Surfactant effect on energy storage performance of hydrothermally synthesized Ni3V2O8.

We report a study to improve the ternary oxide Ni3V2O8's electrochemical energy storage capabilities through correct surfactanization during hydrothermal synthesis. In this study, Ni3V2O8nanomaterials were synthesized in three different forms: one with a cationic surfactant (CTAB), one with an anionic surfactant (SLS), and one without any surfactant. FESEM study reveals that all the synthesized Ni3V2O8nanomaterials had a small stone-like morphology. The electrochemical study showed that anionic surfactant-assisted Ni3V2O8(NVSLS) had a maximum of 972 F g-1specific capacitance at 1 A g-1current density, whereas cationic surfactant-assisted Ni3V2O8(NVCTAB) had the lowest specific capacitance of 162 F g-1. The specific capacitance and the capacitance retention of the NVSLS(85% after 4000 cycles) based electrode was much better than that of the NVCTAB(76% after 4000 cycles) based electrode. The improved energy storage properties of the NVSLSelectrode are attributed to its high diffusion coefficient, high surface area, and enriched elemental nickel, as compared to the NVCTABelectrode. All these excellent electrochemical properties of NVSLSelectrode indicates their potential usage in asymmetric supercapacitor application.

Epigenetics governs senescence.

Petal is one of the most esthetic and essential parts of a flower that fascinates the pollinators to enhance pollination. Petal senescence is a highly controlled and organized natural phenomenon assisted by phytohormones and gene regulation. It is an inelastically programmed event preceding to which petals give rise to color and scent that captivate pollinators, representing a flower's maturity for sexual reproduction. Till today, many genes involved in the petal senescence through genetic as well as epigenetic changes in response to hormones have been identified. In most of the species, petal senescence is controlled by ethylene, whereas others are independent of this hormone. It has also been proved that the increase in the carbohydrate contents like mannitol, inositol and trehalose delayed the senescence in tulips and Gladiolus. An increased sugar content prevents the biosynthesis of EIN3-like mRNA and further upregulates several senescence correlated genes. A wide range of different transcription factors as well as regulators are disparately expressed in ethylene insensitive and ethylene sensitive petal senescence. DcHB30, a downregulating factor, which upon linking physically to DcWRKY75 leads to the upregulation of ethylene promoting petal senescence. Here we describe the role of ethylene in petal senescence through epigenetic changes. Studies show that ethylene causes petal senescence through epigenetic changes. Feng et al. (Plant Physiol 192:546-564, 2023) observed that ARABIDOPSIS HOMOLOG OF TRITHORAX1 (DcATX1) promotes trimethylation of histone 3 (H3) at 4th lysine (H3K4me3) in Carnation. H3K4me3 further stimulates the expression of genes of ethylene biosynthesis and senescence, leading to senescence in Carnation.

Peroxynitrite is essential for aerenchyma formation in rice roots under waterlogging conditions.

MAIN CONCLUSION: In this study, we report that peroxynitrite is necessary for ethylene-mediated aerenchyma formation in rice roots under waterlogging conditions. Plants under waterlogging stress face anoxygenic conditions which reduce their metabolism and induce several adaptations. The formation of aerenchyma is of paramount importance for the survival of plants under waterlogging conditions. Though some studies have shown the involvement of ethylene in aerenchyma formation under waterlogging conditions, the implication of peroxynitrite (ONOO-) in such a developmental process remains elusive. Here, we report an increase in aerenchyma formation in rice roots exposed to waterlogging conditions under which the number of aerenchyma cells and their size was further enhanced in response to exogenous ethephon (a donor of ethylene) or SNP (a donor of nitric oxide) treatment. Application of epicatechin (a peroxynitrite scavenger) to waterlogged plants inhibited the aerenchyma formation, signifying that ONOO- might have a role in aerenchyma formation. Interestingly, epicatechin and ethephon co-treated waterlogged plants were unable to form aerenchyma, indicating the necessity of ONOO- in ethylene-mediated aerenchyma formation under waterlogging conditions. Taken together, our results highlight the role of ONOO- in ethylene-mediated aerenchyma formation in rice and could be used in the future to develop waterlogging stress-tolerant varieties of rice.

GABA: a key player of abiotic stress regulation.

Abiotic stress is considered as the main culprit for reduction of global food production. Recent studies have reported GABA as a major regulator of abiotic stress and thus opening new avenues in research on emerging roles of GABA in abiotic stress acclimation in plants.

Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification.

