Search

A series of semi-crystalline, wide band gap (WBG) photovoltaic polymers were synthesized with varying number and topology of fluorine substituents. To decrease intramolecular charge transfer and to modulate the resulting band gap of D-A type copolymers, electron-releasing alkoxy substituents were attached to electron-deficient benzothiadiazole (A) and electron-withdrawing fluorine atoms (0-4F) were substituted onto a 1,4-bis(thiophen-2-yl)benzene unit (D). Intra- and/or interchain noncovalent Coulombic interactions were also incorporated into the polymer backbone to promote planarity and crystalline intermolecular packing. The resulting optical band gap and the valence level were tuned to 1.93-2.15 eV and -5.37 to -5.67 eV, respectively, and strong interchain organization was observed by differential scanning calorimetry, high-resolution transmission electron microscopy and grazing incidence X-ray scattering measurements. The number of fluorine atoms and their position significantly influenced the photophysical, morphological and optoelectronic properties of bulk heterojunctions (BHJs) with these polymers. BHJ photovoltaic devices showed a high power conversion efficiency (PCE) of up to 9.8% with an open-circuit voltage of 0.94-1.03 V. To our knowledge, this PCE is one of the highest values for fullerene-based single BHJ devices with WBG polymers having a band gap of over 1.90 eV. A tandem solar cell was also demonstrated successfully to show a PCE of 10.3% by combining a diketopyrrolopyrrole-based low band gap polymer.

Abstract Introduction Nocardia cyriacigeorgica bacteraemia has been described in the setting of profound immunodeficiency in only two previous case reports. In both instances, diagnosis was rapidly facilitated by 16S rRNA gene sequencing of blood culture isolates. To the best of our knowledge, we believe that our case is the first presentation of N. cyriacigeorgica bacteraemia associated with acute cytomegalovirus disease in a kidney transplant recipient, which was then followed by severe and fatal pneumonia only seven days later. Case presentation We present the case of a 73-year-old Caucasian woman, a renal transplant recipient, with peripheral vascular disease, hypertension, osteoporosis and vascular dementia who was diagnosed with septicemia and pneumonia. In spite of appropriate anti-microbial therapy for nocardial sepsis, she developed severe pneumonia and acute renal failure. Conclusion This case illustrates a potential for disseminated nocardial infection to produce clinical syndromes that may be indistinguishable from acute cytomegalovirus disease. An atypical presentation (pneumonia and renal failure) of a rare disease (nocardial septicemia) in the setting of renal transplantation is discussed. This case illustrates that the possibility of severe cytomegalovirus disease should be considered in renal transplanted patients diagnosed with nocardial septicemia who subsequently develop severe sepsis, pneumonia, and renal failure. Molecular diagnosis should readily be available to assist with the prompt diagnosis and treatment of these infections in renal transplant patients.

Growth hormone-releasing hormone antagonists (GHRHAnt) have been associated with antitumor and antioxidative activities. The present study investigates for the first time the effects of those compounds towards pro-inflammatory cytokine expression in a murine model of cecal ligation and puncture (CLP) - induced sepsis. The results indicate that GHRHAnt JV-1-36 significantly suppressed IL-1α, IL-6, and pSTAT3 activation in septic lungs. Moreover, GHRHAnt treatment reduced bronchoalveolar lavage fluid (BALF) protein concentration, suggesting a protective effect of that compound in sepsis-induced lung edema. Based on those findings, it is suggested that GHRHAnt may represent an exciting new therapeutic possibility in sepsis-induced endotoxemia and lung injury., Competing Interests: Declaration of competing interest The authors declare no conflict of interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: Diabetes is a metabolic disorder that causes the risk of stroke, heart disease, kidney failure, and other long-term complications because diabetes generates excess sugar in the blood. Machine learning (ML) models can aid in diagnosing diabetes at the primary stage. So, we need an efficient ML model to diagnose diabetes accurately., Methods: In this paper, an effective data preprocessing pipeline has been implemented to process the data and random oversampling to balance the data, handling the imbalance distributions of the observational data more sophisticatedly. We used four different diabetes datasets to conduct our experiments. Several ML algorithms were used to determine the best models to predict diabetes faultlessly., Results: The performance analysis demonstrates that among all ML algorithms, random forest surpasses the current works with an accuracy rate of 86% and 98.48% for Dataset 1 and Dataset 2; extreme gradient boosting and decision tree surpass with an accuracy rate of 99.27% and 100% for Dataset 3 and Dataset 4, respectively. Our proposal can increase accuracy by 12.15% compared to the model without preprocessing., Conclusions: This excellent research finding indicates that the proposed models might be employed to produce more accurate diabetes predictions to supplement current preventative interventions to reduce the incidence of diabetes and its associated costs., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

