Correlation and association analyses in microbiome study integrating multiomics in health and disease

Temporal dynamics of user activities: deep learning strategies and mathematical modeling for long-term and short-term profiling

Profiling social media users is an analytical approach to generate an extensive blueprint of user's personal characteristics, which can be useful for a diverse range of applications, such as targeted marketing and personalized recommendations. Although social user profiling has gained substantial attention in recent years, effectively constructing a collaborative model that could describe long and short-term profiles is still challenging. In this paper, we will discuss the profiling problem from two perspectives; how to mathematically model and track user's behavior over short and long periods and how to enhance the classification of user's activities. Using mathematical equations, our model can define periods in which the user's interests abruptly changed. A dataset consisting of 30,000 tweets was built and manually annotated into 10 topic categories. Bi-LSTM and GRU models are applied to classify the user's activities representing his interests, which then are utilized to create and model the dynamic profile. In addition, the effect of word embedding techniques and pre-trained classification models on the accuracy of the classification process is explored in this research.

An optimised and validated surrogate analyte A-TEEM-PARAFAC-PLS technique for detecting and quantifying the biological oxygen demand in surface water

A 5-day test duration makes BOD5 measurement unsatisfactory and hinders the development of a quick technique. Protein-like fluorescence peaks show a strong correlation between the BOD characteristics and the fluorescence intensities. For identifying and measuring BOD in surface water, a simultaneous absorbance-transmittance and fluorescence excitation-emission matrices (A-TEEM) method combined with PARAFAC (parallel factor) and PLS (partial least squares) analyses was developed using a tyrosine and tryptophan (tyr-trpt) mix as a surrogate analyte for BOD. The use of a surrogate analyte was decided upon due to lack of fluorescent BOD standards. Tyr-trpt mix standard solutions were added to surface water samples to prepare calibration and validation samples. PARAFAC analysis of excitation-emission matrices detected the tyr-trpt mix in surface water. PLS modelling demonstrated significant linearity (R2 = 0.991) between the predicted and measured tyr-trypt mix concentrations, and accuracy and robustness were all acceptable per the ICH Q2 (R2) and ASTM multivariate calibration/validation procedures guidelines. Based on a suitable and workable surrogate analyte method, these results imply that BOD can be detected and quantified using the A-TEEM-PARAFAC-PLS method. Very positive comparability between tyr-trypt mix concentrations was found, suggesting that tyr-trypt mix might eventually take the place of a BOD-based sampling protocol. Overall, this approach offers a novel tool that can be quickly applied in water treatment plant settings and is a step in supporting the trend toward rapid BOD determination in waters. Further studies should demonstrate the wide application of the method using real wastewater samples from various water treatment facilities.

The Roles of NOTCH3 p.R544C and Thrombophilia Genes in Vietnamese Patients With Ischemic Stroke: Study Involving a Hierarchical Cluster Analysis

BACKGROUND

The etiology of ischemic stroke is multifactorial. Several gene mutations have been identified as leading causes of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), a hereditary disease that causes stroke and other neurological symptoms.

OBJECTIVE

We aimed to identify the variants of NOTCH3 and thrombophilia genes, and their complex interactions with other factors.

METHODS

We conducted a hierarchical cluster analysis (HCA) on the data of 100 patients diagnosed with ischemic stroke. The variants of NOTCH3 and thrombophilia genes were identified by polymerase chain reaction with confronting 2-pair primers and real-time polymerase chain reaction. The overall preclinical characteristics, cumulative cutpoint values, and factors associated with these somatic mutations were analyzed in unidimensional and multidimensional scaling models.

RESULTS

We identified the following optimal cutpoints: creatinine, 83.67 (SD 9.19) µmol/L; age, 54 (SD 5) years; prothrombin (PT) time, 13.25 (SD 0.17) seconds; and international normalized ratio (INR), 1.02 (SD 0.03). Using the Nagelkerke method, cutpoint 50% values of the Glasgow Coma Scale score; modified Rankin scale score; and National Institutes of Health Stroke Scale scores at admission, after 24 hours, and at discharge were 12.77, 2.86 (SD 1.21), 9.83 (SD 2.85), 7.29 (SD 2.04), and 6.85 (SD 2.90), respectively.

CONCLUSIONS

The variants of MTHFR (C677T and A1298C) and NOTCH3 p.R544C may influence the stroke severity under specific conditions of PT, creatinine, INR, and BMI, with risk ratios of 4.8 (95% CI 1.53-15.04) and 3.13 (95% CI 1.60-6.11), respectively (Pfisher<.05). It is interesting that although there are many genes linked to increased atrial fibrillation risk, not all of them are associated with ischemic stroke risk. With the detection of stroke risk loci, more information can be gained on their impacts and interconnections, especially in young patients.

mi-Mic: a novel multi-layer statistical test for microbiota-disease associations

mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.

Criteria to evaluate graduate nurse proficiencies in obtaining a health history and perform physical assessment in simulation-based education: A narrative review

BACKGROUND

Simulation is a technique being used increasingly in healthcare education which offers opportunities to evaluate nursing proficiencies. The use of valid and reliable instruments is recognised as the foundation for a robust assessment, however competency-based health assessment courses for graduate nurses can consequently become reductionist in measuring proficiencies.

OBJECTIVE

The specific review question was: In simulation-based education, what are the criteria that evaluate graduate nursing student's competence in obtaining a health history and performance of patient assessment?

METHODS

Eleven studies were included in the review. Papers were critically appraised with The Joanna Briggs Institute quasi-experimental studies checklist. Bloom's taxonomy was used to structure this narrative review.

RESULTS

Seven papers evaluated cognition through questionnaires and two papers used a Likert-scale to determine self-perceived knowledge. Six papers evaluated psychomotor skills with a behavioural checklist. Diversity of application was factored into the studies when testing affective skills. Three papers used Likert-scales to evaluate preparedness, six papers used Likert-scales to evaluate self-confidence and one used a Likert-scale to evaluate autonomy. Three papers used a checklist to evaluate professionalism. Four papers used faculty member/ standardised patient feedback.

CONCLUSION

Reductionist evaluation instruments create a barrier when evaluating competency. The limited validity and reliability of assessment instruments in simulation, as well as the lack of standardisation of affective skills assessment, presents a challenge in simulation research. Affective skills encompass attitudes, behaviours and communication abilities, which pose a significant challenge for standardised assessments due to their subjective nature. This review of the simulation literature highlights a lack of robustness in the evaluation of the affective domain. This paper proposes that simulation assessment instruments should include the standardisation of affective domain proficiencies such as: adaptation to patients' cognitive function, ability to interpret and synthesise relevant information, ability to demonstrate clinical judgement, readiness to act, recognition of professional limitations and faculty/standardised-simulated patient feedback. The incorporation of the affective domain in standardised assessment instruments is important to ensure comprehensive assessment of simulation particularly in the development of health history and physical assessment proficiencies. Attention to all of the domains in Blooms taxonomy during simulation assessment has the potential to better prepare professionals for the patient care setting.

