Genetic variability of Haemonchus contortus isolates in small ruminants from slaughterhouses in Bangladesh.

Haemonchus contortus is a blood-sucking gastrointestinal nematode that infects all ruminants and causes significant economic losses in production. Characterizing the genetic variability of H. contortus populations is crucial for understanding patterns of disease transmission and developing effective control strategies against haemonchosis. This study aimed to identify the genetic variability of H. contortus isolates in small ruminants from slaughterhouses in Bangladesh. During January to December 2015, 400 abomasa samples were collected and 186 were found to be positive for Haemonchus. A 321-bp fragment of the second internal transcribed spacer (ITS-2) of nuclear ribosomal DNA and an 800-bp fragment of the mitochondrial nicotinamide dehydrogenase subunit-4 gene (nad4) were amplified using polymerase chain reaction (PCR) and directly sequenced. The results showed 10 genotypes (ITS-2) and 45 haplotypes (nad4) among the 186 worms. The sequences were 98.5 to 100% identical to reference sequences from the GenBank database. ITS-2 sequence analysis revealed four nucleotide substitutions at positions 30, 41, 42, and 216. There was one transition (C/T) at position 42 and three transversions (C/A at position 30, G/C at position 41, and T/A at position 216). The nad4 gene sequences showed 15 substitutions, all of which were transitions. The pairwise distance of ITS-2 between H. contortus populations ranged from 0.005 to 1.477. The nucleotide diversity (µ) among the populations was 0.009524 using ITS-2 and 0.00394 using nad4. This study indicated low genetic deviation among H. contortus populations in Bangladesh.

MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification.

The monkeypox virus poses a new pandemic threat while we are still recovering from COVID-19. Despite the fact that monkeypox is not as lethal and contagious as COVID-19, new patient cases are recorded every day. If preparations are not made, a global pandemic is likely. Deep learning (DL) techniques are now showing promise in medical imaging for figuring out what diseases a person has. The monkeypox virus-infected human skin and the region of the skin can be used to diagnose the monkeypox early because an image has been used to learn more about the disease. But there is still no reliable Monkeypox database that is available to the public that can be used to train and test DL models. As a result, it is essential to collect images of monkeypox patients. The "MSID" dataset, short form of "Monkeypox Skin Images Dataset", which was developed for this research, is free to use and can be downloaded from the Mendeley Data database by anyone who wants to use it. DL models can be built and used with more confidence using the images in this dataset. These images come from a variety of open-source and online sources and can be used for research purposes without any restrictions. Furthermore, we proposed and evaluated a modified DenseNet-201 deep learning-based CNN model named MonkeyNet. Using the original and augmented datasets, this study suggested a deep convolutional neural network that was able to correctly identify monkeypox disease with an accuracy of 93.19% and 98.91% respectively. This implementation also shows the Grad-CAM which indicates the level of the model's effectiveness and identifies the infected regions in each class image, which will help the clinicians. The proposed model will also help doctors make accurate early diagnoses of monkeypox disease and protect against the spread of the disease.

Face emotion recognition based on infrared thermal imagery by applying machine learning and parallelism.

Over time for the past few years, facial expression identification has been a promising area. However, darkness, lighting conditions, and other factors make facial emotion identification challenging to detect. As a result, thermal images are suggested as a solution to such problems and for a variety of other benefits. Furthermore, focusing on significant regions of a face rather than the entire face is sufficient for reducing processing and improving accuracy at the same time. This research introduces novel infrared thermal image-based approaches for facial emotion recognition. First, the entire image of the face is separated into four pieces. Then, we accepted only four active regions (ARs) to prepare training and testing datasets. These four ARs are the left eye, right eye, and lips areas. In addition, ten-folded cross-validation is proposed to improve recognition accuracy using Convolutional Neural Network (CNN), a machine learning technique. Furthermore, we incorporated a parallelism technique to reduce processing-time in testing and training datasets. As a result, we have seen that the processing time reduces to 50%. Finally, a decision-level fusion is applied to improve the recognition accuracy. As a result, the proposed technique achieves a recognition accuracy of 96.87 %. The achieved accuracy ascertains the robustness of our proposed scheme.

Pandemic disease detection through wireless communication using infrared image based on deep learning.

