A data mining and ab initio study of the interaction between the aromatic and backbone amide groups in proteins

Interactions between aromatic groups and backbone amide groups in protein environments are characterized both through data mining analyses of X‐ray protein structures and through ab initio molecular orbital calculations on a model complex. The data mining analyses of 1029 X‐ray protein structures elucidate the configurational characteristics of the interaction as well as the positions of the interacting moieties involved. On a statistical average, more than seven such interactions occur in a typical protein structure. The configurations of these interactions are restricted with the face‐to‐face orientation as the preferred arrangement. The interaction occurs mainly within a single peptide chain. Both α‐helix and β‐strand secondary structures provide an almost equal number of backbone amides to participate within this interaction. The interaction energy was evaluated with the supermolecular ab initio method at the MP2 level. It is shown that aromatic–amide(backbone) interactions identified in proteins can achieve a stabilization energy of 3.3 kcal/mol. The interaction involves the entirety of the backbone amide rather than only its amine portion. This study concludes that the interaction between aromatic and backbone amide groups is of general significance to protein structure due to its strength and common occurrence. © 2000 John Wiley & Sons, Inc. Int J Quant Chem 80: 44–60, 2000     

典型煤的近红外反射光谱特征的方向性研究

高光谱遥感是煤矿区探测的有效方法,对于煤炭资源调查、矿区环境监测等具有重要意义,其中煤、矸石、植被、水体等被遥测物各个方向的反射光谱特征是煤矿高光谱遥感的基础,为此有必要针对典型煤的方向反射光谱特征进行研究.从我国不同矿区收集了无烟煤、烟煤、褐煤三大类煤中的4种典型煤样,4种煤样按煤阶由高到低顺序包括无烟煤一号、贫煤、...     

A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.     

Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task

Research on the functioning of human cognition has been a crucial problem studied for years. Electroencephalography (EEG) classification methods may serve as a precious tool for understanding the temporal dynamics of human brain activity, and the purpose of such an approach is to increase the statistical power of the differences between conditions that are too weak to be detected using standard EEG methods. Following that line of research, in this paper, we focus on recognizing gender differences in the functioning of the human brain in the attention task. For that purpose, we gathered, analyzed, and finally classified event-related potentials (ERPs). We propose a hierarchical approach, in which the electrophysiological signal preprocessing is combined with the classification method, enriched with a segmentation step, which creates a full line of electrophysiological signal classification during an attention task. This approach allowed us to detect differences between men and women in the P3 waveform, an ERP component related to attention, which were not observed using standard ERP analysis. The results provide evidence for the high effectiveness of the proposed method, which outperformed a traditional statistical analysis approach. This is a step towards understanding neuronal differences between men’s and women’s brains during cognition, aiming to reduce the misdiagnosis and adverse side effects in underrepresented women groups in health and biomedical research.     

The Discovery of Actinospene,a New Polyene Macrolide with Broad Activity against Plant Fungal Pathogens and Pathogenic Yeasts

Phytopathogenic fungi infect crops, presenting a worldwide threat to agriculture. Polyene macrolides are one of the most effective antifungal agents applied in human therapy and crop protection. In this study, we found a cryptic polyene biosynthetic gene cluster in Actinokineospora spheciospongiae by genome mining. Then, this gene cluster was activated via varying fermentation conditions, leading to the discovery of new polyene actinospene (1), which was subsequently isolated and its structure determined through spectroscopic techniques including UV, HR-MS, and NMR. The absolute configuration was confirmed by comparing the calculated and experimental electronic circular dichroism (ECD) spectra. Unlike known polyene macrolides, actinospene (1) demonstrated more versatile post-assembling decorations including two epoxide groups and an unusual isobutenyl side chain. In bioassays, actinospene (1) showed a broad spectrum of antifungal activity against several plant fungal pathogens as well as pathogenic yeasts with minimum inhibitory concentrations ranging between 2 and 10 μg/mL.     

