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The overuse of antibiotics, chemicals and as well as lack of farm biosecurity and good hygiene practices in poultry production are considered to be contributors for occurring foodborne illness and many significant public health threats reported in both developed and developing countries nowadays. Considering the above, a piloted food safety activity was implemented jointly by the Department of Livestock Services and FAO-Food Safety program, Bangladesh in twenty-five selected poultry (broiler) dense subdistricts of the country. Good Agriculture Practices (GAP) and Good Hygiene Practices (GHP) related five (microbial) plus five (chemical) Code of Practices (CoP) were adopted at the farm level (N=500) through farmers’ motivation and intensive participatory training program. It was found that average production cost reduces, the feed conversion ratio decreases and mortality rate decreases that convince increase farm profitability in the best-practiced farms (n=81). In conclusion, Good Agriculture Practices (GAP) and Good Hygiene Practices (GHP) through certifying key control measures can increase profitability in broiler farming and CoP adapted farms found to be less public health hazardous than non-CoP farms in light of food safety and public health grounds. Asian Australas. J. Food Saf. Secur. 2018, 2(2), 45-55

This study describes characterisation of three thermophilic strains of cyanobacteria belonging to Thermosynechococcaceae and their utilisation as a source of thermostable C-phycocyanin. Three strains NIES 2133, NIES 2134 and PCC 6715 have been cultivated in two types of growth media and two temperatures: 45 and 50 °C and assessed for C-phycocyanin productivity. Among these strains Synechococcus lividus sp. PCC 6715, phylogenetically closer to Thermosynechococcus sp. clade than other Synechococcaceae, has been selected for subsequent study to characterise its C-phycocyanin. The strain showed faster growth rates than the other two and appeared to utilise different bicarbonate uptake mechanism based on SbtA, whilst BCT1 mechanism was found in NIES2133 and 2134. Higher growth rates translated to larger overall C-phycocyanin productivity. C-phycocyanin has been purified using chromatographic methods and its thermostability and pH stability has been assessed against that of Spirulina. Results have shown that PCC 6715 C-phycocyanin is one of the most stable proteins of this type reported to date. It exhibits 90% stability after 5-hour incubation at 50 °C and about 70% stability after 2-week incubation at the same temperature. PCC 6715 phycocyanins show high stability at acidic pH at both 4 °C and 50 °C, whilst at 50 °C maintain 100% stability during 4-hour incubation at pH 4.0 to 8.0. PCC 6715 phycocyanin shows good long-term stability characteristics and could be considered as a thermostable replacement for Spirulina phycocyanin. Analysis of molecular model of PCC 6715 C-phycocyanins and crystal structures of other phycocyanins reveals interesting pattern of amino acid substitutions that are present in thermophilic proteins that could have significant impact on their significantly higher thermostability. Phycocyanins from thermostable cyanobacteria exhibit evolutionarily conserved amino acid substitutions localised in the single structural hotspot of the protein that result in additional stabilising interactions within and between protein subunits when compared to proteins from mesophilic counterparts.

Various polycrystalline compositions Ni0.38−xCu0.15+yZn0.47+x−yFe2O4 [(x, y) = (0.00, 0.01)] are prepared through the sol–gel auto-combustion technique and sintered at 850, 950, 1050, and 1150 °C for 5 h in air. The single-phase cubic spinel structures of the compositions are confirmed by X-ray diffraction analysis. No secondary phases are observed in the X-ray diffraction patterns. The lattice constant is found to increase with doping of Zn2+ in place of Ni2+ and decrease with doping of Cu2+ in place of Ni2+. The bulk density of ferrites increases with sintering temperature up to 1050 °C, then decreases. The field emission scanning electron microscopy is used to demonstrate the surface morphology of the materials. The maximum grain size (1.97 µm) is found for the composition Ni0.38Cu0.16Zn0.46Fe2O4. The maximum bulk density (4.42 × 103 kg/m3), maximum initial permeability, and highest relative quality factor (≥ 6000) are observed for the composition Ni0.38Cu0.16Zn0.46Fe2O4 sintered at 1050 °C. The values of dielectric constants, impedance, and AC resistivity are found higher at lower frequencies but become almost constant at higher frequencies, which can be explained based on the hopping mechanism. The investigated ferrites exhibit comparatively higher permeability, lower eddy current loss, and higher resistivity, which make them suitable for wireless power transfer (WPT) applications.

