Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination

Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic network for various shape analysis tasks with competitive performance to supervised methods. In this paper, we propose an unsupervised method for learning a generic and efficient shape encoding network for different shape analysis tasks. The key idea of our method is to jointly encode and learn shape and point features from unlabeled 3D point clouds. For this purpose, we adapt HR-Net to octree-based convolutional neural networks for jointly encoding shape and point features with fused multiresolution subnetworks and design a simple-yet-efficient Multiresolution Instance Discrimination (MID) loss for jointly learning the shape and point features. Our network takes a 3D point cloud as input and output both shape and point features. After training, the network is concatenated with simple task-specific back-end layers and fine-tuned for different shape analysis tasks. We evaluate the efficacy and generality of our method and validate our network and loss design with a set of shape analysis tasks, including shape classification, semantic shape segmentation, as well as shape registration tasks. With simple back-ends, our network demonstrates the best performance among all unsupervised methods and achieves competitive performance to supervised methods, especially in tasks with a small labeled dataset. For fine-grained shape segmentation, our method even surpasses existing supervised methods by a large margin.Comment: Accepted by AAAI 2021. Code: https://github.com/microsoft/O-CNN/blob/master/docs/unsupervised.m

Funneled Landscape Leads to Robustness of Cell Networks: Yeast Cell Cycle

We uncovered the underlying energy landscape for a cellular network. We discovered that the energy landscape of the yeast cell-cycle network is funneled towards the global minimum (G0/G1 phase) from the experimentally measured or inferred inherent chemical reaction rates. The funneled landscape is quite robust against random perturbations. This naturally explains robustness from a physical point of view. The ratio of slope versus roughness of the landscape becomes a quantitative measure of robustness of the network. The funneled landscape can be seen as a possible realization of the Darwinian principle of natural selection at the cellular network level. It provides an optimal criterion for network connections and design. Our approach is general and can be applied to other cellular networks

Continuity of transcriptomes among colorectal cancer subtypes based on meta-analysis

Background: Previous approaches to defining subtypes of colorectal carcinoma (CRC) and other cancers based on transcriptomes have assumed the existence of discrete subtypes. We analyze gene expression patterns of colorectal tumors from a large number of patients to test this assumption and propose an approach to identify potentially a continuum of subtypes that are present across independent studies and cohorts. Results: We examine the assumption of discrete CRC subtypes by integrating 18 published gene expression datasets and \u3e3700 patients, and contrary to previous reports, find no evidence to support the existence of discrete transcriptional subtypes. Using a meta-analysis approach to identify co-expression patterns present in multiple datasets, we identify and define robust, continuously varying subtype scores to represent CRC transcriptomes. The subtype scores are consistent with established subtypes (including microsatellite instability and previously proposed discrete transcriptome subtypes), but better represent overall transcriptional activity than do discrete subtypes. The scores are also better predictors of tumor location, stage, grade, and times of disease-free survival than discrete subtypes. Gene set enrichment analysis reveals that the subtype scores characterize T-cell function, inflammation response, and cyclin-dependent kinase regulation of DNA replication. Conclusions: We find no evidence to support discrete subtypes of the CRC transcriptome and instead propose two validated scores to better characterize a continuity of CRC transcriptomes

Analysis of adverse events following immunization of adsorbed acellular DPT vaccine at different vaccination sites

ObjectiveTo analyze the occurrence of suspected adverse events following immunization （AEFI） after changing the priority vaccination sites of the adsorbed acellular diphtherior-pertussis-tetanus vaccine （hereinafter referred to as DPT vaccine）， so as to provide scientific basis for mass vaccination.MethodsMonitoring data of AEFI for the DPT vaccine in Wujiang District from September 2020 to August 2022 were collected from China's disease prevention and control information system， and the vaccination information of DPT vaccine in all children's vaccination clinics in Wujiang District during the same period was selected. The incidence of AEFI for the DPT vaccine was analyzed and compared.ResultsThe reported incidence of AEFI was significantly lower in the buttocks than that in other sites （P5.0 cm） in the deltoid muscle of the upper arm was significantly higher than that in the buttocks （P<0.05）.ConclusionThe DPT vaccine is safe in different parts of the body and is worth popularizing

Production of biomass-derived monomers through catalytic conversion of furfural and hydroxymethylfurfural

