = 1.40 (1.25 – 1.57) and injection site erythema (297) ROR = 1.88 (1.67 – 2.10) and for Cervarix, headache (672) ROR = 1.14 (1.06 – 1.23) and loss of consciousness (528) ROR = 1.71 (1.57 – 1.87). In total, we collected 406 reports of death and 2461 cases of permanent disability in the ten-year period. The events consisting of incorrect vaccine storage or incorrect administration were not considered. The AEFI analysis showed that the most frequently reported events are non-serious and listed in the corresponding SmPCs. In addition to these, potential safety signals arose regarding less frequent and severe AEFIs that would deserve further investigation. This already happened with the referral of the European Medicines Agency (EMA) for the adverse events POTS (Postural Orthostatic Tachycardia Syndrome) and CRPS (Complex Regional Pain Syndrome) associated with anti-papillomavirus vaccines.

Keywords: adverse drug reactions, pharmacovigilance, safety, vaccines

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Authors: Austin P. Hopkins, R. Daren Harmel, Jim A Ippolito, P. J. A. Kleinman, D. Sahoo

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Field-scale runoff and water quality data are critical to understanding the fate and transport of nutrients applied to agricultural lands and minimizing their off-site transport because it is at that scale that agricultural management decisions are typically made based on hydrologic, soil, and land use factors. However, regional influences such as precipitation, temperature, and prevailing cropping systems and land use patterns also impact nutrient runoff. In the present study, the recently-updated MANAGE (Measured Annual Nutrient loads from Agricultural Environments) database was used to conduct an ecoregion-level analysis of nitrogen and phosphorus runoff from agricultural lands in the North America. Specifically, annual N and P runoff loads for cropland and grasslands in North American Level II EPA ecoregions were presented, and the impact of factors such as land use, tillage, and fertilizer timing and placement on N and P runoff were analyzed. Specifically we compiled annual N and P runoff load data (i.e., dissolved, particulate, and total N and P, kg/ha/yr) for each Level 2 EPA ecoregion and for various agricultural management practices (i.e., land use, tillage, fertilizer timing, fertilizer placement) within each ecoregion to showcase the analyses possible with the data in MANAGE. Potential differences in N and P runoff loads were evaluated between and within ecoregions with statistical and graphical approaches. Non-parametric analyses, mainly Mann-Whitney tests were conducted on median values weighted by the site years of data utilizing R because the data were not normally distributed, and we used Dunn tests and box and whisker plots to visually and statistically evaluate significant differences. Out of the 50 total North American Ecoregions, 11 were found that had significant data and site years to be utilized in the analysis. When examining ecoregions alone, it was observed that ER 9.2 temperate prairies had a significantly higher total N at 11.7 kg/ha/yr than ER 9.4 South Central Semi Arid Prairies with a total N of 2.4. When examining total P it was observed that ER 8.5 Mississippi Alluvial and Southeast USA Coastal Plains had a higher load at 3.0 kg/ha/yr than ER 8.2 Southeastern USA Plains with a load of 0.25 kg/ha/yr. Tillage and Land Use had severe impacts on nutrient loads. In ER 9.2 Temperate Prairies, conventional tillage had a total N load of 36.0 kg/ha/yr while conservation tillage had a total N load of 4.8 kg/ha/yr. In all relevant ecoregions, when corn was the predominant land use, total N levels significantly increased compared to grassland or other grains. In ER 8.4 Ozark-Ouachita, Corn had a total N of 22.1 kg/ha/yr while grazed grassland had a total N of 2.9 kg/ha/yr. There are further intricacies of the interactions that agricultural management practices have on one another combined with ecological conditions and their impacts on the continental aquatic nutrient loads that still need to be explored. This research provides a stepping stone to further understanding of land and resource stewardship and best management practices.

Keywords: water quality, ecoregions, nitrogen, phosphorus, agriculture, best management practices, land use

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Authors: Khethiwe Mtshali, Zamantungwa T. H. Khumalo, Stanford Kwenda, Ismail Arshad, Oriel M. M. Thekisoe

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Ecologically, the gut, mammary glands and bloodstream consist of distinct microbial communities of commensals, mutualists and pathogens, forming a complex ecosystem of niches. The by-products derived from these body sites i.e. faeces, milk and blood, respectively, have many uses in rural communities where they aid in the facilitation of day-to-day household activities and occasional rituals. Thus, although livestock rearing plays a vital role in the sustenance of the livelihoods of rural communities, it may serve as a potent reservoir of different pathogenic organisms that could have devastating health and economic implications. This study aimed to simultaneously explore the microbial profiles of corresponding faecal, milk and blood samples from lactating cows using 16S rRNA metagenomic sequencing. Bacterial communities were inferred through the Divisive Amplicon Denoising Algorithm 2 (DADA2) pipeline coupled with SILVA database v138. All downstream analyses were performed in R v3.6.1. Alpha-diversity metrics showed significant differences between faeces and blood, faeces and milk, but did not vary significantly between blood and milk (Kruskal-Wallis, P < 0.05). Beta-diversity metrics on Principal Coordinate Analysis (PCoA) and Non-Metric Dimensional Scaling (NMDS) clustered samples by type, suggesting that microbial communities of the studied niches are significantly different (PERMANOVA, P < 0.05). A number of taxa were significantly differentially abundant (DA) between groups based on the Wald test implemented in the DESeq2 package (Padj < 0.01). The majority of the DA taxa were significantly enriched in faeces than in milk and blood, except for the genus Anaplasma, which was significantly enriched in blood and was, in turn, the most abundant taxon overall. A total of 30 phyla, 74 classes, 156 orders, 243 families and 408 genera were obtained from the overall analysis. The most abundant phyla obtained between the three body sites were Firmicutes, Bacteroidota, and Proteobacteria. A total of 58 genus-level taxa were simultaneously detected between the sample groups, while bacterial signatures of at least 8 of these occurred concurrently in corresponding faeces, milk and blood samples from the same group of animals constituting a pool. The important taxa identified in this study could be categorized into four potentially pathogenic clusters: i) arthropod-borne; ii) food-borne and zoonotic; iii) mastitogenic and; iv) metritic and abortigenic. This study provides insight into the microbial composition of bovine faeces, milk, and blood and its extent of overlapping. It further highlights the potential risk of disease occurrence and transmission between the animals and the inhabitants of the sampled rural community, pertaining to their unsanitary practices associated with the use of cattle by-products.

