Complete mitogenome and intra-family comparative mitogenomics showed distinct position of Pama Croaker Otolithoides pama.

The Pama Croaker, Otolithoides pama, is an economically important fish species in Bangladesh. Intra-family similarities in morphology and typical barcode sequences of cox1 create ambiguities in its identification. Therefore, morphology and the complete mitochondrial genome of O. pama, and comparative mitogenomics within the family Sciaenidae have been studied. Extracted genomic DNA was subjected to Illumina-based short read sequencing for De-Novo mitogenome assembly. The complete mitogenome of O. pama (Accession: OQ784575.1) was 16,513 bp, with strong AC biasness and strand asymmetry. Relative synonymous codon usage (RSCU) among 13 protein-coding genes (PCGs) of O. pama was also analyzed. The studied mitogenomes including O. pama exhibited consistent sizes and gene orders, except for the genus Johnius which possessed notably longer mitogenomes with unique gene rearrangements. Different genetic distance metrics across 30 species of Sciaenidae family demonstrated 12S rRNA and the control region (CR) as the most conserved and variable regions, respectively, while most of the PCGs undergone a purifying selection. Different phylogenetic trees were congruent with one another, where O. pama was distinctly placed. This study would contribute to distinguishing closely related fish species of Sciaenidae family and can be instrumental in conserving the genetic diversity of O. pama.

Improved reference quality genome sequence of the plastic-degrading greater wax moth, Galleria mellonella.

Galleria mellonella is a pest of honeybees in many countries because its larvae feed on beeswax. However, G. mellonella larvae can also eat various plastics, including polyethylene, polystyrene, and polypropylene, and therefore, the species is garnering increasing interest as a tool for plastic biodegradation research. This paper presents an improved genome (99.3% completed lepidoptera_odb10 BUSCO; genome mode) for G. mellonella. This 472 Mb genome is in 221 contigs with an N50 of 6.4 Mb and contains 13,604 protein-coding genes. Genes that code for known and putative polyethylene-degrading enzymes and their similarity to proteins found in other Lepidoptera are highlighted. An analysis of secretory proteins more likely to be involved in the plastic catabolic process has also been carried out.

Age and sex-specific disability-free life expectancy in urban and rural settings of Bangladesh.

BACKGROUND: Disability-free life expectancy (DFLE) has been used to gain a better understanding of the population's quality of life. OBJECTIVES: The authors aimed to estimate age and sex-specific disability-free life expectancy (DFLE) for urban and rural areas of Bangladesh, as well as to investigate the differences in DFLE between males and females of urban and rural areas. METHODS: Data from the Bangladesh Sample Vital Statistics-2016 and the Bangladesh Household Income and Expenditure Survey (HIES)-2016 were used to calculate the disability-free life expectancy (DFLE) of urban and rural males and females in Bangladesh in 2016. The DFLE was calculated using the Sullivan method. RESULTS: With only a few exceptions, rural areas have higher mortality and disability rates than urban areas. For both males and females, statistically significant differences in DFLE were reported between urban and rural areas between the ages of birth and 39 years. In comparison to rural males and females, urban males and females had a longer life expectancy (LE), a longer disability-free life expectancy, and a higher share of life without disability. CONCLUSION: This study illuminates stark urban-rural disparities in LE and DFLE, especially among individuals aged < 1-39 years. Gender dynamics reveal longer life expectancy but shorter disability-free life expectancy for Bangladeshi women compared to men, emphasizing the need for targeted interventions to address these pronounced health inequalities.

Knowledge, attitudes, and practices regarding antibiotic use in Bangladesh: Findings from a cross-sectional study.

