Does social media usage make employees happy and innovative? Perspectives from information visibility theory

Although social media facilitates the work communications, its dark sides also receive growing attention from both managers and researchers. This study aims to understand whether and how the use of social media impacts employees’ well-being and innovative performance. Borrowing from information visibility theory and related literature, we explore the roles of shared approval and message accessibility afforded by social media in the workplace. The moderation effect of employees’ segmentation preferences has also been examined. The survey analysis results show that both shared approval and message accessibility afforded by social media have a positive effect on employees’ well-being, which subsequently increases their innovative performance. We also find that segmentation preference strengthens the relationship between message accessibility and well-being but weakens the relationship between shared approval and well-being. The findings enhance our understanding of social media usage in the workplace and provide practical guidance for managers

Unified Data-Free Compression: Pruning and Quantization without Fine-Tuning

Structured pruning and quantization are promising approaches for reducing the inference time and memory footprint of neural networks. However, most existing methods require the original training dataset to fine-tune the model. This not only brings heavy resource consumption but also is not possible for applications with sensitive or proprietary data due to privacy and security concerns. Therefore, a few data-free methods are proposed to address this problem, but they perform data-free pruning and quantization separately, which does not explore the complementarity of pruning and quantization. In this paper, we propose a novel framework named Unified Data-Free Compression(UDFC), which performs pruning and quantization simultaneously without any data and fine-tuning process. Specifically, UDFC starts with the assumption that the partial information of a damaged(e.g., pruned or quantized) channel can be preserved by a linear combination of other channels, and then derives the reconstruction form from the assumption to restore the information loss due to compression. Finally, we formulate the reconstruction error between the original network and its compressed network, and theoretically deduce the closed-form solution. We evaluate the UDFC on the large-scale image classification task and obtain significant improvements over various network architectures and compression methods. For example, we achieve a 20.54% accuracy improvement on ImageNet dataset compared to SOTA method with 30% pruning ratio and 6-bit quantization on ResNet-34.Comment: ICCV202

Intriguing Findings of Frequency Selection for Image Deblurring

Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image. Recent progress on image deblurring always designs end-to-end architectures and aims at learning the difference between blurry and sharp image pairs from pixel-level, which inevitably overlooks the importance of blur kernels. This paper reveals an intriguing phenomenon that simply applying ReLU operation on the frequency domain of a blur image followed by inverse Fourier transform, i.e., frequency selection, provides faithful information about the blur pattern (e.g., the blur direction and blur level, implicitly shows the kernel pattern). Based on this observation, we attempt to leverage kernel-level information for image deblurring networks by inserting Fourier transform, ReLU operation, and inverse Fourier transform to the standard ResBlock. 1x1 convolution is further added to let the network modulate flexible thresholds for frequency selection. We term our newly built block as Res FFT-ReLU Block, which takes advantages of both kernel-level and pixel-level features via learning frequency-spatial dual-domain representations. Extensive experiments are conducted to acquire a thorough analysis on the insights of the method. Moreover, after plugging the proposed block into NAFNet, we can achieve 33.85 dB in PSNR on GoPro dataset. Our method noticeably improves backbone architectures without introducing many parameters, while maintaining low computational complexity. Code is available at https://github.com/DeepMed-Lab/DeepRFT-AAAI2023.Comment: AAAI 202