Keywords: microbial profiling, 16S rRNA, NGS, feces, milk, blood, lactating cows, small-scale farmers

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Authors: Mohammad Karimi Moridani, Mohammad Abdi Zadeh, Zahra Shahiazar Mazraeh

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In recent years, the use of intelligent systems in biomedical engineering has increased dramatically, especially in the diagnosis of various diseases. Also, due to the relatively simple recording of the electrocardiogram signal (ECG), this signal is a good tool to show the function of the heart and diseases associated with it. The aim of this paper is to design an intelligent system for automatically detecting a normal electrocardiogram signal from abnormal one. Using this diagnostic system, it is possible to identify a person's heart condition in a very short time and with high accuracy. The data used in this article are from the Physionet database, available in 2016 for use by researchers to provide the best method for detecting normal signals from abnormalities. Data is of both genders and the data recording time varies between several seconds to several minutes. All data is also labeled normal or abnormal. Due to the low positional accuracy and ECG signal time limit and the similarity of the signal in some diseases with the normal signal, the heart rate variability (HRV) signal was used. Measuring and analyzing the heart rate variability with time to evaluate the activity of the heart and differentiating different types of heart failure from one another is of interest to the experts. In the preprocessing stage, after noise cancelation by the adaptive Kalman filter and extracting the R wave by the Pan and Tampkinz algorithm, R-R intervals were extracted and the HRV signal was generated. In the process of processing this paper, a new idea was presented that, in addition to using the statistical characteristics of the signal to create a return map and extraction of nonlinear characteristics of the HRV signal due to the nonlinear nature of the signal. Finally, the artificial neural networks widely used in the field of ECG signal processing as well as distinctive features were used to classify the normal signals from abnormal ones. To evaluate the efficiency of proposed classifiers in this paper, the area under curve ROC was used. The results of the simulation in the MATLAB environment showed that the AUC of the MLP and SVM neural network was 0.893 and 0.947, respectively. As well as, the results of the proposed algorithm in this paper indicated that the more use of nonlinear characteristics in normal signal classification of the patient showed better performance. Today, research is aimed at quantitatively analyzing the linear and non-linear or descriptive and random nature of the heart rate variability signal, because it has been shown that the amount of these properties can be used to indicate the health status of the individual's heart. The study of nonlinear behavior and dynamics of the heart's neural control system in the short and long-term provides new information on how the cardiovascular system functions, and has led to the development of research in this field. Given that the ECG signal contains important information and is one of the common tools used by physicians to diagnose heart disease, but due to the limited accuracy of time and the fact that some information about this signal is hidden from the viewpoint of physicians, the design of the intelligent system proposed in this paper can help physicians with greater speed and accuracy in the diagnosis of normal and patient individuals and can be used as a complementary system in the treatment centers.

Keywords: neart rate variability, signal processing, linear and non-linear features, classification methods, ROC Curve

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Authors: Olga Sukhoveeva

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Carbon dioxide (CO2) is the main component of carbon biogeochemical cycle and one of the most important greenhouse gases (GHG). Agriculture, particularly arable soils, are one the largest sources of GHG emission for the atmosphere including CO2.Models may be used for estimation of GHG emission from agriculture if they can be adapted for different countries conditions. The only model used in officially at national level in United Kingdom and China for this purpose is DNDC (DeNitrification-DeComposition). In our research, the model DNDC is offered for estimation of GHG emission from arable soils in Russia. The aim of our research was to create the method of DNDC using for evaluation of CO2 emission in Russia based on official statistical information. The target territory was European part of Russia where many field experiments are located. At the first step of research the database on climate, soil and cropping characteristics for the target region from governmental, statistical, and literature sources were created. All-Russia Research Institute of Hydrometeorological Information – World Data Centre provides open daily data about average meteorological and climatic conditions. It must be calculated spatial average values of maximum and minimum air temperature and precipitation over the region. Spatial average values of soil characteristics (soil texture, bulk density, pH, soil organic carbon content) can be determined on the base of Union state register of soil recourses of Russia. Cropping technologies are published by agricultural research institutes and departments. We offer to define cropping system parameters (annual information about crop yields, amount and types of fertilizers and manure) on the base of the Federal State Statistics Service data. Content of carbon in plant biomass may be calculated via formulas developed and published by Ministry of Natural Resources and Environment of the Russian Federation. At the second step CO2 emission from soil in this region were calculated by DNDC. Modelling data were compared with empirical and literature data and good results were obtained, modelled values were equivalent to the measured ones. It was revealed that the DNDC model may be used to evaluate and forecast the CO2 emission from arable soils in Russia based on the official statistical information. Also, it can be used for creation of the program for decreasing GHG emission from arable soils to the atmosphere. Financial Support: fundamental scientific researching theme 0148-2014-0005 No 01201352499 ‘Solution of fundamental problems of analysis and forecast of Earth climatic system condition’ for 2014-2020; fundamental research program of Presidium of RAS No 51 ‘Climate change: causes, risks, consequences, problems of adaptation and regulation’ for 2018-2020.

