Crop Phenomics and High-Throughput Phenotyping:Past Decades,Current Challenges,and Future Perspectives

Since whole-genome sequencing of many crops has been achieved,crop functional genomics studies have stepped into the big-data and high-throughput era.However,acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies.Nevertheless,recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years.In this article,we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades.We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies.Finally,we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap.It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

Microstructural characterization,tribological and corrosion behavior of AA7075-TiC composites

Aluminum alloys are the potential materials in the automobile and aerospace sectors due to their lower density,easy forming and excellent corrosion resistance.The demand of high strength-to-weight ratio materials in structural applications needs the engineering industries to seek aluminum alloy with new versions of hard and brittle ceramic particles.The microstructure,hardness,wear and corrosion behaviors of AA7075 composites with 2.5wt.%and 5wt.%TiC particles were studied.Microscopic analysis is evident that the transformation of the strong dendritic morphology to non-dendritic morphology on the incorporation of TiC into AA7075.Furthermore,the precipitation of the second-phase compounds such as Al_(2)CuMg,Al_(2)Cu andFe-rich Al_6(Cu,Fe)/Al_(7)Cu_(2)Fe)is promoted by TiC particles at inter-and intra-dendritic regions.Accordingly,the hardness of composites is improved by grain boundary strengthening and particulate strengthening mechanisms.Both coefficient of friction and wear rate have an inverse relation with TiC concentration.The base alloy without TiC shows adhesive-type wear-induced deformation due to the formation of an oxide film,while composite samples exhibit a mechanically mixed layer and abrasive-type wear behavior.Composite samples shows a higher corrosion rate due to the presence of numerous precipitates which promote pitting corrosion.

MayGAN:Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

Leukemia,often called blood cancer,is a disease that primarily affects white blood cells(WBCs),which harms a person’s tissues and plasma.This condition may be fatal when if it is not diagnosed and recognized at an early stage.The physical technique and lab procedures for Leukaemia identification are considered time-consuming.It is crucial to use a quick and unexpected way to identify different forms of Leukaemia.Timely screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require treatment.Various deep-learning(DL)model-based segmentation and categorization techniques have already been introduced,although they still have certain drawbacks.In order to enhance feature extraction and classification in such a practical way,Mayfly optimization with Generative Adversarial Network(MayGAN)is introduced in this research.Furthermore,Generative Adversarial System(GAS)is integrated with Principal Component Analysis(PCA)in the feature-extracted model to classify the type of blood cancer in the data.The semantic technique and morphological procedures using geometric features are used to segment the cells that makeup Leukaemia.Acute lymphocytic Leukaemia(ALL),acute myelogenous Leukaemia(AML),chronic lymphocytic Leukaemia(CLL),chronic myelogenous Leukaemia(CML),and aberrant White Blood Cancers(WBCs)are all successfully classified by the proposed MayGAN model.The proposed MayGAN identifies the abnormal activity in the WBC,considering the geometric features.Compared with the state-of-the-art methods,the proposed MayGAN achieves 99.8%accuracy,98.5%precision,99.7%recall,97.4%F1-score,and 98.5%Dice similarity coefficient(DSC).

Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

An abnormality that develops in white blood cells is called leukemia.The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery.Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis.This paper proposes a Histogram Threshold Segmentation Classifier(HTsC)for a decision support system.The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images.Arithmetic operations are used to crop the nucleus based on automated approximation.White Blood Cell(WBC)segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach.Through entropy-adaptive mask generation,WBCs accurately detect the circularity region for identification of the nucleus.The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations.Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions.The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images.The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier.The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes,monocytes,neutrophils,eosinophils,and abnormalities in the WBCs.The proposed HTsC identifies the abnormal activity in the WBC,considering the color and shape features.It exhibits a higher classification accuracy value of 99.6%when combined with the other classifiers.The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%,which is approximately 3%–12%higher than the conventional technique.

