Heteroatom–doped hollow carbon microspheres based on amphiphilic supramolecular vesicles and highly crosslinked polyphosphazene for high performance supercapacitor electrode materials

Hybrid hollow polymeric microspheres (HPMSs) are synthesized by encapsulating the supramolecular vesicles with polyphosphazene through a rapid one-step polycondensation reaction. Subsequent carbonization treatments of the HPMSs lead to corresponding hollow carbon microspheres (HCMSs) with well-preserved geometry. The sizes of HPMSs and HCMSs are controlled by the vesicles, which is directly determined by the feeding ratio of the assembly units. Electrodes based on HCMSs showed a specific capacitance of 314.6 F/g at a current density of 0.2 A/g in 6 M KOH electrolyte, 180.0 F/g at a current density of 30 A/g, and high stability of 98.2% of capacity retention after 2000 cycles. Both the high surface area and high heteroatoms level of HCMSs contribute to the excellent capacitive performance. Meanwhile, the hollow carbon structure ensured the satisfactory capacitive performance by increasing utilization efficiency of the surface area as well as achieving short diffusion paths for electrolyte ions

Investigations on the Antifungal Effect of Nerol against Aspergillus flavus

The antifungal efficacy of nerol (NEL) has been proved against Aspergillus flavus by using in vitro and in vivo tests. The mycelial growth of A. flavus was completely inhibited at concentrations of 0.8 μL/mL and 0.1 μL/mL NEL in the air at contact and vapor conditions, respectively. The NEL also had an evident inhibitory effect on spore germination in A. flavus along with NEL concentration as well as time-dependent kinetic inhibition. The NEL presented noticeable inhibition on dry mycelium weight and synthesis of aflatoxin B1 (AFB1) by A. flavus, totally restraining AFB1 production at 0.6 μL/mL. In real food system, the efficacy of the NEL on resistance to decay development in cherry tomatoes was investigated in vivo by exposing inoculated and control fruit groups to NEL vapor at different concentration. NEL vapors at 0.1 μL/mL air concentration significantly reduced artificially contaminated A. flavus and a broad spectrum of fungal microbiota. Results obtained from presented study showed that the NEL had a great antifungal activity and could be considered as a benefit and safe tool to control food spoilage

High-dimensional quantile mediation analysis with application to a birth cohort study of mother-newborn pairs

MOTIVATION: There has been substantial recent interest in developing methodology for high-dimensional mediation analysis. Yet, the majority of mediation statistical methods lean heavily on mean regression, which limits their ability to fully capture the complex mediating effects across the outcome distribution. To bridge this gap, we propose a novel approach for selecting and testing mediators throughout the full range of the outcome distribution spectrum. RESULTS: The proposed high-dimensional quantile mediation model provides a comprehensive insight into how potential mediators impact outcomes via their mediation pathways. This method\u27s efficacy is demonstrated through extensive simulations. The study presents a real-world data application examining the mediating effects of DNA methylation on the relationship between maternal smoking and offspring birthweight. AVAILABILITY AND IMPLEMENTATION: Our method offers a publicly available and user-friendly function qHIMA(), which can be accessed through the R package HIMA at https://CRAN.R-project.org/package=HIMA

COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking

Expert finding, a popular service provided by many online websites such as Expertise Finder, LinkedIn, and AMiner, benefits seeking consultants, collaborators, and candidate qualifications. However, its quality is suffered from a single source of support information for experts. This paper employs AMiner, a free online academic search and mining system, having collected more than over 100 million researcher profiles together with 200 million papers from multiple publication databases, as the basis for investigating the problem of expert linking, which aims at linking any external information of persons to experts in AMiner. A critical challenge is how to perform zero shot expert linking without any labeled linkages from the external information to AMiner experts, as it is infeasible to acquire sufficient labels for arbitrary external sources. Inspired by the success of self supervised learning in computer vision and natural language processing, we propose to train a self supervised expert linking model, which is first pretrained by contrastive learning on AMiner data to capture the common representation and matching patterns of experts across AMiner and external sources, and is then fine-tuned by adversarial learning on AMiner and the unlabeled external sources to improve the model transferability. Experimental results demonstrate that COAD significantly outperforms various baselines without contrastive learning of experts on two widely studied downstream tasks: author identification (improving up to 32.1% in HitRatio@1) and paper clustering (improving up to 14.8% in Pairwise-F1). Expert linking on two genres of external sources also indicates the superiority of the proposed adversarial fine-tuning method compared with other domain adaptation ways (improving up to 2.3% in HitRatio@1).Comment: TKDE under revie