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The center stage of neuro-imaging is currently occupied by studies of functional correlations between brain regions. These correlations define the brain functional networks, which are the most frequently used framework to represent and interpret a variety of experimental findings. In the previous study, we first demonstrated that the relatively stronger blood oxygenated level dependent (BOLD) activations contain most of the information relevant to understand functional connectivity, and subsequent work confirmed that a large compression of the original signals can be obtained without significant loss of information. In this study, we revisit the correlation properties of these epochs to define a measure of nonlinear dynamic directed functional connectivity (nldFC) across regions of interest. We show that the proposed metric provides at once, without extensive numerical complications, directed information of the functional correlations, as well as a measure of temporal lags across regions, overall offering a different and complementary perspective in the analysis of brain co-activation patterns. In this study, we provide further details for the computations of these measures and for a proof of concept based on replicating existing results from an Autistic Syndrome database, and discuss the main features and advantages of the proposed strategy for the study of brain functional correlations.Astrocytes play a central role in inducing concerted phase synchronized neural-wave patterns inside the brain. In this article, we demonstrate that injected radio-frequency signal in underlying heavy metal layer of spin-orbit torque oscillator neurons mimic the neuron phase synchronization effect realized by glial cells. Potential application of such phase coupling effects is illustrated in the context of a temporal "binding problem." We also present the design of a coupled neuron-synapse-astrocyte network enabled by compact neuromimetic devices by combining the concepts of local spike-timing dependent plasticity and astrocyte induced neural phase synchrony.
Environmental risk factors, including community level pollution burden and exposure to smoking and secondhand smoke, have not been evaluated in relation to risk for infection with COVID-19 in high risk, urban Latinx families.

We evaluated risk factors for COVID-19 infection in three, preexisting, longitudinal, Latinx family cohorts in the San Francisco Bay Area from May through September 2020 (N=383 households, 1875 people). All households were previously recruited before the pandemic. For the COVID-19 sub-study, participants responded to a telephone interview where we assessed food consumption patterns, housing and employment status, and history of COVID-19 infection. Secondhand smoke exposure was based on previously collected selfreported data, and environmental pollution exposure was determined from census tract residence. Non-parametric tests and multiple logistic regression were used to assess independent predictors of COVID-19 infection.

Larger household size increased risk for infection (OR=1.58;not allow exposure to secondhand smoke between units, that individuals eat in the home environment, and that large households can safely separate individuals exposed to COVID-19.Digital health technologies hold significant promise to advance both functional and normative health and social care integration. The COVID-19 pandemic has created a window of opportunity to rapidly advance the adoption of digital solutions which can improve activities that support integration at clinical, professional, organizational and system levels. Global examples demonstrate how the pandemic has also created opportunities to use technology to address core values of integrated care like person-centredness and coordination. However, rapid and reactive changes could lead to increased fragmentation and exacerbate health inequity. This perspective paper outlines some of the opportunities and threats to advancing integrated care presented by the rapid adoption of digital health tools, suggesting we maintain a long view to ensure the stage we set today will mean greater integration tomorrow.
Recently, an outbreak of a novel human coronavirus SARS-CoV-2 has become a world health concern leading to severe respiratory tract infections in humans. Virus transmission occurs through person-to-person contact, respiratory droplets, and contaminated hands or surfaces. Accordingly, we aim at reviewing the literature on all information available about the persistence of coronaviruses, including human and animal coronaviruses, on inanimate surfaces and inactivation strategies with biocides employed for chemical and physical disinfection.

A comprehensive search was systematically conducted in main databases from 1998 to 2020 to identify various viral disinfectants associated with HCoV and methods for control and prevention of this newly emerged virus.

