Isolation and Characterization of Bacterial Endophytes from Small Nodules of Field-Grown Peanut.

It is evident that legume root nodules can accommodate rhizobial and non-rhizobial bacterial endophytes. Our recent nodule microbiome study in peanuts described that small nodules can harbor diverse bacterial endophytes. To understand their functional role, we isolated 87 indigenous endophytes from small nodules of field-grown peanut roots and characterized them at molecular, biochemical, and physiological levels. The amplified 16S rRNA genes and phylogenetic analysis of these isolates revealed a wide variety of microorganisms related to the genera Bacillus, Burkholderia, Enterobacter, Herbaspirillum, Mistsuaria, Pantoea, Pseudomonas, and Rhizobia. It was observed that 37% (100% identity) and 56% (>99% identity) of the isolates matched with the amplified sequence variants (ASVs) from our previous microbiome study. All of these isolates were tested for stress tolerance (high temperature, salinity, acidic pH) and phosphate (P) solubilization along with ammonia (NH3), indole-3-acetic acid (IAA), 1-aminocyclopropane-1-carboxylate deaminase (ACCD), and siderophore production. The majority (78%) of the isolates were found to be halotolerant, thermotolerant, and acidophilic, and a few of them showed a significant positive response to the production of IAA, NH3, siderophore, ACCD, and P-solubilization. To evaluate the plant growth promotion (PGP) activity, plant and nodulation assays were performed in the growth chamber conditions for the selected isolates from both the non-rhizobial and rhizobial groups. However, these isolates appeared to be non-nodulating in the tested conditions. Nonetheless, the isolates 2 (Pantoea), 17 (Burkholderia), 21 (Herbaspirillum), 33o (Pseudomonas), and 77 (Rhizobium sp.) showed significant PGP activity in terms of biomass production. Our findings indicate that these isolates have potential for future biotechnological applications through the development of biologicals for sustainable crop improvement.

3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images.

This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D quantum-inspired self-supervised tensor neural network (3-D-QNet). The underlying architecture of 3-D-QNet is composed of a trinity of volumetric layers, viz., input, intermediate, and output layers interconnected using an S -connected third-order neighborhood-based topology for voxelwise processing of 3-D medical image data, suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation of tensor decomposition in quantum formalism leads to faster convergence of network operations to preclude the inherent slow convergence problems faced by the classical supervised and self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3-D-QNet is tailored and tested on the BRATS 2019 Brain MR image dataset and the Liver Tumor Segmentation Challenge (LiTS17) dataset extensively in our experiments. The 3-D-QNet has achieved promising dice similarity (DS) as compared with the time-intensive supervised convolutional neural network (CNN)-based models, such as 3-D-UNet, voxelwise residual network (VoxResNet), Dense-Res-Inception Net (DRINet), and 3-D-ESPNet, thereby showing a potential advantage of our self-supervised shallow network on facilitating semantic segmentation.

A deep fuzzy model for diagnosis of COVID-19 from CT images.

From early 2020, a novel coronavirus disease pneumonia has shown a global "pandemic" trend at an extremely fast speed. Due to the magnitude of its harm, it has become a major global public health event. In the face of dramatic increase in the number of patients with COVID-19, the need for quick diagnosis of suspected cases has become particularly critical. Therefore, this paper constructs a fuzzy classifier, which aims to detect infected subjects by observing and analyzing the CT images of suspected patients. Firstly, a deep learning algorithm is used to extract the low-level features of CT images in the COVID-CT dataset. Subsequently, we analyze the extracted feature information with attribute reduction algorithm to obtain features with high recognition. Then, some key features are selected as the input for the fuzzy diagnosis model to the training model. Finally, several images in the dataset are used as the test set to test the trained fuzzy classifier. The obtained accuracy rate is 94.2%, and the F1-score is 93.8%. Experimental results show that, compared with the deep learning diagnosis methods widely used in medical image analysis, the proposed fuzzy model improves the accuracy and efficiency of diagnosis, which consequently helps to curb the spread of COVID-19.

Soil carbon sequestration, greenhouse gas emissions, and water pollution under different tillage practices.

