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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.

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., (©Hazrat Ali, Rizwan Qureshi, Zubair Shah. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 17.11.2023.)

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Hammad 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.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Siam, Alsaify, Mohammad, Biswas, Ali and Shah.)

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.

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., (© 2023. Springer Nature Limited.)

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., (© 2023. The Author(s).)

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., (© 2023. Institute of Plant and Microbial Biology.)

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/ ., (© 2023. BioMed Central Ltd., part of Springer Nature.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Khan, Ali and Shah.)

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., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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.

Robust speech emotion recognition relies on the quality of the speech features. We present speech features enhancement strategy that improves speech emotion recognition. We used the INTERSPEECH 2010 challenge feature-set. We identified subsets from the features set and applied principle component analysis to the subsets. Finally, the features are fused horizontally. The resulting feature set is analyzed using t-distributed neighbour embeddings (t-SNE) before the application of features for emotion recognition. The method is compared with the state-of-the-art methods used in the literature. The empirical evidence is drawn using two well-known datasets: Berlin Emotional Speech Dataset (EMO-DB) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for two languages, German and English, respectively. Our method achieved an average recognition gain of 11.5% for six out of seven emotions for the EMO-DB dataset, and 13.8% for seven out of eight emotions for the RAVDESS dataset as compared to the baseline study., Competing Interests: Hazrat Ali is an Academic Editor for PeerJ., (© 2022 Kanwal et al.)

Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. The most important question when using multimodal data is how to fuse them-a field of growing interest among researchers. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion of these different data modalities to provide multimodal insights. To this end, in this scoping review, we focus on synthesizing and analyzing the literature that uses AI techniques to fuse multimodal medical data for different clinical applications. More specifically, we focus on studies that only fused EHR with medical imaging data to develop various AI methods for clinical applications. We present a comprehensive analysis of the various fusion strategies, the diseases and clinical outcomes for which multimodal fusion was used, the ML algorithms used to perform multimodal fusion for each clinical application, and the available multimodal medical datasets. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We searched Embase, PubMed, Scopus, and Google Scholar to retrieve relevant studies. After pre-processing and screening, we extracted data from 34 studies that fulfilled the inclusion criteria. We found that studies fusing imaging data with EHR are increasing and doubling from 2020 to 2021. In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. Specifically, early fusion was the most used technique in most applications for multimodal learning (22 out of 34 studies). We found that multimodality fusion models outperformed traditional single-modality models for the same task. Disease diagnosis and prediction were the most common clinical outcomes (reported in 20 and 10 studies, respectively) from a clinical outcome perspective. Neurological disorders were the dominant category (16 studies). From an AI perspective, conventional ML models were the most used (19 studies), followed by DL models (16 studies). Multimodal data used in the included studies were mostly from private repositories (21 studies). Through this scoping review, we offer new insights for researchers interested in knowing the current state of knowledge within this research field., (© 2022. The Author(s).)

Background: Advances in sports medicine, rehabilitation applications and diagnostics of neuromuscular disorders are based on the analysis of skeletal muscle contractions. Recently, medical imaging techniques have transformed the study of muscle contractions, by allowing identification of individual motor units' activity, within the whole studied muscle. However, appropriate image-based simulation models, which would assist the continued development of these new imaging methods are missing. This is mainly due to a lack of models that describe the complex interaction between tissues within a muscle and its surroundings, e.g., muscle fibres, fascia, vasculature, bone, skin, and subcutaneous fat. Herein, we propose a new approach to overcome this limitation., Methods: In this work, we propose to use deep learning to model the authentic intra-muscular skeletal muscle contraction pattern using domain-to-domain translation between in silico (simulated) and in vivo (experimental) image sequences of skeletal muscle contraction dynamics. For this purpose, the 3D cycle generative adversarial network (cycleGAN) models were evaluated on several hyperparameter settings and modifications. The results show that there were large differences between the spatial features of in silico and in vivo data, and that a model could be trained to generate authentic spatio-temporal features similar to those obtained from in vivo experimental data. In addition, we used difference maps between input and output of the trained model generator to study the translated characteristics of in vivo data., Results: This work provides a model to generate authentic intra-muscular skeletal muscle contraction dynamics that could be used to gain further and much needed physiological and pathological insights and assess and overcome limitations within the newly developed research field of neuromuscular imaging., (© 2022. The Author(s).)