COVID-19 has become a pandemic for the entire world, and it has significantly affected the world economy. The importance of early detection and treatment of the infection cannot be overstated. The traditional diagnosis techniques take more time in detecting the infection. Although, numerous deep learning-based automated solutions have recently been developed in this regard, nevertheless, the limitation of computational and battery power in resource-constrained devices makes it difficult to deploy trained models for real-time inference. In this paper, to detect the presence of COVID-19 in CT-scan images, an important weights-only transfer learning method has been proposed for devices with limited runt-time resources. In the proposed method, the pre-trained models are made point-of-care devices friendly by pruning less important weight parameters of the model. The experiments were performed on two popular VGG16 and ResNet34 models and the empirical results showed that pruned ResNet34 model achieved 95.47% accuracy, 0.9216 sensitivity, 0.9567 F-score, and 0.9942 specificity with 41.96% fewer FLOPs and 20.64% fewer weight parameters on the SARS-CoV-2 CT-scan dataset. The results of our experiments showed that the proposed method significantly reduces the run-time resource requirements of the computationally intensive models and makes them ready to be utilized on the point-of-care devices.

Ethylene Renders Silver Nanoparticles Stress Tolerance in Rice Seedlings by Regulating Endogenous Nitric Oxide Accumulation.

Developments in the field of nanotechnology over the past few years have increased the prevalence of silver nanoparticles (AgNPs) in the environment, resulting in increased exposure of plants to AgNPs. Recently, various studies have reported the effect of AgNPs on plant growth at different concentrations. However, identifying the mechanisms and signaling molecules involved in plant responses against AgNPs stress is crucial to find an effective way to deal with the phytotoxic impacts of AgNPs on plant growth and development. Therefore, this study was envisaged to investigate the participation of ethylene in mediating the activation of AgNPs stress tolerance in rice (Oryza sativa L.) through a switch that regulates endogenous nitric oxide (NO) accumulation. Treatment of AgNPs alone hampered the growth of rice seedlings due to severe oxidative stress as a result of decline in sulfur assimilation, glutathione (GSH) biosynthesis and alteration in the redox status of GSH. These results are also accompanied by the higher endogenous NO level. However, addition of ethephon (a donor of ethylene) reversed the AgNP-induced effects. Though the application of silicon nanoparticles (SiNPs) alone promoted the growth of rice seedlings but, interestingly their application in combination with AgNPs enhanced the AgNP-induced toxicity in the seedlings through the same routes as exhibited in the case of AgNPs alone treatment. Interestingly, addition of ethephon reversed the negative effects of SiNPs under AgNPs stress. These results suggest that ethylene might act as a switch to regulate the level of endogenous NO, which in turn could be associated with AgNPs stress tolerance in rice. Furthermore, the results also indicated that addition of l-NG-nitro arginine methyl ester (l-NAME) (an inhibitor of endogenous NO synthesis) also reversed the toxic effects of SiNPs together with AgNPs, further suggesting that the low level of endogenous NO was associated with AgNPs stress tolerance. Overall, the results indicate that the low level of endogenous NO triggers AgNPs stress tolerance, while high level leads to AgNPs toxicity by regulating sulfur assimilation, GSH biosynthesis, redox status of GSH and oxidative stress markers. The results revealed that ethylene might act as a switch for regulating AgNPs stress in rice seedlings by controlling endogenous NO accumulation.

Adaptive windowing based recurrent neural network for drift adaption in non-stationary environment.

In today's digital era, many applications generate massive data streams that must be sequenced and processed immediately. Therefore, storing large amounts of data for analysis is impractical. Now, this infinite amount of evolving data confronts concept drifts in data stream classification. Concept drift is a phenomenon in which the distribution of input data or the relationship between input data and target label changes over time. If the drifts are not addressed, the learning model's performance suffers. Non-stationary data streams must be processed as they arrive, and neural networks' built-in capabilities aid in the processing of huge non-stationary data streams. We proposed an adaptive windowing approach based on a gated recurrent unit, a variant of the recurrent neural network incrementally trained on incoming data (for the real-world airline and synthetic Streaming Ensemble Algorithm (SEA) datasets), and employed elastic weight consolidation with the Fisher information matrix to prevent forgetting. Unlike the traditional fixed window methodology, the proposed model dynamically increases the window size if the prediction is correct and reduces it if drifts occur. As a result, an adaptive recurrent neural network model can adapt to changes in the non-stationary data stream and provide consistent performance. Moreover, the findings revealed that on the airline and the SEA dataset, the proposed model outperforms state-of-the-art methods by achieving 67.74% and 91.70% accuracy, respectively. Further, the results demonstrated that the proposed model has a better accuracy of 3.6% and 1.6% for the SEA and the airline dataset, respectively.

Yoga Pose Estimation and Feedback Generation Using Deep Learning.

