Robust Component-based Network Localization with Noisy Range Measurements

Accurate and robust localization is crucial for wireless ad-hoc and sensor networks. Among the localization techniques, component-based methods advance themselves for conquering network sparseness and anchor sparseness. But component-based methods are sensitive to ranging noises, which may cause a huge accumulated error either in component realization or merging process. This paper presents three results for robust component-based localization under ranging noises. (1) For a rigid graph component, a novel method is proposed to evaluate the graph's possible number of flip ambiguities under noises. In particular, graph's \emph{MInimal sepaRators that are neaRly cOllineaR (MIRROR)} is presented as the cause of flip ambiguity, and the number of MIRRORs indicates the possible number of flip ambiguities under noise. (2) Then the sensitivity of a graph's local deforming regarding ranging noises is investigated by perturbation analysis. A novel Ranging Sensitivity Matrix (RSM) is proposed to estimate the node location perturbations due to ranging noises. (3) By evaluating component robustness via the flipping and the local deforming risks, a Robust Component Generation and Realization (RCGR) algorithm is developed, which generates components based on the robustness metrics. RCGR was evaluated by simulations, which showed much better noise resistance and locating accuracy improvements than state-of-the-art of component-based localization algorithms.Comment: 9 pages, 15 figures, ICCCN 2018, Hangzhou, Chin

Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively

ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries

Existing autonomous driving pipelines separate the perception module from the prediction module. The two modules communicate via hand-picked features such as agent boxes and trajectories as interfaces. Due to this separation, the prediction module only receives partial information from the perception module. Even worse, errors from the perception modules can propagate and accumulate, adversely affecting the prediction results. In this work, we propose ViP3D, a visual trajectory prediction pipeline that leverages the rich information from raw videos to predict future trajectories of agents in a scene. ViP3D employs sparse agent queries throughout the pipeline, making it fully differentiable and interpretable. Furthermore, we propose an evaluation metric for this novel end-to-end visual trajectory prediction task. Extensive experimental results on the nuScenes dataset show the strong performance of ViP3D over traditional pipelines and previous end-to-end models.Comment: Project page is at https://tsinghua-mars-lab.github.io/ViP3

Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology

Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health. The current description and quantification of features on pathology slides are limited, prompting the need for innovative solutions to comprehensively assess diverse phenotypic attributes within Whole Slide Images (WSIs). In particular, understanding the morphological characteristics of podocytes, terminally differentiated glomerular epithelial cells, is crucial for studying glomerular injury. This paper introduces the Spatial Pathomics Toolkit (SPT) and applies it to podocyte pathomics. The SPT consists of three main components: (1) instance object segmentation, enabling precise identification of podocyte nuclei; (2) pathomics feature generation, extracting a comprehensive array of quantitative features from the identified nuclei; and (3) robust statistical analyses, facilitating a comprehensive exploration of spatial relationships between morphological and spatial transcriptomics features.The SPT successfully extracted and analyzed morphological and textural features from podocyte nuclei, revealing a multitude of podocyte morphomic features through statistical analysis. Additionally, we demonstrated the SPT's ability to unravel spatial information inherent to podocyte distribution, shedding light on spatial patterns associated with glomerular injury. By disseminating the SPT, our goal is to provide the research community with a powerful and user-friendly resource that advances cellular spatial pathomics in renal pathology. The implementation and its complete source code of the toolkit are made openly accessible at https://github.com/hrlblab/spatial_pathomics

The influence of metabolic syndrome on age-related hearing loss from the perspective of mitochondrial dysfunction

With the increase in life expectancy in the global population, aging societies have emerged in many countries, including China. As a common sensory defect in the elderly population, the prevalence of age-related hearing loss and its influence on society are increasing yearly. Metabolic syndrome is currently one of the main health problems in the world. Many studies have demonstrated that metabolic syndrome and its components are correlated with a variety of age-related diseases of the peripheral sensory system, including age-related hearing loss. Both age-related hearing loss and metabolic syndrome are high-prevalence chronic diseases, and many people suffer from both at the same time. In recent years, more and more studies have found that mitochondrial dysfunction occurs in both metabolic syndrome and age-related hearing loss. Therefore, to better understand the impact of metabolic syndrome on age-related hearing loss from the perspective of mitochondrial dysfunction, we reviewed the literature related to the relationship between age-related hearing loss and metabolic syndrome and their components to discern the possible role of mitochondria in both conditions