The ever-evolving global landscape of communication, driven by Information Technology advancements, underscores the importance of emotion detection in natural language processing. However, challenges persist in interpreting emotions within linguistically diverse contexts, notably in low-resource languages like Bengali, compounded by the emergence of Banglish. To address this gap, we present "Bengali & Banglish," an extensive dataset comprising 80,098 labelled samples across six emotion classes. Our dataset fills a void in fine-grained emotion classification for Bengali and pioneers in emotion detection in Banglish. We achieve significant performance metrics through meticulous annotation and rigorous evaluation, including a weighted F1 score of 71.30% for Bengali and 64.59% for Banglish using BanglaBERT. Also, our dataset facilitates Bengali-to-Banglish Machine Translation, contributing to the advancement of language processing models. Furthermore, our dataset demonstrates a high Cohen's Kappa score of 93.5%, affirming the reliability and consistency of our annotations. This research underscores the importance of linguistic diversity in NLP and provides a valuable resource for enhancing Emotion Detection capabilities in Bengali and Banglish across digital platforms., (© 2024 The Author(s).)

Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it often calls for specialist testing, such as blood tests, bone marrow aspiration, and biopsy, all of which are highly time-consuming and expensive. It is essential to obtain an early diagnosis of ALL in order to start therapy in a timely and suitable manner. In recent medical diagnostics, substantial progress has been achieved through the integration of artificial intelligence (AI) and Internet of Things (IoT) devices. Our proposal introduces a new AI-based Internet of Medical Things (IoMT) framework designed to automatically identify leukemia from peripheral blood smear (PBS) images. In this study, we present a novel deep learning-based fusion model to detect ALL types of leukemia. The system seamlessly delivers the diagnostic reports to the centralized database, inclusive of patient-specific devices. After collecting blood samples from the hospital, the PBS images are transmitted to the cloud server through a WiFi-enabled microscopic device. In the cloud server, a new fusion model that is capable of classifying ALL from PBS images is configured. The fusion model is trained using a dataset including 6512 original and segmented images from 89 individuals. Two input channels are used for the purpose of feature extraction in the fusion model. These channels include both the original and the segmented images. VGG16 is responsible for extracting features from the original images, whereas DenseNet-121 is responsible for extracting features from the segmented images. The two output features are merged together, and dense layers are used for the categorization of leukemia. The fusion model that has been suggested obtains an accuracy of 99.89%, a precision of 99.80%, and a recall of 99.72%, which places it in an excellent position for the categorization of leukemia. The proposed model outperformed several state-of-the-art Convolutional Neural Network (CNN) models in terms of performance. Consequently, this proposed model has the potential to save lives and effort. For a more comprehensive simulation of the entire methodology, a web application (Beta Version) has been developed in this study. This application is designed to determine the presence or absence of leukemia in individuals. The findings of this study hold significant potential for application in biomedical research, particularly in enhancing the accuracy of computer-aided leukemia detection.