Keywords: arable soils, carbon dioxide emission, DNDC model, European Russia

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Authors: Lina Psarra, Manogj Sundaresan, Purjeet Sutar

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In response to the global imperative for sustainable solutions, this article proposes the development of a comprehensive circularity index applicable to a wide range of products across various industries. The absence of a consensus on using a universal metric to assess circularity performance presents a significant challenge in prioritizing and effectively managing sustainable initiatives. This circularity index serves as a quantitative measure to evaluate the adherence of products, processes, and systems to the principles of a circular economy. Unlike traditional distinct metrics such as recycling rates or material efficiency, this index considers the entire lifecycle of a product in one single metric, also incorporating additional factors such as reusability, scarcity of materials, reparability, and recyclability. Through a systematic approach and by reviewing existing metrics and past methodologies, this work aims to address this gap by formulating a circularity index that can be applied to diverse product portfolio and assist in comparing the circularity of products on a scale of 0%-100%. Project objectives include developing a formula, designing and implementing a pilot tool based on the developed Product Circularity Index (PCI), evaluating the effectiveness of the formula and tool using real product data, and assessing the feasibility of integration into various sustainability initiatives. The research methodology involves an iterative process of comprehensive research, analysis, and refinement where key steps include defining circularity parameters, collecting relevant product data, applying the developed formula, and testing the tool in a pilot phase to gather insights and make necessary adjustments. Major findings of the study indicate that the PCI provides a robust framework for evaluating product circularity across various dimensions. The Excel-based pilot tool demonstrated high accuracy and reliability in measuring circularity, and the database proved instrumental in supporting comprehensive assessments. The PCI facilitated the identification of key areas for improvement, enabling more informed decision-making towards circularity and benchmarking across different products, essentially assisting towards better resource management. In conclusion, the development of the Product Circularity Index represents a significant advancement in global sustainability efforts. By providing a standardized metric, the PCI empowers companies and stakeholders to systematically assess product circularity, track progress, identify improvement areas, and make informed decisions about resource management. This project contributes to the broader discourse on sustainable development by offering a practical approach to enhance circularity within industrial systems, thus paving the way towards a more resilient and sustainable future.

Keywords: circular economy, circular metrics, circularity assessment, circularity tool, sustainable product design, product circularity index

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Authors: Hisham Arab Alkabeya, A. M. Hughes, J. Adams

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Background: People with Rheumatoid Arthritis (RA) continue to experience problems with hand function despite new drug advances and targeted medical treatment. Consequently, it is important to identify the factors that influence the impact of RA disease on hand function. This systematic review identified observational studies that reported factors that influenced the impact of RA on hand function. Methods: MEDLINE, EMBASE, CINAL, AMED, PsychINFO, and Web of Science database were searched from January 1990 up to March 2017. Full-text articles published in English that described factors related to hand functional disability in people with RA were selected following predetermined inclusion and exclusion criteria. Pertinent data were thoroughly extracted and documented using a pre-designed data extraction form by the lead author, and cross-checked by the review team for completion and accuracy. Factors related to hand function were classified under the domains of the International Classification of Functioning, Disability, and Health (ICF) framework and health-related factors. Three reviewers independently assessed the methodological quality of the included articles using the quality of cross-sectional studies (AXIS) tool. Factors related to hand function that was investigated in two or more studies were explored using a best-evidence synthesis. Results: Twenty articles form 19 studies met the inclusion criteria from 1,271 citations; all presented cross-sectional data (five high quality and 15 low quality studies), resulting in at best limited evidence in the best-evidence synthesis. For the factors classified under the ICF domains, the best-evidence synthesis indicates that there was a range of body structure and function factors that were related with hand functional disability. However, key factors were hand strength, disease activity, and pain intensity. Low functional status (physical, emotional and social) level was found to be related with limited hand function. For personal factors, there is limited evidence that gender is not related with hand function; whereas, conflicting evidence was found regarding the relationship between age and hand function. In the domain of environmental factors, there was limited evidence that work activity was not related with hand function. Regarding health-related factors, there was limited evidence that the level of the rheumatoid factor (RF) was not related to hand function. Finally, conflicting evidence was found regarding the relationship between hand function and disease duration and general health status. Conclusion: Studies focused on body structure and function factors, highlighting a lack of investigation into personal and environmental factors when considering the impact of RA on hand function. The level of evidence which exists was limited, but identified that modifiable factors such as grip or pinch strength, disease activity and pain are the most influential factors on hand function in people with RA. The review findings suggest that important personal and environmental factors that impact on hand function in people with RA are not yet considered or reported in clinical research. Well-designed longitudinal, preferably cohort, studies are now needed to better understand the causality between personal and environmental factors and hand functional disability in people with RA.

