In-situ particle analysis with heterogeneous background: a machine learning approach.

We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle-substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle-substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing.

Effect of Light Quality on Seed Potato (Solanum tuberose L.) Tuberization When Aeroponically Grown in a Controlled Greenhouse.

A plant factory equipped with artificial lights is a comparatively new concept when growing seed potatoes (Solanum tuberosum L.) for minituber production. The shortage of disease-free potato seed tubers is a key challenge to producing quality potatoes. Quality seed tuber production all year round in a controlled environment under an artificial light condition was the main purpose of this study. The present study was conducted in a plant factory to investigate the effects of distinct spectrum compositions of LEDs on potato tuberization when grown in an aeroponic system. The study was equipped with eight LED light combinations: L1 = red: blue: green (70 + 25 + 5), L2 = red: blue: green (70 + 20 + 10), L3 = red: blue: green (70 + 15 + 15), L4 = red: blue: green (70 + 10 + 20), L5 = red: blue: far-red (70 + 25 + 5), L6 = red: blue: far-red (70 + 20 + 10), L7 = red: blue: far-red (70 + 15 + 15), L8 = red: blue: far-red (70 + 10 + 20), and L9 = natural light with 300 µmol m-2 s-1 of irradiance, 16/8 h day/night, 65% relative humidity, while natural light was used as the control treatment. According to the findings, treatment L4 recorded a higher tuber number (31/plant), tuber size (>3 g); (9.26 ± 3.01), and GA3 content, along with better plant growth characteristics. Moreover, treatment L4 recorded a significantly increased trend in the stem diameter (11.08 ± 0.25), leaf number (25.32 ± 1.2), leaf width (19 ± 0.81), root length (49 ± 2.1), and stolon length (49.62 ± 2.05) compared to the control (L9). However, the L9 treatment showed the best performance in plant fresh weight (67.16 ± 4.06 g) and plant dry weight (4.46 ± 0.08 g). In addition, photosynthetic pigments (Chl a) (0.096 ± 0.00 mg g-1, 0.093 ± 0.00 mg g-1) were found to be the highest in the L1 and L2 treatments, respectively. However, Chl b and TCL recorded the best results in treatment L4. Finally, with consideration of the plant growth and tuber yield performance, treatment L4 was found to have the best spectral composition to grow quality seed potato tubers.

Dietary Acrylamide: A Detailed Review on Formation, Detection, Mitigation, and Its Health Impacts.

In today's fast-paced world, people increasingly rely on a variety of processed foods due to their busy lifestyles. The enhanced flavors, vibrant colors, and ease of accessibility at reasonable prices have made ready-to-eat foods the easiest and simplest choice to satiate hunger, especially those that undergo thermal processing. However, these foods often contain an unsaturated amide called 'Acrylamide', known by its chemical name 2-propenamide, which is a contaminant formed when a carbohydrate- or protein-rich food product is thermally processed at more than 120 °C through methods like frying, baking, or roasting. Consuming foods with elevated levels of acrylamide can induce harmful toxicity such as neurotoxicity, hepatoxicity, cardiovascular toxicity, reproductive toxicity, and prenatal and postnatal toxicity. This review delves into the major pathways and factors influencing acrylamide formation in food, discusses its adverse effects on human health, and explores recent techniques for the detection and mitigation of acrylamide in food. This review could be of interest to a wide audience in the food industry that manufactures processed foods. A multi-faceted strategy is necessary to identify and resolve the factors responsible for the browning of food, ensure safety standards, and preserve essential food quality traits.

Intimate partner violence and excess fertility among women of reproductive age in Malawi.