Learning Global-aware Kernel for Image Harmonization

Image harmonization aims to solve the visual inconsistency problem in composited images by adaptively adjusting the foreground pixels with the background as references. Existing methods employ local color transformation or region matching between foreground and background, which neglects powerful proximity prior and independently distinguishes fore-/back-ground as a whole part for harmonization. As a result, they still show a limited performance across varied foreground objects and scenes. To address this issue, we propose a novel Global-aware Kernel Network (GKNet) to harmonize local regions with comprehensive consideration of long-distance background references. Specifically, GKNet includes two parts, \ie, harmony kernel prediction and harmony kernel modulation branches. The former includes a Long-distance Reference Extractor (LRE) to obtain long-distance context and Kernel Prediction Blocks (KPB) to predict multi-level harmony kernels by fusing global information with local features. To achieve this goal, a novel Selective Correlation Fusion (SCF) module is proposed to better select relevant long-distance background references for local harmonization. The latter employs the predicted kernels to harmonize foreground regions with both local and global awareness. Abundant experiments demonstrate the superiority of our method for image harmonization over state-of-the-art methods, \eg, achieving 39.53dB PSNR that surpasses the best counterpart by +0.78dB $\uparrow$; decreasing fMSE/MSE by 11.5\%$\downarrow$/6.7\%$\downarrow$ compared with the SoTA method. Code will be available at \href{https://github.com/XintianShen/GKNet}{here}.Comment: 10 pages, 10 figure

Normative Analysis of Individual Brain Differences Based on a Population MRI-Based Atlas of Cynomolgus Macaques

The developmental trajectory of the primate brain varies substantially with aging across subjects. However, this ubiquitous variability between individuals in brain structure is difficult to quantify and has thus essentially been ignored. Based on a large-scale structural magnetic resonance imaging dataset acquired from 162 cynomolgus macaques, we create a species-specific 3D template atlas of the macaque brain, and deploy normative modeling to characterize individual variations of cortical thickness (CT) and regional gray matter volume (GMV). We observed an overall decrease in total GMV and mean CT, and an increase in white matter volume from juvenile to early adult. Specifically, CT and regional GMV were greater in prefrontal and temporal cortices relative to early unimodal areas. Age-dependent trajectories of thickness and volume for each cortical region revealed an increase in the medial temporal lobe, and decreases in all other regions. A low percentage of highly individualized deviations of CT and GMV were identified (0.0021%, 0.0043%, respectively, P \u3c 0.05, false discovery rate [FDR]-corrected). Our approach provides a natural framework to parse individual neuroanatomical differences for use as a reference standard in macaque brain research, potentially enabling inferences regarding the degree to which behavioral or symptomatic variables map onto brain structure in future disease studies

Plasma aldosterone concentrations elevation in hypertensive patients: the dual impact on hyperuricemia and gout

BackgroundPrior research has highlighted the association between uric acid (UA) and the activation of the renin-angiotensin-aldosterone system (RAAS). However, the specific relationship between aldosterone, the RAAS’s end product, and UA-related diseases remains poorly understood. This study aims to clarify the impact of aldosterone on the development and progression of hyperuricemia and gout in hypertensive patients.MethodsOur study involved 34534 hypertensive participants, assessing plasma aldosterone concentration (PAC)’s role in UA-related diseases, mainly hyperuricemia and gout. We applied multiple logistic regression to investigate the impact of PAC and used restricted cubic splines (RCS) for examining the dose-response relationship between PAC and these diseases. To gain deeper insights, we conducted threshold analyses, further clarifying the nature of this relationship. Finally, we undertook subgroup analyses to evaluate PAC’s effects across diverse conditions and among different subgroups.ResultsMultivariate logistic regression analysis revealed a significant correlation between the occurrence of hyperuricemia and gout and the elevation of PAC levels. Compared to the first quartile (Q1) group, groups Q2, Q3, and Q4 all exhibited a significantly increased risk of occurrence. Moreover, the conducted RCS analysis demonstrated a significant nonlinear dose-response relationship, especially when PAC was greater than 14 ng/dL, with a further increased risk of hyperuricemia and gout. Finally, comprehensive subgroup analyses consistently reinforced these findings.ConclusionThis study demonstrates a close association between elevated PAC levels and the development of UA-related diseases, namely hyperuricemia and gout, in hypertensive patients. Further prospective studies are warranted to confirm and validate this relationship

