Classification of the heterogeneous structure of urban landscapes (STURLA) as an indicator of landscape function applied to surface temperature in New York City

Defining landscape structure and key relationships between landscape structure and function is challenging in urban areas characterized by density and patchy spatial patterns. In order to trace the spatial and temporal patterns of urban landscape structures, compare patterns across cities, or inform urban design principles, we need to classify the landscape in a way that captures context and landscape heterogeneity, but can be broadly applied across different cities or landscape variations within a city. In this study, we introduce a simple and reproducible approach for classifying the structure of urban landscapes (STURLA) that utilizes heterogeneous, composite classes which represent combinations of built and natural features, and examine the response of a landscape function – surface temperature.This classification approach is unique in that it develops composite (as opposed to homogeneous) classes, which are defined a posteriori, based on compositions of adjacent structural elements that emerge in the urban landscape, using a cellular grid to define units of analysis. We test the separability of classes that emerge from this approach, and find that it is possible to discern classes – comprised of the mix of land and building covers common in urban areas – which have meaningfully distinct temperature signatures. This classification approach may be extended to multiple cities and ecological indicators in order to offer insight into the relationship between urban landscape structure and ecosystem response, in a way that accounts for interactions among different types of urban landscape surfaces. We suggest that this approach can support spatial prioritization of landscape function needs in urban development and design approaches for improving particular types of functioning, such as reductions in urban heat.     

Harmony Search Algorithm for Word Sense Disambiguation

Word Sense Disambiguation (WSD) is the task of determining which sense of an ambiguous word (word with multiple meanings) is chosen in a particular use of that word, by considering its context. A sentence is considered ambiguous if it contains ambiguous word(s). Practically, any sentence that has been classified as ambiguous usually has multiple interpretations, but just one of them presents the correct interpretation. We propose an unsupervised method that exploits knowledge based approaches for word sense disambiguation using Harmony Search Algorithm (HSA) based on a Stanford dependencies generator (HSDG). The role of the dependency generator is to parse sentences to obtain their dependency relations. Whereas, the goal of using the HSA is to maximize the overall semantic similarity of the set of parsed words. HSA invokes a combination of semantic similarity and relatedness measurements, i.e., Jiang and Conrath (jcn) and an adapted Lesk algorithm, to perform the HSA fitness function. Our proposed method was experimented on benchmark datasets, which yielded results comparable to the state-of-the-art WSD methods. In order to evaluate the effectiveness of the dependency generator, we perform the same methodology without the parser, but with a window of words. The empirical results demonstrate that the proposed method is able to produce effective solutions for most instances of the datasets used.     

Risk estimation and risk prediction using machine-learning methods

After an association between genetic variants and a phenotype has been established, further study goals comprise the classification of patients according to disease risk or the estimation of disease probability. To accomplish this, different statistical methods are required, and specifically machine-learning approaches may offer advantages over classical techniques. In this paper, we describe methods for the construction and evaluation of classification and probability estimation rules. We review the use of machine-learning approaches in this context and explain some of the machine-learning algorithms in detail. Finally, we illustrate the methodology through application to a genome-wide association analysis on rheumatoid arthritis.     

生态系统服务建模技术研究进展

在生态系统服务评估模型的数量、类型及应用大量增加的背景下,为将生态系统服务评估有效整合到决策中,系统比较、甄别不同建模工具并筛选出适合决策需求的生态系统服务评估和模拟方法尤为必要。因此,归纳并总结了国内外现有的生态系统服务评估模型的建模技术,包括:相关关系法、生物-物理过程法以及专家知识法;分别对其原理、差异、优缺点以及适用性进行了详尽阐释。大多数相关模型侧重于统计关系,相对容易创建和扩展,适用于生态系统服务的初始评估;生物-物理过程模型难以构建且不易获取,但提供了探索人-地系统相互作用和长期变化的有效机制;专家知识法有效结合了多种类型的知识体系,关注人类社会与自然系统之间反馈和交互动态的系统整合,但当评估地点发生变化时难以验证。在此基础上,介绍了基于上述3种建模技术的典型生态系统服务综合评估模型的发展和应用现状。各类建模技术面临着实用性和科学准确性之间的权衡。通过对不同建模技术的梳理与整合分析旨在提升当前生态系统服务研究的决策支撑能力,并为国内相关研究提供参考和借鉴。     

How Distance Affects Semantic Integration in Discourse: Evidence from Event-Related Potentials

Event-related potentials were used to investigate whether semantic integration in discourse is influenced by the number of intervening sentences between the endpoints of integration. Readers read discourses in which the last sentence contained a critical word that was either congruent or incongruent with the information introduced in the first sentence. Furthermore, for the short discourses, the first and last sentence were intervened by only one sentence while for the long discourses, they were intervened by three sentences. We found that the incongruent words elicited an N400 effect for both the short and long discourses. However, a P600 effect was only observed for the long discourses, but not for the short ones. These results suggest that although readers can successfully integrate upcoming words into the existing discourse representation, the effort required for this integration process is modulated by the number of intervening sentences. Thus, discourse distance as measured by the number of intervening sentences should be taken as an important factor for semantic integration in discourse.     

