Robust human activity recognition using lesser number of wearable sensors

In recent years, research on the recognition of human physical activities solely using wearable sensors has received more and more attention. Compared to other types of sensory devices such as surveillance cameras, wearable sensors are preferred in most activity recognition applications mainly due to their non-intrusiveness and pervasiveness. However, many existing activity recognition applications or experiments using wearable sensors were conducted in the confined laboratory settings using specifically developed gadgets. These gadgets may be useful for a small group of people in certain specific scenarios, but probably will not gain their popularity because they introduce additional costs and they are unusual in everyday life. Alternatively, commercial devices such as smart phones and smart watches can be better utilized for robust activity recognitions. However, only few prior studies focused on activity recognitions using multiple commercial devices. In this paper, we present our feature extraction strategy and compare the performance of our feature set against other feature sets using the same classifiers. We conduct various experiments on a subset of a public dataset named PAMAP2. Specifically, we only select two sensors out of the thirteen used in PAMAP2. Experimental results show that our feature extraction strategy performs better than the others. This paper provides the necessary foundation towards robust activity recognition using only the commercial wearable devices.NRF (Natl Research Foundation, S’pore)Accepted versio

No casual relationship between T2DM and the risk of infectious diseases: A two-sample mendelian randomization study

Background: In epidemiological studies, it has been proven that the occurrence of type 2 diabetes mellitus (T2DM) is related to an increased risk of infectious diseases. However, it is still unclear whether the relationship is casual. Methods: We employed a two-sample Mendelian randomization (MR) to clarify the causal effect of T2DM on high-frequency infectious diseases: sepsis, skin and soft tissue infections (SSTIs), urinary tract infections (UTIs), pneumonia, and genito-urinary infection (GUI) in pregnancy. And then, we analyzed the genome-wide association study (GWAS) meta-analysis of European-descent individuals and conducted T2DM-related single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) that were associated with genome-wide significance (p \u3c 5 × 10–8). MR estimates were obtained using the inverse variance-weighted (IVW), the MR-Egger regression, the simple mode (SM), weighted median, and weighted mode. Results: The UK Biobank (UKB) cohort (n \u3e 500,000) provided data for GWASs on infectious diseases. MR analysis showed little evidence of a causal relationship of T2DM with five mentioned infections’ (sepsis, SSTI, UTI, pneumonia, and GUI in pregnancy) susceptibility [odds ratio (OR) = 0.99999, p = 0.916; OR = 0.99986, p = 0.233; OR = 0.99973, p = 0.224; OR = 0.99997, p = 0.686; OR, 1.00002, p = 0.766]. Sensitivity analysis showed similar results, indicating the robustness of causality. There were no heterogeneity and pleiotropic bias. Conclusion: T2DM would not be causally associated with high-frequency infectious diseases (including sepsis, SSTI, UTI, pneumonia, and GUI in pregnancy)

Non-targeted Metabolomic Study on Anti-aging Effect of Ripe Pu-erh Tea on D-Galactose-Induced Aging Mice

Delaying aging has become a hot spot of social concern and research. Our previous studies have shown that ripe Pu-erh tea can delay aging in mice by regulating the intestinal flora, but the metabolites in response to endogenous substances in mice are not clear. In this paper, the Morris water maze test was used to detect learning and memory capacity in control, D-galactose-induced aging, and ripe Pu-erh tea-treated mice. Non-targeted metabolomics was used to detect metabolites in the brain tissue and serum of mice from each group for the purpose of exploring the anti-aging effect of ripe Pu-erh tea on D-galactose-induced aging mice, screening differential metabolites among the three groups and analyzing the related metabolic pathways. The results showed that ripe Pu-erh tea improved learning capacity, and regulated 26 differential metabolites in the brain tissue of aging mice, mainly involved in the glycerophospholipid metabolism, vitamin B6 metabolism, histidine metabolism and purine metabolism pathways, among which the glycerophospholipid metabolism and histidine metabolism pathway were the most significant. A total of 11 differential metabolites were identified in serum, mainly involved in the metabolism of vitamin B6 and arachidonic acid, among which vitamin B6 metab olism pathway was the most significant. After the intervention with ripe Pu-erh tea, the contents of glycerophospholipid metabolites including phosphatidylcholine [PC (20:5/20:4)], phosphatidyl ethanlamine [PE (22:2/14:0)], phosphatidylserine [PS (20:5/18:1)] and lysophosphatidylcholine [LysoPC (18:2)], the histidine metabolite carnosine, and the vitamin B6 metabolite pyridoxal 5’-phosphate were significantly increased in aging mice. These results suggest that ripe Pu-erh tea can delay aging by regulating lipid and amino acid metabolism

The psychological outcomes of COVID-19 affected the pandemic-after risk perceptions of nurse clinicians: a latent profile analysis