Keywords: factors, hand function, rheumatoid arthritis, systematic review

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Authors: Sundru Manjulata Devi, Prakash M. Halami

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The immemorial use of fermented foods from vegetables, dairy and other biological sources are of great demand in India because of their health benefits. However, the diversity of Lactobacillus plantarum group (LPG) of vegetable origin has not been revealed yet, particularly with reference to their probiotic functionalities. In the present study, the different species of probiotic Lactobacillus plantarum group (LPG) i.e., L. plantarum subsp. plantarum MTCC 5422 (from fermented cereals), L. plantarum subsp. argentoratensis FG16 (from fermented bamboo shoot) and L. paraplantarum MTCC 9483 (from fermented gundruk) (as characterized by multiplex recA PCR assay) were considered to investigate their relative expression of MUB domains of mub gene (mucin binding protein) by Real time PCR. Initially, the allelic variation in the mub gene was assessed and found to encode three different variants (Type I, II and III). All the three types had 8, 9 and 10 MUB domains respectively (as analysed by Pfam database) and were found to be responsible for adhesion of bacteria to the host intestinal epithelial cells. These domains either get inserted or deleted during speciation or evolutionary events and lead to divergence. The reverse transcriptase qPCR analysis with mubLPF1+R1 primer pair supported variation in amplicon sizes with 300, 500 and 700 bp among different LPG strains. The relative expression of these MUB domains significantly unregulated in the presence of 1% mucin in overnight grown cultures. Simultaneously, the mub gene expressed efficiently by 7 fold in the culture L. paraplantarum MTCC 9483 with 10 MUB domains. An increase in the expression levels for L. plantarum subsp. plantarum MTCC 5422 and L. plantarum subsp. argentoratensis FG16 (MCC 2974) with 9 and 8 repetitive domains was around 4 and 2 fold, respectively. The detection and expression of an integrase (int) gene in the upstream region of mub gene reveals the excision and integration of these repetitive domains. Concurrently, an in vitro adhesion assay to mucin and exclusion of pathogens (such as Listeria monocytogenes and Micrococcus leuteus) was investigated and observed that the L. paraplantarum MTCC 9483 with more adhesion domains has more ability to adhere to mucin and inhibited the growth of pathogens. The production and expression of plantaricin-like bacteriocin (plnNC8 type) in MTCC 9483 suggests the pathogen inhibition. Hence, the expression of MUB domains can act as potential biomarkers in the screening of a novel probiotic LPG strain with adherence property. The present study provides a platform for an easy, rapid, less time consuming, low-cost methodology for the detection of potential probiotic bacteria. It was known that the traditional practices followed in the preparation of fermented bamboo shoots/gundruk/cereals of Indian foods contain different kinds of neutraceuticals for functional food and novel compounds with health promoting factors. In future, a detailed study of these food products can add more nutritive value, consumption and suitable for commercialization.

Keywords: adhesion gene, fermented foods, MUB domains, probiotics

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Authors: Liya Abraham

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Eritrea is located along the Red Sea, north of the Horn of Africa, between Djibouti and Sudan and has a population of about 3.2 million as of 2010. It has six administrative regions; Anseba, Debub, Debubawi K’eyih Bahri, Gash-Barka, Ma'akel, and Semenawi K’eyih Bahri. Eritrea has got its independence in 1991 after 30 years war of liberation. The country is blessed with various medicinal flora and fauna, and marine and terrestrial biodiversity. Traditional Medicine (TM) has been an integral part of the Eritrean culture for centuries. So far, more than 19 TM modalities have been recognized, and are broadly categorized as; herbal, procedure-based and spiritual. Despite the availability of modern medicine to the majority of the population, TM is still widely practiced. The rationale behind widespread use is accessibility, affordability and cultural acceptability. Hence, TM is of great contribution to the Eritrean health care system. As a matter of fact, harnessing the potential contribution of effective and safe TM in order to attain Universal Health Coverage (UHC) has been emphasized in the WHO TM strategy 2014-2023. The Eritrean TM, however, was operating without regulation and reliable scientific justification behind its safety and efficacy. Thus, the Ministry of Health (MoH), in recognition of the role of TM in primary healthcare and safeguard public health, established a regulatory body for TM so-called as Traditional Medicine Unit (TMU) in 2012. The mission of the unit is to ensure rational TM use through an integrated health service delivery system and contribute to the country’s economic and social development. The unit has established its national TM policy in 2017. The activities of the unit are guided by the National TM Advisory Committee (TMAC), responsible for the provision of technical assistance and advisory role. Moreover, the Legal Framework and Code of Ethics and Practice which provide a legal basis for the regulation of TM have also been drafted. In recognition of the importance of TM research and development, the unit launched a nationwide TM survey in 2017 and had surveyed two zones (Gash-Barka and Debub). The findings of the survey were subjected to a research dissemination workshop and publication in international journals. Furthermore, TM-related adverse events reporting tool (Green Form) aiming to guide regulatory interventions and researches have been established by the unit, and ever since reports are flowing. The unit has also been offering training to THPs, pharmacy students and health care professionals regarding TM and its regulatory activities. In addition, as part of the establishment of the national medicinal plants' database and herbal monograph, more than 329 and 30 medicinal plants, have been compiled respectively. In conclusion, TM is still widely accepted and practiced in Eritrea. The TMU ever since its establishment is endeavoring to ensure the safety and efficacy of the TM, and its integration in the mainstream health service delivery system.

