Solvothermal Synthesis and Pyrolysis Toward Heteroatom-Doped Carbon Microspheres for Zinc-Ion Hybrid Capacitors.

Heteroatom-doped porous carbon materials have investigated to promote the energy density of zinc-ion hybrid capacitors (ZICs). Yet, the quest for high-performance carbon materials or cathodes brings to light the question of which dopants facilitate fast energy storage kinetics and various types of pseudocapacitive reactions. Investigation of carbon materials with precise quantitative dopants as the key variable represents an effective appropriate approach to comprehending the intricate role of dopants in energy storage areas. Here, a straightforward solvothermal strategy is demonstrated for a variety of pristine and iron-incorporated polymer microspheres, used as precursors for durable spherical carbons intended for cathode applications in ZICs. The strategy effectively governs the incorporation of dopants within the carbon materials, whilewhile maintaining consistent morphology, microtexture, and pore structure across different carbon variations. The synergistic effect of various dopants enhance the pseudocapacitance and facilitate the ion storage process. In consequence, the optimal cathode delivers considerable capacity (178.8 mAh g-1 at 0.5 A g-1), good energy density (120.2 Wh kg-1 at 336 W kg-1), and excellent cycling stability (101.5% capacity retention at 35 000 cycles). The demonstration showcases a viable method for crafting carbon materials with precise dopants to accommodate the zinc anode, thus enabling high-capacity and high-energy ZICs.

A Strategy for Enhancing Perpendicular Magnetic Anisotropy in Yttrium Iron Garnet Films.

In future information storage and processing, magnonics is one of the most promising candidates to replace traditional microelectronics. Yttrium iron garnet (YIG) films with perpendicular magnetic anisotropy (PMA) have aroused widespread interest in magnonics. Obtaining strong PMA in a thick YIG film with a small lattice mismatch (η) has been fascinating but challenging. Here, a novel strategy is proposed to reduce the required minimum strain value for producing PMA and increase the maximum thickness for maintaining PMA in YIG films by slight oxygen deficiency. Strong PMA is achieved in the YIG film with an η of only 0.4% and a film thickness up to 60 nm, representing the strongest PMA for such a small η reported so far. Combining transmission electron microscopy analyses, magnetic measurements, and a theoretical model, it is demonstrated that the enhancement of PMA physically originates from the reduction of saturation magnetization and the increase of magnetostriction coefficient induced by oxygen deficiency. The Gilbert damping values of the 60-nm-thick YIG films with PMA are on the order of 10-4. This strategy improves the flexibility for the practical applications of YIG-based magnonic devices and provides promising insights for the theoretical understanding and the experimental enhancement of PMA in garnet films.

Longitudinal analysis of mucosa-associated invariant T cells in sepsis reveals their early numerical decline with prognostic implications and a progressive loss of antimicrobial functions.

Sepsis-elicited immunosuppression elevates the risk of secondary infections. We used a clinically relevant mouse model and serial peripheral blood samples from patients to assess the antimicrobial activities of mucosa-associated invariant T (MAIT) cells in sepsis. Hepatic and splenic MAIT cells from B6-MAITCAST mice displayed increased CD69 expression and a robust interferon-γ (IFNγ) production capacity shortly after sublethal cecal ligation and puncture, but not at a late timepoint. Peripheral blood MAIT cell frequencies were reduced in septic patients at the time of intensive care unit (ICU) admission, and more dramatically so among nonsurvivors, suggesting the predictive usefulness of early MAIT cell enumeration. In addition, at ICU admission, MAIT cells from sepsis survivors launched stronger IFNγ responses to several bacterial species compared with those from patients who subsequently died of sepsis. Of note, while low human leukocyte antigen (HLA)-DR+ monocyte frequencies, widely regarded as a surrogate indicator of sepsis-induced immunosuppression, were gradually corrected, the numerical insufficiency of MAIT cells was not resolved over time, and their CD69 expression continued to decline. MAIT cell responses to bacterial pathogens, a major histocompatibility complex-related protein 1 (MR1) ligand, and interleukin (IL)-12 and IL-18 were also progressively lost during sepsis and did not recover by the time of ICU/hospital discharge. We propose that MAIT cell dysfunctions contribute to post-sepsis immunosuppression.