Abstract Background Risk perception among nurses after the COVID-19 pandemic is a crucial factor affecting their attitudes and willingness to work in clinics. Those with poor psychological status could perceive risks sensitively as fears or threats that are discouraging. This article aimed to determine whether psychological outcomes, including post-traumatic stress disorder (PTSD), depression, anxiety, and insomnia, following the COVID-19 pandemic were differentially related to the risk perceptions of nurses working in clinics and increased perceived risk. Method The participants were 668 nurse clinicians from five local hospitals. Risk perceptions and psychological outcomes were measured by adapted questionnaires via the Internet. Latent profile analysis (LPA) identified subgroups of individuals who showed similar profiles regarding the perceived risks in nursing. Multinomial regression and probit regression were used to examine the extent to which sociodemographic and psychological outcomes predicted class membership. Results LPA revealed four classes: groups with low-, mild-, moderate-, and high-level risk perceptions. Membership of the high-level risk perception class was predicted by the severity of psychological outcomes. Anxiety significantly accounted for a moderate increase in risk perceptions, while the symptoms of insomnia, depression, and PTSD accelerated the increase to the high level of risk perception class. Conclusions By classifying groups of nurse clinicians sharing similar profiles regarding risk perceptions and then exploring associated predictors, this study shows the psychological outcomes after COVID-19 significantly impacted pandemic-associated risk perceptions and suggests intervening in nurses' psychological outcomes while simultaneously focusing on work-related worries is important following the outbreak of COVID-19

Estimating breast tissue-specific DNA methylation age using next-generation sequencing data

Background DNA methylation (DNAm) age has been widely accepted as an epigenetic biomarker for biological aging. Emerging evidence suggests that DNAm age can be tissue-specific and female breast tissue ages faster than other parts of the body. The Horvath clock, which estimates DNAm age across multiple tissues, has been shown to be poorly calibrated in breast issue. We aim to develop a model to estimate breast tissue-specific DNAm age. Methods Genome-wide DNA methylation sequencing data were generated for 459 normal, 107 tumor, and 45 paired adjacent-normal breast tissue samples. We determined a novel set of 286 breast tissue-specific clock CpGs using penalized linear regression and developed a model to estimate breast tissue-specific DNAm age. The model was applied to estimate breast tissue-specific DNAm age in different breast tissue types and in tumors with distinct clinical characteristics to investigate cancer-related aging effects. Results Our estimated breast tissue-specific DNAm age was highly correlated with chronological age (r = 0.88; p = 2.9 × 10−31) in normal breast tissue. Breast tumor tissue samples exhibited a positive epigenetic age acceleration, where DNAm age was on average 7 years older than respective chronological age (p = 1.8 × 10−8). In age-matched analyses, tumor breast tissue appeared 12 and 13 years older in DNAm age than adjacent-normal and normal breast tissue (p = 4.0 × 10−6 and 1.0 × 10−6, respectively). Both HER2+ and hormone-receptor positive subtypes demonstrated significant acceleration in DNAm ages (p = 0.04 and 3.8 × 10−6, respectively), while no apparent DNAm age acceleration was observed for triple-negative breast tumors. We observed a non-linear pattern of epigenetic age acceleration with breast tumor grade. In addition, early-staged tumors showed a positive epigenetic age acceleration (p = 0.003) while late-staged tumors exhibited a non-significant negative epigenetic age acceleration (p = 0.10). Conclusions The intended applications for this model are wide-spread and have been shown to provide biologically meaningful results for cancer-related aging effects in breast tumor tissue. Future studies are warranted to explore whether breast tissue-specific epigenetic age acceleration is predictive of breast cancer development, treatment response, and survival as well as the clinical utility of whether this model can be extended to blood samples

Fine mapping and candidate gene analysis of proportion of four-seed pods by soybean CSSLs

Soybean yield, as one of the most important and consistent breeding goals, can be greatly affected by the proportion of four-seed pods (PoFSP). In this study, QTL mapping was performed by PoFSP data and BLUE (Best Linear Unbiased Estimator) value of the chromosome segment substitution line population (CSSLs) constructed previously by the laboratory from 2016 to 2018, and phenotype-based bulked segregant analysis (BSA) was performed using the plant lines with PoFSP extreme phenotype. Totally, 5 ICIM QTLs were repeatedly detected, and 6 BSA QTLs were identified in CSSLs. For QTL (qPoFSP13-1) repeated in ICIM and BSA results, the secondary segregation populations were constructed for fine mapping and the interval was reduced to 100Kb. The mapping results showed that the QTL had an additive effect of gain from wild parents. A total of 14 genes were annotated in the delimited interval by fine mapping. Sequence analysis showed that all 14 genes had genetic variation in promoter region or CDS region. The qRT−PCR results showed that a total of 5 candidate genes were differentially expressed between the plant lines having antagonistic extreme phenotype (High PoFSP > 35.92%, low PoFSP< 17.56%). The results of haplotype analysis showed that all five genes had two or more major haplotypes in the resource population. Significant analysis of phenotypic differences between major haplotypes showed all five candidate genes had haplotype differences. And the genotypes of the major haplotypes with relatively high PoFSP of each gene were similar to those of wild soybean. The results of this study were of great significance to the study of candidate genes affecting soybean PoFSP, and provided a basis for the study of molecular marker-assisted selection (MAS) breeding and four-seed pods domestication

Genome sequences reveal global dispersal routes and suggest convergent genetic adaptations in seahorse evolution