BACKGROUND: Escalating antibiotic resistance presents a notable worldwide dilemma, pointing a large involvement of general population. The objective of this study was to assess knowledge, attitudes, and practices regarding the utilization of antibiotics among Bangladeshi residents. METHODS: A cross-sectional study, conducted from January 01 to April 25, 2022, included 1,947 Bangladeshi adults with a history of antibiotic use, via online surveys and face-to-face interviews using a pretested semi-structured questionnaire. Descriptive statistics, Chi-square tests, and multivariate linear regression models were employed. RESULTS: Mean scores for knowledge, attitudes, and practices were 6.59±1.20, 8.34±1.19, and 12.74±2.59, with correct rates of 73.22%, 92.67%, and 57.91%. Positive predictors for knowledge included being unmarried (ß = 0.10, p = 0.001), higher education (College: ß = 0.09, p = 0.025; Bachelor: ß = 0.22, p<0.001; Master or above: ß = 0.14, p<0.001), various professions (student: ß = 0.57, p<0.001; housewife: ß = 0.33, p<0.001; employee: ß = 0.53, p<0.001; businessman: ß = 0.31, p<0.001; unemployed: ß = 0.15, p<0.001), and residing in semi-urban (ß = 0.32, p<0.001) or urban areas (ß = 0.15, p<0.001). Positive predictors for attitudes included being married (ß = 0.18, p<0.001), specific professions (student: ß = 1.06, p<0.001; housewife: ß = 0.33, p<0.001; employee: ß = 0.86, p<0.001; businessman: ß = 0.37, p<0.001; unemployed: ß = 0.47, p<0.001), higher SES (Lower-middle: ß = 0.22, p<0.001; Middle: ß = 0.26, p<0.001), and residing in semi-urban areas (ß = 0.18, p<0.001); negative predictors included higher education (College: ß = -0.12, p = 0.001; Master or above: ß = -0.09, p = 0.008) and being rich (ß = -0.13, p<0.001). Positive predictors for practices included being married (ß = 0.18, p<0.001), specific professions (student: ß = 0.32, p<0.001; employee: ß = 0.43, p<0.001; businessman: ß = 10, p = 0.034; unemployed: ß = 0.11, p = 0.009), and higher SES (Lower-middle: ß = 0.14, p = 0.009; Middle: ß = 0.38, p<0.001; Higher-middle: ß = 0.15, p = 0.008); negative predictors included higher education (College: ß = -0.21, p<0.001), being rich (ß = -0.12, p<0.001), residing in semi-urban (ß = -0.14, p<0.001) or urban areas (ß = -0.16, p<0.001). CONCLUSIONS: Participants exhibited adequate knowledge and positive attitudes but lagged behind in proper practice of antibiotic use. Proper initiatives should be tailored to enhance prudent antibiotic use and mitigate the risk of antimicrobial resistance.

An open set model for pest identification.

Agricultural pest identification is a prerequisite for increasing crop production and meeting global food demands. Generally, numerous phenotypic and genotypic features are widely utilized for species-level pest identification. However, the approaches are time-consuming and require expert knowledge in relevant fields. Numerous image-based machine learning (ML) models also exist to identify insect pests in agricultural fields. The models are significantly rely on a large, manually curated dataset and are close-set in nature. Our study aims to develop an open set pest identification approach by adding the capability of rejecting any irrelevant inputs. Tephritid fruit flies (Diptera:Tephritidae) are considered as an example since they are the most economically important agricultural pests worldwide. Images of the fruit flies were collected from a publicly available database and filtered to exclude uninformative images using a deep learning model (Inception-V3) and an unsupervised k-means clustering method. For the closed-set identification task, our EfficientNet-B2 model classified four major genera of notorious tephritid flies, namely, Anastrepha, Ceratitis, Rhagoletis, and Bactrocera with an accuracy of 89.65%. We further improvise our proposed model for open-set recognition tasks to leverage the identification beyond the trained datasets. The open set model achieved an overall accuracy of 86.48% and a macro F1-score of 94.44% on the four genera and an unknown class. Our proposed model can be a practical and effective pest identification tool for harmful fruit flies. In addition, the model is easy to implement with existing agricultural pest control systems in an open-world scenario.

Environmental quality and its impact on total fertility rate: an econometric analysis from a new perspective.