Rapid diagnosis to test diseases, such as COVID-19, is a significant issue. It is a routine virus test in a reverse transcriptase-polymerase chain reaction. However, a test like this takes longer to complete because it follows the serial testing method, and there is a high chance of a false-negative ratio (FNR). Moreover, there arises a deficiency of R.T.-PCR test kits. Therefore, alternative procedures for a quick and accurate diagnosis of patients are urgently needed to deal with these pandemics. The infrared image is self-sufficient for detecting these diseases by measuring the temperature at the initial stage. C.T. scans and other pathological tests are valuable aspects of evaluating a patient with a suspected pandemic infection. However, a patient's radiological findings may not be identified initially. Therefore, we have included an Artificial Intelligence (A.I.) algorithm-based Machine Intelligence (MI) system in this proposal to combine C.T. scan findings with all other tests, symptoms, and history to quickly diagnose a patient with a positive symptom of current and future pandemic diseases. Initially, the system will collect information by an infrared camera of the patient's facial regions to measure temperature, keep it as a record, and complete further actions. We divided the face into eight classes and twelve regions for temperature measurement. A database named patient-info-mask is maintained. While collecting sample data, we incorporate a wireless network using a cloudlets server to make processing more accessible with minimal infrastructure. The system will use deep learning approaches. We propose convolution neural networks (CNN) to cross-verify the collected data. For better results, we incorporated tenfold cross-verification into the synthesis method. As a result, our new way of estimating became more accurate and efficient. We achieved 3.29% greater accuracy by incorporating the "decision tree level synthesis method" and "ten-folded-validation method". It proves the robustness of our proposed method.

Prevalence of helminth and protozoan infections in pet birds of Chattogram, Bangladesh.

BACKGROUND: Parasitic diseases such as helminths and protozoa are considered one of the major impediments in the rearing of pet birds. The current study was undertaken to determine the prevalence of helminths and protozoal infections in different captive pet birds in Chattogram metropolitan area, Bangladesh. METHODS: A total of 549 pooled faecal and 311 individual blood samples were collected from different species of pet birds during June 2019 to May 2020. The faecal samples were examined following routine microscopic tests to identify the eggs and oocysts of helminths and protozoan parasites, respectively. Polymerase chain reaction (PCR) was performed to determine the haemoprotozoan parasites. RESULTS: The prevalence of helminth infestations in pet birds was 8.01% (95% confidence interval [CI]: 5.88-10.61), where infestation caused by nematodes was the highest (7.47%, CI: 5.41-10). Ascaridia and Capillaria spp. infestations were the commonest helminths recorded in different groups of pet birds. The overall prevalence of intestinal protozoal infections was 11.11% (CI: 8.61-14.04) in pet birds. The most commonly occurring protozoal infections were Eimeria spp. (7.83%, CI: 5.73-10.41) followed by Isospora spp. (2.91%, CI: 1.67-4.69). The overall prevalence of haemoprotozoan parasites was 2.25% (CI: 0.91-4.58) in different groups of pet birds. The highest prevalence was recorded in Plasmodium spp. (1.29%, CI: 0.35-3.26) followed by Leucocytozoon and Haemoproteus spp. CONCLUSIONS: The data generated in this study are the first of its type, which would be beneficial to the veterinary practice, aviculturists, pet bird owners and breeders in Bangladesh to respond appropriately for the prevention and control of the disease.

Fake news on Facebook and their impact on supply chain disruption during COVID-19.

Social media (SM) fake news has become a serious concern especially during COVID-19. In this study, we develop a research model to investigate to what extent SM fake news contributes to supply chain disruption (SCD), and what are the different SM affordances that contribute to SM fake news. To test the derived hypotheses with survey data, we have applied partial least square based structural equation modelling (PLS-SEM) technique. Further, to identify how different configurations of SC resilience (SCR) capabilities reduce SCD, we have used fuzzy set qualitative comparative analysis (fsQCA). The results show that SM affordances lead to fake news, which increases consumer panic buying (CPB); CPB in turn increases SCD. In addition, SM fake news directly increases SCD. The moderation test suggests that, SCR capability, as a higher-order construct, decreases the effect of CPB on SCD; however, neither of the capabilities individually moderates. Complimentarily, the fsQCA results suggest that no single capability but their three specific configurations reduce SCD. This work offers a new theoretical perspective to study SCD through SM fake news. Our research advances the knowledge of SCR from a configurational lens by adopting an equifinal means towards mitigating disruption. This research will also assist the operations and SC managers to strategize and understand which combination of resilience capabilities is the most effective in tackling disruptions during a crisis e.g., COVID-19. In addition, by identifying the relative role of different SM affordances, this study provides pragmatic insights into SM affordance measures that combat fake news on SM.