Nonlocal Detection of Interlayer Three-Magnon Coupling.

A leading nonlinear effect in magnonics is the interaction that splits a high-frequency magnon into two low-frequency magnons with conserved linear momentum. Here, we report experimental observation of nonlocal three-magnon scattering between spatially separated magnetic systems, viz. a CoFeB nanowire and a yttrium iron garnet (YIG) thin film. Above a certain threshold power of an applied microwave field, a CoFeB Kittel magnon splits into a pair of counterpropagating YIG magnons that induce voltage signals in Pt electrodes on each side, in excellent agreement with model calculations based on the interlayer dipolar interaction. The excited YIG magnon pairs reside mainly in the first excited (n=1) perpendicular standing spin-wave mode. With increasing power, the n=1 magnons successively scatter into nodeless (n=0) magnons through a four-magnon process. Our results demonstrate nonlocal detection of two separately propagating magnons emerging from one common source that may enable quantum entanglement between distant magnons for quantum information applications.

Field-Free Spin-Orbit Torque Switching Enabled by the Interlayer Dzyaloshinskii-Moriya Interaction.

Perpendicularly magnetized structures that are switchable using a spin current under field-free conditions can potentially be applied in spin-orbit torque magnetic random-access memory (SOT-MRAM). Several structures have been developed; however, new structures with a simple stack structure and MRAM compatibility are urgently needed. Herein, a typical structure in a perpendicular spin-transfer torque MRAM, the Pt/Co multilayer and its synthetic antiferromagnetic counterpart with perpendicular magnetic anisotropy, was observed to possess an intrinsic interlayer chiral interaction between neighboring magnetic layers, namely, the interlayer Dzyaloshinskii-Moriya interaction (DMI) effect. Furthermore, using a current parallel to the eigenvector of the interlayer DMI, we switched the perpendicular magnetization of both structures without a magnetic field, owing to the additional symmetry breaking introduced by the interlayer DMI. This SOT switching scheme realized in the Pt/Co multilayer and its synthetic antiferromagnet structure may open a new avenue toward practical perpendicular SOT-MRAM and other SOT devices.

Piezoelectric Strain-Controlled Magnon Spin Current Transport in an Antiferromagnet.

As the core of spintronics, the transport of spin aims at a low-dissipation data process. The pure spin current transmission carried by magnons in antiferromagnetic insulators is natively endowed with superiority such as long-distance propagation and ultrafast speed. However, the traditional control of magnon transport in an antiferromagnet via a magnetic field or temperature variation adds critical inconvenience to practical applications. Controlling magnon transport by electric methods is a promising way to overcome such embarrassment and to promote the development of energy-efficient antiferromagnetic logic. Here, the experimental realization of an electric field-induced piezoelectric strain-controlled magnon spin current transmission through the antiferromagnetic insulator in the Y3Fe5O12/Cr2O3/Pt trilayer is reported. An efficient and nonvolatile manipulation of magnon propagation/blocking is achieved by changing the relative direction between the Néel vector and spin polarization, which is tuned by ferroelastic strain from the piezoelectric substrate. The piezoelectric strain-controlled antiferromagnetic magnon transport opens an avenue for the exploitation of antiferromagnet-based spin/magnon transistors with ultrahigh energy efficiency.

Biogas slurry topdressing as replacement of chemical fertilizers reduces leaf senescence of maize by up-regulating tolerance mechanisms.

