Machine Learning Applications in Studying Mental Health Among Immigrants and Racial and Ethnic Minorities: A Systematic Review

Background: The use of machine learning (ML) in mental health (MH) research is increasing, especially as new, more complex data types become available to analyze. By systematically examining the published literature, this review aims to uncover potential gaps in the current use of ML to study MH in vulnerable populations of immigrants, refugees, migrants, and racial and ethnic minorities. Methods: In this systematic review, we queried Google Scholar for ML-related terms, MH-related terms, and a population of a focus search term strung together with Boolean operators. Backward reference searching was also conducted. Included peer-reviewed studies reported using a method or application of ML in an MH context and focused on the populations of interest. We did not have date cutoffs. Publications were excluded if they were narrative or did not exclusively focus on a minority population from the respective country. Data including study context, the focus of mental healthcare, sample, data type, type of ML algorithm used, and algorithm performance was extracted from each. Results: Our search strategies resulted in 67,410 listed articles from Google Scholar. Ultimately, 12 were included. All the articles were published within the last 6 years, and half of them studied populations within the US. Most reviewed studies used supervised learning to explain or predict MH outcomes. Some publications used up to 16 models to determine the best predictive power. Almost half of the included publications did not discuss their cross-validation method. Conclusions: The included studies provide proof-of-concept for the potential use of ML algorithms to address MH concerns in these special populations, few as they may be. Our systematic review finds that the clinical application of these models for classifying and predicting MH disorders is still under development

Multi-sensor fusion based on multiple classifier systems for human activity identification

Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multi-sensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system. - 2019, The Author(s).This research is supported by University of Malaya BKP Special Grant no vote BKS006-2018.Scopu

Clinicopathological Investigations Among Recurrent Camelpox Outbreaks in Omanis’ Arabian Camels (Camelus dromedarius)

Camelpox remains a widespread viral disease in camelids, with socioeconomic relevance. The present study explored the hematological, biochemical, and histopathological alterations in dromedary-racing camels from the North of Oman infected with camelpox virus diagnosed by real-time PCR. Blood and skin samples were collected from camels with clinical signs and skin lesions (n=4) and from healthy camels (n=3) from 10 different camel herds. The results indicated that the infected camels showed clinical signs, including pyrexia, lacrimation, nasal mucus discharge, affixed and swollen eyelids, emaciation, and pimples on the skin of the head, legs, and abdomen. Hemoglobin, hematocrit, and platelets were significantly greater, with a significant reduction in leukocyte and lymphocyte counts in infected camels than in healthy camels. Infected camels had higher CK and creatinine levels and hepatic-related metabolites, including AST, ALP, AST, GGT, and LDH, than the apparently healthy camels. Histopathological examination of skin scab samples revealed ballooning degeneration of epidermal cells in the presence of typical large eosinophilic intracytoplasmic inclusion bodies and suppurative dermatitis following secondary bacterial infection in all examined infected camels. Camelpox viral DNA was detected using real-time PCR in the blood and skin samples of all infected camels. These findings in dromedary-racing camels associated with a molecular diagnosis of camelpox are described for the first time in the Sultanate of Oman. Therefore, further studies are warranted

Identifying the influential spreaders in multilayer interactions of online social networks

Online social networks (OSNs) portray a multi-layer of interactions through which users become a friend, information is propagated, ideas are shared, and interaction is constructed within an OSN. Identifying the most influential spreaders in a network is a significant step towards improving the use of existing resources to speed up the spread of information for application such as viral marketing or hindering the spread of information for application like virus blocking and rumor restraint. Users communications facilitated by OSNs could confront the temporal and spatial limitations of traditional communications in an exceptional way, thereby presenting new layers of social interactions, which coincides and collaborates with current interaction layers to redefine the multiplex OSN. In this paper, the effects of different topological network structure on influential spreaders identification are investigated. The results analysis concluded that improving the accuracy of influential spreaders identification in OSNs is not only by improving identification algorithms but also by developing a network topology that represents the information diffusion well. Moreover, in this paper a topological representation for an OSN is proposed which takes into accounts both multilayers interactions as well as overlaying links as weight. The measurement results are found to be more reliable when the identification algorithms are applied to proposed topological representation compared when these algorithms are applied to single layer representations