Tillage is a common agricultural practice and a critical component of agricultural systems that is frequently employed worldwide in croplands to reduce climatic and soil restrictions while also sustaining various ecosystem services. Tillage can affect a variety of soil-mediated processes, e.g., soil carbon sequestration (SCS) or depletion, greenhouse gas (GHG) (CO2, CH4, and N2O) emission, and water pollution. Several tillage practices are in vogue globally, and they exhibit varied impacts on these processes. Hence, there is a dire need to synthesize, collate and comprehensively present these interlinked phenomena to facilitate future researches. This study deals with the co-benefits and trade-offs produced by several tillage practices on SCS and related soil properties, GHG emissions, and water quality. We hypothesized that improved tillage practices could enable agriculture to contribute to SCS and mitigate GHG emissions and leaching of nutrients and pesticides. Based on our current understanding, we conclude that sustainable soil moisture level and soil temperature management is crucial under different tillage practices to offset leaching loss of soil stored nutrients/pesticides, GHG emissions and ensuring SCS. For instance, higher carbon dioxide (CO2) and nitrous oxide (N2O) emissions from conventional tillage (CT) and no-tillage (NT) could be attributed to the fluctuations in soil moisture and temperature regimes. In addition, NT may enhance nitrate (NO3-) leaching over CT because of improved soil structure, infiltration capacity, and greater water flux, however, suggesting that the eutrophication potential of NT is high. Our study indicates that the evaluation of the eutrophication potential of different tillage practices is still overlooked. Our study suggests that improving tillage practices in terms of mitigation of N2O emission and preventing NO3- pollution may be sustainable if nitrification inhibitors are applied.

Soil carbon sequestration - An interplay between soil microbial community and soil organic matter dynamics.

Soil carbon sequestration (SCS) refers to the uptake of carbon (C) containing substances from the atmosphere and its storage in soil C pools. Soil microbial community (SMC) play a major role in C cycling and their activity has been considered as the main driver of differences in the potential to store C in soils. The composition of the SMC is crucial for the maintenance of soil ecosystem services, as the structure and activity of SMC also regulates the turnover and delivery of nutrients, as well as the rate of decomposition of soil organic matter (SOM). Quantifying the impact of agricultural practices on both SMC and SCS is key to improve sustainability of soil management. Hence, we discuss the impact of farming practices improving SCS by altering SMC, SOM, and soil aggregates, unraveling their inter-and intra-relationships. Using quantitative and process driven insights from 197 peer-reviewed publications leads to the conclusion that the net benefits from agricultural management to improve SCS would not be sustainable if we overlook the role of soil microbial community. Reintroduction of the decayed microbial community to agricultural soils is crucial for enhancing long-term C storage potential of soils and stabilize them over time. The interactions among SMC, SOM, soil aggregates, and agricultural activities still require more knowledge and research to understand their full contribution to the SCS.

Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation.

Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model exploiting the sophisticated three-level qutrit-inspired quantum information system, referred to as quantum fully self-supervised neural network (QFS-Net), is presented for automated segmentation of brain magnetic resonance (MR) images. The QFS-Net model comprises a trinity of a layered structure of qutrits interconnected through parametric Hadamard gates using an eight-connected second-order neighborhood-based topology. The nonlinear transformation of the qutrit states allows the underlying quantum neural network model to encode the quantum states, thereby enabling a faster self-organized counterpropagation of these states between the layers without supervision. The suggested QFS-Net model is tailored and extensively validated on the Cancer Imaging Archive (TCIA) dataset collected from the Nature repository. The experimental results are also compared with state-of-the-art supervised (U-Net and URes-Net architectures) and the self-supervised QIS-Net model and its classical counterpart. Results shed promising segmented outcomes in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources. The proposed QFS-Net is also investigated on natural gray-scale images from the Berkeley segmentation dataset and yields promising outcomes in segmentation, thereby demonstrating the robustness of the QFS-Net model.

A paradigm shift to CO2 sequestration to manage global warming - With the emphasis on developing countries.

Global land use changes that tend to satisfy the food needs of augmenting population is provoking agricultural soils to act as a carbon (C) source rather than sink. Agricultural management practices are crucial to offset the anthropogenic C emission; hence, Carbon sequestration (CS) in agriculture is a viable option for reversing this cycle, but it is based on hypotheses that must be questioned in order to contribute to the development of new agricultural techniques. This review summarizes a global perspective focusing on 5 developing countries (DC) (Bangladesh, Brazil, Argentina, Nigeria and Mexico) because of their importance on global C budget and on the agricultural sector as well as the impact produced by several global practices such as tillage, agroforestry systems, silvopasture, 4p1000 on CO2 sequestration. We also discussed about global policies regarding CS and tools available to measure CS. We found that among all practices agroforestry deemed to be the most promising approach and conversion from pasture to agroforestry will be favorable to both farmers and in changing climate, (e.g., agroforestry systems can generate 725 Euroeq C credit in EU) while some strategies (e.g. no-tillage) supposed to be less promising and over-hyped. In terms of conservative tillage (no-, reduced-, and minimal tillage systems), global and DC's land use increased. However, the impact of no-tillage is ambiguous since the beneficial impact is only limited to top soil (0-10 cm) as opposed to conventional mechanisms. Grasses, cereals and cover crops have higher potential of CS in their soils. While the 4p1000 initiative appears to be successful in certain areas, further research is needed to validate this possible mode of CS. Furthermore, for effective policy design and implementation to obtain more SOC stock, we strongly emphasize to include farmers globally as they are the one and only sustainable driver, hence, government and associated authorities should take initiatives (e.g., stimulus incentives, C credits) to form C market and promote C plantings. Otherwise, policy failure may occur. Moreover, to determine the true effect of these activities or regulations on CS, we must concurrently analyze SOC stock adjustments using models or direct measurements. Above all, SOC is the founding block of sustainable agriculture and inextricably linked with food security. Climate-smart managing of agriculture is very crucial for a massive SOC stock globally especially in DC's.

Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.

COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.

Statistical-based system combination approach to gain advantages over different machine translation systems.

Every machine translation system has some advantages. We propose an improved statistical system combination approach to achieve the advantages of existing machine translation systems. The primary task is to score all the phrases of the outputs of different machine translation systems selected for combination. Three steps are involved in the proposed statistical system combination approach, viz., alignment, decoding, and scoring. Pair alignment is done in the first step to prevent duplication so that only a single phrase is chosen from various phrases containing the same information. Thus the alignment and scoring strategy are implemented in our approach. Hypotheses are built in the second step. In the third step, we calculate the scores for all the hypotheses. The hypothesis with the highest score is chosen as the final translated output. Wrong scoring can mislead to identify the best part from different systems. It may be noted that a particular phrase may appear in various ways in different translations. To resolve the challenges, we incorporate WordNet in the alignment phase and word2vec in the scoring phase along with the existing factors. We find that the system combination model using WordNet and word2vec injection improves the machine translation accuracy. In this work, we have merged three systems viz., Hierarchical machine translation system, Bing Microsoft Translate, and Google Translate. The broad tests of translation on eight language pairs with benchmark datasets demonstrate that the proposed system achieves better quality than the individual systems and the state-of-the-art system combination models.

Rosai-Dorfman Disease of the Breast With Variable IgG4+ Plasma Cells: A Diagnostic Mimicker of Other Malignant and Reactive Entities.

Rosai-Dorfman disease (RDD) is an uncommon disorder, characterized by an atypical expansion of histiocytes which classically shows emperipolesis and immunoreactivity with S-100 protein. RDD affects the lymph nodes as well as extranodal sites; however, RDD of the breast is exceptionally rare. Herein, we describe the histopathologic features of 22 cases of RDD occurring in the breast, with an emphasis on the differential diagnosis. All cases were notable for an exuberant lymphocytic infiltrate with and without germinal center formation, and the majority (19/22) showed numerous plasma cells: 5 to 132/high-power field (HPF). IgG and IgG4 immunohistochemical stains were available for 13 cases; in no instance were criteria for IgG4-related sclerosing disease met, though in a single case the IgG4/IgG ratio was increased to 25%. Sclerosis was present in the majority of cases (18/22), and was frequently prominent. RDD cells showing emperipolesis were present in all cases (22/22), and ranged from rare (<1/50 HPF) to numerous (>50/50 HPF). Two of the cases in our series were initially misdiagnosed as inflammatory myofibroblastic tumor and plasma cell mastitis with granulomatous inflammation. As emperipolesis can be indistinct, the presence of stromal fibrosis and a prominent lymphoplasmacytic inflammatory infiltrate should prompt a careful search for the characteristic histiocytes, which can be aided by the use of S-100 immunohistochemistry.

Development of an interactive anatomical three-dimensional eye model.

The discrete anatomy of the eye's intricate oculomotor system is conceptually difficult for novice students to grasp. This is problematic given that this group of muscles represents one of the most common sites of clinical intervention in the treatment of ocular motility disorders and other eye disorders. This project was designed to develop a digital, interactive, three-dimensional (3D) model of the muscles and cranial nerves of the oculomotor system. Development of the 3D model utilized data from the Visible Human Project (VHP) dataset that was refined using multiple forms of 3D software. The model was then paired with a virtual user interface in order to create a novel 3D learning tool for the human oculomotor system. Development of the virtual eye model was done while attempting to adhere to the principles of cognitive load theory (CLT) and the reduction of extraneous load in particular. The detailed approach, digital tools employed, and the CLT guidelines are described herein.