Genetic diversity and population structure of Robinia pseudoacacia from six improved variety bases in China as revealed by simple sequence repeat markers

Robinia pseudoacacia is an important afforesta-tion tree introduced to China in 1878.In the present study,we examined the genetic diversity among 687 strains rep-resenting four improved varieties and two secondary prov-enances,comprising 641 clones and 46 seedlings.Ninety-one simple sequence repeats(SSRs)were selected through segregation analysis and polymorphism characterization,and all sampled individuals were genotyped using well-characterized SSR markers.After excluding loci with non-neutral equilibrium,missing locus data and null alleles,we used 36 primer pairs to assess the genetic diversity of these germplasm resources,revealing vast genetic differentiation among the samples,with an average of 8.352 alleles per locus and a mean Shannon′s index of 1.302.At the popula-tion level,the partitioning of variability was assessed using analysis of molecular variance,which revealed 93%and 7%variation within and among collection sites,respectively.Four clusters were detected using structure analysis,indi-cating a degree of genetic differentiation among the six populations.Insights into the genetic diversity and structure of R.pseudoacacia provide a theoretical basis for the con-servation,breeding and sustainable development in China.

刚柔并济的仿生凝胶聚合物电解质助力稳定的锂电池

凝胶聚合物电解质(GPE)因其优良的热稳定性和卓越的电化学性能而具备增强锂离子电池性能的潜力,从而受到越来越多的认可.尽管其具有上述优点,但传统GPE的实际应用通常因其溶胀性和有限的机械强度而受到阻碍.为了解决这些问题,本项研究工作提出了一种通过简单方法构建的刚柔并济的仿生GPE,由聚环氧乙烷(PEO)和聚偏二氟乙烯-六氟丙烯(PVDF-HFP)组成并通过Kevlar纤维织物进行增强.所得的PEO/PVDF-HFP/Kevlar(PPK)GPE表现出2.815 mS cm^(−1)的优异离子电导率和0.571的锂离子迁移数,以及32.59 MPa的超高机械强度.这些特性有助于防止锂枝晶生长并增强LiFePO4电池的电化学性能,从而实现稳定的循环性能.PPK GPE可以为高性能锂离子电池的各种实际应用提供理论基础.

PVDF-HFP-SN-based gel polymer electrolyte for high-performance lithium-ion batteries

Gel polymer electrolytes(GPEs)have attracted extensive attention in lithium-ion batteries due to their high security and excellent electrochemical performance.However,their inferior Li-ion transference number,low room-temperature ionic conductivity,and poor long cycle stability raise challenges in practical applications.Herein,a flexible poly(vinylidene fluoride-cohexafluoropropylene)-butanedinitrile(PVDF-HFP-SN)-based GPE(PSGPE)is synthesized successfully by a general immersion precipitation method.The resultant PSGPEs have numerous connecting pores to ensure sufficient space for liquid electrolytes.Moreover,the reduced crystallinity of PVDF-HFP and the high polarity of SN can reduce the energy barrier of Li-ions shuttling between pores.The synergistic effect possesses a high ionic conductivity of 1.35 mS·cm^(-1)at room temperature with a high Li-ion transference number of 0.69.The PVDF-HFP-SN-based GPE is applied in a LiFePO_(4)/graphite battery,which can realize stable cycling performance for 350 cycles and good rate performance at room temperature.These results demonstrate that the novel PSGPE possesses advantage in simplified production process,which can improve the practicability of gel polymer lithium-ion batteries.

Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning

Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis.The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance,interpretability,and generalizability.Specifically,we argue that a core set of co-altered brain regions(namely‘core regions’)comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients.Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain.We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.

Multimodal biofeedback for Parkinson’s disease motor and nonmotor symptoms

Parkinson’s disease(PD)is a neurodegenerative disordercharacterized by motor retardation,myotonia,quiescent tremor,and postural gait abnormality,as well as nonmotor symptoms such asanxiety and depression.Biofeedback improves motor and nonmotorfunctions of patients by regulating abnormal electroencephalogram(EEG),electrocardiogram(ECG),photoplethysmography(PPG),electromyography(EMG),respiration(RSP),or other physiologicalsignals.Given that multimodal signals are closely related to PDstates,the clinical effect of multimodal biofeedback on patientswith PD is worth exploring.Twenty-one patients with PD in Beijing Rehabilitation Hospital were enrolled and divided into threegroups:multimodal(EEG,ECG,PPG,and RSP feedback signal),EEG(EEG feedback signal),and sham(random feedback signal),and they received biofeedback training five times in two weeks.The combined clinical scale and multimodal signal analysis resultsrevealed that the EEG group significantly improved motor symptomsand increased Berg balance scale scores by regulatingβband activity;the multimodal group significantly improved nonmotorsymptoms and reduced Hamilton rating scale for depression scores by improvingθband activity.Our preliminary results revealed thatmultimodal biofeedback can improve the clinical symptoms of PD,but the regulation effect on motor symptoms is weaker than that ofEEG biofeedback.

Comparison of roller-spreading and blade-spreading processes in powder-bed additive manufacturing by DEM simulations

The roller-spreading and blade-spreading are main powder spreading methods in powder-bed additive manufacturing.The discrete element method was introduced to simulate nylon powder spreading by both roller and blade spreaders.The two spreading processes were compared from several aspects including particle flow behavior,particle contact forces,forces exerted on spreaders,particle segregation and powder layer density.It is found that powder spreading methods mainly affect the movement trajectory of particles,particle contact forces and forces exerted on spreaders.Complicated dispersion and circulation movement of particles occur inside the powder pile by roller-spreading,while particles have relatively weak dispersion by the blade-spreading.The normal force applied to the roller introduces a compacting effect on the powder pile and creates strong force chains that distribute uniformly in the powder pile.Therefore,the powder bed with higher density can be obtained by roller-spreading in thicker powder layer due to the compacting effect.The blade spreader sustains tangential force mainly,so the blade-spreading process limits its application to thicker powder layer.As the powder layer thickness increases,the roller-spreading is more sensitive to segregation index than that of the blade-spreading.The comprehensive comparison of two spreading processes provides criteria for selecting spreading methods.

Overview of CCKS 2020 Task 3: Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records

The China Conference on Knowledge Graph and Semantic Computing(CCKS)2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical records.Two annotated data sets and some other additional resources for these two subtasks were provided for participators.This evaluation competition attracted 354 teams and 46 of them successfully submitted the valid results.The pre-trained language models are widely applied in this evaluation task.Data argumentation and external resources are also helpful.