PURPOSE: Gender inequity and adverse health outcomes continue to be of concern among women in sub-Saharan Africa. We determined prevalence of intimate partner violence and excess fertility (having more children than desired) in reproductive age women in Malawi. We also explored factors associated with these outcomes and with spousal fertility intentions. PATIENTS AND METHODS: In a cross-sectional study, a total of 360 women and 410 men were recruited using multi-stage sampling from communities in a peri-urban setting in Blantyre District, Southern Malawi in 2021. Women and men were separately interviewed by trained study workers using a structured questionnaire. In addition to descriptive analyses, we used univariate and multivariate logistic regression models to assess associations of risk factors with the outcomes of intimate partner violence and excess fertility. RESULTS: Among women, lifetime prevalence of intimate partner violence was 23.1%, and excess fertility was experienced by 25.6%. Intimate partner violence was associated with male partners alcohol consumption (adjusted odds ratio 2.13; P = 0.019). Women were more likely to report excess fertility if they were older (adjusted odds ratio 2.0, P<0.001, for a 5-year increase). Alcohol consumption by the male partner (adjusted odds ratio 2.14; P = 0.025) and women being able to refuse sex with their male partner (adjusted odds ratio 0.50; P = 0.036) were associated with discordant fertility preferences. CONCLUSIONS: Intimate partner violence, excess fertility, and social and health inequities continue to be prevalent in Malawi. These data suggest the underlying proximal and distal factors associated with these adverse outcomes such as alcohol consumption may be addressed through education, couple interactive communication, and community dialogue. To ensure sustainability and effectiveness, strong leadership involvement, both governmental and non-governmental, is needed.

In silico identification and functional prediction of differentially expressed genes in South Asian populations associated with type 2 diabetes.

Type 2 diabetes (T2D) is one of the major metabolic disorders in humans caused by hyperglycemia and insulin resistance syndrome. Although significant genetic effects on T2D pathogenesis are experimentally proved, the molecular mechanism of T2D in South Asian Populations (SAPs) is still limited. Hence, the current research analyzed two Gene Expression Omnibus (GEO) and 17 Genome-Wide Association Studies (GWAS) datasets associated with T2D in SAP to identify DEGs (differentially expressed genes). The identified DEGs were further analyzed to explore the molecular mechanism of T2D pathogenesis following a series of bioinformatics approaches. Following PPI (Protein-Protein Interaction), 867 potential DEGs and nine hub genes were identified that might play significant roles in T2D pathogenesis. Interestingly, CTNNB1 and RUNX2 hub genes were found to be unique for T2D pathogenesis in SAPs. Then, the GO (Gene Ontology) showed the potential biological, molecular, and cellular functions of the DEGs. The target genes also interacted with different pathways of T2D pathogenesis. In fact, 118 genes (including HNF1A and TCF7L2 hub genes) were directly associated with T2D pathogenesis. Indeed, eight key miRNAs among 2582 significantly interacted with the target genes. Even 64 genes were downregulated by 367 FDA-approved drugs. Interestingly, 11 genes showed a wide range (9-43) of drug specificity. Hence, the identified DEGs may guide to elucidate the molecular mechanism of T2D pathogenesis in SAPs. Therefore, integrating the research findings of the potential roles of DEGs and candidate drug-mediated downregulation of marker genes, future drugs or treatments could be developed to treat T2D in SAPs.

Complex interactions between diverse mobile genetic elements drive the evolution of metal-resistant bacterial genomes.

