Search results for: neural pathways

Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

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Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

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Authors: Md. M. Hossain, Regan Roat, Jenica Christopherson, Colette Free, Zhiguang Guo

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Long noncoding RNA (lncRNA) mediated post-transcriptional gene regulation, and their epigenetic landscapes have been shown to be involved in many human diseases. However, their regulation in diabetes through governing islet’s β-cell function and survival needs to be elucidated. Due to the technical and ethical constraints, it is difficult to study their role in β-cell function and survival in human under in vivo condition. In this study, humanized mice have been developed through transplanting human pancreatic islet under the kidney capsule of NOD.SCID mice and induced β-cell death leading to diabetes condition to study lncRNA mediated regulation. For this, human islets from 3 donors (3000 IEQ, purity > 80%) were transplanted under the kidney capsule of STZ induced diabetic NOD.scid mice. After at least 2 weeks of normoglycecemia, lymphocytes from diabetic NOD mice were adoptively transferred and islet grafts were collected once blood glucose reached > 200 mg/dl. RNA from human donor islets, islet grafts from humanized mice with either adoptive lymphocyte transfer (ALT) or PBS control (CTL) were ribodepleted; barcoded fragment libraries were constructed and sequenced on the Ion Proton sequencer. lncRNA expression in isolated human islets, islet grafts from humanized mice with and without induced β-cell death and their regulation in human islets function in vitro under glucose challenge, cytokine mediated inflammation and induced apoptotic condition were investigated. Out of 3155 detected lncRNAs, 299 that highly expressed in islets were found to be significantly downregulated and 224 upregulated in ALT compared to CTL. Most of these are found to be collocated within 5 kb upstream and 1 kb downstream of 788 up- and 624 down-regulated mRNAs. Genomic Regions Enrichment of Annotations Analysis revealed deregulated and collocated genes are related to pancreas endocrine development; insulin synthesis, processing, and secretion; pancreatitis and diabetes. Many of them, that found to be located within enhancer domains for islet specific gene activity, are associated to the deregulation of known islet/βcell specific transcription factors and genes that are important for β-cell differentiation, identity, and function. RNA sequencing analysis revealed aberrant lncRNA expression which is associated to the deregulated mRNAs in β-cell function as well as in molecular pathways related to diabetes. A distinct set of candidate lncRNA isoforms were identified as highly enriched and specific to human islets, which are deregulated in human islets from donors with different BMIs and with type 2 diabetes. These RNAs show an interesting regulation in cultured human islets under glucose stimulation and with induced β-cell death by cytokines. Aberrant expression of these lncRNAs was detected in the exosomes from the media of islets cultured with cytokines. Results of this study suggest that the islet specific lncRNAs are deregulated in human islet with β-cell death, hence important in diabetes. These lncRNAs might be important for human β-cell function and survival thus could be used as biomarkers and novel therapeutic targets for diabetes.

Keywords: β-cell, humanized mouse, pancreatic islet, LncRNAs

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Authors: Ruphi Naz, Altaf Ahmad, Mohammad Anis

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Sialic acid (5-Acetylneuraminic acid, Neu5Ac) is a common sugar found as a terminal residue on glycoconjugates in many animals. Humans brain and the central nervous system contain the highest concentration of sialic acid (as N-acetylneuraminic acid) where these acids play an important role in neural transmission and ganglioside structure in synaptogenesis. Due to its important biological function, sialic acid is attracting increasing attention. To understand metabolic networks, fluxes and regulation, it is essential to be able to determine the cellular and subcellular levels of metabolites. Genetically-encoded fluorescence resonance energy transfer (FRET) sensors represent a promising technology for measuring metabolite levels and corresponding rate changes in live cells. Taking this, we developed a genetically encoded FRET (fluorescence resonance energy transfer) based nanosensor to analyse the sialic acid level in living cells. Sialic acid periplasmic binding protein (sia P) from Haemophilus influenzae was taken and ligated between the FRET pair, the cyan fluorescent protein (eCFP) and Venus. The chimeric sensor protein was expressed in E. coli BL21 (DE3) and purified by affinity chromatography. Conformational changes in the binding protein clearly confirmed the changes in FRET efficiency. So any change in the concentration of sialic acid is associated with the change in FRET ratio. This sensor is very specific to sialic acid and found stable with the different range of pH. This nanosensor successfully reported the intracellular level of sialic acid in bacterial cell. The data suggest that the nanosensors may be a versatile tool for studying the in vivo dynamics of sialic acid level non-invasively in living cells

Keywords: nanosensor, FRET, Haemophilus influenzae, metabolic networks

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Authors: Kelly Jaunzems

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Social media is a powerful instrument of communication, enabling the presentation of information in multiple forms and modes, amplifying the interactions between people, organisations, and stakeholders, and increasing the range of communication channels available. Younger generations are highly engaged with social media and more likely to use this channel than any other to seek information. Given this, it may appear extraordinary that occupational safety and health professionals have yet to seriously engage with social media for communicating safety messages to younger audiences who, in many industries, might be statistically more likely to encounter more workplace harm or injury. Millennials, defined as those born between 1981-2000, have distinctive characteristics that also impact their interaction patterns rendering many traditional occupational safety and health communication channels sub-optimal or near obsolete. Used to immediate responses, 280-character communication, shares, likes, and visual imagery, millennials struggle to take seriously the low-tech, top-down communication channels such as safety noticeboards, toolbox meetings, and passive tick-box online inductions favoured by traditional OSH professionals. This paper draws upon well-established communication findings, which argue that it is important to know a target audience and reach them using their preferred communication pathways, particularly if the aim is to impact attitudes and behaviours. Health practitioners have adopted social media as a communication channel with great success, yet safety practitioners have failed to follow this lead. Using a digital ethnography approach, this paper examines seven organisations’ Facebook posts from two one-month periods one year apart, one in 2018 and one in 2019. Each of the years informs organisation-based case studies. Comparing, contrasting, and drawing upon these case studies, the paper discusses and evaluates the (non) use of social media communication of safety information in terms of user engagement, shareability, and overall appeal. The success of health practitioners’ use of social media provides a compelling template for the implementation of social media into organisations’ safety communication strategies. Highly visible content such as that found on social media allows an organization to become more responsive and engage in two-way conversations with their audience, creating more engaged and participatory conversations around safety. Further, using social media to address younger audiences with a range of tonal qualities (for example, the use of humour) can achieve cut through in a way that grim statistics fail to do. On the basis of 18 months of interviews, filed work, and data analysis, the paper concludes with recommendations for communicating safety information via social media. It proposes exploration of the social media communication formula that, when utilised by safety practitioners, may create an effective social media presence. It is anticipated that such social media use will increase engagement, expand the number of followers and reduce the likelihood and severity of safety-related incidents. The tools offered may provide a path for safety practitioners to reach a disengaged generation of workers to build a cohesive and inclusive conversation around ways to keep people safe at work.

