Is hyperuricemia an independent risk factor for new-onset chronic kidney disease?: a systematic review and meta-analysis based on observational cohort studies

This article discusses the role of interrogation in intelligence during the Second World War, and it focuses on the importance of culture in the collection of Human Intelligence in the European theatre of operation. It argues that cultural issues, including but not limited to language knowledge, provided an added value to interrogation, interviewing and questioning during and after the Second World War, for example through the employment of native speakers, in particular former refugees and enemy aliens. The article also highlights some of the flaws involved in this process, which led to bad prisoner handling and therefore bad intelligence collection. It also tries to complement archival sources with personal accounts and oral histories in order to achieve a deeper understanding of the role of the human being in the collection of intelligence through interrogation and questioning

Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services

Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures. To render GNN-based service for IoT-driven smart applications, traditional model serving paradigms usually resort to the cloud by fully uploading geo-distributed input data to remote datacenters. However, our empirical measurements reveal the significant communication overhead of such cloud-based serving and highlight the profound potential in applying the emerging fog computing. To maximize the architectural benefits brought by fog computing, in this paper, we present Fograph, a novel distributed real-time GNN inference framework that leverages diverse and dynamic resources of multiple fog nodes in proximity to IoT data sources. By introducing heterogeneity-aware execution planning and GNN-specific compression techniques, Fograph tailors its design to well accommodate the unique characteristics of GNN serving in fog environments. Prototype-based evaluation and case study demonstrate that Fograph significantly outperforms the state-of-the-art cloud serving and fog deployment by up to 5.39x execution speedup and 6.84x throughput improvement.Comment: Accepted by IEEE/ACM Transactions on Networkin

Flattening Singular Values of Factorized Convolution for Medical Images

Convolutional neural networks (CNNs) have long been the paradigm of choice for robust medical image processing (MIP). Therefore, it is crucial to effectively and efficiently deploy CNNs on devices with different computing capabilities to support computer-aided diagnosis. Many methods employ factorized convolutional layers to alleviate the burden of limited computational resources at the expense of expressiveness. To this end, given weak medical image-driven CNN model optimization, a Singular value equalization generalizer-induced Factorized Convolution (SFConv) is proposed to improve the expressive power of factorized convolutions in MIP models. We first decompose the weight matrix of convolutional filters into two low-rank matrices to achieve model reduction. Then minimize the KL divergence between the two low-rank weight matrices and the uniform distribution, thereby reducing the number of singular value directions with significant variance. Extensive experiments on fundus and OCTA datasets demonstrate that our SFConv yields competitive expressiveness over vanilla convolutions while reducing complexity

Perancangan Sistem Otomatis Update pada Aplikasi Desktop Abios

Unlike web applications easier to update the latest version, desktop applications more difficult and must involve the user in doing so. It is caused by a desktop application is an application that is installed in the computer user. The purpose of this research is to design an automatic system updates on a desktop application, an example case: Application Binus International Operational Support (ABIOS). This research used literature study and system design. In desktop applications, often there is update the latest applications that are not known to the user who sometimes fatal and disrupt business operations. Generally, developer will inform the changes version to user that they can update the application. In an update of applications, should be done by the system automatically, not manually by users. Once in a while, the user background is not from computer base. After doing the research, it can be concluded that the system automatically updates the application has benefits to users in obtaining information regarding the latest version, and can assist in automatically update the latest application is based on computerization. For further development of this system is expected to operate on multi platforms and or mobile applications

A network‐based variable selection approach for identification of modules and biomarker genes associated with end‐stage kidney disease

AimsIntervention for end‐stage kidney disease (ESKD), which is associated with adverse prognoses and major economic burdens, is challenging due to its complex pathogenesis. The study was performed to identify biomarker genes and molecular mechanisms for ESKD by bioinformatics approach.MethodsUsing the Gene Expression Omnibus dataset GSE37171, this study identified pathways and genomic biomarkers associated with ESKD via a multi‐stage knowledge discovery process, including identification of modules of genes by weighted gene co‐expression network analysis, discovery of important involved pathways by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, selection of differentially expressed genes by the empirical Bayes method, and screening biomarker genes by the least absolute shrinkage and selection operator (Lasso) logistic regression. The results were validated using GSE70528, an independent testing dataset.ResultsThree clinically important gene modules associated with ESKD, were identified by weighted gene co‐expression network analysis. Within these modules, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed important biological pathways involved in ESKD, including transforming growth factor‐β and Wnt signalling, RNA‐splicing, autophagy and chromatin and histone modification. Furthermore, Lasso logistic regression was conducted to identify five final genes, namely, CNOT8, MST4, PPP2CB, PCSK7 and RBBP4 that are differentially expressed and associated with ESKD. The accuracy of the final model in distinguishing the ESKD cases and controls was 96.8% and 91.7% in the training and validation datasets, respectively.ConclusionNetwork‐based variable selection approaches can identify biological pathways and biomarker genes associated with ESKD. The findings may inform more in‐depth follow‐up research and effective therapy.SUMMARY AT A GLANCEThis gene–gene network analysis to identify genes associated with end‐stage renal disease is an important step, albeit early, towards the discovery of biomarkers using peripheral blood cells. The findings also provide insight on disease pathophysiology at the molecular level, and hence therapeutic targets for future research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162799/2/nep13655.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162799/1/nep13655_am.pd