Validity of cold storage and heat treatment on the deactivation of <i>Vibrio parahaemolyticus</i> isolated from fish meat market

Vibrio parahaemolyticus is a zoonotic disease transmitted to humans when handling or consuming improperly cooked fish meat. This study aimed to evaluate the effect of thermal treatment on V. parahaemolyticus isolates. Different heat treatment methods are used to determine the best methods for controlling V. parahaemolyticus, isolated from fish meat, which include microwave, low-temperature long time, and high-temperature short time methods. The V. parahaemolyticus isolates significantly declined in bacteria count when they were kept at 4°C, and 25°C for a long time, and the V. parahaemolyticus isolates significantly declined in bacteria count manner when they were kept at -20°C for a long time. The high temperature and long-time exposure at 75°C/25 minutes by moist heat, 87°C/5 minutes by dry heat, and 70°C/20 minutes by frying heat were enough to kill V. parahaemolyticus isolates. This work can be useful to decrease the hazards of infections related to V. parahaemolyticus and reduce the causes of fish-borne pathogens

Betaine and nano-emulsified vegetable oil supplementation for improving carcass and meat quality characteristics of broiler chickens under heat stress conditions

IntroductionThis research aimed to examine the effects of water-added betaine (BET) and/or nano-emulsified vegetable oil (MAGO) on carcass and meat quality characteristics of broilers raised under thermoneutral (TN) and heat stress (HS) conditions.MethodsOn day 21, 640 birds (Ross 308) were randomly assigned to one of two thermal conditions (thermoneutral 22 ± 1°C and heat stress 32 ± 1°C) each containing four treatment groups: Control, BET, MAGO, and a mixture of both (BETMAGO) in a 2 × 4 factorial arrangement (eight groups). Each group has eight replicates, with ten birds each. The birds' carcass and meat quality characteristics were evaluated at 35 days.Results and discussionThe dressing percentage, breast, leg, wing, heart, initial pH, color change, cooking loss (CL), water-holding capacity (WHC), shear force (SF), and texture profile with exception of springiness significantly affected by the treatments. The results showed that HS had negative effects on carcass weight and relative weights of the breast, spleen, and heart. Moreover, HS increased dressing percentage, wing, initial pH, final core temperature, initial lightness, WHC, and hardness. Significant differences in interactions between treatments and temperature were observed in the spleen, WHC, and SF.ConclusionWater supplemented with BET effectively improved carcass dressing percentage, breast weight, and meat quality in terms of water-holding capacity and tenderness under HS conditions. More studies on the use of BET and/or MAGO at different levels were recommended

Recent advances in mobile touch screen security authentication methods: a systematic literature review

The security of the smartphone touch screen has attracted considerable attention from academics as well as industry and security experts. The maximum security of the mobile phone touch screen is necessary to protect the user’s stored information in the event of loss. Previous reviews in this research domain have focused primarily on biometrics and graphical passwords while leaving out PIN, gesture/pattern and others. In this paper, we present a comprehensive literature review of the recent advances made in mobile touch screen authentication techniques covering PIN, pattern/gesture, biometrics, graphical password and others. A new comprehensive taxonomy of the various multiple class authentication techniques is presented in order to expand the existing taxonomies on single class authentication techniques. The review reveals that the most recent studies that propose new techniques for providing maximum security to smartphone touch screen reveal multi-objective optimization problems. In addition, open research problems and promising future research directions are presented in the paper. Expert researchers can benefit from the review by gaining new insights into touch screen cyber security, and novice researchers may use this paper as a starting point of their inquir