Keywords: social media, workplace safety, communication strategies, young workers

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Authors: Oleg Reva, Ilya Korotetskiy, Marina Lankina, Murat Kulmanov, Aleksandr Ilin

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Molecular docking approaches are widely used for design of new antibiotics and modeling of antibacterial activities of numerous ligands which bind specifically to active centers of indispensable enzymes and/or key signaling proteins of pathogens. Widespread drug resistance among pathogenic microorganisms calls for development of new antibiotics specifically targeting important metabolic and information pathways. A generally recognized problem is that almost all molecular targets have been identified already and it is getting more and more difficult to design innovative antibacterial compounds to combat the drug resistance. A promising way to overcome the drug resistance problem is an induction of reversion of drug resistance by supplementary medicines to improve the efficacy of the conventional antibiotics. In contrast to well established computer-based drug design, modeling of drug resistance reversion still is in its infancy. In this work, we proposed an approach to identification of compensatory genetic variants reducing the fitness cost associated with the acquisition of drug resistance by pathogenic bacteria. The approach was based on an analysis of the population genetic of Mycobacterium tuberculosis and on results of experimental modeling of the drug resistance reversion induced by a new anti-tuberculosis drug FS-1. The latter drug is an iodine-containing nanomolecular complex that passed clinical trials and was admitted as a new medicine against MDR-TB in Kazakhstan. Isolates of M. tuberculosis obtained on different stages of the clinical trials and also from laboratory animals infected with MDR-TB strain were characterized by antibiotic resistance, and their genomes were sequenced by the paired-end Illumina HiSeq 2000 technology. A steady increase in sensitivity to conventional anti-tuberculosis antibiotics in series of isolated treated with FS-1 was registered despite the fact that the canonical drug resistance mutations identified in the genomes of these isolates remained intact. It was hypothesized that the drug resistance phenotype in M. tuberculosis requires an adjustment of activities of many genes to compensate the fitness cost of the drug resistance mutations. FS-1 cased an aggravation of the fitness cost and removal of the drug-resistant variants of M. tuberculosis from the population. This process caused a significant increase in genetic heterogeneity of the Mtb population that was not observed in the positive and negative controls (infected laboratory animals left untreated and treated solely with the antibiotics). A large-scale search for linkage disequilibrium associations between the drug resistance mutations and genetic variants in other genomic loci allowed identification of target proteins, which could be influenced by supplementary drugs to increase the fitness cost of the drug resistance and deprive the drug-resistant bacterial variants of their competitiveness in the population. The approach will be used to improve the efficacy of FS-1 and also for computer-based design of new drugs to combat drug-resistant infections.

Keywords: complete genome sequencing, computational modeling, drug resistance reversion, Mycobacterium tuberculosis

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Authors: Huseyin Apdik, Aysegul Dogan, Selami Demirci, Ezgi Avsar Apdik, Fikrettin Sahin

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Mesenchymal stem cells (MSCs) are crucial cell types for bone maintenance and growth along with resident bone progenitor cells providing bone tissue integrity during osteogenesis and skeletal growth. Any deficiency in this regulation would result in vital bone diseases. Of those, osteoporosis, characterized by a reduction in bone mass and mineral density, is a critical skeletal disease for especially elderly people. The commonly used drugs for the osteoporosis treatment are bisphosphonates (BPs). The most prominent role of BPs is to prevent bone resorption arisen from high osteoclast activity. However, administrations of bisphosphonates may also cause bisphosphonate-induced osteonecrosis of the jaw (BIONJ). Up to the present, the researchers have proposed several circumstances for BIONJ. However, effects of long-term and/or high dose usage of BPs on stem cell’s proliferation, survival, differentiation or maintenance capacity have not been evaluated yet. The present study will be held to; figure out BPs’ effects on MSCs in vitro in the aspect of cell proliferation and toxicity, migration, angiogenic activity, lineage specific gene and protein expression levels, mesenchymal stem cell properties and potential signaling pathways affected by BP treatment. Firstly, mesenchymal stem cell characteristics of Dental Pulp Stem Cells (DPSCs) and Periodontal Ligament Stem Cells (PDLSCs) were proved using flow cytometry analysis. Cell viability analysis was completed to determine the cytotoxic effects of BPs (Zoledronate (Zol), Alendronate (Ale) and Risedronate (Ris)) on DPSCs and PDLSCs by the 3-(4,5-di-methyl-thiazol-2-yl)-5-(3-carboxymethoxy-phenyl)-2-(4-sulfo-phenyl)-2H-tetrazolium (MTS) assay. Non-toxic concentrations of BPs were determined at 24 h under growth condition, and at 21 days under osteogenic differentiation condition for both cells. The scratch assay was performed to evaluate their migration capacity under the usage of determined of BPs concentrations at 24 h. The results revealed that while the scratch closure is 70% in the control group for DPSCs, it was 57%, 66% and 66% in Zol, Ale and Ris groups, respectively. For PDLSs, while wound closure is 71% in control group, it was 65%, 66% and 66% in Zol, Ale and Ris groups, respectively. As future experiments, tube formation assay and aortic ring assay will be done to determinate angiogenesis abilities of DPSCs and PDLSCs treated with BPs. Expression levels of osteogenic differentiation marker genes involved in bone development will be determined using real time-polymerase change reaction (RT-PCR) assay and expression profiles of important proteins involved in osteogenesis will be evaluated using western blotting assay for osteogenically differentiated MSCs treated with or without BPs. In addition to these, von Kossa staining will be performed to measure calcium mineralization status of MSCs.