Background/Aim: The reproducibility of athero­sclerotic lesions was evaluated after the production of cloned-microminipigs and their offspring. Materials and Methods: Cloned-microminipig-parents were produced by microminipig-somatic cell nuclei. These parents were crossbred and delivered males (F1-offspring) were divided into two groups: normal chow diet (NcD)-fed and high-fat/high-cholesterol diet (HcD)-fed groups. One of the F1-offsprings was subjected to cloning, and delivered males (F1-clones) were fed with HcD. After 8 weeks, all animals were necropsied for patho­physiological studies compared to non-cloned-microminipigs. Results: HcD-fed F1-offspring and F1-clones, but not NcD-fed F1-offspring, exhibited increased serum lipid levels and systemic atherosclerosis, which were comparable to those of HcD-fed non-cloned-microminipigs. Homogeneity of variance analysis demonstrated that standard deviation values of serum lipoprotein and aortic atherosclerosis area from HcD-fed animals decreased in F1-offspring and F1-clones. Conclusion: HcD-induced atherogenesis was highly reproducible in F1-offsprings and F1-clones, indicating that the atherosclerosis-prone genomic background was preserved in the cloned-microminipigs, which can be used for studies on human atherosclerosis and related diseases.

To reduce the dispersion of COVID - 19, people need to maintain safe distance from each other. This paper proposes a mobile application solution that keeps track of the COVID - 19 positive individuals in a certain area. With the help of the infected person's position uploaded in the cloud system, a location-based recommendation (i.e. informing people about a danger zone) is provided to the related users. Taking the pandemic into consideration, a proper visualization of users' location is made on the map using geospatial hotspot and location-based services. This paper describes the development of the mobile application that uses GPS data to pinpoint the infected person's location and create a danger zone based on the information. The accuracy of the services (provided by the application) was tested and confirmed through experiments.

Twitter is one of the most popular social networking services in the world. Over the past few years' people have been using Twitter messages on a daily basis to express their views and share their feelings. As a result, the size of data is increasing dramatically creating opportunity for researchers to use these tweets as sources for data mining and extract valuable information. Being popular in Saudi Arabia, we believe that twitter messages (tweets) are a good source to capture the sentiment of people. These twitter messages can be divided mostly into two classes: positive or negative. Our goal is to design and implement a sentiment analyzer that will classify real tweets collected by Twitter API into one of the above categories. We used a machine learning based sentiment analysis method and applied several supervised learning algorithms such as Logistics Regression, Naive Bayes, Support Vector Machine, and Decision Trees. To accomplish this, we preprocess the data-set that we have chosen to train our classifier. We used Bag-of-words and TF-IDF techniques to extract features from the preprocessed tweets. We also used uni-gram, bi-gram, and tri-gram to rank our features to identify best predictive accuracy for the classifier. Among the classification techniques that we used, Logistic regression performs the best in terms of accuracy for the validation and test data while using tri-gram features with stop words and TF-IDF feature extraction technique.

Nicotinic acetylcholine receptors are members of the Cys-loop superfamily of pentameric ligand-gated ion channels. The electric organ of the Torpedo ray is extraordinarily rich in an acetylcholine receptor that is homologous to the human nicotinic receptor found at the neuromuscular junction. Due to this abundant natural source in the fish and the relatively accessible preparation of the neuromuscular junction (compared to a central synapse), this muscle-type receptor and specifically the fish receptors have long been used as the prototype for study of nicotinic receptors. However, an absence of structural detail at high resolution has limited the chemical interpretation of this archetypal nicotinic receptor. One of the main concerns in preparing receptor for high resolution structural analysis was its documented sensitivity to particular detergents and requirements for specific lipids in order to maintain function after reconstitution in a membrane. Here, we present methods for purifying native nicotinic receptor from Torpedo electric tissue that maintains functionality after reconstitution and that is amenable to high resolution structural analysis. The specific developments we describe include detergent exchange during purification, inclusion of specific lipids during purification and for nanodisc reconstitution, and synthesis of a new affinity reagent for rapid isolation of receptors.

Semantic segmentation of 3D medical images is a challenging task due to the high variability of the shape and pattern of objects (such as organs or tumors). Given the recent success of deep learning in medical image segmentation, Neural Architecture Search (NAS) has been introduced to find high-performance 3D segmentation network architectures. However, because of the massive computational requirements of 3D data and the discrete optimization nature of architecture search, previous NAS methods require a long search time or necessary continuous relaxation, and commonly lead to sub-optimal network architectures. While one-shot NAS can potentially address these disadvantages, its application in the segmentation domain has not been well studied in the expansive multi-scale multi-path search space. To enable one-shot NAS for medical image segmentation, our method, named HyperSegNAS, introduces a HyperNet to assist super-net training by incorporating architecture topology information. Such a HyperNet can be removed once the super-net is trained and introduces no overhead during architecture search. We show that HyperSegNAS yields better performing and more intuitive architectures compared to the previous state-of-the-art (SOTA) segmentation networks; furthermore, it can quickly and accurately find good architecture candidates under different computing constraints. Our method is evaluated on public datasets from the Medical Segmentation Decathlon (MSD) challenge, and achieves SOTA performances.

