QUANTIS: Data quality assessment tool by clustering analysis

Automatically generated kinetic networks are ideally validated against a large set of accurate, reproducible, and easy-to-model experimental data. However, although this might seem simple, it proves to be quite challenging. QUANTIS, a publicly available Python package, is specifically developed to evaluate both the precision and accuracy of experimental data and to ensure a uniform, quick processing, and storage strategy that enables automated comparison of developed kinetic models. The precision is investigated with two clustering techniques, PCA and t-SNE, whereas the accuracy is probed with checks for the conservation laws. First, the developed tool processes, evaluates, and stores experimental yield data automatically. All data belonging to a given experiment, both unprocessed and processed, are stored in the form of an HDF5 container. The demonstration of QUANTIS on three different pyrolysis cases showed that it can help in identifying and overcoming instabilities in experimental datasets, reduce mass and molar balance closure discrepancies, and, by evaluating the visualized correlation matrices, increase understanding in the underlying reaction pathways. Inclusion of all experimental data in the HDF5 file makes it possible to automate simulating the experiment with CHEMKIN. Because of the employed InChI string identifiers for molecules, it is possible to automate the comparison experiment/simulation. QUANTIS and the concepts demonstrated therein is a potentially useful tool for data quality assessment, kinetic model validation, and refinement.     

Simulating ion clustering in potassium thiocyanate aqueous solutions with various ion-water models

Using a molecular dynamics simulation technique,we compared several commonly used ion-water models to describe the microscopic structures and dynamics in KSCN aqueous solutions.Results are compared with observations of femtosecond infrared vibrational-energy transfer and anisotropy measurements.The Jorgensen/TIP4P model is found to provide the best reproduction of clustering properties such as percentage of clustered ions,cluster-size distribution,concentration dependence of the water,and ion-rotation time constants.     

Flexible and powerful strategy for qualitative and quantitative analysis of valepotriates in Valeriana jatamansi Jones using high‐performance liquid chromatography with linear ion trap Orbitrap mass spectrometry

Valeriana jatamansi Jones is an important medicinal plant and its quality is closely related to its region of origin. In the current study, we utilized a flexible and powerful strategy for comprehensive evaluation of the quality diversity for 15 regions in China. The method was based on a hybrid linear ion trap‐Orbitrap mass spectrometry platform. For structure characterization, fragmentation patterns were detected by analyzing a series of standard compounds using data dependent multistage mass spectrometry acquisition. A fragment ion database for valepotriates was established, and the acquired data were high throughput filtered by fragment ion search for compound identification. For quantitative purposes, we normalized the mass spectrometry data of 15 samples using SIEVE 2.0 and the differences in composition were analyzed using principal component analysis combined with hierarchical clustering analysis. The results identified a total of 92 compounds from Valeriana jatamansi Jones. Samples from Dali, Kunming, and Baoshan have better qualities and concentrations of the main active constituents. To verify our strategy, we compared the valtrate, acevaltrate, and baldrinal contents using high‐performance liquid chromatography with diode array detector. We developed and validated a comprehensive qualitative and quantitative analytical method to achieve quality control of Valeriana jatamansi Jones.     

基于两阶段灰色聚类模型的新疆经济社会发展水平评价研究

通过两阶段灰色聚类模型选择影响经济社会发展水平,基础设施建设,相关产业发展水平以及区域经济基础四个一级指标的10个二级指标来构建区域经济社会发展水平评价指标体系,以2013年新疆14地州市相关数据为样本,运用基于改进的中心点混合三角白化权函数把新疆各地州市从经济社会发展的角度划分成4个区域,为"新丝绸之路经济带"下新疆各地州的经济与社会发展定位提供依据最后找出属于三、四级经济圈内地区经济与社会发展落后的原因并提出相关建议.     