Easy and Efficient Transformer : Scalable Inference Solution For large NLP model

Recently, large-scale transformer-based models have been proven to be effective over a variety of tasks across many domains. Nevertheless, putting them into production is very expensive, requiring comprehensive optimization techniques to reduce inference costs. This paper introduces a series of transformer inference optimization techniques that are both in algorithm level and hardware level. These techniques include a pre-padding decoding mechanism that improves token parallelism for text generation, and highly optimized kernels designed for very long input length and large hidden size. On this basis, we propose a transformer inference acceleration library -- Easy and Efficient Transformer (EET), which has a significant performance improvement over existing libraries. Compared to Faster Transformer v4.0's implementation for GPT-2 layer on A100, EET achieves a 1.5-4.5x state-of-art speedup varying with different context lengths. EET is available at https://github.com/NetEase-FuXi/EET. A demo video is available at https://youtu.be/22UPcNGcErg

Extremely strong tubular stacking of aromatic oligoamide macrocycles

As the third-generation rigid macrocycles evolved from progenitor 1, cyclic aromatic oligoamides 3, with a backbone of reduced constraint, exhibit extremely strong stacking with an astoundingly high affinity (estimated lower limit of Kdimer \u3e 1013 M–1 in CHCl3), which leads to dispersed tubular stacks that undergo further assembly in solution. Computational study reveals a very large binding energy (–49.77 kcal mol–1) and indicates highly cooperative local dipole interactions that account for the observed strength and directionality for the stacking of 3. In the solid-state, X-ray diffraction (XRD) confirms that the aggregation of 3 results in well-aligned tubular stacks. The persistent tubular assemblies of 3, with their non-deformable sub-nm pore, are expected to possess many interesting functions. One such function, transmembrane ion transport, is observed for 3. Includes supplemental material

Extremely strong tubular stacking of aromatic oligoamide macrocycles

As the third-generation rigid macrocycles evolved from progenitor 1, cyclic aromatic oligoamides 3, with a backbone of reduced constraint, exhibit extremely strong stacking with an astoundingly high affinity (estimated lower limit of Kdimer \u3e 1013 M–1 in CHCl3), which leads to dispersed tubular stacks that undergo further assembly in solution. Computational study reveals a very large binding energy (–49.77 kcal mol–1) and indicates highly cooperative local dipole interactions that account for the observed strength and directionality for the stacking of 3. In the solid-state, X-ray diffraction (XRD) confirms that the aggregation of 3 results in well-aligned tubular stacks. The persistent tubular assemblies of 3, with their non-deformable sub-nm pore, are expected to possess many interesting functions. One such function, transmembrane ion transport, is observed for 3. Includes supplemental material

Genome-wide compound heterozygote analysis highlights alleles associated with adult height in Europeans

Adult height is the most widely genetically studied common trait in humans; however, the trait variance explainable by currently known height-associated single nucleotide polymorphisms (SNPs) identified from the previous genome-wide association studies (GWAS) is yet far from complete given the high heritability of this complex trait. To exam if compound heterozygotes (CH) may explain extra height variance, we conducted a genome-wide analysis to screen for CH in association with adult height in 10,631 Dutch Europeans enriched with extremely tall people, using our recently developed method implemented in the software package CollapsABEL. The analysis identified six regions (3q23, 5q35.1, 6p21.31, 6p21.33, 7q21.2, and 9p24.3), where multiple pairs of SNPs as CH showed genome-wide significant association with height (P < 1.67 × 10−10). Of those, 9p24.3 represents a novel region influencing adult height, whereas the others have been highlighted in the previous GWAS on height based on analysis of individual SNPs. A replication analysis in 4080 Australians of European ancestry confirmed the significant CH-like association at 9p24.3 (P < 0.05). Together, the collapsed genotypes at these six loci explained 2.51% of the height variance (after adjusting for sex and age), compared with 3.23% explained by the 14 top-associated SNPs at 14 loci identified by traditional GWAS in the same data set (P < 5 × 10−8). Overall, our study empirically demonstrates that CH plays an important role in adult height and may explain a proportion of its “missing heritability”. Moreover, our findings raise promising expectations for other highly polygenic complex traits to explain missing heritability identifiable through CH-like associations

Glycated Haemoglobin A1c Variability Score Elicits Kidney Function Decline in Chinese People Living with Type 2 Diabetes

Our aim was to investigate the association of glycated haemoglobin A1c (HbA1c) variability score (HVS) with estimated glomerular filtration rate (eGFR) slope in Chinese adults living with type 2 diabetes. This cohort study included adults with type 2 diabetes attending outpatient clinics between 2011 and 2019 from a large electronic medical record-based database of diabetes in China (WECODe). We estimated the individual-level visit-to-visit HbA1c variability using HVS, a proportion of changes in HbA1c of ≥0.5% (5.5 mmol/mol). We estimated the odds of people experiencing a rapid eGFR annual decline using a logistic regression and differences across HVS categories in the mean eGFR slope using a mixed-effect model. The analysis involved 2397 individuals and a median follow-up of 4.7 years. Compared with people with HVS ≤ 20%, those with HVS of 60% to 80% had 11% higher odds of experiencing rapid eGFR annual decline, with an extra eGFR decline of 0.93 mL/min/1.73 m(2) per year on average; those with HVS > 80% showed 26% higher odds of experiencing a rapid eGFR annual decline, with an extra decline of 1.83 mL/min/1.73 m(2) per year on average. Chinese adults with type 2 diabetes and HVS > 60% could experience a more rapid eGFR decline