Basic research for identification and classification of organophosphorus pesticides in water based on ultraviolet-visible spectroscopy information.

In this study, the goal was to develop a method for detecting and classifying organophosphorus pesticides (OPPs) in bodies of water. Sixty-five samples with different concentrations were prepared for each of the organophosphorus pesticides, namely chlorpyrifos, acephate, parathion-methyl, trichlorphon, dichlorvos, profenofos, malathion, dimethoate, fenthion, and phoxim, respectively. Firstly, the spectral data of all the samples was obtained using a UV-visible spectrometer. Secondly, five preprocessing methods, six manifold learning methods, and five machine learning algorithms were utilized to build detection models for identifying OPPs in water bodies. The findings indicate that the accuracy of machine learning models trained on data preprocessed using convolutional smoothing + first-order derivatives (SG + FD) outperforms that of models trained on data preprocessed using other methods. The backpropagation neural network (BPNN) model exhibited the highest accuracy rate at 99.95%, followed by the support vector machine (SVM) and convolutional neural network (CNN) models, both at 99.92%. The extreme learning machine (ELM) and K-nearest neighbors (KNN) models demonstrated accuracy rates of 99.84% and 99.81%, respectively. Following the application of a manifold learning algorithm to the full-wavelength data set for the purpose of dimensionality reduction, the data was then visualized in the first three dimensions. The results demonstrate that the t-distributed domain embedding (t-SNE) algorithm is superior, exhibiting dense clustering of similar clusters and clear classification of dissimilar ones. SG + FD-t-SNE-SVM ranks highest among the feature extraction models in terms of performance. The feature extraction dimension was set to 4, and the average classification accuracy was 99.98%, which slightly improved the prediction performance over the full-wavelength model. As shown in this study, the ultraviolet-visible (UV-visible) spectroscopy system combined with the t-SNE and SVM algorithms can effectively identify and classify OPPs in waterbodies.

Learning Attention in the Frequency Domain for Flexible Real Photograph Denoising.

Recent advancements in deep learning techniques have pushed forward the frontiers of real photograph denoising. However, due to the inherent pooling operations in the spatial domain, current CNN-based denoisers are biased towards focusing on low-frequency representations, while discarding the high-frequency components. This will induce a problem for suboptimal visual quality as the image denoising tasks target completely eliminating the complex noises and recovering all fine-scale and salient information. In this work, we tackle this challenge from the frequency perspective and present a new solution pipeline, coined as frequency attention denoising network (FADNet). Our key idea is to build a learning-based frequency attention framework, where the feature correlations on a broader frequency spectrum can be fully characterized, thus enhancing the representational power of the network across multiple frequency channels. Based on this, we design a cascade of adaptive instance residual modules (AIRMs). In each AIRM, we first transform the spatial-domain features into the frequency space. Then, a learning-based frequency attention framework is devised to explore the feature inter-dependencies converted in the frequency domain. Besides this, we introduce an adaptive layer by leveraging the guidance of the estimated noise map and intermediate features to meet the challenges of model generalization in the noise discrepancy. The effectiveness of our method is demonstrated on several real camera benchmark datasets, with superior denoising performance, generalization capability, and efficiency versus the state-of-the-art.

Circulating tumor DNA predicts recurrence and assesses prognosis in operable gastric cancer: A systematic review and meta-analysis.

BACKGROUND: Selecting the appropriate patient for further treatment after surgery for gastric cancer can improve the patient prognosis. Circulating tumor DNA (ctDNA) has the potential to predict recurrence and prognosis after gastric cancer surgery, but the results are still inconclusive. As the completed studies had small sample sizes and were inconsistent, a meta-analysis was conducted to assess the effect of ctDNA on recurrence and prognosis after gastric cancer surgery. METHODS: PubMed, Embase, Scopus, and the Web of Science were searched for potentially eligible studies published up to April 7, 2023. Pooled relative risk (RR) and pooled hazard ratio (HR) were calculated to evaluate recurrence, recurrence-free survival (RFS), and overall survival (OS) following gastric cancer surgery. RESULTS: A pooled analysis revealed that patients who were ctDNA positive before and after surgery were at a high risk of gastric cancer recurrence (RR = 1.79, 95% CI: 1.19-2.71; RR = 3.17, 95% CI: 2.36-4.25). The pooled data revealed that ctDNA-positive patients had a poorer RFS and OS (HR = 6.37, 95% CI: 2.70-15.01; HR = 4.58, 95% CI: 1.68-12.49). CONCLUSIONS: ctDNA-positive patients were at a high risk of recurrence after gastric cancer surgery and had a poorer prognosis. Hence, ctDNA-positive patients needed close follow-up and further treatment.