Canine mammary gland tumors are very common and represent a potential model of human breast cancer, and microRNA (miRNAs) are promising biomarkers and therapeutic targets for these tumors. Accordingly, we aimed to identify miRNAs differentially expressed in canine mammary gland tumors using next generation sequencing (NGS), with subsequent confirmatory qPCR and target gene analyses. Mammary gland tissue was collected from healthy dogs (n=7) and dogs with suspected tumors (n=80). A subset of samples was analyzed with NGS to identify differentially expressed miRNAs with CLC Genome Workbench. Normal (n=10), tumor-adjacent (n=6), and tumor-bearing (n=76) mammary gland tissue samples were analyzed for the identified miRNAs using qPCR. An in silico analysis (TargetScan) was performed to predict the miRNAs' target genes using gene ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (DAVID). We identified four miRNAs (cfa-miR-1-3p, cfa-miR-133a-3p, cfa-miR-133b-3p, and cfa-miR-133c-3p) as down regulated in canine mammary gland tumor tissues relative to normal and tumor adjacent tissues. KEGG analysis revealed the potential target genes of cfa-miR-1-3p are related to the Rap1 signaling pathway, adherens junction, and Ras signaling pathway, and those of the miR-133 family are related to the TGF-beta signaling pathway, synaptic vesicle cycle, and sphingolipid signaling pathway. In combination, these target genes are related to the regulation of transcription and DNA binding transcription (GO analysis), and the Hippo signaling pathway, adherens junction, and endocytosis (KEGG analysis). Accordingly, we suggest these four miRNAs are promising potential biomarker candidates for canine mammary gland tumors warranting further investigation.

A skin lesion is a portion of skin that observes abnormal growth compared to other areas of the skin. The ISIC 2018 lesion dataset has seven classes. A miniature dataset version of it is also available with only two classes: malignant and benign. Malignant tumors are tumors that are cancerous, and benign tumors are non-cancerous. Malignant tumors have the ability to multiply and spread throughout the body at a much faster rate. The early detection of the cancerous skin lesion is crucial for the survival of the patient. Deep learning models and machine learning models play an essential role in the detection of skin lesions. Still, due to image occlusions and imbalanced datasets, the accuracies have been compromised so far. In this paper, we introduce an interpretable method for the non-invasive diagnosis of melanoma skin cancer using deep learning and ensemble stacking of machine learning models. The dataset used to train the classifier models contains balanced images of benign and malignant skin moles. Hand-crafted features are used to train the base models (logistic regression, SVM, random forest, KNN, and gradient boosting machine) of machine learning. The prediction of these base models was used to train level one model stacking using cross-validation on the training set. Deep learning models (MobileNet, Xception, ResNet50, ResNet50V2, and DenseNet121) were used for transfer learning, and were already pre-trained on ImageNet data. The classifier was evaluated for each model. The deep learning models were then ensembled with different combinations of models and assessed. Furthermore, shapely adaptive explanations are used to construct an interpretability approach that generates heatmaps to identify the parts of an image that are most suggestive of the illness. This allows dermatologists to understand the results of our model in a way that makes sense to them. For evaluation, we calculated the accuracy, F1-score, Cohen’s kappa, confusion matrix, and ROC curves and identified the best model for classifying skin lesions.

The two fertility stalls that occurred in Bangladesh emerged as substantial barriers in controlling its burgeoning vast population. The first stall occurred during 1996-2000 in the mid-transition of fertility, while the second stall occurred during the recent period 2011-2014 in the late transition of fertility. This article explores the role of proximate determinants in those stalls by using the data of Bangladesh Demographic and Health Surveys. Data have been analyzed using proximate determinants model and a descriptive analysis technique. Findings show that contraceptive use and induced abortion are the main drivers of fertility transition in Bangladesh. Both stalls in Bangladesh are found to be associated with a stall in induced abortion. In addition, declining postpartum infecundability plays a vital role in setting first stall. At the end of this stall, composition of contraceptive use is also found less efficient. While, declining fertility-inhibiting effect of marriage and a marginal increase in contraceptive use are the additional factors that play key roles in setting second stall. The mixture of contraceptive use shows a shift to a more efficient composition in the recent years. Change in the timing of birth does not show any consistent link with any of the fertility stalls.