A comparative study of feature selection and feature extraction methods for financial distress identification

Financial distress identification remains an essential topic in the scientific literature due to its importance for society and the economy. The advancements in information technology and the escalating volume of stored data have led to the emergence of financial distress that transcends the realm of financial statements and its' indicators (ratios). The feature space could be expanded by incorporating new perspectives on feature data categories such as macroeconomics, sectors, social, board, management, judicial incident, etc. However, the increased dimensionality results in sparse data and overfitted models. This study proposes a new approach for efficient financial distress classification assessment by combining dimensionality reduction and machine learning techniques. The proposed framework aims to identify a subset of features leading to the minimization of the loss function describing the financial distress in an enterprise. During the study, 15 dimensionality reduction techniques with different numbers of features and 17 machine-learning models were compared. Overall, 1,432 experiments were performed using Lithuanian enterprise data covering the period from 2015 to 2022. Results revealed that the artificial neural network (ANN) model with 30 ranked features identified using the Random Forest mean decreasing Gini (RF_MDG) feature selection technique provided the highest AUC score. Moreover, this study has introduced a novel approach for feature extraction, which could improve financial distress classification models.

RNA viromes of Dermacentor nuttalli ticks reveal a novel uukuvirus in Qīnghǎi Province, China

Ticks are a major parasite on the Qīnghǎi-Tibet Plateau, western China, and represent an economic burden to agriculture and animal husbandry. Despite research on tick-borne pathogens that threaten humans and animals, the viromes of dominant tick species remain unknown. In this study, we collected Dermacentor nuttalli ticks near Qīnghǎi Lake and identified 13 viruses belonging to at least six families through metagenomic sequencing. Four viruses were of high abundance in pools, including Xīnjiāng tick-associated virus 1 (XJTAV1), and three novel viruses: Qīnghǎi Lake virus 1, Qīnghǎi Lake virus 2 (QHLV1, and QHLV2, unclassified), and Qīnghǎi Lake virus 3 (QHLV3, genus Uukuvirus of family Phenuiviridae in order Bunyavirales), which lacks the M segment. The minimum infection rates of the four viruses among the tick groups were 8.2%, 49.5%, 6.2%, and 24.7%, respectively, suggesting the prevalence of these viruses in D. nuttalli ticks. A putative M segment of QHLV3 was identified from next-generation sequencing data and further characterized for its signal peptide cleavage site, N-glycosylation, and transmembrane region. Furthermore, we probed the L, M, and S segments of other viruses using the putative M segment sequence with sequencing data of other tick pools. By revealing the viromes of D. nuttalli ticks, this study enhances our understanding of tick-borne viral communities in highland regions. The putative M segment identified in a novel uukuvirus suggests that previously identified uukuviruses without M segments should have had the same genome organization as typical bunyaviruses. These results will facilitate virus discovery and our understanding of the phylogeny of tick-borne uukuviruses.

Machine learning-based morphological quantification of replicative senescence in human fibroblasts

Although aging has been investigated extensively at the organismal and cellular level, the morphological changes that individual cells undergo along their replicative lifespan have not been precisely quantified. Here, we present the results of a readily accessible machine learning-based pipeline that uses standard fluorescence microscope and open access software to quantify the minute morphological changes that human fibroblasts undergo during their replicative lifespan in culture. Applying this pipeline in a widely used fibroblast cell line (IMR-90), we find that advanced replicative age robustly increases (+28-79%) cell surface area, perimeter, number and total length of pseudopodia, and nuclear surface area, while decreasing cell circularity, with phenotypic changes largely occurring as replicative senescence is reached. These senescence-related morphological changes are recapitulated, albeit to a variable extent, in primary dermal fibroblasts derived from human donors of different ancestry, age, and sex groups. By performing integrative analysis of single-cell morphology, our pipeline further classifies senescent-like cells and quantifies how their numbers increase with replicative senescence in IMR-90 cells and in dermal fibroblasts across all tested donors. These findings provide quantitative insights into replicative senescence, while demonstrating applicability of a readily accessible computational pipeline for high-throughput cell phenotyping in aging research.

On-site colorimetric food spoilage monitoring with smartphone embedded machine learning

Real-time and on-site food spoilage monitoring is still a challenging issue to prevent food poisoning. At the onset of food spoilage, microbial and enzymatic activities lead to the formation of volatile amines. Monitoring of these amines with conventional methods requires sophisticated, costly, labor-intensive, and time consuming analysis. Here, anthocyanins rich red cabbage extract (ARCE) based colorimetric sensing system was developed with the incorporation of embedded machine learning in a smartphone application for real-time food spoilage monitoring. FG-UV-CD100 films were first fabricated by crosslinking ARCE-doped fish gelatin (FG) with carbon dots (CDs) under UV light. The color change of FG-UV-CD100 films with varying ammonia vapor concentrations was captured in different light sources with smartphones of various brands, and a comprehensive dataset was created to train machine learning (ML) classifiers to be robust and adaptable to ambient conditions, resulting in 98.8% classification accuracy. Meanwhile, the ML classifier was embedded into our Android application, SmartFood++, enabling analysis in about 0.1 s without internet access, unlike its counterpart using cloud operation via internet. The proposed system was also tested on a real fish sample with 99.6% accuracy, demonstrating that it has a great advantage as a potent tool for on-site real-time monitoring of food spoilage by non-specialized personnel.

Statistical normalization methods in microbiome data with application to microbiome cancer research

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

Study of genes polymorphisms in RANK/RANKL/OPG and WNT signaling pathways and their associations with bone parameters in broiler chicken

Limb problems are one of the most common problems with fast-growing meat-type chickens. Various bone abnormalities, which can lead to limping, bone weakness, or even fractures, bring overall discomfort to birds and a loss of production. Genetic aspects are often associated with these side effects on bone stability and are also cited as the dominant cause. These points to a close negative relationship of genetic selection for rapid growth with traits involved in bone integrity. Due to the assumption of an additive genetic background, improvements through genetic tools can be used. Our study is focused on selected genes of important signaling pathways for bone metabolism. We tried to detect polymorphisms that would show associations with selected bone parameters in a total of 48 broilers. Those were fast-growing Ross 308 hybrids and slow-growing Hubbard M22BxJA87A hybrids. The TNFRSF11A and WISP1 genes were tested. A total of fourteen polymorphisms were found, three of them were synonymous and five in the intron. In the case of four polymorphisms found in exons of the TNFRSF11A gene (c.11G > T, c.31G > A, c.37C > G, c.514G > A), associations with the observed bone parameters (bone strength, bone dimensions and bone mass) were demonstrated. The genetic architecture of bone traits is not fully understood, therefore the present study and the knowledge gained can help to increase the potential in poultry breeding processes and thus reduce the death of individuals.

Detection and Quantification of Bisphenol A in Surface Water Using Absorbance-Transmittance and Fluorescence Excitation-Emission Matrices (A-TEEM) Coupled with Multiway Techniques

In the present protocol, we determined the presence and concentrations of bisphenol A (BPA) spiked in surface water samples using EEM fluorescence spectroscopy in conjunction with modelling using partial least squares (PLS) and parallel factor (PARAFAC). PARAFAC modelling of the EEM fluorescence data obtained from surface water samples contaminated with BPA unraveled four fluorophores including BPA. The best outcomes were obtained for BPA concentration (R2 = 0.996; standard deviation to prediction error's root mean square ratio (RPD) = 3.41; and a Pearson's r value of 0.998). With these values of R2 and Pearson's r, the PLS model showed a strong correlation between the predicted and measured BPA concentrations. The detection and quantification limits of the method were 3.512 and 11.708 micro molar (µM), respectively. In conclusion, BPA can be precisely detected and its concentration in surface water predicted using the PARAFAC and PLS models developed in this study and fluorescence EEM data collected from BPA-contaminated water. It is necessary to spatially relate surface water contamination data with other datasets in order to connect drinking water quality issues with health, environmental restoration, and environmental justice concerns.