Keywords: bisphosphonates, bisphosphonate-induced osteonecrosis of the jaw, mesenchymal stem cells, osteogenesis

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Authors: Yehjune Heo

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As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: anti-spoofing, CNN, fingerprint recognition, loss function, optimizer

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Authors: Isaac Kayode Ogunlade

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Data acquisition (DAQ) is the process by which physical phenomena from the real world are transformed into an electrical signal(s) that are measured and converted into a digital format for processing, analysis, and storage by a computer. The DAQ is designed using PIC18F4550 microcontroller, communicating with Personal Computer (PC) through USB (Universal Serial Bus). The research deployed initial knowledge of data acquisition system and embedded system to develop a weather data acquisition device using LM35 sensor to measure weather parameters and the use of Artificial Intelligence(Artificial Neural Network - ANN)and statistical approach(Autoregressive Integrated Moving Average – ARIMA) to predict precipitation (rainfall). The device is placed by a standard device in the Department of Meteorology, Federal University of Technology, Akure (FUTA) to know the performance evaluation of the device. Both devices (standard and designed) were subjected to 180 days with the same atmospheric condition for data mining (temperature, relative humidity, and pressure). The acquired data is trained in MATLAB R2012b environment using ANN, and ARIMAto predict precipitation (rainfall). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correction Square (R2), and Mean Percentage Error (MPE) was deplored as standardize evaluation to know the performance of the models in the prediction of precipitation. The results from the working of the developed device show that the device has an efficiency of 96% and is also compatible with Personal Computer (PC) and laptops. The simulation result for acquired data shows that ANN models precipitation (rainfall) prediction for two months (May and June 2017) revealed a disparity error of 1.59%; while ARIMA is 2.63%, respectively. The device will be useful in research, practical laboratories, and industrial environments.

Keywords: data acquisition system, design device, weather development, predict precipitation and (FUTA) standard device

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Rahaba Marima, Clement Penny

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The Health-related quality of life (HRQoL) for HIV positive patients has improved since the introduction of the highly active antiretroviral treatment (HAART). However, in the present HAART era, HIV co-morbidities such as lung cancer, a non-AIDS (NAIDS) defining cancer have been documented to be on the rise. Under normal physiological conditions, cells grow, repair and proliferate through the cell-cycle as cellular homeostasis is important in the maintenance and proper regulation of tissues and organs. Contrarily, the deregulation of the cell-cycle is a hallmark of cancer, including lung cancer. The association between lung cancer and the use of HAART components such as Efavirenz (EFV) is poorly understood. This study aimed at elucidating the effects of EFV on the cell-cycle genes’ expression in lung cancer. For this purpose, the human cell-cycle gene array composed of 84 genes was evaluated on both normal lung fibroblasts (MRC-5) cells and adenocarcinoma (A549) lung cells, in response to 13µM EFV or 0.01% vehicle. The ±2 up or down fold change was used as a basis of target selection, with p < 0.05. Additionally, RT-qPCR was done to validate the gene array results. Next, In-silico bio-informatics tools, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Reactome, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Ingenuity Pathway Analysis (IPA) were used for gene/gene interaction studies as well as to map the molecular and biological pathways influenced by the identified targets. Interestingly, the DNA damage response (DDR) pathway genes such as p53, Ataxia telangiectasia mutated and Rad3 related (ATR), Growth arrest and DNA damage inducible alpha (GADD45A), HUS1 checkpoint homolog (HUS1) and Role of radiation (RAD) genes were shown to be upregulated following EFV treatment, as revealed by STRING analysis. Additionally, functional enrichment analysis by the KEGG pathway revealed that most of the differentially expressed gene targets function at the cell-cycle checkpoint such as p21, Aurora kinase B (AURKB) and Mitotic Arrest Deficient-Like 2 (MAD2L2). Core analysis by IPA revealed that p53 downstream targets such as survivin, Bcl2, and cyclin/cyclin dependent kinases (CDKs) complexes are down-regulated, following exposure to EFV. Furthermore, Reactome analysis showed a significant increase in cellular response to stress genes, DNA repair genes, and apoptosis genes, as observed in both normal and cancerous cells. These findings implicate the genotoxic effects of EFV on lung cells, provoking the DDR pathway. Notably, the constitutive expression of this pathway (DDR) often leads to uncontrolled cell proliferation and eventually tumourigenesis, which could be the attribute of HAART components’ (such as EFV) effect on human cancers. Targeting the cell-cycle and its regulation holds a promising therapeutic intervention to the potential HAART associated carcinogenesis, particularly lung cancer.

Keywords: cell-cycle, DNA damage response, Efavirenz, lung cancer

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Authors: Casta Guillaume, Robert Jagers, Deborah Rivas-Drake

