Assessing hydrologic alteration: Evaluation of different alternatives according to data availability

The natural flow regime of rivers across the world has been largely modified. Understanding the extent to which the flow regime deviates from natural conditions is necessary for designing sound management and restoration measures. In this regard, ‘Indicators of Hydrologic Alteration’ is currently considered one of the most effective approaches for assessing hydrologic alteration (HA). However, several generalized drawbacks such as the climatic variability between the pre- and post-impacted series and the scarcity of hydrological data in many impaired rivers should be addressed. In this study, a protocol with the following five alternative designs based on data availability is presented: (1) Paired-Before–After Control–Impact (BACIP), (2) Before–After (BA), (3) Control–Impact (CI), (4) Hydrological Classification (HC) and (5) Predicted Hydrological indices (HP). BACIP compares the status of the impacted gauge before and after the perturbation is started, in addition to controlling for natural climatic changes. Hence, it has been considered as the reference benchmark for all other designs. When this protocol was applied to 11 reservoirs situated in the northern third of the Iberian Peninsula, the BA design was able to correctly identify most of the non-significant HA but failed in almost one quarter of the significant alterations. Similarly, BACIP and CI showed an agreement of >80%. This suggests that the method is suitable when proper data are unavailable for BACIP or BA. In addition, our results indicated that the critical thresholds for HA varied depending on the hydrological index being considered. Significant HAs ranged from <5% for the number of days with increasing and decreasing flows to >64% for the duration of low-flow pulses. To delineate adequate thresholds, further research combining hydrological analyses with the biological response to the HA is warranted. Finally, the application of HC and HP designs revealed a significant degree of uncertainty related to the intra-class variability and the predictive error of the models. Therefore, 25% of the analysis could not be evaluated. However, in the evaluable cases, the HC and HP designs correctly assessed >75% of the HA, which highlighted the potential of this method in cases of scarce streamflow data.     

Machine learning approaches in MALDI-MSI: clinical applications

Introduction: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging.

Areas covered: This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing.

Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data.     

注册分类改革视角下药品数据保护制度效应分析

在国际趋势的压力下,兼受跨太平洋伙伴关系协定（TPP）影响,我国实施药品数据保护制度已成大势所趋。鉴于药品数据保护与药品注册审批体系紧密相关,我国化学药注册分类改革后,药品数据保护对医药产业发展必然会带来不同影响。结合我国分类注册改革新标准,在理论上对数据保护制度实施效果进行研究,进而探讨新注册分类下数据保护的实施对1至4类药品及医药产业的影响。     

利用SRAP标记分析贵州砂梨资源遗传多样性

采用SRAP标记技术,对贵州59份砂梨种质的遗传多样性进行分析。结果表明:(1)筛选的15对SRAP引物共扩增出151个位点,其中120个是多态性位点,多态百分率为79.47%。(2)POPGENE分析表明,贵州砂梨资源在物种水平上有效等位基因数(Ne)为1.355 9,Nei’s基因多样性指数(H)为0.216 9,Shannon’s信息指数(I)为0.336 2;在区域品种群水平Ne=1.261 1,H=0.155 5,I=0.235 2,以黔西南地区资源的遗传多样性最高,六盘水市最低。(3)聚类分析显示,在遗传相似系数0.75处,可将59份砂梨资源分为6个组,聚类分析结果具有一定的地理区域特性。研究认为,贵州砂梨种质具有较为丰富的遗传多样性,并表现出明显的地理区域特性;SRAP标记与ISSR标记在揭示贵州砂梨种质遗传多样性上具有高度的一致性,但SRAP标记能在更小的变异范围内揭示更多的遗传信息。     

藏区金腰种质资源调查与分析

金腰属植物因富含黄酮醇类化合物而具有显著的药效,通过对我国西藏、云南、四川、青海以及甘肃5个省(自治区)进行金腰属植物种质资源的科学考察,共收集到19种(含2变种)145份野生种质资源。此外四川省发现金腰新分布2种,分别为秦岭金腰(Chrysosplenium biondianum Engl.)、陕甘金腰(Chrysosplenium qinlingense Z.P.Jien&J.T.Pan),均为中国特有种;甘肃省发现金腰新分布3种,分别为毛金腰(Chrysosplenium pilosum Maxim.)、绵毛金腰(Chrysosplenium lanuginosum Hook.f.&Thomson)和蜕叶金腰(Chrysosplenium henryi Franch.),凭证标本存于中南民族大学植物标本馆(HSN)。金腰属植物作为传统藏药长期以来被用于治疗各种疾病,如长梗金腰(Chrysosplenium axillare Maxim.)的75%乙醇提取物具抗炎症、抗流感病毒活性,从灰花金腰(Chrysosplenium grayanum Maxim.)与裸茎金腰(Chrysosplenium nudicaule Bunge)提取的五环三萜对肿瘤细胞有较强抑制效果。本调查丰富了野生藏药金腰属植物种质资源,为深入研究金腰属植物的分类与药理活性提供了基础。     

The Urfold: Structural similarity just above the superfold level?

