抑郁情绪对阿尔茨海默病患者认知功能的影响

目的:探讨抑郁情绪对阿尔茨海默病(Alzheimer's disease, AD)患者各认知领域功能的影响。方法:对2021年6月至2023年6月就诊于日照市人民医院神经内科门诊及记忆门诊的62例AD轻、中度患者进行详细的神经心理量表评估,采用简易智能状态检查量表(MMSE)、蒙特利尔认知评估量表(MoCA)评价AD轻、中度患者的整体认知功能,并分别对记忆、语言、注意力、视空间、执行功能等5个不同维度的单认知领域功能进行评估,采用汉密尔顿抑郁量表(HAMD)评价患者抑郁状况,HAMD≤7分为AD不伴发抑郁组,HAMD> 7分为AD伴发抑郁组,比较两组总体认知功能及各认知领域功能的情况。结果:62例AD患者中,有28例合并抑郁状态,占比45.16%,AD患者HAMD评分与DST-顺背呈负相关,与TMT-B和Stroop色词测验-时间呈正相关(P <0.05)。合并抑郁的AD患者与不合并抑郁的AD患者DST-顺背、TMT-B和Stroop色词测验-时间得分差异有统计学意义(P <0.05)。结论:抑郁会影响AD患者的注意力、执行功能等认知功能。

Straw mulching alters the composition and loss of dissolved organic matter in farmland surface runoff by inhibiting the fragmentation of soil small macroaggregates

Straw mulching is a widespread practice for reducing the soil carbon loss caused by erosion.However,the effects of straw mulching on dissolved organic matter(DOM)runoff loss from black soil are not well studied.How straw mulching affects the composition and loss of runoff DOM by changing soil aggregates remains largely unclear.Here,a straw mulching treatment was compared to a no mulching treatment(as a control)on sloping farmland with black soil erosion in Northeast China.We divided the soil into large macroaggregates(>2 mm),small macroaggregates(0.25-2 mm),and microaggregates(<0.25 mm).After five rain events,the effects of straw mulching on the concentration(characterized by dissolved organic carbon(DoC)and composition(analyzed by fluorescence spectroscopy)of runoff and soil aggregate DOM were studied.The results showed that straw mulching reduced the runoff amount by 54.7%.Therefore,although straw mulching increased the average DOc concentration in runoff,it reduced the total runoff DOM loss by 48.3%.The composition of runoff DOM is similar to that of soil,as both contain humic-like acid and protein-like components.With straw mulching treatment,the protein-like components in small macroaggregates accumulated and the protein-like components in runoff declined with rain events.Fluorescence spectroscopy technology may help in understanding the hydrological paths of rain events by capturing the dynamic changes of runoff and soil DOM characteristics.A variation partitioning analysis(VPA)indicated that the DOM concentration and composition of microaggregates explained 68.2%of the change in runoff DOM from no mulching plots,while the change in runoff DOM from straw mulching plots was dominated by small macroaggregates at a rate of 55.1%.Taken together,our results demonstrated that straw mulching reduces the fragmentation of small macroaggregates and the loss of microaggregates,thus effecting DOM compositions in soil and reducing the DOM loss in runoff.These results provide a theoretical basis for reducing carbon loss in sloping farmland.

Free-walking:Pedestrian inertial navigation based on dual foot-mounted IMU

The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

Strengthening Network Security: Deep Learning Models for Intrusion Detectionwith Optimized Feature Subset and Effective Imbalance Handling

In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network attacks.We have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and Attention mechanism.These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature subset.Moreover,we address class imbalance in the dataset using focal loss.Finally,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection performance.The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively.In binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods.