In this study, we compared the genomes of three metal-resistant bacteria isolated from mercury-contaminated soil. We identified diverse and novel MGEs with evidence of multiple LGT events shaping their genomic structure and heavy metal resistance. Among the three metal-resistant strains, Sphingobium sp SA2 and Sphingopyxis sp SE2 were resistant to multiple metals including mercury, cadmium, copper, zinc and lead. Pseudoxanthomonas sp SE1 showed resistance to mercury only. Whole genome sequencing by Illumina and Oxford Nanopore technologies was undertaken to obtain comprehensive genomic data. The Sphingobium and Sphingopyxis strains contained multiple chromosomes and plasmids, whereas the Pseudoxanthomonas strain contained one circular chromosome. Consistent with their metal resistance profiles, the strains of Sphingobium and Sphingopyxis contained a higher quantity of diverse metal resistance genes across their chromosomes and plasmids compared to the single-metal resistant Pseudoxanthomonas SE1. In all three strains, metal resistance genes were principally associated with various novel MGEs including genomic islands (GIs), integrative conjugative elements (ICEs), transposons, insertion sequences (IS), recombinase in trio (RIT) elements and group II introns, indicating their importance in facilitating metal resistance adaptation in a contaminated environment. In the Pseudoxanthomonas strain, metal resistance regions were largely situated on a GI. The chromosomes of the strains of Sphingobium and Sphingopyxis contained multiple metal resistance regions, which were likely acquired by several GIs, ICEs, numerous IS elements, several Tn3 family transposons and RIT elements. Two of the plasmids of Sphingobium were impacted by Tn3 family transposons and ISs likely integrating metal resistance genes. The two plasmids of Sphingopyxis harboured transposons, IS elements, an RIT element and a group II intron. This study provides a comprehensive annotation of complex genomic regions of metal resistance associated with novel MGEs. It highlights the critical importance of LGT in the evolution of metal resistance of bacteria in contaminated environments.

Disparities in the maternal healthcare continuum among Bangladeshi women: evidence from nationally representative surveys.

INTRODUCTION: Utilisation of maternal healthcare is low, and it consistently decreases across antenatal to postnatal period in Bangladesh. However, there is paucity of knowledge in Bangladesh to understand gaps and associated factors in seeking continuum of maternal healthcare along the pathway. Therefore, we aimed to assess the trend in socioeconomic and demographic factors and wealth inequity in maternal CoC using the Bangladesh Multiple Indicator Cluster Survey (MICS). METHODS: We performed a secondary analysis on nationally representative data from the last two MICS survey, carried out in 2012-13 and 2019. The study included women of reproductive age (15-49 years) with a live birth within two years preceding the survey. Total of 7,950 and 9,183 respondents were identified from these two surveys, respectively. We used multivariable logistic regression and concentration index to examine the covariates and inequity, respectively, in the utilisation of CoC. RESULTS: Utilisation of ≥4 antenatal care, skilled delivery, and postnatal care for both mother and newborn increased from 13 percent in 2012-13 to 25 percent in 2019 survey. Moreover, wealth inequity persists favouring the rich in utilisation of CoC in Bangladesh. Women belonging to wealthier quintile, urban areas, and non-Muslim families, with higher education, a household head with higher education, media use, and fewer children were most likely to avail complete CoC in both the surveys. CONCLUSION: Overall utilisation of maternal CoC increased between 2012 and 13 and 2019, however, women in Bangladesh still lack the CoC with persistent wealth inequality. Integrated provision of ANC, delivery by SBA, and PNC should be introduced, comprising both private and public health facilities, and targeting women in poor and rural communities. Efforts should also focus on women's education, autonomy, fertility rate, and exposure to media.

Elucidation of anti-hyperglycemic activity of Psidium guajava L. leaves extract on streptozotocin induced neonatal diabetic Long-Evans rats.

