Search results for: pattern recognition receptor

Authors: Davendra S. Malik

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Bhimtal lake is located between 290 21’ N latitude and 790 24’ E longitude, at an elevation of 1332m above mean sea level in the Kumaun region of Uttarakhand of Indian subcontinent. The lake surface area is decreasing in water area, depth level in relation to ecological and biological characteristics due to climatic variations, invasive land use pattern, degraded forest zones and changed agriculture pattern in lake catchment basin. The present study is focused on long and short term effects of climate change on aquatic biodiversity and productivity of Bhimtal lake. The meteorological data of last fifteen years of Bhimtal lake catchment basin revealed that air temperature has been increased 1.5 to 2.1oC in summer, 0.2 to 0.8 C in winter, relative humidity increased 4 to 6% in summer and rainfall pattern changed erratically in rainy seasons. The surface water temperature of Bhimtal lake showed an increasing pattern as 0.8 to 2.6 C, pH value decreased 0.5 to 0.2 in winter and increased 0.4 to 0.6 in summer. Dissolved oxygen level in lake showed a decreasing trend as 0.7 to 0.4mg/l in winter months. The mesotrophic nature of Bhimtal lake is changing towards eutrophic conditions and contributed for decreasing biodiversity. The aquatic biodiversity of Bhimtal lake consisted mainly phytoplankton, zooplankton, benthos and fish species. In the present study, a total of 5 groups of phytoplankton, 3 groups of zooplankton, 11 groups of benthos and 15 fish species were recorded from Bhimtal lake. The comparative data of biodiversity of Bhimtal lake since January, 2000 indicated the changing pattern of phytoplankton biomass were decreasing as 1.99 and 1.08% of Chlorophyceae and Bacilleriophyceae families respectively. The biomass of Cynophyceae was increasing as 0.45% and contributing the algal blooms during summer season in lake. The biomass of zooplankton and benthos were found decreasing in winter season and increasing during summer season. The endemic fish species (18 no.) were found in year 2000-05, as while the fish species (15 no.) were recorded in present study. The relative fecundity of major fish species were observed decreasing trends during their breeding periods in lake. The natural and anthropogenic factors were identified as ecological threats for existing aquatic biodiversity of Bhimtal lake. The present research paper emphasized on the effect of changing pattern of different climatic variables on species composition, biomass of phytoplankton, zooplankton, benthos, and fishes in Bhimtal lake of Kumaun region. The present research data will be contributed significantly to assess the changing pattern of aquatic biodiversity and productivity of Bhimtal lake with different time scale.

Keywords: aquatic biodiversity, Bhimtal lake, climate change, lake ecology

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Authors: Stefan Gruden, Charlott Brunmark, Bo Holmqvist, Erwin D. Brenndorfer, Martin Johansson, Jian Liu, Ying Zhao, Niklas Axen, Moustapha Hassan

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Aim: The study was designed to evaluate the ability of the calcium sulfate-based NanoZolid® drug delivery technology to locally release the epidermal growth factor (EGF) protein while maintaining its biological activity. Methods: NanoZolid-formulated EGF protein labelled with a near-infrared dye (EGF-NIR) depots or EGF-NIR dissolved in PBS were injected subcutaneously into mice bearing EGF receptor (EGFR) positive human A549 lung cancer tumors inoculated subcutaneously. The release and biodistribution of the EGF-NIR were investigated in vivo longitudinally up to 96 hours post-administration, utilizing whole-body fluorescence imaging. In order to confirm the in vivo findings, histological analysis of tumor cryosections was performed to investigate EGF-NIR fluorescent signal and EGFR expression level by immunofluorescence labelling. Results: The in vivo fluorescence imaging showed a controlled release profile of the EGF-NIR loaded in the NanoZolid depots compared to free EGF-NIR. Histological analysis of the tumors further demonstrated a prevailing distribution of EGF-NIR in regions with high levels of EGFR expression. Conclusion: Calcium sulfate based depots can be used to formulate EGF while maintaining its biological activity, e.g., receptor binding capability. This may have good clinical potential for local delivery of biomolecules to enhance treatment efficacy and minimize systemic adverse effects.

