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"Islam, Saidul"
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Confronting the blue revolution : industrial aquaculture and sustainability in the Global South
\"Uses the shrimp farming industry in Bangladesh and across the global South to demonstrate the social and environmental impact of industrialized aquaculture.\"--Page [i].
Book
Cyanide at the origin of metabolism
The emergence of protometabolic reactions that evolved into today’s metabolic pathways is unclear. Now, evidence suggests that the chemical origin of biological carbon metabolism may have relied on the versatility of a single primitive C
1
feedstock molecule — hydrogen cyanide.
Journal Article
Does community pressure matter in cesarean deliveries in Bangladesh? An analysis of nationally representative surveys
Cesarean delivery plays a significant role in reducing maternal and child mortality. However, unjustified cesarean section (C-section) delivery is rising worldwide, including in Bangladesh. C-section delivery rates in Bangladesh have increased from 2.9% in 1999 to 45% in 2022, which is particularly high for first-order births (51%). This study aims to describe the prevalence and determinants of births by C-section for institutional deliveries in Bangladesh’s private and public health facilities. Data from the Bangladesh Demographic and Health Surveys (BDHS) for 2011, 2014, 2017−18, and 2022 are used in this study. Besides the common socio-economic determinants of C-sections, adequate antenatal care (ANC) visits, place of delivery (public/private), and community-level factors including level of illiteracy and prevalence of C-sections in the community were found to have a significant association. After controlling the effect of other variables, women from a community with a high prevalence of C-sections were found to be 11.68 times more likely to have a C-section in their last birth compared to women from a community with a low prevalence of C-sections. Also, the women who had their last birth in private facilities were 8.16 times more likely to have C-sections than women who delivered in public facilities. These findings suggest that the increased rate of C-sections in Bangladesh may be driven by both individual-level and provider-level factors where community pressure plays a vital role. Close monitoring, particularly in private hospitals, and community-level awareness programs about the adversity of C-sections are the proposed policy strategies to avoid unnecessary cesarean deliveries in Bangladesh.
Journal Article
Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal
State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. Users may find it challenging to comprehend and interpret the results. Explainable AI (XAI) can explain the machine learning (ML) outputs and contribution of features in disease prediction models. Electroencephalography (EEG) is a potential predictive tool for understanding cortical impairment caused by an ischemic stroke and can be utilized for acute stroke prediction, neurologic prognosis, and post-stroke treatment. This study aims to utilize ML models to classify the ischemic stroke group and the healthy control group for acute stroke prediction in active states. Moreover, XAI tools (Eli5 and LIME) were utilized to explain the behavior of the model and determine the significant features that contribute to stroke prediction models. In this work, we studied 48 patients admitted to a hospital with acute ischemic stroke and 75 healthy adults who had no history of identified other neurological illnesses. EEG was obtained within three months following the onset of ischemic stroke symptoms using frontal, central, temporal, and occipital cortical electrodes (Fz, C1, T7, Oz). EEG data were collected in an active state (walking, working, and reading tasks). In the results of the ML approach, the Adaptive Gradient Boosting models showed around 80% accuracy for the classification of the control group and the stroke group. Eli5 and LIME were utilized to explain the behavior of the stroke prediction model and interpret the model locally around the prediction. The Eli5 and LIME interpretable models emphasized the spectral delta and theta features as local contributors to stroke prediction. From the findings of this explainable AI research, it is expected that the stroke-prediction XAI model will help with post-stroke treatment and recovery, as well as help healthcare professionals, make their diagnostic decisions more explainable.
