Acrylic pressure sensitive adhesives (PSAs) were prepared by UV polymerization under varying curing conditions of both fast and slow curing, employing high- and low-intensity UV radiation, respectively. The influences of curing conditions and isobornyl acrylate (IBOA) content on PSA performance were comprehensively investigated by measurement of their rheological, thermal, and adhesive properties. In particular, rheological characterization was accomplished by several analytical methods, such as in situ UV rheology, frequency sweep, stress relaxation, and temperature ramp tests, to understand the effect of the UV curing process and IBOA content on the viscoelastic behavior of acrylic PSAs. The slow-cured samples were observed to form more tightly crosslinked networks compared to the fast-cured. On the other hand, at high loading levels of IBOA, in the case of slow curing, the sample exhibited a contrasting trend, having the shortest stress relaxation time and the highest energy dissipation; this was due to molecular chain scission occurring in the crosslinked polymer during UV polymerization. Consequently, we successfully demonstrated the influence of monomer composition of acrylic PSAs, and that of curing conditions employed in UV polymerization. This study provides valuable insights for the development of crosslinked polymer networks of acrylic PSAs for flexible display applications.Dengue virus (DENV) is a major mosquito vector based human pathogenic flavivirus which is causing major threat worldwide, yet the availability of therapeutic treatment and several vaccines, still called for advance treatment and vaccine development. The present top down computational approach is a vaccine development step to find novel super antigenic HLA binding epitopes from DENV proteome. The approach used sequence based screening to find complete conserve and high population coverage, common epitopes among all DENV serotype. Propred and Immune Epitope Data Base were used for sequence based screening with recommended parameters. Among top 29 identified epitopes, five structural protein epitopes viz. 33LQGRGPLKL41, 249VVVLGSQEG257, 172LVGIVTLYL180, 146MKILIGVVI154, 72YIIVGVEPG80 and one nonstructural protein epitope 18LKNDIPMTG26 were showed high conserve nature and high population coverage from complete DENV proteome. Further structure based study involving docking and molecular dynamic simulation to confirm stable behavior of HLA allele-peptide complex to give potent cell mediated immune response. Docking of epitope 72YIIVGVEPG80-DRB1 0401 allele and epitope 33LQGRGPLKL41-B*5101 allele complexes showed the best binding energy of - 7.71 and - 7.20 kcal/mol, respectively and stable binding pattern over the time window during molecular dynamic simulation. This computational approach resulted novel epitopes which can be used in the design and development of short epitope based vaccines as well as diagnosis tools for dengue infection.Since the start of the pandemic caused by the novel coronavirus, COVID-19, more than 106 million people have been infected and global deaths have surpassed 2.4 million. In Chile, the government restricted the activities and movement of people, organizations, and companies, under the concept of dynamic quarantine across municipalities for a predefined period of time. Chile is an interesting context to study because reports to have a higher quantity of infections per million people as well as a higher number of polymerize chain reaction (PCR) tests per million people. The higher testing rate means that Chile has good measurement of the contagious compared to other countries. Further, the heterogeneity of the social, economic, and demographic variables collected of each Chilean municipality provides a robust set of control data to better explain the contagious rate for each city. In this paper, we propose a framework to determine the effectiveness of the dynamic quarantine policy by analyzing different causal models (meta-learners and causal forest) including a time series pattern related to effective reproductive number.  https://www.selleckchem.com/products/evobrutinib.html  Additionally, we test the ability of the proposed framework to understand and explain the spread over benchmark traditional models and to interpret the Shapley Additive Explanations (SHAP) plots. The conclusions derived from the proposed framework provide important scientific information for government policymakers in disease control strategies, not only to analyze COVID-19 but to have a better model to determine social interventions for future outbreaks.Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.[This corrects the article DOI 10.1007/s11469-020-00418-6.].Evidence is mixed concerning whether delayed judgments of learning (JOLs) enhance learning and if so, whether their benefit is similar to retrieval practice. One potential explanation for the mixed findings is the truncated search hypothesis, which states that not all delayed JOLs lead to a full-blown covert retrieval attempt. In three paired-associate learning experiments, we examined the effect of delayed JOLs on later recall by comparing them to conditions of restudy, overt retrieval, and various other delayed JOL conditions. In Experiment 1, after an initial study phase, subjects either restudied word pairs, practiced overt retrieval, or made cue-only or cue-target delayed JOLs. In Experiments 2a and 2b, where conditions were manipulated within-subjects, subjects either restudied word pairs, practiced overt retrieval, made cue-only delayed JOLs, made cue-only delayed JOLs followed by a yes/no retrieval question or, in another condition, by an overt retrieval prompt. The final cued recall tests were delayed by two days.