Worldwide physiological research has aimed to decelerate the aging of crop leaves by optimizing fertilization measures to improve crop or biomass yield. Solid organic fertilizers can be combined with chemical fertilizers to delay the aging of crop leaves. Biogas slurry is a liquid organic fertilizer produced by the anaerobic fermentation of livestock and poultry manure and other resources, and it can partially replace chemical fertilizers in field application via drip irrigation systems. However, the impact of biogas slurry topdressing on leaf aging remains unclear. This study investigated treatments with no topdressing (control, CK) and five topdressing patterns of biogas slurry replacing chemical fertilizer (nitrogen) at 100%, 75%, 50%, 25%, and 0% (100%BS, 75%BS, 50%BS, 25%BS, CF). The effects of different proportions of biogas slurry on leaf senescence rate, photosynthetic pigments, osmotic adjustment substances, antioxidant defense enzymes, and nitrogen metabolism related enzyme activities of maize were analyzed. Subsequently, the mechanisms of biogas slurry topdressing on the leaf senescence rate of maize were explored. The results showed that the mean decreasing rate of relative green leaf area (Vm) treated with biogas slurry decreased by 3.7%-17.1% and the duration of leaf area duration (LAD) increased by 3.7%-17.1% compared with the results for CK. The maximum senescence rate of 100%BS was delayed by 4.4 and 5.6 d compared to the results for CF and CK, respectively. During the senescence of maize leaves, the use of biogas slurry topdressing increased the content of chlorophyll, decreased the water loss and the accumulation rate of malondialdehyde and proline in leaves, and increased the activities of catalase, peroxidase, and superoxide dismutase in the later growth and development periods of maize. In addition, biogas slurry topdressing improved the nitrogen transport efficiency of the leaves and ensured continuous and efficient ammonium assimilation. Furthermore, there was a strong correlation between leaf senescence and the investigated physiological indices. Cluster analysis showed that the 100%BS treatment exhibited the most prominent effect on leaf senescence. Biogas slurry topdressing as a substitute for chemical fertilizer can be potentially used as an anti-aging regulation measure for crops to decrease the damage induced by senescence.

Feature screening with large-scale and high-dimensional survival data.

Data with a huge size present great challenges in modeling, inferences, and computation. In handling big data, much attention has been directed to settings with "large p small n", and relatively less work has been done to address problems with p and n being both large, though data with such a feature have now become more accessible than before, where p represents the number of variables and n stands for the sample size. The big volume of data does not automatically ensure good quality of inferences because a large number of unimportant variables may be collected in the process of gathering informative variables. To carry out valid statistical analysis, it is imperative to screen out noisy variables that have no predictive value for explaining the outcome variable. In this paper, we develop a screening method for handling large-sized survival data, where the sample size n is large and the dimension p of covariates is of non-polynomial order of the sample size n, or the so-called NP-dimension. We rigorously establish theoretical results for the proposed method and conduct numerical studies to assess its performance. Our research offers multiple extensions of existing work and enlarges the scope of high-dimensional data analysis. The proposed method capitalizes on the connections among useful regression settings and offers a computationally efficient screening procedure. Our method can be applied to different situations with large-scale data including genomic data.

COVID-19 impact on mental health.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has posed a significant influence on public mental health. Current efforts focus on alleviating the impacts of the disease on public health and the economy, with the psychological effects due to COVID-19 relatively ignored. In this research, we are interested in exploring the quantitative characterization of the pandemic impact on public mental health by studying an online survey dataset of the United States. METHODS: The analyses are conducted based on a large scale of online mental health-related survey study in the United States, conducted over 12 consecutive weeks from April 23, 2020 to July 21, 2020. We are interested in examining the risk factors that have a significant impact on mental health as well as in their estimated effects over time. We employ the multiple imputation by chained equations (MICE) method to deal with missing values and take logistic regression with the least absolute shrinkage and selection operator (Lasso) method to identify risk factors for mental health. RESULTS: Our analysis shows that risk predictors for an individual to experience mental health issues include the pandemic situation of the State where the individual resides, age, gender, race, marital status, health conditions, the number of household members, employment status, the level of confidence of the future food affordability, availability of health insurance, mortgage status, and the information of kids enrolling in school. The effects of most of the predictors seem to change over time though the degree varies for different risk factors. The effects of risk factors, such as States and gender show noticeable change over time, whereas the factor age exhibits seemingly unchanged effects over time. CONCLUSIONS: The analysis results unveil evidence-based findings to identify the groups who are psychologically vulnerable to the COVID-19 pandemic. This study provides helpful evidence for assisting healthcare providers and policymakers to take steps for mitigating the pandemic effects on public mental health, especially in boosting public health care, improving public confidence in future food conditions, and creating more job opportunities. TRIAL REGISTRATION: This article does not report the results of a health care intervention on human participants.

