The Cerebellar Response to Visual Portion Size Cues Is Associated with the Portion Size Effect in Children.

The neural mechanisms underlying susceptibility to eating more in response to large portions (i.e., the portion size effect) remain unclear. Thus, the present study examined how neural responses to portion size relate to changes in weight and energy consumed as portions increase. Associations were examined across brain regions traditionally implicated in appetite control (i.e., an appetitive network) as well as the cerebellum, which has recently been implicated in appetite-related processes. Children without obesity (i.e., BMI-for-age-and-sex percentile < 90; N = 63; 55% female) viewed images of larger and smaller portions of food during fMRI and, in separate sessions, ate four meals that varied in portion size. Individual-level linear and quadratic associations between intake (kcal, grams) and portion size (i.e., portion size slopes) were estimated. The response to portion size in cerebellar lobules IV-VI was associated with the quadratic portion size slope estimated from gram intake; a greater response to images depicting smaller compared to larger portions was associated with steeper increases in intake with increasing portion sizes. Within the appetitive network, neural responses were not associated with portion size slopes. A decreased cerebellar response to larger amounts of food may increase children's susceptibility to overeating when excessively large portions are served.

Does 'portion size' matter? Brain responses to food and non-food cues presented in varying amounts.

Larger portions of food elicit greater intake than smaller portions of food, particularly when foods are high in energy density (kcal/g; ED). The neural mechanisms underlying this effect remain unclear. The present study used fMRI to assess brain activation to food (higher-ED, lower-ED) and non-food (office supplies) images presented in larger and smaller (i.e., age-appropriate) amounts in 61, 7-8-year-olds (29 male, 32 female) without obesity. Larger amounts of food increased activation in bilateral visual and right parahippocampal areas compared to smaller amounts; greater activation to food amount (larger > smaller) in this cluster was associated with smaller increases in food intake as portions increased. Activation to amount (larger > smaller) was stronger for food than office supplies in primary and secondary visual areas, but, for office supplies only, extended into bilateral parahippocampus, inferior parietal cortex, and additional visual areas (e.g., V7). Activation was greater for higher-vs. lower-ED food images in ventromedial prefrontal cortex for both larger and smaller amounts of food; however, this activation extended into left lateral orbital frontal cortex for smaller amounts only. Activation to food cues did not differ by familial risk for obesity. These results highlight potentially distinct neural pathways for encoding food energy content and quantity.

A review of microplastic threat mitigation in Asian lentic environments.

Microplastic (MP) pollution has evolved into a significant worldwide environmental concern due to its widespread sources, enduring presence, and adverse effects on lentic ecosystems and human well-being. The growing awareness of the hidden threat posed by MPs in lentic ecosystems has emphasized the need for more in-depth research. Unlike marine environments, there remain unanswered questions about MP hotspots, ecotoxic effects, transport mechanisms, and fragmentation in lentic ecosystems. The introduction of MPs represents a novel threat to long-term environmental health, posing unresolved challenges for sustainable management. While MP pollution in lentic ecosystems has garnered global attention due to its ecotoxicity, our understanding of MP hotspots in lakes from an Asian perspective remains limited. Hence, the aim of this review is to provide a comprehensive analysis of MP hotspots, morphological attributes, ecotoxic impacts, sustainable solutions, and future challenges across Asia. The review summarizes the methods employed in previous studies and the techniques for sampling and analyzing microplastics in lake water and sediment. Notably, most studies concerning lake microplastics tend to follow the order of China > India > Pakistan > Nepal > Turkey > Bangladesh. Additionally, this review critically addresses the analysis of microplastics in lake water and sediment, shedding light on the prevalent net-based sampling methods. Ultimately, this study emphasizes the existing research gaps and suggests new research directions, taking into account recent advancements in the study of microplastics in lentic environments. In conclusion, the review advocates for sustainable interventions to mitigate MP pollution in the future, highlighting the presence of MPs in Asian lakes, water, and sediment, and their potential ecotoxicological repercussions on both the environment and human health.

Human Micro-Expressions in Multimodal Social Behavioral Biometrics.

