OPORTUNIDADES PARA EL FORTALECIMIENTO DE LA COOPERACIÓN ECONÓMICA Y COMERCIAL ENTRE CUBA Y SHANDONG

Economic and trade cooperation between the Republic of Cuba and the People's Republic of China has a long history and has achieved gratifying results in the 1960s years of diplomatic relations. China is the second commercial partner of Cuba and its main technological supplier and the province of Shandong is one of the most important provinces within the economic-commercial cooperation between both nations. In the article, the key areas of cooperation are highlighted, with the aim of visualizing them, for their strengthening, highlighting future projections. In addition, some alternatives are shown to provide a reference in the economic and commercial cooperation between Cuba and the Shandong province.La cooperación económica y comercial entre la República de Cuba y la República Popular China tiene una larga historia y ha logrado resultados gratificantes en los 60 años de relaciones diplomáticas. China es el segundo socio comercial de Cuba y su principal proveedor tecnológico y la provincia de Shandong es una de las provincias más importantes dentro de la cooperación económico-comercial entre ambas naciones. En el artículo, se destacan las áreas claves de la cooperación, con el objetivo de visualizarlas, para su fortalecimiento, destacándose las proyecciones futuras. Además, se muestran algunas alternativas para proporcionar una referencia en la cooperación económica y comercial entre Cuba y la provincia de Shandong

Variable Selection in Competing Risks Using the L1-Penalized Cox Model

One situation in survival analysis is that the failure of an individual can happen because of one of multiple distinct causes. Survival data generated in this scenario are commonly referred to as competing risks data. One of the major tasks, when examining survival data, is to assess the dependence of survival time on explanatory variables. In competing risks, as with ordinary univariate survival data, there may be explanatory variables associated with the risks raised from the different causes being studied. The same variable might have different degrees of influence on the risks due to different causes. Given a set of explanatory variables, it is of interest to identify the subset of variables that are significantly associated with the risk corresponding to each failure cause. In this project, we develop a statistical methodology to achieve this purpose, that is, to perform variable selection in the presence of competing risks survival data. Asymptotic properties of the model and empirical simulation results for evaluation of the model performance are provided. One important feature of our method, which is based on the idea of the L1 penalized Cox model, is the ability to perform variable selection in situations where we have high-dimensional explanatory variables, i.e. the number of explanatory variables is larger than the number of observations. The method was applied on a real dataset originated from the National Institutes of Health funded project Genes related to hepatocellular carcinoma progression in living donor and deceased donor liver transplant\u27\u27 to identify genes that might be relevant to tumor progression in hepatitis C virus (HCV) infected patients diagnosed with hepatocellular carcinoma (HCC). The gene expression was measured on Affymetrix GeneChip microarrays. Based on the current available 46 samples, 42 genes show very strong association with tumor progression and deserve to be further investigated for their clinical implications in prognosis of progression on patients diagnosed with HCV and HCC

Parametric frailty models for clustered data with arbitrary censoring: application to effect of male circumcision on HPV clearance

Background In epidemiological studies, subjects are often followed for a period during which study outcomes are measured at selected time points, such as by diagnostic testing performed on biological samples collected at each visit. Although test results may indicate the presence or absence of a disease or condition, they cannot provide information on when exactly it occurred. Such study designs generate arbitrarily censored time-to-event data, which can include left, interval and right censoring. Adding to this complexity, the data may be clustered such that observations within the same cluster are not independent, such as time to recovery of an infectious disease of family or community members. This data structure is observed when evaluating circumcision\u27s effect on clearance of penile high risk human papillomavirus (HR-HPV) infections using data collected from the male circumcision(MC) trial conducted in Rakai, Uganda, where the multiple infections within individual and HPV testings performed at trial follow-up visits gave rise to the clustered data with arbitrary censoring. Methods We describe the use of parametric proportional hazards frailty models and accelerated failure time frailty models to examine the relationship between explanatory variables and the survival outcomes that are subject to arbitrary censoring, while accounting for the correlation within clusters. Standard software such as SAS can be used for parameter estimation. Results Circumcision\u27s effect on HPV infection was a secondary end point in the Rakai MC trial, and HPV genotyping was conducted for penile samples of a subset of trial participants collected at enrollment, 6, 12 and 24-month follow up visits. At enrollment, 36.7% intervention arm men (immediate circumcision) and 36.6% control arm men (delayed circumcision at 2 years) were infected with HR-HPV, with the number of infections per man being 1-5. The proposed models were used to examine whether MC facilitated clearance of the prevalent infections. Results show that clearance of multiple infections within each man is highly correlated, and clearance was 60% faster if a man was circumcised. Conclusions Parametric frailty models provide viable ways to study the relationship between exposure variables and clustered survival outcome that is subject to arbitrary censoring, as is often observed in HPV epidemiology studies

Impact of a community health worker HIV treatment and prevention intervention in an HIV hotspot fishing community in Rakai, Uganda (mLAKE): study protocol for a randomized controlled trial