Directional pumping of water and oil microdroplets on slippery surface.

Transporting water and oil microdroplets is important for applications ranging from water harvesting to biomedical analysis but remains a great challenge. This is due to the amplified contact angle hysteresis and insufficient driving force in the micrometer scale, especially for low-surface energy oil droplets. Coalescence of neighboring droplets, which releases vast additional surface energy, was often required, but its relatively uncontrollable nature brings uncertainties to the droplet motion, and the methodology is not applicable to single droplets. Here we introduce a strategy based on slippery surface with immobilized lubricant menisci to directionally transport microdroplets. By simply mounting hydrogel dots on slippery surface, the raised menisci remotely pump microdroplets via capillary force with high efficiency, regardless of droplet size or surface energy. By proof-of-concept experiments, we demonstrate that our method allows for highly efficient water droplet collection and highly sensitive biomedical analyte detection.

Characterizing the COVID-19 dynamics with a new epidemic model: Susceptible-exposed-asymptomatic-symptomatic-active-removed.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread stealthily and presented a tremendous threat to the public. It is important to investigate the transmission dynamics of COVID-19 to help understand the impact of the disease on public health and the economy. In this article, we develop a new epidemic model that utilizes a set of ordinary differential equations with unknown parameters to delineate the transmission process of COVID-19. The model accounts for asymptomatic infections as well as the lag between symptom onset and the confirmation date of infection. To reflect the transmission potential of an infected case, we derive the basic reproduction number from the proposed model. Using the daily reported number of confirmed cases, we describe an estimation procedure for the model parameters, which involves adapting the iterated filter-ensemble adjustment Kalman filter (IF-EAKF) algorithm. To illustrate the use of the proposed model, we examine the COVID-19 data from Quebec for the period from 2 April 2020 to 10 May 2020 and carry out sensitivity studies under a variety of assumptions. Simulation studies are used to evaluate the performance of the proposed model under a variety of settings.

La maladie à coronavirus 2019 (COVID­19), causée par le coronavirus 2 du syndrome respiratoire aigu sévère (SARS­CoV­2), s'est rapidement propagée et représente une grande menace pour le public. Pour mieux comprendre l'impact de cette maladie sur la santé publique et l'économie, il est important d'étudier la dynamique de sa transmission. A cette fin, les auteurs de cet article proposent un nouveau modèle épidémiologique basé sur un ensemble d'équations différentielles ordinaires avec des paramètres inconnus et qui tient compte des infections asymptomatiques ainsi que du décalage entre l'apparition des symptômes et la date de confirmation de l'infection. Ils en déduisent le taux de reproduction de base qui traduit le potentiel de transmission d'un cas infecté. En utilisant le nombre rapporté de cas confirmés, les auteurs décrivent une procédure d'estimation des paramètres du modèle qui repose sur une adaptation de l'algorithme filtre itéré ­ filtre de Kalman énsemble àjustement (IF­EAKF). Une mise en application du modèle proposé est illustrée à travers l'examen des données COVID­19 du Québec pour la période du 2 avril 2020 au 10 mai 2020. Une analyse de sensibilité du modèle construit est explorée sous diverses hypothèses. Enfin, les auteurs ont fait appel à des études de simulation pour évaluer la performance du modèle proposé et ce sous différents scénarios.

Clinical predictors of driving simulator performance in drivers with multiple sclerosis.

BACKGROUND: Drivers with multiple sclerosis (MS) may experience visual-cognitive impairment that affects their fitness to drive. Due to limitations associated with the on-road assessment, an alternative assessment that measures driving performance is warranted. Whether clinical indicators of on-road outcomes can also predict driving performance outcomes on a driving simulator are not fully understood. OBJECTIVE: This study examined if deficits in immediate verbal/auditory recall (California Verbal Learning Test-Second Edition; CVLT2-IR) and/or slower divided attention (Useful Field of View™; UFOV2) predicted deficits in operational, tactical, or strategic maneuvers assessed on a driving simulator, in drivers with and without MS. METHODS: Participants completed the CVLT2-IR, UFOV2, and a driving simulator assessment of operational, tactical, and strategic maneuvers. RESULTS: Deficits in immediate verbal/auditory recall and slower divided attention predicted adjustment to stimuli errors, pertaining to tactical maneuvers only, in 36 drivers with MS (vs 20 drivers without MS; F(3, 51) = 6.1, p = 0.001, R2 = 0.3, Radj2=0.2). CONCLUSION: The CVLT2-IR and UFOV2 capture the visual and verbal/auditory recall, processing speed, and divided attention required to appropriately adjust to stimuli in a simulated driving environment. Clinicians may use the CVLT2-IR and UFOV2 as precursors to driving performance deficits in drivers with MS.

