Knowledge Transfer in Information Systems Support Community: Network Effects of Bridging and Reaching

This study focuses on two network characteristics of an IS support community – bridging and reaching – and examines their effects on knowledge transfer and IS support professional\u27s productivity. Bridging is studied through Burt’s structural holes (constraint) measure; Reaching is studied through Valente and Foreman’s closeness centrality (radiality) measure. We posit that structural hole access and closeness centrality are positively related to IS support professional’s productivity. To test our hypotheses, we collected archival data comprising 11,409 system usage problem records reported by over 2,000 users during an 11-month post-implementation period of a new enterprise system, SAP/R3, in a large U.S. company. We analyzed the data using social network analysis and analysis of covariance. Our results provide strong support for our hypotheses. Our study offers new insight into traditional arguments on the path-dependency of experience learning and absorptive capacity and suggests several ways for IS professionals to improve their productivity

Preparation and evaluation of PEGylated phospholipid membrane coated layered double hydroxide nanoparticles

AbstractThe aim of the present study was to develop layered double hydroxide (LDH) nanoparticles coated with PEGylated phospholipid membrane. By comparing the size distribution and zeta potential, the weight ratio of LDH to lipid materials which constitute the outside membrane was identified as 2:1. Transmission electron microscopy photographs confirmed the core-shell structure of PEGylated phospholipid membrane coated LDH (PEG-PLDH) nanoparticles, and cell cytotoxicity assay showed their good cell viability on Hela and BALB/C-3T3 cells over the concentration range from 0.5 to 50 μg/mL

Mg2+-dependent facilitation and inactivation of L-type Ca2+ channels in guinea pig ventricular myocytes

AbstractThis study aimed to investigate the intracellular Mg2+ regulation of the L-type Ca2+ channels in guinea pig ventricular myocytes. By adopting the inside-out configuration of the patch clamp technique, single channel currents of the L-type Ca2+ channels were recorded at different intracellular Mg2+ concentrations ([Mg2+]i). At free [Mg2+]i of 0, 10−9, 10−7, 10−5, 10−3, and 10−1 M, 1.4 μM CaM + 3 mM ATP induced channel activities of 44%, 117%, 202%, 181%, 147%, and 20% of the control activity in cell-attached mode, respectively, showing a bell-shaped concentration-response relationship. Moreover, the intracellular Mg2+ modulated the Ca2+ channel gating properties, accounting for alterations in channel activities. These results imply that Mg2+ has a dual effect on the L-type Ca2+ channels: facilitation and inhibition. Lower [Mg2+]i maintains and enhances the basal activity of Ca2+ channels, whereas higher [Mg2+]i inhibits channel activity. Taken together, our data from the application of an [Mg2+]i series suggest that the dual effect of Mg2+ upon the L-type Ca2+ channels exhibits long open-time dependence

Super-Resolution and Segmentation Deep Learning for Breast Cancer Histopathology Image Analysis

Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images\u27 size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model\u27s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings

A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter

Supply chain management is a vital part of ensuring service quality and production efficiency in industrial applications. With the development of cloud computing and data intelligence in modern industries, datacenters have become an important basic support for intelligent applications. However, the increase in the number and complexity of tasks makes datacenters face increasingly heavy task processing demands. Therefore, there are problems of long task completion time and long response time in the task scheduling process of the data center. A multi-swarm particle swarm optimization task scheduling approach based on load balancing is proposed in this paper, aiming to reduce the maximum completion time and response time in task scheduling. The proposed approach improves the fitness evaluation function of particle swarms to facilitate load balancing. The new adaptive inertia weight and initialization method design can improve the search efficiency and convergence speed of particles. Meanwhile, the multi-swarm design can avoid the problem of particles falling into local optimum as much as possible. Finally, the proposed algorithm is verified experimentally using the task dataset released by Alibaba datacenter, and compared with other benchmark algorithms. The results show that the proposed algorithm can improve the task scheduling performance of the datacenter in supply chain management when dealing with different workloads and changes in the number of elastic machines