Keywords: bioresorbable, calcium sulfate, controlled release, NanoZolid

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Ren Moses

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The process of classifying vehicles on a highway is hereby viewed as a pattern recognition problem in which connectionist techniques such as artificial neural networks (ANN) can be used to assign vehicles to their correct classes and hence to establish optimum axle spacing thresholds. In the United States, vehicles are typically classified into 13 classes using a methodology commonly referred to as “Scheme F”. In this research, the ANN model was developed, trained, and applied to field data of vehicles. The data comprised of three vehicular features—axle spacing, number of axles per vehicle, and overall vehicle weight. The ANN reduced the classification error rate from 9.5 percent to 6.2 percent when compared to an existing classification algorithm that is not ANN-based and which uses two vehicular features for classification, that is, axle spacing and number of axles. The inclusion of overall vehicle weight as a third classification variable further reduced the error rate from 6.2 percent to only 3.0 percent. The promising results from the neural networks were used to set up new thresholds that reduce classification error rate.

Keywords: artificial neural networks, vehicle classification, traffic flow, traffic analysis, and highway opera-tions

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Authors: S. Kherchaoui, A. Houacine

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This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.

Keywords: facial expression identification, curvelet coefficient, support vector machine (SVM), recognition system

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Authors: Mohammed Arif, Abdessalam Kifouche

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Speech is a very convenient way of communication between people and machines. It conveys information about the identity of the talker. Since speaker recognition technology is increasingly securing our everyday lives, the objective of this paper is to develop two automatic text-independent speaker verification systems (TI SV) using low-level spectral features and machine learning methods. (i) The first system is based on a support vector machine (SVM), which was widely used in voice signal processing with the aim of speaker recognition involving verifying the identity of the speaker based on its voice characteristics, and (ii) the second is based on Gaussian Mixture Model (GMM) and Universal Background Model (UBM) to combine different functions from different resources to implement the SVM based.

Keywords: speaker verification, text-independent, support vector machine, Gaussian mixture model, cepstral analysis

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Authors: Ingrid Argote, John Archila, Marcelo Becker

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In agriculture, intelligent systems applications have generated great advances in automating some of the processes in the production chain. In order to improve the efficiency of those systems is proposed a vision system to estimate the amount of fruits in sweet orange trees. This work presents a system proposal using capture of thermal images and fuzzy logic. A bibliographical review has been done to analyze the state-of-the-art of the different systems used in fruit recognition, and also the different applications of thermography in agricultural systems. The algorithm developed for this project uses the metrics of the fuzzines parameter to the contrast improvement and segmentation of the image, for the counting algorith m was used the Hough transform. In order to validate the proposed algorithm was created a bank of images of sweet orange Citrus (L.) Osbeck acquired in the Maringá Farm. The tests with the algorithm Indicated that the variation of the tree branch temperature and the fruit is not very high, Which makes the process of image segmentation using this differentiates, This Increases the amount of false positives in the fruit counting algorithm. Recognition of fruits isolated with the proposed algorithm present an overall accuracy of 90.5 % and grouped fruits. The accuracy was 81.3 %. The experiments show the need for a more suitable hardware to have a better recognition of small temperature changes in the image.

Keywords: Agricultural systems, Citrus, Fuzzy logic, Thermal images.

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Authors: Fariba Ebrahimi, Mehdi Ghorbani, Ali Salajegheh

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Comprehensively water management considers economic, environmental, technical and social and also sustainability of water resources for future generations. Grassland management implies cooperative approach and involves all stakeholders and also introduces issues to managers, decision and policy makers. Solving these issues needs integrated and system approach. According to the recognition of actors or key persons in necessary to apply cooperative management of Water. Therefore, based on stakeholder analysis and social network analysis can be used to demonstrate the most effective actors for environmental decisions. In this research, social powers according are specified to social network approach at Water utilizers’ level of Natural in Jirood catchment of Latian basin. In this paper, utilizers of water resources were recognized using field trips and then, trust and collaboration matrix produced using questionnaires. In the next step, degree centrality index were Examined. Finally, geometric position of each actor was illustrated in the network. The results of the research based on centrality index have a key role in recognition of cooperative management of Water in Jirood and also will help managers and planners of water in the case of recognition of social powers in order to organization and implementation of sustainable management of Water.

Keywords: social network analysis, water co-management, social power, centrality index, local stakeholders network, Jirood catchment

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Authors: Martha Serrano-Arias, Martha Frías-Armenta

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Significant results have been found both supporting universality of emotion recognition and cultural background influence. Thus, the aim of this research was to test a Mexican version of the MTSI in different cultures to find differences in their performance. The MTSI-Mx assesses through a scenario approach were subjects must evaluate real persons. Two target persons were used for the construction, a man (FS) and a woman (AD). The items were grouped in four variables: Picture, Video, and FS and AD scenarios. The test was applied to 201 students from Mexico and Germany. T-test for picture and FS scenario show no significance. Video and AD had a significance at the 5% level. Results show slight differences between cultures, although a more comprehensive research is needed to conclude which culture can perform better in this kind of assessments.