BACKGROUND: Psidium guajava L (Guava) belongs to the Myrtaceae family and has been claimed to possess several pharmacological properties including antidiabetic. OBJECTIVE: This study was designed to evaluate the anti-hyperglycemic activity of P guajava L leaves aqueous extract on neonatal streptozotocin-induced type 2 diabetic model rats. METHODS: Streptozotocin was induced (90 mg/kg) intraperitoneally to 48 h old Long Evans rat pups. After three months, 18 male type-2 diabetic model rats were confirmed by OGTT (FG > 7 mmol/L). Therefore, experimental rats were divided into three groups 2) Diabetic water control (10 ml/kg), 3) Gliclazide treated (20 mg/kg), and 4) Extract treated group (1.25g/kg)] Six normal female rats comprised group 1 [Non-diabetic water control (10 ml/kg)]. All rats were treated orally with their respective treatment for 28 consecutive days. Blood samples were collected on 0 days (by tail cut method) and the end day (by cardiac puncture) of the experiment. The anti-hyperglycemic activity was evaluated by measuring fasting glucose, serum insulin, lipid profile, hepatic glycogen content, and intestinal glucose absorption by standard methods. RESULTS: The serum glucose level of extract treated group was decreased by 16% as well as significantly (p<0.05) increased the serum insulin level (M±SD, 0 day vs 28thday; 0.319 ± 0.110 vs 0.600 ± 0.348, µg/L). Moreover, the extract-treated group also significantly (p<0.05) enhanced liver glycogen content and inhibited glucose absorption from the upper intestine. Besides, a significant (p < 0.05) reduction of LDL-cholesterol level was found in the extract-treated group (M±SD, 55 ± 33 vs 14 ± 9, mg/dl) compared with baseline values where other groups did not show any statistically remarkable changes. CONCLUSION: Current study concludes that P guajava leaves aqueous extract enhances insulin secretion from pancreatic beta-cells and promotes glycogen synthesis in the liver. The extract also inhibits glucose absorption from the upper intestine and improves dyslipidemia to some extent. Therefore, possesses the potential for drug development against T2DM.

Chemico-biological interaction unraveled the potential mechanistic pathway of Ixeridium dentatum compounds against atopic dermatitis.

This study aims to investigate the potential therapeutic application of Ixeridium dentatum (ID) in treating atopic dermatitis (AD) through network pharmacology, molecular docking, and molecular dynamic simulation. We employed GC-MS techniques and identified 40 bioactive compounds present in the ID and determined their targets by accessing public databases. The convergence of compounds and dermatitis related targets led to the identification of 32 common genes. Among them, IL1B, PTGS2, IL6, IL2, and RELA, were found to be significant targets which were analyzed using Cytoscape network topology. The KEGG pathway evaluation revealed that these targets were significantly enriched in the C-type lectin receptor signaling pathway. The therapeutic efficacy of Stigmasta-5,22-dien-3-ol, Urea, n-Heptyl-, and 3-Epimoretenol was demonstrated in molecular docking assay, as evidenced by their presence in the core compounds of the compound-target network. Furthermore, these compounds exhibited significant kinetic stability and chemical reactivity in DFT quantum analysis when compared to their co-crystallized ligands and reference drug, indicating their potential as key targets for future research. Among the top three docking complexes, namely IL6-3-Epimoretenol, and IL2- Stigmasta-5,22-dien-3-ol, both demonstrated exceptional dynamic characteristics in molecular dynamics simulations at 100 ns. The feasibility of these compounds could be attributed to the prior traditional interrelationship between ID and AD. Overall, this research elucidates the interplay between AD-associated signaling pathways and target receptors with the bioactive ID. The proposal posits the utilization of antecedent compounds as a substitute for the customary pharmaceutical intervention that obstructs the discharge of cytokines, which incite dermal inflammation in the C-type lectin receptor signaling pathway of atopic dermatitis.

An insight on sources and biodegradation of bioplastics: a review.

Durability and affordability are two main reasons for the widespread consumption of plastic in the world. However, the inability of these materials to undergo degradation has become a significant threat to the environment and human health To address this issue, bioplastics have emerged as a promising alternative. Bioplastics are obtained from renewable and sustainable biomass and have a lower carbon footprint and emit fewer greenhouse gases than petroleum-based plastics. The use of these bioplastics sourced from renewable biomass can also reduce the dependency on fossil fuels, which are limited in availability. This review provides an elaborate comparison of biodegradation rates of potential bioplastics in soil from various sources such as biomass, microorganisms, and monomers. These bioplastics show great potential as a replacement for conventional plastics due to their biodegradable and diverse properties.