FamEvent: An R Package for Generating and Modeling Time-to-Event Data in Family Designs.

FamEvent is a comprehensive R package for simulating and modelling age-at-disease onset in families carrying a rare gene mutation. The package can simulate complex family data for variable time-to-event outcomes under three common family study designs (population, high-risk clinic and multi-stage) with various levels of missing genetic information among family members. Residual familial correlation can be induced through the inclusion of a frailty term or a second gene. Disease-gene carrier probabilities are evaluated assuming Mendelian transmission or empirically from the data. When genetic information on the disease gene is missing, an Expectation-Maximization algorithm is employed to calculate the carrier probabilities. Penetrance model functions with ascertainment correction adapted to the sampling design provide age-specific cumulative disease risks by sex, mutation status, and other covariates for simulated data as well as real data analysis. Robust standard errors and 95% confidence intervals are available for these estimates. Plots of pedigrees and penetrance functions based on the fitted model provide graphical displays to evaluate and summarize the models.

Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID-19: Meta-analysis and sensitivity analysis.

The coronavirus disease-2019 (COVID-19) has been found to be caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensive knowledge of COVID-19 remains incomplete and many important features are still unknown. This manuscript conducts a meta-analysis and a sensitivity study to answer the questions: What is the basic reproduction number? How long is the incubation time of the disease on average? What portion of infections are asymptomatic? And ultimately, what is the case fatality rate? Our studies estimate the basic reproduction number to be 3.15 with the 95% CI (2.41-3.90), the average incubation time to be 5.08 days with the 95% CI (4.77-5.39) (in day), the asymptomatic infection rate to be 46% with the 95% CI (18.48%-73.60%), and the case fatality rate to be 2.72% with 95% CI (1.29%-4.16%) where asymptomatic infections are accounted for.

Parametric and semiparametric estimation methods for survival data under a flexible class of models.

In survival analysis, accelerated failure time models are useful in modeling the relationship between failure times and the associated covariates, where covariate effects are assumed to appear in a linear form in the model. Such an assumption of covariate effects is, however, quite restrictive for many practical problems. To incorporate flexible nonlinear relationship between covariates and transformed failure times, we propose partially linear single index models to facilitate complex relationship between transformed failure times and covariates. We develop two inference methods which handle the unknown nonlinear function in the model from different perspectives. The first approach is weakly parametric which approximates the nonlinear function globally, whereas the second method is a semiparametric quasi-likelihood approach which focuses on picking up local features. We establish the asymptotic properties for the proposed methods. A real example is used to illustrate the usage of the proposed methods, and simulation studies are conducted to assess the performance of the proposed methods for a broad variety of situations.

Emerging Applications of Bioinspired Slippery Surfaces in Biomedical Fields.

In this minireview, we summarize the recent development of liquid-infused slippery surfaces (LISS) for biomedical applications. The selected topics are divided into two parts: the material designs and emerging strategies to fabricate slippery surface, and their applications with strong and direct relevance to biomedical areas including antibiofouling, antithrombosis, medical device coatings and surface enhanced/assisted detection. We also describe the most critical directions in need of development to adapt this new approach to biomedical use.

On-road assessment of fitness-to-drive in persons with MS with cognitive impairment: A prospective study.

