Draft genome sequence of extracellular enzyme-producing Bacillus paralicheniformis strain NBG-07.

The genome of Bacillus paralicheniformis strain NBG-07 was sequenced using Illumina sequencing due to its ability to produce thermostable enzymes of industrial importance. The strain was isolated from the soil. Annotation of the draft genome revealed genes involved in the production of different enzymes, including alpha-amylase, protease, cellulase, and laccase.

Single Stage Adaptive Multi-Attention Network for Image Restoration.

Recently attention-based networks have been successful for image restoration tasks. However, existing methods are either computationally expensive or have limited receptive fields, adding constraints to the model. They are also less resilient in spatial and contextual aspects and lack pixel-to-pixel correspondence, which may degrade feature representations. In this paper, we propose a novel and computationally efficient architecture Single Stage Adaptive Multi-Attention Network (SSAMAN) for image restoration tasks, particularly for image denoising and image deblurring. SSAMAN efficiently addresses computational challenges and expands receptive fields, enhancing robustness in spatial and contextual feature representation. Its Adaptive Multi-Attention Module (AMAM), which consists of Adaptive Pixel Attention Branch (APAB) and an Adaptive Channel Attention Branch (ACAB), uniquely integrates channel and pixel-wise dimensions, significantly improving sensitivity to edges, shapes, and textures. We perform extensive experiments and ablation studies to validate the performance of SSAMAN. Our model shows state-of-the-art results on various benchmarks, for example, on image denoising tasks, SSAMAN achieves a notable 40.08 dB PSNR on SIDD dataset, outperforming Restormer by 0.06 dB PSNR, with 41.02% less computational cost, and achieves a 40.05 dB PSNR on the DND dataset. For image deblurring, SSAMAN achieves 33.53 dB PSNR on GoPro dataset. Code and models are available at Github.

Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study.

BACKGROUND: Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest. OBJECTIVE: In this paper, we propose a machine learning-based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study. METHODS: We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance). RESULTS: After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: "virus of COVID-19," "risk factors of COVID-19," "prevention of COVID-19," "treatment of COVID-19," "health care delivery during COVID-19," "and impact of COVID-19." The most prominent topic, observed in over half of the analyzed studies, was "the impact of COVID-19." CONCLUSIONS: The proposed machine learning-based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.

Challenges and Approaches of Culturing the Unculturable Archaea.

Since Carl Woese's discovery of archaea as a third domain of life, numerous archaeal species have been discovered, yet archaeal diversity is poorly characterized. Culturing archaea is complicated, but several queries about archaeal cell biology, evolution, physiology, and diversity need to be solved by culturing and culture-dependent techniques. Increasing interest in demand for innovative culturing methods has led to various technological and methodological advances. The current review explains frequent hurdles hindering uncultured archaea isolation and discusses features for more archaeal cultivation. This review also discusses successful strategies and available media for archaeal culturing, which might be helpful for future culturing practices.

Multimodal deep learning for liver cancer applications: a scoping review.

Background: Hepatocellular carcinoma is a malignant neoplasm of the liver and a leading cause of cancer-related deaths worldwide. The multimodal data combines several modalities, such as medical images, clinical parameters, and electronic health record (EHR) reports, from diverse sources to accomplish the diagnosis of liver cancer. The introduction of deep learning models with multimodal data can enhance the diagnosis and improve physicians' decision-making for cancer patients. Objective: This scoping review explores the use of multimodal deep learning techniques (i.e., combining medical images and EHR data) in diagnosing and prognosis of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA). Methodology: A comprehensive literature search was conducted in six databases along with forward and backward references list checking of the included studies. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review guidelines were followed for the study selection process. The data was extracted and synthesized from the included studies through thematic analysis. Results: Ten studies were included in this review. These studies utilized multimodal deep learning to predict and diagnose hepatocellular carcinoma (HCC), but no studies examined cholangiocarcinoma (CCA). Four imaging modalities (CT, MRI, WSI, and DSA) and 51 unique EHR records (clinical parameters and biomarkers) were used in these studies. The most frequently used medical imaging modalities were CT scans followed by MRI, whereas the most common EHR parameters used were age, gender, alpha-fetoprotein AFP, albumin, coagulation factors, and bilirubin. Ten unique deep-learning techniques were applied to both EHR modalities and imaging modalities for two main purposes, prediction and diagnosis. Conclusion: The use of multimodal data and deep learning techniques can help in the diagnosis and prediction of HCC. However, there is a limited number of works and available datasets for liver cancer, thus limiting the overall advancements of AI for liver cancer applications. Hence, more research should be undertaken to explore further the potential of multimodal deep learning in liver cancer applications.