Background: Contagious and economically devastating, peste des petits ruminants (PPR) is a viral disease affecting goats and sheep, causing significant losses in livestock productivity and posing a threat to food security and rural livelihoods worldwide., Objectives: This study was conducted to assess the status of goat farmer's knowledge, attitude and practice (KAP) about PPR disease at Sylhet district of Bangladesh., Methods: A comprehensive cross-sectional survey, conducted over 11 weeks, targeted 130 goat owners. Following a pilot study with 20 farmers, a set of 17 validated questions on PPR KAP was validated. Data collection was performed through face-to-face interviews by a trained team using KOBO Toolbox, with interpretation of responses based on established thresholds for knowledge (>65%), attitude (>75%) and practice (>70%)., Results: Overall, 67.7% of participants demonstrated a good level of knowledge about PPR disease. Males exhibited 1.42 times higher odds of knowledge compared to females (odds ratio = 1.42). The middle age group (31-45 years) showed significantly higher knowledge levels (83.3%, p < 0.001). Within this age group, positive practice levels were also notably higher (54.8%). Those with a higher secondary education background exhibited the most positive practice levels (>80%). Participants whose additional income came from non-governmental organization employment showed a higher positive practice level (71.4%), 1.46 times higher than other income sources., Conclusion: Strategic interventions should prioritize female farmers, educational empowerment and collaboration with non-governmental organizations to bolster livestock health and rural livelihoods in Bangladesh as part of national PPR control strategy to fulfil the goals of Office International des Epizooties/World Organization for Animal Health (OIE/WOAH) and Food and Agriculture Organization (FAO) PPR eradication by 2030., (© 2024 The Author(s). Veterinary Medicine and Science published by John Wiley & Sons Ltd.)

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases., (© 2024 The Authors. FEBS Open Bio published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Data-driven approaches are often utilized to model and generate vibrotactile feedback and sounds for rigid stylus-based interaction. Nevertheless, in prior research, these two modalities were typically addressed separately due to challenges related to synchronization and design complexity. To this end, we introduce a novel multimodal multitask deep learning framework. In this paper, we developed a comprehensive end-to-end data-driven system that encompasses the capture of contact acceleration signals and sound data from various texture surfaces. This framework introduces novel encoder-decoder networks for modeling and rendering vibrotactile feedback through an actuator while routing sound to headphones. The proposed encoder-decoder networks incorporate stacked transformers with convolutional layers to capture both local variability and overall trends within the data. To the best of our knowledge, this is the first attempt to apply transformer-based data-driven approach for modeling and rendering of vibrotactile signals as well as sounds during tool-surface interactions. In numerical evaluations, the proposed framework demonstrates a lower RMS error compared to state-of-the-art models for both vibrotactile signals and sound data. Additionally, subjective similarity evaluation also confirm the superiority of proposed method over state-of-the-art., (© 2024. The Author(s).)

Introduction: Lumpy skin disease (LSD) is a highly contagious vector-borne viral disease of cattle. LSD has emerged in Bangladesh in 2019, causing significant economic losses due to its high morbidity and mortality. This research was designed to isolate, identify, and assess the immunogenicity of LSD virus (LSDV) using nodular tissue samples obtained from affected cattle during the 2019-20 outbreak across nine districts of Bangladesh., Methods: To determine the presence of LSDV in nodular tissues, we initially used iiPCR and PCR, followed by histopathological examination. 151 were positive via iiPCR and PCR among the 180 collected samples. The PCR positive 151 samples were then inoculated into 10-day-old embryonated chicken eggs via the CAM route to isolate LSDV, confirmed through PCR. Subsequently, partial sequencing and phylogenetic analysis of the P32 gene were performed to determine the origin of the circulating LSDV strain. The immunogenicity of selected LSDV strains was assessed through an ELISA test., Results: The PCR results revealed a distinct positive band at 192 bp in both the nodular tissue samples and the LSDV isolated from chicken embryo inoculations. Microscopic analysis of the nodular lesions revealed thickening of the epidermis, ballooning degeneration of keratinocytes, and proliferation of follicular epithelia. Additionally, mononuclear infiltration was observed at the demarcation line between infected and healthy tissue, with necrosis of muscular tissues beneath the epidermis. The LSDV isolate from Bangladesh exhibited a close genetic relationship with LSDV strains isolated from neighboring and other regional countries including India, Myanmar, and Mongolia. This observation strongly suggests the possibility of a transboundary spread of the LSD outbreak in Bangladesh during 2019-2020. The results of the immunogenicity test showed that the serum antibody titer remained at a protective level for up to 18 months following secondary immunization with inactivated LSDV antigen. This finding suggests that the inactivated LSDV antigen could be a potential vaccine candidate to protect cattle in Bangladesh against LSDV., Conclusion: In conclusion, our research successfully isolated, identified, and characterized LSDV in cattle nodular tissues from the 2019-20 outbreak in Bangladesh. Furthermore, it provided insights into the probable origin of the circulating strain and investigated a potential vaccine candidate to protect cattle in the region from LSDV., Competing Interests: The authors extend their heartfelt thanks to the Managing Director of FnF Pharmaceuticals Ltd., Bangladesh, for providing financial support that enabled the execution of this research. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Uddin, Hossain, Rahman, Siddique, Kafi, Hossain, Chakraborty, Rahman, Khasruzzaman, Ward and Islam.)

Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed a machine learning-based kidney diseases prediction (ML-CKDP) model with dual objectives: to enhance dataset preprocessing for CKD classification and to develop a web-based application for CKD prediction. The proposed model involves a comprehensive data preprocessing protocol, converting categorical variables to numerical values, imputing missing data, and normalizing via Min-Max scaling. Feature selection is executed using a variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Feature Elimination, Sequential Forward Selection, Lasso Regression, and Ridge Regression to refine the datasets. The model employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT), to predict CKDs. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the Area Under the Curve (AUC) specifically for the classification of positive cases. Random Forest (RF) and AdaBoost (AdaB) achieve a 100% accuracy rate, evident across various validation methods including data splits of 70:30, 80:20, and K-Fold set to 10 and 15. RF and AdaB consistently reach perfect AUC scores of 100% across multiple datasets, under different splitting ratios. Moreover, Naive Bayes (NB) stands out for its efficiency, recording the lowest training and testing times across all datasets and split ratios. Additionally, we present a real-time web-based application to operationalize the model, enhancing accessibility for healthcare practitioners and stakeholders. Web app link: https://rajib-research-kedney-diseases-prediction.onrender.com/., Competing Interests: The authors declare that they have no conflict of interest and no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Enzymatic processing has been a suitable bio-based sustainable application for the textile industry, mitigates the use of harsh chemicals, and minimises environmental impact. Among these enzymes, cellulase enzymes have been extensively used for biopolishing applications. This study introduces an eco-friendly biopolishing of cotton fabric that has been developed by using enzymes extracted from wasted sugarcane bagasse waste in an aqueous medium. Various extraction conditions were explored, and experiments were conducted under diverse time, pH, and temperature settings. The qualitative BUTEXDCE2022C01 testing method was used to assess the biopolishing effects, resulting in a considerable reduction in fabric weight (up to 5.26%) and strength (up to 10.54%). The optimum biopolishing condition was identified to be 1 h at pH 4.8, 55 °C from the fermented solution on day three, indicating the presence of acid cellulase enzyme. The viability of cellulase enzymes has been verified through comparative analysis with commercial samples that had undergone enzyme-biopolishing. Extracted and filtered enzymes exhibited pH stability at room temperature and proved equally effective as industrial enzymes. As textile industries pursue eco-friendly solutions, extracting cellulase from wasted sugarcane bagasse could be a sustainable and alternative option, which also can be sourced locally. Therefore, these findings have wider implications for sustainable enzyme extraction methods and contributions to environmental conservation., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Interstitial iodides are the most critical type of defects in perovskite solar cells that limits efficiency and stability. They can be generated during solution, film, and device processing, further accelerating degradation. Herein, we find that introducing a small amount of a zinc salt- zinc trifluoromethane sulfonate (Zn(OOSCF 3 ) 2 ) in the perovskite solution can control the iodide defects in resultant perovskites ink and films. CF 3 SOO ̶ vigorously suppresses molecular iodine formation in the perovskites by reducing it to iodide. At the same time, zinc cations can precipitate excess iodide by forming a Zn-Amine complex so that the iodide interstitials in the resultant perovskite films can be suppressed. The perovskite films using these additives show improved photoluminescence quantum efficiency and reduce deep trap density, despite zinc cations reducing the perovskite grain size and iodide interstitials. The zinc additives facilitate the formation of more uniform perovskite films on large-area substrates (78-108 cm 2 ) in the blade-coating process. Fabricated minimodules show power conversion efficiencies of 19.60% and 19.21% with aperture areas of 84 and 108 cm 2 , respectively, as certified by National Renewable Energy Laboratory (NREL), the highest efficiency certified for minimodules of these sizes., (© 2024. The Author(s).)