Genome Mining of Pseudomonas Species: Diversity and Evolution of Metabolic and Biosynthetic Potential

Microbial genome sequencing has uncovered a myriad of natural products (NPs) that have yet to be explored. Bacteria in the genus Pseudomonas serve as pathogens, plant growth promoters, and therapeutically, industrially, and environmentally important microorganisms. Though most species of Pseudomonas have a large number of NP biosynthetic gene clusters (BGCs) in their genomes, it is difficult to link many of these BGCs with products under current laboratory conditions. In order to gain new insights into the diversity, distribution, and evolution of these BGCs in Pseudomonas for the discovery of unexplored NPs, we applied several bioinformatic programming approaches to characterize BGCs from Pseudomonas reference genome sequences available in public databases along with phylogenetic and genomic comparison. Our research revealed that most BGCs in the genomes of Pseudomonas species have a high diversity for NPs at the species and subspecies levels and built the correlation of species with BGC taxonomic ranges. These data will pave the way for the algorithmic detection of species- and subspecies-specific pathways for NP development.     

Community detection in social networks based on improved Label Propagation Algorithm and balanced link density

Nowadays, community detection has been raised as one of the key research areas in the online social networks mining. One of the most common algorithms in this field is label propagation algorithm (LPA). Even though the LPA method has advantages such as simplicity in understanding and implementation, as well as linear time complexity, it has an important disadvantage of the uncertainty and instability in outcomes, that is, the algorithm detects and reports different combinations of communities in each run. This problem originates from the nature of random selection in the LPA method. In this paper, a novel method is proposed based on the LPA method and the inherent structure, that is, link density feature, of the input network. The proposed method uses a sensitivity parameter (balance parameter); by choosing the appropriate values for it, the desired qualities of the identified communities can be achieved. The proposed method is called Balanced Link Density-based Label Propagation (BLDLP). In comparison with the basic LPA, the proposed method has an advantage of certainty and stability in the output results, whereas its time complexity is still comparable with the basic LPA and of course lowers than many other approaches. The proposed method has been evaluated on real-world known datasets, such as the Facebook social network and American football clubs, and by comparing it with the basic LPA, the effectiveness of the proposed method in terms of the quality of the communities found and the time complexity has been shown.     

工矿业城市区域水质参数高光谱定量反演

工矿业城市区域易受工业活动、矿产开采影响,使其水环境遭受不同程度的破坏,以至于水体污染问题突出。当前常规水质监测主要采用“以点代面”的工作方式进行野外采样及其室内化验分析,然而环境复杂多变,空间差异大,导致调查点代表性受限,整体精度不高,效率低下,更难以实现区域性动态监测。以因矿兴市的矿业重镇湖北黄石大冶市为研究区,同步开展无人机高光谱航飞、地面光谱测量和水体样品采集测试,分别获得具有49个波段的高光谱影像数据和光谱分辨率为1 nm的水体光谱曲线,其中影像数据波谱范围为505~890 nm,光谱分辨率为7.78 nm,空间分辨率为30 cm。对获取的高光谱影像和光谱数据剔除异常值、光谱定标、辐射校正等预处理后,对比分析研究区内水体的不同光谱吸收、反射及光谱曲线形态特征信息,从而提取出高光谱影像和测量光谱的反射光谱曲线形态特征、去包络线后光谱曲线形态特征、三阶求导后光谱曲线形态特征和光谱四值编码共四大类25个光谱特征。采用皮尔森相关系数分析样品水质参数与光谱特征之间的相关性,以此筛选出存在显著相关的水质参数与光谱特征。在此基础上,采用逐步回归分析方法筛选出最大反射率及其波长位置、对称度、光谱编码Ⅲ、三阶导最大及最小值等光谱特征作为模型变量,构建出水质参数的多元线性反演模型,并对模型进行F检验和t检验。将检验后的反演模型推广运用于研究区内高光谱影像,获得尾矿库、河流、湖泊等典型区域的水质参数反演结果,从而实现“由点到面”水质参数信息的快速获取。结果显示水质参数pH、硬度（Ca2++Mg2+）、钾与氯离子比值（K+/Cl-）、镁与碱度比值[Mg2+/(HCO3-+CO2-₃)]的反演精度较高,其pH的判定系数R2最小为0.669,镁与碱度比值的判定系数R2最大为0.895,相对均方根误差均小于28%;而总溶解固体（TDS）反演精度较低,其判定系数R2仅为0.463,相对均方根误差达36.762%。提出了一种基于光谱曲线形态特征的高光谱遥感水质参数定量反演模型方法,实现了pH值、硬度、镁离子与碱度之比等水质参数的高光谱定量反演,为区域水环境动态监测提供了新方法。     

