新型查尔酮衍生物抗乳腺癌活性的3D-QSAR模型构建及分子设计

为了获得较高抗乳腺癌活性的新型化合物,对18个新型查尔酮衍生物抗乳腺癌活性(pIC_(50))进行了三维定量构效关系(3D-QSAR)研究。其中14个化合物作为训练集用于构建3D-QSAR模型,其余化合物(含模板分子)作为测试集对所建模型进行验证。所建3D-QSAR模型的交叉验证系数R^(2)_(CV)为0.569,非交叉验证系数R^(2)为0.974,说明所建模型具有良好的稳定性和预测能力。该模型中立体场、静电场对pIC_(50)的贡献分别为58.8%和41.2%,表明影响该类化合物抗肿瘤活性的主要因素是取代基的疏水性、空间位阻和电荷分布。通过对模型的分析,设计了5个具有较高pIC_(50)的新化合物,有待通过后续医学实验加以验证。

喹啉酮类BRD4抑制剂的3D-QSAR研究

使用比较分子力场分析法(CoMFA)和比较分子相似性指数法(CoMSIA)对33个已报道的喹啉酮类BRD4抑制剂进行3D-QSAR模型建立,研究了其化学结构和生物活性间的关系,并用计算机辅助药物设计(computer-aided drug design,CADD)设计出7个喹啉酮类抑制剂。结果表明,建立的CoMFA(q^(2)=0.926,r^(2)=0.997,r^(2)_(pred)=0.744)和CoMSIA(q^(2)=0.939,r^(2)=0.991,r^(2)_(pred)=0.786)模型具有较好的预测能力,基于这些模型设计的7个新喹啉酮类BRD4抑制剂具有高活性,并对其进行ADMET性质评价和类药性分析。以上研究结果有助于改造和开发更加有效的喹啉酮类BRD4抑制剂。

Quantifying source-sink relationships in leaf-color modified rice genotypes during grain filling

Leaf-color modification can affect canopy photosynthesis,with potential effects on rice yield and yield components.Modulating source-sink relationships through crop management is often used to improve crop productivity.This study investigated whether and how modifying leaf color alters source-sink relationships and whether current crop cultivation practices remain applicable for leaf-color modified genotypes.Periodically collected data of total biomass and nitrogen(N)accumulation in rice genotypes of four genetic backgrounds and their leaf-color modified variants(greener or yellower)were analyzed,using a recently established modelling method to quantify the source-sink(im)balance during grain filling.Among all leaf-color variants,only one yellower-leaf variant showed a higher source capacity than its normal genotype.This was associated with greater post-flowering N-uptake that prolonged the functional leaf-N duration,and this greater post-flowering N-uptake was possible because of reduced pre-flowering N-uptake.A density experiment showed that current management practices(insufficient planting density accompanied by abundant N application)are unsuitable for the yellower-leaf genotype,ultimately limiting its yield potential.Leaf-color modification affects source-sink relationships by regulating the N trade-off between pre-and post-flowering uptake,as well as N translocation between source and sink organs.To best exploit leaf-color modification for improving crop productivity,adjustments of crop management practices are required.

What diameter?What height?Influence of measures of average tree size on area-based allometric volume relationships

Volume is an important attribute used in many forest management decisions.Data from 83 fixed-area plots located in central New Brunswick,Canada,are used to examine how different measures of stand-level diameter and height influence volume prediction using a stand-level variant of Honer's(1967)volume equation.When density was included in the models(Volume=f(Diameter,Height,Density))choice of diameter measure was more important than choice of height measure.When density was not included(Volume=f(Diameter,Height)),the opposite was true.For models with density included,moment-based estimators of stand diameter and height performed better than all other measures.For models without density,largest tree estimators of stand diameter and height performed better than other measures.The overall best equation used quadratic mean diameter,Lorey's height,and density(root mean square error=5.26 m^3·ha^(-1);1.9%relative error).The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha(root mean square error=32.08 m^(3)·ha^(-1);11.8%relative error).The results of this study have some important implications for height subsampling and LiDAR-derived forest inventory analyses.