Harnessing the power of synthetic data in healthcare: innovation, application, and privacy

Data-driven decision-making in modern healthcare underpins innovation and predictive analytics in public health and clinical research. Synthetic data has shown promise in finance and economics to improve risk assessment, portfolio optimization, and algorithmic trading. However, higher stakes, potential liabilities, and healthcare practitioner distrust make clinical use of synthetic data difficult. This paper explores the potential benefits and limitations of synthetic data in the healthcare analytics context. We begin with real-world healthcare applications of synthetic data that informs government policy, enhance data privacy, and augment datasets for predictive analytics. We then preview future applications of synthetic data in the emergent field of digital twin technology. We explore the issues of data quality and data bias in synthetic data, which can limit applicability across different applications in the clinical context, and privacy concerns stemming from data misuse and risk of re-identification. Finally, we evaluate the role of regulatory agencies in promoting transparency and accountability and propose strategies for risk mitigation such as Differential Privacy (DP) and a dataset chain of custody to maintain data integrity, traceability, and accountability. Synthetic data can improve healthcare, but measures to protect patient well-being and maintain ethical standards are key to promote responsible use.

Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability

Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing multi-in silico features from a population of human ventricular cell models that could capture a representation of the underlying mechanisms contributing to TdP risk to provide a more reliable assessment of drug-induced cardiotoxicity. Method: We generated a virtual population of human ventricular cell models using a modified O'Hara-Rudy model, allowing inter-individual variation. IC 50 and Hill coefficients from 67 drugs were used as input to simulate drug effects on cardiac cells. Fourteen features (dVm dt repol , dVm dt max , Vm peak , Vm resting , APD tri , APD 90 , APD 50 , Ca peak , Ca diastole , Ca tri , CaD 90 , CaD 50 , qNet, qInward) could be generated from the simulation and used as input to several machine learning models, including k-nearest neighbor (KNN), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). Optimization of the machine learning model was performed using a grid search to select the best parameter of the proposed model. We applied five-fold cross-validation while training the model with 42 drugs and evaluated the model's performance with test data from 25 drugs. Result: The proposed ANN model showed the highest performance in predicting the TdP risk of drugs by providing an accuracy of 0.923 (0.908-0.937), sensitivity of 0.926 (0.909-0.942), specificity of 0.921 (0.906-0.935), and AUC score of 0.964 (0.954-0.975). Discussion and conclusion: According to the performance results, combining the electrophysiological model including inter-individual variation and optimization of machine learning showed good generalization ability when evaluated using the unseen dataset and produced a reliable drug-induced TdP risk prediction system.

Mass spectrometry-based analysis of gut microbial metabolites of aromatic amino acids

Small molecules derived from gut microbiota have been increasingly investigated to better understand the functional roles of the human gut microbiome. Microbial metabolites of aromatic amino acids (AAA) have been linked to many diseases, such as metabolic disorders, chronic kidney diseases, inflammatory bowel disease, diabetes, and cancer. Important microbial AAA metabolites are often discovered via global metabolite profiling of biological specimens collected from humans or animal models. Subsequent metabolite identity confirmation and absolute quantification using targeted analysis enable comparisons across different studies, which can lead to the establishment of threshold concentrations of potential metabolite biomarkers. Owing to their excellent selectivity and sensitivity, hyphenated mass spectrometry (MS) techniques are often employed to identify and quantify AAA metabolites in various biological matrices. Here, we summarize the developments over the past five years in MS-based methodology for analyzing gut microbiota-derived AAA. Sample preparation, method validation, analytical performance, and statistical methods for correlation analysis are discussed, along with future perspectives.

Quantifying Decay Due to Wet Atmospheric Deposition on Basalt

The study of building materials is important for a better conservation of built heritage. Worldwide, volcanic stones (including basalt, andesite and dacite) are among the least studied building materials. In this research, the decay of a red basalt due to wet atmospheric deposition was studied. Red basalt was exposed to artificial rain solutions, prepared from rain samples collected weekly from 2014-2019. In this research, the decay of stone-built heritage was indirectly studied emulating wet atmospheric accelerated weathering under three different volume weighted mean (VWM) compositions: global, acid and no-acid categories. Lixiviates were analyzed to better understand the deterioration mechanisms taking place inside the material. Decay was quantified as mass difference, water absorption capacity (WAC) and open porosity (OP) changes. Results show that the methodology used is suitable to research the decay of built heritage. The studied basalt is indeed prone to decay by wet atmospheric deposition. The main decay mechanisms are the washing of insoluble compounds, dissolution of minerals, salt crystallization and cation exchange. WAC and OP showed promising results of their appropriateness as monitoring variables of decay in situ. Acid conditions produce the most severe decay, but Ph effect is not as important as precipitation volume. Non-linear equations relating volume of precipitation with mass difference in red basalt are presented.

Exploring the Frequency and Distribution of Ecological Non-monotonicity in Associations among Ecosystem Constituents

Complex links between biotic and abiotic constituents are fundamental for the functioning of ecosystems. Although non-monotonic interactions and associations are known to increase the stability, diversity, and productivity of ecosystems, they are frequently ignored by community-level standard statistical approaches. Using the copula-based dependence measure qad, capable of quantifying the directed and asymmetric dependence between variables for all forms of (functional) relationships, we determined the proportion of non-monotonic associations between different constituents of an ecosystem (plants, bacteria, fungi, and environmental parameters). Here, we show that up to 59% of all statistically significant associations are non-monotonic. Further, we show that pairwise associations between plants, bacteria, fungi, and environmental parameters are specifically characterized by their strength and degree of monotonicity, for example, microbe-microbe associations are on average stronger than and differ in degree of non-monotonicity from plant-microbe associations. Considering directed and non-monotonic associations, we extended the concept of ecosystem coupling providing more complete insights into the internal order of ecosystems. Our results emphasize the importance of ecological non-monotonicity in characterizing and understanding ecosystem patterns and processes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10021-023-00867-9.

Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods

The monkeypox virus poses a novel public health risk that might quickly escalate into a worldwide epidemic. Machine learning (ML) has recently shown much promise in diagnosing diseases like cancer, finding tumor cells, and finding COVID-19 patients. In this study, we have created a dataset based on the data both collected and published by Global Health and used by the World Health Organization (WHO). Being entirely textual, this dataset shows the relationship between the symptoms and the monkeypox disease. The data have been analyzed, using gradient boosting methods such as Extreme Gradient Boosting (XGBoost), CatBoost, and LightGBM along with other standard machine learning methods such as Support Vector Machine (SVM) and Random Forest. All these methods have been compared. The research aims to provide an ML model based on symptoms for the diagnosis of monkeypox. Previous studies have only examined disease diagnosis using images. The best performance has belonged to XGBoost, with an accuracy of 1.0 in reviews. To check the model's flexibility, k-fold cross-validation is used, reaching an average accuracy of 0.9 in 5 different splits of the test set. In addition, Shapley Additive Explanations (SHAP) helps in examining and explaining the output of the XGBoost model.