Anthropometric measures and arsenic methylation among pregnant women in rural northern Bangladesh.

INTRODUCTION: Arsenic methylation converts inorganic arsenic (iAs) to monomethyl (MMA) and dimethyl (DMA) arsenic compounds. Body mass index (BMI) has been positively associated with arsenic methylation efficiency (higher DMA%) in adults, but evidence in pregnancy is inconsistent. We estimated associations between anthropometric measures and arsenic methylation among pregnant women in rural northern Bangladesh. METHODS: We enrolled pregnant women (n = 784) (median [IQR] gestational week: 14 [13, 15]) in Gaibandha District, Bangladesh from 2018 to 2019. Anthropometric measures were BMI, subscapular and triceps skinfold thicknesses, and mid-upper arm circumference (MUAC), fat area (MUAFA), and muscle area (MUAMA). Arsenic methylation measures were urinary iAs, MMA, and DMA divided by their sum and multiplied by 100 (iAs%, MMA%, and DMA%), primary methylation index (MMA/iAs; PMI), and secondary methylation index (DMA/MMA; SMI). In complete cases (n = 765 [97.6%]), we fitted linear, beta, and Dirichlet regression models to estimate cross-sectional differences in iAs%, MMA%, DMA%, PMI, and SMI per IQR-unit difference in each anthropometric measure, adjusting for drinking water arsenic, age, gestational age, education, living standards index, and plasma folate, vitamin B12, and homocysteine. RESULTS: Median (IQR) BMI, subscapular skinfold thickness, triceps skinfold thickness, MUAC, MUAFA, and MUAMA were 21.5 (19.4, 23.8) kg/m2, 17.9 (13.2, 24.2) mm, 14.2 (10.2, 18.7) mm, 25.9 (23.8, 28.0) cm, 15.3 (10.5, 20.3) cm2, and 29.9 (25.6, 34.2) cm2, respectively. Median (IQR) iAs%, MMA%, DMA%, PMI, and SMI were 12.0 (9.3, 15.2)%, 6.6 (5.3, 8.3)%, 81.0 (77.1, 84.6)%, 0.6 (0.4, 0.7), and 12.2 (9.3, 15.7), respectively. In both unadjusted and adjusted linear models, all anthropometric measures were negatively associated with iAs%, MMA%, and PMI and positively associated with DMA% and SMI. For example, fully adjusted mean differences (95% CI) in DMA% per IQR-unit difference in BMI, subscapular skinfolds thickness, triceps skinfold thickness, MUAC, MUAFA, and MUAMA were 1.72 (1.16, 2.28), 1.58 (0.95, 2.21), 1.74 (1.11, 2.37), 1.45 (0.85, 2.06), 1.70 (1.08, 2.31), and 0.70 (0.13, 1.27) pp, respectively. CONCLUSIONS: Anthropometric measures were positively associated with arsenic methylation efficiency among pregnant women in the early second trimester.

Verbal and Social Autopsy of Adult Deaths and Adult Care-Seeking Pattern in Mozambique, 2019-2020.

In sub-Saharan Africa, recent data about causes of adult death and care-seeking during illnesses are limited. This analysis examines adult deaths using verbal and social autopsy data from a nationally and provincially representative sample registration system in Mozambique. Causes of death among those 18 years and older were assigned using the InSilicoVA algorithm, and underlying social causes were examined using the pathway to survival model. Care-seeking was analyzed in different groups to determine if care was sought from formal providers (doctor, nurse/midwife, and trained community health worker) or other providers (traditional provider, family member, and pharmacist), using χ2 tests and multinomial regression models. Among the 4,040 adult deaths reported during 2019-2020, the major causes were HIV (17%), cancer (13%), injury (10%), cardiovascular diseases (9%), pneumonia (7%), tuberculosis (5%), and maternal causes (3%). Formal care-seeking was more likely among adults who had primary or higher level education (relative risk ratio [RRR]: 1.6, P < 0.001; RRR: 1.7, P < 0.01), were married (RRR: 1.3, P < 0.01), and had highest household wealth (RRR: 3.1, P < 0.001). Formal care-seeking was less likely among adults who were male (RRR: 0.7, P < 0.001), had social capital (RRR: 0.7, P < 0.05), or resided in the southern region (RRR: 0.4, P < 0.001). Information about adult causes of death is useful for formulating policy and for developing, monitoring, and evaluating programs to improve adult health in Mozambique. Care-seeking-related information helps identify barriers for seeking care from formal health providers while emphasizing the need for generating local resources and strengthening outreach health systems service delivery.