A Novel Gradient Boosted Energy Optimization Model(GBEOM)for MANET

Mobile Ad Hoc Network(MANET)is an infrastructure-less network that is comprised of a set of nodes that move randomly.In MANET,the overall performance is improved through multipath multicast routing to achieve the quality of service(quality of service).In this,different nodes are involved in the information data collection and transmission to the destination nodes in the network.The different nodes are combined and presented to achieve energy-efficient data transmission and classification of the nodes.The route identification and routing are established based on the data broadcast by the network nodes.In transmitting the data packet,evaluating the data delivery ratio is necessary to achieve optimal data transmission in the network.Furthermore,energy consumption and overhead are considered essential factors for the effective data transmission rate and better data delivery rate.In this paper,a Gradient-Based Energy Optimization model(GBEOM)for the route in MANET is proposed to achieve an improved data delivery rate.Initially,the Weighted Multi-objective Cluster-based Spider Monkey Load Balancing(WMC-SMLB)technique is utilized for obtaining energy efficiency and load balancing routing.The WMC algorithm is applied to perform an efficient node clustering process from the considered mobile nodes in MANET.Load balancing efficiency is improved with a higher data delivery ratio and minimum routing overhead based on the residual energy and bandwidth estimation.Next,the Gradient Boosted Multinomial ID3 Classification algorithm is applied to improve the performance of multipath multicast routing in MANET with minimal energy consumption and higher load balancing efficiency.The proposed GBEOM exhibits∼4%improved performance in MANET routing.

EsECC_SDN:Attack Detection and Classification Model for MANET

Mobile Ad Hoc Networks(MANET)is the framework for social networking with a realistic framework.In theMANETenvironment,based on the query,information is transmitted between the sender and receiver.In the MANET network,the nodes within the communication range are involved in data transmission.Even the nodes that lie outside of the communication range are involved in the transmission of relay messages.However,due to the openness and frequent mobility of nodes,they are subjected to the vast range of security threats inMANET.Hence,it is necessary to develop an appropriate security mechanism for the dataMANET environment for data transmission.This paper proposed a security framework for the MANET network signature escrow scheme.The proposed framework uses the centralised Software Defined Network(SDN)with an ECC cryptographic technique.The developed security framework is stated as Escrow Elliptical Curve Cryptography SDN(EsECC_SDN)for attack detection and classification.The developed EsECC-SDN was adopted in two stages for attack classification and detection:(1)to perform secure data transmission between nodes SDN performs encryption and decryption of the data;and(2)to detect and classifies the attack in theMANET hyper alert based HiddenMarkovModel Transductive Deep Learning.Furthermore,the EsECC_SDN is involved in the assignment of labels in the transmitted data in the database(DB).The escrow handles these processes,and attacks are evaluated using the hyper alert.The labels are assigned based on the k-medoids attack clustering through label assignment through a transductive deep learning model.The proposed model uses the CICIDS dataset for attack detection and classification.The developed framework EsECC_SDN’s performance is compared to that of other classifiers such as AdaBoost,Regression,and Decision Tree.The performance of the proposed EsECC_SDN exhibits∼3%improved performance compared with conventional techniques.

防风网对煤堆遮风效果的风洞实验研究

为了考察防风网对堆场煤堆的遮风效果,进行了防风网与煤堆之间区域的流场可视化实验。使用风洞模拟大气边界层条件,并将开孔率为38.5%的防风网模型布置在煤堆模型前方。实验中采用粒子成像测速(PIV)系统测量了防风网和堆场区域的平均风速分布,并且获得了煤堆表面的摩擦风速分布,进一步估算煤堆起尘量的变化情况。实验发现,防风网可有效降低煤堆表面迎风区域的摩擦风速,而对背风面的流场分布影响较小。研究结果可为防风网的遮护作用提供一定的实验支持。

High-coverage proteome analysis reveals the first insight of protein modification systems in the pathogenic spirochete Leptospira interrogans

细螺旋体病是感染人和大量动物的类螺旋体的病原的螺旋菌引起的普遍 zoonotic 疾病。由联合计算预言和高精确性的双人脚踏车团系列，我们修订了螺旋体 interrogans serovar Lai，为细螺旋体病负责的免费生活的病原的螺旋菌，为新奇基因的提供的实质的肽证据和新基因边界的染色体注解。随后，我们介绍了蛋白质表示和多重 posttranslational 修正(PTM ) 的高范围的 proteome 分析。约 64.3% 预言的 L。interrogans 蛋白质被检测 2 编目 540 蛋白质。同时，多重 PTM 的侧面并发地被建立，在总数 32 phosphorylated， 46 acetylated 和 155 methylated 蛋白质包含。在 serovar Lai 的 PTM 系统显示出唯一的特征。L 的唯一的象真核细胞一样特征。interrogans 蛋白质修正在 phosphorylation 和精氨酸 methylation 被表明。这系统的分析在建议的初核质而且一个看法提供高范围的蛋白质表示和多重修正的全面信息不仅 evolutionarily 原始的 L。interrogans 与优核质在蛋白质修正系统分享重要类似。