Yoga is a 5000-year-old practice developed in ancient India by the Indus-Sarasvati civilization. The word yoga means deep association and union of mind with the body. It is used to keep both mind and body in equilibration in all flip-flops of life by means of asana, meditation, and several other techniques. Nowadays, yoga has gained worldwide attention due to increased stress levels in the modern lifestyle, and there are numerous methods or resources for learning yoga. Yoga can be practiced in yoga centers, through personal tutors, and can also be learned on one's own with the help of the Internet, books, recorded clips, etc. In fast-paced lifestyles, many people prefer self-learning because the abovementioned resources might not be available all the time. But in self-learning, one may not find an incorrect pose. Incorrect posture can be harmful to one's health, resulting in acute pain and long-term chronic concerns. In this paper, deep learning-based techniques are developed to detect incorrect yoga posture. With this method, the users can select the desired pose for practice and can upload recorded videos of their yoga practice pose. The user pose is sent to train models that output the abnormal angles detected between the actual pose and the user pose. With these outputs, the system advises the user to improve the pose by specifying where the yoga pose is going wrong. The proposed method was compared to several state-of-the-art methods, and it achieved outstanding accuracy of 0.9958 while requiring less computational complexity.

RIPK: a crucial ROS signaling component in plants.

Reactive oxygen species (ROS) are involved in plant development and stress acclimation. Recently, Li et al. reported that ROS production is controlled by receptor-like cytoplasmic kinase (RLCK)-mediated respiratory burst oxidase homolog D (RBOHD) phosphorylation, which subsequently regulates pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI), damage-associated molecular pattern (DAMP)-triggered immunity (DTI), effector-triggered immunity (ETI), and systemic acquired resistance (SAR) in plants, thus enhancing our understanding of biotic stress tolerance.

Enhanced Network Intrusion Detection System.

A reasonably good network intrusion detection system generally requires a high detection rate and a low false alarm rate in order to predict anomalies more accurately. Older datasets cannot capture the schema of a set of modern attacks; therefore, modelling based on these datasets lacked sufficient generalizability. This paper operates on the UNSW-NB15 Dataset, which is currently one of the best representatives of modern attacks and suggests various models. We discuss various models and conclude our discussion with the model that performs the best using various kinds of evaluation metrics. Alongside modelling, a comprehensive data analysis on the features of the dataset itself using our understanding of correlation, variance, and similar factors for a wider picture is done for better modelling. Furthermore, hypothetical ponderings are discussed for potential network intrusion detection systems, including suggestions on prospective modelling and dataset generation as well.

Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering.

This paper proposes a multivariate and online prediction of stock prices via the paradigm of kernel adaptive filtering (KAF). The prediction of stock prices in traditional classification and regression problems needs independent and batch-oriented nature of training. In this article, we challenge this existing notion of the literature and propose an online kernel adaptive filtering-based approach to predict stock prices. We experiment with ten different KAF algorithms to analyze stocks' performance and show the efficacy of the work presented here. In addition to this, and in contrast to the current literature, we look at granular level data. The experiments are performed with quotes gathered at the window of one minute, five minutes, ten minutes, fifteen minutes, twenty minutes, thirty minutes, one hour, and one day. These time windows represent some of the common windows frequently used by traders. The proposed framework is tested on 50 different stocks making up the Indian stock index: Nifty-50. The experimental results show that online learning and KAF is not only a good option, but practically speaking, they can be deployed in high-frequency trading as well.

Implication of nitric oxide and hydrogen sulfide signalling in alleviating arsenate stress in rice seedlings.

Nitric oxide (NO) and hydrogen sulfide (H2S) since their discovery have proven to be game changing molecules in alleviating abiotic stress. They individually play role in plant stress management while the pathways of stress regulation through their crosstalk remain elusive. The current study focuses on investigating the interplay of NO and H2S signalling in the amelioration of arsenate As(V) toxicity in rice seedlings and managing its growth, photosynthesis, sucrose and proline metabolism. Results show that As(V) exposure declined fresh weight (biomass) due to induced cell death in root tips. Moreover, a diminished RuBisCO activity, decline in starch content with high proline dehydrogenase activity and increased total soluble sugars content was observed which further intensified in the presence of Nω-nitro-L-arginine methyl ester hydrochloride (L-NAME, an inhibitor of nitric oxide synthase-like activity), and DL-propargylglycine (PAG, an inhibitor of cysteine desulfhydrase activity). These results correlate with lower endogenous level of NO and H2S. Addition of L-NAME increased As(V) toxicity. Interestingly, addition of SNP reverses effect of L-NAME suggesting that endogenous NO has a role in mitigating As(V) toxicity. Similarly, exogenous H2S also significantly alleviated As(V) stress, while PAG further stimulated As(V) toxicity. Furthermore, application of H2S in the presence of L - NAME and NO in the presence of PAG could still mitigate As(V) toxicity, suggesting that endogenous NO and H2S could independently mitigate As(V) stress.