Development and validation of a prognostic nomogram for predicting cancer-specific survival in advanced endometrial carcinoma after surgery: a retrospective analysis of the SEER Database.

OBJECTIVE: We aimed to construct and validate a prognostic nomogram to predict cancer-specific survival (CSS) after surgery in patients with advanced endometrial carcinoma (EC). DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: The Surveillance, Epidemiology, and End Results (SEER) Database contains cancer incidence and survival data from population-based cancer registries in the USA. A total of 5445 patients from the SEER Database diagnosed with advanced EC between 2004 and 2015 were included and randomised 7:3 into a training cohort (n=3812) and a validation cohort (n=1633). OUTCOME MEASURE: CSS. RESULTS: The nomograms for CSS included 10 variables (positive regional nodes, age, tumour size, International Federation of Gynecology and Obstetrics (FIGO) stage, grade, ethnicity, income, radiation, chemotherapy and historical stage) based on the forward stepwise regression results. They revealed discrimination and calibration using the concordance index (C-index) and area under the time-dependent receiver operating characteristic curve, with a C-index value of 0.7324 (95% CI=0.7181 to 0.7468) and 0.7511 (95% CI=0.7301 to 0.7722) for the training and validation cohorts, respectively. Using calibration plots, a high degree of conformance was shown between the predicted and observed results. Additionally, a comparison of the nomogram and FIGO staging based on changes in the C-index, net reclassification index and integrated discrimination improvement demonstrated that the nomogram had better accuracy and efficacy. CONCLUSIONS: We successfully constructed an accurate and effective nomogram to predict CSS in patients with advanced EC, which may help clinicians determine optimal individualised treatment strategies for patients with advanced EC. The predictive performance of the nomogram was evaluated thoroughly, but only internally. Therefore, further validation using different data sources is warranted in future related studies.

Treatment stratification and prognosis assessment using circulating tumor DNA in locally advanced rectal cancer: A systematic review and meta-analysis.

BACKGROUND: Circulating tumor DNA (ctDNA) is an emerging biomarker for locally advanced rectal cancer (LARC), giving hope for stratified treatment. As the completed studies have small sample sizes and different experimental methods, systematic review and meta-analysis were performed to explore their role in predicting pathological complete response (pCR), tumor recurrence, and prognosis. METHODS: PubMed, Embase, and the Web of Science were searched for potentially eligible studies published up to September 6, 2022. Pooled relative risk (RR) was calculated to predict pCR and tumor recurrence, and pooled hazard ratio (HR) was calculated to evaluate the prognosis of overall survival (OS), recurrence-free survival (RFS), and metastasis-free survival (MRS). RESULTS: Twelve studies published between 2018 and 2022 included 931 patients, and 2544 serum samples were eventually included in the meta-analysis. The pooled revealed that ctDNA-negative patients were more likely to have a pCR (RR = 1.64, 95% confidence interval [CI]: 1.26-2.12). The pooled revealed that ctDNA-positive patients were at high risk of recurrence (RR = 3.37, 95% CI: 2.34-4.85) and had a poorer prognosis for OS (HR = 3.03, 95% CI: 1.86-4.95), RFS (HR = 7.08, 95% CI: 4.12-12.14), and MRS (HR = 2.77, 95% CI: 2.01-3.83). CONCLUSION: ctDNA may be useful for stratifying treatment and assessing prognosis in patients with LARC, but its clinical application still needs to be confirmed in a prospective multicenter study with large samples.