The advent of Social Behavioral Biometrics (SBB) in the realm of person identification has underscored the importance of understanding unique patterns of social interactions and communication. This paper introduces a novel multimodal SBB system that integrates human micro-expressions from text, an emerging biometric trait, with other established SBB traits in order to enhance online user identification performance. Including human micro-expression, the proposed method extracts five other original SBB traits for a comprehensive representation of the social behavioral characteristics of an individual. Upon finding the independent person identification score by every SBB trait, a rank-level fusion that leverages the weighted Borda count is employed to fuse the scores from all the traits, obtaining the final identification score. The proposed method is evaluated on a benchmark dataset of 250 Twitter users, and the results indicate that the incorporation of human micro-expression with existing SBB traits can substantially boost the overall online user identification performance, with an accuracy of 73.87% and a recall score of 74%. Furthermore, the proposed method outperforms the state-of-the-art SBB systems.

Analysis of the fire risks and mitigation approaches in the apparel manufacturing industry: Implications toward operational safety and sustainability.

Fire risks pose a substantial threat to the apparel manufacturing industry since they can lead to immense property damage, potential loss of life, disruption of business operations, and reputational damage. In an emerging economy like Bangladesh, fire-related hazards are crucial due to the numerous deadly industrial fire incidents in recent years. This research, thereby, proposes an integrated multi-criteria decision-making (MCDM) framework to identify and mitigate fire risk hazards in the apparel manufacturing industry. Initially, the study identified 30 significant fire risk factors from the literature review. Then, after expert validation, an integrated Best Worst Method (BWM) and Weighted Sum Model (WSM) framework was utilized to prioritize the fire risk factors. Twenty-three mitigation actions were proposed afterward for the top-ranked risk factors based on National Fire Protection Association (NFPA) codes. An Interpretive Structural Modeling (ISM) with a Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis was later used to explore the interrelationships and dependencies among the mitigation actions. The ranking obtained from the BWM-WSM assessment revealed 'combustible storage unseparated by fire-rated construction,' 'non-standard inspection, testing, and maintenance', and 'inadequate means of egress for the occupant load' as the three most critical fire risk factors. The ISM-MICMAC analysis revealed 'fire-rated construction' and 'standardized detection and protection' as the most-driving mitigation actions. The study outcomes are expected to aid the managers and policymakers in emerging economies in formulating sustainable fire risk management strategies for the apparel industry and thus improve the operational safety and resilience of the sector.

Fuzzy logic, geostatistics, and multiple linear models to evaluate irrigation metrics and their influencing factors in a drought-prone agricultural region.

The quality of water used for irrigation is one of the major threats to maintaining the long-term sustainability of agricultural practices. Although some studies have addressed the suitability of irrigation water in different parts of Bangladesh, the irrigation water quality in the drought-prone region has yet to be thoroughly studied using integrated novel approaches. This study aims to assess the suitability of irrigation water in the drought-prone agricultural region of Bangladesh using traditional irrigation metrics such as sodium percentage (NA%), magnesium adsorption ratio (MAR), Kelley's ratio (KR), sodium adsorption ratio (SAR), total hardness (TH), permeability index (PI), and soluble sodium percentage (SSP), along with novel irrigation indices such as irrigation water quality index (IWQI) and fuzzy irrigation water quality index (FIWQI). Thirty-eight water samples were taken from tube wells, river systems, streamlets, and canals in agricultural areas, then analyzed for cations and anions. The multiple linear regression model predicted that SAR (0.66), KR (0.74), and PI (0.84) were the primary important elements influencing electrical conductivity (EC). Based on the IWQI, all water samples fall into the "suitable" category for irrigation. The FIWQI suggests that 75% of the groundwater and 100% of the surface water samples are excellent for irrigation. The semivariogram model indicates that most irrigation metrics have moderate to low spatial dependence, suggesting strong agricultural and rural influence. Redundancy analysis shows that Na+, Ca2+, Cl-, K+, and HCO3- in water increase with decreasing temperature. Surface water and some groundwater in the southwestern and southeastern parts are suitable for irrigation. The northern and central parts are less suitable for agriculture because of elevated K+ and Mg2+ levels. This study determines irrigation metrics for regional water management and pinpoints suitable areas in the drought-prone region, which provides a comprehensive understanding of sustainable water management and actionable steps for stakeholders and decision-makers.