Food safety and biological control; genomic insights and antimicrobial potential of Bacillus velezensis FB2 against agricultural fungal pathogens.

Development of natural, broad-spectrum, and eco-friendly bio-fungicides is of high interest in the agriculture and food industries. In this context, Bacillus genus has shown great potential for producing a wide range of antimicrobial metabolites against various pathogens. A Bacillus velezensis strain FB2 was isolated from an agricultural field of National Institute for Biotechnology and Genetic Engineering (NIBGE) Faisalabad, Pakistan, exhibiting good antifungal properties. The complete genome of this strain was sequenced, and its antifungal potential was assayed by dual culture method. Moreover, structural characterization of its antifungal metabolites, produced in vitro, were studied. Genome analysis and mining revealed the secondary metabolite gene clusters, encoding non-ribosomal peptides (NRPs) production (e.g., surfactin, iturin and fengycin) and polyketide (PK) synthesis (e.g., difficidin, bacillaene and macrolactin). Furthermore, the Bacillus velezensis FB2 strain was observed to possess in vitro antifungal activity; 41.64, 40.38 and 26% growth inhibition against major fungal pathogens i.e. Alternaria alternata, Fusarium oxysporum and Fusarium solani respectively. Its lipopeptide extract obtained by acid precipitation method was also found effective against the above-mentioned fungal pathogens. The ESI-MS/MS analysis indicated various homologs of surfactin and iturin-A, responsible for their antifungal activities. Overall, this study provides a better understanding of Bacillus velezensis FB2, as a promising candidate for biocontrol purposes, acting in a safe and sustainable way, to control plant pathogens.

Artificial Intelligence-Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review.

BACKGROUND: Transformer-based models are gaining popularity in medical imaging and cancer imaging applications. Many recent studies have demonstrated the use of transformer-based models for brain cancer imaging applications such as diagnosis and tumor segmentation. OBJECTIVE: This study aims to review how different vision transformers (ViTs) contributed to advancing brain cancer diagnosis and tumor segmentation using brain image data. This study examines the different architectures developed for enhancing the task of brain tumor segmentation. Furthermore, it explores how the ViT-based models augmented the performance of convolutional neural networks for brain cancer imaging. METHODS: This review performed the study search and study selection following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search comprised 4 popular scientific databases: PubMed, Scopus, IEEE Xplore, and Google Scholar. The search terms were formulated to cover the interventions (ie, ViTs) and the target application (ie, brain cancer imaging). The title and abstract for study selection were performed by 2 reviewers independently and validated by a third reviewer. Data extraction was performed by 2 reviewers and validated by a third reviewer. Finally, the data were synthesized using a narrative approach. RESULTS: Of the 736 retrieved studies, 22 (3%) were included in this review. These studies were published in 2021 and 2022. The most commonly addressed task in these studies was tumor segmentation using ViTs. No study reported early detection of brain cancer. Among the different ViT architectures, Shifted Window transformer-based architectures have recently become the most popular choice of the research community. Among the included architectures, UNet transformer and TransUNet had the highest number of parameters and thus needed a cluster of as many as 8 graphics processing units for model training. The brain tumor segmentation challenge data set was the most popular data set used in the included studies. ViT was used in different combinations with convolutional neural networks to capture both the global and local context of the input brain imaging data. CONCLUSIONS: It can be argued that the computational complexity of transformer architectures is a bottleneck in advancing the field and enabling clinical transformations. This review provides the current state of knowledge on the topic, and the findings of this review will be helpful for researchers in the field of medical artificial intelligence and its applications in brain cancer.

Extremal quantum correlation generation using a hybrid channel.