Analysis of genetic diversity and genetic structure of Qinchuan cattle conservation population using whole-genome resequencing.

In the conservation of livestock and poultry resources, population genetic diversity and genetic structure of the conservation population are important factors affecting the effectiveness of conservation. In this study, whole-genome resequencing technology was used to detect genomic variation in 100 Qinchuan cattle (30 bulls and 70 cows). By analyzing population genetic diversity, runs of homozygosity (ROH) distribution features, kinship relationships, and family structure, the conservation effectiveness of Qinchuan cattle was comprehensively evaluated. The results showed that a total of 20,968,017 high-quality SNPs were detected in 100 Qinchuan cattle, the average minimum allele frequency was 0.191±0.124, the average polymorphic information content was 0.279±0.131, and the average observed heterozygosity was 0.275±0.131, the average expected heterozygosity is 0.279±0.131, indicating that the genetic diversity of the Qinchuan cattle conservation population is relatively rich. The average identity by state (IBS) distance of the Qinchuan conservation population was 0.243±0.020, with a value of 0.242±0.021 for the bulls. The results of the kinship G-matrix were consistent with the results of the IBS distance matrix, both showing that some individuals in the conservation population had close kinship. A total of 8258 genomic ROH were detected in 100 Qinchuan cattle, with a total length of 9.64 GB. The average length of ROH fragments was 1.167±1.203 Mb, 69.35% of the ROH were short ROH with a length of 0.5~1 Mb, and the average total length of ROH per individual was 96.40 Mb. The average inbreeding coefficient based on ROH was 0.039±0.039, with a value of 0.044±0.035 for the bulls, indicating that some bulls had a certain degree of inbreeding accumulation. The results of the phylogenetic tree combined with kinship analysis showed that the individuals in the Qinchuan cattle conservation population could be divided into eight families, including seven families with bulls and one family without bulls. This study demonstrated that the genetic diversity of the Qinchuan conservation population is relatively rich, with no significant inbreeding accumulation, but there is a risk of inbreeding among some individuals. Therefore, it is necessary to strengthen selection and mating to ensure the sustainable development of Qinchuan cattle resources.

Flexible and Generalized Real Photograph Denoising Exploiting Dual Meta Attention.

Supervised deep learning techniques have been widely explored in real photograph denoising and achieved noticeable performances. However, being subject to specific training data, most current image denoising algorithms can easily be restricted to certain noisy types and exhibit poor generalizability across testing sets. To address this issue, we propose a novel flexible and well-generalized approach, coined as dual meta attention network (DMANet). The DMANet is mainly composed of a cascade of the self-meta attention blocks (SMABs) and collaborative-meta attention blocks (CMABs). These two blocks have two forms of advantages. First, they simultaneously take both spatial and channel attention into account, allowing our model to better exploit more informative feature interdependencies. Second, the attention blocks are embedded with the meta-subnetwork, which is based on metalearning and supports dynamic weight generation. Such a scheme can provide a beneficial means for self and collaborative updating of the attention maps on-the-fly. Instead of directly stacking the SMABs and CMABs to form a deep network architecture, we further devise a three-stage learning framework, where different blocks are utilized for each feature extraction stage according to the individual characteristics of SMAB and CMAB. On five real datasets, we demonstrate the superiority of our approach against the state of the art. Unlike most existing image denoising algorithms, our DMANet not only possesses a good generalization capability but can also be flexibly used to cope with the unknown and complex real noises, making it highly competitive for practical applications.

Circulation tumour DNA in predicting recurrence and prognosis in operable colorectal cancer patients: A meta-analysis.