The study investigates the antecedents that influence social distancing behavior during the COVID-19 outbreak and develops a hypothetical model integrating additional pertinent variables into the classic theory of planned behavior (TPB) to grasp the social distancing behavior accurately. The extended model was investigated employing structural equation modeling using the data collected through Snowball Technology from 331 respondents. The results unearth significant influences of all the predictor variables on their outcome variables. Surprisingly, the supportive government strategy has no crucial impact on perceived behavioral control toward social distancing. Furthermore, the study reports no trivial difference between young and old and high-low income groups on the intention-actual social distancing. The study outlines policy interventions to prevent mass transmission of the COVID-19 catastrophe. Lastly, limitations are specified to open up novel insights for future researchers., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Till the day the rectal cancer deaths in the world is in eighth position. For rectal cancer surgery, short-term benefits are expected to be similar for laparoscopic resection of rectum (LRR) and open resection of rectum (ORR). In Bangladesh though the rectal cancer is the sixteenth most common cause of cancer deaths, there is lack of data regarding outcomes of laparoscopic and open surgical approaches for carcinoma rectum. Purpose of study was to compare oncopathological outcomes by quality of surgical resection between LRR and ORR groups. The quasi experimental study was conducted among 46 subjects who attended in the Department of Colorectal Surgery, Bangabandhu Sheikh Mujib Medical University, Bangladesh from July 2020 to June 2021 with rectal adenocarcinoma within 15cm from the anal verge. Equal number of subjects were allocated for LRR and ORR group. Oncopathological end points such as circumferential resection margin (CRM), distal resection margin (DRM), lymph nodes and quality of mesorectal excision were assessed and compared. Here every patients have given their written consent for this study. Mean age of the subjects for LRR and ORR were 45.47±12.66 and 44.47±12.48 years respectively. Majority of the subjects were in age above 40 years (67.0%). The proportion of male (56.5%) were higher than those of female (43.5%). The complete resection was better in LRR (91.3%) than ORR (87.0%) group though statistically not significant. CRM was lower in LRR (0.0%) than ORR (13.0%) group in respect of frequency distribution. DRM was negative for both LRR and ORR group (95.7% each) and it was not statistically significant. ORR met the National Comprehensive Cancer Network (NCCN) criteria of harvesting 12 lymph nodes were as in LRR group 10 lymph nodes were harvested. The mean harvested lymph nodes were 12.2±5.55 and 10.1±5.55 in LRR and ORR group respectively. The study demonstrated that LRR is better (though statistically not significant) in respect of complete resection and CRM while in harvesting lymph nodes, ORR met the NCCN criteria but LRR does not. There is no difference observed regarding DRM in both groups. On oncopathological point of view both the group showed almost equally effective results. Laparoscopic surgery can be opted as the standard operative technique for surgical management of rectal cancer.