Keywords: emotion recognition, MTSI, social intelligence, transcultural study

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Authors: N. E. Gallagher, C. E. Neville, N. Lyner, J. Yarnell, C. C. Patterson, J. E. Gallacher, Y. Ben-Shlomo, A. Fehily, J. V. Woodside

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Hearing loss is highly prevalent in older people and can reduce quality of life substantially. Emerging research suggests that potentially modifiable risk factors, including risk factors previously related to cardiovascular disease risk, may be associated with a decreased or increased incidence of hearing loss. This has prompted investigation into the possibility that certain nutrients, foods or dietary patterns may also be associated with incidence of hearing loss. The aim of this study was to determine any associations between dietary patterns and hearing loss in men enrolled in the Caerphilly study. The Caerphilly prospective cohort study began in 1979-1983 with recruitment of 2512 men aged 45-59 years. Dietary data was collected using a self-administered, semi-quantitative, 56-item food frequency questionnaire (FFQ) at baseline (1979-1983), and 7-day weighed food intake (WI) in a 30% sub-sample, while pure-tone unaided audiometric threshold was assessed at 0.5, 1, 2 and 4 kHz, between 1984 and 1988. Principal components analysis (PCA) was carried out to determine a posteriori dietary patterns and multivariate linear and logistic regression models were used to examine associations with hearing level (pure tone average (PTA) of frequencies 0.5, 1, 2 and 4 kHz in decibels (dB)) for linear regression and with hearing loss (PTA>25dB) for logistic regression. Three dietary patterns were determined using PCA on the FFQ data- Traditional, Healthy, High sugar/Alcohol avoider. After adjustment for potential confounding factors, both linear and logistic regression analyses showed a significant and inverse association between the Healthy pattern and hearing loss (P<0.001) and linear regression analysis showed a significant association between the High sugar/Alcohol avoider pattern and hearing loss (P=0.04). Three similar dietary patterns were determined using PCA on the WI data- Traditional, Healthy, High sugar/Alcohol avoider. After adjustment for potential confounding factors, logistic regression analyses showed a significant and inverse association between the Healthy pattern and hearing loss (P=0.02) and a significant association between the Traditional pattern and hearing loss (P=0.04). A Healthy dietary pattern was found to be significantly inversely associated with hearing loss in middle-aged men in the Caerphilly study. Furthermore, a High sugar/Alcohol avoider pattern (FFQ) and a Traditional pattern (WI) were associated with poorer hearing levels. Consequently, the role of dietary factors in hearing loss remains to be fully established and warrants further investigation.

Keywords: ageing, diet, dietary patterns, hearing loss

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Authors: Martial Amou

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This study was carried out to assess the situation of households’ livelihood under a changing climate pattern in the Zio district of Togo, West Africa. The study examined three important aspects: (i) assessment of households’ livelihood situation under a changing climate pattern, (ii) farmers’ perception and understanding of local climate change, (iii) determinants of adaptation strategies undertaken in cropping pattern to climate change. To this end, secondary sources of data, and survey data collected from 235 farmers in four villages in the study area were used. Adapted conceptual framework from Sustainable Livelihood Framework of DFID, two steps Binary Logistic Regression Model and descriptive statistics were used in this study as methodological approaches. Based on Sustainable Livelihood Approach (SLA), various factors revolving around the livelihoods of the rural community were grouped into social, natural, physical, human, and financial capital. Thus, the study came up that households’ livelihood situation represented by the overall livelihood index in the study area (34%) is below the standard average households’ livelihood security index (50%). The natural capital was found as the poorest asset (13%) and this will severely affect the sustainability of livelihood in the long run. The result from descriptive statistics and the first step regression (selection model) indicated that most of the farmers in the study area have clear understanding of climate change even though they do not have any idea about greenhouse gases as the main cause behind the issue. From the second step regression (output model) result, education, farming experience, access to credit, access to extension services, cropland size, membership of a social group, distance to the nearest input market, were found to be the significant determinants of adaptation measures undertaken in cropping pattern by farmers in the study area. Based on the result of this study, recommendations are made to farmers, policy makers, institutions, and development service providers in order to better target interventions which build, promote or facilitate the adoption of adaptation measures with potential to build resilience to climate change and then improve rural livelihood.