Identifying Mixtures of Mixtures Using Bayesian Estimation

The use of a finite mixture of normal distributions in model-based clustering allows us to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework, we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior, where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this prior allows us to estimate the model using standard MCMC sampling methods. In combination with a post-processing approach which resolves the label switching issue and results in an identified model, our approach allows us to simultaneously (1) determine the number of clusters, (2) flexibly approximate the cluster distributions in a semiparametric way using finite mixtures of normals and (3) identify cluster-specific parameters and classify observations. The proposed approach is illustrated in two simulation studies and on benchmark datasets. Supplementary materials for this article are available online.     

Interspecific Variation between the American Quercus virginiana and Mediterranean Quercus Species in Terms of Seed Nutritional Composition,Phytochemical Content,and Antioxidant Activity

This study aimed to evaluate a complete nutritional composition in the seeds Quercus virginiana to compare this nutritional composition with three Mediterranean Quercus species. We analyzed the seed morphometry, proximate composition, phytochemicals, and antioxidant capacity. The seed of Q. virginiana presented the smaller seed size and weight, while Q. suber presented the highest values. Moreover, Q. virginiana seeds showed the highest amounts of sugar and total lipids, digestibility, energy, palmitic acid, and stearic acid. On the other hand, Q. virginiana seeds showed the lowest values of linoleic acid. Moreover, Q. coccifera seeds presented the highest total phenolics and flavonoids contents and antioxidant activity. The clustering analysis revealed a significant similarity in seed morphometry and nutritional composition between the Mediterranean Q. ilex and Q. suber, grouping with the American Q. virginiana, but to a considerable distance; by contrast, the Mediterranean Q. coccifera was the most distant in the clustering analysis. The content of phenolics and flavonoids and digestibility value were the variables that contributed to the separation to a greater extent in the clustering of the four species. The nutritional and biological activity assessment of plant seed may be considered as an essential mission to find new sustainable sources and novel chemical agents. In this sense, Quercus seeds may be an alternative and a competitive food source for the agri-food industry.     

Investigating the Disordered and Membrane-Active Peptide A-Cage-C Using Conformational Ensembles

The driving forces and conformational pathways leading to amphitropic protein-membrane binding and in some cases also to protein misfolding and aggregation is the subject of intensive research. In this study, a chimeric polypeptide, A-Cage-C, derived from α-Lactalbumin is investigated with the aim of elucidating conformational changes promoting interaction with bilayers. From previous studies, it is known that A-Cage-C causes membrane leakages associated with the sporadic formation of amorphous aggregates on solid-supported bilayers. Here we express and purify double-labelled A-Cage-C and prepare partially deuterated bicelles as a membrane mimicking system. We investigate A-Cage-C in the presence and absence of these bicelles at non-binding (pH 7.0) and binding (pH 4.5) conditions. Using in silico analyses, NMR, conformational clustering, and Molecular Dynamics, we provide tentative insights into the conformations of bound and unbound A-Cage-C. The conformation of each state is dynamic and samples a large amount of overlapping conformational space. We identify one of the clusters as likely representing the binding conformation and conclude tentatively that the unfolding around the central W23 segment and its reorientation may be necessary for full intercalation at binding conditions (pH 4.5). We also see evidence for an overall elongation of A-Cage-C in the presence of model bilayers.     

Multi-way dual Cheeger constants and spectral bounds of graphs

We introduce a set of multi-way dual Cheeger constants and prove universal higher-order dual Cheeger inequalities for eigenvalues of normalized Laplace operators on weighted finite graphs. Our proof proposes a new spectral clustering phenomenon deduced from metrics on real projective spaces. We further extend those results to a general reversible Markov operator and find applications in characterizing its essential spectrum.     

回归的能源有效网络大数据流汇聚算法研究

为了降低传感器网络数据流汇聚时的能源消耗,提出了一种基于回归的能源有效数据流汇聚算法。首先,将传感器节点分为活跃节点和能源有效节点。然后,以活跃节点为中心点将所有节点进行聚类,并应用回归方法通过活跃节点的数据流对能源有效节点的数据进行预测。接下来,通过节点预测值的累积误差不断修正活跃节点集。最后,应用活跃节点的数据流信息对能源有效节点的数据进行预测。实验表明,本文提出的算法与其它相关算法相比具有更好的预测准确性。