An epigenetic modification is DNA N4-methylcytosine (4mC) that affects several biological functions without altering the DNA nucleotides, including DNA conformation, cell development, replication, stability, and DNA structural changes. To prevent restriction enzyme from damaging self-DNA, 4mC performs a critical role in restriction-modification functions. Existing studies mainly focused on finding hand-crafted features to identify 4mC locations, but these methods are inefficient due to high time consuming and high costs. In our research work, we propose a 4mC-CGRU which is a deep learning-based computational model with a standard encoding method to identify the 4mC sites from DNA sequences that learned autonomous feature selection in the Rosaceae genome, particularly in Rosa chinensis (R. chinensis) and Fragaria vesca (F. vesca). The proposed model consists of a convolutional neural network (CNN) and a gated recurrent unit network (GRU)-based model for identifying 4mC sites from Fragaria vesca and Rosa chinensis in the genomes. The CNN model extracts useful features from the datasets and the GRU classifies the DNA sequences. Thus, our approach can automatically extract important features to detect relative sites from DNA sequence. The performance analysis shows that the proposed model consistently outperforms over the state-of-the-art works in detecting 4mC sites., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The Internet of Things (IoT) is a transformative technology that is reshaping industries and daily life, leading us towards a connected future that is full of possibilities and innovations. In this paper, we present a robust framework for the application of Internet of Things (IoT) technology in the agricultural sector in Bangladesh. The framework encompasses the integration of IoT, data mining techniques, and cloud monitoring systems to enhance productivity, improve water management, and provide real-time crop forecasting. We conducted rigorous experimentation on the framework. We achieve an accuracy of 87.38% for the proposed model in predicting data harvest. Our findings highlight the effectiveness and transparency of the framework, underscoring the significant potential of the IoT in transforming agriculture and empowering farmers with data-driven decision-making capabilities. The proposed framework might be very impactful in real-life agriculture, especially for monsoon agriculture-based countries like Bangladesh.

Objective: Whole genome sequencing (WGS) of Aeromonas veronii Alim&#95;AV&#95;1000 isolated from ulcerative lesions of Shing fish (stringing catfish; Heteropneustes fossilis ) was performed during the outbreak year 2021., Materials and Methods: Using next-generation sequencing (Illumina) technology, WGS was accomplished, resulting in the sequencing, assembly, and analysis of the entire genome of the A. veronii strain. Moreover, the genomic features, virulence factors, antimicrobial resistome, and phylogenetic analysis for the molecular evolution of this strain were also examined., Results: The genome size of the A. veronii Alim&#95;AV&#95;1000 strain was 4,494,515 bp, with an average G+C content of 58.87%. Annotation revealed the known transporters and genes linked to virulence, drug targets, and antimicrobial resistance., Conclusion: The findings of the phylogenetic analysis revealed that the strain of the present study has a close relationship with the China strain TH0426 and strain B56. This study provides novel information on A. veronii isolated from Shing fish in Bangladesh., Competing Interests: The authors confirmed no competing interests., (Copyright: © Journal of Advanced Veterinary and Animal Research.)

Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods.

