Present-day activity and seismic potential of the north Qinling fault, southern ordos block, central China, as revealed from GPS data and seismicity

The North Qinling Fault, located at the boundary of the North China Block and the South China Block, represents an important tectonic structure between the Weihe Basin and the Qinling Mountains, and controls the subsidence and expansion of the Weihe Basin. This fault has been highly active and has caused strong earthquakes since the Holocene and in a pre-seismic stage currently, as indicated by the many paleoearthquake traces found along it. To determine the present-day activity and seismic potential of the North Qinling Fault, by inverting GPS data, we produced fault locking depth, slip rate, and regional strain fields maps; moreover, based on seismicity, we produced a seismic b-value map. Combining this information with modern seismicity, we were able to comprehensively analyze the seismic potential of different fault segments. Our inversion of GPS data showed that the slip rate of the western segment of the fault (Qingjiangkou–Xitangyu) and the correspondent locking depth are 1.33 mm/a and 13.54 km, respectively, while the slip rate of the middle segment (Xitangyu–Fengyukou) and the correspondent locking depth are 0.45 mm/a and 8.58 km, respectively; finally, the slip rate of the eastern segment (Xitangyu–Daiyu) and the correspondent locking depth are 0.36 mm/a and 21.46 km, respectively. The locking depths of the western and middle segments of the fault are shallower than 90% of the seismic cutoff depth, while the locking depth of the eastern segment of the fault is similar to 90% of the seismic cutoff depth, indicating that “deep creep” occurs in the western and middle segments, while the eastern segment is locked. Modern small earthquakes have involved the western and middle segments of the fault, while the eastern segment has acted as a seismic gap with weak seismicity, characterized by a higher shear strain value and a lower b-value. These characteristics reflect the relationship between the locking depth and seismicity distribution. The results of our comprehensive analysis, combined with field geological surveys, show that the eastern segment of the North Qinling Fault has a strong seismic potential and is presently locked

A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph

The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a challenge for knowledge engineering to construct a high-quality domain knowledge graph for multi-source heterogeneous data. We propose a complete process framework for constructing a knowledge graph that combines structured data and unstructured data, which includes data processing, information extraction, knowledge fusion, data storage, and update strategies, aiming to improve the quality of the knowledge graph and extend its life cycle. Specifically, we take the construction process of an enterprise knowledge graph as an example and integrate enterprise register information, litigation-related information, and enterprise announcement information to enrich the enterprise knowledge graph. For the unstructured text, we improve existing model to extract triples and the F1-score of our model reached 72.77%. The number of nodes and edges in our constructed enterprise knowledge graph reaches 1,430,000 and 3,170,000, respectively. Furthermore, for each type of multi-source heterogeneous data, we apply corresponding methods and strategies for information extraction and data storage and carry out a detailed comparative analysis of graph databases. From the perspective of practical use, the informative enterprise knowledge graph and its timely update can serve many actual business needs. Our proposed enterprise knowledge graph has been deployed in HuaRong RongTong (Beijing) Technology Co., Ltd. and is used by the staff as a powerful tool for corporate due diligence. The key features are reported and analyzed in the case study. Overall, this paper provides an easy-to-follow solution and practice for domain knowledge graph construction, as well as demonstrating its application in corporate due diligence

Whole-Exome Sequencing Identifies Rare and Low-Frequency Coding Variants Associated with LDL Cholesterol

Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98th or <2nd percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels

Deep-learning models require large amounts of accurately labeled data. However, for medical image segmentation, high-quality labels rely on expert experience, and less-experienced operators provide noisy labels. How one might mitigate the negative effects caused by noisy labels for 3D medical image segmentation has not been fully investigated. In this paper, our purpose is to propose a novel hybrid robust-learning architecture to combat noisy labels for 3D medical image segmentation. Our method consists of three components. First, we focus on the noisy annotations of slices and propose a slice-level label-quality awareness method, which automatically generates label-quality scores for slices in a set. Second, we propose a shape-awareness regularization loss based on distance transform maps to introduce prior shape information and provide extra performance gains. Third, based on a re-weighting strategy, we propose an end-to-end hybrid robust-learning architecture to weaken the negative effects caused by noisy labels. Extensive experiments are performed on two representative datasets (i.e., liver segmentation and multi-organ segmentation). Our hybrid noise-robust architecture has shown competitive performance, compared to other methods. Ablation studies also demonstrate the effectiveness of slice-level label-quality awareness and a shape-awareness regularization loss for combating noisy labels

A Skeleton-Based Rehabilitation Exercise Assessment System With Rotation Invariance

Automated exercise assessment is of great importance for patients under rehabilitation exercise who require professional guidance. Among the existing approaches, the skeleton-based assessment model that classifies the correctness of the exercise has attracted much attention due to its relative ease of implementation and convenience in use. However, there are two problems with this approach. The first problem is its sensitivity to the orientation of the human skeleton. To solve this problem, we propose a novel rotation-invariant descriptor, the dot product matrix of the human skeleton, and prove mathematically that this descriptor discards only the orientation message that we do not expect while preserving all other useful information. Lack of feedback from the system is the second problem, because the exercisers do not know which parts of their exercises are incorrect. Therefore, we develop a visualization method for our system based on Gradient-Weighted Class Activation Mapping (Grad-CAM) and an quantitative metric called Overlap Ratio (OvR) to measure the quality of the visualization result. To demonstrate the effect of our method, we conduct experiments on two public datasets and a self-generated push-up dataset. The experimental results show that our rotation-invariant descriptor can achieve absolute robustness to orientation even under severe angle perturbations. In terms of accuracy and OvR, our method even outperforms previous works in most cases, indicating that the rotation-invariant descriptor helps the assessment model to extract more stable features. The visualization results are also informative to correct the movements; some examples are presented in this paper. The code of this paper and our push-up dataset are publicly available at https://github.com/Kelly510/RehabExerAssess

Des stress-tests pour une mobilité durable : une approche par l’accessibilité. Rapport final

Stress tests for a sustainable mobility: an accessibility approach. - The project called “stress-tests for a sustainable mobility: an accessibility approach” aims to develop stress-tests to propose a sustainable mobility analysis. First, comparing the Lyon Urban Area and the Munich metropolitan Region, municipalities are tested regarding their susceptibility to sharp increases in mobility costs by means of the Vulnerability Assessment. The concept is in this research adapted to regional vulnerability in the case of dramatic increases in mobility costs.This vulnerability analysis is coupled with a “stress-tests” approach at a household’s level. Stress-tests implement shocks on fuel prices and CO2 emission. It highlights shocks impact on different household’s daily mobility and location choices.Ce projet vise à développer des « stress-tests » dans le cadre d’une analyse de la mobilité durable. A partir d’une comparaison entre les aires d’étude de l’Aire Urbaine de Lyon et de la Région Métropolitaine de Munich, il mobilise le concept de vulnérabilité pour réaliser un état des lieux à l’échelle communale des zones les plus sensibles à une hausse potentielle du coût de la mobilité automobile.Cette étude en termes de vulnérabilité est complétée, dans un second temps, par une analyse à partir de « stress-tests » pour envisager l’impact de chocs sur la mobilité sur différents profils de ménages français et allemands

Additional file 1 of Chinese visceral adiposity index and its transition patterns: impact on cardiovascular and cerebrovascular diseases in a national cohort study

Supplementary Material