Virulence and genetic analysis of Puccinia graminis tritici in the Indian sub-continent from 2016 to 2022 and evaluation of wheat varieties for stem rust resistance

Wheat stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), has re-emerged as one of the major concerns for global wheat production since the evolution of Ug99 and other virulent pathotypes of Pgt from East Africa, Europe, Central Asia, and other regions. Host resistance is the most effective, economic, and eco-friendly approach for managing stem rust. Understanding the virulence nature, genetic diversity, origin, distribution, and evolutionary pattern of Pgt pathotypes over time and space is a prerequisite for effectively managing newly emerging Pgt isolates through host resistance. In the present study, we monitored the occurrence of stem rust of wheat in India and neighboring countries from 2016 to 2022, collected 620 single-pustule isolates of Pgt from six states of India and Nepal, analyzed them on Indian stem rust differentials, and determined their virulence phenotypes and molecular genotypes. The Ug99 type of pathotypes did not occur in India. Pathotypes 11 and 40A were most predominant during these years. Virulence phenotyping of these isolates identified 14 Pgt pathotypes, which were genotyped using 37 Puccinia spp.-specific polymorphic microsatellites, followed by additional phylogenetic analyses using DARwin. These analyses identified three major molecular groups, demonstrating fewer lineages, clonality, and long-distance migration of Pgt isolates in India. Fourteen of the 40 recently released Indian wheat varieties exhibited complete resistance to all 23 Pgt pathotypes at the seedling stage. Twelve Sr genes were postulated in 39 varieties based on their seedling response to Pgt pathotypes. The values of slow rusting parameters i.e. coefficient of infection, area under disease progress curve, and infection rates, assessed at adult plant stage at five geographically different locations during two crop seasons, indicated the slow rusting behavior of several varieties. Six Sr genes (Sr2, Sr57, Sr58, Sr24, Sr31, and Sr38) were identified in 24 wheat varieties using molecular markers closely linked to these genes. These findings will guide future breeding programs toward more effective management of wheat stem rust.

MLATE: Machine learning for predicting cell behavior on cardiac tissue engineering scaffolds

Cardiovascular disease is one of the leading causes of mortality worldwide and is responsible for millions of deaths annually. One of the most promising approaches to deal with this problem, which has spread recently, is cardiac tissue engineering (CTE). Many researchers have tried developing scaffolds with different materials, cell lines, and fabrication methods to help regenerate heart tissue. Machine learning (ML) is one of the hottest topics in science and technology, revolutionizing many fields and changing our perspective on solving problems. As a result of using ML, some scientific issues have been resolved, including protein-folding, a challenging problem in biology that remained unsolved for 50 years. However, it is not well addressed in tissue engineering. An AI-based software was developed by our group called MLATE (Machine Learning Applications in Tissue Engineering) to tackle tissue engineering challenges, which highly depend on conducting costly and time-consuming experiments. For the first time, to the best of our knowledge, a CTE scaffold dataset was created by collecting specifications from the literature, including different materials, cell lines, and fabrication methods commonly used in CTE scaffold development. These specifications were used as variables in the study. Then, the CTE scaffolds were rated based on cell behaviors such as cell viability, growth, proliferation, and differentiation on the scaffold on a scale of 0-3. These ratings were considered a function of the variables in the gathered dataset. It should be stated that this study was merely based on information available in the literature. Then, twenty-eight ML algorithms were applied to determine the most effective one for predicting cell behavior on CTE scaffolds fabricated by different materials, compositions, and methods. The results indicated the high performance of XGBoost with an accuracy of 87%. Also, by implementing ensemble learning algorithms and using five algorithms with the best performance, an accuracy of 93% with the AdaBoost Classifier and Voting Classifier was achieved. Finally, the open-source software developed in this study was made available for everyone by publishing the best model along with a step-by-step guide to using it online at: https://github.com/saeedrafieyan/MLATE.

Physical activity-induced alterations of the gut microbiota are BMI dependent

Physical inactivity is one of the leading causes of chronic metabolic disease including obesity. Increasing physical activity (PA) has been shown to improve cardiometabolic and musculoskeletal health and to be associated with a distinct gut microbiota composition in trained athletes. However, the impact of PA on the gut microbiota is inconclusive for individuals performing PA in their day-to-day life. This study examined the role of PA and hand-grip strength on gut microbiome composition in middle-aged adults (40-65 years, n = 350) with normal (18.5-24.9 kg/m2 ) and overweight (25-29.9 kg/m2 ) body mass index (BMI). PA was recorded using the International Physical Activity Questionnaire, and hand-grip strength was measured using a dynamometer. Serum samples were assessed for lipidomics while DNA was extracted from fecal samples for microbiome analysis. Overweight participants showed a higher concentration of triacylglycerols, and lower concentrations of cholesteryl esters, sphingomyelin, and lyso-phosphotidylcholine lipids (p < .05) compared with those with normal BMI. Additionally, overweight participants had a lower abundance of the Oscillibacter genus (p < .05). The impact of PA duration on the gut microbiome was BMI dependent. In normal but not overweight participants, high PA duration showed greater relative abundance of commensal taxa such as Actinobacteria and Proteobacteria phyla, as well as Collinsella and Prevotella genera (p < .05). Furthermore, in males with normal BMI, a stronger grip strength was associated with a higher relative abundance of Faecalibacterium and F. prausnitzii (p < .05) compared with lower grip strength. Taken together, data suggest that BMI plays a significant role in modeling PA-induced changes in gut microbiota.

Development of a lateral flow dipstick test for the detection of 4 strains of Salmonella spp. in animal products and animal production environmental samples based on loop-mediated isothermal amplification

OBJECTIVE

This study aimed to develop loop-mediated isothermal amplification (LAMP) combined with lateral flow dipstick (LFD) and compare it with LAMP-AGE, polymerase chain reaction (PCR), and standard Salmonella culture as reference methods for detecting Salmonella contamination in animal products and animal production environmental samples.

METHODS

The SalInvA01 primer, derived from the InvA gene and designed as a new probe for LFD detection, was used in developing this study. Adjusting for optimal conditions by temperature, time, and reagent concentration includes evaluating the specificity and limit of detection. The sampling of 120 animal product samples and 350 animal production environmental samples was determined by LAMP-LFD, comparing LAMP-AGE, PCR, and the culture method.

RESULTS

Salmonella was amplified using optimal conditions for the LAMP reaction and a DNA probe for LFD at 63°C for 60 minutes. The specificity test revealed no cross-reactivity with other microorganisms. The limit of detection of LAMP-LFD in pure culture was 3×102 CFU/mL (6 CFU/reaction) and 9.01 pg/μL in genomic DNA. The limit of detection of the LAMP-LFD using artificially inoculated in minced chicken samples with 5 hours of pre-enrichment was 3.4×104 CFU/mL (680 CFU/reaction). For 120 animal product samples, Salmonella was detected by the culture method, LAMP-LFD, LAMP-AGE, and PCR in 10/120 (8.3%). In three hundred fifty animal production environmental samples, Salmonella was detected in 91/350 (26%) by the culture method, equivalent to the detection rates of LAMP-LFD and LAMP-AGE, while PCR achieved 86/350 (24.6%). When comparing sensitivity, specificity, positive predictive value, and accuracy, LAMP-LFD showed the best results at 100%, 95.7%, 86.3%, and 96.6%, respectively. For Kappa index of LAMP-LFD, indicated nearly perfect agreement with culture method.

CONCLUSION

The LAMP-LFD Salmonella detection, which used InvA gene, was highly specific, sensitive, and convenient for identifying Salmonella. Furthermore, this method could be used for Salmonella monitoring and primary screening in animal products and animal production environmental samples.