Assessing the critical success factors for implementing industry 4.0 in the pharmaceutical industry: Implications for supply chain sustainability in emerging economies.

The emerging technologies of Industry 4.0 (I4.0) are crucial to incorporating agility, sustainability, smartness, and competitiveness in the business model, enabling long-term sustainability practices in the pharmaceutical supply chain (PSC). By leveraging the latest technologies of I4.0, pharmaceutical companies can gain real-time visibility into their supply chain (SC) operations, allowing them to make data-driven decisions that improve SC performance, efficiency, resilience, and sustainability. However, to date, no research has examined the critical success factors (CSFs) that enable the pharmaceutical industry to adopt I4.0 successfully to enhance overall SC sustainability. This study, therefore, analyzed the potential CSFs for adopting I4.0 to increase all facets of sustainability in the PSC, especially from the perspective of an emerging economy like Bangladesh. Initially, sixteen CSFs were identified through a comprehensive literature review and expert validation. Later, the finalized CSFs were clustered into three relevant groups and analyzed using a Bayesian best-worst method (BWM)-based multi-criteria decision-making (MCDM) framework. The study findings revealed that "sufficient investment for technological advancement", "digitalized product monitoring and traceability", and "dedicated and robust research and development (R&D) team" are the top three CSFs to adopt I4.0 in the PSC. The study's findings can aid industrial practitioners, managers, and policymakers in creating effective action plans for efficiently adopting I4.0 in PSC to avail of its competitive benefits and ensure a sustainable future for the pharmaceutical industry.

Managing the invisible threat of microplastics in marine ecosystems: Lessons from coast of the Bay of Bengal.

Invisible microplastics (MP) have become a significant problem worldwide in recent years. Although many studies have highlighted the sources, effects, and fate of MPs pollution on various ecosystems in developed countries, there is limited information on MPs in the marine ecosystem along the northeastern coast of the Bay of Bengal (BoB). Coastal ecosystems along the BoB coasts are critical to a biodiverse ecology that supports human survival and resource extraction. However, the multi-environmental hotspots, ecotoxicity effects, transport mechanisms, fates, and intervention measures to control MP pollution initiatives along the BoB coasts have received little attention. Therefore, this review aims to highlight the multi-environmental hotspots, ecotoxicity effects, sources, fates, and intervention measures of MP in the northeastern BoB to understand how MP spreads in the nearshore marine ecosystem. This study critically evaluates the hotspots and ecotoxic effects of pollution from MP on the coastal multi-environment, e.g., soil, sediment, salt, water, and fish, as well as current intervention measures and additional mitigation recommendations. This study identified the northeastern part of the BoB as a hotspot for MP. In addition, the transport mechanisms and fate of MP in different environmental compartments are highlighted, as are research gaps and potential future research areas. Research on the ecotoxic effects of MP on BoB marine ecosystems must be a top priority, given the increasing use of plastics and the presence of significant marine products worldwide. The knowledge gained from this study would inform decision-makers and stakeholders in a way that could reduce the impact of the legacy of micro- and nanoplastics in the area. This study also proposes structural and non-structural measures to mitigate the effects of MPs and promote sustainable management.

Personal protective equipment-derived pollution during Covid-19 era: A critical review of ecotoxicology impacts, intervention strategies, and future challenges.