DEVELOPMENT OF YOGURT BASED ON LACTOSE-FREE MILK WITH A FUNCTIONAL BIOACTIVE COMPOUND

В последние годы проблема создания функциональных продуктов питания получила развитие в виде научных разработок, что позволяет создавать современные продукты целенаправленного действия и высокой биологической ценности. Целью данного ис-следования была разработка технологии производства йогурта на основе молока, не содержащего лактозы, и растительного компонента (экстракт альгината натрия, полученный с варьированием мощности ультразвуковой обработки) для обеспечения функциональных характеристик продукта. В рамках исследования было приготовлено 6 образцов йогурта. В исследуемых образцах оценивались такие показатели, как активная и титруемая кислотность, синерезис (0,5; 1; 1,5 часа), вязкость, определение массовой доли кефирана (экзополисахарида). Все подготовленные образцы имели неповрежденный сгусток, и на поверхности было обнаружено небольшое отделение сыворотки. Эта технология и рецептура перспективны для создания нового йогурта, обеспечивающего здоровье населения. При увеличении жирности молока, используемого для приготовления образцов йогурта, наблюдается увеличение содержания экзополисахарида кефирана. Наибольшее содержание экзополисахарида кефирана было обнаружено в йогурте образца 1, приготовленном на основе молока 3,5 % жирности с добавлением экстракта альгината натрия № 1. Содержание в 1 г йогурта (образец 1) EPS kefiran составляет 174,52 г. Эта технология и рецептура являются многообещающими для создания нового йогурта для обеспечения общественного здравоохранения.In recent years, the problem of creating functional food products has been developed in the form of scientific developments, which makes it possible to create modern products of pur-poseful action and high biological value. The purpose of this study was to develop a technology for the production of yogurt based on lactose-free milk, and a vegetable component (sodium alginate extract obtained with the variability of ultrasonic processing power) to ensure the functional charac-teristics of the product. As part of the study, 6 yogurt samples were prepared. In the studied samples, such indicators as active and titrated acidity, syneresis (0.5, 1, 1.5 hours), viscosity, determination of the mass fraction of kefiran (exopolysaccharide) were evaluated. All the prepared samples had an undisturbed clot, and a slight separation of serum was found on the surface. This technology and formulation is promising for the formation of a new yogurt to ensure the health of the population. With an increase in the fat content of milk used for the preparation of yogurt samples, an increase in the content of kefiran exopolysaccharide is observed. The highest content of kefiran exopolysaccharide was found in sample 1 yogurt prepared on the basis of milk of 3.5 % fat content with the addition of sodium alginate extract No. 1. The content in 1 g of yogurt (sample 1) EPS kefiran is 174.52 g. This technology and formulation is promising for the formation of a new yogurt to ensure public health

Urinary microbiota signatures associated with different types of urinary diversion: a comparative study

BackgroundRadical cystectomy and urinary diversion (UD) are gold standards for non-metastatic muscle-invasive bladder cancer. Orthotopic neobladder (or Studer), ileal conduit (or Bricker) and cutaneous ureterostomy (CU) are mainstream UD types. Little is known about urinary microbiological changes after UD. MethodsIn this study, urine samples were collected from healthy volunteers and patients with bladder cancer who had received aforementioned UD procedures. Microbiomes of samples were analyzed using 16S ribosomal RNA gene sequencing, and microbial diversities, distributions and functions were investigated and compared across groups. ResultsHighest urine microbial richness and diversity were observed in healthy controls, followed by Studer patients, especially those without hydronephrosis or residual urine, α-diversity indices of whom were remarkably higher than those of Bricker and CU groups. Studer UD type was the only independent factor favoring urine microbial diversity. The urine microflora structure of the Studer group was most similar to that of the healthy individuals while that of the CU group was least similar. Studer patients and healthy volunteers shared many similar urine microbial functions, while Bricker and CU groups exhibited opposite characteristics. ConclusionOur study first presented urinary microbial landscapes of UD patients and demonstrated the microbiological advantage of orthotopic neobladder. Microbiota might be a potential tool for optimization of UD management