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Civically engaged youth are critical to maintaining and/or improving the functioning of local, national and global communities and their institutions. The present study investigated how school climate and academic beliefs (academic self-efficacy and school belonging) may inform emergent civic behaviors (emergent citizenship) among self-identified middle school youth of color (African American, Multiracial or Mixed, Latino, Asian American or Pacific Islander, Native American, and other). Study aims: 1) Understand whether and how school climate is associated with civic engagement behaviors, directly and indirectly, by fostering a positive sense of connection to the school and/or engendering feelings of self-efficacy in the academic domain. Accordingly, we examined 2) The association of youths’ sense of school connection and academic self-efficacy with their personally responsible and participatory civic behaviors in school and community contexts—both concurrently and longitudinally. Data from two subsamples of a larger study of social/emotional development among middle school students were used for longitudinal and cross sectional analysis. The cross-sectional sample included 324 6th-8th grade students, of which 43% identified as African American, 20% identified as Multiracial or Mixed, 18% identified as Latino, 12% identified as Asian American or Pacific Islander, 6% identified as Other, and 1% identified as Native American. The age of the sample ranged from 11 – 15 (M = 12.33, SD = .97). For the longitudinal test of our mediation model, we drew on data from the 6th and 7th grade cohorts only (n =232); the ethnic and racial diversity of this longitudinal subsample was virtually identical to that of the cross-sectional sample. For both the cross-sectional and longitudinal analyses, full information maximum likelihood was used to deal with missing data. Fit indices were inspected to determine if they met the recommended thresholds of RMSEA below .05 and CFI and TLI values of at least .90. To determine if particular mediation pathways were significant, the bias-corrected bootstrap confidence intervals for each indirect pathway were inspected. Fit indices for the latent variable mediation model using the cross-sectional data suggest that the hypothesized model fit the observed data well (CFI = .93; TLI =. 92; RMSEA = .05, 90% CI = [.04, .06]). In the model, students’ perceptions of school climate were significantly and positively associated with greater feelings of school connectedness, which were in turn significantly and positively associated with civic engagement. In addition, school climate was significantly and positively associated with greater academic self-efficacy, but academic self-efficacy was not significantly associated with civic engagement. Tests of mediation indicated there was one significant indirect pathway between school climate and civic engagement behavior. There was an indirect association between school climate and civic engagement via its association with sense of school connectedness, indirect association estimate = .17 [95% CI: .08, .32]. The aforementioned indirect association via school connectedness accounted for 50% (.17/.34) of the total effect. Partial support was found for the prediction that students’ perceptions of a positive school climate are linked to civic engagement in part through their role in students’ sense of connection to school.

Keywords: civic engagement, early adolescence, school climate, school belonging, developmental niche

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Authors: Nicole Selvi Hill, Archchana Radhakrishnan

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Introduction: Traumatic brain injury (TBI) is a leading cause of mortality among trauma patients. In the management of TBI, the main principle is avoiding cerebral ischemia, as this is a strong determiner of neurological outcomes. The use of vasoactive drugs, such as vasopressin, has an important role in maintaining cerebral perfusion pressure to prevent secondary brain injury. Current guidelines do not suggest a preferred vasoactive drug to administer in the management of TBI, and there is a paucity of information on the therapeutic potential of vasopressin following TBI. Vasopressin is also an endogenous anti-diuretic hormone (AVP), and pathways mediated by AVP play a large role in the underlying pathological processes of TBI. This creates an overlap of discussion regarding the therapeutic potential of vasopressin following TBI. Currently, its popularity lies in vasodilatory and cardiogenic shock in the intensive care setting, with increasing support for its use in haemorrhagic and septic shock. Methodology: This is a review article based on a literature review. An electronic search was conducted via PubMed, Cochrane, EMBASE, and Google Scholar. The aim was to identify clinical studies looking at the therapeutic administration of vasopressin in severe traumatic brain injury. The primary aim was to look at the neurological outcome of patients. The secondary aim was to look at surrogate markers of cerebral perfusion measurements, such as cerebral perfusion pressure, cerebral oxygenation, and cerebral blood flow. Results: Eight papers were included in the final number. Three were animal studies; five were human studies, comprised of three case reports, one retrospective review of data, and one randomised control trial. All animal studies demonstrated the benefits of vasopressors in TBI management. One animal study showed the superiority of vasopressin in reducing intracranial pressure and increasing cerebral oxygenation over a catecholaminergic vasopressor, phenylephrine. All three human case reports were supportive of vasopressin as a rescue therapy in catecholaminergic-resistant hypotension. The retrospective review found vasopressin did not increase cerebral oedema in TBI patients compared to catecholaminergic vasopressors; and demonstrated a significant reduction in the requirements of hyperosmolar therapy in patients that received vasopressin. The randomised control trial results showed no significant differences in primary and secondary outcomes between TBI patients receiving vasopressin versus those receiving catecholaminergic vasopressors. Apart from the randomised control trial, the studies included are of low-level evidence. Conclusion: Studies favour vasopressin within certain parameters of cerebral function compared to control groups. However, the neurological outcomes of patient groups are not known, and animal study results are difficult to extrapolate to humans. It cannot be said with certainty whether vasopressin’s benefits stand above usage of other vasoactive drugs due to the weaknesses of the evidence. Further randomised control trials, which are larger, standardised, and rigorous, are required to improve knowledge in this field.

Keywords: catecholamines, cerebral perfusion pressure, traumatic brain injury, vasopressin, vasopressors

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Authors: David A. Harness

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Natural evolution of ATP cognitive systems is to meet AI peer review standards. ATP process of axiom selection from Mizar to prove a conjecture would be further refined, as in all human and machine learning, by solving the real world problem of the proposed AI peer review challenge: Determine which conjecture forms the higher confidence level constructive proof between Standard Model of Physics SU(n) lattice gauge group operation vs. present non-standard 4D GEM EOS SU(n) lattice gauge group spatially extended operation in which the photon and electron are the first two trace angular momentum invariants of a gravitoelectromagnetic (GEM) energy momentum density tensor wavetrain integration spin-stress pressure-volume equation of state (EOS), initiated via 32 lines of Mathematica code. Resulting gravitoelectromagnetic spectrum ranges from compressive through rarefactive of the central cosmological constant vacuum energy density in units of pascals. Said self-adjoint group operation exclusively operates on the stress energy momentum tensor of the Einstein field equations, introducing quantization directly on the 4D spacetime level, essentially reformulating the Yang-Mills virtual superpositioned particle compounded lattice gauge groups quantization of the vacuum—into a single hyper-complex multi-valued GEM U(1) × SU(1,3) lattice gauge group Planck spacetime mesh quantization of the vacuum. Thus the Mizar corpus already contains all of the axioms required for relevant DeepMath premise selection and unambiguous formal natural language parsing in context deep learning.

