The novel coronavirus severe acute respiratory syndrome coronavirus 2 causing the Coronavirus disease (COVID-19) pandemic has ravaged the world with over 72 million total cases and over 1.6 million deaths worldwide as of early December 2020. An overwhelming preponderance of cases and deaths is observed within the elderly population, and especially in those with pre-existing conditions and comorbidities. Aging causes numerous biological changes in the immune system, which are linked to age-related illnesses and susceptibility to infectious diseases. Age-related changes influence the host immune response and therefore not only weaken the ability to fight respiratory infections but also to mount effective responses to vaccines. Immunosenescence and inflamm-aging are considered key features of the aging immune system wherein accumulation of senescent immune cells contribute to its decline and simultaneously increased inflammatory phenotypes cause immune dysfunction. Age-related quantitative and qualitative changes in the immune system affect cells and soluble mediators of both the innate and adaptive immune responses within lymphoid and non-lymphoid peripheral tissues. These changes determine not only the susceptibility to infections, but also disease progression and clinical outcomes thereafter. Furthermore, the response to therapeutics and the immune response to vaccines are influenced by age-related changes within the immune system. Therefore, better understanding of the pathophysiology of aging and the immune response will not only help understand age-related diseases but also guide targeted management strategies for deadly infectious diseases like COVID-19.

The integrity of the endothelial barrier between circulating blood and tissue is important for blood vessel function and, ultimately, for organ homeostasis. Here, we developed a vessel-on-a-chip with perfused endothelialized channels lined with human bone marrow stromal cells, which adopt a mural cell-like phenotype that recapitulates barrier function of the vasculature. In this model, barrier function is compromised upon exposure to inflammatory factors such as LPS, thrombin, and TNFα, as has been observed in vivo. Interestingly, we observed a rapid physical withdrawal of mural cells from the endothelium that was accompanied by an inhibition of endogenous Rac1 activity and increase in RhoA activity in the mural cells themselves upon inflammation. Using a system to chemically induce activity in exogenously expressed Rac1 or RhoA within minutes of stimulation, we demonstrated RhoA activation induced loss of mural cell coverage on the endothelium and reduced endothelial barrier function, and this effect was abrogated when Rac1 was simultaneously activated. We further showed that N-cadherin expression in mural cells plays a key role in barrier function, as CRISPRmediated knockout of N-cadherin in the mural cells led to loss of barrier function, and overexpression of N-cadherin in CHO cells promoted barrier function. In summary, this bicellular model demonstrates the continuous and rapid modulation of adhesive interactions between endothelial and mural cells and its impact on vascular barrier function and highlights an in vitro platform to study the biology of perivascular-endothelial interactions.

In today's world, where technology is advancing every single day, new methodologies are being developed, and are brought in everyday use making our lives simpler, faster, safer, and powerful. Similarly, Human Activity Recognition (HAR) is getting more popular with all the revolutions made in the technologies. Sensor Network Technology is used in industrial applications, smart homes and system. A massive amount of data can be obtained from these sensors which are linked to the human body. Recognition of Human Activities using these sensors, and wearable technologies has been actively studied. Behavior Recognition seeks to distinguish one or more people's activities and goals through a collection of observations on the actions and environmental conditions of the person. Health surveillance, aged treatment, and plenty of other domains can be used to automatically understand the behavioral context. An existing dataset consisting of 10 subjects (5 females, 5 males) is being used in the paper, which incorporates both young and old volunteers between 19 and 60 years of old with weights ranging from 55 to 85 kg. The dataset reflects motion data collected when subjects are engaged in 11 separate (static and dynamic) smart home activities: computer usage (1 min), telephone conversation (1 min), vacuum cleaning (1 min), book reading (1 min), TV watching (1 min), ironing (1 min), walking (1 min), exercise (1 min), cooking (1 min), drinking (20 times), hair brushing (1 min) (20 times). Most of the activities are similar because of the multi sensor environment which makes it more difficult. Using three tri axial IMU (inertial measurement unit), Magnetometer, Accelerometer, Gyroscope sensors attached to