BACKGROUND: Environmental quality significantly affects various aspects of human existence. This study employs ecological footprint as a proxy to assess the impact of environmental quality on the TFR, measured as births per woman. This study investigates the extent to which ecological footprint indicators impact on the TFR in across 31 countries between from 1990 to 2017. METHODS: We gathered data on ecological footprints, specifically carbon, agricultural land, grazing land, forest products, and fisheries, from the Global Footprint Network. Information on the TFR, Human Development Index (HDI), and per capita Gross National Income (GNI) were sourced from the World Bank and the United Nations. We applied static panel and quantile regression models to scrutinize the connection between the ecological footprint and TFR, showing how the former influences the latter. RESULTS: The outcomes reveal that, in both fixed and random effects models, factors including HDI, carbon, and fishing grounds exert a negative influence on TFR, all at a significance level of p < 0.01. Conversely, cropland and forest product footprints exhibited a favorable impact on the TFR (p < 0.01). Furthermore, GNI per capita positively affected the TFR in both models, with a p-value of 0.01. Quantiles regression analysis demonstrated that HDI and carbon footprint had a negative impact on TFR across all quantiles. This statistical significance is maintained for all quantiles, although it is only significant for the carbon footprint up to the 60th quantile, at p < 0.01. CONCLUSIONS: This study establishes a negative correlation between specific ecological footprint indicators, such as carbon and fishing grounds, and TFR. Conversely, there was a positive correlation between the footprint of forest products and the TFR. The primary conclusion drawn is that there is heterogeneity in the results regarding the relationship between ecological footprint and TFR. Moreover, the ecological footprint indicators considered in this study did not uniformly influence TFR. Each ecological footprint indicator exhibited distinct effects on the TFR, displaying either positive or negative correlation coefficients. Future research endeavors may delve into how ecological footprints impact other population dynamics, such as mortality and migration.

Association of dietary diversity of 6-23 months aged children with prenatal and postnatal obstetric care: evidence from a nationwide cross-sectional study.

BACKGROUND: Dietary diversity is a key determinant of infant and young child eating patterns for a variety of food groups taken by children between the ages of 6-23 months. The study aimed to examine the association between prenatal and postnatal obstetric care factors of mother and child's dietary diversity, and specific food practices in Bangladesh. METHODS: This study analyzed the data of 2497 children between the age of 6-23 extracted from the latest countrywide Bangladesh Demographic Health Survey 2017-2018 and explored relationships between prenatal and postnatal obstetric care received by mother and dietary diversity score (DDS), minimum dietary diversity (MDD), and introduction of solid, semi-solid, and soft foods (ISSSF) of their children. RESULTS: Findings revealed that ≥ 4 antenatal care (ANC) visits care visits increased the DDS (adjusted [Formula: see text]: 0.32, 95% CI [0.21, 0.43]), increased the likelihood of MDD (AOR 1.54, 95% CI [1.23, 1.93]), and ISSSF (AOR 1.24, 95% CI [1.08, 1.48]), consuming eggs (AOR 1.47, 95% CI [1.23, 1.76]), and vitamin A vegetables and fruits (AOR 1.38, 95% CI [1.15, 1.66]). Moreover, DDS (adjusted ß: 0.05, 95% CI [0.00, 0.11]) and MDD (AOR 1.66, 95% CI [1.31, 2.11]) are linked to childbirth in a medical facility. The C-section delivery influences the DDS (adjusted [Formula: see text]: 0.05, 95% CI [0.00, 0.10]), MDD (AOR 1.39, 95% CI [1.10, 1.75]), and ISSSF (AOR 1.22, 95% CI [1.02, 1.48]). Besides, postnatal visits within 48 h of delivery linked to MDD (AOR 0.66, 95% CI [0.49, 0.89]) and ISSSF (AOR 0.76, 95% CI [0.59, 0.97]), and physicians or professionals providing postnatal checkups were significantly associated with DDS (adjusted [Formula: see text]: 0.09, 95% CI [0.02, 0.16]), MDD (AOR 1.69, 95% CI [1.26, 2.26]), and ISSSF (AOR 1.30, 95% CI [1.04, 1.62]). CONCLUSION: Knowledge of child nutritional feeding should emphasize during prenatal and postnatal obstetric care of mother, particularly during antenatal and postnatal visits, C-section delivery, and birth in a healthcare facility to eradicate malnutrition and establish healthy child feeding practices.

Positive impacts of COVID-19 Lock down in Bangladesh: An online investigation.

Background: This research focuses on the positive impacts of the COVID-19 lockdown on society and the environment, despite acknowledging the widespread negative effects of the pandemic and lockdown measures. The research was aimed at pinpointing and evaluating the beneficial results stemming from these measures. Method: Data for the study was collected through an online survey distributed via Google forms to adults over the age of 18 across the country. A total of 1230 participants completed the survey, mostly from rural areas (61.7%), providing valuable insights for analysis. The questionnaire encompassed personal, family, and cohesive social data, along with information on the environment and biodiversity. The study utilized structural equation modeling (SEM) and confirmatory factor analysis (CFA) to analyze the data and examine correlations between variables. Results: The findings indicated that the COVID-19 lockdown had positive implications for individuals and society, leading to increased health consciousness, improved family relationships, and constructive social attitudes. Moreover, restrictions on access to natural tourist destinations and parks during the lockdown contributed to positive changes in biodiversity. These results highlight the importance of adopting appropriate measures during pandemics to foster personal and social well-being, as well as the preservation of natural environments and biodiversity. Conclusion: This study emphasizes the need for further research to promote sustainable living in similar situations. By understanding the data appropriately, individuals can play a constructive role in future pandemics, leading to positive outcomes for both society and the environment.