Reconceptualizing cybersecurity awareness capability in the data-driven digital economy.

Data breaches have become a formidable challenge for business operations in the twenty-first century. The emergence of big data in the ever-growing digital economy has created the necessity to secure critical organizational information. The lack of cybersecurity awareness exposes organizations to potential cyber threats. Thus, this research aims to identify the various dimensions of cybersecurity awareness capabilities. Drawing on the dynamic capabilities framework, the findings of the study show personnel (knowledge, attitude and learning), management (training, culture and strategic orientation) and infrastructure capabilities (technology and data governance) as thematic dimensions to tackle cybersecurity awareness challenges.

Biomedical image retrieval using adaptive neuro-fuzzy optimized classifier system.

The quantity of scientific images associated with patient care has increased markedly in recent years due to the rapid development of hospitals and research facilities. Every hospital generates more medical photographs, resulting in more than 10 GB of data per day being produced by a single image appliance. Software is used extensively to scan and locate diagnostic photographs to identify patient's precise information, which can be valuable for medical science research and advancement. An image recovery system is used to meet this need. This paper suggests an optimized classifier framework focused on a hybrid adaptive neuro-fuzzy approach to accomplish this goal. In the user query, similarity measurement, and the image content, fuzzy sets represent the vagueness that occurs in such data sets. The optimized classifying method 'hybrid adaptive neuro-fuzzy is enhanced with the improved cuckoo search optimization. Score values are determined by utilizing the linear discriminant analysis (LDA) of such classified images. The preliminary findings indicate that the proposed approach can be more reliable and effective at estimation than can existing approaches.

Facial expression recognition based on active region of interest using deep learning and parallelism.

The automatic facial expression tracking method has become an emergent topic during the last few decades. It is a challenging problem that impacts many fields such as virtual reality, security surveillance, driver safety, homeland security, human-computer interaction, medical applications. A remarkable cost-efficiency can be achieved by considering some areas of a face. These areas are termed Active Regions of Interest (AROIs). This work proposes a facial expression recognition framework that investigates five types of facial expressions, namely neutral, happiness, fear, surprise, and disgust. Firstly, a pose estimation method is incorporated and to go along with an approach to rotate the face to achieve a normalized pose. Secondly, the whole face-image is segmented into four classes and eight regions. Thirdly, only four AROIs are identified from the segmented regions. The four AROIs are the nose-tip, right eye, left eye, and lips respectively. Fourthly, an info-image-data-mask database is maintained for classification and it is used to store records of images. This database is the mixture of all the images that are gained after introducing a ten-fold cross-validation technique using the Convolutional Neural Network. Correlations of variances and standard deviations are computed based on identified images. To minimize the required processing time in both training and testing the data set, a parallelism technique is introduced, in which each region of the AROIs is classified individually and all of them run in parallel. Fifthly, a decision-tree-level synthesis-based framework is proposed to coordinate the results of parallel classification, which helps to improve the recognition accuracy. Finally, experimentation on both independent and synthesis databases is voted for calculating the performance of the proposed technique. By incorporating the proposed synthesis method, we gain 94.499%, 95.439%, and 98.26% accuracy with the CK+ image sets and 92.463%, 93.318%, and 94.423% with the JAFFE image sets. The overall accuracy is 95.27% in recognition. We gain 2.8% higher accuracy by introducing a decision-level synthesis method. Moreover, with the incorporation of parallelism, processing time speeds up three times faster. This accuracy proves the robustness of the proposed scheme.

Lumpy skin disease outbreak in cattle population of Chattogram, Bangladesh.

