GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition

Gait recognition is an emerging biological recognition technology that identifies and verifies individuals based on their walking patterns. However, many current methods are limited in their use of temporal information. In order to fully harness the potential of gait recognition, it is crucial to consider temporal features at various granularities and spans. Hence, in this paper, we propose a novel framework named GaitGS, which aggregates temporal features in the granularity dimension and span dimension simultaneously. Specifically, Multi-Granularity Feature Extractor (MGFE) is proposed to focus on capturing the micro-motion and macro-motion information at the frame level and unit level respectively. Moreover, we present Multi-Span Feature Learning (MSFL) module to generate global and local temporal representations. On three popular gait datasets, extensive experiments demonstrate the state-of-the-art performance of our method. Our method achieves the Rank-1 accuracies of 92.9% (+0.5%), 52.0% (+1.4%), and 97.5% (+0.8%) on CASIA-B, GREW, and OU-MVLP respectively. The source code will be released soon.Comment: 14 pages, 6 figure

Randomized clinical trial comparing abluminal biodegradable polymer sirolimus-eluting stents with durable polymer sirolimus-eluting stents: Nine months angiographic and 5-year clinical outcomes

Background: The biodegradable polymer drug-eluting stents (DES) were developed to improve vascular healing. However, further data and longer-term follow-up are needed to confirm safety and efficacy of these stents. This randomized clinical trial aimed to compare safety and efficacy of 2 sirolimus-eluting stents (SES): Cordimax—a novel abluminal biodegradable polymer SES and Cypher Select—a durable polymer SES, at 9 months angiographic and 5-year clinical follow-up. Methods: We randomized 402 patients with coronary artery disease to percutaneous coronary intervention with Cordimax (n = 202) or Cypher select (n = 200). Angiographic follow-up was performed at 9 months after the index procedure and clinical follow-up annually up to 5 years. The primary endpoint was angiographic in-stent late luminal loss (LLL). Secondary endpoints included angiographic restenosis rate, target vessel revascularization (TVR), and major adverse cardiac events (MACEs; defined as cardiac death, myocardial infarction, or TVR) at 5-year follow-up. Results: Cordimax was noninferior to Cypher select for in-stent LLL (0.25 ± 0.47 vs 0.18 ± 0.49 mm; P = 0.587) and in-stent mean diameter stenosis (22.19 ± 12.21% vs 19.89 ± 10.79%; P = 0.064) at 9 months angiographic follow-up. The MACE rates were not different at 1 year (5.9% vs 4.0%, P = 0.376); however, MACE rates from 2 to 5 years were lower in the Cordimax group (6.8% vs 13.1%; P = 0.039). Conclusion: Abluminal biodegradable polymer SES is noninferior to durable polymer SES at 9-month angiographic and 1-year clinical follow-up. However, MACE rates from 2 to 5 years were less in the abluminal biodegradable polymer group.National Key Technology Research and Development Program in the Twelfth Five-year Plan Period of China (No. 2014BAI11B04)

OSNet & MNetO: Two Types of General Reconstruction Architectures for Linear Computed Tomography in Multi-Scenarios

Recently, linear computed tomography (LCT) systems have actively attracted attention. To weaken projection truncation and image the region of interest (ROI) for LCT, the backprojection filtration (BPF) algorithm is an effective solution. However, in BPF for LCT, it is difficult to achieve stable interior reconstruction, and for differentiated backprojection (DBP) images of LCT, multiple rotation-finite inversion of Hilbert transform (Hilbert filtering)-inverse rotation operations will blur the image. To satisfy multiple reconstruction scenarios for LCT, including interior ROI, complete object, and exterior region beyond field-of-view (FOV), and avoid the rotation operations of Hilbert filtering, we propose two types of reconstruction architectures. The first overlays multiple DBP images to obtain a complete DBP image, then uses a network to learn the overlying Hilbert filtering function, referred to as the Overlay-Single Network (OSNet). The second uses multiple networks to train different directional Hilbert filtering models for DBP images of multiple linear scannings, respectively, and then overlays the reconstructed results, i.e., Multiple Networks Overlaying (MNetO). In two architectures, we introduce a Swin Transformer (ST) block to the generator of pix2pixGAN to extract both local and global features from DBP images at the same time. We investigate two architectures from different networks, FOV sizes, pixel sizes, number of projections, geometric magnification, and processing time. Experimental results show that two architectures can both recover images. OSNet outperforms BPF in various scenarios. For the different networks, ST-pix2pixGAN is superior to pix2pixGAN and CycleGAN. MNetO exhibits a few artifacts due to the differences among the multiple models, but any one of its models is suitable for imaging the exterior edge in a certain direction.Comment: 13 pages, 13 figure

An association study of NRAMP1, VDR, MBL and their interaction with the susceptibility to tuberculosis in a Chinese population