Keywords: efficacy, regulation, safety, traditional medicine, traditional medicine unit, universal health coverage

Procedia PDF Downloads 187

Authors: Niamh Herlihy, Helen Fischer, Rainer Sauerborn, Anneliese Depoux, Avner Bar-Hen, Antoine Flauhault, Stefanie Schütte

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In the recent decades, there has been an increase in the number of publications both in the scientific and grey literature on the potential health risks associated with climate change. Though interest in climate change and health is growing, there are still many gaps to adequately assess our future health needs in a warmer world. Generating a greater understanding of the health impacts of climate change could be a key step in inciting the changes necessary to decelerate global warming and to target new strategies to mitigate the consequences on health systems. A long term and broad overview of existing scientific literature in the field of climate change and health is currently missing in order to ensure that all priority areas are being adequately addressed. We conducted a scoping review of published peer-reviewed literature on climate change and health from two large databases, PubMed and Web of Science, between 1990 and 2015. A scoping review allowed for a broad analysis of this complex topic on a meta-level as opposed to a thematically refined literature review. A detailed search strategy including specific climate and health terminology was used to search the two databases. Inclusion and exclusion criteria were applied in order to capture the most relevant literature on the human health impact of climate change within the chosen timeframe. Two reviewers screened the papers independently and any differences arising were resolved by a third party. Data was extracted, categorized and coded both manually and using R software. Analytics and infographics were developed from results. There were 7269 articles identified between the two databases following the removal of duplicates. After screening of the articles by both reviewers 3751 were included. As expected, preliminary results indicate that the number of publications on the topic has increased over time. Geographically, the majority of publications address the impact of climate change and health in Europe and North America, This is particularly alarming given that countries in the Global South will bear the greatest health burden. Concerning health outcomes, infectious diseases, particularly dengue fever and other mosquito transmitted infections are the most frequently cited. We highlight research gaps in certain areas e.g climate migration and mental health issues. We are developing a database of the identified climate change and health publications and are compiling a report for publication and dissemination of the findings. As health is a major co-beneficiary to climate change mitigation strategies, our results may serve as a useful source of information for research funders and investors when considering future research needs as well as the cost-effectiveness of climate change strategies. This study is part of an interdisciplinary project called 4CHealth that confronts results of the research done on scientific, political and press literature to better understand how the knowledge on climate change and health circulates within those different fields and whether and how it is translated to real world change.

Keywords: climate change, health, review, mapping

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Authors: Jie Lin, Yiwen Pan, Li Zhang, Zhangyong Xia

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Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of lipids, immune cells, and extracellular matrix in the arterial walls. This pathological process can lead to the formation of plaques that can obstruct blood flow and trigger various cardiovascular diseases such as heart attack and stroke. The underlying molecular mechanisms still remain unclear, although many studies revealed the dysfunction of endothelial cells, recruitment and activation of monocytes and macrophages, and the production of pro-inflammatory cytokines and chemokines in atherosclerosis. This study aimed to identify hub genes involved in the progression of atherosclerosis and to analyze their biological function in silico, thereby enhancing our understanding of the disease’s molecular mechanisms. Through the analysis of microarray data, we examined the gene expression in media and neo-intima from plaques, as well as distant macroscopically intact tissue, across a cohort of 32 hypertensive patients. Initially, 112 differentially expressed genes (DEGs) were identified. Subsequent immune infiltration analysis indicated a predominant presence of 27 immune cell types in the atherosclerosis group, particularly noting an increase in monocytes and macrophages. In the Weighted gene co-expression network analysis (WGCNA), 10 modules with a minimum of 30 genes were defined as key modules, with blue, dark, Oliver green and sky-blue modules being the most significant. These modules corresponded respectively to monocyte, activated B cell, and activated CD4 T cell gene patterns, revealing a strong morphological-genetic correlation. From these three gene patterns (modules morphology), a total of 2509 key genes (Gene Significance >0.2, module membership>0.8) were extracted. Six hub genes (CD36, DPP4, HMOX1, PLA2G7, PLN2, and ACADL) were then identified by intersecting 2509 key genes, 102 DEGs with lipid-related genes from the Genecard database. The bio-functional analysis of six hub genes was estimated by a robust classifier with an area under the curve (AUC) of 0.873 in the ROC plot, indicating excellent efficacy in differentiating between the disease and control group. Moreover, PCA visualization demonstrated clear separation between the groups based on these six hub genes, suggesting their potential utility as classification features in predictive models. Protein-protein interaction (PPI) analysis highlighted DPP4 as the most interconnected gene. Within the constructed key gene-drug network, 462 drugs were predicted, with ursodeoxycholic acid (UDCA) being identified as a potential therapeutic agent for modulating DPP4 expression. In summary, our study identified critical hub genes implicated in the progression of atherosclerosis through comprehensive bioinformatic analyses. These findings not only advance our understanding of the disease but also pave the way for applying similar analytical frameworks and predictive models to other diseases, thereby broadening the potential for clinical applications and therapeutic discoveries.

Keywords: atherosclerosis, hub genes, drug prediction, bioinformatics

Procedia PDF Downloads 66

Authors: Vanessa H. S. Zago, Ana Maria H. de Avila, Paula P. Costa, Welington Corozolla, Liriam S. Teixeira, Eliana C. de Faria