The synthesis of biomass-derived monomers has received great attention in recent years, motivated by the depletion of fossil fuels and environmental issues. Moreover, the intrinsic functionality within the biomass or biomass-derived chemicals has great potential to produce new types of monomers containing multiple acid or alcohol groups, thereby leading to materials with novel properties. Given their versatile functional groups and easy production from cellulose/hemicellulose, furfural and hydroxymethylfurfural were regarded as very important biomass-derived platform chemicals through which multiple monomers can be produced via heterogeneous catalysis. In the current review, recent development in heterogeneous catalysis for the production of 6 bio-based monomers, furfuryl alcohol, 2,5-bis(hydroxymethyl)furan, 2,5-bis-(hydroxymethyl)tetrahydrofuran, 1,5-pentanediol, 1,6-hexanediol, and 2,5-furandicarboxylic acid, from furfural and hydroxymethylfurfural is reviewed and summarized. The major challenge is how to efficiently and selectively convert specific functional group(s) in furfural and hydroxymethylfurfural during the production of these monomers. Additionally, catalyst stability issues need to be addressed due to necessity of using hot water as reaction medium and high tendency of carbonaceous deposit formation on catalyst surface. The current review mainly focuses on efforts of catalytic site design and modification, including selection of metal/support, use of synergy between metal and support, tuning metal size, use of inverse catalysts, adding catalytic promoters, constructing bimetallic sites, etc., to realize efficient, selective and stable production of bio-based monomers from furfural and hydroxymethylfurfural

Federated Learning-Based Insulator Fault Detection for Data Privacy Preserving

Insulators are widely used in distribution network transmission lines and serve as critical components of the distribution network. The detection of insulator faults is essential to ensure the safe and stable operation of the distribution network. Traditional insulator detection methods often rely on manual identification, which is time-consuming, labor-intensive, and inaccurate. The use of vision sensors for object detection is an efficient and accurate detection method that requires minimal human intervention. Currently, there is a considerable amount of research on the application of vision sensors for insulator fault recognition in object detection. However, centralized object detection requires uploading data collected from various substations through vision sensors to a computing center, which may raise data privacy concerns and increase uncertainty and operational risks in the distribution network. Therefore, this paper proposes a privacy-preserving insulator detection method based on federated learning. An insulator fault detection dataset is constructed, and Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models are trained within the federated learning framework for insulator fault detection. Most of the existing insulator anomaly detection methods use a centralized model training method, which has the advantage of achieving a target detection accuracy of over 90%, but the disadvantage is that the training process is prone to privacy leakage and lacks privacy protection capability. Compared with the existing insulator target detection methods, the proposed method can also achieve an insulator anomaly detection accuracy of more than 90% and provide effective privacy protection. Through experiments, we demonstrate the applicability of the federated learning framework for insulator fault detection and its ability to protect data privacy while ensuring test accuracy

Frailty status and risk of irritable bowel syndrome in middle-aged and older adults: A large-scale prospective cohort studyResearch in context

Summary: Background: Frailty is a public health problem for ageing society, however, evidence is lacking regarding its impact on intestinal functions. We aimed to examine prospective relationships of frailty and pre-frailty in middle-aged and older adults with incident irritable bowel syndrome (IBS) in a large-scale population-based cohort. Methods: Participants (aged 37–73 years) free of IBS, coeliac disease, inflammatory bowel disease and any cancer at baseline were included, using data from the UK Biobank (collected 2006–2010, 22 assessment centres). Participants without available primary care data were excluded. Frailty status was assessed using Fried phenotype including five criteria (weight loss, exhaustion, low grip strength, low physical activity, slow walking pace). Participants who met at least three criteria were defined as frail, and those who fulfilled one or two criteria were defined as pre-frail. Primary outcome was incident IBS. Cox proportional hazard model was conducted to examine the associated risk of incident IBS. Findings: Among 176,423 participants (mean age 56.19 years), 7994 (4.5%) and 78,957 (44.8%) were frail and pre-frail at baseline. During a median of 13.2-year follow-up, 4155 cases of incident IBS were identified. Compared with non-frail individuals, those with frail (HR = 1.80, 95% CI: 1.59–2.04) and pre-frail (HR = 1.21, 1.14–1.30) showed significantly higher risk of developing IBS after multivariable adjustment (Ptrend < 0.001). Specifically, the positive association was not only observed in older adults (HR = 1.69, 1.37–2.08 for frail; 1.24, 1.12–1.39 for pre-frail), but also in middle-aged adults (HR = 1.90, 1.62–2.22 for frail; 1.19, 1.10–1.30 for pre-frail), both with Ptrend < 0.001. Further sensitivity analysis and subgroup analysis indicated similar results. Interpretation: Frailty and pre-frailty in middle-aged and older adults are associated with increased risk of incident clinical diagnosis of IBS. Funding: National Natural Science Foundation of China (No. 82070550) &amp; National Key Research and Development Program of China (2022YFC2504002, 2022YFC2504003)