During the COVID-19 pandemic, people used personal protective equipment (PPE) to lessen the spread of the virus. The release of microplastics (MPs) from discarded PPE is a new threat to the long-term health of the environment and poses challenges that are not yet clear. PPE-derived MPs have been found in multi-environmental compartments, e.g., water, sediments, air, and soil across the Bay of Bengal (BoB). As COVID-19 spreads, healthcare facilities use more plastic PPE, polluting aquatic ecosystems. Excessive PPE use releases MPs into the ecosystem, which aquatic organisms ingest, distressing the food chain and possibly causing ongoing health problems in humans. Thus, post-COVID-19 sustainability depends on proper intervention strategies for PPE waste, which have received scholarly interest. Although many studies have investigated PPE-induced MPs pollution in the BoB countries (e.g., India, Bangladesh, Sri Lanka, and Myanmar), the ecotoxicity impacts, intervention strategies, and future challenges of PPE-derived waste have largely gone unnoticed. Our study presents a critical literature review covering the ecotoxicity impacts, intervention strategies, and future challenges across the BoB countries (e.g., India (162,034.45 tons), Bangladesh (67,996 tons), Sri Lanka (35,707.95 tons), and Myanmar (22,593.5 tons). The ecotoxicity impacts of PPE-derived MPs on human health and other environmental compartments are critically addressed. The review's findings infer a gap in the 5R (Reduce, Reuse, Recycle, Redesign, and Restructure) Strategy's implementation in the BoB coastal regions, hindering the achievement of UN SDG-12. Despite widespread research advancements in the BoB, many questions about PPE-derived MPs pollution from the perspective of the COVID-19 era still need to be answered. In response to the post-COVID-19 environmental remediation concerns, this study highlights the present research gaps and suggests new research directions considering the current MPs' research advancements on COVID-related PPE waste. Finally, the review suggests a framework for proper intervention strategies for reducing and monitoring PPE-derived MPs pollution in the BoB countries.

Evaluating barriers to sustainable boiler operation in the apparel manufacturing industry: Implications for mitigating operational hazards in the emerging economies.

The efficiency with which conventional boilers perform, in terms of sustainability, is affected by a variety of factors. Unsustainable boiler operating practices are still surprisingly frequent in developing countries, resulting in environmental liabilities and catastrophic accidents. It is a serious problem in developing countries like Bangladesh, where boilers are utilized extensively in the apparel manufacturing sector. However, no research has yet examined the challenges or barriers associated with sustainable boiler operation in the apparel manufacturing sector. This study, thereby, utilizes an integrated MCDM approach, combining the fuzzy theory and the decision-making trial and evaluation laboratory (DEMATEL) method, to identify, prioritize, and explore the relations among the barriers to sustainable boiler operation in the apparel manufacturing industry, from an emerging economy perspective. The barriers were initially identified from the literature and a visual survey of 127 factories. After expert validation, thirteen barriers were finally selected to be analyzed utilizing the fuzzy DEMATEL method. The study findings revealed that 'Absence of water treatment facilities', 'Fossil fuel burning and GHG emissions', and 'Excessive consumption of groundwater' are the three most prominent barriers to sustainable boiler operation. The cause-effect relations among the barriers suggest that 'Inadequate compliance with safety and hazard regulations' is the most influential and 'Fossil fuel burning and GHG emissions' is the most influenced barrier. This study is expected to guide the managers and policymakers of the apparel manufacturing sector in successfully overcoming the barriers to sustainable boiler operation, thus mitigating the operational hazards and achieving the sustainable development goals (SDGs).

Assessing the challenges to medical waste management during the COVID-19 pandemic: Implications for the environmental sustainability in the emerging economies.

Emerging economies are struggling with proper and efficient management of waste due to their constrained resources and weak management. In recent days, this crisis has worsened due to the outbreak of the highly contagious COVID-19 pandemic. To avoid building up stockpiles and contaminating communities with potentially contagious medical waste (MW), and to ensure sustainability in the current and post-COVID-19 era, it is a dire need to develop and implement a safe and efficient medical waste management (MWM) system. This research, thereby, aims to identify, assess, and prioritize the key challenges to efficient and sustainable MWM to mitigate the impacts of the disruptions caused by situations like the pandemic in emerging economies. An integrated approach consisting of the Best-Worst Method (BWM), Interpretive Structural Modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) has been proposed to achieve the objectives. Based on the literature review and expert feedback, a total of seventeen challenges were identified and later prioritized by using BWM. The top twelve challenges have been further analyzed using ISM-MICMAC to examine their interrelationships. This study reveals that lack of proper law enforcement and insufficient financial support from investors and the government are two crucial challenges for efficient MWM implementation. The research insights can assist healthcare facility administrators, practitioners, and city managers in identifying the associated challenges and shaping strategic decisions for establishing and managing efficient MWM systems to ensure sustainable development in the post-COVID-19 era.