Real-time face mask position recognition system based on MobileNet model.

COVID-19 is a highly contagious disease that was first identified in 2019, and has since taken more than six million lives world wide till date, while also causing considerable economic, social, cultural and political turmoil. As a way to limit its spread, the World Health Organization and medical experts have advised properly wearing face masks, social distancing and hand sanitization, besides vaccination. However, people wear masks sometimes uncovering their mouths and/or noses consciously or unconsciously, thereby lessening the effectiveness of the protection they provide. A system capable of automatic recognition of face mask position could alert and ensure that an individual is wearing a mask properly before entering a crowded public area and putting themselves and others at risk. We first develop and publicly release a dataset of face mask images, which are collected from 391 individuals of different age groups and gender. Then, we study six different architectures of pre-trained deep learning models, and finally propose a model developed by fine tuning the pre-trained state of the art MobileNet model. We evaluate the performance (accuracy, F1-score, and Cohen's Kappa) of this model on the proposed dataset and MaskedFace-Net, a publicly available synthetic dataset created by image editing. Its performance is also compared to other existing methods. The proposed MobileNet is found as the best model providing an accuracy, F1-score, and Cohen's Kappa of 99.23%, 99.22%, and 99.19%, respectively for face mask position recognition. It outperforms the accuracy of the best existing model by about 2%. Finally, an automatic face mask position recognition system has been developed, which can recognize if an individual is wearing a mask correctly or incorrectly. The proposed model performs very well with no drop in recognition accuracy from real images captured by a camera.

The Pregnancy, Arsenic, and Immune Response (PAIR) Study in rural northern Bangladesh.

BACKGROUND: Arsenic exposure and micronutrient deficiencies may alter immune reactivity to influenza vaccination in pregnant women, transplacental transfer of maternal antibodies to the foetus, and maternal and infant acute morbidity. OBJECTIVES: The Pregnancy, Arsenic, and Immune Response (PAIR) Study was designed to assess whether arsenic exposure and micronutrient deficiencies alter maternal and newborn immunity and acute morbidity following maternal seasonal influenza vaccination during pregnancy. POPULATION: The PAIR Study recruited pregnant women across a large rural study area in Gaibandha District, northern Bangladesh, 2018-2019. DESIGN: Prospective, longitudinal pregnancy and birth cohort. METHODS: We conducted home visits to enrol pregnant women in the late first or early second trimester (11-17 weeks of gestational age). Women received a quadrivalent seasonal inactivated influenza vaccine at enrolment. Follow-up included up to 13 visits between enrolment and 3 months postpartum. Arsenic was measured in drinking water and maternal urine. Micronutrient deficiencies were assessed using plasma biomarkers. Vaccine-specific antibody titres were measured in maternal and infant serum. Weekly telephone surveillance ascertained acute morbidity symptoms in women and infants. PRELIMINARY RESULTS: We enrolled 784 pregnant women between October 2018 and March 2019. Of 784 women who enrolled, 736 (93.9%) delivered live births and 551 (70.3%) completed follow-up visits to 3 months postpartum. Arsenic was detected (≥0.02 µg/L) in 99.7% of water specimens collected from participants at enrolment. The medians (interquartile ranges) of water and urinary arsenic at enrolment were 5.1 (0.5, 25.1) µg/L and 33.1 (19.6, 56.5) µg/L, respectively. Water and urinary arsenic were strongly correlated (Spearman's ⍴ = 0.72) among women with water arsenic ≥ median but weakly correlated (⍴ = 0.17) among women with water arsenic < median. CONCLUSIONS: The PAIR Study is well positioned to examine the effects of low-moderate arsenic exposure and micronutrient deficiencies on immune outcomes in women and infants. REGISTRATION: NCT03930017.