The preservation of quantum correlations requires optimal procedures and the proper design of the transmitting channels. In this regard, we address designing a hybrid channel comprising a single-mode cavity accompanied by a super-Gaussian beam and local dephasing parts based on the dynamics of quantum characteristics. We choose two-level atoms and various functions such as traced-distance discord, concurrence, and local-quantum uncertainty to analyze the effectiveness of the hybrid channel to preserve quantum correlations along with entropy suppression discussed using linear entropy. The joint configuration of the considered fields is found to not only preserve but also generate quantum correlations even in the presence of local dephasing. Most importantly, within certain limits, the proposed channel can be readily regulated to generate maximal quantum correlations and complete suppression of the disorder. Besides, compared to the individual parts, mixing the Fock state cavity, super-Gaussian beam, and local dephasing remains a resourceful choice for the prolonged quantum correlations' preservation. Finally, we present an interrelationship between the considered two-qubit correlations' functions, showing the deviation between each two correlations and of the considered state from maximal entanglement under the influence of the assumed hybrid channel.

Biofilms as Battlefield Armor for Bacteria against Antibiotics: Challenges and Combating Strategies.

Bacterial biofilms are formed by communities, which are encased in a matrix of extracellular polymeric substances (EPS). Notably, bacteria in biofilms display a set of 'emergent properties' that vary considerably from free-living bacterial cells. Biofilms help bacteria to survive under multiple stressful conditions such as providing immunity against antibiotics. Apart from the provision of multi-layered defense for enabling poor antibiotic absorption and adaptive persistor cells, biofilms utilize their extracellular components, e.g., extracellular DNA (eDNA), chemical-like catalase, various genes and their regulators to combat antibiotics. The response of biofilms depends on the type of antibiotic that comes into contact with biofilms. For example, excessive production of eDNA exerts resistance against cell wall and DNA targeting antibiotics and the release of antagonist chemicals neutralizes cell membrane inhibitors, whereas the induction of protein and folic acid antibiotics inside cells is lowered by mutating genes and their regulators. Here, we review the current state of knowledge of biofilm-based resistance to various antibiotic classes in bacteria and genes responsible for biofilm development, and the key role of quorum sensing in developing biofilms and antibiotic resistance is also discussed. In this review, we also highlight new and modified techniques such as CRISPR/Cas, nanotechnology and bacteriophage therapy. These technologies might be useful to eliminate pathogens residing in biofilms by combating biofilm-induced antibiotic resistance and making this world free of antibiotic resistance.

Integrated aquatic and terrestrial food production enhances micronutrient and economic productivity for nutrition-sensitive food systems.

Integrated aquaculture-agriculture (IAA) is a form of crop diversification where aquatic and terrestrial foods are grown together on a single parcel of land. We compare economic and nutrient productivity per hectare for 12 distinct IAA combinations, identified from a representative survey of 721 farms in southern Bangladesh. Just under half of households integrate agriculture into their aquaculture production. Regression analyses show positive associations between the integration of terrestrial foods into aquatic farming systems and nutrient productivity, but that nutrient productivity is partly disconnected from economic productivity. However, we find that production of specific combinations of aquatic foods and vegetables can simultaneously improve nutrient productivity and economic productivity, thereby promoting nutrition-sensitive agriculture (NSA). The approach demonstrated here can be applied to the design of NSA programmes that are important for realizing nutrition-sensitive food systems.

Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review.

BACKGROUND: Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications. Recently, many researchers have developed vision transformer-based AI methods for lung cancer diagnosis and prognosis. OBJECTIVE: This scoping review aims to identify the recent developments on vision transformer-based AI methods for lung cancer imaging applications. It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field. METHODS: In this review, we searched Pubmed, Scopus, IEEEXplore, and Google Scholar online databases. The search terms included intervention terms (vision transformers) and the task (i.e., lung cancer, adenocarcinoma, etc.). Two reviewers independently screened the title and abstract to select relevant studies and performed the data extraction. A third reviewer was consulted to validate the inclusion and exclusion. Finally, the narrative approach was used to synthesize the data. RESULTS: Of the 314 retrieved studies, this review included 34 studies published from 2020 to 2022. The most commonly addressed task in these studies was the classification of lung cancer types, such as lung squamous cell carcinoma versus lung adenocarcinoma, and identifying benign versus malignant pulmonary nodules. Other applications included survival prediction of lung cancer patients and segmentation of lungs. The studies lacked clear strategies for clinical transformation. SWIN transformer was a popular choice of the researchers; however, many other architectures were also reported where vision transformer was combined with convolutional neural networks or UNet model. Researchers have used the publicly available lung cancer datasets of the lung imaging database consortium and the cancer genome atlas. One study used a cluster of 48 GPUs, while other studies used one, two, or four GPUs. CONCLUSION: It can be concluded that vision transformer-based models are increasingly in popularity for developing AI methods for lung cancer applications. However, their computational complexity and clinical relevance are important factors to be considered for future research work. This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis. We provide an interactive dashboard on lung-cancer.onrender.com/ .