BACKGROUND: Selecting the appropriate patient for further treatment after surgery and adjuvant chemotherapy (ACT) for colorectal cancer (CRC) can improve the patient's prognosis. Circulating tumour DNA (ctDNA) has the potential to predict recurrence and prognosis after CRC surgery and ACT, but the results are still inconclusive. OBJECTIVES: As the completed studies have small sample sizes and different experimental methods, a meta-analysis was conducted to assess the ctDNA on recurrence and prognosis after CRC surgery and ACT. METHODS: PubMed, Embase, the Web of Science and the Cochrane Library were searched for potentially eligible studies published up to 6 March 2022. Pooled relative risk (RR) and pooled hazard ratio (HR) were calculated to evaluate recurrence and the prognosis of recurrence-free survival (RFS) following CRC surgery and ACT. RESULTS: Fourteen studies published between 2014 and 2022 included 2393 patients, and 7189 serum samples were eventually included in the meta-analysis. The pooled revealed that ctDNA-positive patients were at high risk of recurrence after CRC surgery (RR = 4.43, 95% CI: 3.58-5.48, p < .05) and had a poorer prognosis for RFS (HR = 7.26, 95% CI: 5.48-9.62, p < .05). The pooled revealed that ctDNA-positive patients were at high risk of recurrence after ACT (RR = 5.77 95% CI: 4.33-7.69, p < .05) and had a poorer prognosis for RFS (HR = 13.96, 95% CI: 8.71-22.4, p < .05). CONCLUSION: ctDNA-positive patients were at a high risk of recurrence after CRC surgery and ACT and had a poorer prognosis. Hence, ctDNA-positive patients required close follow-up and further treatments.

Cholesterol sulfate alleviates ulcerative colitis by promoting cholesterol biosynthesis in colonic epithelial cells.

Cholesterol sulfate, produced by hydroxysteroid sulfotransferase 2B1 (SULT2B1), is highly abundant in the intestine. Herein, we study the functional role and underlying intestinal epithelial repair mechanisms of cholesterol sulfate in ulcerative colitis. The levels of cholesterol and cholesterol sulfate, as well as the expression of Sult2b1 and genes involved in cholesterol biosynthesis, are significantly higher in inflamed tissues from patients with ulcerative colitis than in intestinal mucosa from healthy controls. Cholesterol sulfate in the gut and circulation is mainly catalyzed by intestinal epithelial SULT2B1. Specific deletion of the Sult2b1 gene in the intestinal epithelial cells aggravates dextran sulfate sodium-induced colitis; however, dietary supplementation with cholesterol sulfate ameliorates this effect in acute and chronic ulcerative colitis in mice. Cholesterol sulfate promotes cholesterol biosynthesis by binding to Niemann-Pick type C2 protein and activating sterol regulatory element binding protein 2 in colonic epithelial cells, thereby alleviates ulcerative colitis. In conclusion, cholesterol sulfate contributes to the healing of the mucosal barrier and exhibits therapeutic efficacy against ulcerative colitis in mice.

Diagnostic Accuracy of Wireless Capsule Endoscopy in Polyp Recognition Using Deep Learning: A Meta-Analysis.

Aim: As the completed studies have small sample sizes and different algorithms, a meta-analysis was conducted to assess the accuracy of WCE in identifying polyps using deep learning. Method: Two independent reviewers searched PubMed, Embase, the Web of Science, and the Cochrane Library for potentially eligible studies published up to December 8, 2021, which were analysed on a per-image basis. STATA RevMan and Meta-DiSc were used to conduct this meta-analysis. A random effects model was used, and a subgroup and regression analysis was performed to explore sources of heterogeneity. Results: Eight studies published between 2017 and 2021 included 819 patients, and 18,414 frames were eventually included in the meta-analysis. The summary estimates for the WCE in identifying polyps by deep learning were sensitivity 0.97 (95% confidence interval (CI), 0.95-0.98); specificity 0.97 (95% CI, 0.94-0.98); positive likelihood ratio 27.19 (95% CI, 15.32-50.42); negative likelihood ratio 0.03 (95% CI 0.02-0.05); diagnostic odds ratio 873.69 (95% CI, 387.34-1970.74); and the area under the sROC curve 0.99. Conclusion: WCE uses deep learning to identify polyps with high accuracy, but multicentre prospective randomized controlled studies are needed in the future.