BACKGROUND: Lumpy skin disease (LSD) is an important viral disease causing significant economic losses in commercial livestock production. In mid-2019, an outbreak of LSD has been reported in cattle population from different parts of Bangladesh including Chattogram division. A cross-sectional surveillance study was undertaken from August 2019 to December 2019 to investigate the prevalence and associated risk factors of LSD in cattle in Chattogram district. METHODS: A total of 3,327 cattle from 19 commercial farms were examined for the LSD specific skin lesions and associated risk factors. A total of 120 skin biopsies were collected from the suspected animal for the confirmation of the disease using molecular detection and histopathological examination. Partial genome sequencing and phylogenetic analyses were performed on selected viral isolates. RESULTS: The overall clinical prevalence of LSD in the study population was 10% (95% confidence interval [CI]: 9.4%-11%) where the highest farm level outbreak frequency was 63.33% (95% CI: 45.51%-78.13%) and the lowest 4.22% (95% CI: 3.39%-5.25%). Crossbred and female cattle showed a significantly higher prevalence of the disease compared to their counterparts. Introduction of new animals in farms was found to be one of the most significant risk factors in the transmission of the disease. All suspected skin biopsies were positive for LSD virus (LSDV) infection with granulomatous and pyogranulomatous dermatitis was revealed on histopathology. Phylogenetic analysis based on the inverted terminal repeat region of the LSDV gene suggested that the locally circulating strain was closely related to the strains isolated from the Middle East and North African countries. CONCLUSIONS: The data generated in this study would be beneficial to the field veterinarians and animal health decision makers in the country as well as it will aid in taking appropriate measures to prevent further relapse or outbreak of this disease in future.

Goat Genomic Resources: The Search for Genes Associated with Its Economic Traits.

Goat plays a crucial role in human livelihoods, being a major source of meat, milk, fiber, and hides, particularly under adverse climatic conditions. The goat genomics related to the candidate gene approach is now being used to recognize molecular mechanisms that have different expressions of growth, reproductive, milk, wool, and disease resistance. The appropriate literature on this topic has been reviewed in this article. Several genetic characterization attempts of different goats have reported the existence of genotypic and morphological variations between different goat populations. As a result, different whole-genome sequences along with annotated gene sequences, gene function, and other genomic information of different goats are available in different databases. The main objective of this review is to search the genes associated with economic traits in goats. More than 271 candidate genes have been discovered in goats. Candidate genes influence the physiological pathway, metabolism, and expression of phenotypes. These genes have different functions on economically important traits. Some genes have pleiotropic effect for expression of phenotypic traits. Hence, recognizing candidate genes and their mutations that cause variations in gene expression and phenotype of an economic trait can help breeders look for genetic markers for specific economic traits. The availability of reference whole-genome assembly of goats, annotated genes, and transcriptomics makes comparative genomics a useful tool for systemic genetic upgradation. Identification and characterization of trait-associated sequence variations and gene will provide powerful means to give positive influences for future goat breeding program.

Hematological and biochemical reference values of Asian house shrews (Suncus murinus) in Bangladesh.

BACKGROUND AND AIM: Determining reference values for hematological and biochemical parameters of Asian house shrew (Suncus murinus) is important for wildlife research to protect human health in surrounding communities. This study aimed to establish the reference values for selected hematology and serum clinical chemistry analyses that may contribute to research on shrew in future. MATERIALS AND METHODS: Blood samples (n=51) were collected from shrews between July and December 2015, Bangladesh, to estimate the levels of hemoglobin (Hb), packed cell volume (PCV), total leukocyte count (TLC), total erythrocyte count (TEC), lymphocyte, monocyte, neutrophil, eosinophil, basophil, calcium, phosphorus (P), sodium (Na), chloride (Cl), urea, glucose, total protein (TP), creatinine, and alanine transaminase (ALT). RESULTS: Although the values did not differ significantly among sexes, age was found to be a significant factor. Hb, PCV, TEC, glucose, and P were higher in males; eosinophil, Na, Cl, TP, and ALT were higher among females. Adults had significantly greater urea and glucose (p<0.05) while juveniles had insignificantly higher values for TLC, PCV, neutrophil, P, and TP. CONCLUSION: This study provides the first reference values for this species in Bangladesh and can be used to guide wildlife research studies.

The genome of the Black Bengal goat (Capra hircus).

