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Notes on 113 fungal taxa are compiled in this paper, including 11 new genera, 89 new species, one new subspecies, three new combinations and xx reference specimens. A wide geographic and taxonomic range of fungal taxa are detailed. In the Ascomycota the new genera Angustospora (Testudinaceae), Camporesia (Xylariaceae), Clematidis, Crassiparies (Pleosporales genera incertae sedis), Farasanispora, Longiostiolum (Pleosporales genera incertae sedis), Multilocularia (Parabambusicolaceae), Neophaeocryptopus (Dothideaceae), Parameliola (Pleosporales genera incertae sedis), and Towyspora (Lentitheciaceae) are introduced. Newly introduced species are Angustospora nilensis, Aniptodera

BackgroundIn genomics, a commonly encountered problem is to extract a subset of variables out of a large set of explanatory variables associated with one or several quantitative or qualitative response variables. An example is to identify associations between codon-usage and phylogeny based definitions of taxonomic groups at different taxonomic levels. Maximum understandability with the smallest number of selected variables, consistency of the selected variables, as well as variation of model performance on test data, are issues to be addressed for such problems.ResultsWe present an algorithm balancing the parsimony and the predictive performance of a model. The algorithm is based on variable selection using reduced-rank Partial Least Squares with a regularized elimination. Allowing a marginal decrease in model performance results in a substantial decrease in the number of selected variables. This significantly improves the understandability of the model. Within the approach we have tested and compared three different criteria commonly used in the Partial Least Square modeling paradigm for variable selection; loading weights, regression coefficients and variable importance on projections. The algorithm is applied to a problem of identifying codon variations discriminating different bacterial taxa, which is of particular interest in classifying metagenomics samples. The results are compared with a classical forward selection algorithm, the much used Lasso algorithm as well as Soft-threshold Partial Least Squares variable selection.ConclusionsA regularized elimination algorithm based on Partial Least Squares produces results that increase understandability and consistency and reduces the classification error on test data compared to standard approaches.

Through the remarkable progress in technology, it is getting easier and easier to generate vast amounts of variables from a given sample. The selection of variables is imperative for data reduction and for understanding the modeled relationship. Partial least squares (PLS) regression is among the modeling approaches that address high throughput data. A considerable list of variable selection methods has been introduced in PLS. Most of these methods have been reviewed in a recently conducted study. Motivated by this, we have therefore conducted a comparison of available methods for variable selection within PLS. The main focus of this study was to reveal patterns of dependencies between variable selection method and data properties, which can guide the choice of method in practical data analysis. To this aim, a simulation study was conducted with data sets having diverse properties like the number of variables, the number of samples, model complexity level, and information content. The results indicate that the above factors like the number of variables, number of samples, model complexity level, information content and variant of PLS methods, and their mutual higher‐order interactions all significantly define the prediction capabilities of the model and the choice of variable selection strategy.

ABSTRACT. Pastoralism and predation are two major concomitantly known facts and matters of concern for conservation biologists worldwide. Pastoralist-predator conflict constitutes a major social-ecological concern in the Pamir mountain range encompassing Afghanistan, Pakistan, and Tajikistan, and affects community attitudes and tolerance toward carnivores. Very few studies have been conducted to understand the dynamics of livestock predation by large carnivores like snow leopards (Panthera uncia) and wolves (Canis lupus), owing to the region's remoteness and inaccessibility. This study attempts to assess the intensity of livestock predation (and resulting perceptions) by snow leopards and wolves across the Afghani, Pakistani, and Tajik Pamir range during the period January 2008-June 2012. The study found that livestock mortality due to disease is the most serious threat to livestock (an average 3.5 animal heads per household per year) and ultimately to the rural economy (an average of US$352 per household per year) as compared to predation (1.78 animal heads per household per year, US$191) in the three study sites. Overall, 1419 (315 per year) heads of livestock were reportedly killed by snow leopards (47%) and wolves (53%) in the study sites. People with comparatively smaller landholdings and limited earning options, other than livestock rearing, expressed negative attitudes toward both wolves and snow leopards and vice versa. Education was found to be an effective solution to dilute people's hatred for predators. Low public tolerance of the wolf and snow leopard in general explained the magnitude of the threat facing predators in the Pamirs. This will likely continue unless tangible and informed conservation measures like disease control and predation compensation programs are taken among others.

Advances in technology make it happen to have massive amount of information in the form of multiple variables per object. The use of multivariate approaches for modeling the real-life phenomena is natural in such situation. There are numerous multivariate approaches in the literature, and its a challenge to stay updated on the possibilities. Partial least squares (PLSs) are one of the many modeling approaches for high-throughput data, and its use in different fields to address the variety of problems has been increased in recent years. We therefore present an overview of PLS's applications. The objective of this paper is to give a comprehensive overview on the advances in PLS algorithm together with its applications for regression, classification, variable selection, and survival analysis problems covering genomics, chemometrics, neuroinformatics, process control, computer vision, econometric, environmental studies, and so on. We have mainly presented different PLS approaches and their applications, so that the reader can easily get an understanding of possibility to use PLS for their own field. For further reading, literature references together with software availability are provided. Figure 2. Illustration of partial least squares (PLS) algorithm, and how to use it for regression, classification, variable selection, and survival analysis, is presented. For regression, trained regression coefficients together with test data provide the fitted response, while variable selection is actually to find the subset of X. In classification and survival analysis, usually, the influential PLS scores are respectively used with linear discriminant analysis (LDA) or with quadratic discriminant analysis (QDA) and proportional hazard regression.

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