Vegetation diversity pattern during spring season in relation to topographic and edaphic variables in sub-tropical zone.

BACKGROUND: The present study was conducted to explore the diversity pattern of spring vegetation under the influence of topographic and edaphic variables in sub-tropical zone, District Malakand. In the present vegetation study, 252 species of 80 families were recorded in the study area. It included 39 species of trees, 43 species of shrubs, 167 species of herbs and 3 climber species. As a whole, 12 communities were established on the basis of topographic and edaphic characteristics in 12 different stations. RESULTS: The results of the present study revealed that all diversity indices (species diversity, evenness index, species richness index, maturity index) during spring showed that the communities in plains lying at lower altitudes had higher diversity while the communities formed at high altitudes had lower diversity. The results of the similarity index showed that there was low similarity (below 50%) amongst the communities in different stations. CONCLUSIONS: It can be concluded that variations in topographic and edaphic factors affect species diversity and communities pattern.

Phylogenetic diversity and functional potential of the microbial communities along the Bay of Bengal coast.

The Bay of Bengal, the world's largest bay, is bordered by populous countries and rich in resources like fisheries, oil, gas, and minerals, while also hosting diverse marine ecosystems such as coral reefs, mangroves, and seagrass beds; regrettably, its microbial diversity and ecological significance have received limited research attention. Here, we present amplicon (16S and 18S) profiling and shotgun metagenomics data regarding microbial communities from BoB's eastern coast, viz., Saint Martin and Cox's Bazar, Bangladesh. From the 16S barcoding data, Proteobacteria appeared to be the dominant phylum in both locations, with Alteromonas, Methylophaga, Anaerospora, Marivita, and Vibrio dominating in Cox's Bazar and Pseudoalteromonas, Nautella, Marinomonas, Vibrio, and Alteromonas dominating the Saint Martin site. From the 18S barcoding data, Ochrophyta, Chlorophyta, and Protalveolata appeared among the most abundant eukaryotic divisions in both locations, with significantly higher abundance of Choanoflagellida, Florideophycidae, and Dinoflagellata in Cox's Bazar. The shotgun sequencing data reveals that in both locations, Alteromonas is the most prevalent bacterial genus, closely paralleling the dominance observed in the metabarcoding data, with Methylophaga in Cox's Bazar and Vibrio in Saint Martin. Functional annotations revealed that the microbial communities in these samples harbor genes for biofilm formation, quorum sensing, xenobiotics degradation, antimicrobial resistance, and a variety of other processes. Together, these results provide the first molecular insight into the functional and phylogenetic diversity of microbes along the BoB coast of Bangladesh. This baseline understanding of microbial community structure and functional potential will be critical for assessing impacts of climate change, pollution, and other anthropogenic disturbances on this ecologically and economically vital bay.

Agonistic antibacterial potential of Loigolactobacillus coryniformis BCH-4 metabolites against selected human pathogenic bacteria: An in vitro and in silico approach.

Lactic acid bacteria are known to produce numerous antibacterial metabolites that are active against various pathogenic microbes. In this study, bioactive metabolites from the cell free supernatant of Loigolactobacillus coryniformis BCH-4 were obtained by liquid-liquid extraction, using ethyl acetate, followed by fractionation, using silica gel column chromatography. The collected F23 fraction effectively inhibited the growth of pathogenic bacteria (Escherichia coli, Bacillus cereus, and Staphylococcus aureus) by observing the minimum inhibitory concentration (MIC) and minimum bactericidal concentrations (MBC). The evaluated values of MIC were 15.6 ± 0.34, 3.9 ± 0.59, and 31.2 ± 0.67 µg/mL and MBC were 15.6 ± 0.98, 7.8 ± 0.45, and 62.5 ± 0.23 µg/mL respectively, against the above-mentioned pathogenic bacteria. The concentration of F23 fraction was varying from 1000 to 1.9 µg/mL. Furthermore, the fraction also exhibited sustainable biofilm inhibition. Using the Electrospray Ionization Mass Spectrometry (ESI-MS/MS), the metabolites present in the bioactive fraction (F23), were identified as phthalic acid, myristic acid, mangiferin, 16-hydroxylpalmatic acid, apigenin, and oleandomycin. By using in silico approach, docking analysis showed good interaction of identified metabolites and receptor proteins of pathogenic bacteria. The present study suggested Loigolactobacillus coryniformis BCH-4, as a promising source of natural bioactive metabolites which may receive great benefit as potential sources of drugs in the pharmacological sector.