Evaluation and Influence Factor of Green Efficiency of China’s Agricultural Innovation from the Perspective of Technical Transformation

Agricultural innovation is important for the green transformation of agriculture.Based on the perspective of technology transformation,this paper builds a theoretical analysis framework and evaluation index system for green efficiency of agricultural innovation,and discusses the evolution laws and influencing factors of the green efficiency of China’s agricultural innovation from 2005 to 2017 utilizing the DEA model,Malmquist index,and Tobit regression analysis.The results show that:1)The overall green efficiency of China’s agricultural innovation is not high,the green efficiency of agricultural innovation in eastern China is mainly driven by pure technical efficiency,while that in central and western China is mainly driven by the scale efficiency.The green efficiency of agricultural innovation shows significant spatial differences,and the low efficiency and relatively low-efficiency regions moved to central and southeastern China.2)Technical progress is the main force affecting the change of green total factor productivity of China’s agricultural innovation,seeing a trend of decrease followed by an increase.Pure technical efficiency and scale efficiency exhibit an increasing-decreasing trend,and gradually transform into key factors that restrict the improvement of the green total factor productivity of agricultural innovation.3)Agricultural technologies’diffusion,absorption,and implementation are three influencing factors of the green efficiency of agricultural innovation.The local level of informatization,the number of agricultural technicians in enterprises and institutions,average education level of residents,and the level of agricultural mechanization have positive impacts on the promotion of the green efficiency of agricultural innovation,promoting the diffusion,absorption and implementation of agricultural innovation technology can significantly improve the green efficiency of agricultural innovation.

Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall

The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services. The teleconnection from E1 Nifio to Yangtze River basin rainfall meant that the strong E1 Nifio in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system. This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin, building on previous work demonstrating the retrospective skill of such a forecast. A simple forecasting methodology is presented, in which the forecast probabilities are derived from the historical relationship between hindcast and observations. Its performance for 2016 is discussed. The heavy rainfall in the May-June-July period was correctly forecast well in advance. August saw anomalously low rainfall, and the forecasts for the June-July-August period correctly showed closer to average levels. The forecasts contributed to the confidence of decision-makers across the Yangtze River basin. Trials of climate services such as this help to promote appropriate use of seasonal forecasts, and highlight areas for future improvements.

ALK和ROS1:肺癌治疗的联合靶点

间变性淋巴瘤激酶(anaplastic lymphoma kinase,ALK)基因重排可见于包括非小细胞肺癌(non small cell lung cancer,NSCLC)在内的多种恶性肿瘤中。ALK融合基因使激酶具有异常活性,而野生型ALK激酶域突变也可使它被激活。ALK基因重排使得NSCLC中出现了新的分子亚型,该亚型对ALK抑制剂高度耐药。克唑替尼(crizotinib)是一个口服小分子ATP模拟化合物,它最初作为MET抑制剂被开发,随后被发现具有抗ALK活性的脱靶效应(off target),并被美国FDA批准用于治疗ALK阳性的NSCLC患者。近来在NSCLC患者中还发现了ROS1受体酪氨酸激酶染色体重排,而克唑替尼正处于治疗该分子亚型NSCLC患者的临床试验中。任何计算机辅助药物设计都是依赖其分子结构和配体的药物设计方法,每种方法的详细信息中均应重点强调利用这二者,以开发多靶点小分子激酶抑制剂。此类多靶点小分子激酶抑制剂均可对ROS1和ALK重排的NSCLC有抑制增殖作用。因此,本综述重点强调了关于靶向这些激酶的重要性,以及在优化出效能更佳、选择性更强的ROS1和ALK激酶抑制剂中所取得的进步。

Predictors of emotional awareness during childhood

Emotional awareness, the ability to identify and describe one’s own emotions, and those of other people, has been widely studied in adult samples, while only few studies have been conducted during childhood. Aim of the present study was to investigate the predictability of the main variables usually associated with the study of emotional awareness. The LEAS-C was administered to a group of 781 children aged 8 to 13 years old. The results showed that both gender and verbal skills are important predictors of children’s emotional awareness. Moreover, cognitive ability and emotion recognition also predicted LEAS-C scores. Finally, age emerged as a significant predictor of the complexity of children’s emotional awareness. The assessment of children’s emotional awareness may be significant for different health-related purposes, e.g. prevention and psychological support.