Diagnostic accuracy of probe-based confocal laser endomicroscopy and tissue sampling by endoscopic retrograde cholangiopancreatography in indeterminate biliary strictures: a meta­analysis.

Probe-based confocal laser endomicroscopy (pCLE), also known as optical biopsy, is a new endoscopic technique that provides real-time magnification of 1000 × microscopic tissue information to diagnose indeterminate biliary strictures. Tissue sampling by endoscopic retrograde cholangiopancreatography (ERCP) is routinely performed to evaluate indeterminate biliary strictures. To evaluate the accuracy of pCLE and tissue sampling by ERCP in the diagnosis of indeterminate biliary strictures, 18 articles were included from 2008 to 2021 through Embase, PubMed, Web of Science, and Cochrane library databases. The summary estimates for the pCLE diagnosis of indeterminate biliary strictures were: sensitivity 0.88 (95% confidence interval (CI), 0.84-0.91); specificity 0.79 (95% CI 0.74-0.83); and Diagnostic Odds Ratio (DOR) 24.63 (95% CI 15.76-38.48). The summary estimates for tissue sampling by ERCP diagnosis for indeterminate biliary strictures were: sensitivity 0.54 (95% CI 0.49-0.59); specificity 0.96 (95% CI 0.94-0.98); and DOR 11.31 (95% CI 3.90-32.82). The area under the sROC curve of pCLE diagnosis of indeterminate biliary strictures is 0.90 higher than 0.65 of tissue sampling by ERCP. The pCLE is a better approach than tissue sampling by ERCP for the diagnosis of indeterminate biliary strictures by providing real-time microscopic images of the bile ducts.

Meta PID Attention Network for Flexible and Efficient Real-World Noisy Image Denoising.

Recent deep convolutional neural networks for real-world noisy image denoising have shown a huge boost in performance by training a well-engineered network over external image pairs. However, most of these methods are generally trained with supervision. Once the testing data is no longer compatible with the training conditions, they can exhibit poor generalization and easily result in severe overfitting or degrading performances. To tackle this barrier, we propose a novel denoising algorithm, dubbed as Meta PID Attention Network (MPA-Net). Our MPA-Net is built based upon stacking Meta PID Attention Modules (MPAMs). In each MPAM, we utilize a second-order attention module (SAM) to exploit the channel-wise feature correlations with second-order statistics, which are then adaptively updated via a proportional-integral-derivative (PID) guided meta-learning framework. This learning framework exerts the unique property of the PID controller and meta-learning scheme to dynamically generate filter weights for beneficial update of the extracted features within a feedback control system. Moreover, the dynamic nature of the framework enables the generated weights to be flexibly tweaked according to the input at test time. Thus, MPAM not only achieves discriminative feature learning, but also facilitates a robust generalization ability on distinct noises for real images. Extensive experiments on ten datasets are conducted to inspect the effectiveness of the proposed MPA-Net quantitatively and qualitatively, which demonstrates both its superior denoising performance and promising generalization ability that goes beyond those of the state-of-the-art denoising methods.

Impact of 9-Minute Withdrawal Time on the Adenoma Detection Rate: A Multicenter Randomized Controlled Trial.

