2018年中国4省脱粒小麦中9种真菌毒素污染情况调查

目的 调查2018年湖北、安徽、江苏和河北4省脱粒小麦中5种镰刀菌毒素和4种黄曲霉毒素（aflatoxins，AFs）的污染情况。方法 从4省采集2018年5~6月收获的脱粒小麦371份，经乙腈-水（84:16，V:V）提取、Mycosep 226多功能净化柱净化后，采用高效液相色谱串联质谱同时测定样品中脱氧雪腐镰刀菌烯醇（deoxynivalenol，DON）、3-乙酰基-DON（3-acetyl-DON，3-Ac-DON）、15-乙酰基-DON（15-acetyl-DON，15-Ac-DON）、雪腐镰刀菌烯醇（nivalenol，NIV）、玉米赤霉烯酮（zearalenone，ZEN）和4种AF, 共9种毒素的含量。 结果 4省脱粒小麦受多种镰刀菌毒素, 尤其是DON的污染较为严重，而AF污染相对较轻。DON的检出率最高,为90.3%，阳性样品中DON平均污染水平为2706.3 μg/kg，45.3%样品DON的含量超过国家限量标准1000 μg/kg；4种AF的检出率和平均污染水平均较低，其中AFB1和AFB2的检出率最高，均为3.2%，2份样品AFB1含量超过国家限量标准5 μg/kg，仅1份样品检出AFG2，所有样品均未检出AFG1。从地区来看，湖北省脱粒小麦中各毒素污染最严重，尤其是DON、其检出率和平均污染水平分别为100%和6314.9 μg/kg；江苏省样品中ZEN的平均污染水平为1971.2 μg/kg，远高于其它省份。Spearman相关性分析表明，5种镰刀菌毒素的污染具有显著相关性，DON与已检出的3种AF及3种AF间的污染相关性均较弱或无显著相关性。 结论 4省脱粒小麦受5种镰刀菌毒素的污染较为严重且具有显著相关性。污染程度为湖北>安徽>江苏>河北。因此，有必要对4省脱粒小麦中多种毒素的协同污染情况进行持续监测。     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

Hybridizing adaptive large neighborhood search with kernel search: a new solution approach for the nurse routing problem with incompatible services and minimum demand

The average age of the population has grown steadily in recent decades along with the number of people suffering from chronic diseases and asking for treatments. Hospital care is expensive and often unsafe, especially for older individuals. This is particularly true during pandemics as the recent SARS-CoV-2. Hospitalization at home has become a valuable alternative to face efficiently a huge increase in treatment requests while guaranteeing a high quality of service and lower risk to fragile patients. This new model of care requires the redefinition of health services organization and the optimization of scarce resources (e.g., available nurses). In this paper, we study a Nurse Routing Problem that tries to find a good balance between hospital costs reduction and the well-being of patients, also considering realistic operational restrictions like maximum working times for the nurses and possible incompatibilities between services jointly provided to the same patient. We first propose a Mixed Integer Linear Programming formulation for the problem and use some valid inequalities to strengthen it. A simple branch-and-cut algorithm is proposed and validated to derive ground benchmarks. In addition, to efficiently solve the problem, we develop an Adaptive Large Neighborhood Search hybridized with a Kernel Search and validate its performance over a large set of different realistic working scenarios. Computational tests show how our matheuristic approach manages to find good solutions in a reasonable amount of time even in the most difficult settings. Finally, some interesting managerial insights are discussed through an economic analysis of the operating context.     

超高压处理对苦杏仁分离蛋白结构和致敏性的影响

苦杏仁中含有多种生物活性组分和营养素，具有较高的营养价值，但苦杏仁具有致敏性，过敏人群食用含有苦杏仁的食品后可能会产生过敏的风险。为探究超高压处理对苦杏仁分离蛋白结构和致敏性的影响，对杏仁分离蛋白进行不同压力处理，并利用圆二色谱、外源荧光光谱和酶联免疫吸附实验对其空间结构和致敏性进行表征。结果表明：苦杏仁分离蛋白100~500 MPa压力处理苦杏仁分离蛋白900 s后，苦杏仁分离蛋白其二级结构未发生显著变化。随着压力强度和保压时间的增加，苦杏仁分离蛋白的三级结构展开，荧光强度增加，疏水性基团暴露，表面疏水性逐渐增大，致敏性显著降低。苦杏仁分离蛋白致敏性的降低，可能是苦杏仁的过敏原构象表位发生了变化。该研究结果对开发安全的苦杏仁食品和饮料具有重要意义。     

A one-class peeling method for multivariate outlier detection with applications in phase I SPC