Trends and long-term variation explaining nutritional determinants of child linear growth: analysis of Bangladesh Demographic and Health Surveys 1996-2018.

OBJECTIVE: To examine the height-for-age z-score (HAZ) of 0-35 months' children along with stunting prevalence to identify trends, changes and available nutrition-sensitive and specific determinants that could help explain the long-term variation in child linear growth using successive Bangladesh Demographic and Health Surveys (BDHS) data from 1996 to 2018. DESIGN: The BDHS pooled data are used for determining the key outcome variables HAZ, stunting and severe stunting. Trends, kernel-weighted local polynomial smoothing illustrations, pooled multivariable linear probability model (LPM), ordinary least squares method (OLS) and regression decomposition were used. PARTICIPANTS: Mothers having 0-35 months' children, the most critical age range for growth faltering. RESULTS: The mean HAZ increased by 0·91(±1·53) with 0·041 annual average change, while the percentages of stunting (-26·63 ± 0·54) and severe stunting (-21·12 ± 0·48) showed a reduction with 1·21 and 0·96 average annual changes, respectively. The average HAZ improvement (0·42 ± 1·56) in urban areas was less than the rural areas (1·16 ± 1·44). Similar patterns followed for stunting and severe stunting. The prenatal doctor visits (3064·65 %), birth in a medical facility (1054·32 %), breastfeeding initiation (153·18 %) and asset index (144·73 %) demonstrated a huge change. The findings of OLS, LPM and regression decomposition identified asset index, birth order, paternal and maternal education, bottle-fed, prenatal doctor visit, birth in a medical facility, vaccination, maternal BMI and ever-breastfed as influencing factors to predict the long-term changes of stunting and severe stunting. CONCLUSION: The nutrition-sensitive and specific factors identified through regression decomposition describing long-term variation in child linear growth should be focused further to attain the sustainable development goals.

Influencing factors associated with maternal delivery at home in urban areas: a cross-sectional analysis of the Bangladesh Demographic and Health Survey 2017-2018 data.

BACKGROUND: The associated factors and patterns of giving birth in home settings of rural areas have been extensively studied in Bangladeshi literature. However, urban areas still need to be explored, particularly with recent data. Therefore, the authors aimed to investigate the influential determinants of delivery at home in urban areas of Bangladesh. MATERIALS AND METHODS: In this study, 1699 urban-dwelling women who had given birth within the previous 60 months of the survey and lived in urban areas were used. The secondary data were extracted from the latest Bangladesh Demographic and Health Survey 2017-2018. Descriptive statistics and logistic regression were applied along with the association among selected variables were examined by the Chi-square test. RESULTS: Findings depict that 36.49% of women who lived in urban areas of Bangladesh delivered at home, whereas, 63.51% delivered at different govt. and private health care facilities. Women who lived in Chittagong [adjusted odds ratio (AOR) = 2.11, 95% CI 1.24-3.60], Barisal [AOR = 2.05, 95% CI 1.16-3.64] and Sylhet [AOR = 1.92, 95% CI 1.08-3.43] divisions have more likelihood to deliver at home (36.85%). Urban women following Christian religion [AOR = 10.71, 95% CI 1.32-86.68] have higher odds of delivering child at home (0.47%). Urban women having three or more children before her latest delivery (22.37%) and who are employed (29.37%) have more likelihood to deliver at home. However, women aged between 25 and 34 years (43.50%), who have higher education (25.90%), play the role of household head (9.06%), have parity of more than two births (2.24%), and read daily newspapers (68.69%) had a lower chance of delivery at home. Furthermore, women from wealthier families (89.12%) and more antenatal care (ANC) visits (94.93%) were less likely to have a delivery at home. CONCLUSION: Despite significant progress in women and reproductive health in Bangladesh, the proportion of delivery in the home in urban areas is alarming and should be emphasized more. The authors believe the identified factors will help design interventions and policy development on this issue.