Insights into Euphorbia diversity: Probing the contrasts between Euphorbia fischeriana Steud and Euphorbia ebracteolata Hayata

In traditional Chinese medicine(TCM),Euphorbia fischeriana Steud(E.fischeriana)and Euphorbia ebracteolata Hayata(E.ebracteolata),commonly referred to as“Langdu”,are widely extensively utilized for treating lymphatic tuberculosis and ringworm[1].Both plant species are perennial herbaceous plants mainly distributed in northeastern China,Mongolia,Russia(Siberia),and Republic of Korea[2].There have been many reports on the chemical constituents and pharmacological effects of the two plant species,which has made more and more researchers realize that there may be differences between E.fischeriana and E.ebracteolata.In some cases,long-term improper use of herbal medicines can even lead to life-threatening conditions[3,4].Therefore,it is essential to employ an effective technology to differentiate between these two plants based on their chemical constituents and biological activities,so as to reduce the harm caused by the mixing and misuse of medicinal materials.Therefore,the present paper describes a study of the differences between E.ebracteolata and E.fischeriana,using untargeted plant metabolomics and biological activity evaluations.This study aims to provide valuable insight into their equivalence and potential interchangeability in TCM and clinical medication.

下一代风险评估在化妆品原料安全性评价的应用研究进展

下一代风险评估(Next Generation Risk Assessment,NGRA)是在传统的风险评估基础上发展出的一套以暴露为导向、以假设为驱动的新式风险评估框架体系。随着科学技术的发展、3R原则的推广,化妆品安全性评价和风险评估方法也在不断进步。国外已有一些采用NGRA开展化妆品原料风险评估的探索性案例研究,这些案例分析展示了NGRA的评估过程,有助于提高对NGRA原则的理解。文章通过对国际化妆品监管合作组织(ICCR)提出的NGRA原则、框架体系、评估工具以及国外应用NGRA开展化妆品原料安全性评价的案例分析等内容进行系统性的梳理和介绍,提出我国化妆品原料安全性评价面临的挑战,希望为提高我国化妆品技术支撑能力提供研究思路和技术储备。

基于平台数据的成人高等教育信息化改革的路径探索

互联网时代,在网络信息技术的加持下,线上学习平台迎来迅猛发展普及的契机,为成人学习者提供了巨大的资源、方便灵活的学习方式以及良好的交互性能,同时也衍生出海量化、多样化、层级复杂的学习数据信息。厘清和明确数据价值意蕴及关联关系,是成人高等教育发展进程面临的挑战和发展方向。充分挖掘、整合和分析平台数据资源,洞察数据蕴藏内涵,掌握数据演变规律,明晰映射交互关系,借力平台数据从适性教学、高效学习、科学管理、实效评价四个维度进行认知思考,助力优化教育教学,从而全面提升人才培养质量和水平。正确把握平台数据在数字化教育改革系统工程中的地位和作用,将是成人高等教育进一步拓展信息化发展空间和高质量内涵建设的突破口,是推动信息化向智慧化转型的新理念新思路新方法。

日本防晒产品的管理制度及对我国的启示

我国的化妆品类产品在日本按照化妆品和医药部外品进行分类管理。其中防晒产品是非常具有代表性的一类,既包括化妆品又包括医药部外品。通过对日本化妆品和医药部外品的法律法规及文献进行分析,研究和总结了产品分类、测试方法、原料管理、产品备案审批制度和标签管理等技术要求及监管要求;结合我国化妆品的监管实际情况,对两国的法规要求和管理现状进行了对比分析,为我国化妆品监管提供了参考,在我国化妆品行业蓬勃发展、监管法规逐步完善的现状下,进一步促进行业的高质量发展。

QTL mapping for berry shape based on a high-density genetic map constructed by whole-genome resequencing in grape