Three anti-inflammatory polysaccharides from Lonicera japonica Thunb.:insights into the structure-function relationships

This study demonstrates the feasibility of producing three polysaccharides(neutral LJP-1,acidic LJP-2 and acidic LJP-3)with significant in vitro and in vivo anti-inflammatory activities from the flowers of Lonicera japonica.The three polysaccharides differed in chemical composition,molecular weight(Mw)distribution,glycosidic linkage pattern,functional groups and morphology.They exhibited excellent protective effects(in a dose-dependent manner)in lipopolysaccharide-injured RAW264.7 macrophages and Cu SO4-damaged zebrafish via reducing NO production and inhibiting the overexpressions of inflammation-related transcription factors,inflammatory proteins and cytokines in the NF-κB/MAPK signaling pathways.Their antiinflammatory effects varied owing to their different molecular characteristics and chemical compositions.Overall,LJP-2 at 400μg/m L was the most effective.LJP-2 consisted mainly of→5)-α-L-Araf(1→,→4)-α-LGalp A(1→and→2)-α-L-Rhap(1→residues with terminal T-β-D-Glcp.Thus,honeysuckle flowers are good sources of anti-inflammatory polysaccharides,and precise fractionation enables the production of potent antiinflammatory agents for the development of functional foods and healthcare products.

Characterizing permeability-porosity relationships of porous rocks using a stress sensitivity model in consideration of elastic-structural deformation and tortuosity sensitivity

Clarifying the relationship between stress sensitivities of permeability and porosity is of great significance in guiding underground resource mining.More and more studies focus on how to construct stress sensitivity models to describe the relationship and obtain a comprehensive stress sensitivity of porous rock.However,the limitations of elastic deformation calculation and incompleteness of considered tortuosity sensitivity lead to the fact that the existing stress sensitivity models are still unsatisfactory in terms of accuracy and generalization.Therefore,a more accurate and generic stress sensitivity model considering elastic-structural deformation of capillary cross-section and tortuosity sensitivity is proposed in this paper.The elastic deformation is derived from the fractal scaling model and Hooke's law.Considering the effects of elastic-structural deformation on tortuosity sensitivity,an empirical formula is proposed,and the conditions for its applicability are clarified.The predictive performance of the proposed model for the permeability-porosity relationships is validated in several sets of publicly available experimental data.These experimental data are from different rocks under different pressure cycles.The mean and standard deviation of relative errors of predicted stress sensitivity with respect to experimental data are 2.63%and 1.91%.Compared with other models,the proposed model has higher accuracy and better predictive generalization performance.It is also found that the porosity sensitivity exponent a,which can describe permeability-porosity relationships,is 2 when only elastic deformation is considered.a decreases from 2 when structural deformation is also considered.In addition,a may be greater than 3 due to the increase in tortuosity sensitivity when tortuosity sensitivity is considered even if the rock is not fractured.

Monitor and Acceptance among Couples in Romantic Relationships:Actor-Partner Interdependence Models Based on the Monitor and Acceptance Theory

Communication could be an essential part of couples in their daily life.Based on Monitor and Acceptance Theory(MAT),the present study explored the mediating role of communication in the relationship between mindfulness and relationship quality among college-student couples.The research examined the dynamic relationship of monitoring and acceptance to relationship satisfaction in the Actor-Partner Interdependence Model(APIM),and the mediating effect of positive or negative communications in these relationships.A total of 96 pairs of couples in the universities in Nanjing,China participated in the research.Momentary measurements were used to measure the momentary levels of their monitor,acceptance,positive/negative communication,and relationship satisfaction.A Hierarchical Linear Model(HLM)was used to deal with the APIM.Results showed that the women’s monitor facet of state mindfulness negatively predicted men’s relationship satisfaction through women’s negative communication,and the women’s acceptance facet of state mindfulness positively predicted women’s relationship satisfaction through women’s positive and negative communication at the within-person level.The study highlights the importance of cooperation in monitoring and acceptance for couples to own and hold high levels of relationship satisfaction.

