An Analysis of the Approaches to Building Learning Party Organizations in Colleges and Universities

The Fourth Plenary Session of the Seventeenth Central Committee of the Chinese Communist Party assigned the strategic task of building a learning Marxist Party, which is a major initiative made from the overall perspective of promoting the cause of socialism with Chinese characteristics and of improving the building of Party. So the Party organizations at all levels should make joint efforts to implement the task of building a learning Party from all aspects and in all areas. Higher education plays an overall, fundamental, leading and humanistic role in the socialist construction in China, and the Party building is an important part of the Communist Party of China itself. In this sense, it is particularly important to promote the building of learning Party organizations in colleges and universities so as to build a comprehensive learning Party. And the writer is firmly convinced that it is of great significance to actively explore the approaches to building learning Party organizations in colleges and universities.

Sex Differences in Quality of Life and Clinical Outcomes in Patients with Heart Failure

Background: Heart failure (HF) is generally associated with poor quality of life (QoL). Limited data are available characterizing health-related QoL (HRQL) in Chinese patients with HF. Methods: We used the Minnesota Living with Heart Failure Questionnaire (MLHFQ) to record QoL in 4082 patients with HF from China who were followed up over 12 months in the Heart Failure Registry of Patient Outcomes (HERO) study. Baseline HRQL and differences in QoL between women and men with heart failure were compared. We used multivariable Cox regression with adjustment for variables to assess the association between MLHFQ summary scores and a composite of all-cause mortality and HF hospitalization. Result: At baseline, the mean MLHFQ in the overall population was 42.9 ± 19.57; the scores for physical and emotional domains were 22.0 ± 8.69 and 8.66 ± 6.08, respectively. Women had a higher (poorer) MLHFQ summary score (44.27 ± 19.13) than men (41.63 ± 19.90) (P<0.001). Female patients also had higher MLHFQ physical and emotional scores than male patients (P<0.001). The specific scores of the questionnaire were higher in women than men. NYHA class was the strongest independent predictor of MLHFQ score (β=6.12 unit increment; P<0.001). Sex was not independently associated with higher MLHFQ scores after multivariable adjustments. The 12-month mortality in the overall cohort was 19.6%, the hospitalization rate was 24.4%, and the composite endpoint was 40.15%. A 10-point increase in MLHFQ score was associated with higher risk of mortality (female and male HRs=1.19 [95% CI 1.12–1.26]; P<0.001 and 1.18 [95% CI 1.12–1.24]; P<0.001, respectively) and composite outcomes (HRs=1.08 [95% CI 1.04–1.13]; P<0.001 and 1.11 [95% CI 1.07–1.14]; P<0.001, respectively). Females did not show a significant association between HRQL and hospitalization (HR=1.04 [95% CI 0.99–1.09]; P=0.107). Conclusion: Quality of life was largely poorer in women than men, but was similar between sexes in terms of physical burden and emotional limitation. HRQL is an independent predictor of all-cause death and HF hospitalization in patients with HF

Fair Causal Feature Selection

Causal feature selection has recently received increasing attention in machine learning. Existing causal feature selection algorithms select unique causal features of a class variable as the optimal feature subset. However, a class variable usually has multiple states, and it is unfair to select the same causal features for different states of a class variable. To address this problem, we employ the class-specific mutual information to evaluate the causal information carried by each state of the class attribute, and theoretically analyze the unique relationship between each state and the causal features. Based on this, a Fair Causal Feature Selection algorithm (FairCFS) is proposed to fairly identifies the causal features for each state of the class variable. Specifically, FairCFS uses the pairwise comparisons of class-specific mutual information and the size of class-specific mutual information values from the perspective of each state, and follows a divide-and-conquer framework to find causal features. The correctness and application condition of FairCFS are theoretically proved, and extensive experiments are conducted to demonstrate the efficiency and superiority of FairCFS compared to the state-of-the-art approaches

MAP3K19 regulatory variation in populations with African ancestry may increase COVID-19 severity

To identify ancestry-linked genetic risk variants associated with COVID-19 hospitalization, we performed an integrative analysis of two genome-wide association studies and resolved four single nucleotide polymorphisms more frequent in COVID-19-hospitalized patients with non-European ancestry. Among them, the COVID-19 risk SNP rs16831827 shows the largest difference in minor allele frequency (MAF) between populations with African and European ancestry and also shows higher MAF in hospitalized COVID-19 patients among cohorts of mixed ancestry (odds ratio [OR] = 1.20, 95% CI: 1.10-1.30) and entirely African ancestry (OR = 1.30, 95% CI: 1.02-1.67). rs16831827 is an expression quantitative trait locus of MAP3K19. MAP3K19 expression is induced during ciliogenesis and most abundant in ciliated tissues including lungs. Single-cell RNA sequencing analyses revealed that MAP3K19 is highly expressed in multiple ciliated cell types. As rs16831827∗T is associated with reduced MAP3K19 expression, it may increase the risk of severe COVID-19 by reducing MAP3K19 expression

The volatile anesthetic isoflurane differentially inhibits voltage-gated sodium channel currents between pyramidal and parvalbumin neurons in the prefrontal cortex