BACKGROUND & AIMS: Although current quality indicators of colonoscopy recommend 6 minutes as the minimum standard for withdrawal time (WT), the impact of a WT longer than 6 minutes on neoplasia detection is unclear. METHODS: A multicenter randomized controlled trial involving 1027 patients was conducted from January 2018 to July 2019. Participants were randomly divided into a 9-minute (n = 514) and 6-minute (n = 513) WT group, and a timer was used to adjust the withdrawal speed. The primary outcome was the adenoma detection rate (ADR). RESULTS: Intention-to-treat analysis showed a significantly higher ADR in the 9-minute versus 6-minute WT group (36.6% vs. 27.1%, P = .001). Prolonging WT from 6 to 9 minutes significantly increased ADR of the proximal colon (21.4% vs. 11.9%, P < .001) as well as of the less experienced colonoscopists (36.8% vs. 23.5%, P = .001). Improvements were also observed in the polyp detection rate (58.0% vs. 47.8%, P < .001), and mean number of polyps and adenomas detected per colonoscopy (1.1 vs. 0.9, P = .002; 0.5 vs. 0.4, P = .008, respectively). The higher ADRs in 9-minute WT were also confirmed by the per-protocol (PP) analysis and subgroup analyses, with an increased rate of sessile serrated lesion detection in the 9-minute WT by PP analysis (4.0% vs. 1.3%, P = .04). Multivariate logistic regression demonstrated that the 9-minute WT was independently associated with increased ADR (P = .005). CONCLUSIONS: Prolonging WT from 6 to 9 minutes significantly improved ADR, especially in the proximal colon and for less experienced colonoscopists. A 9-minute WT benchmark should be considered as one of the quality indicators of colonoscopy. ClinicalTrials.gov (identifier, NCT03399045).

PID Controller-Guided Attention Neural Network Learning for Fast and Effective Real Photographs Denoising.

Real photograph denoising is extremely challenging in low-level computer vision since the noise is sophisticated and cannot be fully modeled by explicit distributions. Although deep-learning techniques have been actively explored for this issue and achieved convincing results, most of the networks may cause vanishing or exploding gradients, and usually entail more time and memory to obtain a remarkable performance. This article overcomes these challenges and presents a novel network, namely, PID controller guide attention neural network (PAN-Net), taking advantage of both the proportional-integral-derivative (PID) controller and attention neural network for real photograph denoising. First, a PID-attention network (PID-AN) is built to learn and exploit discriminative image features. Meanwhile, we devise a dynamic learning scheme by linking the neural network and control action, which significantly improves the robustness and adaptability of PID-AN. Second, we explore both the residual structure and share-source skip connections to stack the PID-ANs. Such a framework provides a flexible way to feature residual learning, enabling us to facilitate the network training and boost the denoising performance. Extensive experiments show that our PAN-Net achieves superior denoising results against the state-of-the-art in terms of image quality and efficiency.

Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network.

Human resource planning is the prerequisite of human resource management, and the basic work of human resource planning is to predict human resource demand. Scientific and reasonable human resource demand forecasting results can provide important data support for enterprise human resource planning and strategic decision-making so that human resources management can play a better role in the realization of corporate goals. Because human resource demand is affected by many factors, there is a high degree of nonlinearity and uncertainty between each factor and personnel demand, as well as the incompleteness and inaccuracy of corporate human resource data. In this paper, the self-organizing feature mapping (SOM) artificial neural network prediction model is selected as the prediction model, and the input and output process of sample data is converted into the optimal solution process of the nonlinear function. In the application of the model, the human resource demand prediction index system is used as the input of the SOM neural network and the total number of employees in the enterprise is used as the output so that the problem of nonlinear fitting between human resource demand-influencing factors and human resource demand can be solved. Finally, through the empirical analysis of the enterprise, the model forecasting process is explained and the human resource demand forecast is realized.

IGHG1 upregulation promoted gastric cancer malignancy via AKT/GSK-3ß/ß-Catenin pathway.

BACKGROUND: Despite current advances in gastric cancer treatment, disease metastasis and chemo-resistance remain as major hurdles against better overall prognosis. Previous studies indicated that IGHG1 as well as -Catenin serve as important regulators of tumor cellular malignancy. Therefore, understanding detailed molecular mechanism and identifying druggable target will be of great potentials in future therapeutic development. METHODS: Surgical tissues and gastric cancer cell lines were retrieved to evaluate IGHG1 expression for patients with or without lymph node/distal organ metastasis. Functional assays including CCK8 assay, Edu assay, sphere formation assay and transwell assay, wound healing assay, etc. were subsequently performed to evaluate the impact of IGHG1/-catenin axis on tumor cell proliferation, migration and chemo-resistance. RESULTS: Gastric cancer tissues and tumor cell lines demonstrated significantly higher level of IGHG1. Functional study further demonstrated that IGHG1 promoted proliferative and migration as well as chemo-resistance of gastric cancer tumor cells. Further experiments indicated that IGHG1 activated AKT/GSK-3/-Catenin axis, which played crucial role in regulation of proliferative and chemo-resistance of gastric cancer cells. CONCLUSION: This study provided novel evidences that IGHG1 acted as oncogene by promotion of gastric cancer cellular proliferation, migration and chemo-resistance. Our research further suggested that IGHG1/AKT/GSK-3ß/ß-Catenin axis acted as novel pathway which regulated gastric cancer cellular malignant behavior. Our research might inspire future therapy development to promote overall prognosis of gastric cancer patients.