Background In Bangladesh, about 30 percent of the total births a woman on average have (2.3 births) are unwanted. Reduction in unwanted births is very important for family wellbeing, and control of rapid population growth. This reduction requires in-depth knowledge about the factors associated with the change in unwanted childbearing. This study examines the contributions of population structure, external factors that is family planning programmes, and their interaction to the change in the proportion of women having unwanted birth during 2004 ̶ 2007, 2007 ̶ 2011 and 2011 ̶ 2014. Methods This study uses the notion of standardization approach for measuring the effects of family planning programmes and non-programme factors on unwanted childbearing. Using Bangladesh Demographic and Health Survey data, we employ both aggregate and detailed three-fold decomposition technique to decompose the changes in the proportion having unwanted birth into the contributions of the three different factors. In decomposing the changes, we use logistic regression estimates. Results About 10.3 percent women are found to have unwanted birth in 2014. This percentage remains almost unchanged during 2004−2007, but declines significantly during 2007−2011 and 2011−2014. Population structure exerts significant reducing effect on proportion having unwanted birth during all the study periods, while external factors exerts significant negative effect only during 2011−2014. Interaction effect is found non-significant during all the study periods. All the factors found significantly associated with unwanted childbearing in logistic regression analysis do not show significant effect on its changes in detailed decomposition analysis. Structural effects of respondent’s age at marriage and education are found significantly negative during all the study periods. Conclusions In Bangladesh, significant reducing effect of population structure amid non-declining family planning programmes reduces the proportion having unwanted birth, while simultaneous reducing effects of structure and family planning programmes during a period result in a more pronounced decline. Substantial decline in women marrying before age 18 and having no education is likely to play a vital role in resulting a population structure that may have significant reducing effect on the proportion having unwanted birth.

The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal part of the biosignal classification in the brain-computer interface (BCI) applications. Currently, this bio-engineering based technology is being employed by researchers in various fields to develop cutting edge applications. The classification of real-time MI-EEG signal is the core computing and challenging task in these applications. It is well-known that the existing classification methods are not so accurate due to the high dimensionality and dynamic behaviors of the real-time EEG data. To improve the classification performance of real-time BCI applications, this paper presents a clustering-based ensemble technique and a developed brain game that distinguishes different human thoughts. At first, we have gathered the brain signals, extracted and selected informative features from these signals to generate training and testing sets. After that, we have constructed several classifiers using Artificial Neural Network (ANN), Support Vector Machine (SVM), naïve Bayes, Decision Tree (DT), Random Forest, Bagging, AdaBoost and compared the performance of these existing approaches with suggested clustering-based ensemble technique. On average, the proposed ensemble technique improved the classification accuracy of roughly 5 to 15% compared to the existing methods. Finally, we have developed the targeted brain game employing our suggested ensemble technique. In this game, real-time EEG signal classification and prediction tabulation through animated ball are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. All relevant codes are available via open repository at: https://github.com/mrzResearchArena/MI-EEG.

The nicotinic acetylcholine receptor, a pentameric ligand-gated ion channel, converts the free energy of binding of the neurotransmitter acetylcholine into opening of its central pore. Here we present the first high-resolution structure of the receptor type found in muscle-endplate membrane and in the muscle-derived electric tissues of fish. The native receptor was purified from Torpedo electric tissue and functionally reconstituted in lipids optimal for cryo-electron microscopy. The receptor was stabilized in a closed state by the binding of α-bungarotoxin. The structure reveals the binding of a toxin molecule at each of two subunit interfaces in a manner that would block the binding of acetylcholine. It also reveals a closed gate in the ion-conducting pore, formed by hydrophobic amino acid side chains, located ∼60 Å from the toxin binding sites. The structure provides a framework for understanding gating in ligand-gated channels and how mutations in the acetylcholine receptor cause congenital myasthenic syndromes.

The paper focuses on investigating the casual relationship among Energy Consumption, Real GDP, and Industry Value Added of Bangladesh using the World Bank Development Indicators data set. The Granger causality approach has been applied to identify the short-run causality direction for all possible pairs of dynamic variables of the study. Results from the approach indicate the unidirectional short run causal relationship from Real GDP to Energy Consumption, while another unidirectional short-run causality has been found from Industry Value Added to Real GDP. The concept of cointegration and Vector Error Correction Model (VECM) are employed to find the long-run relationships among the variables. Our results show the existence of long run relationship between each pair of variables. Furthermore, the Variance Decomposition (VDC) techniques and Impulse Response Function (IRF) was also used to measure the extent/degree of dynamic properties of the variables. Bangladesh has an emerging economy with limited energy resources. Here, the evidence from our study would help policymakers in setting the appropriate energy consumption policies that will enhance and sustain economic growth for the welfare/development of this country.

