A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation

This paper presents a novel framework for simultaneously implementing localization and segmentation, which are two of the most important vision-based tasks for robotics. While the goals and techniques used for them were considered to be different previously, we show that by making use of the intermediate results of the two modules, their performance can be enhanced at the same time. Our framework is able to handle both the instantaneous motion and long-term changes of instances in localization with the help of the segmentation result, which also benefits from the refined 3D pose information. We conduct experiments on various datasets, and prove that our framework works effectively on improving the precision and robustness of the two tasks and outperforms existing localization and segmentation algorithms.Comment: 7 pages, 5 figures.This work has been accepted by ICRA 2019. The demo video can be found at https://youtu.be/Bkt53dAehj

Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution

Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video superresolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the SpatialTemporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https: //vlis2022.github.io/cvpr23/egvsr.Comment: Accepted by CVPR202

The Analysis of Key Factors Related to ADCs Structural Design

Antibody–drug conjugates (ADCs) have developed rapidly in recent decades. However, it is complicated to map out a perfect ADC that requires optimization of multiple parameters including antigens, antibodies, linkers, payloads, and the payload-linker linkage. The therapeutic targets of the ADCs are expected to express only on the surface of the corresponding target tumor cells. On the contrary, many antigens usually express on normal tissues to some extent, which could disturb the specificity of ADCs and limit their clinical application, not to mention the antibody is also difficult to choose. It requires to not only target and have affinity with the corresponding antigen, but it also needs to have a linkage site with the linker to load the payloads. In addition, the linker and payload are indispensable in the efficacy of ADCs. The linker is required to stabilize the ADC in the circulatory system and is brittle to release free payload while the antibody combines with antigen. Also, it is a premise that the dose of ADCs will not kill normal tissues and the released payloads are able to fulfill the killing potency in tumor cells at the same time. In this review, we mainly focus on the latest development of key factors affecting ADCs progress, including the selection of antibodies and antigens, the optimization of payload, the modification of linker, payload-linker linkage, and some other relevant parameters of ADCs

Artificial neural network system for cell classification using single cell RNA expression

We implemented an automated system for single-cell classification using artificial neural networks (ANN). Our system takes single-cell gene expression sparse matrices and trains ANN to classify cell types and subtypes. The assemblies of ANNs predict cell classes by voting. We tested the system in a case study where we trained ANNs with a dataset containing approximately 120,000 single cells and tested the resulting model using an independent data set of 13,000 single cells. The overall accuracy of the 5-class classification was 95%. We trained and tested a total of 100 ANNs in 10 cycles. The prediction system demonstrated excellent reproducibility. The analysis of misclassifications indicated that 2% were likely classification errors, while the remaining 3% were likely due to mislabeled types and subtypes in the test set

Genetic Structures of Copy Number Variants Revealed by Genotyping Single Sperm

Copy number variants (CNVs) occupy a significant portion of the human genome and may have important roles in meiotic recombination, human genome evolution and gene expression. Many genetic diseases may be underlain by CNVs. However, because of the presence of their multiple copies, variability in copy numbers and the diploidy of the human genome, detailed genetic structure of CNVs cannot be readily studied by available techniques.Single sperm samples were used as the primary subjects for the study so that CNV haplotypes in the sperm donors could be studied individually. Forty-eight CNVs characterized in a previous study were analyzed using a microarray-based high-throughput genotyping method after multiplex amplification. Seventeen single nucleotide polymorphisms (SNPs) were also included as controls. Two single-base variants, either allelic or paralogous, could be discriminated for all markers. Microarray data were used to resolve SNP alleles and CNV haplotypes, to quantitatively assess the numbers and compositions of the paralogous segments in each CNV haplotype.This is the first study of the genetic structure of CNVs on a large scale. Resulting information may help understand evolution of the human genome, gain insight into many genetic processes, and discriminate between CNVs and SNPs. The highly sensitive high-throughput experimental system with haploid sperm samples as subjects may be used to facilitate detailed large-scale CNV analysis

Disease Burden of Chronic Kidney Disease Due to Hypertension From 1990 to 2019: A Global Analysis

Background: Although it is widely known that hypertension is an important cause of chronic kidney disease (CKD), little detailed quantitative research exists on the burden of CKD due to hypertension.Objective: The objective of the study is to estimate the global disease burden of CKD due to hypertension and to evaluate the association between the socioeconomic factors and country-level disease burden of CKD due to hypertension.Methods: We extracted the disability-adjusted life-year (DALY) numbers, rates, and age-standardized rates of CKD due to hypertension from the Global Burden of Disease Study 2019 database to investigate the time trends of the burden of CKD due to hypertension from 1990 to 2019. Stepwise multiple linear regression analysis was performed to evaluate the correlations between the age-standardized DALY rate and socioeconomic factors and other related factors obtained from open databases.Results: Globally, from 1990 to 2019, DALY numbers caused by CKD due to hypertension increased by 125.2% [95% confidential interval (CI), 124.6 to 125.7%]. The DALY rate increased by 55.7% (55.3 to 56.0%) to 128.8 (110.9 to 149.2) per 100,000 population, while the age-standardized DALYs per 100,000 population increased by 10.9% (10.3 to 11.5%). In general, males and elderly people tended to have a higher disease burden. The distribution disparity in the burden of CKD due to hypertension varies greatly among countries. In the stepwise multiple linear regression model, inequality-adjusted human development index (IHDI) [β = −161.1 (95% CI −238.1 to −84.2), P < 0.001] and number of physicians per 10,000 people [β = −2.91 (95% CI −4.02 to −1.80), P < 0.001] were significantly negatively correlated with age-standardized DALY rate when adjusted for IHDI, health access and quality (HAQ), number of physicians per 10,000 people, and population with at least some secondary education.Conclusion: Improving the average achievements and equality of distribution in health, education, and income, as well as increasing the number of physicians per 10,000 people could help to reduce the burden of CKD due to hypertension. These findings may provide relevant information toward efforts to optimize health policies aimed at reducing the burden of CKD due to hypertension