Journal Article
Prebiotic selection and assembly of proteinogenic amino acids and natural nucleotides from complex mixtures
A central problem for the prebiotic synthesis of biological amino acids and nucleotides is to avoid the concomitant synthesis of undesired or irrelevant by-products. Additionally, multistep pathways require mechanisms that enable the sequential addition of reactants and purification of intermediates that are consistent with reasonable geochemical scenarios. Here, we show that 2-aminothiazole reacts selectively with two- and three-carbon sugars (glycolaldehyde and glyceraldehyde, respectively), which results in their accumulation and purification as stable crystalline aminals. This permits ribonucleotide synthesis, even from complex sugar mixtures. Remarkably, aminal formation also overcomes the thermodynamically favoured isomerization of glyceraldehyde into dihydroxyacetone because only the aminal of glyceraldehyde separates from the equilibrating mixture. Finally, we show that aminal formation provides a novel pathway to amino acids that avoids the synthesis of the non-proteinogenic α,α-disubstituted analogues. The common physicochemical mechanism that controls the proteinogenic amino acid and ribonucleotide assembly from prebiotic mixtures suggests that these essential classes of metabolite had a unified chemical origin.
2-aminothiazole — a hybrid of prebiotic amino acid and nucleotide precursors — sequentially accumulates and purifies glycolaldehyde and glyceraldehyde from complex mixtures in the order required for ribonucleotide synthesis, dynamically resolves glyceraldehyde from its ketose-isomer dihydroxyacetone, and provides the first strategy to select natural amino acids from abiotic aldehydes and ketones.
Journal Article
Heated gas bubbles enrich, crystallize, dry, phosphorylate and encapsulate prebiotic molecules
Non-equilibrium conditions must have been crucial for the assembly of the first informational polymers of early life, by supporting their formation and continuous enrichment in a long-lasting environment. Here, we explore how gas bubbles in water subjected to a thermal gradient, a likely scenario within crustal mafic rocks on the early Earth, drive a complex, continuous enrichment of prebiotic molecules. RNA precursors, monomers, active ribozymes, oligonucleotides and lipids are shown to (1) cycle between dry and wet states, enabling the central step of RNA phosphorylation, (2) accumulate at the gas–water interface to drastically increase ribozymatic activity, (3) condense into hydrogels, (4) form pure crystals and (5) encapsulate into protecting vesicle aggregates that subsequently undergo fission. These effects occur within less than 30 min. The findings unite, in one location, the physical conditions that were crucial for the chemical emergence of biopolymers. They suggest that heated microbubbles could have hosted the first cycles of molecular evolution.
High concentrations of prebiotic molecules and dry–wet cycles are difficult to achieve in a submerged system. Now, it has been shown that temperature gradients across gas bubbles in submerged rock pores can provide these conditions. Molecules are continuously accumulated at the warm side of bubbles at the gas–water interface, which enables or enhances many prebiotically relevant processes.
Journal Article
Households’ vulnerability assessment: empirical evidence from cyclone-prone area of Bangladesh
Despite Bangladesh being vulnerable to cyclones, there is a dearth of research on cyclone vulnerability assessment. Assessing a household's vulnerability is considered a crucial step in avoiding the adverse effects of catastrophe risks. This research was conducted in the cyclone-prone district of Barguna, Bangladesh. This study's purpose is to evaluate this region's vulnerability. A questionnaire survey was conducted using a convenience sample technique. A door-to-door survey of 388 households in two Unions of Patharghata Upazila, Barguna district, was conducted. Forty-three indicators were selected to assess cyclone vulnerability. The results were quantified using an index-based methodology with a standardized scoring method. Where applicable, descriptive statistics have been obtained. In terms of vulnerability indicators, we also utilized the chi-square test to compare Kalmegha and Patharghata Union. When appropriate, the non-parametric Mann–Whitney U test was employed to evaluate the relationship between the Vulnerability Index Score (VIS) and the union. According to the results, the environmental vulnerability (0.53 ± 0.17) and the composite vulnerability index (0.50 ± 0.08) were significantly greater in Kalmegha Union than in Patharghata Union. They faced inequity in government assistance (71%) and humanitarian aid (45%) from national and international organizations. However, 83% of them underwent evacuation practices. 39% were satisfied with the WASH conditions at the cyclone shelter, whereas around half were dissatisfied with the status of the medical facilities. Most of them (96%) rely only on surface water for drinking. National and international organizations should have a comprehensive plan for disaster risk reduction that encompasses all individuals, regardless of race, geography, or ethnicity.