Monkeypox is a double-stranded DNA virus with an envelope and is a member of the Poxviridae family's Orthopoxvirus genus. This virus can transmit from human to human through direct contact with respiratory secretions, infected animals and humans, or contaminated objects and causing mutations in the human body. In May 2022, several monkeypox affected cases were found in many countries. Because of its transmitting characteristics, on July 23, 2022, a nationwide public health emergency was proclaimed by WHO due to the monkeypox virus. This study analyzed the gene mutation rate that is collected from the most recent NCBI monkeypox dataset. The collected data is prepared to independently identify the nucleotide and codon mutation. Additionally, depending on the size and availability of the gene dataset, the computed mutation rate is split into three categories: Canada, Germany, and the rest of the world. In this study, the genome mutation rate of the monkeypox virus is predicted using a deep learning-based Long Short-Term Memory (LSTM) model and compared with Gated Recurrent Unit (GRU) model. The LSTM model shows "Root Mean Square Error" (RMSE) values of 0.09 and 0.08 for testing and training, respectively. Using this time series analysis method, the prospective mutation rate of the 50th patient has been predicted. Note that this is a new report on the monkeypox gene mutation. It is found that the nucleotide mutation rates are decreasing, and the balance between bi-directional rates are maintained., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Pathan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Alzheimer's disease (AD) is one of the leading causes of dementia among older people. In addition, a considerable portion of the world's population suffers from metabolic problems, such as Alzheimer's disease and diabetes. Alzheimer's disease affects the brain in a degenerative manner. As the elderly population grows, this illness can cause more people to become inactive by impairing their memory and physical functionality. This might impact their family members and the financial, economic, and social spheres. Researchers have recently investigated different machine learning and deep learning approaches to detect such diseases at an earlier stage. Early diagnosis and treatment of AD help patients to recover from it successfully and with the least harm. This paper proposes a machine learning model that comprises GaussianNB, Decision Tree, Random Forest, XGBoost, Voting Classifier, and GradientBoost to predict Alzheimer's disease. The model is trained using the open access series of imaging studies (OASIS) dataset to evaluate the performance in terms of accuracy, precision, recall, and F1 score. Our findings showed that the voting classifier attained the highest validation accuracy of 96% for the AD dataset. Therefore, ML algorithms have the potential to drastically lower Alzheimer's disease annual mortality rates through accurate detection., Competing Interests: Conflict of interestThe authors declare that there are no conflicts of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Metal-halide perovskites (MHPs) have been successfully exploited for converting photons to charges or vice versa in applications of solar cells, light-emitting diodes and solar fuels 1-3 , for which all these applications involve strong light. Here we show that self-powered polycrystalline perovskite photodetectors can rival the commercial silicon photomultipliers (SiPMs) for photon counting. The photon-counting capability of perovskite photon-counting detectors (PCDs) is mainly determined by shallow traps, despite that deep traps also limit charge-collection efficiency. Two shallow traps with energy depth of 5.8 ± 0.8 millielectronvolts (meV) and 57.2 ± 0.1 meV are identified in polycrystalline methylammonium lead triiodide, which mainly stay at grain boundaries and the surface, respectively. We show that these shallow traps can be reduced by grain-size enhancement and surface passivation using diphenyl sulfide, respectively. It greatly suppresses dark count rate (DCR) from >20,000 counts per second per square millimetre (cps mm -2 ) to 2 cps mm -2 at room temperature, enabling much better response to weak light than SiPMs. The perovskite PCDs can collect γ-ray spectra with better energy resolution than SiPMs and maintain performance at high temperatures up to 85 °C. The zero-bias operation of perovskite detectors enables no drift of noise and detection property. This study opens a new application of photon counting for perovskites that uses their unique defect properties., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Lantana camara L. is generally considered an invasive plant species throughout the world. Research works carried out in recent years have proved its significance as a source of antimicrobial lead molecules. The aim of this research was to identify the antibacterial substance(s) in this plant species found locally and to test its antibacterial effect against selected bacterial strains. Plant samples were collected from the University of Dhaka campus. Ethanol and ethyl acetate extracts of the plant leaves were tested against Escherichia coli, Bacillus subtilis, Pneumococcus and Klebsiella. Both the ethanol and the ethyl acetate extracts showed significant activity against Bacillus subtilis. In disk diffusion antibacterial assay, ethanol extract showed greater activity than ethyl acetate extract against Bacillus subtilis and the zones of inhibition were 14 mm and 12 mm respectively. However, ethyl acetate extract showed greater activity than ethanol extract in TLC bioautography assay. Ethyl acetate and ethanol extracts showed very little activity against Pneumococcus and Klebsiella, but no antibacterial potential against Escherichia coli. Fractionation of the ethyl acetate extract by TLC and bioautography detection of antibacterial activity in TLC encouraged further purification of the lead active compound(s). Phytochemical composition analysis of the ethyl acetate extract showed the presence of alkaloids, steroids, phenolic compounds and glycosides.