Effect of Substituting CaO with BaO and CaO/Al₂O₃ Ratio on the Viscosity of CaO–BaO–Al₂O₃–CaF₂–Li₂O Mold Flux System

The effect of substituting CaO with BaO and CaO/Al2O3 ratio on the viscosity of CaO&#8315;BaO&#8315;Al2O3&#8315;CaF2&#8315;Li2O mold flux system was studied by rotational viscosity method. The results showed that the viscosity increased with increasing BaO as a substitute for CaO, while the viscosity decreased with the increase in CaO/Al2O3 ratio. The viscous activation energy of the slags is from 92.1 kJ&#183;mol&#8722;1 to 133.4 kJ&#183;mol&#8722;1. Either the Arhenius or the Weymann&#8315;Frenkel equation can be applied to establish the viscosity prediction model. In this paper, the Weymann&#8315;Frenkel equation and a new optical basicity with regard to Al2O3 as an acidic oxide were applied to the modified NPL model for predicting the viscosity of CaO&#8315;BaO&#8315;Al2O3&#8315;CaF2&#8315;Li2O mold flux system. The estimated viscosity is in good agreement with the measured viscosity

Non-alcoholic fatty liver is associated with increased risk of irritable bowel syndrome: a prospective cohort study.

BACKGROUND: The relationship between non-alcoholic fatty liver degree as well as non-alcoholic fatty liver disease (NAFLD) and irritable bowel syndrome (IBS) remains poorly understood. We aimed to investigate the prospective association of non-alcoholic fatty liver degree as well as NAFLD with incident IBS in a large-scale population-based cohort. METHODS: Participants free of IBS, coeliac disease, inflammatory bowel disease, alcoholic liver disease, and any cancer at baseline from the UK Biobank were included. Non-alcoholic fatty liver degree was measured by a well-validated fatty liver index (FLI), with FLI ≥ 60 as an indicator of NAFLD. Primary outcome was incident IBS. Cox proportional hazard model was used to investigate the associated risk of incident IBS. RESULTS: Among 396,838 participants (mean FLI was 48.29 ± 30.07), 153,203(38.6%) were with NAFLD diagnosis at baseline. During a median of 12.4-year follow-up, 7129 cases of incident IBS were identified. Compared with non-NAFLD, NAFLD patients showed a 13% higher risk of developing IBS (HR = 1.13, 95%CI: 1.05-1.17) after multivariable adjustment. Compared with the lowest, the highest FLI quartile was associated with a significantly increased risk of IBS (HRQ4 VS Q1 = 1.21, 1.13-1.30, Ptrend < 0.001). Specifically, the positive association between non-alcoholic fatty liver degree and IBS was also observed by per SD change of FLI (adjusted HR = 1.08, 1.05-1.10). Further sensitivity analysis and subgroup analysis indicated similar results, with the positive association particularly observed in females, but not in males. CONCLUSIONS: High degree of non-alcoholic fatty liver as well as non-alcoholic fatty liver disease is associated with increased risk of incident IBS. Further studies are warranted to confirm the findings and elucidate the underlying biological mechanisms

Enhancing Robot Task Planning and Execution through Multi-Layer Large Language Models

Large language models have found utility in the domain of robot task planning and task decomposition. Nevertheless, the direct application of these models for instructing robots in task execution is not without its challenges. Limitations arise in handling more intricate tasks, encountering difficulties in effective interaction with the environment, and facing constraints in the practical executability of machine control instructions directly generated by such models. In response to these challenges, this research advocates for the implementation of a multi-layer large language model to augment a robot’s proficiency in handling complex tasks. The proposed model facilitates a meticulous layer-by-layer decomposition of tasks through the integration of multiple large language models, with the overarching goal of enhancing the accuracy of task planning. Within the task decomposition process, a visual language model is introduced as a sensor for environment perception. The outcomes of this perception process are subsequently assimilated into the large language model, thereby amalgamating the task objectives with environmental information. This integration, in turn, results in the generation of robot motion planning tailored to the specific characteristics of the current environment. Furthermore, to enhance the executability of task planning outputs from the large language model, a semantic alignment method is introduced. This method aligns task planning descriptions with the functional requirements of robot motion, thereby refining the overall compatibility and coherence of the generated instructions. To validate the efficacy of the proposed approach, an experimental platform is established utilizing an intelligent unmanned vehicle. This platform serves as a means to empirically verify the proficiency of the multi-layer large language model in addressing the intricate challenges associated with both robot task planning and execution