Identification of Scaffold Proteins RACK1 and Hic-5 as Regulators of Endothelial Sprouting Resoponses

Angiogenesis is defined as growth of new blood vessels from pre-existing ones and occurs during normal physiological development as well as pathological conditions like cancer. During angiogenesis, normally quiescent endothelial cells (ECs) are activated in response to external pro-angiogenic cues and undergo rapid morphogenic changes such as proliferation, migration, invasion, and lumen formation to extend new sprouts into surrounding three-dimensional (3D) matrix. To better understand angiogenic regulation, we utilized an in vitro model wherein sphingosine 1-phosphate (S1P) and pro-angiogenic growth factors (GF) synergize to induce rapid and robust endothelial sprouting in 3D collagen matrices. In a proteomic screen designed to identify molecules relevant to angiogenesis, we found up-regulated levels of receptor for activated C kinase 1 (RACK1) and the intermediate filament protein, vimentin. RACK1 depletion reduced EC invasion. Silencing of vimentin or RACK1 decreased cell adhesion and attenuated with focal adhesion kinase (FAK) activation, which is indispensable for successful angiogenesis. Moreover, pro-angiogenic GFs enhanced RACK1 and vimentin association. RACK1, vimentin, and FAK, formed an intermolecular complex during S1P- and GF- induced invasion. Also, depletion of RACK1 decreased vimentin and FAK association, suggesting a role for RACK1 in stabilizing vimentin-FAK interactions during sprouting. In an independent study, we identified focal adhesion (FA) scaffold protein, hydrogen peroxide inducible clone 5 (Hic-5), as a critical regulator of angiogenesis. Hic-5 depletion interfered with endothelial invasion and lumen formation. S1P induced rapid Hic-5 translocation to FAs and Hic-5 silencing attenuated FAK expression and activation. S1P induced a novel interaction between Hic-5 and membrane type 1 matrix-metalloproteinase (MT1-MMP). In vitro binding experiments revealed that LIM2 domain of Hic-5 was required for MT1-MMP binding. Moreover, Hic-5 and MT1-MMP levels were up-regulated in detergent resistant membrane fractions of invading ECs, indicative of their crosstalk. Hic-5 silencing interfered with S1P- induced MT1-MMP membrane translocation, a critical event for successful angiogenesis. Since MT1-MMP and FAK interaction has been reported to be essential for matrix degradation at FA sites, we further tested if Hic-5 mediated this interaction. Our results indicated that presence of Hic-5 significantly enhanced FAK and MT1-MMP complex formation. In conclusion, we report that scaffold proteins RACK1 and Hic-5 regulate successful endothelial sprouting responses in 3D matrices

Analysis of Construction and Demolition Waste and its Applications Based on Recent Studies

Construction and Demolition Waste C & D waste is becoming a havoc each coming day. According to government agencies like Building Material Promotion Council (BMPTC) and Centre for Fly Ash Research and Management (C-FARM) estimated 165 million tonnes from construction. Out of municipal solid waste approximately 15% to 20% of solid waste comes from construction and demolition projects. Centre of Science and Environment (CSE) says in their latest release analysis of the C&D waste management sector, titled Another Brick off the Wall, India manages to recover and recycle only about 1% of its construction and demolition (C&D) waste), as the official recycling capacity is a mere 6,500 tons per day (TPD)- just about 1%. In this paper, we will analyze the C & D waste management to maintain the sustainable approach

Deep Knowledge-Infusion For Explainable Depression Detection

Discovering individuals depression on social media has become increasingly important. Researchers employed ML/DL or lexicon-based methods for automated depression detection. Lexicon based methods, explainable and easy to implement, match words from user posts in a depression dictionary without considering contexts. While the DL models can leverage contextual information, their black-box nature limits their adoption in the domain. Though surrogate models like LIME and SHAP can produce explanations for DL models, the explanations are suitable for the developer and of limited use to the end user. We propose a Knolwedge-infused Neural Network (KiNN) incorporating domain-specific knowledge from DepressionFeature ontology (DFO) in a neural network to endow the model with user-level explainability regarding concepts and processes the clinician understands. Further, commonsense knowledge from the Commonsense Transformer (COMET) trained on ATOMIC is also infused to consider the generic emotional aspects of user posts in depression detection. The model is evaluated on three expertly curated datasets related to depression. We observed the model to have a statistically significant (p<0.1) boost in performance over the best domain-specific model, MentalBERT, across CLEF e-Risk (25% MCC increase, 12% F1 increase). A similar trend is observed across the PRIMATE dataset, where the proposed model performed better than MentalBERT (2.5% MCC increase, 19% F1 increase). The observations confirm the generated explanations to be informative for MHPs compared to post hoc model explanations. Results demonstrated that the user-level explainability of KiNN also surpasses the performance of baseline models and can provide explanations where other baselines fall short. Infusing the domain and commonsense knowledge in KiNN enhances the ability of models like GPT-3.5 to generate application-relevant explanations.13 pages, 2 figure

Study of COVID-19 Seroprevalence Among Healthcare Workers at Dedicated COVID Hospital in Southern Rajasthan

Background: Coronavirus disease 2019 (COVID-19), a pandemic, has affected approximately 90,000 healthcare workers (HCWs) worldwide and 548 HCWs in India with an infection rate of 6%. Seroprevalence studies can provide relevant information which is useful for assessing the level of exposure among hospital personnel, to avoid unnecessary quarantines and for healthcare resource planning. Aims and objectives: Study of COVID-19 seroprevalence, clinical profile and outcomes among HCWs working at a dedicated COVID hospital in southern Rajasthan. Material and methods: It was a cross-sectional study conducted among 100 HCWs posted in various wards of dedicated COVID hospital at the RNT Medical College, Udaipur, Rajasthan, India, over a period of 2 months from April 2020 to May 2020. Results: Out of 100 HCWs, 68% were male and 32% were female with mean age 31.90 years and 16% had seropositive response. Majority, i.e., 81% seropositive HCWs were asymptomatic and all had good outcome (discharged). Conclusion: It is advisable that this high-risk population of HCWs should follow infection prevention and control (IPC) protocol as well as institutional quarantine protocol, screening and training at timely interval to protect themselves

Optimal nodes selection in wireless sensor and actor networks based on prioritized mutual exclusion approach

In this research paper we study the problem of mutual exclusionin the context ofwireless sensor and actor network (WSAN) and propose two novel approaches tosolve it. The major requirements for any proposed approach in such context are: (1) theproposed approach must select the minimum number of actor nodes to act on the givenevent region, (2) the overlaps between acting ranges should be minimum, (3) wastage ofresources should be less, and finally, whole event region must be covered by one or morethan one actors as per their applicability. We have proposed two algorithms, centralizedprioritized h-out-of-k mutual exclusion algorithm (CPMEA), and distributed prioritizedh-out-of-k mutual exclusion algorithm (DPMEA) in this research paper. Both proposedapproaches construct an actor cover set with similar optimality. The simulation resultsshow the performance in terms of size of actor cover set, overlapped region, nonoverlappedregion and maximum actor coverage degree. We have also compared ourobtained results with previously proposed benchmark algorithms