Machine learning for detecting DNA attachment on SPR biosensor.

Optoelectric biosensors measure the conformational changes of biomolecules and their molecular interactions, allowing researchers to use them in different biomedical diagnostics and analysis activities. Among different biosensors, surface plasmon resonance (SPR)-based biosensors utilize label-free and gold-based plasmonic principles with high precision and accuracy, allowing these gold-based biosensors as one of the preferred methods. The dataset generated from these biosensors are being used in different machine learning (ML) models for disease diagnosis and prognosis, but there is a scarcity of models to develop or assess the accuracy of SPR-based biosensors and ensure a reliable dataset for downstream model development. Current study proposed innovative ML-based DNA detection and classification models from the reflective light angles on different gold surfaces of biosensors and associated properties. We have conducted several statistical analyses and different visualization techniques to evaluate the SPR-based dataset and applied t-SNE feature extraction and min-max normalization to differentiate classifiers of low-variances. We experimented with several ML classifiers, namely support vector machine (SVM), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbors (KNN), logistic regression (LR) and random forest (RF) and evaluated our findings in terms of different evaluation metrics. Our analysis showed the best accuracy of 0.94 by RF, DT and KNN for DNA classification and 0.96 by RF and KNN for DNA detection tasks. Considering area under the receiver operating characteristic curve (AUC) (0.97), precision (0.96) and F1-score (0.97), we found RF performed best for both tasks. Our research shows the potentiality of ML models in the field of biosensor development, which can be expanded to develop novel disease diagnosis and prognosis tools in the future.

Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis.

BACKGROUND: With the global spread of COVID-19, the world has seen many patients, including many severe cases. The rapid development of machine learning (ML) has made significant disease diagnosis and prediction achievements. Current studies have confirmed that omics data at the host level can reflect the development process and prognosis of the disease. Since early diagnosis and effective treatment of severe COVID-19 patients remains challenging, this research aims to use omics data in different ML models for COVID-19 diagnosis and prognosis. We used several ML models on omics data of a large number of individuals to first predict whether patients are COVID-19 positive or negative, followed by the severity of the disease. RESULTS: On the COVID-19 diagnosis task, we got the best AUC of 0.99 with our multilayer perceptron model and the highest F1-score of 0.95 with our logistic regression (LR) model. For the severity prediction task, we achieved the highest accuracy of 0.76 with an LR model. Beyond classification and predictive modeling, our study founds ML models performed better on integrated multi-omics data, rather than single omics. By comparing top features from different omics dataset, we also found the robustness of our model, with a wider range of applicability in diverse dataset related to COVID-19. Additionally, we have found that omics-based models performed better than image or physiological feature-based models, proving the importance of the omics-based dataset for future model development. CONCLUSIONS: This study diagnoses COVID-19 positive cases and predicts accurate severity levels. It lowers the dependence on clinical data and professional judgment, by leveraging the utilization of state-of-the-art models. our model showed wider applicability across different omics dataset, which is highly transferable in other respiratory or similar diseases. Hospital and public health care mechanisms can optimize the distribution of medical resources and improve the robustness of the medical system.

Sense and Manner of WASH and Their Coalition With Disease and Nutritional Status of Under-five Children in Rural Bangladesh: A Cross-Sectional Study.

This study aimed to assess the knowledge and practice of caregivers and their relationship to the disease and nutritional status of children under 5 years of age in rural areas of Sylhet, Bangladesh. A total of 110 households with at least a child aged 6 to 59 months were selected by simple random method from 10 rural communities of three Upazila of Sylhet from September 2019 to February 2020. Descriptive statistics were used to assess the "Water Access, Sanitation, and Hygiene" (WASH) knowledge and practice, and multivariate chi-square analyses were performed to assess associations among diseases and nutritional status with WASH following a structured questionnaire. The study found a significant association between WASH with childhood disease and nutritional status, and 65% of children were found to be in a diseased state and 35% of children were found in a no exposure of disease state within the last 6 months. The findings sketched that mother with poor WASH knowledge and practice was at greater risk for disease outbreaks, disease frequency, and duration. The highest incidence of diarrhea was 17% in children aged 12 to 23 months. A significant effect of WASH was also found in children's nutritional status, which was reflected in the ratio of stunted, underweight, and wasted children. Integrated convergent work focusing on providing clean water within the household, stopping open defecation, promoting handwashing, behavior change, and poverty alleviation is needed to improve the situation. Health, nutrition, and livelihood programs should be uninterrupted, and mothers or caregivers should be encouraged to participate in these programs.