Increasing incidences of resistance to antibiotics by pathogenic bacteria is a worldwide concern and isolation of antibiotic-resistant strains of Niallia circulans (formerly known as Bacillus circulans), an opportunistic human pathogen, has been reported. Due to their lack of ethical constraints as well as their cost-effective rearing, invertebrates have been commonly used to study infection by bacteria pathogenic to humans. In this study, we demonstrate that a foodborne strain of N. circulans kills larvae of the silkworm, Bombyx mori within 48 h after hemolymph injection. The infected larvae turned black with an increase in the phenoloxidase (PO) activity in the hemolymph. Midgut injection of N. circulans resulted in the killing of larvae within 96 h. A significant increase in bacterial load was observed in the hemolymph 12 h after infection. The viable hemocyte number decreased to 48% within 12 h of injection. RT-qPCR analysis revealed that upon hemolymph infection with N. circulans the expression of the antimicrobial peptide (AMP) genes, Bmdefensin-B and Bmgloverin-3, were upregulated 2.5- and 1.8-fold, respectively, whereas 1.6-fold upregulation was observed for BmToll-2 in the larval fat body. Therapeutic effects of antibiotics like tetracycline, imipenem, ceftriaxone, ampicillin, and clindamycin were observed against N. circulans in the Bombyx larvae with varying efficacies. Results from this study suggest that larvae of B. mori can be used as infection models for screening therapeutics that are effective against N. circulans.

DNA (Deoxyribonucleic Acid) N4-methylcytosine (4mC), a kind of epigenetic modification of DNA, is important for modifying gene functions, such as protein interactions, conformation, and stability in DNA, as well as for the control of gene expression throughout cell development and genomic imprinting. This simply plays a crucial role in the restriction-modification system. To further understand the function and regulation mechanism of 4mC, it is essential to precisely locate the 4mC site and detect its chromosomal distribution. This research aims to design an efficient and high-throughput discriminative intelligent computational system using the natural language processing method "word2vec" and a multi-configured 1D convolution neural network (1D CNN) to predict 4mC sites. In this article, we propose a grid search-based multi-layer dynamic ensemble system (GS-MLDS) that can enhance existing knowledge of each level. Each layer uses a grid search-based weight searching approach to find the optimal accuracy while minimizing computation time and additional layers. We have used eight publicly available benchmark datasets collected from different sources to test the proposed model's efficiency. Accuracy results in test operations were obtained as follows: 0.978, 0.954, 0.944, 0.961, 0.950, 0.973, 0.948, 0.952, 0.961, and 0.980. The proposed model has also been compared to 16 distinct models, indicating that it can accurately predict 4mC.

COVID-19 is a severe respiratory contagious disease that has now spread all over the world. COVID-19 has terribly impacted public health, daily lives and the global economy. Although some developed countries have advanced well in detecting and bearing this coronavirus, most developing countries are having difficulty in detecting COVID-19 cases for the mass population. In many countries, there is a scarcity of COVID-19 testing kits and other resources due to the increasing rate of COVID-19 infections. Therefore, this deficit of testing resources and the increasing figure of daily cases encouraged us to improve a deep learning model to aid clinicians, radiologists and provide timely assistance to patients. In this article, an efficient deep learning-based model to detect COVID-19 cases that utilizes a chest X-ray images dataset has been proposed and investigated. The proposed model is developed based on ResNet50V2 architecture. The base architecture of ResNet50V2 is concatenated with six extra layers to make the model more robust and efficient. Finally, a Grad-CAM-based discriminative localization is used to readily interpret the detection of radiological images. Two datasets were gathered from different sources that are publicly available with class labels: normal, confirmed COVID-19, bacterial pneumonia and viral pneumonia cases. Our proposed model obtained a comprehensive accuracy of 99.51% for four-class cases (COVID-19/normal/bacterial pneumonia/viral pneumonia) on Dataset-2, 96.52% for the cases with three classes (normal/ COVID-19/bacterial pneumonia) and 99.13% for the cases with two classes (COVID-19/normal) on Dataset-1. The accuracy level of the proposed model might motivate radiologists to rapidly detect and diagnose COVID-19 cases.