Selecting appropriate spectrum becomes one of the key challenges for IoT devices in satisfying the QoS aspects of the applications while integrated with Cognitive Radio (CR). Most of the existing research is focused on maximizing the spectrum utilization by merging CR with IoT paradigm whereas QoS aspects of IoT applications have largely been neglected. We propose a spectrum selection mechanism that can be employed by IoT devices to meet the QoS requirements of IoT applications. The approach includes identifying the QoS requirements of IoT applications, matching appropriate spectrum (in CR) satisfying the QoS requirements. This QoS aware spectrum matching strategy provides IoT applications a suitable solutions for satisfying the QoS requirements.

Internet of things (IoT) has changed human lifestyle by introducing various smart applications. In recent years, sophisticated automation systems have become an essential outcome of the IoT paradigm. Due to the characteristics of IoT applications, devices need to communicate with each other seamlessly. New networking technologies and architectures have also been designed to support the communication requirements of current and future IoT devices. Wireless and radio communications are very desirable for achieving communication among the devices with various proximity. As there is a rapid growth in the number of IoT devices networked based on wireless transmission, radio frequency resource needs to be allocated efficiently to enhance radio spectrum utilization. In wireless connection, Cognitive Radio (CR) is an opportunistic radio access technology targeted to improve the spectrum usage and to mitigate the excessive contention of radio communication. In this chapter, we have identified the functional similarities between IoT and CR, and the challenges that are important to be addressed to integrate CR technology for IoT. We have also proposed a framework for cognitive radio enabled IoT that provides efficient radio spectrum utilization for IoT.

The consistency of user satisfaction on mobile application has been more competitive because of the rapid growth of multi-featured applications. The analysis of user reviews or opinions can play a major role to understand the user’s emotions or demands. Several approaches in different areas of sentiment analysis have been proposed recently. The main objective of this work is to assist the developers in identifying the user’s opinion on their apps whether positive or negative. A sentiment analysis based approach has been proposed in this paper. NLP-based techniques Bags-of-Words, N-Gram, and TF-IDF along with Machine Learning Classifiers, namely, KNN, Random Forest (RF), SVM, Decision Tree, Naive Byes have been used to determine and generate a well-fitted model. It’s been found that RF provides 87.1% accuracy, 91.4% precision, 81.8% recall, 86.3% F1-Score. 88.9% of accuracy, 90.8% of precision, 86.4% of recall, and 88.5% of F1-Score are obtained from SVM.

There has been an increasing demand to store a large quantity of images generated in social media, eHealth, and other popular domains. Due to the rapid uses of digital images, an efficient and faster compression technique is needed for any cloud-based applications. This paper introduces and describes an idea of compressing images using a distributed system to reduce the storage requirement of data (with images). The proposed strategy can provide storage efficiency with an easier way to compress and reconstruct the image in future when needed.

Keyphrases are single or multiple word phrases of a document which describe the principal topics of that document. These keyphrases help readers to get an overview of the document. In this paper, we proposed a system that uses the combination of Convolutional Neural Network and Bidirectional Long Short-Term Memory (BiLSTM) Recurrent Neural Network (RNN) to automatically detect keyphrases from a document. We also used some preprocessing steps to clean and generate candidates keyphrases to train the model. Convolutional Neural Network can analyze semantic meanings of sentences. Bidirectional LSTM can learn the relations among words in the sentences. A Bengali pre-trained word embedding is used in this work.

The presence of missing values in daily rainfall data may hamper the analyses to determine effective results for solving problems of hydrological, agricultural, and climatological issues. The study attempts to select an appropriate method for estimating the missing value of daily rainfall data of Bangladesh. For this purpose, eight methods and seven comparison techniques are employed. For imputation of missing values employing these methods, three sets of daily rainfall data (1, 5, and 10% missing values) with 1000 repetitions are considered randomly for five regions of the country. These samples are artificially created as missing and then imputation for these missing values is made applying the selected methods. The relative performance of the methods are examined using some comparison criteria. The following observations can be made from the study regarding the choice of the appropriate missing value estimation technique: for imputation of the missing values of daily rainfall data, the arithmetic average method for rainfall stations Chittagong and Rajshahi in the south-east region and the north-west region, respectively, is found as the best methods. Further, the single best estimator method for rainfall stations Sylhet and Dhaka in the north-east region and the mid-region, respectively, and the EM-MCMC method for rainfall station Khulna of the south-east region are also identified as the best methods in respect of Kolmogorov-Smirnov test, the lowest bias of estimate, the value of S index, etc.