Modeling Rett Syndrome Using TALEN-Edited MECP2 Mutant Cynomolgus Monkeys

Gene-editing technologies have made it feasible to create nonhuman primate models for human genetic disorders. Here, we report detailed genotypes and phenotypes of TALEN-edited MECP2 mutant cynomolgus monkeys serving as a model for a neurodevelopmental disorder, Rett syndrome (RTT), which is caused by loss-of-function mutations in the human MECP2 gene. Male mutant monkeys were embryonic lethal, reiterating that RTT is a disease of females. Through a battery of behavioral analyses, including primate-unique eye-tracking tests, in combination with brain imaging via MRI, we found a series of physiological, behavioral, and structural abnormalities resembling clinical manifestations of RTT. Moreover, blood transcriptome profiling revealed that mutant monkeys resembled RTT patients in immune gene dysregulation. Taken together, the stark similarity in phenotype and/or endophenotype between monkeys and patients suggested that gene-edited RTT founder monkeys would be of value for disease mechanistic studies as well as development of potential therapeutic interventions for RTT

Functional Differences in the Backward Shifts of CA1 and CA3 Place Fields in Novel and Familiar Environments

Insight into the processing dynamics and other neurophysiological properties of different hippocampal subfields is critically important for understanding hippocampal function. In this study, we compared shifts in the center of mass (COM) of CA3 and CA1 place fields in a familiar and completely novel environment. Place fields in CA1 and CA3 were simultaneously recorded as rats ran along a closed loop track in a familiar room followed by a session in a completely novel room. This process was repeated each day over a 4-day period. CA3 place fields shifted backward (opposite to the direction of motion of the rat) only in novel environments. This backward shift gradually diminished across days, as the novel environment became more familiar with repeated exposures. Conversely, CA1 place fields shifted backward across all days in both familiar and novel environments. Prior studies demonstrated that CA1 place fields on average do not exhibit a backward shift during the first exposure to an environment in which the familiar cues are rearranged into a novel configuration, although CA3 place fields showed a strong backward shift. Under the completely novel conditions of the present study, no dissociation was observed between CA3 and CA1 during the first novel session (although a strong dissociation was observed in the familiar sessions and the later novel sessions). In summary, this is the first study to use simultaneous recordings in CA1 and CA3 to compare place field COM shift and other associated properties in truly novel and familiar environments. This study further demonstrates functional differentiation between CA1 and CA3 as the plasticity of CA1 place fields is affected differently by exposure to a completely novel environment in comparison to an altered, familiar environment, whereas the plasticity of CA3 place fields is affected similarly during both types of environmental novelty

Influenza A Virus Entry Inhibitors Targeting the Hemagglutinin

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Bioavailability of heavy metals bounded to PM2.5 in Xi'an, China: seasonal variation and health risk assessment

Studying the characteristics and health risks of heavy metals in atmospheric fine particulate matter (PM2.5) is a crucial component of understanding atmospheric pollution in China. Integrated 24 h PM2.5 samples were collected in winter and summer 2016 in Xi'an, China. The pollution levels, speciation, and health risks of seven PM2.5-bound metal elements (Al, As, Cd, Cr, Ni, Pb, and Zn) were investigated in this study. The average concentration of PM2.5 was 50.1 +/- 30.4 mu g m(-3) and was higher in winter than in summer. Significant seasonal variations in the elements were also observed. The average concentration ratios of Al, As, Cd, Cr, and Pb decreased in summer by 17.5%, 6.4%, 42.5%, 34.1%, and 61.4% compared with their concentrations in winter, respectively, whereas Ni and Zn increased by 37.7% and 7.6% in summer. The soluble and exchangeable fraction (F1) accounted for large proportions of Cd and Pb concentrations, indicating their greater hazard to the environment and human health. Al, As, and Cr mainly existed in the residual state (F4), which had relatively high stability in particulate matter. Ni was consistently distributed in different forms (F1-F4). The bioavailability evaluation indicated that Pb, Cd, Ni, and Zn were potential bioavailable element which exhibited strong biological toxicity. Although the concentration of Pb was very low, its BI value was the highest. The carcinogenic and non-carcinogenic risks of Cr and As were relatively high, and thus require attention from the government and environmental management departments