Keywords: climate change, rural livelihood, cropping pattern, adaptation, Zio District

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Authors: Sakineh Abbasi Bourondaragh

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The aim of this research is effectiveness of integrative behavioral couples therapy on the communication patterns of couples applying for divorce. We selected (N=20) reports from Tabriz Family Judicial Complex (FJC) of couples which have conflict in their marital relationships. All of reports were released during 2012. First, they were randomly divided into two experimental and control groups and all the couples were given pre-test. They participated in twelve therapy sessions. Then the experimental group was exposed to an experimental intervention, but the control group was not received experimental intervention. The subjects were treated. At the end of treatment, a post-test was performed about subjects (each of two groups).The results showed that integrative behavioral couple therapy could increase and improve communication patterns. The findings also showed that integrative behavioral couples therapy had increased mutual constructive pattern and decreased demand/withdraw pattern and mutual avoidance pattern of CPQ sub-scale. Steady change indicator showed that the difference is clinically meaningful.

Keywords: integrative behavioral couple therapy, communication patterns, cognitive sciences, Family Judicial Complex

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Authors: Darshna Sharma, Suban K. Sahoo

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The detection of intracellular fluoride in human cancer cell HeLa was achieved by chemosensors derived from vitamin B6 cofactors using fluorescence imaging technique. These sensors were first synthesized by condensation of pyridoxal/pyridoxal phosphate with 2-amino(thio)phenol. The anion recognition ability was explored by experimental (UV-VIS, fluorescence and 1H NMR) and theoretical DFT [(B3LYP/6-31G(d,p)] methods in DMSO and mixed DMSO-H2O system. All the developed sensors showed both naked-eye detectable color change and remarkable fluorescence enhancement in the presence of F- and AcO-. The anion recognition was occurred through the formation of hydrogen bonded complexes between these anions and sensor, followed by the partial deprotonation of sensor. The detection limit of these sensors were down to micro(nano) molar level of F- and AcO-.

Keywords: chemosensors, fluoride, acetate, turn-on, live cells imaging, DFT

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Authors: Sung-Bok Choi, Hyun-Jun Shin, Hyoung-Kyu Song

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This paper proposes a single relay selection scheme in cooperative communication. Decode and forward scheme is considered when a source node wants to cooperate with a single relay for data transmission. To use the proposed single relay selection scheme, the source node make a little different pattern signal which is not complex pattern and broadcasts it. The proposed scheme does not require the channel state information between the source node and candidates of the relay during the relay selection. Therefore, it is able to be used in many fields.

Keywords: relay selection, cooperative communication, df, channel codes

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Authors: Yong-Sung Kim, Je-Seok Shin, Ho-Jun Jo, Jin-O Kim

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Home Energy Management System (HEMS) which makes the residential consumers contribute to the demand response is attracting attention in recent years. An aim of HEMS is to minimize their electricity cost by controlling the use of their appliances according to electricity price. The use of appliances in HEMS may be affected by some conditions such as external temperature and electricity price. Therefore, the user’s usage pattern of appliances should be modeled according to the external conditions, and the resultant usage pattern is related to the user’s comfortability on use of each appliances. This paper proposes a methodology to model the usage pattern based on the historical data with the copula function. Through copula function, the usage range of each appliance can be obtained and is able to satisfy the appropriate user’s comfort according to the external conditions for next day. Within the usage range, an optimal scheduling for appliances would be conducted so as to minimize an electricity cost with considering user’s comfort. Among the home appliance, electric heater (EH) is a representative appliance which is affected by the external temperature. In this paper, an optimal scheduling algorithm for an electric heater (EH) is addressed based on the method of branch and bound. As a result, scenarios for the EH usage are obtained according to user’s comfort levels and then the residential consumer would select the best scenario. The case study shows the effects of the proposed algorithm compared with the traditional operation of the EH, and it also represents impacts of the comfort level on the scheduling result.

Keywords: load scheduling, usage pattern, user’s comfort, copula function, branch and bound, electric heater

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Ajay Kadam, Ramesh Kagalkar

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The proposed research objective is to add to a framework for programmed recognition of sound. In this framework the real errand is to distinguish any information sound stream investigate it & anticipate the likelihood of diverse sounds show up in it. To create and industrially conveyed an adaptable sound web crawler a flexible sound search engine. The calculation is clamor and contortion safe, computationally productive, and hugely adaptable, equipped for rapidly recognizing a short portion of sound stream caught through a phone microphone in the presence of frontal area voices and other predominant commotion, and through voice codec pressure, out of a database of over accessible tracks. The algorithm utilizes a combinatorial hashed time-recurrence group of stars examination of the sound, yielding ordinary properties, for example, transparency, in which numerous tracks combined may each be distinguished.