Floristic composition and edaphic relationships in ferruginous campo rupestre reference ecosystems

Land use change and occupation have led to modifications in the environment causing loss of biodiversity and ecosystem services throughout the planet.Some environments with high economic relevance,such as the ferruginous campo rupestre(rupestrian grassland known as Canga in Brazil),are even more susceptible to severe impacts due to their extreme habitat conditions and low resilience.The determination of reference ecosystems based on the intrinsic characteristics of the ecosystem is essential for conservation as well as to the implementation of ecological restoration.We proposed the reference ecosystem of the three main types of habitats of the ferruginous campo rupestre based on their floristic composition.We described the floristic composition of each habitat and evaluated the physicochemical properties of the soils and the relationship between plants and soils.All three habitats showed high diversity of plant species and many endemic species,such as Chamaecrista choriophylla,Cuphea pseudovaccinium,Lychnophora pinaster,and Vellozia subalata.The distribution of vegetation was strongly related with the edaphic characteristics,with a set of species more adapted to high concentration of base saturation,fine sand,organic carbon,and iron,while another set of species succeeded in more acidic soils with higher S and silt concentration.We provide support for the contention that the ferruginous campo rupestre is a mosaic of different habitats shaped by intrinsic local conditions.Failure to recognize the floristic composition of each particular habitat can lead to inappropriate restoration,increased habitat homogenization and increased loss of biodiversity and ecosystem services.This study also advances the knowledge base for building the reference ecosystem for the different types of ferruginous campo rupestre habitats,as well as a key database for highlighting those species contribute most to community assembly in this diverse and threatened tropical mountain ecosystem.

Different mechanisms underlie similar species-area relationships in two tropical archipelagoes

Despite much research in the field of island biogeography,mechanisms regulating insular diversity remain elusive.Here,we aim to explore mechanisms underlying plant species-area relationships in two tropical archipelagoes in the South China Sea.We found positive plant species-area relationships for both coral and continental archipelagoes.However,our results showed that different mechanisms contributed to similar plant species-area relationships between the two archipelagoes.For coral islands,soil nutrients and spatial distance among communities played major roles in shaping plant community structure and species diversity.By contrast,the direct effect of island area,and to a lesser extent,soil nutrients determined plant species richness on continental islands.Intriguingly,increasing soil nutrients availability(N,P,K)had opposite effects on plant diversity between the two archipelagoes.In summary,the habitat quality effect and dispersal limitation are important for regulating plant diversity on coral islands,whereas the passive sampling effect,and to a lesser extent,the habitat quality effect are important for regulating plant diversity on continental islands.More generally,our findings indicate that island plant species-area relationships are outcomes of the interplay of both niche and neutral processes,but the driving mechanisms behind these relationships depends on the type of islands.

Modern pollen-vegetation relationships along altitudinal vegetation belts in the Leigong Mountain of southwestern China

Understanding the distribution,dispersal,and correlation of modern pollen with vegetation in mountainous regions is essential for establishing accurate modern analogs for fossil pollen records.This study,conducted in Leigong Mountain on the YunnanGuizhou Plateau of southwestern China,involved the collection of 35 surface soil samples from diverse vegetation communities along an elevational gradient ranging from 1210 to 1875 meters.The results reveal a close correspondence between modern pollen assemblages and vegetation zones.Principal Component Analysis(PCA)results indicate that pollen assemblages can effectively distinguish between subtropical montane evergreen broad-leaved forest(SEBF)and subtropical montane deciduous broadleaved forest(SDBF).However,both SEBF and SDBF show significant overlap with subtropical montane evergreen-deciduous broad-leaved mixed forest(SEMF).Detrended Correspondence Analysis(DCA)results clearly distinguish the three vegetation zones,and the first axis of DCA shows a significant positive correlation with elevation(p<0.01,R=0.48).Discriminant Analysis(DA)successfully assigns 94.4%of the modern pollen samples to their respective vegetation zones.Pollen taxa such as Impatiens,Astertype,and Rosaceae exhibit significant indicative capabilities for the SEBF zone,effectively distinguishing this vegetation zone from others.Pinus and Alnus display overrepresentation in the Leigong Mountain region,while Quercus(D,deciduous-type)and Poaceae exhibit high representation in the SEBF zone.In the SEBF zone,both pollen diversity and richness are the lowest.Our study reveals the complex relationship between the richness and diversity of pollen and vegetation.The diversity and richness of tree and shrub pollen are found to be lower than those of the corresponding plants.The pollen-vegetation relationship elucidated in this study serves as a critical reference for reconstructing ancient environments from fossil pollen retrieved in this region.

Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.

The Risk Role of Defeat on the Mental Health of College Students:A Moderated Mediation Effect of Academic Stress and Interpersonal Relationships

Background:College students face significant academic and physiological changes,making them more susceptible to psychological issues such as depression,self-injury,and suicidal ideation.Feelings of defeat can exacerbate these risks by increasing academic stress.However,interpersonal relationships can moderate the impact of academic stress on students’mental health.Utilizing the presage–process–product model,this study aims to empirically investigate how feelings of defeat influence depression,self-injury,and suicidal ideation among college students.Additionally,it explores the mediating role of academic stress and the moderating role of various types of interpersonal relationships.Methods:A total of 1612 college students(750 females,862 males,mean age=19.64±0.62 years)were recruited through cluster sampling.Data were collected via offline questionnaires administered by a trained psychology teacher and a postgraduate student,ensuring high reliability with two examiners per class.Latent profile analysis(LPA)was used to examine the impact of defeat on mental health outcomes,while mediation analysis was conducted to assess the roles of academic stress and interpersonal relationships.Results:1.Defeat is identified as a significant risk factor for mental health issues among college students;2.Four distinct patterns of interpersonal relationships were identified:the interpersonal-relationship risk group,the father–child-relationship high-risk group,the general interpersonal-relationship group,and the superior interpersonal-relationship group;3.Academic stress partially mediates the relationship between defeat and mental health issues such as depression,self-injury,and suicidal ideation;4.Different interpersonal relationship models moderate the impact of academic stress on depression and suicidal ideation.Conclusion:Defeat is a significant risk factor for mental health problems in college students.Academic stress partially mediates the negative impact of defeat on mental health,while patterns of interpersonal relationships moderate this impact.Effective early prevention and intervention should focus on monitoring students’stress levels and fostering warm,positive parent–child relationships.

Research on the Evaluation System of Community Daycare Center Planning and Implementation from the Perspective of Subject-Object Relationships

Through literature review,we found that evaluations of urban planning implementation often focus narrowly on either the subject(stakeholders)or the object(projects),while micro-level aspects such as site selection and construction are primarily considered within the context of planning implementation.There is insufficient research on the evaluation of implementation.Community daycare centers play a crucial role in the community home care model,yet there is relatively little research on their usage efficiency,satisfaction levels,and spatial evaluation of planning implementation.Based on the theoretical understanding of the human environment,including the subject,object,and their interactions,an evaluation system for the planning and implementation of community day care centers was constructed,incorporating subjective evaluation,objective evaluation,and comparative analysis.

自分泌运动因子(ATX)抑制剂的3D-QSAR和分子对接研究

自分泌运动因子(ATX)在心血管疾病、炎症和肿瘤的病理过程中发挥重要作用。对具有抑制ATX活性的吲哚类化合物进行了三维定量构效关系(3D-QSAR)和分子对接研究。采用CoMFA和CoMSIA模型对40个ATX抑制剂构建3D-QSAR模型,阐明了抑制剂结构与生物活性之间的关系,同时利用Surflex-Dock进行分子对接,研究该类化合物与ATX蛋白的结合模式。CoMFA模型(q^(2)=0.668,r^(2)=0.992)和CoMSIA模型(q^(2)=0.727,r^(2)=0.988)表明其具有良好的预测能力和机制可解释能力,分子对接表明上述化合物与ATX均能很好地结合,Ser306、Glu232和His121是影响上述化合物与ATX结合的主要氨基酸残基。本研究为进一步设计合成ATX抑制剂提供理论依据,对开发高效ATX抑制剂具有指导意义。