Grape berry shape is an important agricultural trait.Clarifying its genetic basis is significant for cultivating grape varieties that meet market demands.However,the current study by forward genetics has not achieved in-depth results.Here,a high-density map was constructed to identify quantitative trait loci(QTLs)for berry shape.A total of 358709 polymorphic SNPs were obtained using whole-genome resequencing(WGS)based on 208 F2 individuals derived from round grape‘E42-6’and oblong grape‘Rizamat’.The 1635.65 cM high-density map was divided into 19 linkage groups with an average distance of 0.37 cM.Using this map,three significant QTLs for fruit shape index(ShI:ratio of berry length to berry width)identified over three years were mapped onto LG4 and LG5,including one stable QTL on Chr5 with the genomic region of 0.47–1.94 Mb.Combining with gene annotation and expression patterns based on RNA-seq data from two contrasting F2 individuals with round and oblong berry(their average ShI was 1.89 and 1.10,respectively)at four developmental stages,four candidate genes were selected from the above QTLs.They were mainly involved in DNA replication,cell wall modification,and phytohormone biosynthesis.Further analysis of RNA-seq data revealed that several important phytohormone synthesis and metabolic pathways were enriched based on differentially expressed genes(DEGs),which was consistent with the results of QTL mapping for genes related to plant hormone biosynthesis in the F2 population.Furthermore,a comparison of plant hormone content showed that there were significant differences in IAA and tZ content between the two contrasting F2 individuals at different developmental stages.Our findings provide molecular insights into the genetic variation in grape berry shape.Stable QTLs and their tightly linked markers offer the possibility of marker-assisted selection to accelerate berry shape breeding.

Monitoring the in vivo siRNA release from lipid nanoparticles based on the fluorescence resonance energy transfer principle

The siRNA-loaded lipid nanoparticles have attracted much attention due to its significant gene silencing effect and successful marketization.However,the in vivo distribution and release of siRNA still cannot be effectively monitored.In this study,based on the fluorescence resonance energy transfer(FRET)principle,a fluorescence dye Cy5-modified survivin siRNA was conjugated to nanogolds(Au-DR-siRNA),which were then wrapped with lipid nanoparticles(LNPs)for monitoring the release behaviour of siRNA in vivo.The results showed that once Au-DR-siRNA was released from the LNPs and cleaved by the Dicer enzyme to produce free siRNA in cells,the fluorescence of Cy5 would change from quenched state to activated state,showing the location and time of siRNA release.Besides,the LNPs showed a significant antitumor effect by silencing the survivin gene and a CT imaging function superior to iohexol by nanogolds.Therefore,this work provided not only an effective method for monitoring the pharmacokinetic behaviour of LNP-based siRNA,but also a siRNA delivery system for treating and diagnosing tumors.

Belief Propagation List Decoding for Polar Codes:Performance Analysis and Software Implementation on GPU

Belief propagation(BP)decoding outputs soft information and can be naturally used in iterative receivers.BP list(BPL)decoding provides comparable error-correction performance to the successive cancellation list(SCL)decoding.In this paper,we firstly introduce an enhanced code construction scheme for BPL decoding to improve its errorcorrection capability.Then,a GPU-based BPL decoder with adoption of the new code construction is presented.Finally,the proposed BPL decoder is tested on NVIDIA RTX3070 and GTX1060.Experimental results show that the presented BPL decoder with early termination criterion achieves above 1 Gbps throughput on RTX3070 for the code(1024,512)with 32 lists under good channel conditions.

Mitigating thermal runaway hazard of high-energy lithium-ion batteries by poison agent

Lithium-ion batteries with high-energy density are extensively commercialized in long-range electric vehicles. However, they are poor in thermal stability and pose fire or explosion, which has attracted the global attention. This study describes a new route to mitigate the battery thermal runaway(TR) hazard by poison agents. First, the self-destructive cell is built using the embedded poison layer. Then, the poisoning mechanism and paths are experimentally investigated at the material, electrode, and cell levels. Finally, the proposed route is verified by TR tests. The results show the TR hazard can be significantly reduced in the self-destructive cell based on a new reaction sequence regulation. Specifically, the maximum temperature of the self-destructive cell is more than 300℃ lower than that of the normal cell during TR. The drop in maximum temperature can reduce total heat release and the probability of TR propagation in the battery system, significantly improving battery safety.