Keywords: fingerprinting, pure tone, white noise, hash function

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Authors: Elragig Aiman, Dreiwi Hanan, Townley Stuart, Elmabrook Idriss

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In this paper, we reconsider the analysis of the Oregonator model. We highlight an error in this analysis which leads to an incorrect depiction of the parameter region in which diffusion driven instability is possible. We believe that the cause of the oversight is the complexity of stability analyses based on eigenvalues and the dependence on parameters of matrix minors appearing in stability calculations. We regenerate the parameter space where Turing patterns can be seen, and we use the common Lyapunov function (CLF) approach, which is numerically reliable, to further confirm the dependence of the results on diffusion coefficients intensities.

Keywords: diffusion driven instability, common Lyapunov function (CLF), turing pattern, positive-definite matrix

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Authors: K. A. Gladkova, N. P. Babushkina, E. Y. Bragina

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Purpose: Tuberculosis (TB), caused by Mycobacterium tuberculosis, is one of the major public health problems worldwide. It is clear that the immune response to M. tuberculosis infection is a relationship between inflammatory and anti-inflammatory responses in which Tumour Necrosis Factor-α (TNF-α) plays key roles as a pro-inflammatory cytokine. TNF-α involved in various cell immune responses via binding to its two types of membrane-bound receptors, TNFRSF1A and TNFRSF1B. Importantly, some variants of the TNFRSF1B gene have been considered as possible markers of host susceptibility to TB. However, the possible impact of such TNF-α and its receptor genes polymorphism on TB cases in Tomsk is missing. Thus, the purpose of our study was to investigate polymorphism of TNF-α (rs1800629) and its receptor TNFRSF1B (rs652625 and rs525891) genes in population of Tomsk and to evaluate their possible association with the development of pulmonary TB. Materials and Methods: The population distribution features of genes polymorphisms were investigated and made case-control study based on group of people from Tomsk. Human blood was collected during routine patients examination at Tomsk Regional TB Dispensary. Altogether, 234 TB-positive patients (80 women, 154 men, average age is 28 years old) and 205 health-controls (153 women, 52 men, average age is 47 years old) were investigated. DNA was extracted from blood plasma by phenol-chloroform method. Genotyping was carried out by a single-nucleotide-specific real-time PCR assay. Results: First, interpopulational comparison was carried out between healthy individuals from Tomsk and available data from the 1000 Genomes project. It was found that polymorphism rs1800629 region demonstrated that Tomsk population was significantly different from Japanese (P = 0.0007), but it was similar with the following Europeans subpopulations: Italians (P = 0.052), Finns (P = 0.124) and British (P = 0.910). Polymorphism rs525891 clear demonstrated that group from Tomsk was significantly different from population of South Africa (P = 0.019). However, rs652625 demonstrated significant differences from Asian population: Chinese (P = 0.03) and Japanese (P = 0.004). Next, we have compared healthy individuals versus patients with TB. It was detected that no association between rs1800629, rs652625 polymorphisms, and positive TB cases. Importantly, AT genotype of polymorphism rs525891 was significantly associated with resistance to TB (odds ratio (OR) = 0.61; 95% confidence interval (CI): 0.41-0.9; P < 0.05). Conclusion: To the best of our knowledge, the polymorphism of TNFRSF1B (rs525891) was associated with TB, while genotype AT is protective [OR = 0.61] in Tomsk population. In contrast, no significant correlation was detected between polymorphism TNF-α (rs1800629) and TNFRSF1B (rs652625) genes and alveolar TB cases among population of Tomsk. In conclusion, our data expands the molecular particularities associated with TB. The study was supported by the grant of the Russia for Basic Research #15-04-05852.

Keywords: polymorphism, tuberculosis, TNF-α, TNFRSF1B gene

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Authors: Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

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This study investigates the task of identifying musical instruments in polyphonic compositions using Convolutional Neural Networks (CNNs) from spectrogram inputs, focusing on binary classification. The model showed promising results, with an accuracy of 97% on solo instrument recognition. When applied to polyphonic combinations of 1 to 10 instruments, the overall accuracy was 64%, reflecting the increasing challenge with larger ensembles. These findings contribute to the field of Music Information Retrieval (MIR) by highlighting the potential and limitations of current approaches in handling complex musical arrangements. Future work aims to include a broader range of musical sounds, including electronic and synthetic sounds, to improve the model's robustness and applicability in real-time MIR systems.

