Deep Clustering Survival Machines with Interpretable Expert Distributions

Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. In this study, we propose a hybrid survival analysis method, referred to as deep clustering survival machines, that combines the discriminative and generative mechanisms. Similar to the mixture models, we assume that the timing information of survival data is generatively described by a mixture of certain numbers of parametric distributions, i.e., expert distributions. We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions. This method also facilitates interpretable subgrouping/clustering of all instances according to their associated expert distributions. Extensive experiments on both real and synthetic datasets have demonstrated that the method is capable of obtaining promising clustering results and competitive time-to-event predicting performance

Experimental Investigations into Bubble Characteristics in a Fluidized Bed through Electrostatic Imaging

Fluidized beds are widely applied in many industrial processes. In order to control and optimise the operation of a fluidized bed, it is necessary to develop reliable methods for the measurement of bubble characteristics to monitor the status of the bed. Electrostatic sensing methods based on the detection of charges on particles have been applied to characterize the particle motion in a fluidized bed. However, there is limited research on the measurement of bubble characteristics using the electrostatic methods due to complex electrostatic phenomena around the bubbles. In this paper, an imaging method using a two-dimensional electrostatic sensor array is employed for the experimental investigations into the bubble behaviors in a two-dimensional fluidized bed. The bubble size, shape, rising velocity and generation frequency are measured. Moreover, an optical imaging system is employed to obtain reference information to evaluate the performance of the electrostatic imaging method. Experimental results show that the bubble characteristics measured from the electrostatic sensor array have a good agreement with the results from the optical imaging system. The relative root mean square error between the bubble shapes measured from the electrostatic sensor array and from the optical system is 0.239 with a standard deviation within 4.7%

Dual-mobility cup total hip arthroplasty improves the quality of life compared to internal fixation in femoral neck fractures patients with severe neuromuscular disease in the lower extremity after stroke: a retrospective study

BackgroundThis study aimed to demonstrate that dual-mobility cup total hip arthroplasty (DMC-THA) can significantly improve the quality of life (QOL) of elderly femoral neck fracture patients with severe neuromuscular disease in unilateral lower extremities due to stroke hemiplegia compared to internal fixation (IF).MethodsFifty-eight cases of severe neuromuscular disease in the unilateral lower extremities with muscle strength < grade 3/5 due to stroke were retrospectively examined From January 2015 to December 2020. Then, patients were divided into DMC and IF groups. The QOL was examined using the EQ-5D and SF-36 outcome measures. The physical and mental statuses were assessed using the Barthel Index (BI) and e Fall Efficacy Scale-International (FES-I), respectively.ResultsPatients in the DMC group had higher BI scores than those in the IF group at different time point. Regarding mental status, the FES-I mean score was 42.1 ± 5.3 in the DMC group and 47.3 ± 5.6 in the IF group (p = 0.002). For the QOL, the mean SF-36 score was 46.1 ± 18.3 for the health component and 59.5 ± 15.0 for the mental component in the DMC group compared to 35.3 ± 16.2 (p = 0.035), and 46.6 ± 17.4 (p = 0.006) compared to the IF group. The mean EQ-5D-5L values were 0.733 ± 0.190 and 0.303 ± 0.227 in the DMC and IF groups (p = 0.035), respectively.ConclusionDMC-THA significantly improved postoperative QOL compared to IF in elderly patients with femoral neck fractures and severe neuromuscular dysfunction in the lower extremity after stroke. The improved outcomes were related to the enhanced early, rudimentary motor function of patients

Evaluating Large Language Models: A Comprehensive Survey

Large language models (LLMs) have demonstrated remarkable capabilities across a broad spectrum of tasks. They have attracted significant attention and been deployed in numerous downstream applications. Nevertheless, akin to a double-edged sword, LLMs also present potential risks. They could suffer from private data leaks or yield inappropriate, harmful, or misleading content. Additionally, the rapid progress of LLMs raises concerns about the potential emergence of superintelligent systems without adequate safeguards. To effectively capitalize on LLM capacities as well as ensure their safe and beneficial development, it is critical to conduct a rigorous and comprehensive evaluation of LLMs. This survey endeavors to offer a panoramic perspective on the evaluation of LLMs. We categorize the evaluation of LLMs into three major groups: knowledge and capability evaluation, alignment evaluation and safety evaluation. In addition to the comprehensive review on the evaluation methodologies and benchmarks on these three aspects, we collate a compendium of evaluations pertaining to LLMs' performance in specialized domains, and discuss the construction of comprehensive evaluation platforms that cover LLM evaluations on capabilities, alignment, safety, and applicability. We hope that this comprehensive overview will stimulate further research interests in the evaluation of LLMs, with the ultimate goal of making evaluation serve as a cornerstone in guiding the responsible development of LLMs. We envision that this will channel their evolution into a direction that maximizes societal benefit while minimizing potential risks. A curated list of related papers has been publicly available at https://github.com/tjunlp-lab/Awesome-LLMs-Evaluation-Papers.Comment: 111 page