We suspect that there is a level of granularity of protein structure intermediate between the classical levels of “architecture” and “topology,” as reflected in such phenomena as extensive three‐dimensional structural similarity above the level of (super)folds. Here, we examine this notion of architectural identity despite topological variability, starting with a concept that we call the “Urfold.” We believe that this model could offer a new conceptual approach for protein structural analysis and classification: indeed, the Urfold concept may help reconcile various phenomena that have been frequently recognized or debated for years, such as the precise meaning of “significant” structural overlap and the degree of continuity of fold space. More broadly, the role of structural similarity in sequence?structure?function evolution has been studied via many models over the years; by addressing a conceptual gap that we believe exists between the architecture and topology levels of structural classification schemes, the Urfold eventually may help synthesize these models into a generalized, consistent framework. Here, we begin by qualitatively introducing the concept.     

Distributional dynamics of a specialized subterranean community oppose the classical understanding of the preferred subterranean habitats

Troglobionts are organisms that are specialized for living in a subterranean environment. These organisms reside prevalently in the deepest zones of caves and in shallow subterranean habitats, and complete their entire life cycles therein. Because troglobionts in most caves depend on organic matter resources from the surface, we hypothesized that they would also select the sections of caves nearest the surface, as long as environmental conditions were favorable. Over 1 year, we analyzed, in monthly intervals, the annual distributional dynamics of a subterranean community consisting of 17 troglobiont species, in relation to multiple environmental factors. Cumulative standardized annual species richness and diversity clearly indicated the existence of two ecotones within the cave: between soil and shallow subterranean habitats, inhabited by soil and shallow troglobionts; and between the transition and inner cave zones, where the spatial niches of shallow and deep troglobionts overlap. The mean standardized annual species richness and diversity showed inverse relationships, but both contributed to a better insight into the dynamics of subterranean fauna. Regression analyses revealed that temperatures in the range 7–10°C, high moisture content of substrate, large cross section of the cave, and high pH of substrate were the most important ecological drivers governing the spatiotemporal dynamics of troglobionts. Overall, this study shows general trends in the annual distributional dynamics of troglobionts in shallow caves and reveals that the distribution patterns of troglobionts within subterranean habitats may be more complex than commonly assumed.     

A Multiplexed Assay for Exon Recognition Reveals that an Unappreciated Fraction of Rare Genetic Variants Cause Large-Effect Splicing Disruptions

1. Download high-res image (142KB)
2. Download full-size image

     

Composition and ecological drivers of the kwongan scrub and woodlands in the northern Swan Coastal Plain,Western Australia

The nature of community patterns and environmental drivers in kwongan mediterranean‐type shrubland on nutrient‐poor soils occurring in Western Australia remain poorly examined. We aimed to determine whether (i) classification of the kwongan vegetation of the northern Swan Coastal Plain would be ecologically informative and (ii) which environmental drivers underpin the plant community patterns. The study area was positioned on the northern Swan Coastal Plain, locality of Cooljarloo (30°39′ S, 115°22′ E), situated 170 km north of Perth, Western Australia. Compositional (518 species × 337 relevés) and environmental data set (29 variables × 87 relevés) describing time since last fire, soil chemical and physical properties, and terrain characteristics were analysed using classification and ordination techniques. OptimClass assisted in the selection of a robust data transformation, resemblance function and clustering algorithm to identify the vegetation patterns. Major ecological drivers of the vegetation patterns were detected using distance‐based redundancy analysis (db‐RDA). Classification revealed major groupings of Wet Heath and Banksia Woodland distinguishable by the high prevalence of myrtyoid and proteoid taxa, respectively. On floristic‐sociological grounds, we recognised four Wet Heath and two Banksia Woodland communities. The Wet Heath was constrained to areas of higher litter depth (db‐RDA axis 1: 9%). Soil chemical and physical properties explained the highest proportion (17%) of the compositional variance, while the terrain‐ and fire‐related variables explained 2% and <0.001%, respectively. While fire explained little compositional variance overall, a separate db‐RDA analysis found that it may play an important pattern‐structuring role within Banksia Woodlands. Fine‐scale compositional patterns correspond only to a small extent to environmental data; the substantial unexplained variance may be due to slow‐acting neutral and stochastic processes.