The recent advancements in artificial intelligence (AI) and the Internet of Medical Things (IoMT) have opened new horizons for healthcare technology. AI models, however, rely on large data that must be shared with the centralized entity developing the model. Data sharing leads to privacy preservation and legal issues. Federated Learning (FL) enables the training of AI models on distributed data. Hence, a large amount of IoMT data can be put into use without the need for sharing the data. This paper presents the opportunities offered by FL for privacy preservation in IoMT data. With FL, the complicated dynamics and agreements for data-sharing can be avoided. Furthermore, it describes the use cases of FL in facilitating collaborative efforts to develop AI for COVID-19 diagnosis. Since handling data from multiple sites poses its challenges, the paper also highlights the critical challenges associated with FL developments for IoMT data. Addressing these challenges will lead to gaining maximum benefit from data-driven AI technologies in IoMT.

Background: Research on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood., Objective: This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code., Methods: A search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as "generative adversarial networks" and "GANs," and application keywords, such as "COVID-19" and "coronavirus." The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included., Results: This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51%) studies used chest X-ray images, while 21 (37%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4%) studies., Conclusions: Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to improve the training of the convolutional neural network (CNN) models trained for the diagnosis of COVID-19. In addition, GANs have also contributed to enhancing the CNNs' performance through the superresolution of the images and segmentation. This review also identified key limitations of the potential transformation of GAN-based methods in clinical applications., (©Hazrat Ali, Zubair Shah. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 29.06.2022.)

This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier, an ensemble model, and automated machine learning (AutoML) model. We compare the performance of all models in a five-fold cross-validation scheme using four evaluation measures. Experimental results demonstrate that the AutoML model outperformed individual and ensemble models in all evaluation measures.

We studied the suitability of Artificial Intelligence (AI)-based models to predict vaccine-critical tweets on the social media platform Twitter. We manually labeled a sample of 800 tweets as either "vaccine-critical" (i.e, anti-vaccine tweets, mentioned concerns related to vaccine safety and efficacy, and are against vaccine mandates or vaccine passports) or "other" (i.e., tweets that are neutral, report news, or are ambiguous) and used them to train and test AI-based models for automatically predicting vaccine-critical tweets. We fine-tuned two pre-trained deep learning-based language models, BERT and BERTweet, and implemented four classical AI-based models, Random Forest, Logistics Regression, Linear Support Vector Machines, and Multinomial Naïve Bayes. We evaluated these AI-based models using f1 score, accuracy, precision, and recall in three-fold cross-validation. We found that BERTweet outperformed all other models using these measures.

A four-level double lambda closed atomic configuration is considered to study the polarization plane rotation of the probe beam through cold as well as thermal Rb[Formula: see text] atomic medium by varying the spontaneously generated coherence (SGC). Magnetic field and strong coupling field are applied to the atomic configuration. The light-matter interaction leads to enhanced the magneto-optical rotation. The intensity of the applied fields plays promising role in the generation and enhancement of birefringence. It ultimately enhances the polarization plane rotation of the probe beam in the Doppler medium. In the presence of both SGC and Doppler broadening effects, the optical rotation and transmission of the weak light beam are modified and controlled as well, which have potential applications in magnetometery and laser frequency stabilization., (© 2022. The Author(s).)