A ResNet attention model for classifying mosquitoes from wing-beating sounds.

Mosquitoes are vectors of numerous deadly diseases, and mosquito classification task is vital for their control programs. To ease manual labor and time-consuming classification tasks, numerous image-based machine-learning (ML) models have been developed to classify different mosquito species. Mosquito wing-beating sounds can serve as a unique classifier for mosquito classification tasks, which can be adopted easily in field applications. The current study aims to develop a deep neural network model to identify six mosquito species of three different genera, based on their wing-beating sounds. While existing models focused on raw audios, we developed a comprehensive pre-processing step to convert raw audios into more informative Mel-spectrograms, resulting in more robust and noise-free extracted features. Our model, namely 'Wing-beating Network' or 'WbNet', combines the state-of-art residual neural network (ResNet) model as a baseline, with self-attention mechanism and data-augmentation technique, and outperformed other existing models. The WbNet achieved the highest performance of 89.9% and 98.9% for WINGBEATS and ABUZZ data respectively. For species of Aedes and Culex genera, our model achieved 100% precision, recall and F1-scores, whereas, for Anopheles, the WbNet reached above 95%. We also compared two existing wing-beating datasets, namely WINGBEATS and ABUZZ, and found our model does not need sophisticated audio devices, hence performed better on ABUZZ audios, captured on usual mobile devices. Overall, our model has potential to serve in mosquito monitoring and prevalence studies in mosquito eradication programs, along with potential implementation in classification tasks of insect pests or other sound-based classifications.

Population differences and domestication effects on mating and remating frequencies in Queensland fruit fly.

Females of many insect species are unreceptive to remating for a period following their first mating. This inhibitory effect may be mediated by either the female or her first mate, or both, and often reflects the complex interplay of reproductive strategies between the sexes. Natural variation in remating inhibition and how this phenotype responds to captive breeding are largely unexplored in insects, including many pest species. We investigated genetic variation in remating propensity in the Queensland fruit fly, Bactrocera tryoni, using strains differing in source locality and degree of domestication. We found up to threefold inherited variation between strains from different localities in the level of intra-strain remating inhibition. The level of inhibition also declined significantly during domestication, which implied the existence of genetic variation for this trait within the starting populations as well. Inter-strain mating and remating trials showed that the strain differences were mainly due to the genotypes of the female and, to a lesser extent, the second male, with little effect of the initial male genotype. Implications for our understanding of fruit fly reproductive biology and population genetics and the design of Sterile Insect Technique pest management programs are discussed.

How do teachers' perceptions of climate change vary in terms of importance, causes, impacts and mitigation? A comparative study in Bangladesh.

Since climate change education is an integral element in the increasingly urgent global approach to solving the problem of climate change, understanding perceptions of climate change among teachers in different academic institutions could play a significant role in how and to what extent institutions address the need to educate learners on this subject, specifically, and participate in other climate change mitigation strategies, in general. This study, therefore, examines teachers' perceptions of climate change regarding its significance, causes, consequences, and mitigation, through analysis of a self-administered structured questionnaire completed by 95 teachers from public and private educational institutions in the city of Sylhet in Bangladesh. Results from descriptive and inferential statistical analysis show that public and private school teachers have an understanding of climate change regarding its significance, causes, consequences, and mitigation. The results also reveal that the sociodemographic characteristics of teachers, as well as academic-related factors, influence their perceptions of climate change. This research concludes that teachers in public institutions have a greater awareness and understanding of climate change than those working in private schools. The research findings have broader implications for further research and policy recommendations and, in particular, draw the attention of the Departments of Education and Disaster Management in Bangladesh or in countries with similar contexts to introduce environmental and disaster education and training opportunities for teachers.

The link between climate change, food security and fertility: The case of Bangladesh.