Abstract Background Effective yet practical strategies are needed to increase engagement in HIV treatment and prevention services, particularly in high-HIV-prevalence hotspots. We designed a community-based intervention called “Health Scouts” to promote uptake and adherence to HIV services in a highly HIV-prevalent fishing community in Rakai, Uganda. Using a situated Information, Motivation, and Behavioral skills theory framework, the intervention consists of community health workers, called Health Scouts, who use motivational interviewing strategies and mobile health tools to promote engagement in HIV treatment and prevention services. Methods/design The Health Scout intervention is being evaluated through a pragmatic, parallel, cluster-randomized controlled trial with an allocation ratio of 1:1. The study setting is a single high-HIV-prevalence fishing community in Rakai, Uganda divided into 40 contiguous neighborhood clusters each containing about 65 households. Twenty clusters received the Health Scout Intervention; 20 clusters received standard of care. The Health Scout intervention is delivered within the community at the household level, targeting all residents aged 15 years or older. The primary programmatic outcomes are self-reported HIV care, antiretroviral therapy, and male circumcision coverage; the primary biologic outcome is population-level HIV viremia prevalence. Follow-up is planned for about 3 years. Discussion HIV treatment and prevention service engagement remains suboptimal in HIV hotspots. New, community-based implementation approaches are needed. If found to be effective in this trial, the Health Scout intervention may be an important component of a comprehensive HIV response. Trial registration ClinicalTrials.gov, ID: NCT02556957 . Registered on 20 September 2015.https://deepblue.lib.umich.edu/bitstream/2027.42/138964/1/13063_2017_Article_2243.pd

Effects of Genital Ulcer Disease and Herpes Simplex Virus Type 2 on the Efficacy of Male Circumcision for HIV Prevention: Analyses from the Rakai Trials

Ron Gray and colleagues analyze data from two circumcision trials in Uganda to assess how HSV-2 status and genital ulcer disease affect the procedure's ability to reduce HIV infection

An analysis of lithium-ion battery state-of-health through physical experiments and mathematical modelling

Lithium-ion batteries are ubiquitous in modern society. The high power and energy density of lithium-ion batteries compared to other forms of electrochemical energy storage make them very popular in a wide range of applications, most notably electric vehicles (EVs) and portable devices such as mobile phones and laptop computers. However, despite the numerous advantages of lithium-ion batteries over other forms of energy sources, their performance and durability still suffer from aging and degradation. The purpose of the work presented in this thesis is to investigate how different load cycle properties affect the cycle life and aging processes of lithium-ion cells. To do so, two approaches are taken: physical experiments and mathematical modeling. In the first approach, the cycle life of commercial lithium-ion cells of LiNiCoAlO₂ chemistry was tested using three different current rates to simulate low-, medium-, and high-power consuming applications. The batteries are discharged/charged repeatedly under the three conditions, all while temperature, voltage, current, and capacity are recorded. Data arising from the experiments are then analyzed, with the goal of quantifying battery degradation based on capacity fade and voltage drop. The results are then used to build two predictive models to estimate lithium-ion battery state-of-health (SoH): the decreasing battery V₀₊ model and the increasing CV charge capacity model. Furthermore, a simple thermal model fitted from the battery temperature profiles is able to predict peak temperature under different working conditions, which may be the solution to temperature sensitive applications such as cellphones. The limitation to physical experiments is that they can be costly and extremely time-consuming. On the other hand, mathematical modeling and simulation can provide insight, such as the internal states of the battery, that is either impractical or impossible to find using physical experiments. Examples include lithium-ion intercalation and diffusion in electrodes and electrolytes, various side-reactions, double-layer effects, and lithium concentration variations across the electrode layer. Thus, in the second approach, work focuses on implementing the pseudo-two-dimensional (P2D) model, the most widely accepted electrochemical model on lithium-ion batteries. The P2D model comprises highly-nonlinear, tightly-coupled partial differential equations that calculate lithium concentration, ionic flux, battery temperature and potential to significant accuracy. The unparalleled prediction abilities of the P2D model, however, are shadowed by the low computational efficiency. Thus, much of this work focuses on reducing model complexity to shorten effective simulation time, allowing for use in applications, such as a battery management system, that have limited computational resources. In the end, four model reductions have been identified and successfully implemented, with each one achieving a certain standard of accuracy.Science, Faculty ofMathematics, Department ofGraduat

Mask usage, social distancing, racial, and gender correlates of COVID-19 vaccine intentions among adults in the US

Vaccine hesitancy could become a significant impediment to addressing the COVID-19 pandemic. The current study examined the prevalence of COVID-19 vaccine hesitancy and factors associated with vaccine intentions. A national panel survey by the National Opinion Research Center (NORC) was designed to be representative of the US household population. Sampled respondents were invited to complete the survey between May 14 and 18, 2020 in English or Spanish. 1,056 respondents completed the survey—942 via the web and 114 via telephone. The dependent variable was assessed by the item “If a vaccine against the coronavirus becomes available, do you plan to get vaccinated, or not?” Approximately half (53.6%) reported intending to be vaccinated, 16.7% did not intend, and 29.7% were unsure. In the adjusted stepwise multinominal logistic regression, Black and Hispanic respondents were significantly less likely to report intending to be vaccinated as were respondents who were females, younger, and those who were more politically conservative. Compared to those who reported positive vaccine intentions, respondents with negative vaccine intentions were significantly less likely to report that they engaged in the COVID-19 prevention behaviors of wearing masks (aOR = 0.53, CI = 0.37–0.76) and social distancing (aOR = 0.22, CI = 0.12–0.42). In a sub-analysis of reasons not to be vaccinated, significant race/ethnic differences were observed. This national survey indicated a modest level of COVID-19 vaccine intention. These data suggest that public health campaigns for vaccine uptake should assess in greater detail the vaccine concerns of Blacks, Hispanics, and women to tailor programs

The differential expression pattern of gene (a) (Affy ID "205336 at") and gene (b) (Affy ID "205055 at")

<p><b>Copyright information:</b></p><p>Taken from "A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy"</p><p>http://www.biomedcentral.com/1471-2164/9/98</p><p>BMC Genomics 2008;9():98-98.</p><p>Published online 26 Feb 2008</p><p>PMCID:PMC2276496.</p><p></p