Keywords: automated theorem proving, constructive quantum field theory, information theory, neural networks

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Authors: J. DeBoard, R. Dietrich, J. Hughes, K. Yurko, G. Harms

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Alzheimer’s disease (AD) is a predominant type of dementia and is likely a major cause of neural network impairment. The pathogenesis of this neurodegenerative disorder has yet to be fully elucidated. There are currently no known cures for the disease, and the best hope is to be able to detect it early enough to impede its progress. Beyond age and genetics, another prevalent risk factor for AD might be traumatic brain injury (TBI), which has similar neurodegenerative hallmarks. Our research focuses on obtaining information and methods to be able to predict when neurodegenerative effects might occur at a clinical level by observation of events at a cellular and molecular level in model mice. First, we wish to introduce our evidence that brain damage can be observed via brain imaging prior to the noticeable loss of neuromuscular control in model mice of AD. We then show our evidence that some blood biomarkers might be able to be early predictors of AD in the same model mice. Thus, we were interested to see if we might be able to predict which mice might show long-term neurodegenerative effects due to differing degrees of TBI and what level of TBI causes further damage and earlier death to the AD model mice. Upon application of TBIs via an apparatus to effectively induce extremely mild to mild TBIs, wild-type (WT) mice and AD mouse models were tested for cognition, neuromuscular control, olfactory ability, blood biomarkers, and brain imaging. Experiments are currently still in process, and more results are therefore forthcoming. Preliminary data suggest that neuromotor control diminishes as well as olfactory function for both AD and WT mice after the administration of five consecutive mild TBIs. Also, seizure activity increases significantly for both AD and WT after the administration of the five TBI treatment. If future data supports these findings, important implications about the effect of TBI on those at risk for AD might be possible.

Keywords: Alzheimer's disease, blood biomarker, neurodegeneration, neuromuscular control, olfaction, traumatic brain injury

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Authors: M. Bilal Ishfaq, Adnan N. Qureshi

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COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

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Authors: Mara Aparecida De Castilho Lopes. Maria Eliza Mattosinho Bernardes

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The education of deaf individuals in Brazil is grounded in the bilingual approach, which presupposes Brazilian Sign Language (Libras) as the first language for these students. In this perspective, Portuguese should be taught as a second language in its written form, ensuring that deaf students also have access to various academic subjects in sign language. Brazilian legislation (Federal Decree No. 5626 of 2005) mandates the teaching of Brazilian Sign Language in university teacher training programs, but there is no pre-established minimum workload. As a result, there is a significant disparity in the teaching and quality of teacher education across the Brazilian territory. Added to this fact is the general lack of awareness within society regarding the linguistic status of Libras, leading to a shortage of competent teachers for its use and instruction, particularly in higher education. Recently, Federal Law No. 14191 of 2021 established bilingual education for the deaf as a mode of instruction, indicating the need for adjustments in teacher training within higher education teacher preparation programs. Given this context, the objective of the present study was to analyze the teaching proposals for Brazilian Sign Language for students in teacher training programs at public universities in Brazil, presenting alternatives to overcome the current models and academic pathways of teaching and learning. In addition to analyzing Brazilian teaching models, an analysis of a continuing education model for teachers in a French institution was also conducted - considering the historical Franco-Brazilian path of deaf education in Brazil. The analysis of the current teacher training model for deaf education in Brazil revealed that initial exposure to sign language and its linguistic structure is not sufficient to provide future teachers with opportunities to reflect on bilingual teaching methods and practices, as seen in other definitions of bilingualism - bilingual education for proficient listeners in two oral languages. As a result, a training proposal was developed for an experimental interdisciplinary course, integrating the curriculum of an initial and continuing teacher training program alongside the Alfredo Bossi Chair at the University of São Paulo. This proposal is structured into three disciplines, which constitute consecutive moments in teacher education: Fundamental Aspects of Brazilian Sign Language, Bilingual Teaching Methodology, and Teaching Investigation Project - interdisciplinary engagement in the field of deafness. The last offered discipline represents an interdisciplinary supervised internship proposal, considering the multi-professional context that constitutes deaf education within a bilingual approach. In interdisciplinary work within the field of deafness, dialogue between teachers and other professionals who work with deaf students from different perspectives - teachers, speech therapists, and sign language interpreters - is frequently necessary. Through alternative avenues, these actions aim to direct the linguistic development of deaf students within their learning processes. Based on the innovative curriculum proposal described here, the intention is to contribute to the enhancement of teacher education in Brazil, with the goal of ensuring bilingual education for deaf students.

Keywords: bilingual education, teacher training, historical-cultural approach, interdisciplinary education, inclusive education

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Authors: Animish Jain

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This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

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Authors: Andrés Quiroz, Emilio Hormazabal, Ana Mutis, Fernando Ortega, Manuel Chacón-Fuentes, Leonardo Parra

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Red clover (Trifolium pratense L.) is an important forage species in temperate regions of the world. The main limitation of this species worldwide is a lack of persistence related to the high mortality of plants due to a complex of biotic and abiotic factors, determining a life span of two or three seasons. Because of the importance of red clover in Chile, a red clover breeding program was started at INIA Carillanca Research Center in 1989, with the main objective of improving the survival of plants, forage yield, and persistence. The main selection criteria for selecting new varieties have been based on agronomical parameters and biotic factors. The main biotic factor associated with red clover mortality in Chile is Hylastinus obscurus (Coleoptera: Curculionidae). Both larval and adults feed on the roots, causing weakening and subsequent death of clover plants. Pesticides have not been successful for controlling infestations of this root borer. Therefore, alternative strategies for controlling this pest are a high priority for red clover producers. Currently, the role of semiochemical in the interaction between H. obscurus and red clover plants has been widely studied for our group. Specifically, from the red clover foliage has been identified limonene is eliciting repellency from the root borer. Limonene is generated in the plant from two independent biosynthetic pathways, the mevalonic acid, and deoxyxylulose pathway. Mevalonate pathway enzymes are localized in the cytosol, whereas the deoxyxylulose phosphate pathway enzymes are found in plastids. In summary, limonene can be determinated by enzymatic bioassay using GPP as substrate and by limonene synthase expression. Therefore, the main objective of this work was to study genetic variation of limonene in material provided by INIA´s Red Clover breeding program. Protein extraction was carried out homogenizing 250 mg of leave tissue and suspended in 6 mL of extraction buffer (PEG 1500, PVP-30, 20 mM MgCl2 and antioxidants) and stirred on ice for 20 min. After centrifugation, aliquots of 2.5 mL were desalted on PD-10 columns, resulting in a final volume of 3.5 mL. Protein determination was performed according to Bradford with BSA as a standard. Monoterpene synthase assays were performed with 50 µL of protein extracts transferred into gas-tight 2 mL crimp seal vials after addition of 4 µL MgCl₂ and 41 µL assay buffer. The assay was started by adding 5 µL of a GPP solution. The mixture was incubated for 30 min at 40 °C. Biosynthesized limonene was quantified in a GC equipped with a chiral column and using synthetic R and S-limonene standards. The enzymatic the production of R and S-limonene from different Superqueli-Carillanca genotypes is shown in this work. Preliminary results showed significant differences in limonene content among the genotypes analyzed. These results constitute an important base for selecting genotypes with a high content of this repellent monoterpene towards H. obscurus.