In phase I of statistical process control (SPC), control charts are often used as outlier detection methods to assess process stability. Many of these methods require estimation of the covariance matrix, are computationally infeasible, or have not been studied when the dimension of the data, $p$, is large. We propose the one-class peeling (OCP) method, a flexible framework that combines statistical and machine learning methods to detect multiple outliers in multivariate data. The OCP method can be applied to phase I of SPC, does not require covariance estimation, and is well suited to high-dimensional data sets with a high percentage of outliers. Our empirical evaluation suggests that the OCP method performs well in high dimensions and is computationally more efficient and robust than existing methodologies. We motivate and illustrate the use of the OCP method in a phase I SPC application on a $N=354$, $p=1917$ dimensional data set containing Wikipedia search results for National Football League (NFL) players, teams, coaches, and managers. The example data set and R functions, OCP.R and OCPLimit.R, to compute the respective OCP distances and thresholds are available in the supplementary materials.     

船体结构冰载荷反演方法及试验验证

冰载荷是影响船舶冰区航行期间结构安全的重要环境载荷。船舶的冰压监测通常采用应变传感器，合理地布放传感器是识别冰载荷的基础。通过对比船体外板结构试验中的冲击载荷和不同测试方案下的应变信号，确定了最佳应变传感器布放方案；采用Green核函数方法建立了船体外板结构应变冲击载荷间的响应关系，并对采集信号在噪声影响下反演的不适定性进行了分析；采用Tikhonov正则化方法克服了载荷反演过程中出现的数值不稳定问题；最后将试验中的响应用到载荷识别分析中，反演的载荷可以较为准确地反映冲击载荷的时域特征并且载荷识别精度良好。     

Association of TaD14-4D,a Gene Involved in Strigolactone Signaling,with Yield Contributing Traits in Wheat

Tillering is a crucial agronomic trait of wheat; it determines yield and plant architecture. Strigolactones (SLs) have been reported to inhibit plant branching. D14, a receptor of SLs, has been described to affect tillering in rice, yet it has seldomly been studied in wheat. In this study, three TaD14 homoeologous genes, TaD14-4A, TaD14-4B, and TaD14-4D, were identified. TaD14-4A, TaD14-4B, and TaD14-4D were constitutively expressed, and TaD14-4D had a higher expression level in most tissues. TaD14 proteins were localized in both cytoplasm and nucleus. An SNP and a 22 bp insertion/deletion (Indel) at the exon regions of TaD14-4D were detected, forming three haplotypes, namely 4D-HapI, 4D-HapII, and 4D-HapIII. Due to the frameshift mutation in the coding region of 4D-HapII, the interaction of 4D-HapII with TaMAX2 and TaD53 was blocked, which led to the blocking of SL signal transduction. Based on the two variation sites, two molecular markers, namely dCAPS-250 and Indel-747, were developed. Association analysis suggested that haplotypes of TaD14-4D were associated with effective tillering number (ETN) and thousand kernel weight (TKW) simultaneously in four environments. The favorable haplotype 4D-HapIII underwent positive selection in global wheat breeding. This study provides insights into understanding the function of natural variations of TaD14-4D and develops two useful molecular markers for wheat breeding.     

日本寒冷地带北海道抑制粳稻生成白未熟粒的栽培法（网络首发、推荐阅读）

白未熟粒的多发会使整粒率下降，降低检查等级，因此急需抑制其发生。白未熟粒中，乳白粒和基部未熟粒的发生率存在品种间差异。切除止叶减少光合作用，增肥导致每平方米稻谷数量的增加，疏植和育苗时高温引起早期异常抽穗所致的齐穗不良，以及收割时期的延迟等会造成白未熟粒的增多。此外倒伏还会导致青未熟粒、白未熟粒增多。在初期生长良好的情况下，从分蘖期开始的深水灌溉抑制了过剩的分蘖产生，增加了粒重和糙米产量，使糙米品质得以提高。由于成熟期的土壤过于干燥，因腹白粒产生引起的品质降低和千粒重减少会导致减产。从抽穗期到齐穗期10天后的茎叶到稻谷的每粒稻谷非结构性碳水化合物（NSC）流转量，以及粗糙米产量减去同一流转量的每粒稻谷NSC增加量越多，这些白未熟粒的产生就越少。为避免白未熟粒的多发，必须使施肥量、种植密度、育苗温室的温度管理、移植时期、成熟期的土壤水分和收割时期合理化，促进初期生长，用深水抑制过剩的分蘖。通过改善以上的栽培方法，以及1990年代中期后在共同干燥调制储藏设施中使用色选机选别糙米，北海道的一等米比例超过了日本全国的平均水平。     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.