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Objectives: The outcome of cardiovascular disease is affected by environment and climate. This study evaluated the possible relationships between climatic and environmental changes and the occurrence of biological rhythms in serum lipids and lipoproteins in a large population sample in the city of Campinas, State of Sao Paulo, Brazil. In addition, it determined the temporal variations of death due to atherosclerotic events in Campinas during the time window examined. Methods: A large 8-year retrospective study was carried out to evaluate the lipid profiles of individuals attended at the University of Campinas (Unicamp). The study population comprised 27.543 individuals of both sexes and of all ages. Normolipidemic and dyslipidemic individuals classified according to Brazilian guidelines on dyslipidemias, participated in the study. For the same period, the temperature, relative humidity and daily brightness records were obtained from the Centro de Pesquisas Meteorologicas e Climaticas Aplicadas a Agricultura/Unicamp and frequencies of death due to atherosclerotic events in Campinas were acquired from the Brazilian official database DATASUS, according to the International Classification of Diseases. Statistical analyses were performed using both Cosinor and ARIMA temporal analysis methods. For cross-correlation analysis between climatic and lipid parameters, cross-correlation functions were used. Results: Preliminary results indicated that rhythmicity was significant for LDL-C and HDL-C in the cases of both normolipidemic and dyslipidemic subjects (n =respectively 11.892 and 15.651 both measures increasing in the winter and decreasing in the summer). On the other hand, for dyslipidemic subjects triglycerides increased in summer and decreased in winter, in contrast to normolipidemic ones, in which triglycerides did not show rhythmicity. The number of deaths due to atherosclerotic events showed significant rhythmicity, with maximum and minimum frequencies in winter and summer, respectively. Cross-correlation analyzes showed that low humidity and temperature, higher thermal amplitude and dark cycles are associated with increased levels of LDL-C and HDL-C during winter. In contrast, TG showed moderate cross-correlations with temperature and minimum humidity in an inverse way: maximum temperature and humidity increased TG during the summer. Conclusions: This study showed a coincident rhythmicity between low temperatures and high concentrations of LDL-C and HDL-C and the number of deaths due to atherosclerotic cardiovascular events in individuals from the city of Campinas. The opposite behavior of cholesterol and TG suggest different physiological mechanisms in their metabolic modulation by climate parameters change. Thus, new analyses are underway to better elucidate these mechanisms, as well as variations in lipid concentrations in relation to climatic variations and their associations with atherosclerotic disease and death outcomes in Campinas.

Keywords: atherosclerosis, climatic variations, lipids and lipoproteins, associations

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Authors: Francesca Marsili

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The reliability assessment of existing structures with probabilistic methods is becoming an increasingly important and frequent engineering task. However probabilistic reliability methods are based on an exhaustive knowledge of the stochastic modeling of the variables involved in the assessment; at the moment standards for the modeling of variables are absent, representing an obstacle to the dissemination of probabilistic methods. The framework according to probability distribution functions (PDFs) are established is represented by the Bayesian statistics, which uses Bayes Theorem: a prior PDF for the considered parameter is established based on information derived from the design stage and qualitative judgments based on the engineer past experience; then, the prior model is updated with the results of investigation carried out on the considered structure, such as material testing, determination of action and structural properties. The application of Bayesian statistics arises two different kind of problems: 1. The results of the updating depend on the engineer previous experience; 2. The updating of the prior PDF can be performed only if the structure has been tested, and quantitative data that can be statistically manipulated have been collected; performing tests is always an expensive and time consuming operation; furthermore, if the considered structure is an ancient building, destructive tests could compromise its cultural value and therefore should be avoided. In order to solve those problems, an interesting research path is represented by investigating Artificial Intelligence (AI) techniques that can be useful for the automation of the modeling of variables and for the updating of material parameters without performing destructive tests. Among the others, one that raises particular attention in relation to the object of this study is constituted by Case-Based Reasoning (CBR). In this application, cases will be represented by existing buildings where material tests have already been carried out and an updated PDFs for the material mechanical parameters has been computed through a Bayesian analysis. Then each case will be composed by a qualitative description of the material under assessment and the posterior PDFs that describe its material properties. The problem that will be solved is the definition of PDFs for material parameters involved in the reliability assessment of the considered structure. A CBR system represent a good candi¬date in automating the modelling of variables because: 1. Engineers already draw an estimation of the material properties based on the experience collected during the assessment of similar structures, or based on similar cases collected in literature or in data-bases; 2. Material tests carried out on structure can be easily collected from laboratory database or from literature; 3. The system will provide the user of a reliable probabilistic description of the variables involved in the assessment that will also serve as a tool in support of the engineer’s qualitative judgments. Automated modeling of variables can help in spreading probabilistic reliability assessment of existing buildings in the common engineering practice, and target at the best intervention and further tests on the structure; CBR represents a technique which may help to achieve this.

Keywords: reliability assessment of existing buildings, Bayesian analysis, case-based reasoning, historical structures

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Authors: C. Silveira, A. Ascenso, J. Ferreira, A. I. Miranda, P. Tuccella, G. Curci