Genetic correlations between Alzheimer's disease and gut microbiome genera

A growing body of evidence suggests that dysbiosis of the human gut microbiota is associated with neurodegenerative diseases like Alzheimer's disease (AD) via neuroinflammatory processes across the microbiota-gut-brain axis. The gut microbiota affects brain health through the secretion of toxins and short-chain fatty acids, which modulates gut permeability and numerous immune functions. Observational studies indicate that AD patients have reduced microbiome diversity, which could contribute to the pathogenesis of the disease. Uncovering the genetic basis of microbial abundance and its effect on AD could suggest lifestyle changes that may reduce an individual's risk for the disease. Using the largest genome-wide association study of gut microbiota genera from the MiBioGen consortium, we used polygenic risk score (PRS) analyses with the "best-fit" model implemented in PRSice-2 and determined the genetic correlation between 119 genera and AD in a discovery sample (ADc12 case/control: 1278/1293). To confirm the results from the discovery sample, we next repeated the PRS analysis in a replication sample (GenADA case/control: 799/778) and then performed a meta-analysis with the PRS results from both samples. Finally, we conducted a linear regression analysis to assess the correlation between the PRSs for the significant genera and the APOE genotypes. In the discovery sample, 20 gut microbiota genera were initially identified as genetically associated with AD case/control status. Of these 20, three genera (Eubacterium fissicatena as a protective factor, Collinsella, and Veillonella as a risk factor) were independently significant in the replication sample. Meta-analysis with discovery and replication samples confirmed that ten genera had a significant correlation with AD, four of which were significantly associated with the APOE rs429358 risk allele in a direction consistent with their protective/risk designation in AD association. Notably, the proinflammatory genus Collinsella, identified as a risk factor for AD, was positively correlated with the APOE rs429358 risk allele in both samples. Overall, the host genetic factors influencing the abundance of ten genera are significantly associated with AD, suggesting that these genera may serve as biomarkers and targets for AD treatment and intervention. Our results highlight that proinflammatory gut microbiota might promote AD development through interaction with APOE. Larger datasets and functional studies are required to understand their causal relationships.

Machine learning based identification of structural brain alterations underlying suicide risk in adolescents

Suicide is the third leading cause of death for individuals between 15 and 19 years of age. The high suicide mortality rate and limited prior success in identifying neuroimaging biomarkers indicate that it is crucial to improve the accuracy of clinical neural signatures underlying suicide risk. The current study implements machine-learning (ML) algorithms to examine structural brain alterations in adolescents that can discriminate individuals with suicide risk from typically developing (TD) adolescents at the individual level. Structural MRI data were collected from 79 adolescents who demonstrated clinical levels of suicide risk and 79 demographically matched TD adolescents. Region-specific cortical/subcortical volume (CV/SCV) was evaluated following whole-brain parcellation into 1000 cortical and 12 subcortical regions. CV/SCV parameters were used as inputs for feature selection and three ML algorithms (i.e., support vector machine [SVM], K-nearest neighbors, and ensemble) to classify adolescents at suicide risk from TD adolescents. The highest classification accuracy of 74.79% (with sensitivity = 75.90%, specificity = 74.07%, and area under the receiver operating characteristic curve = 87.18%) was obtained for CV/SCV data using the SVM classifier. Identified bilateral regions that contributed to the classification mainly included reduced CV within the frontal and temporal cortices but increased volume within the cuneus/precuneus for adolescents at suicide risk relative to TD adolescents. The current data demonstrate an unbiased region-specific ML framework to effectively assess the structural biomarkers of suicide risk. Future studies with larger sample sizes and the inclusion of clinical controls and independent validation data sets are needed to confirm our findings.

Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine

Since 1978, with the first IVF (in vitro fertilization) baby birth in Manchester (England), more than eight million IVF babies have been born throughout the world, and many new techniques and discoveries have emerged in reproductive medicine. To summarize the modern technology and progress in reproductive medicine, all scientific papers related to reproductive medicine, especially papers related to reproductive translational medicine, were fully searched, manually curated and reviewed. Results indicated whether male reproductive medicine or female reproductive medicine all have made significant progress, and their markers have experienced the progress from karyotype analysis to single-cell omics. However, due to the lack of comprehensive databases, especially databases collecting risk exposures, disease markers and models, prevention drugs and effective treatment methods, the application of the latest precision medicine technologies and methods in reproductive medicine is limited.

Depression and fatigue in active IBD from a microbiome perspective-a Bayesian approach to faecal metagenomics

BACKGROUND

Extraintestinal symptoms are common in inflammatory bowel diseases (IBD) and include depression and fatigue. These are highly prevalent especially in active disease, potentially due to inflammation-mediated changes in the microbiota-gut-brain axis. The aim of this study was to investigate the associations between structural and functional microbiota characteristics and severity of fatigue and depressive symptoms in patients with active IBD.

METHODS

We included clinical data of 62 prospectively enrolled patients with IBD in an active disease state. Patients supplied stool samples and completed the questionnaires regarding depression and fatigue symptoms. Based on taxonomic and functional metagenomic profiles of faecal gut microbiota, we used Bayesian statistics to investigate the associative networks and triangle motifs between bacterial genera, functional modules and symptom severity of self-reported fatigue and depression.

RESULTS

Associations with moderate to strong evidence were found for 3 genera (Odoribacter, Anaerotruncus and Alistipes) and 3 functional modules (pectin, glycosaminoglycan and central carbohydrate metabolism) with regard to depression and for 4 genera (Intestinimonas, Anaerotruncus, Eubacterium and Clostridiales g.i.s) and 2 functional modules implicating amino acid and central carbohydrate metabolism with regard to fatigue.

CONCLUSIONS

This study provides the first evidence of association triplets between microbiota composition, function and extraintestinal symptoms in active IBD. Depression and fatigue were associated with lower abundances of short-chain fatty acid producers and distinct pathways implicating glycan, carbohydrate and amino acid metabolism. Our results suggest that microbiota-directed therapeutic approaches may reduce fatigue and depression in IBD and should be investigated in future research.

Machine Learning-Based Quantification of (-)-trans-Δ-Tetrahydrocannabinol from Human Saliva Samples on a Smartphone-Based Paper Microfluidic Platform

(-)-trans-Δ-Tetrahydrocannabinol (THC) is a major psychoactive component in cannabis. Despite the recent trends of THC legalization for medical or recreational use in some areas, many THC-driven impairments have been verified. Therefore, convenient, sensitive, quantitative detection of THC is highly needed to improve its regulation and legalization. We demonstrated a biosensor platform to detect and quantify THC with a paper microfluidic chip and a handheld smartphone-based fluorescence microscope. Microfluidic competitive immunoassay was applied with anti-THC-conjugated fluorescent nanoparticles. The smartphone-based fluorescence microscope counted the fluorescent nanoparticles in the test zone, achieving a 1 pg/mL limit of detection from human saliva samples. Specificity experiments were conducted with cannabidiol (CBD) and various mixtures of THC and CBD. No cross-reactivity to CBD was found. Machine learning techniques were also used to quantify the THC concentrations from multiple saliva samples. Multidimensional data were collected by diluting the saliva samples with saline at four different dilutions. A training database was established to estimate the THC concentration from multiple saliva samples, eliminating the sample-to-sample variations. The classification algorithms included k-nearest neighbor (k-NN), decision tree, and support vector machine (SVM), and the SVM showed the best accuracy of 88% in estimating six different THC concentrations. Additional validation experiments were conducted using independent validation sample sets, successfully identifying positive samples at 100% accuracy and quantifying the THC concentration at 80% accuracy. The platform provided a quick, low-cost, sensitive, and quantitative point-of-care saliva test for cannabis.