Molecular detection and prevalence of Rotavirus with acute gastroenteritis among the children of rural and urban areas / Detecção molecular e prevalência de rotavírus com gastroenterite aguda entre as crianças de áreas rurais e urbanas

Rotavirus is the main infective agent of acute gastroenteritis (AGE) in children under the age of five years and causing significant morbidity as well as mortality throughout the world. The study was carried out to detect the prevalence rate, genotypes strain and risk factors of Rotavirus among the children of rural and urban areas of district Bannu Khyber Pakhtunkhwa Pakistan. A total of 180 stool samples were collected from children under the age of 5 years from two major hospitals of Bannu from January to December (2015). The samples were analyzed by Reverse-transcriptase Polymerase Chain Reaction (RT-PCR) for the detection of Rotavirus, positive samples were further processed for genotyping (G and P type) through specific PCR. Of the total, 41 (23%) samples were positive for Rotavirus. The most prevalent G genotypes found were: G3, G8, G9 (each 29%), followed by G10 (15%), and G11 (10%). Whereas the prevalent P genotypes were: P-8 (25%), P-4 and P-10 (each 20%), P-9 (15%), followed by P-6 and P-11 (each 10%). Moreover, Rotavirus infection was more prevalent in summer (23.73%) and winter (22.7%) than spring (20%) and autumn (21.4%). Rotavirus infection exhibited high frequency in June (14%), October (8%) and November (6%). It is concluded that Rotavirus is more prevalent in children and various genotypes (G and P) of Rotavirus are present in the study area. Lack of studies, awareness and rarer testing of Rotavirus are the principal reasons of virus prevalence in district Bannu, Pakistan.

O rotavírus é o principal agente infeccioso da gastroenterite aguda (AGE) em crianças menores de 5 anos e causa de morbidade e mortalidade significativas em todo o mundo. O estudo foi realizado para detectar a taxa de prevalência, cepa de genótipos e fatores de risco de rotavírus entre as crianças de áreas rurais e urbanas do distrito de Bannu Khyber Pakhtunkhwa, Paquistão. Um total de 180 amostras de fezes foi coletada de crianças menores de 5 anos de dois grandes hospitais de Bannu de janeiro a dezembro (2015). As amostras foram analisadas por reação em cadeia da polimerase transcriptase reversa (RT-PCR) para detecção de rotavírus; as amostras positivas foram posteriormente processadas para genotipagem (tipo G e P) através de PCR específica. Do total, 41 (23%) amostras foram positivas para rotavírus. Os genótipos G mais prevalentes encontrados foram: G3, G8, G9 (cada 29%), seguidos de G10 (15%) e G11 (10%). Considerando que os genótipos P prevalentes foram: P-8 (25%), P-4 e P-10 (cada 20%), P-9 (15%), seguido por P-6 e P-11 (cada 10%). Além disso, a infecção por rotavírus foi mais prevalente no verão (23,73%) e inverno (22,7%) do que na primavera (20%) e no outono (21,4%). A infecção por rotavírus apresentou alta frequência em junho (14%), outubro (8%) e novembro (6%). Conclui-se que o rotavírus é mais prevalente em crianças e vários genótipos (G e P) do rotavírus estão presentes na área de estudo. A falta de estudos, conhecimento e testes mais raros de rotavírus são as principais razões da prevalência do vírus no distrito de Bannu, Paquistão.

Molecular detection and prevalence of Rotavirus with acute gastroenteritis among the children of rural and urban areas