SummaryObjectivesTo investigate natural-resistance-associated macrophage protein 1 (NRAMP1), mannose-binding lectin (MBL), vitamin D receptor (VDR) gene polymorphisms and their interaction with susceptibility to pulmonary tuberculosis (PTB) in a Chinese population.MethodsA case-control study was conducted in PTB (n=151), age- and sex- matched healthy controls (HCs) (n=453). Genetic polymorphisms of NRAMP1 (INT4, D543NA and 3′UTR), MBL (HL, PQ, XY and AB) and VDR (FokI and Taq) were analyzed by using PCR-restriction fragment length polymorphism (RFLP) and PCR- single- strand conformation polymorphism (SSCP) techniques. Multifactor dimensionality reduction (MDR) analysis was carried out to assess the effects of the interaction between SNPs.ResultsThe distribution of NRAMP1- 3′UTR (TGTG/del), MBL- HL (H/L) and FokI (F/f) were significantly different between PTB patients and HCs (p<0.05). HPYA (OR: 1.88; 95% CI: 1.22-2.91), LPXA (OR: 3.17; 95% CI: 1.69- 5.96), LQYA (OR: 3.52; 95%CI: 1.50-8.23) and LPYB (OR: 12.37; 95%CI: 3.75- 40.85) of MBL were risk haplotypes for PTB. The TGTG- H- f (OR: 1.70; 95%CI: 1.10-2.62) and del- H-f (OR: 3.48; 95% CI: 1.45-8.37) of 3′UTR- HL- FokI were also high-risk haplotypes associated with tuberculosis.ConclusionsOur study suggests that genotypes of many polymorphic genes are associated with TB, it is necessary to further explore the mechanism of genotypes and gene-gene interaction in susceptibility to tuberculosis

Reconstruct gene regulatory network using slice pattern model

<p>Abstract</p> <p>Background</p> <p>Gene expression time series array data has become a useful resource for investigating gene functions and the interactions between genes. However, the gene expression arrays are always mixed with noise, and many nonlinear regulatory relationships have been omitted in many linear models. Because of those practical limitations, inference of gene regulatory model from expression data is still far from satisfactory.</p> <p>Results</p> <p>In this study, we present a model-based computational approach, Slice Pattern Model (SPM), to identify gene regulatory network from time series gene expression array data. In order to estimate performances of stability and reliability of our model, an artificial gene network is tested by the traditional linear model and SPM. SPM can handle the multiple transcriptional time lags and more accurately reconstruct the gene network. Using SPM, a 17 time-series gene expression data in yeast cell cycle is retrieved to reconstruct the regulatory network. Under the reliability threshold, <it>θ </it>= 55%, 18 relationships between genes are identified and transcriptional regulatory network is reconstructed. Results from previous studies demonstrate that most of gene relationships identified by SPM are correct.</p> <p>Conclusion</p> <p>With the help of pattern recognition and similarity analysis, the effect of noise has been limited in SPM method. At the same time, genetic algorithm is introduced to optimize parameters of gene network model, which is performed based on a statistic method in our experiments. The results of experiments demonstrate that the gene regulatory model reconstructed using SPM is more stable and reliable than those models coming from traditional linear model.</p

Low dose triptolide reverses chemoresistance in adult acute lymphoblastic leukemia cells via reactive oxygen species generation and DNA damage response disruption

Chemoresistance represents a major challenge for treatment of acute lymphoblastic leukemia (ALL). Thus, new drugs to overcome chemoresistance in ALL are urgently needed. To this end, we established a cytarabine (araC)-resistant ALL cell line (NALM-6/R), which interestingly displayed cross-resistance towards doxorubicin (ADM). Here we report that low dose of triptolide (TPL), a natural product used for treating inflammatory diseases such as arthritis, could reverse araC and ADM resistance and in NALM-6/R cells as well as primary cells from patients with relapsed or refractory (R/R) ALL, reflected by inhibition of cell proliferation and induction of apoptosis in vitro, and repression of tumor growth in vivo in a mouse xenograft model. Mechanistically, these events were associated with impaired mitochondrial membrane potential and increased reactive oxygen species (ROS) production. Co-treatment with TPL and araC or ADM upregulated pro-apoptotic caspase-9 protein, inhibited checkpoint kinase 1 (Chk1) and 2 (Chk2) phosphorylation, and induced γH2A.X (a DNA damage marker). Notably, the combination regimen of TPL and conventional chemotherapeutics also rapidly diminished tumor burden in a patient with R/R ALL. Together, these findings provide preclinical evidence for repurposing use of TPL in combination with chemotherapeutic agents to treat R/R ALL as an alternative salvage regimen

Evidence of elevation-dependent warming from the Chinese Tian Shan

The phenomenon in which the warming rate of air temperature is amplified with elevation is termed elevation-dependent warming (EDW). It has been clarified that EDW can accelerate the retreat of glaciers and melting of snow, which can have significant impacts on the regional ecological environment. Owing to the lack of high-density ground observations in high mountains, there is widespread controversy regarding the existence of EDW. Current evidence is mainly derived from typical high-mountain regions such as the Swiss Alps, the Colorado Rocky Mountains, the tropical Andes and the Tibetan Plateau–Himalayas. Rare evidence in other mountain ranges has been reported, especially in arid regions. In this study, EDW features (regional warming amplification and altitude warming amplification) in the Chinese Tian Shan (CTM) were detected using a unique high-resolution (1 km, 6-hourly) air temperature dataset (CTMD) from 1979 to 2016. The results showed that there were significant EDW signals at different altitudes on different timescales. The CTM showed significant regional warming amplification in spring, especially in March, and the warming trends were greater than those of continental China with respect to three temperatures (minimum temperature, mean temperature and maximum temperature). The significance values of EDW above different altitude thresholds are distinct for three temperatures in 12 months. The warming rate of the minimum temperature in winter showed a significant elevation dependence (p<0.01), especially above 3000 m. The greatest altitudinal gradient in the warming rate of the maximum temperature was found above 4000 m in April. For the mean temperature, the warming rates in June and August showed prominent altitude warming amplification but with different significance above 4500 m. Within the CTM, the Tolm Mountains, the eastern part of the Borokoonu Mountains, the Bogda Mountains and the Balikun Mountains are representative regions that showed significant altitude warming amplification on different timescales. This new evidence could partly explain the accelerated melting of snow in the CTM, although the mechanisms remain to be explored