GWAS Identifies Novel Susceptibility Loci on 6p21.32 and 21q21.3 for Hepatocellular Carcinoma in Chronic Hepatitis B Virus Carriers

Genome-wide association studies (GWAS) have recently identified KIF1B as susceptibility locus for hepatitis B virus (HBV)–related hepatocellular carcinoma (HCC). To further identify novel susceptibility loci associated with HBV–related HCC and replicate the previously reported association, we performed a large three-stage GWAS in the Han Chinese population. 523,663 autosomal SNPs in 1,538 HBV–positive HCC patients and 1,465 chronic HBV carriers were genotyped for the discovery stage. Top candidate SNPs were genotyped in the initial validation samples of 2,112 HBV–positive HCC cases and 2,208 HBV carriers and then in the second validation samples of 1,021 cases and 1,491 HBV carriers. We discovered two novel associations at rs9272105 (HLA-DQA1/DRB1) on 6p21.32 (OR = 1.30, P = 1.13×$10^{−19}$) and rs455804 (GRIK1) on 21q21.3 (OR = 0.84, P = 1.86×$10^{−8}$), which were further replicated in the fourth independent sample of 1,298 cases and 1,026 controls (rs9272105: OR = 1.25, P = 1.71×$10^{−4}$; rs455804: OR = 0.84, P = 6.92×$10^{−3}$). We also revealed the associations of HLA-DRB1*0405 and 0901*0602, which could partially account for the association at rs9272105. The association at rs455804 implicates GRIK1 as a novel susceptibility gene for HBV–related HCC, suggesting the involvement of glutamate signaling in the development of HBV–related HCC

Interferon gamma (IFN-γ) disrupts energy expenditure and metabolic homeostasis by suppressing SIRT1 transcription

Chronic inflammation impairs metabolic homeostasis and is intimately correlated with the pathogenesis of type 2 diabetes. The pro-inflammatory cytokine IFN-γ is an integral part of the metabolic inflammation circuit and contributes significantly to metabolic dysfunction. The underlying mechanism, however, remains largely unknown. In the present study, we report that IFN-γ disrupts the expression of genes key to cellular metabolism and energy expenditure by repressing the expression and activity of SIRT1 at the transcription level. Further analysis reveals that IFN-γ requires class II transactivator (CIITA) to repress SIRT1 transcription. CIITA, once induced by IFN-γ, is recruited to the SIRT1 promoter by hypermethylated in cancer 1 (HIC1) and promotes down-regulation of SIRT1 transcription via active deacetylation of core histones surrounding the SIRT1 proximal promoter. Silencing CIITA or HIC1 restores SIRT1 activity and expression of metabolic genes in skeletal muscle cells challenged with IFN-γ. Therefore, our data delineate an IFN-γ/HIC1/CIITA axis that contributes to metabolic dysfunction by suppressing SIRT1 transcription in skeletal muscle cells and as such shed new light on the development of novel therapeutic strategies against type 2 diabetes

Clinicopathological Significance and Prognostic Value of DNA Methyltransferase 1, 3a, and 3b Expressions in Sporadic Epithelial Ovarian Cancer

Altered DNA methylation of tumor suppressor gene promoters plays a role in human carcinogenesis and DNA methyltransferases (DNMTs) are responsible for it. This study aimed to determine aberrant expression of DNMT1, DNMT3a, and DNMT3b in benign and malignant ovarian tumor tissues for their association with clinicopathological significance and prognostic value. A total of 142 ovarian cancers and 44 benign ovarian tumors were recruited for immunohistochemical analysis of their expression. The data showed that expression of DNMT1, DNMT3a, and DNMT3b was observed in 76 (53.5%), 92 (64.8%) and 79 (55.6%) of 142 cases of ovarian cancer tissues, respectively. Of the serious tumors, DNMT3a protein expression was significantly higher than that in benign tumor samples (P = 0.001); DNMT3b was marginally significant down regulated in ovarian cancers compared to that of the benign tumors (P = 0.054); DNMT1 expression has no statistical difference between ovarian cancers and benign tumor tissues (P = 0.837). Of the mucious tumors, the expression of DNMT3a, DNMT3b, and DNMT1 was not different between malignant and benign tumors. Moreover, DNMT1 expression was associated with DNMT3b expression (P = 0.020, r = 0.195). DNMT1 expression was associated with age of the patients, menopause status, and tumor localization, while DNMT3a expression was associated with histological types and serum CA125 levels and DNMT3b expression was associated with lymph node metastasis. In addition, patients with DNMT1 or DNMT3b expression had a trend of better survival than those with negative expression. Co-expression of DNMT1 and DNMT3b was significantly associated with better overall survival (P = 0.014). The data from this study provided the first evidence for differential expression of DNMTs proteins in ovarian cancer tissues and their associations with clinicopathological and survival data in sporadic ovarian cancer patients