Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification

This paper considers the domain adaptive person re-identification (re-ID) problem: learning a re-ID model from a labeled source domain and an unlabeled target domain. Conventional methods are mainly to reduce feature distribution gap between the source and target domains. However, these studies largely neglect the intra-domain variations in the target domain, which contain critical factors influencing the testing performance on the target domain. In this work, we comprehensively investigate into the intra-domain variations of the target domain and propose to generalize the re-ID model w.r.t three types of the underlying invariance, i.e., exemplar-invariance, camera-invariance and neighborhood-invariance. To achieve this goal, an exemplar memory is introduced to store features of the target domain and accommodate the three invariance properties. The memory allows us to enforce the invariance constraints over global training batch without significantly increasing computation cost. Experiment demonstrates that the three invariance properties and the proposed memory are indispensable towards an effective domain adaptation system. Results on three re-ID domains show that our domain adaptation accuracy outperforms the state of the art by a large margin. Code is available at: https://github.com/zhunzhong07/ECNComment: To appear in CVPR 201

Dynamic Model of Spur Gear Pair with Modulation Internal Excitation

In the actual measurements, vibration and noise spectrum of gear pair often exhibits sidebands around the gear mesh harmonic orders. In this study, a nonlinear time-varying dynamic model of spur gear pair was established to predict the modulation sidebands caused by the AM-FM modulation internal excitation. Here, backlash, modulation time-varying mesh stiffness, and modulation transmission error are considered. Then the undamped natural mode was studied. Numerical simulation was made to reveal the dynamic characteristic of a spur gear under modulation condition. The internal excitation was shown to exhibit obvious modulation sideband because of the modulation time-varying mesh stiffness and modulation transmission error. The Runge-Kutta method was used to solve the equations for analyzing the dynamic characteristics with the effect of modulation internal excitation. The result revealed that the response under modulation excitation exhibited obvious modulation sideband. The response under nonmodulation condition was also calculated for comparison. In addition, an experiment was done to verify the prediction of the modulation sidebands. The calculated result was consistent with the experimental result

The effects of indoor air pollution from solid fuel use on cognitive function among middle-aged and older population in China

Objectives Growing evidence has linked outdoor air pollution exposure with higher risk of cognitive impairments. However, the role of indoor air pollution in cognitive decline is not well elaborated. By using nationally representative longitudinal data, this study aimed to explore the effects of indoor air pollution from solid fuel use on cognitive function among middle-aged and older individuals in China. Methods Data were obtained from 2011-2015 waves of CHARLS (China Health and Retirement Longitudinal Study). Scores from the Telephone Interview of Cognitive Status and figure drawing/word recall tests were used to measure cognitive function in 39,482 individuals. Exposure to indoor air pollution was measured as use of solid fuel for cooking. Solid fuel was defined as coal, biomass charcoal, wood, and straw; clean fuel was defined as liquefied gas, natural gas, and electricity. Linear mixed effect models were applied to examine the effect of indoor air pollution from solid fuel use on cognitive function. Results Participants had an average global cognitive function of 9.67 (SD=4.13). Solid fuel users made up 49.71% of participants, but this proportion was much greater among those living in rural areas (64.22%). Compared with clean fuel users, solid fuel users had worse cognitive function. On average, solid fuel users had a 0.81 (95%CI: -0.89,-0.73) lower global cognition score, 0.63 (95%CI: -0.69,-0.57) lower mental health score, and 0.16 (95%CI: -0.22,-0.14) lower episodic memory score. These effects were stronger among participants who are female, aged 65 years old and above, have education level of primary school and below, or have cardiovascular diseases. Conclusions These results provide evidence for the role of indoor air pollution in neurobehavioral disorders in China. Promotion of practices like expanded use of clean fuel and improved stoves in households may be crucial to significantly reduce indoor air pollution and protect mental health

PSSP-RFE: Accurate Prediction of Protein Structural Class by Recursive Feature Extraction from PSI-BLAST Profile, Physical-Chemical Property and Functional Annotations

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets

Role of CTSC in Glioblastoma Based on Oncomine and TCGA Database

Background and objective Glioblastoma (GBM) is one of the malignant tumors causing death worldwide. Most patients were found in the middle and late stages and had poor prognosis. The purpose of this study was to investigate the expression and significance of CTSC in GBM. Methods The information about CTSC in Oncomine database was collected and analyzed twice. The role of CTSC in GBM was meta-analyzed. The expression of CTSC in glioma cell lines was retrieved by CCLE database, and the survival of patients was analyzed by TCGA database. Results A total of 1,459 different types of CTSC were collected in Oncomine database, 134 of which had statistical differences in CTSC expression, 89 of which had increased CTSC expression and 45 of which had decreased CTSC expression. A total of 50 studies involving the expression of CTSC in GBM cancer and normal tissues included 1,189 samples. Compared with the control group, CTSC was highly expressed in GBM (P < 0.05). Moreover, CTSC was highly expressed in glioma cell lines. There was a correlation between the expression of CTSC and the overall survival rate of GBM. The overall survival rate of patients with high expression of CTSC was worse, while the prognosis of patients with low expression of SPC24 was better (P < 0.05). Conclusion Through the in-depth mining of oncomine gene chip database, we propose that CTSC is highly expressed in GBM tissues and is related to the prognosis of GBM, which may provide an important theoretical basis for the treatment of glioma