An efficient 3D cell-based discrete fracture-matrix flow model for digitally captured fracture networks

Complex hydraulic fracture networks are critical for enhancing permeability in unconventional reservoirs and mining indus-tries.However,accurately simulating the fluid flow in realistic fracture networks(compared to the statistical fracture net-works)is still challenging due to the fracture complexity and computational burden.This work proposes a simple yet efficient numerical framework for the flow simulation in fractured porous media obtained by 3D high-resolution images,aiming at both computational accuracy and efficiency.The fractured rock with complex fracture geometries is numerically constructed with a cell-based discrete fracture-matrix model(DFM)having implicit fracture apertures.The flow in the complex fractured porous media(including matrix flow,fracture flow,as well as exchange flow)is simulated with a pipe-based cell-centered finite volume method.The performance of this model is validated against analytical/numerical solutions.Then a lab-scale true triaxial hydraulically fractured shale sample is reconstructed,and the fluid flow in this realistic fracture network is simu-lated.Results suggest that the proposed method achieves a good balance between computational efficiency and accuracy.The complex fracture networks control the fluid flow process,and the opened natural fractures behave as primary fuid pathways.Heterogeneous and anisotropic features of fluid flow are well captured with the present model.

QTL detection and candidate gene analysis of grape white rot resistance by interspecific grape (Vitis vinifera L. × Vitis davidii Foex.) crossing

Grape white rot,a devastating disease of grapevines caused by Coniella diplodiella(Speg.)Sacc.,leads to significant yield losses in grape.Breeding grape cultivars resistant to white rot is essential to reduce the regular use of chemical treatments.In recent years,Chinese grape species have gained more attention for grape breeding due to their high tolerance to various biotic and abiotic factors along with changing climatic conditions.In this study,we employed whole-genome resequencing(WGR)to genotype the parents of‘Manicure Finger’(Vitis vinifera,female)and‘0940’(Vitis davidii,male),along with 101 F1 mapping population individuals,thereby constructing a linkage genetic map.The linkage map contained 9337 single-nucleotide polymorphism(SNP)markers with an average marker distance of 0.3 cM.After 3 years of phenotypic evaluation of the progeny for white rot resistance,we confirmed one stable quantitative trait locus(QTL)for white rot resistance on chromosome 3,explaining up to 17.9%of the phenotypic variation.For this locus,we used RNA-seq to detect candidate gene expression and identified PR1 as a candidate gene involved in white rot resistance.Finally,we demonstrated that recombinant PR1 protein could inhibit the growth of C.diplodiella and that overexpression of PR1 in susceptible V.vinifera increased grape resistance to the pathogen.

Design Strategies for Cellular Nanosponges as Medical Countermeasures

The interest in using therapeutic nanoparticles to bind with harmful molecules or pathogens and subsequently neutralize their bioactivity has grown tremendously.Among various nanomedicine platforms,cell membrane-coated nanoparticles,namely,“cellular nanosponges,”stand out for their broadspectrum neutralization capability challenging to achieve in traditional countermeasure technologies.Such ability is attributable to their cellular function-based rather than target structure-based working principle.Integrating cellular nanosponges with various synthetic substrates further makes their applications exceptionally versatile and adaptive.This review discusses the latest cellular nanosponge technology focusing on how the structure–function relationship in different designs has led to versatile and potent medical countermeasures.Four design strategies are discussed,including harnessing native cell membrane functions for biological neutralization,functionalizing cell membrane coatings to enhance neutralization capabilities,combining cell membranes and functional cores for multimodal neutralization,and integrating cellular nanosponges with hydrogels for localized applications.Examples in each design strategy are selected,and the discussion is to highlight their structure–function relationships in complex disease settings.The review may inspire additional design strategies for cellular nanosponges and fulfill even broader medical applications.