Keywords: binary classifier, CNN, spectrogram, instrument

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Authors: Rahul Magazine, Shobitha Rao

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The aim of this study was to study the prescribing pattern of drugs in patients with ARDS (Acute Respiratory Distress Syndrome) managed at a tertiary care hospital. This observational study was conducted at Kasturba Hospital, Karnataka, India. Data of patients admitted from January 2010 to December 2012 was collected. A total of 150 patients of ARDS were included. Data included patients’ age, gender, clinical disorders precipitating ARDS, and prescribing pattern of drugs. The mean age of the study population was 42.92±13.91 years. 48% of patients were less than 40 years of age. Infection was the cause of ARDS in 81.3% of subjects. Antibiotics were prescribed in all the subjects and beta-lactams were prescribed in 97.3%. 41.3% were prescribed corticosteroids, 39.3% diuretics and 89.3% intravenous fluids. Infection was the commonest etiology for ARDS, and beta-lactams were the commonest antibiotics prescribed. Corticosteroids and diuretics were prescribed in a significant number of patients. Most of the patients received intravenous fluids.

Keywords: acute respiratory syndrome, beta lactams, corticosteroids, Acute Respiratory Distress Syndrome (ARDS)

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Authors: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan

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We propose to record Activities of Daily Living (ADLs) of elderly people using a vision-based system so as to provide better assistive and personalization technologies. Current ADL-related research is based on data collected with help from non-elderly subjects in laboratory environments and the activities performed are predetermined for the sole purpose of data collection. To obtain more realistic datasets for the application, we recorded ADLs for the elderly with data collected from real-world environment involving real elderly subjects. Motivated by the need to collect data for more effective research related to elderly care, we chose to collect data in the room of an elderly person. Specifically, we installed Kinect, a vision-based sensor on the ceiling, to capture the activities that the elderly subject performs in the morning every day. Based on the data, we identified 12 morning activities that the elderly person performs daily. To recognize these activities, we created a HARELCARE framework to investigate into the effectiveness of existing Human Activity Recognition (HAR) algorithms and propose the use of a transfer learning algorithm for HAR. We compared the performance, in terms of accuracy, and training progress. Although the collected dataset is relatively small, the proposed algorithm has a good potential to be applied to all daily routine activities for healthcare purposes such as evidence-based diagnosis and treatment.

Keywords: daily activity recognition, healthcare, IoT sensors, transfer learning

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Authors: Atsuko Okazaki, Jurg Ott

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Some genetic diseases (‘digenic traits’) are due to the interaction between two DNA variants. For example, certain forms of Retinitis Pigmentosa (a genetic form of blindness) occur in the presence of two mutant variants, one in the ROM1 gene and one in the RDS gene, while the occurrence of only one of these mutant variants leads to a completely normal phenotype. Detecting such digenic traits by genetic methods is difficult. A common approach to finding disease-causing variants is to compare 100,000s of variants between individuals with a trait (cases) and those without the trait (controls). Such genome-wide association studies (GWASs) have been very successful but hinge on genetic effects of single variants, that is, there should be a difference in allele or genotype frequencies between cases and controls at a disease-causing variant. Frequent pattern mining (FPM) methods offer an avenue at detecting digenic traits even in the absence of single-variant effects. The idea is to enumerate pairs of genotypes (genotype patterns) with each of the two genotypes originating from different variants that may be located at very different genomic positions. What is needed is for genotype patterns to be significantly more common in cases than in controls. Let Y = 2 refer to cases and Y = 1 to controls, with X denoting a specific genotype pattern. We are seeking association rules, ‘X → Y’, with high confidence, P(Y = 2|X), significantly higher than the proportion of cases, P(Y = 2) in the study. Clearly, generally available FPM methods are very suitable for detecting disease-associated genotype patterns. We use fpgrowth as the basic FPM algorithm and built a framework around it to enumerate high-frequency digenic genotype patterns and to evaluate their statistical significance by permutation analysis. Application to a published dataset on opioid dependence furnished results that could not be found with classical GWAS methodology. There were 143 cases and 153 healthy controls, each genotyped for 82 variants in eight genes of the opioid system. The aim was to find out whether any of these variants were disease-associated. The single-variant analysis did not lead to significant results. Application of our FPM implementation resulted in one significant (p < 0.01) genotype pattern with both genotypes in the pattern being heterozygous and originating from two variants on different chromosomes. This pattern occurred in 14 cases and none of the controls. Thus, the pattern seems quite specific to this form of substance abuse and is also rather predictive of disease. An algorithm called Multifactor Dimension Reduction (MDR) was developed some 20 years ago and has been in use in human genetics ever since. This and our algorithms share some similar properties, but they are also very different in other respects. The main difference seems to be that our algorithm focuses on patterns of genotypes while the main object of inference in MDR is the 3 × 3 table of genotypes at two variants.