向列相液晶分子结构与黏度关系研究及BPNN-QSAR模型建立

用于光学、微波通信调谐等器件的向列相液晶材料需要具备高响应速度来实现应用需求.液晶器件响应速度与液晶的旋转黏度、液晶的双折射率等因素相关.微波器件用向列相液晶,常采用大π-电子共轭体系、大极性基团来提高液晶分子的双折射率和介电各向异性,实现宽相位调制量,也因此增大了液晶材料黏度,影响了微波器件的响应速度.本文以液晶黏度因素为主线,对本课题组设计合成的42种向列相液晶在25℃时的黏度用旋转流变仪进行测试,从液晶化合物的结构角度分析影响液晶黏度的因素.首次建立向列相液晶分子结构与黏度的BPNN-QSAR定量构效模型,模型测试组预测值跟真实值之间的相关系数q^(2)=0.607>0.5,说明模型可用于液晶化合物的黏度性能预测,并对影响黏度性能的分子结构描述符进行了探讨.从实际应用出发结合本课题研究,设计了两个系列7个大双折射率液晶分子,BPNN模型测试黏度量度小于同类型分子,实验测试值与模型测试值相近.

陕西茯茶抗氧化成分的提取及其定量构效关系(QSAR)研究

以陕西茯砖黑毛茶为原料,基于溶剂萃取法的单因素实验对有效成分进行提取,通过正交实验测定陕西茯茶提取物对DPPH和ABTS+自由基的清除率来反映其有效成分的抗氧化能力。研究结果表明利用乙醇-水溶液萃取法获得的提取物具有较强的抗氧化成分,抗氧化成分提取的最佳条件为:40%的乙醇溶液,提取温度45℃、提取时间45 min、料液比为1∶60。研究发现该有效成分对DPPH的清除率高达85%,高于对ABTS+自由基的清除率(约67%)。对茯茶素分子的化学物理属性与抗氧化的关联性进行了定量构效关系的研究,揭示出含有酚羟基的茯茶素分子的抗氧化性与分子中氢键给体的数量存在线性关系。总之,本文对陕西茯茶中多酚的提取工艺以及抗氧化性能的定量构效关系(QSAR)的研究可以为陕西茯茶抗氧化产品的开发提供理论依据。

基于极限梯度提升算法和特征筛选方法的羊角月牙藻(Selenastrum capricornutum)急性毒性定量构效关系(QSAR)模型的建立与应用

藻类是水生食物网中主要的初级生产者,对水生生态系统的可持续性起着重要作用。随着社会发展、工业进步和人类活动,大量化学品被释放到水生环境中,对藻类产生了极大的威胁。若藻类受到危害势必会影响其他水生生物,因此有必要开展藻类的毒性评估。藻类的毒性评估需要大量的毒性数据,通过实验的方法获得水生毒性数据成本较高且比较耗时,定量构效关系(QSAR)是解决这类问题的一种良好的替代方法。本研究基于Web of Science与中国知网数据库文献中的53条急性毒性数据,利用极限梯度提升(XGB)算法和特征筛选方法建立了羊角月牙藻(Selenastrum capricornutum)急性毒性的QSAR模型。最优模型的训练集决定系数(R^(2)_(TR))达到了0.97,验证集决定系数(Q^(2)_(EXT))达到了0.78,留一法交叉验证系数(Q^(2)_(LOO))也达到了0.51,表明建立的QSAR模型具有较好的拟合优度、稳健性和预测能力。机理解释结果表明,化合物的拓扑电荷数、总原子序数和电负性是影响羊角月牙藻急性毒性的关键因素。在此基础上,采用建立的QSAR模型和EPI Suite分别预测了16种典型多环芳烃(PAHs)对藻类的急性毒性,并对其进行了毒性分级。研究结果为藻类的急性毒性数据的获取提供了一个高效预测工具,有利于加快化学品的水环境风险评估工作。

