New Characterizations in Turnstile Streams with Applications

Recently, [Li, Nguyen, Woodruff, STOC 2014] showed any 1-pass constant probability streaming algorithm for computing a relation f on a vector x in {-m, -(m-1), ..., m}^n presented in the turnstile data stream model can be implemented by maintaining a linear sketch Ax mod q, where A is an r times n integer matrix and q = (q_1, ..., q_r) is a vector of positive integers. The space complexity of maintaining Ax mod q, not including the random bits used for sampling A and q, matches the space of the optimal algorithm. We give multiple strengthenings of this reduction, together with new applications. In particular, we show how to remove the following shortcomings of their reduction: 1. The Box Constraint. Their reduction applies only to algorithms that must be correct even if x_{infinity} = max_{i in [n]} |x_i| is allowed to be much larger than m at intermediate points in the stream, provided that x is in {-m, -(m-1), ..., m}^n at the end of the stream. We give a condition under which the optimal algorithm is a linear sketch even if it works only when promised that x is in {-m, -(m-1), ..., m}^n at all points in the stream. Using this, we show the first super-constant Omega(log m) bits lower bound for the problem of maintaining a counter up to an additive epsilon*m error in a turnstile stream, where epsilon is any constant in (0, 1/2). Previous lower bounds are based on communication complexity and are only for relative error approximation; interestingly, we do not know how to prove our result using communication complexity. More generally, we show the first super-constant Omega(log(m)) lower bound for additive approximation of l_p-norms; this bound is tight for p in [1, 2]. 2. Negative Coordinates. Their reduction allows x_i to be negative while processing the stream. We show an equivalence between 1-pass algorithms and linear sketches Ax mod q in dynamic graph streams, or more generally, the strict turnstile model, in which for all i in [n], x_i is nonnegative at all points in the stream. Combined with [Assadi, Khanna, Li, Yaroslavtsev, SODA 2016], this resolves the 1-pass space complexity of approximating the maximum matching in a dynamic graph stream, answering a question in that work. 3. 1-Pass Restriction. Their reduction only applies to 1-pass data stream algorithms in the turnstile model, while there exist algorithms for heavy hitters and for low rank approximation which provably do better with multiple passes. We extend the reduction to algorithms which make any number of passes, showing the optimal algorithm is to choose a new linear sketch at the beginning of each pass, based on the output of previous passes

Construction of an infectious cloning system of porcine reproductive and respiratory syndrome virus and identification of glycoprotein 5 as a potential determinant of virulence and pathogenicity

Porcine reproductive and respiratory syndrome virus (PRRSV) infection of pigs causes a variety of clinical manifestations, depending on the pathogenicity and virulence of the specific strain. Identification and characterization of potential determinant(s) for the pathogenicity and virulence of these strains would be an essential step to precisely design and develop effective anti-PRRSV intervention. In this study, we report the construction of an infectious clone system based on PRRSV vaccine strain SP by homologous recombination technique, and the rescue of a chimeric rSP-HUB2 strain by replacing the GP5 and M protein-coding region from SP strain with the corresponding region from a highly pathogenic strain PRRSV-HUB2. The two recombinant viruses were shown to be genetically stable and share similar growth kinetics, with rSP-HUB2 exhibiting apparent growth and fitness advantages. Compared to in cells infected with PRRSV-rSP, infection of cells with rSP-HUB2 showed significantly more inhibition of the induction of type I interferon (IFN-β) and interferon stimulator gene 56 (ISG56), and significantly more promotion of the induction of proinflammatory cytokines IL-6, IL-8, ISG15 and ISG20. Further overexpression, deletion and mutagenesis studies demonstrated that amino acid residue F16 in the N-terminal region of the GP5 protein from HUB2 was a determinant for the phenotypic difference between the two recombinant viruses. This study provides evidence that GP5 may function as a potential determinant for the pathogenicity and virulence of highly pathogenic PRRSV

Gut-joint axis in knee synovitis: gut fungal dysbiosis and altered fungi–bacteria correlation network identified in a community-based study

Objectives: Knee synovitis is a highly prevalent and potentially curable condition for knee pain; however, its pathogenesis remains unclear. We sought to assess the associations of the gut fungal microbiota and the fungi–bacteria correlation network with knee synovitis. Methods: Participants were derived from a community-based cross-sectional study. We performed an ultrasound examination of both knees. A knee was defined as having synovitis if its synovium was ≥4 mm and/or Power Doppler (PD) signal was within the knee synovium area (PD synovitis). We collected faecal specimens from each participant and assessed gut fungal and bacterial microbiota using internal transcribed spacer 2 and shotgun metagenomic sequencing. We examined the relation of α-diversity, β-diversity, the relative abundance of taxa and the interkingdom correlations to knee synovitis. Results: Among 977 participants (mean age: 63.2 years; women: 58.8%), 191 (19.5%) had knee synovitis. β-diversity of the gut fungal microbiota, but not α-diversity, was significantly associated with prevalent knee synovitis. The fungal genus Schizophyllum was inversely correlated with the prevalence and activity (ie, control, synovitis without PD signal and PD synovitis) of knee synovitis. Compared with those without synovitis, the fungi–bacteria correlation network in patients with knee synovitis was smaller (nodes: 93 vs 153; edges: 107 vs 244), and the average number of neighbours was fewer (2.3 vs 3.2). Conclusion: Alterations of gut fungal microbiota and the fungi–bacteria correlation network are associated with knee synovitis. These novel findings may help understand the mechanisms of the gut-joint axis in knee synovitis and suggest potential targets for future treatment