Nitric oxide and hydrogen sulfide: an indispensable combination for plant functioning.

Nitric oxide (NO) and hydrogen sulfide (H2S) are gasotransmitters, which are involved in almost all plant physiological and stress-related processes. With its antioxidant regulatory properties, NO on its own ameliorates plant stress, while H2S, a foul-smelling gas, has differential effects. Recent studies have shown that these signaling molecules are involved in intertwined pathway networks. This is due to the contrasting effects of NO and H2S depending on cell type, subcellular compartment, and redox status, as well as the flux and dosage of NO and H2S in different plant species and cellular contexts. Here, we provide a comprehensive review of the complex networks of these molecules, with particular emphasis on root development, stomatal movement, and plant cell death.

Staphylococcal Pyopericardium: A Rare and Fatal Complication Following a Common Viral Disease.

How to cite this article: Sarma U, Mishra V, Aier I, et al. Staphylococcal Pyopericardium: A Rare and Fatal Complication Following a Common Viral Disease. Indian J Crit Care Med 2021;25(6):739-741.

A transfer learning with structured filter pruning approach for improved breast cancer classification on point-of-care devices.

BACKGROUND AND OBJECTIVE: A significant progress has been made in automated medical diagnosis with the advent of deep learning methods in recent years. However, deploying a deep learning model for mobile and small-scale, low-cost devices is a major bottleneck. Further, breast cancer is more prevalent currently, and ductal carcinoma being its most common type. Although many machine/deep learning methods have already been investigated, still, there is a need for further improvement. METHOD: This paper proposes a novel deep convolutional neural network (CNN) based transfer learning approach complemented with structured filter pruning for histopathological image classification, and to bring down the run-time resource requirement of the trained deep learning models. In the proposed method, first, the less important filters are pruned from the convolutional layers and then the pruned models are trained on the histopathological image dataset. RESULTS: We performed extensive experiments using three popular pre-trained CNNs, VGG19, ResNet34, and ResNet50. With VGG19 pruned model, we achieved an accuracy of 91.25% outperforming earlier methods on the same dataset and architecture while reducing 63.46% FLOPs. Whereas, with the ResNet34 pruned model, the accuracy increases to 91.80% with 40.63% fewer FLOPs. Moreover, with the ResNet50 model, we achieved an accuracy of 92.07% with 30.97% less FLOPs. CONCLUSION: The experimental results reveal that the pre-trained model's performance complemented with filter pruning exceeds original pre-trained models. Another important outcome of the research is that the pruned model with reduced resource requirements can be deployed in point-of-care devices for automated diagnosis applications with ease.

Tocilizumab plus standard care versus standard care in patients in India with moderate to severe COVID-19-associated cytokine release syndrome (COVINTOC): an open-label, multicentre, randomised, controlled, phase 3 trial.

