Family, Community, and a Life of Politics

A first-person narrative about life in Dayton, Ohio, composed as part of the Facing Project, a nationwide storytelling initiative

Estimating ammonia emissions from cropland in China based on the establishment of agro-region-specific models

ACKNOWLEDGMENTS This work was financially supported by Natural Science Foundation of China under a grant numbers 41877546 and U1612441, and a BBSRC-Newton Funded project (BB/N013484/1). This work also contributes to the activities of Top-notch Academic Programs Project of Jiangsu Higher Education Institution of China (PPZY2015A061), and Program for Student Innovation through Research and Training (1913A22).Peer reviewedPostprin

Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer

Data-driven methods have become increasingly more prominent for musculoskeletal modelling due to their conceptually intuitive simple and fast implementation. However, the performance of a pre-trained data-driven model using the data from specific subject(s) may be seriously degraded when validated using the data from a new subject, hindering the utility of the personalised musculoskeletal model in clinical applications. This paper develops an active physics-informed deep transfer learning framework to enhance the dynamic tracking capability of the musculoskeletal model on the unseen data. The salient advantages of the proposed framework are twofold: 1) For the generic model, physics-based domain knowledge is embedded into the loss function of the data-driven model as soft constraints to penalise/regularise the data-driven model. 2) For the personalised model, the parameters relating to the feature extraction will be directly inherited from the generic model, and only the parameters relating to the subject-specific inference will be finetuned by jointly minimising the conventional data prediction loss and the modified physics-based loss. In this paper, we use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Moreover, convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework, and the physics law between muscle forces and joint kinematics is utilised as the soft constraints. Results of comprehensive experiments on a self-collected dataset from eight healthy subjects indicate the effectiveness and great generalization of the proposed framework.Comment: arXiv admin note: text overlap with arXiv:2207.0143

MLVSNet: Multi-level Voting Siamese Network for 3D visual tracking

Benefiting from the excellent performance of Siamese-based trackers, huge progress on 2D visual tracking has been achieved. However, 3D visual tracking is still under-explored. Inspired by the idea of Hough voting in 3D object detection, in this paper, we propose a Multi-level Voting Siamese Network (MLVSNet) for 3D visual tracking from outdoor point cloud sequences. To deal with sparsity in outdoor 3D point clouds, we propose to perform Hough voting on multi-level features to get more vote centers and retain more useful information, instead of voting only on the fi-nal level feature as in previous methods. We also design an efficient and lightweight Target-Guided Attention (TGA) module to transfer the target information and highlight the target points in the search area. Moreover, we propose a Vote-cluster Feature Enhancement (VFE) module to exploit the relationships between different vote clusters. Extensive experiments on the 3D tracking benchmark of KITTI dataset demonstrate that our MLVSNet outperforms state-of-the-art methods with significant margins. Code will be available at https://github.com/CodeWZT/MLVSNet

Effects of species-dominated patches on soil organic carbon and total nitrogen storage in a degraded grassland in China

Background Patchy vegetation is a very common phenomenon due to long-term overgrazing in degraded steppe grasslands, which results in substantial uncertainty associated with soil carbon (C) and nitrogen (N) dynamics because of changes in the amount of litter accumulation and nutrition input into soil. Methods We investigated soil C and N stocks beneath three types of monodominant species patches according to community dominance. Stipa krylovii patches, Artemisia frigida patches, and Potentilla acaulis patches represent better to worse vegetation conditions in a grassland in northern China. Results The results revealed that the soil C stock (0–40 cm) changed significantly, from 84.7 to 95.7 Mg ha−1, and that the soil organic carbon content (0–10 cm) and microbial biomass carbon (0–10 and 10–20 cm) varied remarkably among the different monodominant species communities (P < 0.05). However, soil total nitrogen and microbial biomass nitrogen showed no significant differences among different plant patches in the top 0–20 cm of topsoil. The soil C stocks under the P. acaulis and S. krylovii patches were greater than that under the A. frigida patch. Our study implies that accurate estimates of soil C and N storage in degenerated grassland require integrated analyses of the concurrent effects of differences in plant community composition

Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia : a systematic literature review and external validation study

Background People with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures. Methods A systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). Results Forty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52-0.60, 0.50-0.59, and 0.50-0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54-0.73, 0.52-0.67, and 0.59-0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05). Conclusions In Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need.Peer reviewe

Postbiotics in colorectal cancer: intervention mechanisms and perspectives

Colorectal cancer (CRC) is a common malignancy affecting the gastrointestinal tract worldwide. The etiology and progression of CRC are related to factors such as environmental influences, dietary structure, and genetic susceptibility. Intestinal microbiota can influence the integrity of the intestinal mucosal barrier and modulate intestinal immunity by secreting various metabolites. Dysbiosis of the intestinal microbiota can affect the metabolites of the microbial, leading to the accumulation of toxic metabolites, which can trigger chronic inflammation or DNA damage and ultimately lead to cellular carcinogenesis and the development of CRC. Postbiotics are preparations of inanimate microorganisms or their components that are beneficial to the health of the host, with the main components including bacterial components (e.g., exopolysaccharides, teichoic acids, surface layer protein) and metabolites (e.g., short-chain fatty acids, tryptophan metabolite, bile acids, vitamins and enzymes). Compared with traditional probiotics, it has a more stable chemical structure and higher safety. In recent years, it has been demonstrated that postbiotics are involved in regulating intestinal microecology and improving the progression of CRC, which provides new ideas for the prevention and diagnosis of CRC. In this article, we review the changes in intestinal microbiota in different states of the gut and the mechanisms of anti-tumor activity of postbiotic-related components, and discuss the potential significance of postbiotics in the diagnosis and treatment of CRC. This reviews the changes and pathogenesis of intestinal microbiota in the development of CRC, and summarizes the relevant mechanisms of postbiotics in resisting the development of CRC in recent years, as well as the advantages and limitations of postbiotics in the treatment process of CRC

Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study

BACKGROUND: People with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures. METHODS: A systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). RESULTS: Forty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52-0.60, 0.50-0.59, and 0.50-0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54-0.73, 0.52-0.67, and 0.59-0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05). CONCLUSIONS: In Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need.This work was supported by 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (Grant no. ZYGD18017 to NT)

Novel Human Bocavirus in Children with Acute Respiratory Tract Infection

Human bocavirus (HBoV) and HBoV2, two human bocavirus species, were found in 18 and 10 of 235 nasopharyngeal aspirates, respectively, from children hospitalized with acute respiratory tract infection. Our results suggest that, like HBoV, HBoV2 is distributed worldwide and may be associated with respiratory and enteric diseases