OBJECTIVES: Black Bengal goat (Capra hircus), a member of the Bovidae family with the unique traits of high prolificacy, skin quality and low demand for food is the most socioeconomically significant goat breed in Bangladesh. Furthermore, the aptitude of adaptation and disease resistance capacity of it is highly notable which makes its whole genome information an area of research interest. DATA DESCRIPTION: The genomic DNA of a local (Chattogram, Bangladesh) healthy male Black Bengal goat (Capra hircus) was extracted and then sequenced. Sequencing was completed using the Illumina HiSeq 2500 sequencing platform and the draft assembly was generated using the "ARS1" genome as the reference. MAKER gene annotation pipeline was utilized to annotate 26,458 gene models. Genome completeness was assessed using BUSCO (Benchmarking Universal Single-Copy Orthologs) which showed 82.5% completeness of the assembled genome.

Prevalence and diversity of gastrointestinal helminths in free-ranging Asian house shrew (Suncus murinus) in Bangladesh.

BACKGROUND AND AIM: Asian house shrew (Suncus murinus), a widely distributed small mammal in the South Asian region, can carry helminths of zoonotic importance. The aim of the study was to know the prevalence and diversity of gastrointestinal (GI) helminths in free-ranging Asian house shrew (S. murinus) in Bangladesh. MATERIALS AND METHODS: A total of 86 Asian house shrews were captured from forest areas and other habitats of Bangladesh in 2015. Gross examination of the whole GI tract was performed for gross helminth detection, and coproscopy was done for identification of specific eggs or larvae. RESULTS: The overall prevalence of GI helminth was 77.9% (67/86), with six species including nematodes (3), cestodes (2), and trematodes (1). Of the detected helminths, the dominant parasitic group was from the genus Hymenolepis spp.(59%), followed by Strongyloides spp.(17%), Capillaria spp. (10%), Physaloptera spp. (3%), and Echinostoma spp.(3%). CONCLUSION: The finding shows that the presence of potential zoonotic parasites (Hymenolepis spp. and Capillaria spp.) in Asian house shrew is ubiquitous in all types of habitat (forest land, cropland and dwelling) in Bangladesh. Therefore, further investigation is crucial to examine their role in the transmission of human helminthiasis.

The first complete mitogenome of Indian star tortoise (Geochelone elegans).

The complete mitochondrial genome of Indian star tortoise (Geochelone elegans) was characterized having 16,446 bp nucleotides encoding 37 genes in circular orientation comprising 13 protein-coding genes, 22 tRNA genes and two rRNA genes. The lengths of 12S and 16S ribosomal RNA were 973 bp and 1600 bp. A non-coding control region (D-Loop) of 966 bp was identified between tRNAPro and tRNAPhe having seven interrupted tandem repeats. A single A + 1 frameshift insertion in the ND3 gene (ND3-174) was also discovered. The complete mitogenome of G. elegans would contribute in deeper understanding of the evolutionary dynamics and conservation effort of vulnerable testudine families.

Identification of non-cerebral cyst: Zoonotic Taenia multiceps in domestic goat in Bangladesh.

AIM: This study was performed to identify the non-cerebral Taenia multiceps cyst through molecular phylogeny of the 12S rRNA gene. MATERIALS AND METHODS: Eight cyst samples were collected from 385 examined slaughtered goats during October 2015-September 2016 from three slaughterhouses in Chittagong City Corporation. Cysts were removed from the thigh muscle, and scolices were collected for light microscopic examination and molecular identification. The DNA was extracted and analyzed by polymerase chain reaction using 12S rRNA gene primers. Cyst samples were also preserved in 10% buffered formalin for histopathological study. RESULTS: T. multiceps non-cerebral cyst is 2.1% prevalent in goat in this area. Under light microscopic examination, scolex was found with four suckers and a rostellum with the double crown of 32 hooks and hooklets. Molecularly, all the samples were amplified with 12S rRNA gene fragments yielded 270 base pair amplicon. Zenker's necrosis with focal to diffuse infiltration of lymphocytes and eosinophil was also found around the cyst wall in histopathological examination. CONCLUSION: Although the non-cerebral form of the cysts produced by T. multiceps is genetically identical with the cerebral cyst, previously published data indicated that cerebral T. multiceps cyst is predominant in other parts of the world as well as in Bangladesh. This study showed that non-cerebral cyst is also prevalent in this country which is very important for public health concern. This study depicts an idea of non-cerebral form of zoonotic T. multiceps cyst which will be helpful in taenia cyst control and prevention.