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Poor air quality is one of the main environmental causes of premature deaths worldwide, and mainly in cities, where the majority of the population lives. It is a consequence of successive land cover (LC) and use changes, as a result of the intensification of human activities. Knowing these landscape modifications in a comprehensive spatiotemporal dimension is, therefore, essential for understanding variations in air pollutant concentrations. In this sense, the use of air quality models is very useful to simulate the physical and chemical processes that affect the dispersion and reaction of chemical species into the atmosphere. However, the modelling performance should always be evaluated since the resolution of the input datasets largely dictates the reliability of the air quality outcomes. Among these data, the updated LC is an important parameter to be considered in atmospheric models, since it takes into account the Earth’s surface changes due to natural and anthropic actions, and regulates the exchanges of fluxes (emissions, heat, moisture, etc.) between the soil and the air. This work aims to evaluate the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), when different LC classifications are used as an input. The influence of two LC classifications was tested: i) the 24-classes USGS (United States Geological Survey) LC database included by default in the model, and the ii) CLC (Corine Land Cover) and specific high-resolution LC data for Portugal, reclassified according to the new USGS nomenclature (33-classes). Two distinct WRF-Chem simulations were carried out to assess the influence of the LC on air quality over Europe and Portugal, as a case study, for the year 2015, using the nesting technique over three simulation domains (25 km², 5 km² and 1 km² horizontal resolution). Based on the 33-classes LC approach, particular emphasis was attributed to Portugal, given the detail and higher LC spatial resolution (100 m x 100 m) than the CLC data (5000 m x 5000 m). As regards to the air quality, only the LC impacts on tropospheric ozone concentrations were evaluated, because ozone pollution episodes typically occur in Portugal, in particular during the spring/summer, and there are few research works relating to this pollutant with LC changes. The WRF-Chem results were validated by season and station typology using background measurements from the Portuguese air quality monitoring network. As expected, a better model performance was achieved in rural stations: moderate correlation (0.4 – 0.7), BIAS (10 – 21µg.m^-3) and RMSE (20 – 30 µg.m^-3), and where higher average ozone concentrations were estimated. Comparing both simulations, small differences grounded on the Leaf Area Index and air temperature values were found, although the high-resolution LC approach shows a slight enhancement in the model evaluation. This highlights the role of the LC on the exchange of atmospheric fluxes, and stresses the need to consider a high-resolution LC characterization combined with other detailed model inputs, such as the emission inventory, to improve air quality assessment.

Keywords: land use, spatial resolution, WRF-Chem, air quality assessment

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Authors: Koteswara Rao Pagolu, Raghava Rao Tamanam

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The present study was aimed to find out the prevalence of DD's in Visakhapatnam, one of the north coastal districts of Andhra Pradesh, India during a span of five years. A cross-sectional investigation was held at District early intervention center (DEIC), Visakhapatnam from 2016 to 2020. To identify the pattern and trend of different DD's including seasonal variations, a retrospective analysis of the health center's inpatient database for the past 5 years was done. Male and female children aged 2 months-18 years are included in the study with the prior permission of the concerned medical officer. The screening tool developed by the Ministry of health and family welfare, India, was used for the study. Among 26,423 cases, children with birth defects are 962, 2229 with deficiencies, 7516 with diseases, and 15716 with disabilities were admitted during the study period. From birth defects, congenital deafness occurred in large numbers with 22.66%, and neural tube defect observed in a small number of cases with 0.83% during the period. From the side of deficiencies, severe acute malnutrition has mostly occurred (66.80 %) and a small number of children were affected with goiter (1.70%). Among the diseases, dental carriers (67.97%) are mostly found and these cases were at peak during the years 2016 and 2019. From disabilities, children with vision impairment (20.55%) have mostly approached the center. Over the past 5 years, the admission rate of down's syndrome and congenital deafness cases showed a rising trend up to 2019 and then declined. Hearing impairment, motor delay, and learning disorder showed a steep rise and gradual decline trend, whereas severe anemia, vitamin-D deficiency, otitis media, reactive airway disease, and attention deficit hyperactivity disorder showed a declining trend. However, congenital heart diseases, dental caries, and vision impairment admission rates showed a zigzag pattern over the past 5 years. This center had inadequate diagnostic facilities related to genetic disease management. For advanced confirmation, the cases are referred to a district government hospital or private diagnostic laboratories in the city for genetic tests. Information regarding the overall burden and pattern of admissions in the health center is obtained by the review of DEIC records. Through this study, it is observed that the incidence of birth defects, as well as genetic disease burden, is high in the Visakhapatnam district. Hence there is a need for strengthening of management services for these diseases in this region.

Keywords: child health screening, developmental delays, district early intervention center, genetic disease management, infrastructural facility, Visakhapatnam district

Procedia PDF Downloads 213

Authors: Xiaoying Zhang

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Forest school – an outdoor educational experience that is undertaken in an outdoor environment with trees – becomes an emerging field of early childhood education recently. In China, the benefits of natural outdoor education to children and young people’s wellness have raised attention. Although different types of outdoor-based activities have been involved in some pre-school of China, few study and practice have been conducted in terms of the notion of forest school. To comprehend the impact of forest school for children and young people, this study aims to systematically review articles on the topic of forest school in preschool education from an ecological perspective, i.e. from individual level (e.g., behavior and mental health) to microsystem level (e.g., the relationship between teachers and children) to ecosystem level. Based on PRISMA framework flow, using the key words of “Forest School” and “Early Childhood Education” for searching in Web-of-science database, a total of 33 articles were identified. Sample participants of 13 studies were not preschool children, five studies were not on forest school theme, and two literature review articles were excluded for further analysis. Finally, 13 articles were eligible for thematic analysis. According to Bronfenbrenner's ecological systems theory, there are some fingdings, on the individual level, current forest school studies are concerned about the children behavioral experience in forest school, how these experience may relate to their achievement or to develop children’s wellbeing/wellness, and how this type of learning experience may enhance children’s self-awareness on risk and safety issues. On the microsystem/mesosystem level, this review indicated that pedagogical development for forest school, risk perception from teachers and parents, social development between peers, and adult’s role in the participation of forest school were concerned, explored and discussed most frequently. On the macrosystem, the conceptualization of forest school is the key theme. Different forms of presentation in various countries with diverse cultures could provide various models of forest school education. However, there was no study investigating forest school on an ecosystem level. As for the potential benefits of physical health and mental wellness that results from forest school, it informs us to reflect the system of preschool education from the ecological perspective for Chinese children. For instance, most Chinese kindergartens ignored the significance of natural outdoor activities for children. Preschool education in China is strongly oriented by primary school system, which means pre-school children are expected to be trained as primary school students to do different subjects, such as math. Hardly any kindergarteners provide the opportunities for children and young people to take risks in a natural environment like forest school does. However, merely copying forest school model for a Chinese preschool education system will be less effective. This review of different level concerns could inform us that the localization the idea of forest school to adapt to a Chinese political, educational and cultural background. More detailed results and profound discussions will be presented in the full paper.