Identifying the role of vision transformer for skin cancer-A scoping review.

Introduction: Detecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between lesions and besieging tissues can lead to incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, but variations in symptoms and underlying effects hinder their performance. Objective: This scoping review synthesizes and analyzes the literature that uses vision transformers for skin lesion detection. Methods: The review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Revise) guidelines. The review searched online repositories such as IEEE Xplore, Scopus, Google Scholar, and PubMed to retrieve relevant articles. After screening and pre-processing, 28 studies that fulfilled the inclusion criteria were included. Results and discussions: The review found that the use of vision transformers for skin cancer detection has rapidly increased from 2020 to 2022 and has shown outstanding performance for skin cancer detection using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular skin lesion shapes, and many other unwanted challenges, the review also discusses the key problems that obfuscate the trustworthiness of vision transformers in skin cancer diagnosis. This review provides new insights for practitioners and researchers to understand the current state of knowledge in this specialized research domain and outlines the best segmentation techniques to identify accurate lesion boundaries and perform melanoma diagnosis. These findings will ultimately assist practitioners and researchers in making more authentic decisions promptly.

Revolutionizing Healthcare with Foundation AI Models.

ChatGPT is a foundation Artificial Intelligence (AI) model that has opened up new opportunities in digital healthcare. Particularly, it can serve as a co-pilot tool for doctors in the interpretation, summarization, and completion of reports. Furthermore, it can build upon the ability to access the large literature and knowledge on the internet. So, chatGPT could generate acceptable responses for the medical examination. Hence. It offers the possibility of enhancing healthcare accessibility, expandability, and effectiveness. Nonetheless, chatGPT is vulnerable to inaccuracies, false information, and bias. This paper briefly describes the potential of Foundation AI models to transform future healthcare by presenting ChatGPT as an example tool.

Endosulfan affects embryonic development synergistically under elevated ambient temperature.

In the present study, we determined the developmental toxicity of endosulfan at an elevated ambient temperature using the zebrafish animal model. Zebrafish embryos of various developmental stages were exposed to endosulfan through E3 medium, raised under two selected temperature conditions (28.5 °C and an elevated temperature of 35 °C), and monitored under the microscope. Zebrafish embryos of very early developmental stages (cellular cleavage stages, such as the 64-cell stage) were highly sensitive to the elevated temperature as 37.5% died and 47.5% developed into amorphous type, while only 15.0% of embryos developed as normal embryos without malformation. Zebrafish embryos that were exposed concurrently to endosulfan and an elevated temperature showed stronger developmental defects (arrested epiboly progress, shortened body length, curved trunk) compared to the embryos exposed to either endosulfan or an elevated temperature. The brain structure of the embryos that concurrently were exposed to the elevated temperature and endosulfan was either incompletely developed or malformed. Furthermore, the stress-implicated genes hsp70, p16, and smp30 regulations were synergistically affected by endosulfan treatment under the elevated thermal condition. Overall, the elevated ambient temperature synergistically enhanced the developmental toxicity of endosulfan in zebrafish embryos.

Shaped Microwave Field in a Three-Level Closed Loop Dense Atomic System.

In this work, we investigate the atomic properties of a three-level system under the effect of a shaped microwave field. The system is simultaneously driven by a powerful laser pulse and a weak constant probe that drives the ground state to an upper level. Meanwhile, an external microwave field drives the upper state to the middle transition with shaped waveforms. Hence, two situations are considered: one in which the atomic system is controlled by a strong laser pump and a classical constant microwave field, and another in which both the microwave and pump laser fields are shaped. Finally, for sake of comparison, we investigate the tanh-hyperbolic, the Gaussian and the power of the exponential microwave form in the system. Our results reveal that shaping the external microwave field has a significant impact on the absorption and dispersion coefficient dynamics. In comparison with the classical scenario, where usually the strong pump laser is considered to have a major role in controlling the absorption spectrum, we show that shaping the microwave field leads to distinct results.