Effect of sucrose, trehalose, maltose and xylose on rheology, water mobility and microstructure of gluten-free model dough based on high hydrostatic pressure treated starches.

Due to functional and physicochemical properties, starch in its native state has limited range of applications. Simultaneously, information on effects of different sugars and their interactions with modified starch on gluten-free model dough is also limited. To better overcome these restrictions, the effects of sucrose, trehalose, maltose and xylose on rheology, water mobility and microstructure of gluten-free dough prepared with high hydrostatic pressure (HHP) treated maize (MS), potato (PS) and sweet potato starch (SS) were investigated. MS, PS and SS dough with trehalose exhibited a lower degree of dependence of G' on frequency sweep (z'), higher strength (K) and relative elastic part of maximum creep compliance (Je/Jmax), suggesting stable network structure formation. Total gas production (VT) of MS dough with maltose, PS dough with sucrose and SS dough with trehalose was increased from 588 to 1454 mL, 537 to 1498 mL and 637 to 1455 mL respectively. Higher weakly bound water (T22) was found in the dough with trehalose at 60 min of fermentation, suggesting more hydrogen bonds and stable network. Thus, trehalose might be a potential improver in HHP treated starch-based gluten-free products.

Investigating problematic uses of Facebook and other Internet activities among university students in Bangladesh during the COVID-19 pandemic.

Problematic use of Internet (PUI) and problematic use of Facebook (PUF) has been linked to escalating behavioral health issues among university students and has increased during the COVID-19 pandemic. This study estimated the prevalence of and explored associated factors for PUI and PUF among Bangladeshi university students during the COVID-19 pandemic. A cross-sectional online survey was undertaken among 1101 Bangladeshi university students between November and December 2020. The Internet Addiction Test and Facebook Addiction Scale were used to assess PIU and PUF, respectively. A multiple linear regression analysis was performed to adjust for confounders. Among the participants, PUI and PUF were found in 39.3% and 37.1%, respectively. The multiple linear regression model indicated PUI was significantly associated with participants residing in a village, arts majors, those unsatisfied with their major, having mediocre parental relationships, failure in romantic relationships, physical comorbidities, longer use of the Internet, using the Internet for purposes other than education, using social media, and downloading movies/TV series. PUF was significantly associated with village residence, lower income, arts majors, failure in romantic relationships, longer use of the Internet, using the Internet for purposes other than education, and downloading movies/TV series. Both PUI and PUF have been prevalent among Bangladeshi university students during the COVID-19 pandemic. Longitudinal & exploratory studies are warranted in the future to identify causal factors for PUI and PUF and appropriate interventions should be designed quickly for this population.

Prevalence and associated factors of depression among Bangladeshi university students: A cross-sectional study.

Objective:  The study aimed to assess the prevalence of depression and its associated factors among university students in Bangladesh. Participants: A total of 403 undergraduate students from Patuakhali Science and Technology University, and Barisal University participated in the study. Method: A cross-sectional study was conducted using Beck's Depression Inventory (BDI). Result: The prevalence of depression (BDI ≥ 14) was 47.14%. Depression was 3.4 times (95% CI: 1.6-7.1), 3.8 times (95% CI: 1.7-8.6), and 3.9 times (95% CI: 1.5-8.9) higher among 2nd, 3rd, and 4th-year students, respectively than 1st-year students. Students with a history of stressful life events (aOR = 2.7, 95% CI: 1.7-4.4), suicidal attempts (aOR = 3.0, 95% CI: 1.2-7.9), and who received inadequate monthly allowance from their family (aOR = 0.53 95% CI: 0.3-0.9) were more likely to develop depressive symptoms. Conclusion: This study reports a high level of depression among university students which needs further discussion, exploration, and calls for designing appropriate interventions.