BACKGROUND: Global randomised controlled trials of the anti-IL-6 receptor antibody tocilizumab in patients admitted to hospital with COVID-19 have shown conflicting results but potential decreases in time to discharge and burden on intensive care. Tocilizumab reduced progression to mechanical ventilation and death in a trial population enriched for racial and ethnic minorities. We aimed to investigate whether tocilizumab treatment could prevent COVID-19 progression in the first multicentre randomised controlled trial of tocilizumab done entirely in a lower-middle-income country. METHODS: COVINTOC is an open-label, multicentre, randomised, controlled, phase 3 trial done at 12 public and private hospitals across India. Adults (aged ≥18 years) admitted to hospital with moderate to severe COVID-19 (Indian Ministry of Health grading) confirmed by positive SARS-CoV-2 PCR result were randomly assigned (1:1 block randomisation) to receive tocilizumab 6 mg/kg plus standard care (the tocilizumab group) or standard care alone (the standard care group). The primary endpoint was progression of COVID-19 (from moderate to severe or from severe to death) up to day 14 in the modified intention-to-treat population of all participants who had at least one post-baseline assessment for the primary endpoint. Safety was assessed in all randomly assigned patients. The trial is completed and registered with the Clinical Trials Registry India (CTRI/2020/05/025369). FINDINGS: 180 patients were recruited between May 30, 2020, and Aug 31, 2020, and randomly assigned to the tocilizumab group (n=90) or the standard care group (n=90). One patient randomly assigned to the standard care group inadvertently received tocilizumab at baseline and was included in the tocilizumab group for all analyses. One patient randomly assigned to the standard care group withdrew consent after the baseline visit and did not receive any study medication and was not included in the modified intention-to-treat population but was still included in safety analyses. 75 (82%) of 91 in the tocilizumab group and 68 (76%) of 89 in the standard care group completed 28 days of follow-up. Progression of COVID-19 up to day 14 occurred in eight (9%) of 91 patients in the tocilizumab group and 11 (13%) of 88 in the standard care group (difference -3·71 [95% CI -18·23 to 11·19]; p=0·42). 33 (36%) of 91 patients in the tocilizumab group and 22 (25%) of 89 patients in the standard care group had adverse events; 18 (20%) and 15 (17%) had serious adverse events. The most common adverse event was acute respiratory distress syndrome, reported in seven (8%) patients in each group. Grade 3 adverse events were reported in two (2%) patients in the tocilizumab group and five (6%) patients in the standard care group. There were no grade 4 adverse events. Serious adverse events were reported in 18 (20%) patients in the tocilizumab group and 15 (17%) in the standard care group; 13 (14%) and 15 (17%) patients died during the study. INTERPRETATION: Routine use of tocilizumab in patients admitted to hospital with moderate to severe COVID-19 is not supported. However, post-hoc evidence from this study suggests tocilizumab might still be effective in patients with severe COVID-19 and so should be investigated further in future studies. FUNDING: Medanta Institute of Education and Research, Roche India, Cipla India, and Action COVID-19 India.

Regulation of ascorbate-glutathione cycle by exogenous nitric oxide and hydrogen peroxide in soybean roots under arsenate stress.

The role of nitric oxide (NO) and hydrogen peroxide (H2O2) is well known for regulating plant abiotic stress responses. However, underlying mechanisms are still poorly understood. Therefore, the present study investigated the involvement of NO and H2O2 signalling in the regulation of arsenate toxicity (AsV) in soybean roots employing a pharmacological approach. Results show that AsV toxicity declined root length and biomass due to greater As accumulation in the cell wall and cellular organelles. Arsenate induced cell death due to enhanced levels of reactive oxygen species, lipid and protein oxidation and down-regulation in ascorbate-glutathione cycle and redox states of ascorbate and glutathione. These results correlate with lower endogenous level of NO. Interestingly, addition of L-NAME increased AsV toxicity. However, addition of SNP reverses effect of L-NAME, suggesting that endogenous NO has a role in mitigating AsV toxicity. Exogenous H2O2 also demonstrated capability of alleviating AsV stress, while NAC reversed the protective effect of H2O2. Furthermore, DPI application further increased AsV toxicity, suggesting that endogenous H2O2 is also implicated in mitigating AsV stress. SNP was not able to mitigate AsV toxicity in the presence of DPI, suggesting that H2O2 might have acted downstream of NO in accomplishing amelioration of AsV toxicity.

Strengthening the Capacity of Emergency Medical Services in Low and Middle Income Countries using Dispatcher-Coordinated Taxis.

International standards recommend provision of 1 ambulance for every 50,000 people to fulfil demand for transporting patients to definitive care facilities in Low and Middle Income Countries (LMICs). Governments' consistent attempt to build capacity of emergency medical services (EMS) in LMICs has been financially demanding. This study is an attempt to assess the feasibility of capacity building of existing EMS in Delhi, India by using taxis as an alternative mode of transport for emergency transportation of road traffic crash victims to enable improvement in response time for road traffic crashes where time criticality is deemed important. Performance of the proposed system is evaluated based on response time, coverage and distance. The system models the performance and quantifies the taxi - ambulance configuration for achieving EMS performance within international standards.

COVID-19 Pneumonia with Delayed Viral Clearance in a Patient with Active Drug-resistant Pulmonary Tuberculosis.

COVID pneumonia patient presents with fever, cough, and breathing difficulty. Many respiratory pathogens have such clinical presentations and pulmonary tuberculosis (PTB) is one of them, which is prevalent in the Indian subcontinent. Herein, we are presenting a case of dual infection with severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) and drug-resistant PTB [likely multidrug resistance (MDR)] in a patient with chronic kidney disease (CKD) and type 2 diabetes mellitus, a clinical course further complicated by a prolonged viral clearance. HOW TO CITE THIS ARTICLE: Sarma U, Mishra V, Goel J, Yadav S, Sharma S, Sherawat RK. COVID-19 Pneumonia with Delayed Viral Clearance in a Patient with Active Drug-resistant Pulmonary Tuberculosis. Indian J Crit Care Med 2020;24(11):1132-1134.