Low-Temperature Growing Anatase TiO2/SnO2 Multi-dimensional Heterojunctions at MXene Conductive Network for High-Efficient Perovskite Solar Cells

A multi-dimensional conductive heterojunction structure,composited by TiO2,SnO2,and Ti3C2TX MXene,is facilely designed and applied as electron transport layer in efficient and stable planar perovskite solar cells.Based on an oxygen vacancy scramble effect,the zero-dimensional anatase TiO2 quantum dots,surrounding on two-dimensional conductive Ti3C2TX sheets,are in situ rooted on three-dimensional SnO2 nanoparticles,constructing nanoscale TiO2/SnO2 heterojunctions.The fabrication is implemented in a controlled lowtemperature anneal method in air and then in N2 atmospheres.With the optimal MXene content,the optical property,the crystallinity of perovskite layer,and internal interfaces are all facilitated,contributing more amount of carrier with effective and rapid transferring in device.The champion power conversion efficiency of resultant perovskite solar cells achieves 19.14%,yet that of counterpart is just 16.83%.In addition,it can also maintain almost 85%of its initial performance for more than 45 days in 30–40%humidity air;comparatively,the counterpart declines to just below 75%of its initial performance.

A New Method for Scene Classification from the Remote Sensing Images

The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of areas.In recent years,a lot of interest has been generated in researching remote sensing image scene classification.Remote sensing image scene retrieval,and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding(RSISU)research.In the last several years,the number of deep learning(DL)methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs,providing new research and development possibilities for RS image classification.A new network called Pass Over(POEP)is proposed that utilizes both feature learning and end-to-end learning to solve the problem of picture scene comprehension using remote sensing imagery(RSISU).This article presents a method that combines feature fusion and extraction methods with classification algorithms for remote sensing for scene categorization.The benefits(POEP)include two advantages.The multi-resolution feature mapping is done first,using the POEP connections,and combines the several resolution-specific feature maps generated by the CNN,resulting in critical advantages for addressing the variation in RSISU data sets.Secondly,we are able to use Enhanced pooling tomake the most use of themulti-resolution feature maps that include second-order information.This enablesCNNs to better cope with(RSISU)issues by providing more representative feature learning.The data for this paper is stored in a UCI dataset with 21 types of pictures.In the beginning,the picture was pre-processed,then the features were retrieved using RESNET-50,Alexnet,and VGG-16 integration of architectures.After characteristics have been amalgamated and sent to the attention layer,after this characteristic has been fused,the process of classifying the data will take place.We utilize an ensemble classifier in our classification algorithm that utilizes the architecture of a Decision Tree and a Random Forest.Once the optimum findings have been found via performance analysis and comparison analysis.

Chromophobe renal cell carcinoma: Novel molecular insights and clinicopathologic updates

Chromophobe renal cell carcinoma(ChRCC)is the third most common renal cell carcinoma(RCC)subtype,which predominantly occurs in sporadic setting.ChRCCs are considered to originate from the intercalated cell of distal tubules with two main morphological variants,classic and eosinophilic.Most ChRCCs carry a favorable clinical outcome.Histology alone is limited in predicting the behavior of ChRCCs that do not have overtly aggressive morphologic findings such as necrosis and sarcomatoid features.Along with positive CD117 expression,classic ChRCCs generally express diffuse and uniform CK7,while eosinophilic variant demonstrates more heterogeneous CK7 expression(rare or patchy).Multiple losses of chromosomes 1,2,6,10,13,17,and 21 are considered to be the genetic hallmarks of classic and eosinophilic ChRCCs,while chromosomal gains are known to be associated with sarcomatoid ChRCCs.TP53 and PTEN are the two most frequently mutated genes in ChRCCs.The major challenge in the differential diagnosis of ChRCCs includes considerations around the eosinophilic variant(of ChRCCs),where it may share overlapping features with oncocytoma or other recent emergent oncocytic tumors.Most eosinophilic ChRCCs share expression of the recently described biomarkers,LINC01187 and FOXI1,with classic ChRCCs,however,a subset of eosinophilic-like ChRCCs with lower biomarker expression have been demonstrated to harbor MTOR gene mutations.Overall,the morphologic features of ChRCCs and genetic profile with combinations of chromosomal losses and gains suggest this tumor entity to represent a distinct,yet heterogeneous group of renal neoplasms.

