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Despite the fact that much of the research has been performed on ZnO-based nanoparticles, still a lot of work is unexplored. The synthesis and characterization of the ZnO nanorods have been co-annealed using a simple combustion method and used for gas sensor and photocatalytic degradations applications. Herein pure and In, Sn and Sb tri-dopants were used, i.e. 0.5 at.wt% 1.0 at.wt% and 1.5 at.wt%, while their effects co-annealed on glass substrate at different temperatures at 500 °C and 1100° have been studied. These samples were coated onto the chosen substrate using spin coating technique. Crystallite scale was measured to the range of 30–50 nm. At such temperatures, the grain size measured for the samples was in range of 50–70 nm. This showed that the prepared nanorods are well crystalline and have strong optical properties to handle. Studies of X-ray diffraction showed the influential point (101). These coated samples designed for nitrogen gas sensing have been tested for the development of smart and functional instruments. Furthermore, it was observed that the samples prepared at higher temperatures exhibit better recovery and better reaction time. Valance ion process explains the gas sensors fast reaction and long recovery time. Thus prepared ZnO nanoparticles have photocatalytic degradation (99.86%) only in 55 min. We observed optimum exposure at an operating temperature of 105 °C. It is notable that morphology of susceptible layer nanoparticles is preserved based on different tri-doping concentrations. The concentration of T2-ZnO nanoparticles for photodegradation of the DR-31 dye and NO 2 gas sensing applications were 1.0 at.wt%

Existing table question answering datasets contain abundant factual questions that primarily evaluate a QA system’s comprehension of query and tabular data. However, restricted by their short-form answers, these datasets fail to include question–answer interactions that represent more advanced and naturally occurring information needs: questions that ask for reasoning and integration of information pieces retrieved from a structured knowledge source. To complement the existing datasets and to reveal the challenging nature of the table-based question answering task, we introduce FeTaQA, a new dataset with 10K Wikipedia-based , , , pairs. FeTaQA is collected from noteworthy descriptions of Wikipedia tables that contain information people tend to seek; generation of these descriptions requires advanced processing that humans perform on a daily basis: Understand the question and table, retrieve, integrate, infer, and conduct text planning and surface realization to generate an answer. We provide two benchmark methods for the proposed task: a pipeline method based on semantic parsing-based QA systems and an end-to-end method based on large pretrained text generation models, and show that FeTaQA poses a challenge for both methods.

Immune checkpoint inhibitors have made significant advances in available cancer treatment options towards progression-free and overall survival in cancer patients by potentiating own anti-tumor immune response. Anti-programmed death (PD-1) and anti-cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) have been increasingly associated with neurologic complications. LE is a rare complication and like many complications secondary to immunotherapy, there is no standard for evaluation and treatment. Anti-GAD65-associated LE has been associated with thymic carcinoma. We describe a patient who presented with progressive memory loss 2 weeks after her third cycle of Ipilimumab and Nivolumab with associated elevated Anti-GAD65 levels. Treatment with IVIG and PLEX led to complete resolution of her symptoms and improvement in her brain imaging and CSF findings.

In recent days, the usage of big data in different applications has improved rapidly, and also, it faces more complications due to enormous data. Generally, big data offers decision-making support to the decision-makers with high accuracy. The growth of communication and data contents is improved effectively according to the speed, velocity, size, and values for providing better knowledge to tackle upcoming complicated tasks and problems. On the other side, multi-criteria-aided decision-making technique is considered to tackle multiple problems presented in big data analysis. To achieve optimal outcomes, an automated model of big data analytics for improving the decision-making is proposed by utilizing the advanced methods. Initially, the big data is gathered from benchmark available sources. Consequently, the essential features are extracted based on the Map Reduce approach, where the features are analyzed by Spatial Incremental Principal Component Analysis (SI-PCA). Especially, in big data analytics, the Bidirectional Recurrent Neural Network (BiRNN) model facilitates increasing the overfitting issues that affects data quality. This issue is rectified by implementing the Adaptive Multiplicative BiRNN (AM-BiRNN) to enable accurate predictions to strengthen the decision-making performance. In the end, the resultant features are given as input to the AM-BiRNN. For further enhancement, the hyperparameters are optimally tuned by Improved Random Function-based Sculptor Optimization Algorithm (IRF-SOA). Finally, the validation of the model is done to achieve the high effective results. When compared with other state-of-the-art techniques, the impressive outcomes proved that the recommended system can provide a better decision-making outcome. Here, the experimental findings of the developed model show 93.15% of accuracy, and 87.09% of sensitivity, respectively.