Abstract Rotavirus is the main infective agent of acute gastroenteritis (AGE) in children under the age of five years and causing significant morbidity as well as mortality throughout the world. The study was carried out to detect the prevalence rate, genotypes strain and risk factors of Rotavirus among the children of rural and urban areas of district Bannu Khyber Pakhtunkhwa Pakistan. A total of 180 stool samples were collected from children under the age of 5 years from two major hospitals of Bannu from January to December (2015). The samples were analyzed by Reverse-transcriptase Polymerase Chain Reaction (RT-PCR) for the detection of Rotavirus, positive samples were further processed for genotyping (G and P type) through specific PCR. Of the total, 41 (23%) samples were positive for Rotavirus. The most prevalent G genotypes found were: G3, G8, G9 (each 29%), followed by G10 (15%), and G11 (10%). Whereas the prevalent P genotypes were: P-8 (25%), P-4 and P-10 (each 20%), P-9 (15%), followed by P-6 and P-11 (each 10%). Moreover, Rotavirus infection was more prevalent in summer (23.73%) and winter (22.7%) than spring (20%) and autumn (21.4%). Rotavirus infection exhibited high frequency in June (14%), October (8%) and November (6%). It is concluded that Rotavirus is more prevalent in children and various genotypes (G and P) of Rotavirus are present in the study area. Lack of studies, awareness and rarer testing of Rotavirus are the principal reasons of virus prevalence in district Bannu, Pakistan.

Resumo O rotavírus é o principal agente infeccioso da gastroenterite aguda (AGE) em crianças menores de 5 anos e causa de morbidade e mortalidade significativas em todo o mundo. O estudo foi realizado para detectar a taxa de prevalência, cepa de genótipos e fatores de risco de rotavírus entre as crianças de áreas rurais e urbanas do distrito de Bannu Khyber Pakhtunkhwa, Paquistão. Um total de 180 amostras de fezes foi coletada de crianças menores de 5 anos de dois grandes hospitais de Bannu de janeiro a dezembro (2015). As amostras foram analisadas por reação em cadeia da polimerase transcriptase reversa (RT-PCR) para detecção de rotavírus; as amostras positivas foram posteriormente processadas para genotipagem (tipo G e P) através de PCR específica. Do total, 41 (23%) amostras foram positivas para rotavírus. Os genótipos G mais prevalentes encontrados foram: G3, G8, G9 (cada 29%), seguidos de G10 (15%) e G11 (10%). Considerando que os genótipos P prevalentes foram: P-8 (25%), P-4 e P-10 (cada 20%), P-9 (15%), seguido por P-6 e P-11 (cada 10%). Além disso, a infecção por rotavírus foi mais prevalente no verão (23,73%) e inverno (22,7%) do que na primavera (20%) e no outono (21,4%). A infecção por rotavírus apresentou alta frequência em junho (14%), outubro (8%) e novembro (6%). Conclui-se que o rotavírus é mais prevalente em crianças e vários genótipos (G e P) do rotavírus estão presentes na área de estudo. A falta de estudos, conhecimento e testes mais raros de rotavírus são as principais razões da prevalência do vírus no distrito de Bannu, Paquistão.

The role of reinforcement learning and value-based decision-making frameworks in understanding food choice and eating behaviors.

The obesogenic food environment includes easy access to highly-palatable, energy-dense, "ultra-processed" foods that are heavily marketed to consumers; therefore, it is critical to understand the neurocognitive processes the underlie overeating in response to environmental food-cues (e.g., food images, food branding/advertisements). Eating habits are learned through reinforcement, which is the process through which environmental food cues become valued and influence behavior. This process is supported by multiple behavioral control systems (e.g., Pavlovian, Habitual, Goal-Directed). Therefore, using neurocognitive frameworks for reinforcement learning and value-based decision-making can improve our understanding of food-choice and eating behaviors. Specifically, the role of reinforcement learning in eating behaviors was considered using the frameworks of (1) Sign-versus Goal-Tracking Phenotypes; (2) Model-Free versus Model-Based; and (3) the Utility or Value-Based Model. The sign-and goal-tracking phenotypes may contribute a mechanistic insight on the role of food-cue incentive salience in two prevailing models of overconsumption-the Extended Behavioral Susceptibility Theory and the Reactivity to Embedded Food Cues in Advertising Model. Similarly, the model-free versus model-based framework may contribute insight to the Extended Behavioral Susceptibility Theory and the Healthy Food Promotion Model. Finally, the value-based model provides a framework for understanding how all three learning systems are integrated to influence food choice. Together, these frameworks can provide mechanistic insight to existing models of food choice and overconsumption and may contribute to the development of future prevention and treatment efforts.

Prevalence of Dental Caries in Children in Mymensingh and Its Associated Risk Factors: A Cross-Sectional Study.