Seahorses have a circum-global distribution in tropical to temperate coastal waters. Yet, seahorses show many adaptations for a sedentary, cryptic lifestyle: they require specific habitats, such as seagrass, kelp or coral reefs, lack pelvic and caudal fins, and give birth to directly developed offspring without pronounced pelagic larval stage, rendering long-range dispersal by conventional means inefficient. Here we investigate seahorses’ worldwide dispersal and biogeographic patterns based on a de novo genome assembly of Hippocampus erectus as well as 358 re-sequenced genomes from 21 species. Seahorses evolved in the late Oligocene and subsequent circum-global colonization routes are identified and linked to changing dynamics in ocean currents and paleo-temporal seaway openings. Furthermore, the genetic basis of the recurring “bony spines” adaptive phenotype is linked to independent substitutions in a key developmental gene. Analyses thus suggest that rafting via ocean currents compensates for poor dispersal and rapid adaptation facilitates colonizing new habitats.Fil: Chunyan, Li. Southern Marine Science and Engineering Guangdong Laboratory; China. Pilot National Laboratory for Marine Science and Technology; China. Chinese Academy of Sciences; República de ChinaFil: Olave, Melisa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina. University of Konstanz; AlemaniaFil: Hou, Yali. Chinese Academy of Sciences; República de ChinaFil: Geng, Qi. Chinese Academy of Sciences; República de China. Southern Marine Science and Engineering Guangdong Laboratory; ChinaFil: Schneider, Ralf. University Of Konstanz; Alemania. Helmholtz Centre for Ocean Research Kie; AlemaniaFil: Zeixa, Gao. Huazhong Agricultural University; ChinaFil: Xiaolong, Tu. Allwegene Technologies ; ChinaFil: Xin, Wang. Chinese Academy of Sciences; República de ChinaFil: Furong, Qi. China National Center for Bioinformation; China. University of Chinese Academy of Sciences; ChinaFil: Nater, Alexander. University of Konstanz; AlemaniaFil: Kautt, Andreas F.. University of Konstanz; Alemania. Harvard University; Estados UnidosFil: Wan, Shiming. Chinese Academy of Sciences; República de ChinaFil: Yanhong, Zhang. Chinese Academy of Sciences; República de ChinaFil: Yali, Liu. Chinese Academy of Sciences; República de ChinaFil: Huixian, Zhang. Chinese Academy of Sciences; República de ChinaFil: Bo, Zhang. Chinese Academy of Sciences; República de ChinaFil: Hao, Zhang. Chinese Academy of Sciences; República de ChinaFil: Meng, Qu ,. Chinese Academy of Sciences; República de ChinaFil: Shuaishuai, Liu. Chinese Academy of Sciences; República de ChinaFil: Zeyu, Chen. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Zhong, Jia. Chinese Academy of Sciences; República de ChinaFil: Zhang, He. BGI-Shenzhen; ChinaFil: Meng, Lingfeng. BGI-Shenzhen; ChinaFil: Wang, Kai. Ludong University; ChinaFil: Yin, Jianping. Chinese Academy of Sciences; República de ChinaFil: Huang, Liangmin. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Venkatesh, Byrappa. Institute of Molecular and Cell Biology; SingapurFil: Meyer, Axel. University of Konstanz; AlemaniaFil: Lu, Xuemei. Chinese Academy of Sciences; República de ChinaFil: Lin, Qiang. Chinese Academy of Sciences; República de China. Southern Marine Science and Engineering Guangdong Laboratory; China. Pilot National Laboratory for Marine Science and Technology; China. University of Chinese Academy of Sciences; Chin

Single-cell transcriptome and antigen-immunoglobin analysis reveals the diversity of B cells in non-small cell lung cancer

Background Malignant transformation and progression of cancer are driven by the co-evolution of cancer cells and their dysregulated tumor microenvironment (TME). Recent studies on immunotherapy demonstrate the efficacy in reverting the anti-tumoral function of T cells, highlighting the therapeutic potential in targeting certain cell types in TME. However, the functions of other immune cell types remain largely unexplored. Results We conduct a single-cell RNA-seq analysis of cells isolated from tumor tissue samples of non-small cell lung cancer (NSCLC) patients, and identify subtypes of tumor-infiltrated B cells and their diverse functions in the progression of NSCLC. Flow cytometry and immunohistochemistry experiments on two independent cohorts confirm the co-existence of the two major subtypes of B cells, namely the naïve-like and plasma-like B cells. The naïve-like B cells are decreased in advanced NSCLC, and their lower level is associated with poor prognosis. Co-culture of isolated naïve-like B cells from NSCLC patients with two lung cancer cell lines demonstrate that the naïve-like B cells suppress the growth of lung cancer cells by secreting four factors negatively regulating the cell growth. We also demonstrate that the plasma-like B cells inhibit cancer cell growth in the early stage of NSCLC, but promote cell growth in the advanced stage of NSCLC. The roles of the plasma-like B cell produced immunoglobulins, and their interacting proteins in the progression of NSCLC are further validated by proteomics data. Conclusion Our analysis reveals versatile functions of tumor-infiltrating B cells and their potential clinical implications in NSCLC