SARS-CoV Infection in a Restaurant from Palm Civet

Contact with food animals was associated with SARS-CoV infection in the People’s Republic of China

Assessment of a Novel VEGF Targeted Agent Using Patient-Derived Tumor Tissue Xenograft Models of Colon Carcinoma with Lymphatic and Hepatic Metastases

The lack of appropriate tumor models of primary tumors and corresponding metastases that can reliably predict for response to anticancer agents remains a major deficiency in the clinical practice of cancer therapy. It was the aim of our study to establish patient-derived tumor tissue (PDTT) xenograft models of colon carcinoma with lymphatic and hepatic metastases useful for testing of novel molecularly targeted agents. PDTT of primary colon carcinoma, lymphatic and hepatic metastases were used to create xenograft models. Hematoxylin and eosin staining, immunohistochemical staining, genome-wide gene expression analysis, pyrosequencing, qRT-PCR, and western blotting were used to determine the biological stability of the xenografts during serial transplantation compared with the original tumor tissues. Early passages of the PDTT xenograft models of primary colon carcinoma, lymphatic and hepatic metastases revealed a high degree of similarity with the original clinical tumor samples with regard to histology, immunohistochemistry, genes expression, and mutation status as well as mRNA expression. After we have ascertained that these xenografts models retained similar histopathological features and molecular signatures as the original tumors, drug sensitivities of the xenografts to a novel VEGF targeted agent, FP3 was evaluated. In this study, PDTT xenograft models of colon carcinoma with lymphatic and hepatic metastasis have been successfully established. They provide appropriate models for testing of novel molecularly targeted agents

Activation of HCA2 regulates microglial responses to alleviate neurodegeneration in LPS-induced in vivo and in vitro models

Abstract Background Previous studies have shown a close association between an altered immune system and Parkinson's disease (PD). Neuroinflammation inhibition may be an effective measure to prevent PD. Recently, numerous reports have highlighted the potential of hydroxy-carboxylic acid receptor 2 (HCA2) in inflammation-related diseases. Notably, the role of HCA2 in neurodegenerative diseases is also becoming more widely known. However, its role and exact mechanism in PD remain to be investigated. Nicotinic acid (NA) is one of the crucial ligands of HCA2, activating it. Based on such findings, this study aimed to examine the effect of HCA2 on neuroinflammation and the role of NA-activated HCA2 in PD and its underlying mechanisms. Methods For in vivo studies, 10-week-old male C57BL/6 and HCA2−/− mice were injected with LPS in the substantia nigra (SN) to construct a PD model. The motor behavior of mice was detected using open field, pole-climbing and rotor experiment. The damage to the mice's dopaminergic neurons was detected using immunohistochemical staining and western blotting methods. In vitro, inflammatory mediators (IL-6, TNF-α, iNOS and COX-2) and anti-inflammatory factors (Arg-1, Ym-1, CD206 and IL-10) were detected using RT-PCR, ELISA and immunofluorescence. Inflammatory pathways (AKT, PPARγ and NF-κB) were delineated by RT-PCR and western blotting. Neuronal damage was detected using CCK8, LDH, and flow cytometry assays. Results HCA2−/− increases mice susceptibility to dopaminergic neuronal injury, motor deficits, and inflammatory responses. Mechanistically, HCA2 activation in microglia promotes anti-inflammatory microglia and inhibits pro-inflammatory microglia by activating AKT/PPARγ and inhibiting NF-κB signaling pathways. Further, HCA2 activation in microglia attenuates microglial activation-mediated neuronal injury. Moreover, nicotinic acid (NA), a specific agonist of HCA2, alleviated dopaminergic neuronal injury and motor deficits in PD mice by activating HCA2 in microglia in vivo. Conclusions Niacin receptor HCA2 modulates microglial phenotype to inhibit neurodegeneration in LPS-induced in vivo and in vitro models

Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks

The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life