Topic Structure Affects Semantic Integration: Evidence from Event-Related Potentials

This study investigated whether semantic integration in discourse context could be influenced by topic structure using event-related brain potentials. Participants read discourses in which the last sentence contained a critical word that was either congruent or incongruent with the topic established in the first sentence. The intervening sentences between the first and the last sentence of the discourse either maintained or shifted the original topic. Results showed that incongruent words in topic-maintained discourses elicited an N400 effect that was broadly distributed over the scalp while those in topic-shifted discourses elicited an N400 effect that was lateralized to the right hemisphere and localized over central and posterior areas. Moreover, a late positivity effect was only elicited by incongruent words in topic-shifted discourses, but not in topic-maintained discourses. This suggests an important role for discourse structure in semantic integration, such that compared with topic-maintained discourses, the complexity of discourse structure in topic-shifted condition reduces the initial stage of semantic integration and enhances the later stage in which a mental representation is updated.     

Chaos game representation of gene structure.

This paper presents a new method for representing DNA sequences. It permits the representation and investigation of patterns in sequences, visually revealing previously unknown structures. Based on a technique from chaotic dynamics, the method produces a picture of a gene sequence which displays both local and global patterns. The pictures have a complex structure which varies depending on the sequence. The method is termed Chaos Game Representation (CGR). CGR raises a new set of questions about the structure of DNA sequences, and is a new tool for investigating gene structure.     

A comparison and user-based evaluation of models of textual information structure in the context of cancer risk assessment

Background  

Many practical tasks in biomedicine require accessing specific types of information in scientific literature; e.g. information about the results or conclusions of the study in question. Several schemes have been developed to characterize such information in scientific journal articles. For example, a simple section-based scheme assigns individual sentences in abstracts under sections such as Objective, Methods, Results and Conclusions. Some schemes of textual information structure have proved useful for biomedical text mining (BIO-TM) tasks (e.g. automatic summarization). However, user-centered evaluation in the context of real-life tasks has been lacking.     

A review of wetland inventory and classification in Australia

Studies of wetlands in Australia, as in other countries, have taken a wide variety of approaches to defining, surveying and classifying these environments. Past and current approaches in Australia are reviewed for each of the States and Territories which provide the context for much of the natural resource investigation in the country. While there are obvious advantages of national, and perhaps international, agreement on definition and types of wetlands, a variety of approaches to inventory and classification will always be necessary for particular purposes. More fundamental than general agreement on approaches is the need for wetland scientists and managers to maximise the accuracy of survey information, to test the assumptions involved in the use of classifications, and to ensure that the classifications they use are the most appropriate for their purposes. The issue of a global wetland classification scheme is discussed on the basis of a representative range of views by Australian wetland workers.     

Palynology of Acmadenia (Diosminae: Rutaceae) and its taxonomic implications

The palynology of Acmadenia (Diosminae: Rutaceae), a taxonomically problematic genus, was investigated to determine its taxonomic significance. Pollen of 32 of the 33 species, and one undescribed species, was investigated by LM, SEM and TEM techniques. Exine structure is extremely variable in proportion to the size of the genus, with seven distinct types and four subtypes being discerned. Species groupings elucidated by the pollen types suggest relationships between species which were not previously apparent. Pollen data supports morphological evidence for the re‐classification of Acmadenia. A redefined Acmadenia is proposed to consist of 23 species, whereas the remainder of the taxa should be referred to new or closely related existing genera.     