Data-Driven Estimation of a Driving Safety Tolerance Zone Using Imbalanced Machine Learning

Predicting driving behavior and crash risk in real-time is a problem that has been heavily researched in the past years. Although in-vehicle interventions and gamification features in post-trip dashboards have emerged, the connection between real-time driving behavior prediction and the triggering of such interventions is yet to be realized. This is the focus of the European Horizon2020 project “i-DREAMS”, which aims at defining, developing, testing and validating a ‘Safety Tolerance Zone’ (STZ) in order to prevent drivers from risky driving behaviors using interventions both in real-time and post-trip. However, the data-driven conceptualization of STZ levels is a challenging task, and data class imbalance might hinder this process. Following the project principles and taking the aforementioned challenges into consideration, this paper proposes a framework to identify the level of risky driving behavior as well as the duration of the time spent in each risk level by private car drivers. This aim is accomplished by four classification algorithms, namely Support Vector Machines (SVMs), Random Forest (RFs), AdaBoost, and Multilayer Perceptron (MLP) Neural Networks and imbalanced learning using the Adaptive Synthetic technique (ADASYN) in order to deal with the unbalanced distribution of the dataset in the STZ levels. Moreover, as an alternative approach of risk prediction, three regression algorithms, namely Ridge, Lasso, and Elastic Net are used to predict time duration. The results showed that RF and MLP outperformed the rest of the classifiers with 84% and 82% overall accuracy, respectively, and that the maximum speed of the vehicle during a 30 s interval, is the most crucial predictor for identifying the driving time at each safety level.

Covid-19 vaccination intensions among literate Ghanaians: Still the need to dissipate fear and build trust on vaccine efficacy?

Purpose

The study examined Covid-19 vaccinations intentions among literate Ghanaians and how it is been influenced by vaccine mistrust and the fear of the unforeseen side effects.

Design/Methodology/Approach

We used cross sectional data collected from 223 respondents by means of questionnaire disseminated through social media from 16th to 20th April, 2021. Likert-scale questions were asked regarding the knowledge, attitudes and perceptions of literate Ghanaians towards COVID-19 vaccines. Kruskal-Wallis and sample t-test were performed to ascertain the differences in vaccination intentions between key socioeconomic variables. A pairwise correlation was performed to examine the relationship between vaccination intensions and fear of the unforeseen, mistrust of the vaccine and concerns of profiteering. Finally, a binary probit regression model was fitted to examine the predictive effect of key variables on respondent’s vaccination intentions.

Findings

The results revealed a relatively low level of knowledge about the safety and efficacy of the COVID-19 vaccines. The sample t-test showed that males have a relatively positive attitude towards the COVID-19 vaccines than females at 5% level of significance. Mistrust of vaccine safety and efficacy have a significant negative influence on vaccination intensions at 1% significance level.

Originality/Value

This study provides the Ghanaian government and other stakeholders with useful information to aid in educational campaigns on the safety and effectiveness of the COVID-19 vaccine. More campaign efforts towards females could help increase uptake given their relatively poor attitudes towards the vaccine.

An Exploratory Study of Online Job Portal Data of the ICT Sector in Bangladesh: Analysis, Recommendations and Preliminary Implications for ICT Curriculum Reform

Many countries wish to achieve digital transformation, especially during the COVID-19 pandemic. The digital skills demand is changing fast. The time-series online job portal data for the ICT industry in Bangladesh provides an opportunity to analyze high demand job titles and skills over time. These time-series data address the question of the speed of changes in job titles and skills and responsiveness of computer science and engineering (CSE) curricula. This study gathers online job portal data of the ICT industry in Bangladesh from 2016 to 2021. Natural language processing is used to group similar skills and job titles following the O*NET Online taxonomy. In addition to the descriptive statistics, the statistical significance test and correlation analysis are conducted. The analysis could identify high demand ICT job titles (Software Developers, Computer System Engineers/Architects, Web Developers, Project Management Specialists) and skills (API, Database, JavaScript) but Computer System Engineer/Architect job titles and API skills are increasing fast. The shift from networking to JavaScript and UI Design is also noteworthy after COVID-19. The preliminary curricula analysis suggests the responsiveness of the CSE program, but online job portal data analysis might provide opportunities for developing unique CSE specialization, courses and curricula.

Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars

The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability prior to the construction procedure. In this study, the application of Tree-Based machine learning techniques is implemented to analyze the ultimate strain of non-spliced and spliced steel reinforcements. In this regard, a database containing the results of 225 experimental tests was collected based on the research investigations available in peer-reviewed international publications. The database included the mechanical properties of both non-spliced and mechanically spliced bars. For better accuracy, the databases of other splicing methods such as lap and welded-spliced methods were excluded from this research. The database was categorized as two sub-databases: training (85%) and testing (15%) of the developed models. Various effective parameters such as splice technique, steel grade of the bar, diameter of the steel bar, coupler geometry—including length and outer diameter along with the testing temperatures—were defined as the input variables for analyzing the ultimate strain using tree-based approaches including Decision Trees and Random Forest. The predicted outcomes were compared to the actual values and the precision of the prediction models was assessed via performance metrics, along with a Taylor diagram. Based on the reported results, the reliability of the proposed ML-based methods was acceptable (with an R2 ≥ 85%) and they were time-saving and cost-effective compared to more complicated, time-consuming, and expensive experimental examinations. More importantly, the models proposed in this study can be further considered as a part of a comprehensive prediction model for estimating the stress-strain behavior of steel bars.

Further evidence from common garden rearing experiments of heritable traits separating lean and siscowet lake charr (Salvelinus namaycush) ecotypes

Genetic evidence of selection for complex and polygenically regulated phenotypes can easily become masked by neutral population genetic structure and phenotypic plasticity. Without direct evidence of genotype‐phenotype associations it can be difficult to conclude to what degree a phenotype is heritable or a product of environment. Common garden laboratory studies control for environmental stochasticity and help to determine the mechanism that regulate traits. Here we assess lipid content, growth, weight, and length variation in full and hybrid F₁ crosses of deep and shallow water sympatric lake charr ecotypes reared for nine years in a common garden experiment. Redundancy analysis (RDA) and quantitative‐trait‐loci (QTL) genomic scans are used to identify associations between genotypes at 19,714 single nucleotide polymorphisms (SNPs) aligned to the lake charr genome and individual phenotypes to determine the role that genetic inheritance plays in ecotype phenotypic diversity. Lipid content, growth, length, and weight differed significantly among lake charr crosses throughout the experiment suggesting that pedigree plays a large roll in lake charr development. Polygenic scores of 15 SNPs putatively associated with lipid content and/or condition factor indicated that ecotype distinguishing traits are polygenically regulated and additive. A QTL identified on chromosome 38 contained >200 genes, some of which were associated with lipid metabolism and growth, demonstrating the complex nature of ecotype diversity. The results of our common garden study further indicate that lake charr ecotypes observed in nature are predetermined at birth and that ecotypes differ fundamentally in lipid metabolism and growth.

Predictive Modelling of Statistical Downscaling Based on Hybrid Machine Learning Model for Daily Rainfall in East-Coast Peninsular Malaysia

In recent years, climate change has demonstrated the volatility of unexpected events such as typhoons, flooding, and tsunamis that affect people, ecosystems and economies. As a result, the importance of predicting future climate has become even direr. The statistical downscaling approach was introduced as a solution to provide high-resolution climate projections. An effective statistical downscaling scheme aimed to be developed in this study is a two-phase machine learning technique for daily rainfall projection in the east coast of Peninsular Malaysia. The proposed approaches will counter the emerging issues. First, Principal Component Analysis (PCA) based on a symmetric correlation matrix is applied in order to rectify the issue of selecting predictors for a two-phase supervised model and help reduce the dimension of the supervised model. Secondly, two-phase machine learning techniques are introduced with a predictor selection mechanism. The first phase is a classification using Support Vector Classification (SVC) that determines dry and wet days. Subsequently, regression estimates the amount of rainfall based on the frequency of wet days using Support Vector Regression (SVR), Artificial Neural Networks (ANNs) and Relevant Vector Machines (RVMs). The comparison between hybridization models’ outcomes reveals that the hybrid of SVC and RVM reproduces the most reasonable daily rainfall prediction and considers high-precipitation extremes. The hybridization model indicates an improvement in predicting climate change predictions by establishing a relationship between the predictand and predictors.