Keywords: head space, limonene enzymatic determination, red clover, Hylastinus obscurus

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Authors: Susan Diamond

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Machine Learning workloads have traditionally been run in high-performance computing (HPC) environments, where users log in to dedicated machines and utilize the attached GPUs to run training jobs on huge datasets. Training of large neural network models is very resource intensive, and even after exploiting parallelism and accelerators such as GPUs, a single training job can still take days. Consequently, the cost of hardware is a barrier to entry. Even when upfront cost is not a concern, the lead time to set up such an HPC environment takes months from acquiring hardware to set up the hardware with the right set of firmware, software installed and configured. Furthermore, scalability is hard to achieve in a rigid traditional lab environment. Therefore, it is slow to react to the dynamic change in the artificial intelligent industry. Watson Deep Learning as a service, a cloud-based deep learning platform that mitigates the long lead time and high upfront investment in hardware. It enables robust and scalable sharing of resources among the teams in an organization. It is designed for on-demand cloud environments. Providing a similar user experience in a multi-tenant cloud environment comes with its own unique challenges regarding fault tolerance, performance, and security. Watson Deep Learning as a service tackles these challenges and present a deep learning stack for the cloud environments in a secure, scalable and fault-tolerant manner. It supports a wide range of deep-learning frameworks such as Tensorflow, PyTorch, Caffe, Torch, Theano, and MXNet etc. These frameworks reduce the effort and skillset required to design, train, and use deep learning models. Deep Learning as a service is used at IBM by AI researchers in areas including machine translation, computer vision, and healthcare. 

Keywords: deep learning, machine learning, cognitive computing, model training

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Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

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Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh

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In brainwaves research using electroencephalogram (EEG) signals, finding the most relevant and effective feature set for identification of activities in the human brain is a big challenge till today because of the random nature of the signals. The feature extraction method is a key issue to solve this problem. Finding those features that prove to give distinctive pictures for different activities and similar for the same activities is very difficult, especially for the number of activities. The performance of a classifier accuracy depends on this quality of feature set. Further, more number of features result in high computational complexity and less number of features compromise with the lower performance. In this paper, a novel idea of the selection of optimal feature set using a heuristically optimized deep autoencoder is presented. Using various feature extraction methods, a vast number of features are extracted from the EEG signals and fed to the autoencoder deep neural network. The autoencoder encodes the input features into a small set of codes. To avoid the gradient vanish problem and normalization of the dataset, a meta-heuristic search algorithm is used to minimize the mean square error (MSE) between encoder input and decoder output. To reduce the feature set into a smaller one, 4 hidden layers are considered in the autoencoder network; hence it is called Heuristically Optimized Deep Autoencoder (HO-DAE). In this method, no features are rejected; all the features are combined into the response of responses of the hidden layer. The results reveal that higher accuracy can be achieved using optimal reduced features. The proposed HO-DAE is also compared with the regular autoencoder to test the performance of both. The performance of the proposed method is validated and compared with the other two methods recently reported in the literature, which reveals that the proposed method is far better than the other two methods in terms of classification accuracy.

Keywords: autoencoder, brainwave signal analysis, electroencephalogram, feature extraction, feature selection, optimization

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Authors: Yuli Hou, Jianping Sun, Mengdan Gao, Hui Liu, Ling Qin, Ang Li, Dongfu Li, Yonghong Zhang, Yan Zhao

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Background and Aims: Interferon-induced transmembrane protein 3 (IFITM3) is a component of ISG (Interferon-Stimulated Gene) family. IFITM3 has been recognized as a key signal molecule regulating cell growth in some tumors. However, the function of IFITM3 rs12252-CC genotype in the hepatocellular carcinoma (HCC) remains unknown to author’s best knowledge. A cohort study was employed to clarify the relationship between IFITM3 rs12252-CC genotype and HCC progression, and cellular experiments were used to investigate the correlation of function of IFITM3 and the progress of HCC. Methods: 336 candidates were enrolled in study, including 156 with HBV related HCC and 180 with chronic Hepatitis B infections or liver cirrhosis. Polymerase chain reaction (PCR) was employed to determine the gene polymorphism of IFITM3. The functions of IFITM3 were detected in PLC/PRF/5 cell with different treated:LV-IFITM3 transfected with lentivirus to knockdown the expression of IFITM3 and LV-NC transfected with empty lentivirus as negative control. The IFITM3 expression, proliferation and migration were detected by Quantitative reverse transcription polymerase chain reaction (qRT-PCR), QuantiGene Plex 2.0 assay, western blotting, immunohistochemistry, Cell Counting Kit(CCK)-8 and wound healing respectively. Six samples (three infected with empty lentiviral as control; three infected with LV-IFITM3 vector lentiviral as experimental group ) of PLC/PRF/5 were sequenced at BGI (Beijing Genomics Institute, Shenzhen,China) using RNA-seq technology to identify the IFITM3-related signaling pathways and chose PI3K/AKT pathway as related signaling to verify. Results: The patients with HCC had a significantly higher proportion of IFITM3 rs12252-CC compared with the patients with chronic HBV infection or liver cirrhosis. The distribution of CC genotype in HCC patients with low differentiation was significantly higher than that in those with high differentiation. Patients with CC genotype found with bigger tumor size, higher percentage of vascular thrombosis, higher distribution of low differentiation and higher 5-year relapse rate than those with CT/TT genotypes. The expression of IFITM3 was higher in HCC tissues than adjacent normal tissues, and the level of IFITM3 was higher in HCC tissues with low differentiation and metastatic than high/medium differentiation and without metastatic. Higher RNA level of IFITM3 was found in CC genotype than TT genotype. In PLC/PRF/5 cell with knockdown, the ability of cell proliferation and migration was inhibited. Analysis RNA sequencing and verification of RT-PCR found out the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin(PI3K/AKT/mTOR) pathway was associated with knockdown IFITM3.With the inhibition of IFITM3, the expression of PI3K/AKT/mTOR signaling pathway was blocked and the expression of vimentin was decreased. Conclusions: IFITM3 rs12252-CC with the higher expression plays a vital role in the progress of HCC by regulating HCC cell proliferation and migration. These effects are associated with PI3K/AKT/mTOR signaling pathway.