Structural Classification of Wild Boar (Sus scrofa) Vocalizations

Determining whether a species' vocal communication system is graded or discrete requires definition of its vocal repertoire. In this context, research on domestic pig (Sus scrofa domesticus) vocalizations, for example, has led to significant advances in our understanding of communicative functions. Despite their close relation to domestic pigs, little is known about wild boar (Sus scrofa) vocalizations. The few existing studies, conducted in the 1970s, relied on visual inspections of spectrograms to quantify acoustic parameters and lacked statistical analysis. Here, we use objective signal processing techniques and advanced statistical approaches to classify 616 calls recorded from semi‐free ranging animals. Based on four spectral and temporal acoustic parameters—quartile Q25, duration, spectral flux, and spectral flatness—extracted from a multivariate analysis, we refine and extend the conclusions drawn from previous work and present a statistically validated classification of the wild boar vocal repertoire into four call types: grunts, grunt‐squeals, squeals, and trumpets. While the majority of calls could be sorted into these categories using objective criteria, we also found evidence supporting a graded interpretation of some wild boar vocalizations as acoustically continuous, with the extremes representing discrete call types. The use of objective criteria based on modern techniques and statistics in respect to acoustic continuity advances our understanding of vocal variation. Integrating our findings with recent studies on domestic pig vocal behavior and emotions, we emphasize the importance of grunt‐squeals for acoustic approaches to animal welfare and underline the need of further research investigating the role of domestication on animal vocal communication.     

真核生物转座子鉴定和分类计算方法

重复序列是真核生物基因组的重要组成成分,根据其序列特征及在基因组中的存在形式,可以进一步分为串联重复、片段重复和散在重复。其中,散在重复大多起源于转座子。根据转座介质的不同,转座子又可分为DNA和逆转录转座子。转座子的转座和扩增对基因的进化和基因组的稳定具有显著的影响;同时与其他类型的重复序列相比,转座子的结构和分类更为复杂多样,使得对转座子的鉴定和分类更为复杂和困难。鉴于此,文章简要概括了转座子的功能及分类,总结了真核生物转座子鉴定、分类和注释的3个步骤:(1)重复序列库的构建;(2)重复序列的校正和分类;(3)基因组注释。着重介绍了每一步骤所采用的不同计算方法,比较了不同方法的优缺点。只有把多种方法结合起来使用才能实现全基因组转座子的精确鉴定、分类和注释,这将为转座子的全基因组鉴定和分类提供借鉴意义。     

QuateXelero: An Accelerated Exact Network Motif Detection Algorithm

Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks’ structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network.     

Weakly supervised learning of information structure of scientific abstracts--is it accurate enough to benefit real-world tasks in biomedicine?

MOTIVATION: Many practical tasks in biomedicine require accessing specific types of information in scientific literature; e.g. information about the methods, results or conclusions of the study in question. Several approaches have been developed to identify such information in scientific journal articles. The best of these have yielded promising results and proved useful for biomedical text mining tasks. However, relying on fully supervised machine learning (ml) and a large body of annotated data, existing approaches are expensive to develop and port to different tasks. A potential solution to this problem is to employ weakly supervised learning instead. In this article, we investigate a weakly supervised approach to identifying information structure according to a scheme called Argumentative Zoning (az). We apply four weakly supervised classifiers to biomedical abstracts and evaluate their performance both directly and in a real-life scenario in the context of cancer risk assessment. RESULTS: Our best weakly supervised classifier (based on the combination of active learning and self-training) performs well on the task, outperforming our best supervised classifier: it yields a high accuracy of 81% when just 10% of the labeled data is used for training. When cancer risk assessors are presented with the resulting annotated abstracts, they find relevant information in them significantly faster than when presented with unannotated abstracts. These results suggest that weakly supervised learning could be used to improve the practical usefulness of information structure for real-life tasks in biomedicine.     

Adapting lacunarity techniques for gradient-based analyses of landscape surfaces

Typically, landscapes are modeled in the form of categorical map patterns, i.e. as mosaics made up of basic elements which are presumed to possess sharp and well-defined boundary lines. Many landscape ecological concepts are based upon this perception. In reality, however, the spatial value progressions of environmental parameters tend to be “gradual” rather than “abrupt”. Therefore, gradient approaches have shifted to the forefront of scientific interest recently. Appropriate methods are needed for the implementation of such approaches. Lacunarity analysis may provide a suitable starting point in this context. We propose adapted versions of standard lacunarity techniques for analyzing ecological gradients in general and the heterogeneity of physical landscape surfaces in particular. A simple way of customizing lacunarity analysis for quantifying the heterogeneity of digital elevation models is to use the value range for defining the box mass used in the calculation process. Furthermore, we demonstrate how lacunarity analysis can be combined with metrics derived from surface metrology, such as the “Average Surface Roughness”. Finally, the “classical” lacunarity approach is used in combination with simple landform indices. The methods are tested using different data sets, including high-resolution digital elevation models. In summary, lacunarity analysis is adopted in order to establish a gradient-based approach for terrain analysis and proves to be a valuable concept for comparing three-dimensional surface patterns in terms of their degree of “heterogeneity”. The proposed developments are meant to serve as a stimulus for making increased use of this simple but effective technique in landscape ecology. They offer a large potential for expanding the methodical spectrum of landscape structure analysis towards gradient-based approaches. Methods like lacunarity analysis are promising, since they do not rely on predefined landscape units or patches and thus enable ecologists to effectively deal with the complexity of natural systems.     