Spatial Analysis of Air Quality Assessment in Two Cities in Nigeria: A Comparison of Perceptions with Instrument-Based Methods

The air quality (AQ) in urban contexts is a major concern, especially in the developing world. The environmental and social challenges created by poor AQ have continued to increase despite improvements in monitoring AQ using earth observation (EO) satellites, static and mobile ground-based sensors and models. However, these types of equipment can be expensive to purchase, operate, and maintain, especially for cities of the developing world, and, as a result, there is growing interest in the elicitation of residents’ perceptions of AQ. However, there is a need to analyse how the results obtained from sensor measurements and models match the AQ as perceived by residents. This study explored AQ in multiple locations in two developing world cities (Abuja and Enugu) in Nigeria by analysing the perceptions of 262 residents and how these compared with findings obtained from ground-based instruments. The results suggest that the perceived AQ of the locations broadly matches those obtained using instruments, although there were statistically significant differences between respondent groups based on the demographic factors of income-education (Abuja) and age (Enugu). This research supports the contention that perceptual AQ assessment provides a valuable source of data for policy and decision-makers when addressing poor AQ and can support action in the absence of instrument-based measurements.

Association of Strabismus With Mood Disorders, Schizophrenia, and Anxiety Disorders Among Children

Importance

Children with strabismus have poorer functional vision and decreased quality of life than those without strabismus.

Objective

To evaluate the association between strabismus and mental illness among children.

Design, Setting, and Participants

This cross-sectional study analyzed claims data from the OptumLabs Data Warehouse, a longitudinal deidentified commercial insurance claims database, from 12 005 189 patients enrolled in the health plan between January 1, 2007, and December 31, 2017. Eligibility criteria included age younger than 19 years at the time of strabismus diagnosis, enrollment in the health plan between 2007 and 2018, and having at least 1 strabismus claim based on International Classification of Diseases, Ninth Revision, Clinical Modification and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Clinical Modification codes. Controls were children in the same database with no eye disease codes other than refractive error reported. Demographic characteristics and mental illness claims were compared. Statistical analysis was conducted from December 1, 2018, to July 31, 2021.

Main Outcomes and Measures

Presence of mental illness claims.

Results

Among the 12 005 189 patients (6 095 523 boys [50.8%]; mean [SD] age, 8.0 [5.9] years) in the study, adjusted odds ratios for the association of mental illnesses with strabismus were 2.01 (95% CI, 1.99-2.04) for anxiety disorder, 1.83 (95% CI, 1.76-1.90) for schizophrenia, 1.64 (95% CI, 1.59-1.70) for bipolar disorder, 1.61 (95% CI, 1.59-1.63) for depressive disorder, and 0.99 (95% CI, 0.97-1.02) for substance use disorder. There was a moderate association between each strabismus type (esotropia, exotropia, and hypertropia) and anxiety disorder, schizophrenia, bipolar disorder, and depressive disorder; odds ratios ranged from 1.23 (95% CI, 1.17-1.29) for the association between esotropia and bipolar disorder to 2.70 (95% CI, 2.66-2.74) for the association between exotropia and anxiety disorder.

Conclusions and Relevance

This cross-sectional study suggests that there was a moderate association between strabismus and anxiety disorder, schizophrenia, bipolar disorder, and depressive disorder but not substance use disorder. Recognizing that these associations exist should encourage mental illness screening and treatment for patients with strabismus.

A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals

In the Motor Imagery (MI)-based Brain Computer Interface (BCI), users' intention is converted into a control signal through processing a specific pattern in brain signals reflecting motor characteristics. There are such restrictions as the limited size of the existing datasets and low signal to noise ratio in the classification of MI Electroencephalogram (EEG) signals. Machine learning (ML) methods, particularly Deep Learning (DL), have overcome these limitations relatively. In this study, three hybrid models were proposed to classify the EEG signal in the MI-based BCI. The proposed hybrid models consist of the convolutional neural networks (CNN) and the Long-Short Term Memory (LSTM). In the first model, the CNN with different number of convolutional-pooling blocks (from shallow to deep CNN) was examined; a two-block CNN model not affected by the vanishing gradient descent and yet able to extract desirable features employed; the second and third models contained pre-trained CNNs conducing to the exploration of more complex features. The transfer learning strategy and data augmentation methods were applied to overcome the limited size of the datasets by transferring learning from one model to another. This was achieved by employing two powerful pre-trained convolutional neural networks namely ResNet-50 and Inception-v3. The continuous wavelet transform (CWT) was used to generate images for the CNN. The performance of the proposed models was evaluated on the BCI Competition IV dataset 2a. The mean accuracy vlaues of 86%, 90%, and 92%, and mean Kappa values of 81%, 86%, and 88% were obtained for the hybrid neural network with the customized CNN, the hybrid neural network with ResNet-50 and the hybrid neural network with Inception-v3, respectively. Despite the promising performance of the three proposed models, the hybrid neural network with Inception-v3 outperformed the two other models. The best obtained result in the present study improved the previous best result in the literature by 7% in terms of classification accuracy. From the findings, it can be concluded that transfer learning based on a pre-trained CNN in combination with LSTM is a novel method in MI-based BCI. The study also has implications for the discrimination of motor imagery tasks in each EEG recording channel and in different brain regions which can reduce computational time in future works by only selecting the most effective channels.

Poor Air Quality in Urban Settings: A Comparison of Perceptual Indicators, Causes and Management in Two Cities

Poor air quality (PAQ) is a global concern, especially in urban areas, and is often seen as an important element of social sustainability given its negative impact on health and quality of life. However, little research has been undertaken in cities of the developing world to explore how residents perceive poor air quality, its main causes, what control measures should be used to address PAQ and where the main responsibility rests for implementing control measures. The research described in this paper sought to address these points, using a questionnaire-based survey (n = 262) in Nigeria’s federal capital city of Abuja (n = 137) and the state-capital city of Enugu (n = 125). The survey took place during the COVID-19 pandemic (October 2020 to March 2021), and was stratified to ensure representation across a number of demographic groups such as gender, age, education and income. The results were analysed using the Kruskal–Wallis non-parametric test and Hochberg’s post hoc test available in SPSS version 28. The study found that the ranking of perceptual indicators and the main causes of PAQ had much agreement between respondents from both cities and between demographic groups. Smoke, odour and dust particles were perceived to be the most important indicators of PAQ, while the main sources of PAQ were waste and bush burning, vehicle use and power generators. The two most preferred control measures were proper waste management and the avoidance of bush burning. However, there was a significant difference between the two cities in terms of the main organisations responsible for addressing PAQ, with respondents from Abuja citing the federal government, while those from Enugu cited the state government. Interestingly, younger people in Enugu noted that the government should take more responsibility in controlling PAQ than did the older demographic in that city, but this difference was not seen in Abuja. Overall, this study reveals that residents in these two Nigerian cities clearly recognise their exposure to PAQ and it suggests that these perceptual indicators, and views on sources and interventions should be central to designing policies to control this important issue.

