A Locality-based Neural Solver for Optical Motion Capture

We present a novel locality-based learning method for cleaning and solving optical motion capture data. Given noisy marker data, we propose a new heterogeneous graph neural network which treats markers and joints as different types of nodes, and uses graph convolution operations to extract the local features of markers and joints and transform them to clean motions. To deal with anomaly markers (e.g. occluded or with big tracking errors), the key insight is that a marker's motion shows strong correlations with the motions of its immediate neighboring markers but less so with other markers, a.k.a. locality, which enables us to efficiently fill missing markers (e.g. due to occlusion). Additionally, we also identify marker outliers due to tracking errors by investigating their acceleration profiles. Finally, we propose a training regime based on representation learning and data augmentation, by training the model on data with masking. The masking schemes aim to mimic the occluded and noisy markers often observed in the real data. Finally, we show that our method achieves high accuracy on multiple metrics across various datasets. Extensive comparison shows our method outperforms state-of-the-art methods in terms of prediction accuracy of occluded marker position error by approximately 20%, which leads to a further error reduction on the reconstructed joint rotations and positions by 30%. The code and data for this paper are available at https://github.com/non-void/LocalMoCap.Comment: Siggraph Asia 2023 Conference Pape

The LncRNA signature associated with cuproptosis as a novel biomarker of prognosis in immunotherapy and drug screening for clear cell renal cell carcinoma

Cuproptosis is a new form of cell death, the second form of metal ion-induced cell death defined after ferroptosis. Recently, cuproptosis has been suggested to be associated with tumorigenesis. However, the relationship between cuproptosis and patient prognosis in clear cell renal cell carcinoma (ccRCC) in the context of immunotherapy remains unknown. The aim of this study was to investigate the correlation between cuproptosis-related long non-coding RNA (lncRNA) and ccRCC in terms of immunity as well as prognosis. Clinical information on lncRNAs associated with differences in cuproptosis genes in ccRCC and normal tissues was collected from The Cancer Genome Atlas (TCGA) dataset. Univariate Cox regression was used to screen lncRNAs. A total of 11 lncRNAs closely associated with cuproptosis were further screened and established using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression, and the samples were randomly divided into training and test groups. A risk prognostic model was constructed using the training group, and the model was validated using the test group. We investigated the predictive ability of the prognostic risk model in terms of clinical prognosis, tumor mutation, immune escape, immunotherapy, tumor microenvironment, immune infiltration levels, and tumor drug treatment of ccRCC. Using the median risk score, patients were divided into low and high-risk groups. Kaplan-Meier curves showed that the overall survival (OS) of patients in the high-risk group was significantly worse than low-risk group (p < 0.001). Receiver operating characteristic (ROC) curves further validated the reliability of our model. The model consistently and accurately predicted prognosis at 1, 3, and 5 years, with an AUC above 0.7. Tumor cell genes generally precede morphological abnormalities; therefore, the model we constructed can effectively compensate for the traditional method of evaluating the prognosis of patients with renal cancer, and our model was also clinically meaningful in predicting ccRCC staging. In addition, lower model risk scores determined by mutational load indicated a good chance of survival. The high-risk group had greater recruitment of immune cells, while the anti-immune checkpoint immunotherapy was less efficacious overall than that of the low-risk group. Tumor and immune-related pathways were enriched, and anti-tumor agents were selected to improve the survival of ccRCC. This prognostic risk model is based on the levels of cuproptosis-associated lncRNAs and provides a new perspective in the clinical assessment and precise treatment of ccRCC

Differential Effects of Short Term Feeding of a Soy Protein Isolate Diet and Estrogen Treatment on Bone in the Pre-Pubertal Rat