Keywords: early childhood education, ecological system, education development prospects in China, forest school

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Authors: Kai Warsoenke, Maik Mackiewicz

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To achieve a high quality of assembled car body structures, tolerancing is used to ensure a geometric accuracy of the single car body parts. There are two main techniques to determine the required tolerances. The first is tolerance analysis which describes the influence of individually tolerated input values on a required target value. Second is tolerance synthesis to determine the location of individual tolerances to achieve a target value. Both techniques are based on classical statistical methods, which assume certain probability distributions. To ensure competitiveness in both saturated and dynamic markets, production processes in vehicle manufacturing must be flexible and efficient. The dimensional specifications selected for the individual body components and the resulting assemblies have a major influence of the quality of the process. For example, in the manufacturing of forming tools as operating equipment or in the higher level of car body assembly. As part of the metrological process monitoring, manufactured individual parts and assemblies are recorded and the measurement results are stored in databases. They serve as information for the temporary adjustment of the production processes and are interpreted by experts in order to derive suitable adjustments measures. In the production of forming tools, this means that time-consuming and costly changes of the tool surface have to be made, while in the body shop, uncertainties that are difficult to control result in cost-intensive rework. The stored measurement results are not used to intelligently design tolerances in future processes or to support temporary decisions based on real-world geometric data. They offer potential to extend the tolerancing methods through data analysis and machine learning models. The purpose of this paper is to examine real-world measurement data from individual car body components, as well as assemblies, in order to develop an approach for using the data in short-term actions and future projects. For this reason, the measurement data will be analyzed descriptively in the first step in order to characterize their behavior and to determine possible correlations. In the following, a database is created that is suitable for developing machine learning models. The objective is to create an intelligent way to determine the position and number of measurement points as well as the local tolerance range. For this a number of different model types are compared and evaluated. The models with the best result are used to optimize equally distributed measuring points on unknown car body part geometries and to assign tolerance ranges to them. The current results of this investigation are still in progress. However, there are areas of the car body parts which behave more sensitively compared to the overall part and indicate that intelligent tolerancing is useful here in order to design and control preceding and succeeding processes more efficiently.

Keywords: automotive production, machine learning, process optimization, smart tolerancing

Procedia PDF Downloads 116

Authors: Naiara Ozamiz Etcebarria, Sonia Ruiz De Azua Garcia, Agurtzane Ortiz Jauregi, Virginia Guillen Cañas

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Introduction: The most relevant competencies of a health professional are an adequate communication capacity, which will influence the satisfaction of professionals and patients, therapeutic compliance, conflict prevention, clinical outcomes´ improvement and efficiency of health services. The ability of Active listening , empathy, assertiveness and social skills, are important abilities to develop in all professions in which there is a relationship with other people. In the field of health, it is even more important to have adequate qualities so that the treatment with the patient will be adequate and satisfactory. We conducted a research with students of third year in the Degree of Medicine with the objectives: - to know how the active listening, empathy, assertiveness and social skills of students are. - to know if there are differences according to different demographic variables, such as sex, language, age, number of siblings and interest in the subject. Material and Methods: The students of the Third year in the Degree of Medicine (N = 212) participated voluntarily. Sociodemographic data were collected. Descriptive and comparative analysis of the averages of the students with respect to active listening, empathy, assertiveness and social skills were performed. Once the questionnaires were collected, they were entered into the SPSS 21 database. Four communicational aspects were evaluated: The active listening questionnaire, the TECA empathy questionnaire, the ACDA questionnaire and the EHS questionnaire Social Skills Scale. The active listening questionnaire assesses these factors: Listening without interruption and less contradiction, Listening with 100% attention, Listening beyond words, Listening encouraging the other to go deeper. The TECA questionnaire of cognitive and affective empathy evaluates: Adoption of perspectives, Emotional Comprehension, Emphasizing stress, Empathic joy. The EHS questionnaire Social Skills Scale: Self-expression in social situations, Defending one's own rights as a consumer, Expressing anger or dissatisfaction, Refusing to do and cutting interactions off, Making requests, Initiating positive interactions with the other sex. The ACDA questionnaire Assertiveness Assessment Scale evaluates self-assertiveness and heteroaservitivity. Applicability: To train these skills is so important for clinical practice of medical students and these capabilities that can be measured in a longitudinal way time. Ethical-legal aspects: The data were anonymous. The study was approved by the Ethics Committee. Results: The students of the Third year in the Degree of Medicine (34.4% Basque speakers and 65.6% Spanish speakers) with average age 20.93, (27.8% men and 72.2% women). There are no differences in social skills between men and women. The Basque speaker students of are more heteroactive (ACDA) than Spanish students. Active listening has a high correlation with social skills, especially with self-expression in social situations. Listening without interruption has a high correlation with self-expression in social situations and initiating positive interactions with the opposite sex. Adoption of perspectives presents a high correlation with auto- assertiveness. Emotional understanding presents a high correlation with positive interactions with the opposite sex. Empathic joy correlates with self-assertiveness, self-expression in social situations, and initiating positive interactions with the opposite sex.

Keywords: active listening, assertiveness, communicational skills, empathy, students of medicine

Procedia PDF Downloads 303