Expedient Synthesis of Core Disaccharide Building Blocks from Natural Polysaccharides for Heparan Sulfate Oligosaccharide Assembly

The complex sulfation motifs of heparan sulfate glycosaminoglycans (HS GAGs) play critical roles in many important biological processes. However, an understanding of their specific functions has been hampered by an inability to synthesize large numbers of diverse, yet defined, HS structures. Here, we describe a new approach to access the four core disaccharides required for HS/heparin oligosaccharide assembly from natural polysaccharides. The use of disaccharides as minimal precursors rather than monosaccharides greatly accelerates the synthesis of HS GAGs, providing key disaccharide and tetrasaccharide intermediates in about half the number of steps compared to traditional strategies. Rapid access to such versatile intermediates will enable the generation of comprehensive libraries of sulfated oligosaccharides for unlocking the ‘sulfation code’ and understanding the roles of specific GAG structures in physiology and disease

Diagnosis of post-neurosurgical bacterial meningitis in patients with aneurysmal subarachnoid hemorrhage based on the immunity-related proteomics signature of the cerebrospinal fluid

IntroductionPost-neurosurgical bacterial meningitis (PNBM) is a serious complication for patients who receive neurosurgical treatment, but the diagnosis is difficult given the complicated microenvironment orchestrated by sterile brain injury and pathogenic infection. In this study, we explored potential diagnostic biomarkers and immunological features using a proteomics platform.MethodsA total of 31 patients with aneurysmal subarachnoid hemorrhage (aSAH) who received neurosurgical treatment were recruited for this study. Among them, 15 were diagnosed with PNBM. The remaining 16 patients were categorized into the non-PNBM group. Proteomics analysis of the cerebrospinal fluid (CSF) was conducted on the Olink platform, which contained 92 immunity-related molecules.ResultsWe found that the expressions of 27 CSF proteins were significantly different between the PNBM and non-PNBM groups. Of those 27 proteins, 15 proteins were upregulated and 12 were downregulated in the CSF of the PNBM group. The receiver operating characteristic curve analysis indicated that three proteins (pleiotrophin, CD27, and angiopoietin 1) had high diagnostic accuracy for PNBM. Furthermore, we also performed bioinformatics analysis to explore potential pathways and the subcellular localization of the proteins.ConclusionIn summary, we found a cohort of immunity-related molecules that can serve as potential diagnostic biomarkers for PNBM in patients with aSAH. These molecules also provide an immunological profile of PNBM

Molecular Precision Engineering for Efficient Binary Organic Photovoltaics through Energy Level and Fibrillar Structure Modulation

Adjusting the energy levels and fibrillar morphology is paramount to enhancing the power conversion efficiency (PCE) of organic solar cells (OSCs). In the present study, an increase in the open-circuit voltage (VOC) is facilitated through the elongation of the alkyl chain within AQx (namely AQx-8), aiming to decrease the free volume ratio (FVR). This reduction in FVR attenuates electron-phonon coupling, thereby augmenting emission efficiency and diminishing the non-radiative energy loss (ΔEnr). To further refine the energy levels and morphological characteristics, the external undecyl chain of AQx-8 is substituted with a shorter carbon chain and cyclohexane noted for its considerable steric hindrance (AQx-H). This alteration significantly mitigates intermolecular aggregation, expands the bandgap, and elevates the lowest unoccupied molecular orbital (LUMO) energy level, culminating in an elevated VOC of 0.923 V in devices based on AQx-H. Morphological analysis reveals that blends based on AQx-H exhibit an enhanced multi-length-scale fibrillar structure, which is conducive to exciton dissociation and charge transport, thereby contributing to a high fill factor (FF) nearing 80%. Consequently, this study reports one of the highest binary PCEs documented, standing at 19.5% (with certification at 19.0%)