Mechanical analysis of the femoral neck dynamic intersection system with different nail angles and clinical applications

BACKGROUND The femoral neck dynamic intersection system(FNS)is mechanically more stable than other internal fixation techniques.Current studies have confirmed that the structural design of FNS has good biomechanical properties in European and American populations.However,whether the suitability of the FNS's 130°main nail angle design for Asian populations has been thoroughly investigated remains unclear.AIM To compare the biomechanical stability differences among different main nail angles of the FNS in the treatment of femoral neck fractures in Asian populations.METHODS Computed tomography data of the femur of healthy adult male volunteers were imported into Mimics software to create a three-dimensional model of the femur.The model was adapted to the curve using Geomagic software and imported into Solidworks software to construct the Pauwels I femoral neck fracture model and design the FNS internal fixation model using different main nail angles.Afterward,the models were assembled with the FNS fracture model and meshed using the preprocessing Hypermesh software.Subsequently,they were imported into Abaqus software to analyze and evaluate the biomechanical effects of different angles of the FNS main nail on the treatment of femoral neck fractures.RESULTS The peak displacement of the proximal femur under different angles of FNS fixation under stress was 7.446 millimeters in the 120°group and 7.416 millimeters in the 125°group;in the 130°,135°,and 140°FNS fixation groups,the peak displacement was 7.324 millimeters,8.138 millimeters,and 8.246 millimeters,respectively.In the 120°and 125°FNS fixation groups,the maximum stresses were concentrated at the main nail and the anti-rotation screw,which intersected the fracture line of the femur neck,resulting in peak stresses of 200.7 MPa and 138.8 MPa,respectively.Peak stresses of 208.8 MPa,219.8 MPa,and 239.3 MPa were observed on the angular locking plate distal to the locking screw in the 130°,135°,and 140°fixation groups.CONCLUSION FNS has significant stress distribution properties,a minimal proximal femoral displacement,and an optimal stability for treating femoral neck fractures in Asian populations when performed with a 130°main nail angle.

基于Box-Behnken设计-响应面法结合G1-熵权法优化普济消毒口服液的提取工艺

目的优选普济消毒口服液的提取工艺。方法基于Box-Behnken设计,以挥发油提取率为指标,优选挥发油的提取工艺;基于Box-Behnken设计结合G1-熵权法主客观组合赋权法,以(R,S)-告依春、橙皮苷、黄芩苷、盐酸小檗碱、牛蒡苷的提取率与提取液干膏率为指标,优选水提取工艺。结果普济消毒口服液挥发油的最佳提取工艺为加水10倍量,浸泡时间为1.5 h,提取时间5 h;水提取最佳工艺为加水10倍量,提取时间为1.8 h,提取次数2次。在最优提取条件下,3批验证试验组间均无明显差异。结论优选的提取工艺稳定可行,可为普济消毒口服液的后续试验研究及工业化生产提供依据。

辛醇对苯甲羟肟酸浮选锡石的影响和机理（英文）

研究了辛醇对苯甲羟肟酸(BHA)浮选锡石纯矿物的影响,分析了辛醇和BHA在锡石表面的吸附机理。实验室浮选实验结果表明,在广泛pH区间内,单一辛醇对锡石没有浮选性,但是作为辅助捕收剂,辛醇可以显著降低捕收剂BHA的用量,并且使锡石仍保持较高回收率。红外光谱和吸附量研究表明,单一辛醇在锡石表面没有发生吸附,但是辛醇与BHA形成吸附组合体而吸附在锡石表面,增强了锡石表面的疏水性;同时辛醇使BHA在锡石表面的吸附量增加,从而降低了BHA的用量。

Progress and Challenges of Reference Standard and Its New Form: Digital Reference Standard

Number and quantities of reference standards (RS) needed for the quality control of drugs are increasing, bringing great pressure to the calibration and using company. This manuscript summarized the four generations for the development of RS including physical RS, paper atlas, substitute RS and electronic databases. The advantages and disadvantages of each generation were summarized. The concept of digital RS (DRS) was proposed based on this, and summed up the definition, advantages, and technical architecture of DRS. The 10 characteristics of five aspects of the DRS were discussed including digital, multi-dimension, big data, cloud computing, internet, internet of things, sharing, multi-terminal, intelligence, and compliance certification. Then, the necessity of its formation and application in the medicine holistic quality control of internet plus era was discussed in this manuscript.