BackgroundHow volatile anesthetics work remains poorly understood. Modulations of synaptic neurotransmission are the direct cellular mechanisms of volatile anesthetics in the central nervous system. Volatile anesthetics such as isoflurane may reduce neuronal interaction by differentially inhibiting neurotransmission between GABAergic and glutamatergic synapses. Presynaptic voltage-dependent sodium channels (Nav), which are strictly coupled with synaptic vesicle exocytosis, are inhibited by volatile anesthetics and may contribute to the selectivity of isoflurane between GABAergic and glutamatergic synapses. However, it is still unknown how isoflurane at clinical concentrations differentially modulates Nav currents between excitatory and inhibitory neurons at the tissue level.MethodsIn this study, an electrophysiological recording was applied in cortex slices to investigate the effects of isoflurane on Nav between parvalbumin (PV+) and pyramidal neurons in PV-cre-tdTomato and/or vglut2-cre-tdTomato mice.ResultsIsoflurane at clinically relevant concentrations produced a hyperpolarizing shift in the voltage-dependent inactivation and slowed the recovery time from the fast inactivation in both cellular subtypes. Since the voltage of half-maximal inactivation was significantly depolarized in PV+ neurons compared to that of pyramidal neurons, isoflurane inhibited the peak Nav currents in pyramidal neurons more potently than those of PV+ neurons (35.95 ± 13.32% vs. 19.24 ± 16.04%, P = 0.036 by the Mann-Whitney test).ConclusionsIsoflurane differentially inhibits Nav currents between pyramidal and PV+ neurons in the prefrontal cortex, which may contribute to the preferential suppression of glutamate release over GABA release, resulting in the net depression of excitatory-inhibitory circuits in the prefrontal cortex

Atomically Intimate Contact between Solid Electrolytes and Electrodes for Li Batteries

Solid electrolytes, as a promising replacement for the flammable liquid electrolyte in conventional Li-ion batteries, may greatly alleviate the safety issues and improve the energy density. However, mainstream electrodes are also solid. If solid electrolytes were employed, creating intimate electrode-electrolyte contact similar to that between solid and liquid would be quite difficult. Here we discovered that, by forming epitaxial interfaces, such a seamless solid-solid contact can happen between two widely studied systems: the Li-rich layered electrode and perovskite solid electrolyte. Atomic-resolution electron microscopy unambiguously demonstrated that the former can be epitaxially embedded into the latter. The solid-solid composite electrode formed this way exhibited a rate capability no lower than the one based on solid-liquid contact. With the periodic misfit dislocations reconciling structural differences, such epitaxy can tolerate large lattice mismatch, and thus may occur between many layered electrodes and perovskite solid electrolytes

F-DDIA: A Framework for Detecting Data Injection Attacks in Nonlinear Cyber-Physical Systems

Data injection attacks in a cyber-physical system aim at manipulating a number of measurements to alter the estimated real-time system states. Many researchers recently focus on how to detect such attacks. However, most of the detection methods do not work well for the nonlinear systems. In this paper, we present a compressive sampling methodology to identify the attack, which allows determining how many and which measurement signals are launched. The sparsity feature is used. Generally, our methodology can be applied to both linear and nonlinear systems. The experimental testing, which includes realistic load patterns from NYISO with various attack scenarios in the IEEE 14-bus system, confirms that our detector performs remarkably well

CE-BLAST makes it possible to compute antigenic similarity for newly emerging pathogens

Major challenges in vaccine development include rapidly selecting or designing immunogens for raising cross-protective immunity against different intra-or inter-subtypic pathogens, especially for the newly emerging varieties. Here we propose a computational method, Conformational Epitope (CE)-BLAST, for calculating the antigenic similarity among different pathogens with stable and high performance, which is independent of the prior binding-assay information, unlike the currently available models that heavily rely on the historical experimental data. Tool validation incorporates influenza-related experimental data sufficient for stability and reliability determination. Application to dengue-related data demonstrates high harmonization between the computed clusters and the experimental serological data, undetectable by classical grouping. CE-BLAST identifies the potential cross-reactive epitope between the recent zika pathogen and the dengue virus, precisely corroborated by experimental data. The high performance of the pathogens without the experimental binding data suggests the potential utility of CE-BLAST to rapidly design cross-protective vaccines or promptly determine the efficacy of the currently marketed vaccine against emerging pathogens, which are the critical factors for containing emerging disease outbreaks.Peer reviewe

Broadband nonlinear modulation of incoherent light using a transparent optoelectronic neuron array

Nonlinear optical processing of ambient natural light is highly desired in computational imaging and sensing applications. A strong optical nonlinear response that can work under weak broadband incoherent light is essential for this purpose. Here we introduce an optoelectronic nonlinear filter array that can address this emerging need. By merging 2D transparent phototransistors (TPTs) with liquid crystal (LC) modulators, we create an optoelectronic neuron array that allows self-amplitude modulation of spatially incoherent light, achieving a large nonlinear contrast over a broad spectrum at orders-of-magnitude lower intensity than what is achievable in most optical nonlinear materials. For a proof-of-concept demonstration, we fabricated a 10,000-pixel array of optoelectronic neurons, each serving as a nonlinear filter, and experimentally demonstrated an intelligent imaging system that uses the nonlinear response to instantly reduce input glares while retaining the weaker-intensity objects within the field of view of a cellphone camera. This intelligent glare-reduction capability is important for various imaging applications, including autonomous driving, machine vision, and security cameras. Beyond imaging and sensing, this optoelectronic neuron array, with its rapid nonlinear modulation for processing incoherent broadband light, might also find applications in optical computing, where nonlinear activation functions that can work under ambient light conditions are highly sought.Comment: 20 Pages, 5 Figure