Measuring Changes in Perceptions of Access to Pet Support Care in Underserved Communities

Understanding social, economic, and structural barriers to accessing pet care services is important for improving the health and welfare of companion animals in underserved communities in the U.S. From May 2018-December 2019, six questions from the validated One Health Community Assessment were used to measure perceptions of access to pet care in two urban and two rural zip codes. One urban and one rural community received services from a pet support outreach program (Pets for Life), while the other served as a comparison community. After propensity score matching was performed to eliminate demographic bias in the sample (Urban = 512 participants, Rural = 234 participants), Generalized Estimating Equations were employed to compare the six measures of access to pet care between the intervention and comparison communities. The urban community with the Pets for Life intervention was associated with a higher overall measure of access to pet care compared to the urban site that did not have the Pets for Life intervention. When assessing each of the six measures of access to care, the urban community with the Pets for Life intervention was associated with higher access to affordable pet care options and higher access to pet care service providers who offer payment options than the community without the Pets for Life intervention. Further analyses with a subset of Pets for Life clients comparing pre-intervention and post-intervention survey responses revealed statistically significant positive trends in perceptions of two of the six measures of access to pet care. This study provides evidence that community-based animal welfare programming has the potential to increase perceptions of access to pet support services.

Microbial interaction-driven community differences as revealed by network analysis

Diversity and compositional analysis are the most common approaches in deciphering microbial community differences. However, these approaches neglect microbial structural differences driven by microbial interactions. In this study, the microbiota data were generated from 12 rectal digesta samples collected from steers in which the Shiga toxin 2 gene (stx2) was not expressed (defined as Stx2- group) in the bacteria, and those with stx2 expressed (defined as Stx2+ group) and used to explore whether microbial networks affect gut microbiota and foodborne pathogen virulence in cattle. Although the Shannon and Chao1 indices of rectal digesta microbial communities did not differ between the two groups (P > 0.05), 24 and 13 taxa were identified to be group-specific genera for Stx2- and Stx2+ microbial communities, respectively. The network analysis indicated 12 and 14 generalists (microbes that were densely connected with other taxa) in microbial communities for Stx2- and Stx2+ groups, and 8 out of 12 generalists and 6 out of 14 generalists were designated to Stx2- and Stx2+ group-specific genera, respectively. However, the 66 core genera were not classified as network generalists. Natural connectivity measurements revealed that the higher stability of the Stx2- microbial network in comparison to the Stx2+ network, suggesting that the structure of each microbial community was inherently different even when their diversity and composition were comparable. Group-specific genera intensely interacted with other taxa in the co-occurrence network, indicating that characterizing microbial networks together with group-specific genera could be an alternative approach to identify variation in microbial communities.

The evolving impact of coronavirus (COVID-19) pandemic on public transportation in Ghana

BACKGROUND

People's mobility improves as a result of transportation, allowing for greater accessibility. However, the COVID-19 pandemic, which began in December 2019 in Wuhan, China, has had a significant effect on mobility and accessibility to public transport.

OBJECTIVES

Looking at the nature of public transports in Ghana with passengers sitting close to each other coupled with the restrictions on mobility, this paper sought to investigate the effect of the pandemic on public transportation in Ghana.

METHOD

The study attempts to use an online and paper-based questionnaire that included questions on usual mode of transportation, usual mode of public transportation prior to and throughout the time of COVID-19, factors influencing public transport mode option prior to and throughout the time of COVID-19 from 1005 commuters. Statistical analysis included descriptive analysis and in addition quantitative comparative analysis. Non-parametric tests were used for inferential statistical analysis. To understand whether usual mode of transportation has evolved due to COVID-19, Wilcoxon signed-ranked test was used to contrast factors influencing mode option prior to and throughout the time of COVID-19.

RESULTS

Results explained that the major impact of COVID-19 on public transportation were social distancing and increase in the cost of transportation. There was a sharp decline in the use of para transit services with high occupancy such as "trotro" to the use of taxis. Commuters considered physical distancing, occupants wearing face masks, cleanliness of vehicle and safety from traffic accidents as essential factors influencing public transport mode choice during COVID-19. The results of the cross-tabulation analysis, which looked at the relationship between the effect of COVID-19 on transportation and the effect of COVID-19 on social, economic and religious activities showed these variables were insignificant, X2 (2, N = 1005) = 3.057, p = .217.

CONCLUSION

On the basis of the study findings, some recommendations were made for policymakers and stakeholders in the transport industry in order to make it much safer and conducive to travel on public transport in a pandemic in the country.

Meta-computational techniques' for managing spare data: An application in off-pump heart surgery

BACKGROUND AND OBJECTIVES

This research looked at the key considerations to remember when selecting a model for working with sparse data. In the presence of sparse evidence, it proposes ideal conditions for conducting meta-analysis.

METHODS

Monte Carlo simulations were used to produce study results, and three forms of continuity correction were used in the research. Besides, meta-analytical approaches were used to measure the cumulative effect of treatment and estimate each method's efficiency. A clinical trial in off-pump surgery met the main objectives of this research. Meta-analysis methods were used to determine the outcome of postoperative risk results. After that, with a total population of 3030, Monte Carlo simulations were used to produce research data to run fixed and random-effect models with three continuity correction forms. The type of consistency adjustment used, group imbalances, statistical analysis used, and variance values between studies all affect meta-analytical methods' results.

RESULTS

MSE values for balanced groups are normally zero. While the Arc-sine variation approach does a decent job of coping with inconsistent results on the effect of treatment, it has concerns with boundary estimates of variance between tests. Furthermore, using continuity correction methods introduces bias and imprecise medication outcome calculations. The spectrum of statistical analysis, such as fixed effects and random effects, can be inferred as completely based on data in samples. The sensitivity analysis of correction decisions could increase the reliability of meta-analysis approaches by enabling researchers to analyze various effect estimation findings.

CONCLUSION

This research study can be expanded upon by identifying alternative approaches to continuity correction methods and resolving boundary estimate problems. The range of statistical analysis, such as fixed effects and random effects, can be entirely dependent on the samples' type of data. The sensitivity analysis of correction decisions could improve the efficiency of meta-analysis methods by allowing researchers to investigate a wide range of effect estimation results.

GutBalance: a server for the human gut microbiome-based disease prediction and biomarker discovery with compositionality addressed

The compositionality of the microbiome data is well-known but often neglected. The compositional transformation pertains to the supervised learning of microbiome data and is a critical step that decides the performance and reliability of the disease classifiers. We value the excellent performance of the distal discriminative balance analysis (DBA) method, which selects distal balances of pairs and trios of bacteria, in addressing the classification of high-dimensional microbiome data. By applying this method to the species-level abundances of all the disease phenotypes in the GMrepo database, we build a balance-based model repository for the classification of human gut microbiome-related diseases. The model repository supports the prediction of disease risks for new sample(s). More importantly, we highlight the concept of balance-disease associations rather than the conventional microbe-disease associations and develop the human Gut Balance-Disease Association Database (GBDAD). Each predictable balance for each disease model indicates a potential biomarker-disease relationship and can be interpreted as a bacteria ratio positively or negatively correlated with the disease. Furthermore, by linking the balance-disease associations to the evidenced microbe-disease associations in MicroPhenoDB, we surprisingly found that most species-disease associations inferred from the shotgun metagenomic datasets can be validated by external evidence beyond MicroPhenoDB. The balance-based species-disease association inference will accelerate the generation of new microbe-disease association hypotheses in gastrointestinal microecology research and clinical trials. The model repository and the GBDAD database are deployed on the GutBalance server, which supports interactive visualization and systematic interrogation of the disease models, disease-related balances and disease-related species of interest.