Development of puffed ginseng-rice snack from ginseng powder and map rice flour using steam and compression process.

A new manufacturing method for producing a puffed ginseng-rice snack (PGRS) was developed using ginseng powder and map rice flour through a steam and compression process (SCP). The physical and sensory properties of the PGRS were characterized. The pellets for puffing were prepared from ginseng powder and map rice flour. The pellets were subjected to 16, 18, and 20% moisture contents and were puffed at 225, 235, and 245° C. The specific volumes of the PGRSs increased with heating temperature and moisture content. However, the breaking strength of the PGRSs decreased. In addition, the SCP imposed special features in the PGRSs that made them more acceptable. The Hunter L-value increased with heating temperature and moisture content. These results indicate that a PGRS with functional additives could be effectively developed into a functional food with the use of a puffing machine, and that the PGRS shows potential as a new snack product.

Identification of Bangladeshi domestic cats with GM1 gangliosidosis caused by the c.1448G>C mutation of the feline GLB1 gene: case study.

GM1 gangliosidosis is a fatal, progressive neurodegenerative lysosomal storage disease caused by mutations in the ß-galactosidase (GLB1) gene. In feline GM1 gangliosidosis, a pathogenic mutation (c.1448G>C) in the feline GLB1 gene was identified in Siamese cats in the United States and Japan and in Korat cats in Western countries. The present study found the homozygous c.1448G>C mutation in 2 apparent littermate native kittens in Bangladesh that were exhibiting neurological signs. This is the first identification of GM1 gangliosidosis in native domestic cats in Southeast Asia. This pathogenic mutation seems to have been present in the domestic cat population in the Siamese region and may have been transferred to pure breeds such as Siamese and Korat cats originating in this region.

Bacillary hemoglobinuria in Japanese black cattle in Hiroshima, Japan: a case study.

Three Japanese Black cows housed with 6 other cows exhibited main clinical symptoms of severe hemoglobinuria. Hematological analyses conducted after antibiotic therapy demonstrated severe anemia, and biochemical analyses indicated both severe hemolysis and disruption of hepatic function. One of the three cows died. Based on the above analyses and observation of typical clinical symptoms, a speculative diagnosis of bacillary hemoglobinuria was made, and immediate high-dose antibiotic treatment improved the general conditions of the surviving animals. Blood samples from the other 2 cows were collected sequentially after antibacterial therapy. Clostridium haemolyticum was detected by a nested polymerase chain reaction analysis of the blood samples. The cows were diagnosed with the second recorded occurrence of bacillary hemoglobinuria in Japan.

Sodium 2-propenyl thiosulfate derived from garlic induces phase II detoxification enzymes in rat hepatoma H4IIE cells.

There is evidence that onions and garlic protect against cancer in humans. It has been suggested that this effect is partly due to the organosulfur compounds in Allium vegetables and that these substances act through induction of phase II detoxification enzymes. Here, we hypothesized that alk(en)yl thiosulfates, sodium n-propyl thiosulfate (NPTS), and sodium 2-propenyl thiosulfate (2PTS), which were identified in onions and garlic, respectively, may induce phase II enzymes. Therefore, rat hepatoma cells (H4IIE) were cultured with 1 to 100 micromol/L of NPTS or 2PTS for 48 hours at 37 degrees C; and the activities and messenger RNA (mRNA) expression levels of phase II enzymes in H4IIE cells were investigated. The effects of diallyl trisulfide and tert-butylhydroquinone, known as phase II inducers, were also examined as positive controls and compared with the responses of NPTS and 2PTS. Quinone reductase (QR) activity and mRNA expression levels of QR and epoxide hydrolase 1 were significantly increased by 2PTS (P < .05-.005). In particular, QR activity was increased at a relatively low concentration of 2PTS (10 micromol/L). However, glutathione S-transferase activity and mRNA expression levels of glutathione S-transferase A5 and uridine diphosphate glucuronosyl transferase 1A1 were not changed by 2PTS. In contrast, NPTS did not affect the activities and mRNA expression levels of these phase II enzymes. These results show that 2PTS can induce phase II enzymes, and its inductive effect is comparable or superior to that of diallyl trisulfide and tert-butylhydroquinone.