Purpose This paper aims to explore the impact of employer branding dimensions i.e. social value, interest value, economic value, development value and application value on turnover intentions (TIs) of employees working in Indian information technology (IT) sector organizations. Design/methodology/approach A total of 380 junior-, middle- and senior-level executives have been surveyed using a structured questionnaire to measure employees’ perception with respect to the dimensions of employer branding and TIs. Hypotheses have been tested using multiple regression analysis. Findings Employer branding dimensions are negatively correlated with employees’ TIs, and two dimensions (social value and development value) are significant predictors of TIs. Practical implications Higher perceived value in employer brand reduces the TIs. Higher employee retention rates further lead to reduction in the cost of hiring and training of new employees, thereby contributing to the profitability of any organization. Hence, practical relevance is there for handling employee turnover and theoretical importance is for further enhancing the talent management concepts. Originality/value Uniqueness of this study lies in its approach. The role of organizational-level factors rather than individualistic characteristics has been analyzed as predictors of the employees’ decision to leave their organization. Furthermore, the sample of progressive Indian IT sector executives adds to the originality of the work.

Farmers’ physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt. It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices. The study in question examines the significance of tool renting and sharing in the workplace. Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it. The following is a manual pilot study of 562 farmers in India to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment. The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment. Farmers are divided into three groups according to the results of this poll: small, moderate, and large. Training and testing splits were used on the same data set in order to get a better understanding of the target variables. The data set for the survey was standardized in order to remove ambiguity. In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees. K-nearest neighbors was the most often used model, followed by logistic regression and decision trees. In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard. Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods.

An oncology fellow shares her experience navigating a cancer drug shortage alongside her patient.

Folding and cellular localization of many proteins of Gram-negative bacteria rely on a network of chaperones and secretion systems. Among them is the lipase-specific foldase Lif, a membrane-bound steric chaperone that tightly binds ( K D  = 29 nM) and mediates folding of the lipase LipA, a virulence factor of the pathogenic bacterium P. aeruginosa . Lif consists of five-domains, including a mini domain MD1 essential for LipA folding. However, the molecular mechanism of Lif-assisted LipA folding remains elusive. Here, we show in in vitro experiments using a soluble form of Lif ( s Lif) that isolated MD1 inhibits s Lif-assisted LipA activation. Furthermore, the ability to activate LipA is lost in the variant s Lif Y99A , in which the evolutionary conserved amino acid Y99 from helix α1 of MD1 is mutated to alanine. This coincides with an approximately three-fold reduced affinity of the variant to LipA together with increased flexibility of s Lif Y99A in the complex as determined by polarization-resolved fluorescence spectroscopy. We have solved the NMR solution structures of P. aeruginosa MD1 and variant MD1 Y99A revealing a similar fold indicating that a structural modification is likely not the reason for the impaired activity of variant s Lif Y99A . Molecular dynamics simulations of the s Lif:LipA complex in connection with rigidity analyses suggest a long-range network of interactions spanning from Y99 of s Lif to the active site of LipA, which might be essential for LipA activation. These findings provide important details about the putative mechanism for LipA activation and point to a general mechanism of protein folding by multi-domain steric chaperones.

Statins are known to be anti-inflammatory, but the mechanism remains poorly understood. Here, we show that macrophages, either treated with statin in vitro or from statin-treated mice, have reduced cholesterol levels and higher expression of Jmjd3 , a H3K27me3 demethylase. We provide evidence that lowering cholesterol levels in macrophages suppresses the adenosine triphosphate (ATP) synthase in the inner mitochondrial membrane and changes the proton gradient in the mitochondria. This activates nuclear factor kappa-B (NF-κB) and Jmjd3 expression, which removes the repressive marker H3K27me3. Accordingly, the epigenome is altered by the cholesterol reduction. When subsequently challenged by the inflammatory stimulus lipopolysaccharide (M1), macrophages, either treated with statins in vitro or isolated from statin-fed mice, express lower levels proinflammatory cytokines than controls, while augmenting anti-inflammatory Il10 expression. On the other hand, when macrophages are alternatively activated by IL-4 (M2), statins promote the expression of Arg1 , Ym1 , and Mrc1 . The enhanced expression is correlated with the statin-induced removal of H3K27me3 from these genes prior to activation. In addition, Jmjd3 and its demethylase activity are necessary for cholesterol to modulate both M1 and M2 activation. We conclude that upregulation of Jmjd3 is a key event for the anti-inflammatory function of statins on macrophages.

Long-term exposure to organic solvents is known to affect human health posing serious occupational hazards. Organic solvents are genotoxic, and they can cause genetic changes in the exposed employees' somatic or germ cells. Chemicals such as benzene, toluene, and gasoline induce an excessive amount of genotoxicity results either in genetic polymorphism or culminates in deleterious mutations when concentration crosses the threshold limits. The impact of genotoxicity is directly related to the time of exposure, types, and quantum of solvent. Genotoxicity affects almost all the physiological systems, but the most vulnerable ones are the nervous system, reproductive system, and blood circulatory system. Based on the available literature report, we propose to evaluate the outcomes of such chemicals on the exposed humans at the workplace. Attempts would be made to ascertain if the long-term exposure makes a person resistant to such chemicals. This may seem to be a far-fetched idea but has not been studied. The health prospect of this study is envisaged to complement the already existing data facilitating a deeper understanding of the genotoxicity across the population. This would also demonstrate if it correlates with the demographic profile of the population and contributes to comorbidity and epidemiology.