BACKGROUND: Children suffer from various oral and periodontal diseases. Dental caries is one of the most prevalent oral diseases among children in the world. This study was conducted to identify the prevalence and risk factors of dental caries in children in Mymensingh, Bangladesh. METHODS: A cross-sectional study was conducted on 362 pediatric patients who attended the Dental Unit of Mymensingh Medical College from March to September 2019. The sample size was calculated using a statistical formula and the children were selected using a systematic random sampling technique. Children and their guardians were interviewed and data were recorded using a structured questionnaire. Risk factors were analyzed using multivariate logistic regression. RESULTS: The overall prevalence of dental caries was 82.7%. The prevalence of caries was significantly higher in aged children (8-10 years) and also in rural, low-income, and illiterate families. Seven significant risk factors were identified that included residence in the rural area (OR: 7.31 [1.73-30.83]), a parental income of BDT ≤ 20,000 per month (OR: 4.75 [1.49-15.05]), reduced duration (≤1 min) of teeth cleaning (OR: 18.54 [2.05-168.17]), teeth cleaning before breakfast (OR: 93.30 [10.95-795.32]), the spoon-feeding method (OR: 12.57 [2.09-75.61]), long-term (37-48 months) breastfeeding (OR: 212.53 [8.69-5195.25]), and family oral problem (OR: 8.20 [2.57-26.16]). CONCLUSIONS: The prevalence of dental caries among the children in Mymensingh is very high and was associated with residence in rural areas, parental income, reduced duration of teeth cleaning, teeth cleaning before breakfast, the spoon-feeding method, long-term breastfeeding, and family oral problems.

Heterogeneity in PFC-amygdala connectivity in middle childhood, and concurrent interrelations with inhibitory control and anxiety symptoms.

The prefrontal cortex (PFC) is a key brain area in considering adaptive regulatory behaviors. This includes regulatory projections to regions of the limbic system such as the amygdala, where the nature of functional connections may confer lower risk for anxiety disorders. The PFC is also associated with behaviors like executive functioning. Inhibitory control is a behavior encompassed by executive functioning and is generally viewed favorably for adaptive socioemotional development. Yet, some research suggests that high levels of inhibitory control may actually be a risk factor for some maladaptive developmental outcomes, like anxiety disorders. In a sample of 51 children ranging from 7 to 9 years old, we examined resting state functional connectivity between regions of the PFC and the amygdala. We used Subgrouping Group Iterative Multiple Model Estimation (S-GIMME) to identify and characterize data-driven subgroups of individuals with similar networks of connectivity between these brain regions. Generated subgroups were collapsed into children characterized by the presence or absence of recovered connections between the PFC and amygdala. For subsets of children with available data (N = 38-44), we then tested whether inhibitory control, as measured by a stop signal task, moderated the relation between these subgroups and child-reported anxiety symptoms. We found an inverse relation between stop-signal reaction times and reported count of anxiety symptoms when covarying for connectivity group, suggesting that greater inhibitory control was actually related to greater anxiety symptoms, but only when accounting for patterns of PFC-amygdala connectivity. These data suggest that there is a great deal of heterogeneity in the nature of functional connections between the PFC and amygdala during this stage of development. The findings also provide support for the notion of high levels of inhibitory control as a risk factor for anxiety, but trait-level biopsychosocial factors may be important to consider in assessing the nature of risk.

Head Kinematics in Youth Ice Hockey by Player Speed and Impact Direction.

Hockey is a fast-paced sport known for body checking, or intentional collisions used to separate opponents from the puck. Exposure to these impacts is concerning, as evidence suggests head impact exposure (HIE), even if noninjurious, can cause long-term brain changes. Currently, there is limited understanding of the effect of impact direction and collision speed on HIE. Video analysis was used to determine speed and direction for 162 collisions from 13 youth athletes. These data were paired with head kinematic data collected with an instrumented mouthpiece. Relationships between peak resultant head kinematics and speeds were evaluated with linear regression. Mean athlete speeds and relative velocity between athletes ranged from 2.05 to 2.76 m/s. Mean peak resultant linear acceleration, rotational velocity, and rotational acceleration were 13.1 g, 10.5 rad/s, and 1112 rad/s2, respectively. Significant relationships between speeds and head kinematics emerged when stratified by contact characteristics. HIE also varied by direction of collision; most collisions occurred in the forward-oblique (ie, offset from center) direction; frontal collisions had the greatest magnitude peak kinematics. These findings indicate that HIE in youth hockey is influenced by speed and direction of impact. This study may inform future strategies to reduce the severity of HIE in hockey.