BACKGROUND: Previous reports suggest that beneficial effects of soy on bone quality are due to the estrogenic actions of isoflavone phytochemicals associated with the protein. However, mechanistic studies comparing the effects of soy diet and estrogens on bone, particularly in rapidly growing animals are lacking. METHODOLOGY AND PRINCIPAL FINDINGS: We studied the effects of short term feeding of soy protein isolate (SPI) on bone in comparison to the effects of 17β-estradiol (E2) in pre-pubertal rats. Female rats were weaned to one of 4 treatments: 1) a control casein-based diet (CAS); 2) CAS with subcutaneous E2 (10 µg/kg/d) (CAS+E2); 3) a SPI-containing diet (SPI); or 4) SPI with subcutaneous E2 (SPI) or SPI with 10 µg/kg/d E2 (SPI+E2) for 14 days beginning on postnatal day 20. SPI increased while E2 decreased bone turnover compared to CAS. In contrast, both treatments decreased serum sclerostin levels. Microarray analysis of RNA isolated from bone revealed 652 genes regulated by SPI, 491 genes regulated by E2, and 266 genes regulated by both SPI diet and E2 compared to CAS. The expression of caveolin-1, a protein localized in the cell membrane, was down-regulated (p<0.05) in rats fed SPI, but not by E2 compared to rats fed casein. Down-regulation of caveolin-1 by SPI was associated with increased BMP2, Smad and Runx2 expression in bone and osteoblasts (p<0.05). CONCLUSIONS/SIGNIFICANCE: These results suggest SPI and E2 have different effects on bone turnover prior to puberty. Approximately half of the genes are regulated in the same direction by E2 or SPI, but in combination, SPI blocks the estrogen effects and returns the profile towards control levels. In addition, there are E2 specific and SPI-specific gene changes related to regulation of bone formation

Association of Adiponectin SNP+45 and SNP+276 with Type 2 Diabetes in Han Chinese Populations: A Meta-Analysis of 26 Case-Control Studies

Recently, many studies have reported that the SNP+45(T>G) and SNP+276(G>T) polymorphisms in the adiponectin gene are associated with type 2 diabetes (T2DM) in the Chinese Han population. However, the previous studies yielded many conflicting results. Thus, a meta-analysis of the association of the adiponectin gene with T2DM in the Chinese Han population is required. In the current study, we first determined the distribution of the adiponectin SNP+276 polymorphism in T2DM and nondiabetes (NDM) control groups. Our results suggested that the genotype and allele frequencies for SNP+276 did not differ significantly between the T2DM and NDM groups. Then, a meta-analysis of 23 case-control studies of SNP+45, with a total of 4161 T2DM patients and 3709 controls, and 11 case-control studies of SNP+276, with 2533 T2DM patients and 2212 controls, was performed. All subjects were Han Chinese. The fixed-effects model and random-effects model were applied for dichotomous outcomes to combine the results of the included studies. The results revealed a trend towards an increased risk of T2DM for the SNP+45G allele as compared with the SNP+45T allele (OR = 1.34; 95% CI, 1.11–1.62; P<0.01) in the Chinese Han population. However, there was no association between SNP+276 and T2DM (OR = 0.90; 95% CI, 0.73–1.10; P = 0.31). The results of our association study showed there was no association between the adiponectin SNP+276 polymorphism and T2DM in the Yunnan Han population. The meta-analysis results suggested that the SNP+45G allele might be a susceptibility allele for T2DM in the Chinese Han population. However, we did not observe an association between SNP+276 and T2DM

Real-Time Component Composition using Hierarchical Timed Automata *

In component-based software development, it is important to use formal models to describe component composition. However, the existing component composition models generally ignore real-time issues. We present a formal integration model based on Hierarchical Timed Automata (HTA) for real-time software system. We present formal definition of components and different component composition techniques. We then introduce the notions of composable and compatible composition, and use Multiset Labeled Transition Systems to represent the interface actions of HTA to perform compositional verification. This hierarchical and unified framework establishes the foundation for formal analysis of real-time properties of the system. Key words: component, real-time, hierarchical timed automata, labeled transition systems 1

Studies on preparation of aceclofenac pellets by centrifugal granulator

To prepare aceclofenac pellets by centrifugal granulation. Using 90~100 g of starch pellets as the core pellets,100 g of aceclofenac mixed with 50 g of microcrystalline cellulose(MCC) and 4 g talc, methyl cellulose (HPMC) as binder,the aceclofenac pellets were prepared by centrifugal granulation .And evaluate the quality of the pellets. The aceclofenac pellets had high yield and less losses,the pellets had a partical size of 0.70~0.88 mm（18~24 mesh）and had uniform particle size .the moisture ,drug content and dissolution meet the requirement . Aceclofenac pellets were prepared by the process of centrifugal granulation .The preparation prescription and process parameters were optimized by single factor method .and the pellets meet the standard requirements