BACKGROUND: Cognitive impairment is common in multiple sclerosis (MS). In other populations, cognitive impairment is known to affect fitness-to-drive. Few studies have focused on fitness-to-drive in MS and no studies have solely focused on the influence of cognitive impairment. OBJECTIVE: To assess fitness-to-drive in persons with MS with cognitive impairment and low physical disability. METHODS: Persons with MS, aged 18-59 years with EDSS ⩽ 4.0, impaired processing speed, and impairment on at least one measure of memory or executive function, were recruited. Cognition was assessed using the Minimal Assessment of Cognitive Function battery. A formal on-road driving assessment was conducted. Chi-square analysis examined the association between the fitness-to-drive (pass/fail) and the neuropsychological test results (normal/impaired). Bayesian statistics predicting failure of the on-road assessment were calculated. RESULTS: Of 36 subjects, eight (22.2%) were unfit to drive. Only the BVMTR-IR, measuring visual-spatial memory, predicted on-road driving assessment failure ( X2 ( df = 1, N = 36) = 3.956; p = 0.047) with a sensitivity of 100%, but low specificity (35.7%) due to false positives (18/25). CONCLUSION: In persons with MS and impaired processing speed, impairment on the BVMTR-IR should lead clinicians to address fitness-to-drive.

Analysis of panel data under hidden mover-stayer models.

Analysis of panel data is often challenged by the presence of heterogeneity and state misclassification. In this paper, we propose a hidden mover-stayer model to facilitate heterogeneity for a population that consists of two subpopulations each of movers or of stayers and to simultaneously account for state misclassification. We develop an inference procedure based on the expectation-maximization algorithm by treating the mover-stayer indicator and underlying true states as latent variables. We evaluate the performance of the proposed method and investigate the impact of ignoring misclassification through simulation studies. The proposed method is applied to analyze the data arising from the Waterloo Smoking Prevention Project. Copyright © 2017 John Wiley & Sons, Ltd.

(-)-Epigallocatechin-3-gallate attenuates cognitive deterioration in Alzheimer's disease model mice by upregulating neprilysin expression.

Epigenetic changes are involved in learning and memory, and histone deacetylase (HDAC) inhibitors are considered potential therapeutic agents for Alzheimer's disease (AD). We previously reported that (-)-epigallocatechin-3-gallate (EGCG) acts as an HDAC inhibitor. Here, we demonstrate that EGCG reduced ß-amyloid (Aß) accumulation in vitro and rescued cognitive deterioration in senescence-accelerated mice P8 (SAMP8) via intragastric administration of low- and high-dose EGCG (5 and 15 mg/kg, respectively) for 60 days. The AD brain has decreased levels of the rate-limiting degradation enzyme of Aß, neprilysin (NEP). We found an association between EGCG-induced reduction in Aß accumulation and elevated NEP expression. Further, NEP silencing prevented the EGCG-induced Aß downregulation. Our findings suggest that EGCG might be effective for treating AD.

Survival prediction in high-grade gliomas using CT perfusion imaging.

Patients with high-grade gliomas usually have heterogeneous response to surgery and chemoirradiation. The objectives of this study were (1) to evaluate serial changes in tumor volume and perfusion imaging parameters and (2) to determine the value of these data in predicting overall survival (OS). Twenty-nine patients with World Health Organization grades III and IV gliomas underwent magnetic resonance (MR) and computed tomography (CT) perfusion examinations before surgery, and 1, 3, 6, 9, and 12 months after radiotherapy. Serial measurements of tumor volumes and perfusion parameters were evaluated by receiver operating characteristic analysis, Cox proportional hazards regression, and Kaplan-Meier survival analysis to determine their values in predicting OS. Higher trends in blood flow (BF), blood volume (BV), and permeability-surface area product in the contrast-enhancing lesions (CEL) and the non-enhancing lesions (NEL) were found in patients with OS < 18 months compared to those with OS ≥ 18 months, and these values were significant at selected time points (P < 0.05). Only CT perfusion parameters yielded sensitivities and specificities of ≥ 70% in predicting 18 and 24 months OS. Pre-surgery BF in the NEL and BV in the CEL and NEL 3 months after radiotherapy had sensitivities and specificities >80% in predicting 24 months OS in patients with grade IV gliomas. Our study indicated that CT perfusion parameters were predictive of survival and could be useful in assessing early response and in selecting adjuvant treatment to prolong survival if verified in a larger cohort of patients.