Cyanobacteria have shown great potential for energy and environmental applications. Cyanobacterial resources, however, are still largely unexplored. Here, forty-nine out of one hundred thirty-two cyanobacterial isolates recovered from thermal springs of western Sichuan, China, were characterized. The phylogenetic analysis of 16S rRNA gene sequences categorized the isolates into three genera, Thermosynechococcus (63.3%), Leptolyngbya (34.7%) and Stanieria (2.0%). Based on sequence similarity, five phylotypes were identified as either putative new species of genus Leptolyngbya or possibly completely new genera. Temperature test showed that all isolates were thermotolerant and twenty-five isolates were capable of growth at temperature of 60 °C, suggesting that these isolates may have strong biotechnological potential. Additionally, three isolates exhibited NaHCO3 tolerance as high as 1 M, indicating that the isolates are promising candidates for bicarbonate-based cyanobacteria production system. Overall, this research laid solid basis for taxonomy and future exploration of extremophilic cyanobacteria for biotechnological and environmental applications.

The rapid and early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections is key to control the current Coronavirus disease 2019 (COVID-19) pandemic. The present study was conducted to clinically evaluate a rapid diagnostic test (RDT) kit, Standard Q COVID-19 Ag Test (SD Biosensor®, Republic of Korea), with reference to the standard real-time RT-PCR for detection of COVID-19 cases in Bangladesh. Nasopharyngeal swabs were taken from 900 COVID-19 suspected patients. Among them, 34.11% (n = 307) were diagnosed as COVID-19 cases by RT-PCR assay, of which 85% (n = 261) were also detectable using the RDT. The overall sensitivity and specificity of the RDT compared to RT-PCR were 85.02% and 100%, respectively, regardless of age, sex, and type of SARS-CoV-2 variants. Most of the RT-PCR positive cases (94%) were found within the first five days of disease onset, and the sensitivity of RDT was 85.91% for the same samples. The positive predictive value (PPV) of the RDT was 100%, and the negative predictive value (NPV) was 92.8%. The Cohen's kappa value of 0.882 indicated excellent agreement between the RDT and RT-PCR assays. The findings of this study showed the potential use of SARS-CoV-2 antigen-based RDT to expedite the diagnostic process and onward COVID-19 management in Bangladesh., SARS-CoV-2; COVID-19; Clinical evaluation; Rapid antigen test; RT-PCR; Bangladesh.

Periplasmic heme-binding proteins (PBPs) in Gram-negative bacteria are components of the heme acquisition system. These proteins shuttle heme across the periplasmic space from outer membrane receptors to ATP-binding cassette (ABC) heme importers located in the inner-membrane. In the present study, we characterized the structures of PBPs found in the pathogen Burkholderia cenocepacia (BhuT) and in the thermophile Roseiflexus sp. RS-1 (RhuT) in the heme-free and heme-bound forms. The conserved motif, in which a well-conserved Tyr interacts with the nearby Arg coordinates on heme iron, was observed in both PBPs. The heme was recognized by its surroundings in a variety of manners including hydrophobic interactions and hydrogen bonds, which was confirmed by isothermal titration calorimetry. Furthermore, this study of 3 forms of BhuT allowed the first structural comparison and showed that the heme-binding cleft of BhuT adopts an "open" state in the heme-free and 2-heme-bound forms, and a "closed" state in the one-heme-bound form with unique conformational changes. Such a conformational change might adjust the interaction of the heme(s) with the residues in PBP and facilitate the transfer of the heme into the translocation channel of the importer.

In this study, multivariate statistical techniques in collaboration with GIS are used to assess the roadside surface water quality of Savar region. Nineteen water samples were collected in dry season and 15 water quality parameters including TSS, TDS, pH, DO, BOD, Cl−, F−, NO3 2−, NO2 −, SO4 2−, Ca, Mg, K, Zn and Pb were measured. The univariate overview of water quality parameters are TSS 25.154 ± 8.674 mg/l, TDS 840.400 ± 311.081 mg/l, pH 7.574 ± 0.256 pH unit, DO 4.544 ± 0.933 mg/l, BOD 0.758 ± 0.179 mg/l, Cl− 51.494 ± 28.095 mg/l, F− 0.771 ± 0.153 mg/l, NO3 2− 2.211 ± 0.878 mg/l, NO2 − 4.692 ± 5.971 mg/l, SO4 2− 69.545 ± 53.873 mg/l, Ca 48.458 ± 22.690 mg/l, Mg 19.676 ± 7.361 mg/l, K 12.874 ± 11.382 mg/l, Zn 0.027 ± 0.029 mg/l, Pb 0.096 ± 0.154 mg/l. The water quality data were subjected to R-mode PCA which resulted in five major components. PC1 explains 28% of total variance and indicates the roadside and brick field dust settle down (TDS, TSS) in the nearby water body. PC2 explains 22.123% of total variance and indicates the agricultural influence (K, Ca, and NO2 −). PC3 describes the contribution of nonpoint pollution from agricultural and soil erosion processes (SO4 2−, Cl−, and K). PC4 depicts heavy positively loaded by vehicle emission and diffusion from battery stores (Zn, Pb). PC5 depicts strong positive loading of BOD and strong negative loading of pH. Cluster analysis represents three major clusters for both water parameters and sampling sites. The site based on cluster showed similar grouping pattern of R-mode factor score map. The present work reveals a new scope to monitor the roadside water quality for future research in Bangladesh.