雌激素的3D-QSAR模型构建及其在饲料与鸡肉中含量的测定以及对人体的影响

雌激素对人体有显著的内分泌干扰效应.本文以雌激素受体相对亲和力(relative binding affinity,RBA)作为生物活性,对雌激素进行了三维定量构效关系(3D-QSAR)计算,得到具有较好预测能力的CoMFA(交叉验证相关系数q^(2)=0.721,非交叉验证相关系数r^(2)=0.925)和CoMSIA(q^(2)=0.824,r^(2)=0.961)模型.并对国内各地区鸡肉进行了雌激素含量检测与雌激素效应评估.植物雌激素在鸡饲料和鸡肉中检出率为69%和84%;天然雌激素在鸡饲料和鸡肉中检出率为53%和50%.检测及评估结果显示绝大部分鸡肉可以安全食用,但鸡肉中含有的己烯雌酚、17α-乙炔雌二醇以及香芹酚所具有的雌激素效应值得引起重视.

保健食品中添加地平类降压药急性毒性的3D-QSAR模型建立

该研究旨在通过建立三维定量构效关系(3-dimensional-quantitative structure-activity relationship,3D-QSAR)模型,探究1,4-二氢吡啶(1,4-dihydropyridines,1,4-DHPs)结构与急性毒性之间的构效关系以及引起急性毒性变化的可能机制。将11种1,4-DHPs三维结构和半数致死量(lethal dose,50%,LD_(50))值进行相应处理后,分别采用比较分子力场分析(comparative molecular field analysis,CoMFA)和比较分子相似性指数分析(comparative molecular similarity index analysis,CoMSIA)构建3D-QSAR模型;随后进行3D-QSAR模型内外部验证和应用域确定。结果显示:(1)CoMFA和CoMSIA模型的交叉验证系数(q^(2))分别为0.509和0.521,非交叉验证系数(R^(2))分别为0.988和0.989,对急性毒性的预测与试验相符合;(2)1,4-DHPs急性毒性变化主要受立体场、静电场、疏水场和氢键受体场影响,表现为在1,4-DHPs的吡啶3号位取代基上施加4种分子场,在吡啶5号位取代基上施加立体场和氢键受体场,在苯环取代基上施加立体场、静电场和疏水场均可影响1,4-DHPs的急性毒性变化。因此,该研究建立出具有较高预测能力的1,4-DHPs急性毒性3D-QSAR模型,不仅能为预测1,4-DHPs急性毒性提供技术支持,也可为新型1,4-DHPs类钙通道阻滞剂的设计提供思路。

Optimizing Spatial Relationships in GCN to Improve the Classification Accuracy of Remote Sensing Images

Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation.With the continuous develop-ment of artificial intelligence technology,the use of deep learning methods for interpreting remote-sensing images has matured.Existing neural networks disregard the spatial relationship between two targets in remote sensing images.Semantic segmentation models that combine convolutional neural networks(CNNs)and graph convolutional neural networks(GCNs)cause a lack of feature boundaries,which leads to the unsatisfactory segmentation of various target feature boundaries.In this paper,we propose a new semantic segmentation model for remote sensing images(called DGCN hereinafter),which combines deep semantic segmentation networks(DSSN)and GCNs.In the GCN module,a loss function for boundary information is employed to optimize the learning of spatial relationship features between the target features and their relationships.A hierarchical fusion method is utilized for feature fusion and classification to optimize the spatial relationship informa-tion in the original feature information.Extensive experiments on ISPRS 2D and DeepGlobe semantic segmentation datasets show that compared with the existing semantic segmentation models of remote sensing images,the DGCN significantly optimizes the segmentation effect of feature boundaries,effectively reduces the noise in the segmentation results and improves the segmentation accuracy,which demonstrates the advancements of our model.