HTML5在智能设备上的机会

自推出App Store以来,苹果在移动应用市场上的斩获吸引了各手机及移动服务商积极投入相关研究与开发工作,并陆续推出自己的解决方案,移动应用俨然进入了春秋战国时代。

阿伏伽德罗常数:单位和标度因子(英文)

阿伏伽德罗常数的重要性在于它可用作测量物质的量的单位 ,它的数值还可用作度量宏观数量与等量的微观原子数量之间的标度因子。问题起因是严格限定阿伏伽德罗常数 6位有效数字是否无用 ,使得进一步改进测量不确定度至 <1 0 -7NA 的提议悬而不决。测量不确定度的改进与提出改变有效数值是完全不同的 ,果真如此 ,有效数字此类改变都会很好地隐含在不确定度括号内的数字里。问题是基于我们的物质“微粒”特性观念和其紧密相连“数值”概念 ,阿伏伽德罗常数作为物质数量单位基础的确立将非常有用于国际标准单位制 ( SI) ,不仅简化了建立化学量测量结果向国际标准单位制的溯源 (即物质量国际标准单位制的确立 ) 。

The Architectural Unit Setting up and Architectural Characteristics of Néré, <i>Parkia biglobosa</i>, Jack, R. Br. (Fabaceae)

Parkia biglobosa is a much-loved and over-exploited African savannah species for its socio-economic importance. Knowing and taking into account its architectural unit, which is the basis for diagnosing phenology, productivity and tree health, could provide a new perspective on its sustainable management. The aim of this study is to establish the architectural development in Parkia biglobosa by retrospective analysis. To achieve this objective, 390 individuals of all sizes ranging from seedlings to senescent trees were observed and analysed under various soil and climatic conditions in Côte d’Ivoire. The results showed that Parkia biglobosa is a light plant but shading tolerant. It is a mixed vegetative axis plant, the stem is orthotropic* in its proximal part and plagiotropic* (collapsing) in its distal part in young stage. The tree then transitions to an adult and old stage into a tree with a plagiotropic* axis in the proximal and distal parts, the trunk is built up by superimposing collapsed relay axes that gradually straighten, branching is sympodial*, growth is defined and sexuality is terminal and lateral. The ontogeny takes place in three phases: initiation of development and establishment of the crown (young), then flowering and establishment of the architectural unity (adult) and finally the death of secondary axes in the crown, duplication of the architecture by a series of partial and total reiterations (old). The level of organisation is 5: the phytomere, the module or growth unit, the axis, the architectural unit and the reiterated complex. Retrospective analysis of the modules showed that the dimensions of the growth units are indicators of morphological variation and species adaptation to a changing climate (P < 0.05). However, the equations generated by the morphological and habitat dimension linkage models are not significant (R2 and r < 0.7) to be used as a guide for field data collection. This study represents an initiation into the architectural study of this species and the information provided will serve as a basis for further research into the architecture in relation to the sustainable use of this species.

Structural and electrical transport properties of Dirac-like semimetal PdSn4 under high pressure

We conducted in-situ high-pressure synchrotron x-ray diffraction(XRD) and electrical transport measurements on Dirac-like semimetal Pd Sn4 in diamond anvil cells with quasi-hydrostatic pressure condition up to 44.5 GPa–52.0 GPa. The XRD data show that the ambient orthorhombic phase(Ccca) is stable with pressures to 44.5 GPa, and the lattice parameters and unit-cell volume decrease monotonously upon compression. The temperature dependence of the resistance exhibits a metallic conduction and follows a Fermi-liquid behavior below 50 K, both of which keep unchanged upon compression to 52.0 GPa. The magnetoresistance curve at 5 K maintains a linear feature in a magnetic field range of 2.5 T–7 T with increasing pressure to 20.0 GPa. Our results may provide pressure-transport constraints on the robustness of the Dirac fermions.

国际‘绿工厂’的策略比较分析

想象有一天，工业区再也不流出污水，反而是能降低水污染的化合物，汽车排放的不再是二氧化碳，却可以洁净空气……