Keywords: digenic traits, DNA variants, epistasis, statistical genetics

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Authors: Jaishree Ranganathan, MuthuPriya Shanmugakani Velsamy, Shamika Kulkarni, Angelina Tzacheva

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Emotions play an important role in everyday life. An-alyzing these emotions or feelings from social media platforms like Twitter, Facebook, blogs, and forums based on user comments and reviews plays an important role in various factors. Some of them include brand monitoring, marketing strategies, reputation, and competitor analysis. The opinions or sentiments mined from such data helps understand the current state of the user. It does not directly provide intuitive insights on what actions to be taken to benefit the end user or business. Actionable Pattern Mining method provides suggestions or actionable recommendations on what changes or actions need to be taken in order to benefit the end user. In this paper, we propose automatic classification of emotions in Twitter data using Recurrent Neural Network - Gated Recurrent Unit. We achieve training accuracy of 87.58% and validation accuracy of 86.16%. Also, we extract action rules with respect to the user emotion that helps to provide actionable suggestion.

Keywords: emotion mining, twitter, recurrent neural network, gated recurrent unit, actionable pattern mining

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Authors: Sara A. Al-Jaberi, Salma Ben-Salem, Meriam Messedi, Fatma Ayadi, Lihadh Al-Gazali, Bassam R. Ali

Abstract:

Background: The chemokine receptor components play crucial roles in the immune system and some of them serve as co-receptors for the HIV virus. Several studies have documented those variants in chemokine receptors are correlated with susceptibility and resistance to infection with HIV virus. For example, mutations in the chemokine receptor 5 gene (CCR5) resulting in loss-of-function (such as the homozygous CCR5Δ32) confer high degree of resistance to HIV infection. Heterozygotes for these variants exhibit slow progression to AIDS. The prevalence of CCR5 polymorphisms varies among ethnic and geographical groups. For example, the CCR5 Δ32 variant is present in 10–15% of north Europeans but is rarely encountered among Africans. This study aims to identify the prevalence of some CCR5 variants in two geographically distant Arab populations (namely Emiratis and Tunisians). Methodology: The prevalence of CCR5 gene variants including CCR5Δ32, FS299, C101X, A29S and C178R has been determined using PCR and direct DNA sequencing. A total of 403 unrelated healthy individuals (253 Emiratis and 150 Tunisians) were genotyped for the CCR5Δ32 variant using PCR amplification and gel electrophoresis. In addition, 200 Emiratis have been screened for other SNPs using Sanger DNA sequencing. Results: Among Emiratis, the allele frequency of the CCR5Δ32 variant has been found to be 0.002. In addition, two variants L55Q and A159 were found at a frequency of 0.002.Moreover, the prevalence of the CCR5Δ32 variant in Tunisians was estimated to be 0.013 which is relatively higher than its frequency in Emiratis but lower than Europeans. Conclusion: We conclude that the allele frequency of the most critical CCR5 polymorphism (Δ32) is extremely low among Emiratis compared to other Arabs and North Europeans. In addition, very low allele frequencies of other CCR5 polymorphisms have been detected among Emiratis.

Keywords: chemokine receptors, CCR5Δ32, CCR5 polymorphisms, Emiratis, Arab populations

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Authors: Shaojian Li

Abstract:

The traditional Chinese patterns, as an integral part of Chinese culture, possess unique values in history, culture, and art. However, with the passage of time and societal changes, many of these traditional patterns are at risk of being lost, damaged, or forgotten. To undertake the digital preservation and protection of these traditional patterns, this paper will collect and organize images of traditional Chinese patterns. It will provide exhaustive and comprehensive semantic annotations, creating a resource library of traditional Chinese pattern images. This will support the digital preservation and application of traditional Chinese patterns.

Keywords: digitization of cultural heritage, traditional Chinese patterns, digital humanities, database construction

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Authors: Fatima Muhammad Shitu

Abstract:

This study seeks to investigate the effect of the keyword strategy on lexico–semantic acquisition, recognition, retention and comprehension in an ESL context. The aim of the study is to determine whether the keyword strategy can be used to enhance acquisition. As a quasi- experimental research, the objectives of the study include: To determine the extent to which the scores obtained by the subjects, who were trained on the use of the keyword strategy for acquisition, differ at the pre-tests and the post–tests and also to find out the relationship in the scores obtained at these tests levels. The sample for the study consists of 300 hundred undergraduate ESL Students in the Federal College of Education, Kano. The seventy-five lexical items for acquisition belong to the lexical field category known as register, and they include Medical, Agriculture and Photography registers (MAP). These were divided in the ratio twenty-five (25) lexical items in each lexical field. The testing technique was used to collect the data while the descriptive and inferential statistics were employed for data analysis. For the purpose of testing, the two kinds of tests administered at each test level include the WARRT (Word Acquisition, Recognition, and Retention Test) and the CCPT (Cloze Comprehension Passage Test). The results of the study revealed that there are significant differences in the scores obtained between the pre-tests, and the post–tests and there are no correlations in the scores obtained as well. This implies that the keyword strategy has effectively enhanced the acquisition of the lexical items studied.