Insight of novel biomarkers for papillary thyroid carcinoma through multiomics

IntroductionThe overdiagnosing of papillary thyroid carcinoma (PTC) in China necessitates the development of an evidence-based diagnosis and prognosis strategy in line with precision medicine. A landscape of PTC in Chinese cohorts is needed to provide comprehensiveness.Methods6 paired PTC samples were employed for whole-exome sequencing, RNA sequencing, and data-dependent acquisition mass spectrum analysis. Weighted gene co-expression network analysis and protein-protein interactions networks were used to screen for hub genes. Moreover, we verified the hub genes' diagnostic and prognostic potential using online databases. Logistic regression was employed to construct a diagnostic model, and we evaluated its efficacy and specificity based on TCGA-THCA and GEO datasets.ResultsThe basic multiomics landscape of PTC among local patients were drawn. The similarities and differences were compared between the Chinese cohort and TCGA-THCA cohorts, including the identification of PNPLA5 as a driver gene in addition to BRAF mutation. Besides, we found 572 differentially expressed genes and 79 differentially expressed proteins. Through integrative analysis, we identified 17 hub genes for prognosis and diagnosis of PTC. Four of these genes, ABR, AHNAK2, GPX1, and TPO, were used to construct a diagnostic model with high accuracy, explicitly targeting PTC (AUC=0.969/0.959 in training/test sets).DiscussionMultiomics analysis of the Chinese cohort demonstrated significant distinctions compared to TCGA-THCA cohorts, highlighting the unique genetic characteristics of Chinese individuals with PTC. The novel biomarkers, holding potential for diagnosis and prognosis of PTC, were identified. Furthermore, these biomarkers provide a valuable tool for precise medicine, especially for immunotherapeutic or nanomedicine based cancer therapy

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Parameter Estimation for Uniformly Accelerating Moving Target in the Forward Scatter Radar Network

Passive radar based on the global navigation satellite positioning system (GNSS) has become the focus of attention in the field of radar. A parameter estimation method is proposed in the forward scatter radar (FSR) network based on GNSS to extend the application scenarios. For uniformly accelerating moving targets, only the instant times when the target crosses the individual baselines are used to retrieve the target motion parameters. The target position, velocity, and acceleration information can be obtained. Firstly, the minimum network configuration is derived theoretically. Then, the effects of crossing time error, station location error, transmitting/receiving station deployment, and target height on the accuracy are analyzed through Monte Carlo simulations. Finally, the simulation results indicate that the target position estimation error is in the order of 100 m. This paper provides the fundamental theory of aerial target positioning with a GNSS-based FSR network

Sparse regularized joint projection model for identifying associations of non-coding RNAs and human diseases

Current human biomedical research shows that human diseases are closely related to non-coding RNAs, so it is of great significance for human medicine to study the relationship between diseases and non-coding RNAs. Current research has found associations between non-coding RNAs and human diseases through a variety of effective methods, but most of the methods are complex and targeted at a single RNA or disease. Therefore, we urgently need an effective and simple method to discover the associations between non-coding RNAs and human diseases. In this paper, we propose a sparse regularized joint projection model (SRJP) to identify the associations between non-coding RNAs and diseases. First, we extract information through a series of ncRNA similarity matrices and disease similarity matrices and assign average weights to the similarity matrices of the two sides. Then we decompose the similarity matrices of the two spaces into low-rank matrices and put them into SRJP. In SRJP, we innovatively use the projection matrix to combine the ncRNA side and the disease side to identify the associations between ncRNAs and diseases. Finally, the regularization term in SRJP effectively improves the robustness and generalization ability of the model. We test our model on different datasets involving three types of ncRNAs: circRNA, microRNA and long non-coding RNA. The experimental results show that SRJP has superior ability to identify and predict the associations between ncRNAs and diseases. © 2022 The Author(s)Funding: The National Natural Science Foundation of China (NSFC 62172296, 62172076, 61902271, 61972280), Excellent Young Scientists Fund in Hunan Province (2022JJ20077), and the Municipal Government of Quzhou (Grant Number 2021D004).</p

Vanadium Hexacyanoferrate as a High-Capacity and High-Voltage Cathode for Aqueous Rechargeable Zinc Ion Batteries

Prussian blue analogs (PBAs) are widely used as electrode materials for secondary batteries because of their cheapness, ease of synthesis, and unique structural properties. Nevertheless, the unsatisfactory capacity and cyclic stability of PBAs are seriously preventing their practical applications. Here, vanadium hexacyanoferrate (VHCF) is successfully prepared and used as a cathode for aqueous zinc-ion batteries (AZIBs). When using 3 M Zn(CF3SO3)2 as the electrolyte, a high capacity of ~230 mA h g&minus;1 and a high voltage of ~1.2 V can be achieved. The XRD result and XPS analysis indicate that the outstanding Zn2+ storage capability is due to the presence of dual electrochemical redox centers in VHCF (Fe2+ &#8651; Fe3+ and V5+ &#8651; V4+ &#8651; V3+). However, the battery shows a short cycle life (7.1% remaining capacity after 1000 cycles) due to the dissolution of VHCF. To elongate the cycle life of the battery, a high-concentration hybrid electrolyte is used to reduce the activity of water molecules. The improved battery exhibits an impressive capacity of 235.8 mA h g&minus;1 and good capacity retention (92.9%) after 1000 cycles

Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features

Abstract Background Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited. Results In this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset. Conclusions The framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features