Keywords: IFITM3, interferon-induced transmembrane protein 3, HCC, hepatocellular carcinoma, PI3K/ AKT/mTOR, phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin

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Authors: Sharon Millen, Sarah Miller, Laura Dunne, Clare McGeady, Laura Neeson

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This paper presents the findings from a randomised controlled trial (RCT) and process evaluation of the Lifestart parenting programme. Lifestart is a structured child-centred programme of information and practical activity for parents of children aged from birth to five years of age. It is delivered to parents in their own homes by trained, paid family visitors and it is offered to parents regardless of their social, economic or other circumstances. The RCT evaluated the effectiveness of the programme and the process evaluation documented programme delivery and included a qualitative exploration of parent and child outcomes. 424 parents and children participated in the RCT: 216 in the intervention group and 208 in the control group across the island of Ireland. Parent outcomes included: parental knowledge of child development, parental efficacy, stress, social support, parenting skills and embeddedness in the community. Child outcomes included cognitive, language and motor development and social-emotional and behavioural development. Both groups were tested at baseline (when children were less than 1 year old), mid-point (aged 3) and at post-test (aged 5). Data were collected during a home visit, which took two hours. The process evaluation consisted of interviews with parents (n=16 at baseline and end-point), and focus groups with Lifestart Coordinators (n=9) and Family Visitors (n=24). Quantitative findings from the RCT indicated that, compared to the control group, parents who received the Lifestart programme reported reduced parenting-related stress, increased knowledge of their child’s development, and improved confidence in their parenting role. These changes were statistically significant and consistent with the hypothesised pathway of change depicted in the logic model. There was no evidence of any change in parents’ embeddedness in the community. Although four of the five child outcomes showed small positive change for children who took part in the programme, these were not statistically significant and there is no evidence that the programme improves child cognitive and non-cognitive skills by immediate post-test. The qualitative process evaluation highlighted important challenges related to conducting trials of this magnitude and design in the general population. Parents reported that a key incentive to take part in study was receiving feedback from the developmental assessment, which formed part of the data collection. This highlights the potential importance of appropriate incentives in relation to recruitment and retention of participants. The interviews with intervention parents indicated that one of the first changes they experienced as a result of the Lifestart programme was increased knowledge and confidence in their parenting ability. The outcomes and pathways perceived by parents and described in the interviews are also consistent with the findings of the RCT and the theory of change underpinning the programme. This hypothesises that improvement in parental outcomes, arising as a consequence of the programme, mediate the change in child outcomes. Parents receiving the Lifestart programme reported great satisfaction with and commitment to the programme, with the role of the Family Visitor being identified as one of the key components of the programme.

Keywords: parent-child relationship, parental self-efficacy, parental stress, school readiness

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Mustafa Metin Donma, Orkide Donma

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Childhood obesity is an important clinical problem because it may lead to chronic diseases during the adulthood period of the individual. Obesity is a metabolic disease associated with low-grade inflammation. The liver occurs at the center of metabolic pathways. Adropin, fibroblast growth factor-21 (FGF-21), and fetuin-A are hepatokines. Due to the immense participation of the liver in glucose metabolism, these liver-derived factors may be associated with insulin resistance (IR), which is a phenomenon discussed within the scope of obesity problems. The aim of this study is to determine the concentrations of adropin, FGF-21, and fetuin-A in childhood obesity, to point out possible differences between the obesity groups, and to investigate possible associations among these three hepatokines in obese and morbidly obese children. A total of one hundred and thirty-two children were included in the study. Two obese groups were constituted. The groups were matched in terms of mean ± SD values of ages. Body mass index values of obese and morbidly obese groups were 25.0 ± 3.5 kg/m² and 29.8 ± 5.7 kg/m², respectively. Anthropometric measurements including waist circumference, hip circumference, head circumference, and neck circumference were recorded. Informed consent forms were taken from the parents of the participants. The ethics committee of the institution approved the study protocol. Blood samples were obtained after overnight fasting. Routine biochemical tests, including glucose- and lipid-related parameters, were performed. Concentrations of the hepatokines (adropin, FGF-21, fetuin A) were determined by enzyme-linked immunosorbent assay. Insulin resistance indices such as homeostasis model assessment for IR (HOMA-IR), alanine transaminase-to aspartate transaminase ratio (ALT/AST), diagnostic obesity notation model assessment laboratory index, diagnostic obesity notation model assessment metabolic syndrome index as well as obesity indices such as diagnostic obesity notation model assessment-II index, and fat mass index were calculated using the previously derived formulas. Statistical evaluation of the study data as well as findings of the study was performed by SPSS for Windows. Statistical difference was accepted significant when p is smaller than 0.05. Statistically significant differences were found for insulin, triglyceride, high-density lipoprotein cholesterol levels of the groups. A significant increase was observed for FGF-21 concentrations in the morbidly obese group. Higher adropin and fetuin-A concentrations were observed in the same group in comparison with the values detected in the obese group (p > 0.05). There was no statistically significant difference between the ALT/AST values of the groups. In all of the remaining IR and obesity indices, significantly increased values were calculated for morbidly obese children. Significant correlations were detected between HOMA-IR and each of the hepatokines. The highest one was the association with fetuin-A (r=0.373, p=0.001). In conclusion, increased levels observed in adropin, FGF-21, and fetuin-A have shown that these hepatokines possess increasing potential going from obese to morbid obese state. Out of the correlations found with the IR index, the most affected hepatokine was fetuin-A, the parameter possibly used as the indicator of the advanced obesity stage.