Journal Article
Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic
Climate change is causing more frequent and extraordinary extreme weather events that are already negatively affecting crop production. There is a need for improved climate risk assessment by developing smart adaptation strategies for sustainable future crop production. This study aims to assess yield impacts of extreme temperatures and rainfall variability on wheat, and winter and summer season-planted maize in northwestern Bangladesh. Utilizing a machine learning approach, future yield patterns were predicted for these crops under various climate change scenarios. Additionally, the study developed adaptation strategies focusing on prediction of optimum sowing windows for wheat and maize to minimize climate risk-related yield losses jeopardizing food security. A fuzzy logical model was applied, incorporating a set of fuzzy rules to estimate the probable yields of wheat and maize (winter and summer growing seasons). Key climatic variables (temperature and rainfall) were added as model inputs, enabling the model to handle uncertainty and nonlinear interactions in the climate–yield relationship. Findings demonstrated that climate change has significant negative impacts at the different phenological stages of both wheat and maize (winter and summer seasons), with yield levels generally showing notable declines. Only small variations in optimal temperature and rainfall patterns affected crop yields significantly. Moreover, maize summer yield was consistently lower than maize winter as the temperature prevails high during the maize summer season (April to July). The study found that the wheat crop, maize winter, and maize summer have as optimal planting windows November 1–7, November 1–10, and February 20 - March 7, respectively. Such adaptation would ensure maximum yield and effective reduction of climate change risks. Outcomes of this study contribute to multiple Sustainable Development Goals (SDGs), especially three; zero hunger (SDG2), climate action (SDG13), and life on land (SDG14). These adaptations identified in this study can support policymakers and stakeholders to combat the impact of extreme climate – and achieving optimal yield. The approach is also applicable to other regions of the country and similar monsoon climates.
Journal Article
Implementing the ‘Integrated Model for Supervision’ for mental health and psychosocial support programming within humanitarian emergencies: A mixed-methods evaluation across six humanitarian contexts
The ‘Integrated Model for Supervision’ (IMS) offers important guidance for how to provide supportive supervision within mental health and psychosocial support (MHPSS) programming in humanitarian emergencies. The current study sought to (i) describe how the IMS was implemented following IMS training; (ii) assess whether delivery of the IMS training is associated with changes in a selection of theoretically supported quantitative outcomes; and (iii) elicit feedback on the IMS and its implementation process.
Data was collected from a participant pool of n = 119 individuals from six different humanitarian organisations that had previously participated in an IMS training. For the first and third objectives, interviews at 6- and 12-months post-training were conducted and thematically analysed. For the second objective, timepoint comparison analytical techniques were used across five distinct timepoints.
Quantitative findings showed significant increases in participant self-efficacy, supervision knowledge, and supervision confidence, alongside some evidence of reductions in participant burnout. Qualitatively, participants underscored the IMS's efficacy in creating supportive supervision structures within their organisations, identified barriers and facilitators to implementation and proposed strategies for sustainability. Additionally, they highlighted positive impacts of implementing the IMS on staff, organisational culture, and service quality.
This study supports the effectiveness and acceptability of the IMS in enhancing the capacity of organisations to provide supportive supervision in humanitarian contexts, as key to promote the wellbeing of humanitarian MHPSS workers and the quality of the services they deliver. Challenges remain, however, to ensure sustainable implementation of the IMS, which guide ongoing efforts towards its improvement.
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•Training on ‘Integrated Model for Supervision’ (IMS) benefits humanitarian workers.•Benefits of the IMS include better mental health and perceived improvement in service quality.•However, barriers exist towards IMS implementation within organisations.•Challenges include low organisational awareness of its importance and limited organisational resources.•Strategies include flexible implementation and refresher IMS training.
Journal Article