Multiple two-sample testing under arbitrary covariance dependency with an application in imaging mass spectrometry

Large-scale hypothesis testing has become a ubiquitous problem in high-dimensional statistical inference, with broad applications in various scientific disciplines. One relevant application is constituted by imaging mass spectrometry (IMS) association studies, where a large number of tests are performed simultaneously in order to identify molecular masses that are associated with a particular phenotype, for example, a cancer subtype. Mass spectra obtained from matrix-assisted laser desorption/ionization (MALDI) experiments are dependent, when considered as statistical quantities. False discovery proportion (FDP) estimation and  control under arbitrary dependency structure among test statistics is an active topic in modern multiple testing research. In this context, we are concerned with the evaluation of associations between the binary outcome variable (describing the phenotype) and multiple predictors derived from MALDI measurements. We propose an inference procedure in which the correlation matrix of the test statistics is utilized. The approach is based on multiple marginal models. Specifically, we fit a marginal logistic regression model for each predictor individually. Asymptotic joint normality of the stacked vector of the marginal regression coefficients is established under standard regularity assumptions, and their (limiting) correlation matrix is estimated. The proposed method extracts common factors from the resulting empirical correlation matrix. Finally, we estimate the realized FDP of a thresholding procedure for the marginal p-values. We demonstrate a practical application of the proposed workflow to MALDI IMS data in an oncological context.     

Structural genomics: correlation blocks, population structure, and genome architecture

An integration of the pattern of genome-wide inter-site associations with evolutionary forces is important for gaining insights into the genomic evolution in natural or artificial populations. Here, we assess the inter-site correlation blocks and their distributions along chromosomes. A correlation block is broadly termed as the DNA segment within which strong correlations exist between genetic diversities at any two sites. We bring together the population genetic structure and the genomic diversity structure that have been independently built on different scales and synthesize the existing theories and methods for characterizing genomic structure at the population level. We discuss how population structure could shape correlation blocks and their patterns within and between populations. Effects of evolutionary forces (selection, migration, genetic drift, and mutation) on the pattern of genome-wide correlation blocks are discussed. In eukaryote organisms, we briefly discuss the associations between the pattern of correlation blocks and genome assembly features in eukaryote organisms, including the impacts of multigene family, the perturbation of transposable elements, and the repetitive nongenic sequences and GC-rich isochores. Our reviews suggest that the observable pattern of correlation blocks can refine our understanding of the ecological and evolutionary processes underlying the genomic evolution at the population level.     

Genetic classification of populations using supervised learning

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case-control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available.In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.     

Derivation of regional crop sequences as an indicator for potential GMO dispersal on large spatial scales

A methodological approach is presented which aims to visualise the constraints for crop sequence planning in agriculture in a regional, large-scale context. In particular, the relationship between the scope of oilseed rape cultivation and the overall regional cropping structure, the share of particular farm types and the interactions between single crops have been analysed. The identified constraints have been applied to specify current and regionally typical crop sequences as input data for large-scale ex ante assessments, here exemplary for the genome dispersal risk in the case of GM oilseed rape cultivation.The regional and spatio-temporal variation of crop sequences for oilseed rape was analysed and generalised through a combination of analytical, classification and up-scaling techniques. In order to anticipate and assess the dispersal risks of transgenic oilseed rape, the methodology was tuned on crop sequences, which strongly influence the temporal dispersal of genetically modified oilseed rape. The regional cropping patterns for oilseed rape were analysed for the four northernmost German federal states: Schleswig-Holstein, Mecklenburg-Western Pomerania, Lower Saxony and Brandenburg. For typical regional crop clusters, specific crop sequences were derived, taking into account the constraints between crops and the weights for the particular crops as related to farm type. Real land-use data obtained at particular research sites were used to precisely determine the frequency of the single crops, as well as to discover sub-dominant crop combinations, which may have a high impact on dispersal processes. The introduced methodology stresses the following aspects: (i) reflection of the current situation due to links to periodically updated statistical data, (ii) implementation of the relationships and constraints between the different crops through statistical analyses, (iii) transfer of extensive, spatially limited agricultural data and expert knowledge to a large-scale context and (iv) integration of sub-dominant measures that are highly sensitive for particular processes.