For medical image analysis, there is always an immense need for rich details in an image. Typically, the diagnosis will be served best if the fine details in the image are retained and the image is available in high resolution. In medical imaging, acquiring high-resolution images is challenging and costly as it requires sophisticated and expensive instruments, trained human resources, and often causes operation delays. Deep learning based super resolution techniques can help us to extract rich details from a low-resolution image acquired using the existing devices. In this paper, we propose a new Generative Adversarial Network (GAN) based architecture for medical images, which maps low-resolution medical images to high-resolution images. The proposed architecture is divided into three steps. In the first step, we use a multi-path architecture to extract shallow features on multiple scales instead of single scale. In the second step, we use a ResNet34 architecture to extract deep features and upscale the features map by a factor of two. In the third step, we extract features of the upscaled version of the image using a residual connection-based mini-CNN and again upscale the feature map by a factor of two. The progressive upscaling overcomes the limitation for previous methods in generating true colors. Finally, we use a reconstruction convolutional layer to map back the upscaled features to a high-resolution image. Our addition of an extra loss term helps in overcoming large errors, thus, generating more realistic and smooth images. We evaluate the proposed architecture on four different medical image modalities: (1) the DRIVE and STARE datasets of retinal fundoscopy images, (2) the BraTS dataset of brain MRI, (3) the ISIC skin cancer dataset of dermoscopy images, and (4) the CAMUS dataset of cardiac ultrasound images. The proposed architecture achieves superior accuracy compared to other state-of-the-art super-resolution architectures., (© 2022. The Author(s).)

The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications., (© 2022. The Author(s).)

Artificial intelligence (AI) techniques can contribute to the early diagnosis of prostate cancer. Recently, there has been a sharp increase in the literature on AI techniques for prostate cancer diagnosis. This review article presents a summary of the AI methods that detect and diagnose prostate cancer using different medical imaging modalities. Following the PRISMA-ScR principle, this review covers 69 studies selected from 1441 searched papers published in the last three years. The application of AI methods reported in these articles can be divided into three broad categories: diagnosis, grading, and segmentation of tissues that have prostate cancer. Most of the AI methods leveraged convolutional neural networks (CNNs) due to their ability to extract complex features. Some studies also reported traditional machine learning methods, such as support vector machines (SVM), decision trees for classification, LASSO, and Ridge regression methods for features extraction. We believe that the implementation of AI-based tools will support clinicians to provide better diagnosis plans for prostate cancer.

U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy, X-rays, fundus, and computed tomography. The average gain achieved in IoU score is 1.5 ± 0.37% and in dice score is 0.9 ± 0.33% over UNET++, whereas, 4.21 ± 2.72 in IoU and 3.47 ± 1.89 in dice score over R2U-Net across different medical imaging segmentation datasets., Competing Interests: Conflict of interestThe authors declare no conflict of interest., (© The Author(s) 2022.)

Text detection in natural scene images for content analysis is an interesting task. The research community has seen some great developments for English/Mandarin text detection. However, Urdu text extraction in natural scene images is a task not well addressed. In this work, firstly, a new dataset is introduced for Urdu text in natural scene images. The dataset comprises of 500 standalone images acquired from real scenes. Secondly, the channel enhanced Maximally Stable Extremal Region (MSER) method is applied to extract Urdu text regions as candidates in an image. Two-stage filtering mechanism is applied to eliminate non-candidate regions. In the first stage, text and noise are classified based on their geometric properties. In the second stage, a support vector machine classifier is trained to discard non-text candidate regions. After this, text candidate regions are linked using centroid-based vertical and horizontal distances. Text lines are further analyzed by a different classifier based on HOG features to remove non-text regions. Extensive experimentation is performed on the locally developed dataset to evaluate the performance. The experimental results show good performance on test set images. The dataset will be made available for research use. To the best of our knowledge, the work is the first of its kind for the Urdu language and would provide a good dataset for free research use and serve as a baseline performance on the task of Urdu text extraction., Competing Interests: Hazrat Ali is an Academic Editor for PeerJ Computer Science., (© 2021 Ali et al.)

In calcareous soils, phosphorus (P) availability to plant is impaired due to the formation of insoluble complexes with calcium and magnesium. Therefore, this study was executed to compare the P use efficiency (PUE) of four different P sources [rock phosphate (RP), acidulated rock phosphate (ARP), single super phosphate (SSP) and di ammonium phosphate (DAP)] alone or pre-treated with organic amendments (farm yard manure (FYM) enriched compost, simple compost and humic acid (HA)) along with control in maize crop under calcareous soils. All treatments irrespective of P sources received 90 kg P 2 O 5 ha -1 . Phosphorus application regardless of its sources and combination with organic amendments significantly improved maize growth, yield as well as P uptake and PUE. Rock phosphate when applied alone was recorded inferior but its performance significantly improved with compost or its pre-addition with FYM and HA, that further enhanced upon acidulation. Maize grain yield increased by 21, 22.2, 67.9 and 94% with RP, ARP, ARP enriched compost and ARP+ compost respectively, over control. Similarly, PUE of DAP improved from 31.7 to 43.1 and 39 with sample and enriched compost correspondingly. Post-harvest soil and grain P were at par for SSP, ARP and DAP alone or in conjugation with organic amendments when averaged across the amendments. These results suggested that pretreatment of P sources with organic amendments is an economical and more feasible approach to improve maize yield and PUE. Moreover, on-farm acidulation of RP may give at par results with SSP and DAP with cheaper rate and hence recommended for P management in maize in alkaline calcareous soils., Competing Interests: The authors declare that they have no competing interests., (© 2021 Mussarat et al.)