Artificial Intelligence-Driven Image Analysis of Bacterial Cells and Biofilms.

The current study explores an artificial intelligence framework for measuring the structural features from microscopy images of the bacterial biofilms. Desulfovibrio alaskensis G20 (DA-G20) grown on mild steel surfaces is used as a model for sulfate reducing bacteria that are implicated in microbiologically influenced corrosion problems. Our goal is to automate the process of extracting the geometrical properties of the DA-G20 cells from the scanning electron microscopy (SEM) images, which is otherwise a laborious and costly process. These geometric properties are a biofilm phenotype that allow us to understand how the biofilm structurally adapts to the surface properties of the underlying metals, which can lead to better corrosion prevention solutions. We adapt two deep learning models: (a) a deep convolutional neural network (DCNN) model to achieve semantic segmentation of the cells, (d) a mask region-convolutional neural network (Mask R-CNN) model to achieve instance segmentation of the cells. These models are then integrated with moment invariants approach to measure the geometric characteristics of the segmented cells. Our numerical studies confirm that the Mask-RCNN and DCNN methods are 227x and 70x faster respectively, compared to the traditional method of manual identification and measurement of the cell geometric properties by the domain experts.

Cannabinoid accumulation in hemp depends on ROS generation and interlinked with morpho-physiological acclimation and plasticity under indoor LED environment.

Manipulation of growth and development of cannabis (Cannabis sativa L.) has received considerable interest by the scientific community due to its high value in medicinal and recreational use worldwide. This study was conducted to investigate the effects of LED spectral changes on reactive oxygen species (ROS) and cannabinoid accumulation by provoking growth, pigmentation, photosynthesis, and secondary metabolites production of cannabis grown in an indoor environment. After three weeks of vegetative growth under greenhouse condition, plants were further grown for 90 days in a plant factory treated with 4 LED light compositions with a canopy-level photosynthetic photon flux density (PPFD) of 300 µmol m-2 s-1 for 16 h. Photosynthetic pigments and photosynthetic rate were linearly increased up to 60 days and then sharply decreased which was found most prominent in L3: MB 240 (Red 85% + Blue 15%) and L4: PF 240 (Red 70% + Blue 30%) LED light compositions. A high concentration of H2O2 was also observed in L3 and L4 treatments which provoked lipid peroxidation in later growth stage. In addition, higher accumulation of cannabinoid was observed under L4 treatment in most cases. It is also evident that higher ROS created a cellular stress in plant as indicated by higher osmolyte synthesis and enzyme activity which initiate quick maturation along with higher cannabinoids accumulation in cannabis plant. Therefore, it can be concluded that ROS metabolism has a crucial role in morpho-physiological acclimation and cannabinoid accumulation in hemp plants. The findings of this study provide further insight on the use of LED light to maximize the production of cannabinoid.

Effect of Ion Pair on Contact Angle for Phosphonium Ionic Liquids.

The wettability of ionic liquids (ILs) is relevant to their use in various applications. However, a mechanistic understanding of how the cation-anion pair affects wettability is still evolving. Here, focusing on phosphonium ILs, wettability was characterized in terms of contact angle using experiments and classical molecular dynamics simulations. Both experiments and simulations showed that the contact angle was affected by the anion and increased as benzoate < salicylate < saccharinate. Further, the simulations showed that the contact angle decreased with increasing cation alkyl chain length for these anions paired with five different tetra-alkyl-phosphonium cations. The trends were explained in terms of adhesive and cohesive energies in the simulations and then correlated to the atomic scale differences between the anions and the cations.