The analysis of 62 studies shows that human coronaviruses such as severe acute respiratory syndrome (SARS) coronavirus, Middle East respiratory syndrome (MERS) coronavirus or endemic human coronaviruses (HCoV), canine coronavirus (CCV), transmissible gastro results of the present study can help researchers, policymakers, health decision makers, and people perceive and take the correct measures to control and prevent further transmission of COVID-19. Prevention and decontamination will be the main ways to stop the ongoing outbreak of COVID-19.The novel coronavirus (COVID-19) has led to significant stressors and shifts in social life, yet social interactions experienced by people with trauma exposure during the COVID pandemic is largely unknown. This study assessed frequency of interactions, social support given and received, and prosocial behavior using online survey methods (N=1049). We examined differences in experiences across three groups no trauma exposure, trauma-exposed with low PTSD symptoms, and trauma-exposed with high PTSD symptoms. We also explored correlations between social interactions and PTSD symptom clusters. Results indicated significant differences across groups and the high PTSD group reported stronger associations between social interaction variables and symptom clusters, on average.Background The preoperative systemic inflammation response index (SIRI), based on peripheral neutrophil (N), monocyte (M), and lymphocyte (L) counts, has shown mounting evidence as an effective prognostic indicator in some malignant tumors. The aim of the present study was to evaluate the prognostic significance of pre-treatment SIRI in gastric cancer patients who received neoadjuvant chemotherapy (NACT). Methods This retrospective study comprised 107 patients with advanced gastric cancer treated with NACT between July 2007 and September 2015 in our hospital. SIRI was calculated from peripheral venous blood samples obtained prior to treatment. The best cutoff value for SIRI by receiver operating characteristic (ROC) curve was 1.2 (low SIRI less then 1.21, high SIRI ≥1.21). The clinical outcomes of disease-free survival (DFS) and overall survival (OS) were analyzed by Kaplan-Meier survival analysis and compared using the log-rank test. Univariate and multivariate analyses were performed by the Cox proportional hazards regression model. Results The results demonstrated that the low SIRI group was statistically associated with gender, primary tumor site, white blood cell, neutrophil, and monocyte counts, NLR (neutrophil to lymphocyte ratio), MLR (monocyte to lymphocyte ratio), and PLR (platelet to lymphocyte ratio). The SIRI was predictive for DFS and OS by univariate and multivariate analysis; the low SIRI group had better median DFS and OS than the high SIRI group (median DFS 27.03 vs. 22.33 months, median OS 29.73 vs. 24.43 months). The DFS and OS in the low SIRI group were longer than the high SIRI group. selleckchem Conclusions SIRI may qualify as a useful, reliable, and convenient prognostic indicator in patients with advanced gastric cancer to help physicians to provide personalized prognostication for gastric cancer patients treated with NACT.Classification with imbalanced data is a common and challenging problem in many practical machine learning problems. Ensemble learning is a popular solution where the results from multiple base classifiers are synthesized to reduce the effect of a possibly skewed distribution of the training set. In this paper, binary classifiers based on Gaussian processes are chosen as bases for inferring the predictive distributions of test latent variables. We apply a Gaussian process latent variable model where the outputs of the Gaussian processes are used for making the final decision. The tests of the new method in both synthetic and real data sets show improved performance over standard approaches.Identifying uterine contractions with the aid of machine learning methods is necessary vis-á-vis their use in combination with fetal heart rates and other clinical data for the assessment of a fetus wellbeing. In this paper, we study contraction identification by processing noisy signals due to uterine activities. We propose a complete four-step method where we address the imbalanced classification problem with an ensemble Gaussian process classifier, where the Gaussian process latent variable model is used as a decision-maker. The results of both simulation and real data show promising performance compared to existing methods.Alloxan (ALX) and streptozotocin (STZ) are extensively used to induce type 1 diabetes (T1D) in animal models. This study is aimed at evaluating the differences in immune parameters caused by ALX and STZ. T1D was induced either with ALX or with STZ, and the animals were followed for up to 180 days. Both ALX and STZ induced a decrease in the total number of circulating leukocytes and lymphocytes, with an increase in granulocytes when compared to control mice (CT). STZ-treated mice also exhibited an increase in neutrophils and a reduction in the lymphocyte percentage in the bone marrow. In addition, while the STZ-treated group showed a decrease in total CD3+, CD4-CD8+, and CD4+CD8+ T lymphocytes in the thymus and CD19+ B lymphocytes in the pancreas and spleen, the ALX group showed an increase in CD4-CD8+ and CD19+ only in the thymus. Basal levels of splenic interleukin- (IL-) 1β and pancreatic IL-6 in the STZ group were decreased. Both diabetic groups showed atrophy of the thymic medulla and degeneration of pancreatic islets of Langerhans composed of inflammatory infiltration and hyperemia with vasodilation. ALX-treated mice showed a decrease in reticuloendothelial cells, enhanced lymphocyte/thymocyte cell death, and increased number of Hassall's corpuscles. Reduced in vitro activation of splenic lymphocytes was found in the STZ-treated group. Furthermore, mice immunized with ovalbumin (OVA) showed a more intense antigen-specific paw edema response in the STZ-treated group, while production of anti-OVA IgG1 antibodies was similar in both groups. Thereby, important changes in immune cell parameters in vivo and in vitro were found at an early stage of T1D in the STZ-treated group, whereas alterations in the ALX-treated group were mostly found in the chronic phase of T1D, including increased mortality rates. These findings suggest that the effects of ALX and STZ influenced, at different times, lymphoid organs and their cell populations.
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