A new tool for assessing the risk of fall in children with severe disability: development of the ALICE scale.

Introduction: It is known in the literature that the main cause of physical impairment in children with severe disabilities is falling, which can worsen their already compromised condition. There are no specific scales for this population in the literature, neither in Italian nor in other languages. We created and validated a scale for assessing the risk of falling in children with severe disabilities. Study design: Observational prospective study. Methods: We enrolled children (inpatients or day-hospital) admitted to the "Santa Maria Bambina Centre" of the "Fondazione Onlus Sacra Famiglia" in Cesano Boscone, Milan; the Content Validity Index of the Scale was calculated to assess the content validity of a new scale (ALICE). Cronbach's alpha coefficient (α) was used to examine internal consistency, Spearman's rho coefficient to test inter-rater reliability. Sensitivity, specificity, positive and negative predictive values were calculated. Results: Out of 48 patients enrolled, 14 fell (29.2%). The ALICE scale, with cut-off set at 16, showed a sensitivity of 100%, a specificity of 88.2%, a positive predictive value of 77.8% and a negative predictive value of 100%. The Content Validity Index of the Scale (=0.93), inter-rater reliability (rho=0.91, p<0.001) and Cronbach's alpha (=0.72) were satisfactory. Conclusions: The ALICE scale seems reliable and valid in the disabled population and can be applied by nurses. Further studies with larger samples and a multicentre design are needed.

KinectGaitNet: Kinect-Based Gait Recognition Using Deep Convolutional Neural Network.

Over the past decade, gait recognition had gained a lot of attention in various research and industrial domains. These include remote surveillance, border control, medical rehabilitation, emotion detection from posture, fall detection, and sports training. The main advantages of identifying a person by their gait include unobtrusiveness, acceptance, and low costs. This paper proposes a convolutional neural network KinectGaitNet for Kinect-based gait recognition. The 3D coordinates of each of the body joints over the gait cycle are transformed to create a unique input representation. The proposed KinectGaitNet is trained directly using the 3D input representation without the necessity of the handcrafted features. The KinectGaitNet design allows avoiding gait cycle resampling, and the residual learning method ensures high accuracy without the degradation problem. The proposed deep learning architecture surpasses the recognition performance of all state-of-the-art methods for Kinect-based gait recognition by achieving 96.91% accuracy on UPCV and 99.33% accuracy on the KGB dataset. The method is the first, to the best of our knowledge, deep learning-based architecture that is based on a unique 3D input representation of joint coordinates. It achieves performance higher than previous traditional and deep learning methods, with fewer parameters and shorter inference time.

Evaluating factors contributing to the failure of information system in the banking industry.

The increasing use of Information Technology (IT) has led to many security and other related failures in the banks and other financial institutions in Bangladesh. In this paper, we investigated the factors contributing to the failurein the IT system of the banking industry in Bangladesh. Based on the experts' opinions and weight on the specified evaluating criteria, an empirical test was conducted using a rough set theory to produce a framework for the IT system failure factors. In this study, an extended approach involving the integration of rough set theory based flexible Failure Mode and Effect Analysis (FMEA) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has beenapplied to help the managers of the corresponding field to identify the factors responsible for the failure of the IT system in the banking industries and then prioritize them accordingly, for the ease of decision-making.In this research, eleven such failure factors were identified, which were then quantitatively analyzed to facilitate managers in crucial decision-making. It was observed that cyber-attack, database hack risks, server failure, network interruption, broadcast data error, and virus effect were the most significant factors for the failure of the IT system. The framework developed in this research can be utilized to assist in efficient decision-makingin other serviceindustries where IT systems play a key role. To the best of the knowledge, this is the first study thatempirically tested key failure factors of the IT system for the banking sector using an integrated method.