Keywords: adropin, fetuin A, fibroblast growth factor-21, insulin resistance, pediatric obesity

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Authors: Sascha Kraus, Wolfgang Hora, Philipp Stieg, Thomas Niemand, Ferdinand Thies, Matthias Filser

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In most countries, small and medium-sized enterprises (SME) are the backbone of the economy due to their impact on job creation, innovation and wealth creation. Moreover, the ongoing globalization makes it inevitable – even for SME that traditionally focused on their domestic markets – to internationalize their business activities to realize further growth and survive in international markets. Thus, internationalization has become one of the most common growth strategies for SME and has received increasing scholarly attention over the last two decades. One the downside internationalization can be also regarded as the most complex strategy that a firm can undertake. Particularly for family firms, that are often characterized by limited financial capital, a risk-averse nature and limited growth aspirations, it could be argued that family firms are more likely to face greater challenges when taking the pathway to internationalization. Especially the triangulation of family, ownership, and management (so-called ‘familiness’) manifests in a unique behavior and decision-making process which is often characterized by the importance given to noneconomic goals and distinguishes a family firm from other businesses. Taking this into account, the concept of socio-emotional wealth (SEW) has been evolved to describe the behavior of family firms. In order to investigate how different internal and external firm characteristics shape internationalization success of family firms, we drew on a sample consisting of 297 small and medium-sized family firms from Germany, Austria, Switzerland, and Liechtenstein. Thus, we include SEW as essential family firm characteristic and added the two major intra-organizational characteristics, entrepreneurial orientation (EO), absorptive capacity (AC) as well as collaboration intensity (CI) and relational knowledge (RK) as two major external network characteristics. Based on previous research we assume that these characteristics are important to explain internationalization success of family firm SME. Regarding the data analysis, we applied a Fuzzy Set Qualitative Comparative Analysis (fsQCA), an approach that allows identifying configurations of firm characteristics, specifically used to study complex causal relationships where traditional regression techniques reach their limits. Results indicate that several combinations of these family firm characteristics can lead to international success, with no permanently required key characteristic. Instead, there are many roads to walk down for family firms to achieve internationalization success. Consequently, our data states that family owned SME are heterogeneous and internationalization is a complex and dynamic process. Results further show that network related characteristics occur in all sets, thus represent an essential element in the internationalization process of family owned SME. The contribution of our study is twofold, as we investigate different forms of international expansion for family firms and how to improve them. First, we are able to broaden the understanding of the intersection between family firm and SME internationalization with respect to major intra-organizational and network-related variables. Second, from a practical perspective, we offer family firm owners a basis for setting up internal capabilities to achieve international success.

Keywords: entrepreneurial orientation, family firm, fsQCA, internationalization, socio-emotional wealth

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Authors: Anika Chebrolu

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Mono-targeted drugs can be of limited efficacy against complex diseases. Recently, multi-target drug design has been approached as a promising tool to fight against these challenging diseases. However, the scope of current computational approaches for multi-target drug design is limited. DeepLig presents a de-novo drug discovery platform that uses reinforcement learning to generate and optimize novel, potent, and multitargeted drug candidates against protein targets. DeepLig’s model consists of two networks in interplay: a generative network and a predictive network. The generative network, a Stack- Augmented Recurrent Neural Network, utilizes a stack memory unit to remember and recognize molecular patterns when generating novel ligands from scratch. The generative network passes each newly created ligand to the predictive network, which then uses multiple Graph Attention Networks simultaneously to forecast the average binding affinity of the generated ligand towards multiple target proteins. With each iteration, given feedback from the predictive network, the generative network learns to optimize itself to create molecules with a higher average binding affinity towards multiple proteins. DeepLig was evaluated based on its ability to generate multi-target ligands against two distinct proteins, multi-target ligands against three distinct proteins, and multi-target ligands against two distinct binding pockets on the same protein. With each test case, DeepLig was able to create a library of valid, synthetically accessible, and novel molecules with optimal and equipotent binding energies. We propose that DeepLig provides an effective approach to design multi-targeted drug therapies that can potentially show higher success rates during in-vitro trials.

Keywords: drug design, multitargeticity, de-novo, reinforcement learning

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Authors: Mohammed Abu Shquier

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This paper introduces a word segmentation method using the novel BP-LSTM-CRF architecture for processing semantic output training. The objective of web morphological analysis tools is to link a formal morpho-syntactic description to a lemma, along with morpho-syntactic information, a vocalized form, a vocalized analysis with morpho-syntactic information, and a list of paradigms. A key objective is to continuously enhance the proposed system through an inductive learning approach that considers semantic influences. The system is currently under construction and development based on data-driven learning. To evaluate the tool, an experiment on homograph analysis was conducted. The tool also encompasses the assumption of deep binary segmentation hypotheses, the arbitrary choice of trigram or n-gram continuation probabilities, language limitations, and morphology for both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), which provide justification for updating this system. Most Arabic word analysis systems are based on the phonotactic morpho-syntactic analysis of a word transmitted using lexical rules, which are mainly used in MENA language technology tools, without taking into account contextual or semantic morphological implications. Therefore, it is necessary to have an automatic analysis tool taking into account the word sense and not only the morpho-syntactic category. Moreover, they are also based on statistical/stochastic models. These stochastic models, such as HMMs, have shown their effectiveness in different NLP applications: part-of-speech tagging, machine translation, speech recognition, etc. As an extension, we focus on language modeling using Recurrent Neural Network (RNN); given that morphological analysis coverage was very low in dialectal Arabic, it is significantly important to investigate deeply how the dialect data influence the accuracy of these approaches by developing dialectal morphological processing tools to show that dialectal variability can support to improve analysis.

Keywords: NLP, DL, ML, analyser, MSA, RNN, CNN

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