煤炭矿区植被冠层光谱土地复垦敏感性分析

煤矿区土地复垦及复垦监测工作,对于我国土地利用和生态环境治理具有重要意义。微生物复垦技术能够促进植物吸收利用矿质养分和水分,增强土壤肥力,对矿区生态恢复具有显著作用。监测和评价土地复垦效应对植物生长影响的传统方法,通常采用野外采集植物和土壤样本并进行室内分析,但这些方法不仅破坏植物根系原状土壤,造成植株损伤,而且耗费人力、物力,时效性差。高光谱遥感技术具有数据获取速度快、信息量大、精度高且无须离体破坏植株等优点,对于土地复垦监测有非常大的潜力。目前,土地复垦效应遥感监测相关研究仍以观测盆栽大豆、玉米等作物的叶片光谱分析为主。实际上,卫星遥感数据观测到的是冠层光谱,并非叶片光谱,但目前还没有通过植被冠层光谱对矿区土地复垦进行监测的研究成果出现。植被冠层光谱不仅受到叶片光谱的影响,还受到植株长势、下垫面等其他因素的影响,光谱特征变化更为复杂。矿区植被冠层光谱特征对于土地复垦效应的敏感度分析,是对矿区植被理化参量进行定量反演的基础,也是限制高光谱技术应用于大面积土地复垦监测的主要瓶颈。于煤炭矿区土地复垦实验基地开展野外冠层光谱观测实验,获取了接菌组和对照组野外植株冠层光谱数据,并从光谱波形变化和光谱特征参量变化两方面综合分析了植被冠层光谱对土地复垦的敏感性。冠层光谱波形方面,分别采用标准差和光谱敏感度作为组内和组间光谱波形差异的有效指标;冠层光谱特征参量方面,选取了植被红边、黄边、蓝边、绿峰、红谷等典型光谱特征,计算获取其位置、斜率、面积等特征参量,并通过描述性统计和单因素方差分析研究了这些冠层光谱特征参量对土地复垦效应的敏感性,挑选出矿区土地复垦监测的有效特征参量。研究表明,接菌组和对照组冠层光谱的主要波形变化趋势一致,但接菌组植株的生长状况更稳定,不同植株之间差异较小,且绿峰和红谷两个特征更突出。这说明土地复垦能够减少植株间冠层光谱差异,增强植被典型光谱特征,而绿峰和红谷对土地复垦有较高的光谱敏感度。光谱特征参量方面,绿峰、红谷、红边波长在土地复垦作用下显著向长波方向移动,而此前叶片光谱研究中对土地复垦较敏感的红边、蓝边斜率变化并不显著。这说明,野外植被冠层光谱分析结果与实验室植被叶片光谱分析的结果并不完全一致,这可能和植被类型、生长周期、土壤背景光谱干扰等因素相关。在采用卫星或航拍遥感数据进行矿区植被环境监测时,所获取的都是植被冠层光谱,因此本研究所得到的结论具有更强的参考意义和实际应用价值。     

基于多支持度的增量式关联规则挖掘算法

传统的基于关联规则的挖掘算法采用的是统一的最小支持度,但是在实际的事务数据库中数据项的重要性是不同的。针对目前多支持度和增量式关联规则更新维护的局限性,提出一种基于多支持度的增量式关联规则挖掘算法。允许用户根据不同项的重要性设置权值,有利于发现更多有趣的规则。采用矩阵的向量内积策略,结合动态剪枝,无需多次扫描事务数据库,不生成庞大候选集。实验结果验证了算法的有效性。更多还原