Real-world efficacy and safety of pyrotinib in patients with HER2-positive metastatic breast cancer: A prospective real-world study

Background: Pyrotinib, a novel irreversible EGFR/HER2 dual tyrosine kinase inhibitor, shows encouraging anticancer activity and acceptable tolerability in multiple phase II and phase III randomized clinical trials, but the real-world data of pyrotinib, especially the outcomes in HER2-positive metastatic breast cancer, have been rarely reported. Here, we evaluated the treatment outcomes of pyrotinib in real-world practice in patients with HER2-positive metastatic breast cancer (MBC).Methods: This was a prospective, real-world, observational cohort study. Through the Breast Cancer Information Management System, HER-2 positive MBC patients treated with pyrotinib between 2017/06 and 2020/09 were included. Provider-reported objective response rate, progression-free survival (PFS), and overall survival (OS) were considered in the assessment of treatment outcomes. Tumor responses to pyrotinib treatment were calculated using RECIST 1.1. Adverse events were evaluated using clinical records.Results: The trial involved 113 individuals who were receiving pyrotinib treatment, with an average age of 51 years. Complete response, partial response and stable disease were observed in 9 (8.0%), 66 (58.4%), and 17 (15.0%) patients, respectively, while progressive disease was recorded in 20 (17.7%) patients. After a median follow-up of 17.2 months, the median PFS was 14.1. The most common adverse events of any grade were diarrhea (87.6%), vomiting (31.9%), and palmar-plantar erythrodysesthesia (26.6%). Among the patients with brain metastases, the median PFS and OS were 15.2 and 19.8 months, respectively. In addition, pyrotinib has similar efficacy in various subtypes of HER2-positive MBC patients, as shown by the lack of a significant difference of PFS and OS among pyrotinib-treated patients with or without brain metastases, or patients using pyrotinib as first-line, second-line, third-line or beyond therapies.Conclusion: Our real-world results demonstrated equivalent clinical efficacy in HER-2 positive MBC patients compared to phase II and phase III clinical trials with pyrotinib, and promising outcomes in patients with brain metastases

Genome-wide analysis of regulatory proteases sequences identified through bioinformatics data mining in Taenia solium

Background Cysticercosis remains a major neglected tropical disease of humanity in many regions, especially in sub-Saharan Africa, Central America and elsewhere. Owing to the emerging drug resistance and the inability of current drugs to prevent re-infection, identification of novel vaccines and chemotherapeutic agents against Taenia solium and related helminth pathogens is a public health priority. The T. solium genome and the predicted proteome were reported recently, providing a wealth of information from which new interventional targets might be identified. In order to characterize and classify the entire repertoire of protease-encoding genes of T. solium, which act fundamental biological roles in all life processes, we analyzed the predicted proteins of this cestode through a combination of bioinformatics tools. Functional annotation was performed to yield insights into the signaling processes relevant to the complex developmental cycle of this tapeworm and to highlight a suite of the proteases as potential intervention targets. Results Within the genome of this helminth parasite, we identified 200 open reading frames encoding proteases from five clans, which correspond to 1.68% of the 11,902 protein-encoding genes predicted to be present in its genome. These proteases include calpains, cytosolic, mitochondrial signal peptidases, ubiquitylation related proteins, and others. Many not only show significant similarity to proteases in the Conserved Domain Database but have conserved active sites and catalytic domains. KEGG Automatic Annotation Server (KAAS) analysis indicated that ~60% of these proteases share strong sequence identities with proteins of the KEGG database, which are involved in human disease, metabolic pathways, genetic information processes, cellular processes, environmental information processes and organismal systems. Also, we identified signal peptides and transmembrane helices through comparative analysis with classes of important regulatory proteases. Phylogenetic analysis using Bayes approach provided support for inferring functional divergence among regulatory cysteine and serine proteases. Conclusion Numerous putative proteases were identified for the first time in T. solium, and important regulatory proteases have been predicted. This comprehensive analysis not only complements the growing knowledge base of proteolytic enzymes, but also provides a platform from which to expand knowledge of cestode proteases and to explore their biochemistry and potential as intervention targets

VLBI Technology Development at SHAO

VLBI technology development made significant progress at SHAO in the last few years. The development status of the Chinese DBBC, the software and FPGA-based correlators, and the new VLBI antenna, as well as VLBI applications are summarized in this paper