Textile dye effluents are believed to be toxic as they might exert various harmful effects on living organisms including genotoxicity. These effluents are the main source of direct and continuous input of pollutants into the aquatic ecosystems. In this study, dye effluents from a local silk dyeing industry were analysed for their genotoxic potential by the Allium cepa genotoxicity test. The A. cepa test is characterised as a genotoxicity test where the roots of A. cepa are grown in different concentrations of the test material. The macroscopic results clearly showed that the toxicity of the dye effluents prompted A. cepa root growth inhibition, and this effect increased with higher concentrations of the effluents. At the cellular level, no dividing cells were found at higher concentrations such as 60%, 80% and 100% of the effluents. However, at a lower concentration of 20%, dividing cells were identified, although the mitotic index was much lower than that of the control. Microscopic analysis show...

The over-growth of phytoplankton causes harmful algal blooms (HABs) in marine ecological environments. Mobility measurement is important in understanding the action of HABs. In this study, the mobility of Amphidinium carterae Hulburt (A. carterae) was investigated using high-frequency ultrasound in the laboratory. Mobility in response to light was illustrated with M-mode images reconstructed from echoed signals. This study suggests that mobility of the swimming speed of A. carterae in response to light can be measured and calculated with M-mode images through high-frequency ultrasound. This finding may be helpful in understanding the fundamental behavior of HABs.

Coronavirus disease (COVID-19) was unknown before the outbreak began in Wuhan, China in December 2019. The virus rapidly spread in different countries across the globe. The World Health Organization (WHO) declared it as a pandemic on the 11th March 2020.1 The pandemic has revealed many areas of public health preparedness those are lacking both in developed and developing countries. Digital interventions provide many opportunities for strengthening health systems.2 It could be a vital resources and could play a critical role in global response to this public health emergency. Digital health is the use of digital information and communication technologies for efficient and timely delivery of health care services, aimed at promoting and protecting the health of the individuals and communities. Furthermore, digital technologies are also being used for conducting monitoring and surveillance of the health programmes, health education, research, development of human resources including continued professional development, risk analysis speacially the risk commucation. Data being generated through services, research, monitoring and surveillance are also used for health management and decision making. Bangladesh Med Res Counc Bull 2020; 46(2): 66-67