Climate change is likely to worsen the food security situation through its impact on food production, which may indirectly affect fertility behaviour. This study examines the direct and indirect effects of climate change (e.g., temperature and precipitation) via the production of major crops, as well as their short- and long-term effects on the total fertility rate (TFR) in Bangladesh. We used structural equation modelling (SEM) to perform path analysis and distinguish the direct influence of climate change on fertility and its indirect influence on fertility through food security. We also applied the error correction model (ECM) to analyze the time-series data on temperature and precipitation, crop production and fertility rate of Bangladesh from 1966 to 2015. The results show that maximum temperature has a direct effect and indirect negative effect-via crop production-on TFR, while crop production has a direct positive effect and indirect negative effect-via infant mortality-on TFR. In the short term, TFR responds negatively to the maximum temperature but positively in the long term. The effect of rainfall on TFR is found to be direct, positive, but mainly short-term. Although indicators of economic development play an important part in the fertility decline in Bangladesh, some climate change parameters and crop production are non-negligible factors.

Supplemental nitrite increases choroidal neovascularization in mice.

Low doses of nitrite, close to physiological levels, increase blood flow in normal and ischemic tissues through a nitric oxide (NO) dependent mechanism. Given that nitrite therapy and dietary supplementation with vegetables high in nitrate (e.g. beets) are gaining popularity we decided to determine if low doses of nitrite impact the development of choroidal neovascularization (CNV), a key feature of wet age related macular degeneration (AMD). Sodium nitrite (at 50 mg/L, 150 mg/L, and 300 mg/L), nitrate (1 g/L) or water alone were provided in the drinking water of C57BL/6 J mice aged 2 or 12 months. Mice were allowed to drink ad libitum for 1 week at which time laser-induced choroidal neovascularization (L-CNV) was induced. The mice continued to drink the supplemented water ad libitum for a further 14 days at which point optical coherence tomography (OCT) was performed to determine the volume of the CNV lesion. Blood was drawn to determine nitrite and nitrate levels and eyes taken for histology. CNV volume was 2.86 × 107 µm3 (±0.4 × 107) in young mice on water alone but CNV volume more than doubled to >6.9 × 107 µm3 (±0.8 × 107) in mice receiving 300 mg/L nitrite or 7.34 × 107 µm3 (±1.4 × 107) in 1 g/L nitrate (p < 0.01). A similar trend was observed in older mice. CNV volume was 5.3 × 107 µm3 (±0.5 × 107) in older mice on water alone but CNV volume almost doubled to approximately 9.3 × 107 µm3 (±1.1 × 107) in mice receiving 300 mg/L nitrite or 8.7 × 107 µm3 (±0.9 × 107) 1 g/L nitrate (p < 0.01). Plasma nitrite levels were highest in young mice receiving 150 mg/L in the drinking water with no changes in plasma nitrate observed. In older mice, drinking water nitrite did not significantly change plasma nitrite, but plasma nitrate was increased. Plasma nitrate was elevated in both young and old mice provided with nitrate supplemented drinking water. Our data demonstrate that the CNV lesion is larger in older mice compared to young and that therapeutic levels of oral nitrite increase the volume of CNV lesions in both young and older mice. Therapeutic nitrite or nitrate supplementation should be used with caution in the elderly population prone to CNV.

Potential role for age as a modulator of oral nitrate reductase activity.

Reduction of salivary nitrate to nitrite by oral nitrate reductase (NR) expressing bacteria has emerged as an integral pathway in regulating nitric oxide (NO) homeostasis and signaling. The oral microbiome is critical for this pathway. Variations in this pathway may underlie variable responses in the magnitude by which dietary or therapeutic nitrate modulates NO-signaling. The relationships between oral microbes and NR activity, and the factors that affect this relationship remain unclear however. Using a cross-sectional study design, the objective of this study was to determine the relationships between oral microbes and oral NR activity using a protocol that directly measures initial NR activity. Tongue swabs were collected from 28 subjects ranging in age from 21 to 73y. Initial NR activity showed a bell-shaped dependence with age, with activity peaking at ~40-50y and being lower but similar between younger (20-30y) and older (51-73) individuals. Microbiome relative abundance and diversity analyses, using 16s sequencing, demonstrated differences across age and identified both NR expressing and non-expressing bacteria in modulating initial NR activity. Finally, initial NR activity was measured in 3mo and 13mo old C57BL/6J mice. No differences in bacterial number were observed. However initial NR activity was significantly (80%) lower in 13mo old mice. Collectively, these data suggest that age is a variable in NR activity and may modulate responsiveness to dietary nitrate.