Aim: To evaluate antimicrobial activity of extracellular metabolites (EMs) of endophytic fungal isolates (EFIs) from Azadirachta indica . Materials & methods: EFIs were identified by internal transcribed spacer (ITS) sequencing. Antimicrobial activity, and minimum inhibitor concentration (MIC) and minimum bactericidal concentration (MBC) were determined using agar diffusion and microdilution method, respectively. Results: Seventeen EFIs were isolated from different organs of A. indica . Eight of them were identified based on ITS sequencing. The EMs of EFIs inhibited the growth of six multidrug-resistant (MDR) bacterial superbugs and three phytopathogenic fungi. The MDR bacterial superbugs are resistant to six commercial antibiotics of different generations but susceptible to EMs of EFIs. The MIC (0.125-1.0 μg/μl), MBC (0.5-4.0 μg/μl) and minimum fungicidal concentration (1.0-4.0 μg/μl) of the EMs from EFIs are lower enough. Conclusion: The EMs of the EFIs have promising antimicrobial activity against MDR bacteria and phytopathogenic fungi.

In this work, we have studied new double perovskite materials, A 2 1+ B 2+ B 3+ X 6 1- , where A 2 1+ = Cs, B 2+ = Li, Na, B 3+ = Al, Ga, In, and X 6 1- . We used the all electron full-potential linearized augmented plane wave (FP-LAPW+lo) method within the framework of density functional theory. We used the mBJ approximation and WC-GGA as exchange-correlation functionals. We optimized the lattice constants with WC-GGA. Band structures were calculated with and without spin-orbit coupling (SOC). Further, band structures for Cs 2 LiGaBr 6 and Cs 2 NaGaBr 6 were calculated with SOC + mBJ to correct the band gap values with respect to experimental value. We obtained direct bandgaps at Γ-point of 1.966 eV for Cs 2 LiGaBr 6 and 1.762 eV for Cs 2 NaGaBr 6 , which are similar to the parent organic-inorganic perovskite (MAPI) CH 3 NH 3 PbI 3 ( E g = 1.6 eV). Total and partial density of states were analyzed to understand the orbital contribution of Cs, Na, Li, Ga and Br near the Fermi level. The optical properties in terms of real and imaginary ε , refractive index n , extinction coefficient k , optical conduction σ , absorption I , and reflectivity R were calculated. A study of the elastic and mechanical properties shows that both materials are thermodynamically stable. A stable, direct bandgap and a gap value close to those of MAPI make Cs 2 NaGaBr 6 a great competitor in the Pb-free hybrid perovskite solar cells world., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Contamination of freshwater ecosystems such as rivers with hazardous heavy metals is an environmental problem of public health concern. Accumulation of potentially toxic heavy metals in freshwater fish causes a potential health threat to their consumers including humans. The present research aimed to (1) investigate the accumulation of four potentially toxic heavy metals viz. Cr, Ni, Cd and Pb in muscle tissue of the commercially important freshwater fish Schizothorax plagiostomus at different sites of River Swat, River Panjkora and River Barandu in northern Pakistan (2) to compare gross metal accumulation in the fish samples by calculating metal pollution index (MPI) and (3) to assess potential risk to the health of the fish consumers. Samples were analyzed for the heavy metals by flame atomic absorption spectrophotometry (FAAS). Risk analysis showed that Ni risk was higher at River Panjkora and River Barandu, Pb risk was higher at River Swat and River Panjkora while Cd risk was quite low at all the three rivers.