Coronavirus disease (COVID-19) is an infectious disease caused by the most recently discovered novel coronavirus, renamed as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). 1 It was unknown before the outbreak began in Wuhan, China, in December 2019. 2 The outbreak was linked epidemiologically to the Hua Nan seafood and wet animal wholesale market in Wuhan, and the market was subsequently closed on 1 January 2020. 3 The virus rapidly spread to all provinces in China, as well as a number of countries overseas, and was declared a Public Health Emergency of International Concern by the Director General of the World Health Organization on 30 January 2020.4 Subsequently, on 11 March 2020, the WHO declared COVID-18 a pandemic.5 It is the first pandemic caused by a coronavirus. 6 Around the globe, hundreds of thousands have been infected and tens of thousands people died including frontline workforce including physicians, nurses and others. Bangladesh reported its first confirmed COVID-19 case on 8 March 2020, after three people, two men and a woman tested positive for the coronavirus. Two of the infected are recently returned from Italy, and the other one is a female family member of the infected male. On the 18 March, 2020, Bangladesh confirmed the first death from COVID-19. 7 In Bangladesh, till 30 March 2020, 49 confirmed cases and there were five deaths due to COVID-19.8 This pandemic-a global calamity, is not only a health concern, it is a threat to life and livelihoods worldwide. In addition to health, major disruptions are also occurred in business, education, transports and others areas. It causes interruption in every aspect of day to day life. To prevent and control infections, the immediate challenges ahead are to conduct the tests, isolation of infected cases, tracing of the contacts and quarantine, and appropriate measures for the overseas returnees. An effective risk communication with community engagement is critical to reduce the stigma, fake news, psychological stress. It is essential to bring courage and mental strength of the frontline fighters, and support for the poor and daily wage earners etc. Aimed at preventing and control of SARS-COV-2, government of Bangladesh has already initiated steps including enhancement of public awareness on hand hygiene, respiratory hygiene, social distancing, wearing of masks, avoidance of public gatherings, campaign against myths, fake news and stigma; preparing the health care services including expansion of hospital facilities, training and protective measures for the health workforce and other frontline fighters. Furthermore, steps are being taken conducting RT PCR tests, isolating infected cases, tracing contacts, quarantine the contacts and overseas returnees, and other necessary measures. The government has declared the general holiday in Bangladesh including closure of the educational institutes and office and workplaces, to prevent and control of infections. Necessary steps have been initiated for the social and economic protections of the vulnerable including expansion of the existing social safety net programmes. Aimed at adequate and timely response to the COVID-19, the Directorate General of Health Services (DGHS), the Ministry of Health and Family Welfare, developed a number of guidelines and manuals for the containment of this pandemic disease. For an effective and timely preparedness and response, the DGHS has developed ‘National Preparedness and Response Plan for COVID-19, Bangladesh’.9 For better response, well-coordinated and cooperated global efforts, including exchange of information, scientific knowledge, research findings, expertise and best practices are important. All countries should implement WHO guidelines and recommendations. In Bangladesh, the Ministry of Health and Family Welfare alone cannot mitigate this pandemic. Strengthening of the coordinated efforts among the ministries, and effective and timely engagement of the non-government and private sectors are strongly recommended. Intensification of RT-PCR lab tests for case detection, and isolation and management of cases, and to trace the contacts and ensure quarantine, surveillance, and research, serological tests to detect SARS-CoV-2 specific immunoglobulins (IgG and IgM) to estimate the population exposure, strengthening public awareness and risk communication, strict implementation of personal hygiene, use of face mask, social distancing and other measures are thus suggested to prevent and control COVID-19 in Bangladesh. Bangladesh Med Res Counc Bull 2020; 46(1): 01-02

MicroRNAs (miRNAs) dysregulation contribute the cancer pathogenesis. However, the miRNA profile of canine oral melanoma (COM), one of the frequent malignant melanoma in dogs is still unrevealed. The aim of this study is to reveal the miRNA profile in canine oral melanoma. MiRNAs profile of oral tissues from normal healthy dogs and COM patients were compared by next-generation sequencing. Along with tumour suppressor miRNAs, we report 30 oncogenic miRNAs in COM. The expressions of miRNAs were further confirmed by quantitative real-time PCR (qPCR). Pathway analysis showed that deregulated miRNAs impact on cancer and signalling pathways. Three oncogenic miRNAs targets (miR-450b, 301a, and 223) from human study also were down-regulated in COM and had a significant negative correlation with their respective miRNA. Furthermore, we found that miR-450b expression is higher in metastatic cells and regulated MMP9 expression through a PAX9-BMP4-MMP9 axis. In silico analysis indicated that miR-126, miR-20b, and miR-106a regulated the highest numbers of differentially expressed transcription factors with respect to human melanoma. Chromosomal enrichment analysis revealed the X chromosome was enriched with oncogenic miRNAs. We comprehensively analyzed the miRNA&rsquo, s profile in COM which will be a useful resource for developing therapeutic interventions in both species.

Among small non-coding RNAs (sncRNAs/sRNAs), the functional regulation of microRNAs (miRNAs) has been studied in canine oral melanoma (COM). However, the expression level of other sncRNAs, like small nucleolar RNAs (snoRNAs), small nuclear RNAs (snRNAs), transfer RNA-derived fragments (tRFs) and PIWI-interacting RNAs (piRNAs), in COM is unknown. The aim of this study was to investigate sncRNAs other than miRNAs in COM from our small RNA sequencing project (PRJNA516252). We found that several snRNAs and piRNAs were upregulated, whereas tRFs and snoRNAs were downregulated in COM. Upregulation of U1 snRNA and piR-972, and downregulation of tRNA-ser (1) and snoRA24 was confirmed in dog melanoma tissue and cell lines by quantitative reverse transcription PCR. Consistently, the expression of tRNA-ser (1) and snoRA24 in plasma of COM cases was also decreased. Finally, we found a similar expression trend of U1 and snoRA24 in the human cutaneous melanoma cell line, MEWO, compared with human epidermal melanocyte cells (HEMa-Lp). In our study, snRNA, snoRNA, tRFs and piRNA were dysregulated during melanoma progression. Moreover, the melanoma-associated expression of U1 and snoRA24 was similar in human and dog melanoma.