Antecedents of Knowledge Hiding and Their Impact on Organizational Performance.

Research on knowledge management has rapidly increased in the last decade, leaving a huge gap on how, why, and what triggers knowledge hiding in inter-organizational setups. Furthermore, the fostering factors for knowledge sharing have also remained unexplored because the employees in an organization are unwilling to share their knowledge with others for several reasons. The current study has attempted to explore the reasons that make employees hide their knowledge from other employees in order to excel. The individual factors considered in this study that make employees hide their knowledge are the lack of rewards for knowledge sharing, internal competition, and psychological entitlement. Furthermore, the interesting consequent factor of knowledge hiding in this study was found to be significant. The moderating role of employees' social status has a significant moderating effect on the relationship between knowledge-hiding behavior and organizational performance. The population of the study was the managerial employees of financial institutions of China and the sample size taken in his study was 446 via convenient sampling technique. The independent factors in this study found significant results of knowledge-hiding behavior, thus approving the mediating role of knowledge hiding in the organizational performance of the financial institutions of China. The software used in this study for the data analysis was smart PLS and the technique used was partial least square SEM for the measurement of the hypothesis of the study. The study's findings also have certain implications for policymaking in financial institutions that may hinder knowledge hiding practices and support the uninterrupted flow of knowledge among employees.

Impact of Knowledge Hiding Behaviors on Workplace Invincibility: Mediating Role of Psychological Contract Breach.

In recent years, knowledge hiding has gained much popularity in the knowledge management literature. Apart from that, antecedents and consequences of knowledge hiding are being scrutinized at present. There have been many studies on the triggering forces of knowledge hiding; however, the uncivil behaviors at the workplace have led the organizations and employees in trouble due to its possible associating factors, which is well explained by social influence theory. One such factor that this study has identified is knowledge hiding behavior at the workplace. This is a quantitative cross-sectional study based on a survey. The population taken in this study is the middle and low-level managerial staff of the software houses located in China. The respondents were selected based on convenient random sampling, and a sample size of 287 is used in this study. The data collected were employed with the partial least square structural equation modeling using Smart-PLS 3. The findings of this study show that apart from evasive hiding, playing dumb and rationalized hiding plays a significant role in predicting workplace incivility. In addition, psychological contract breach (PCB) has been taken as the mediating variable. The violation of psychological contracts among employees can indulge them in negative feelings that may convert to workplace incivility at any available opportunity of revenge which is well explained by social influence theory. Workplace incivility cannot be completely eradicated from the organizations; however, it can be controlled by making relevant policies. The civility among the employees can be attained by due managerial interventions and training of the employees considering the protection of victims and due punishment to the perpetrator.

Synthesis of 1-benzylisoindoline and 1-benzyl-tetrahydroisoquinoline through nucleophilic addition of organozinc reagents to N,O-acetals.

A new approach to access 1-benzylisoindoline and 1-benzyl-tetrahydroisoquinoline has been developed through nucleophilic addition of organozinc reagents to N,O-acetals. A number of substituted organozinc reagents were amenable for this transformation, and the desired products were obtained with excellent yields. Moreover, Sc(OTf)3 proved to be an effective catalyst for the formation of 1-benzylisoindoline and 1-benzyl-tetrahydroisoquinoline using such nucleophilic addition.