Keywords: keyword, lexical, semantics, strategy

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Authors: Thiago Spilborghs Bueno Meyer, Plinio Thomaz Aquino Junior

Abstract:

Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs.

Keywords: emotion recognition, speech, deep learning, human-robot interaction, neural networks

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Authors: Shiliang Wu, Huanxin Zhang

Abstract:

Mercury has been well-known for its negative effects on wildlife, public health as well as the ecosystem. Once emitted into atmosphere, mercury can be transformed into different forms or enter the ecosystem through dry deposition or wet deposition. Some fraction of the mercury will be reemitted back into the atmosphere and be subject to the same cycle. In addition, the relatively long lifetime of elemental mercury in the atmosphere enables it to be transported long distances from source regions to receptor regions. Global change such as climate change and land use/land cover change impose significant challenges for mercury pollution control besides the efforts to regulate mercury anthropogenic emissions. In this study, we use a global chemical transport model (GEOS-Chem) to examine the potential impacts from changes in climate and land use/land cover on the global budget of mercury as well as its atmospheric transport, chemical transformation, and deposition. We carry out a suite of sensitivity model simulations to separate the impacts on atmospheric mercury associated with changes in climate and land use/land cover. Both climate change and land use/land cover change are found to have significant impacts on global mercury budget but through different pathways. Land use/land cover change primarily increase mercury dry deposition in northern mid-latitudes over continental regions and central Africa. Climate change enhances the mobilization of mercury from soil and ocean reservoir to the atmosphere. Also, dry deposition is enhanced over most continental areas while a change in future precipitation dominates the change in mercury wet deposition. We find that 2000-2050 climate change could increase the global atmospheric burden of mercury by 5% and mercury deposition by up to 40% in some regions. Changes in land use and land cover also increase mercury deposition over some continental regions, by up to 40%. The change in the lifetime of atmospheric mercury has important implications for long-range transport of mercury. Our case study shows that changes in climate and land use and cover could significantly affect the source-receptor relationships for mercury.

Keywords: mercury, toxic pollutant, atmospheric transport, deposition, climate change

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Authors: Chutamas Thepmalee, Aussara Panya, Mutita Junking, Jatuporn Sujjitjoon, Nunghathai Sawasdee, Pa-Thai Yenchitsomanus

Abstract:

Cholangiocarcinoma (CCA) is an aggressive malignancy of bile duct epithelial cells in which the standard treatments, including surgery, radiotherapy, chemotherapy, and targeted therapy are partially effective. Many solid tumors including CCA escape host immune responses by creating tumor microenvironment and generating immunosuppressive cytokines such as interleukin-10 (IL-10) and transforming growth factor-β (TGF-β). These cytokines can inhibit dendritic cell (DC) differentiation and function, leading to decreased activation and response of effector CD4+ and CD8+ T cells for cancer cell elimination. To overcome the effects of these immunosuppressive cytokines and to increase ability of DC to activate effector CD4+ and CD8+ T cells, we generated self-differentiated DCs (SD-DCs) with down-regulation of IL-10 and TGF-β receptors for activation of effector CD4+ and CD8+ T cells. Human peripheral blood monocytes were initially transduced with lentiviral particles containing the genes encoding GM-CSF and IL-4 and then secondly transduced with lentiviral particles containing short-hairpin RNAs (shRNAs) to knock-down mRNAs of IL-10 and TGF-β receptors. The generated SD-DCs showed up-regulation of MHC class II (HLA-DR) and co-stimulatory molecules (CD40 and CD86), comparable to those of DCs generated by convention method. Suppression of IL-10 and TGF-β receptors on SD-DCs by specific shRNAs significantly increased levels of IFN-γ and also increased cytolytic activity of DC-activated effector T cells against CCA cell lines (KKU-213 and KKU-100), but it had little effect to immortalized cholangiocytes (MMNK-1). Thus, SD-DCs with down-regulation of IL-10 and TGF-β receptors increased activation of effector T cells, which is a recommended method to improve DC function for the preparation of DC-activated effector T cells for adoptive T-cell therapy.

Keywords: cholangiocarcinoma, IL-10 receptor, self-differentiated dendritic cells, TGF-β receptor

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