Longitudinal associations of concurrent falls and fear of falling with functional limitations differ by living alone or not

BackgroundFalls and fear of falling (FOF) are independent risk factors for functional limitations in older adults. However, the combined effect of falls and FOF on functional limitations and the moderating role of living alone or not is unclear. We aimed to examine (1) the independent and combined effect of falls and FOF on functional limitations in older adults and (2) whether living alone moderates these associations.MethodsWe used data from the National Health and Aging Trends Study (NHATS) and included 5,950 U.S. community-dwelling older adults aged 65 and older from Round 1 (Year 2011) and Round 2 (Year 2012). Falls and FOF were ascertained by asking participants whether they had any falls in the last year and whether they had worried about falling in the previous month at R1. Assessed functional limitations included any difficulties with mobility, self-care, or household activities at R2. Poisson regression models were used to examine the longitudinal associations of falls and FOF with functional limitations and the moderation effects of baseline living alone.ResultsOf the 5,950 participants, 16.3% had falls only; 14.3% had FOF only; 14.3% had both, and 55.1% had neither at baseline. In the adjusted model, those who experienced concurrent falls and FOF in R1 had a higher risk of functional limitations at R2 than those with neither (Mobility: Incidence risk ratio [IRR] = 1.34, 95% CI: 1.24–1.45; Self-care: IRR = 1.18, 95% CI: 1.11–1.26; Household: IRR = 1.20, 95% CI: 1.11–1.30). Moreover, living alone significantly moderated the longitudinal associations of concurrent falls and FOF with mobility activity limitations.ConclusionThe findings suggest that strategies to improve falls and FOF together could potentially help prevent functional limitations. Older adults who live with others and have falls or FOF should receive interventions to promote their mobility activities

B-Cell Receptor-Associated Protein 31 Promotes Metastasis via AKT/β-Catenin/Snail Pathway in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most lethal cancer worldwide, characterized with high heterogeneity and inclination to metastasize. Emerging evidence suggests that BAP31 gets involved in cancer progression with different kinds. It still remains unknown whether and how BAP31 plays a role in HCC metastasis. Epithelial–mesenchymal transition (EMT) has been a common feature in tumor micro-environment, whose inducer TGF-β increased BAP31 expression in this research. Elevated expression of BAP31 was positively correlated with tumor size, vascular invasion and poor prognosis in human HCC. Ectopic expression of BAP31 promoted cell migration and invasion while BAP31 knockdown markedly attenuated metastatic potential in HCC cells and mice orthotopic xenografts. BAP31 induced EMT process, and enhanced the expression level of EMT-related factor Snail and decreased contents and membrane distribution of E-cadherin. BAP31 also activated AKT/β-catenin pathway, which mediated its promotional effects on HCC metastasis. AKT inhibitor further counteracted the activated AKT/β-catenin/Snail upon BAP31 over-expression. Moreover, silencing Snail in BAP31-overexpressed cells impaired enhanced migratory and invasive abilities of HCC cells. In HCC tissues, BAP31 expression was positively associated with Snail. In conclusion, BAP31 promotes HCC metastasis by activating AKT/β-catenin/Snail pathway. Thus, our study implicates BAP31 as potential prognostic biomarker, and provides valuable information for HCC prognosis and treatment

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

Biosafety and mental health: Virus induced cognitive decline

Biological agents threats people's life through different ways, one of which lies in the impairment of cognition. It is believed cognitive decline may result from biological agents mediated neuron damage directly, or from the activation of the host immune response to eradicate the pathogen. However, direct linkage between infections and cognitive decline is very limited. Here we focus on the mechanisms of how different biological virus or they induced systemic and local inflammation link to the cognitive impairment, focusing on the roles of activated microglia and several molecular pathways mediated neurotoxicity

A Staged Finite-Time Control Strategy for Formation of Underactuated Unmanned Surface Vehicles

The formation control issue for a group of underactuated unmanned surface vehicles (USVs) is discussed in the paper, and a staged finite-time control strategy for the USVs is proposed. Firstly, we try to steer each USV to its own starting point in the formation for a limited time, under the initial condition that each of these vehicles is parked at random. To deal with the nonholonomic behavior of the system, the dynamics of the USV is transformed into cascade systems. Then, the finite-time controller is designed for each vehicle based on homogeneity theory. After each USV reaches its own starting point with desired orientation, the model of the vehicle is decomposed into two subsystems under the Serret-Frenet frame. In order to maintain the formation pattern, two finite-time distributed controllers are developed for the surge subsystem and the yaw subsystem, respectively. The settling time for the staged control strategy is limited. Numerical simulations are carried out to illustrate the effectiveness of the proposed formation control strategy

A Communication Framework with Unified Efficiency and Secrecy

Future wireless networks are confronted with the pressure to meet the requirements of network efficiency and communication secrecy, which require a new communication framework to unify and jointly optimize efficiency and secrecy. The challenges are to extend the current communication architecture and reveal the relationship of efficiency and secrecy, so that unification and joint optimization can be achieved. Artificial Intelligence (AI) is a powerful tool to solve these problems. In this article, a unified efficiency and secrecy communication framework (UESCF) is proposed to provide the theoretical bases for the applications of AI technology in wireless networks. Under the proposed framework, AI technologies are applied to generate the network common knowledge and communication private knowledge, with the purpose of improving efficiency and secrecy. The goal of this article is to establish the UESCF for future wireless networks and offer an overview to illuminate the potential applications of AI in the proposed framework. 2002-2012 IEEE.This work was supported by the National Natural Science Foundation of China (91538203 and 61871257); the new strategic industries development projects of Shenzhen City (JCYJ20170307145820484); the Joint Research Foundation of the General Armaments Department and the Ministry of Education (6141A02033322); and the Beijing Innovation Center for Future Chips, Tsinghua University.Scopus2-s2.0-8506466144

Representing Urban Forms: A Collective Learning Model with Heterogeneous Human Mobility Data

Human mobility data refers to records of human movements, such as cellphone traces, vehicle GPS trajectories, geo-tagged posts, and photos. While successfully mining human mobility data can benefit many applications such as city planning, transportation, urban economics, and public safety, it is very challenging to model large-scale Heterogeneous Human Mobility Data (HHMD) that are generated from different resources. In this paper, we develop a general collective learning approach to model HHMD at an individual level towards identifying and quantifying the urban forms of residential communities. Specifically, our proposed method exploits two geographic regularities among HHMD. First, we jointly capture the correlations among residential communities, urban functions, temporal effects, and user mobility patterns by analogizing communities as documents and mobility patterns as words. Also, we further combine explicit LASSO analysis and significant testing into latent representation learning as a regularization term by analogizing compatible Point-of-Interests (POIs) as the meta-data of communities. In this way, we can learn the urban forms, including a mix of functions and corresponding portfolios, of residential communities from HHDM and POIs. We further leverage these learned results to address two application problems: real estate ranking and restaurant popularity prediction. Finally, we conduct intensive evaluations with a variety of real-world data, where experimental results demonstrate the effectiveness of our proposed modeling method and its successful applications for other problems