MOFs are porous materials with adjustable porosity ensuing a tenable surface area and stability. MOFs consist of metal containing joint where organic ligands are linked with coordination bonding rendering a unique architecture favouring the diverse applications in attachment of enzymes, Chemical catalysis, Gases storage and separation, biomedicals. In the past few years immobilization of soluble enzymes on/in MOF has been the topic of interest for scientists working in diverse field. The activity of enzyme, reusability, storage, chemical and thermal stability, affinity with substrate can be greatly improved by immobilizing of enzyme on MOFs. Along with improvement in enzymes properties, the high loading of enzyme is also observed while using MOFs as immobilization support. In this review a detail study of immobilization on/in Metalorganic Frameworks (MOFs) have been described. Furthermore, strategies for the enzyme immobilization on MOFs and resulting in improved catalytic performance of immobilized enzymes have been reported., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Diseases have been recognized as the major obstacle to the shrimp ( Penaeus monodon ) and prawn ( Macrobrachium rosenbergii ) aquaculture production in Bangladesh. This study provides an assessment of shrimp and prawn diseases/syndromes, health management practices, and occupational health hazards associated with the handling of chemical and biological products to prevent and treat shrimp and prawn diseases. A survey was conducted using a semi-structured questionnaire with 380 shrimp and prawn farmers in the southwest of Bangladesh during February and June of 2016. The farms were categorized on the basis of the three cropping patterns: shrimp polyculture, prawn polyculture, and shrimp and prawn polyculture. Eight different diseases and/or symptoms were reported by the surveyed farmers. The white spot disease and the broken antenna and rostrum symptom were the most common in shrimp and prawn species, respectively. In total, 35 chemical and biological products (4 antibiotics, 15 disinfectants, 13 pesticides, 2 feed additives and probiotics) were used to treat and/or prevent diseases in the all farm categories. The major constraints for disease management were limited access to disease diagnostic service, inadequate product application information and lack of knowledge on better management practices. Handling chemicals and preparation of medicated feed with bare hands was identified as a potential occupational health hazard. This study suggests improvements in farmers' knowledge and skill in disease diagnostics and health management practices, and appropriate handling of potentially hazardous chemicals., (© 2018 The Authors.)

ARL13B encodes for the ADP-ribosylation factor-like 13B GTPase, which is required for normal cilia structure and Sonic hedgehog (Shh) signaling. Disruptions in cilia structure or function lead to a class of human disorders called ciliopathies. Joubert syndrome is characterized by a wide spectrum of symptoms, including a variable degree of intellectual disability, ataxia, and ocular abnormalities. Here we report a novel homozygous missense variant c.[223G>A] (p.(Gly75Arg) in the ARL13B gene, which was identified by whole-exome sequencing of a trio from a consanguineous family with multiple-affected individuals suffering from intellectual disability, ataxia, ocular defects, and epilepsy. The same variant was also identified in a second family. We saw a striking difference in the severity of ataxia between affected male and female individuals in both families. Both ARL13B and ARL13B-c.[223G>A] (p.(Gly75Arg) expression rescued the cilia length and Shh defects displayed by Arl13b hennin (null) cells, indicating that the variant did not disrupt either ARL13B function. In contrast, ARL13B-c.[223G>A] (p.(Gly75Arg) displayed a marked loss of ARL3 guanine nucleotide-exchange factor activity, with retention of its GTPase activities, highlighting the correlation between its loss of function as an ARL3 guanine nucleotide-exchange factor and Joubert syndrome.

Penicillium chrysogenum, which lacks the roqA gene, processes synthetic, exogenously added histidyltryptophanyldiketopiperazine (HTD) to yield a set of roquefortine-based secondary metabolites also produced by the wild-type strain. Feeding a number of synthetic HTD analogues to the ΔroqA strain gives rise to the biosynthesis of a number of new roquefortine D derivatives, depending on the nature of the synthetic HTD added. Besides delivering semisynthetic roquefortine analogues, the mutasynthesis studies presented here also shed light on the substrate preferences and molecular mechanisms employed by the roquefortine C/D biosynthesis gene cluster, knowledge that may be tapped for the future development of more complex semisynthetic roquefortine-based secondary metabolites., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)