Habitat fragmentation and extinction rates within freshwater fish communities: a faunal relaxation approach

Aim To estimate population extinction rates within freshwater fish communities since the fragmentation of palaeo‐rivers due to sea level rise at the end of the Pleistocene; to combine this information with rates estimated by other approaches (population surveys, fossil records); and to build an empirical extinction–area relationship. Location Temperate rivers from the Northern Hemisphere, with a special focus on rivers discharging into the English Channel, in north‐western France. Methods (1) French rivers. We used a faunal relaxation approach to estimate extinction rates in coastal rivers after they became isolated by the sea level rise. Tributaries within the Seine were used to build a species–area relationship for a non‐fragmented river system to predict species richness in coastal rivers before their fragmentation. (2) Other rivers. Extinction rates obtained for four other Holarctic river systems fragmented at the end of the Pleistocene, the fragmented populations of one salmonid species (Japan) and the fossil records from the Mississippi Basin were included in the study. Results (1) French rivers. Within strictly freshwater fish species, rare and/or habitat specialist species were the most affected by fragmentation. In contrast, euryhaline species were not affected. A negative relationship between extinction rate and river basin size was observed. (2) Other rivers. Our study established a common scaling relationship for freshwater fish population extinction rates that spans seven orders of magnitude in river basin size. Main conclusions This study strongly suggests that extinctions of fish populations occurred within French coastal rivers after they became isolated 8000 years ago. The patterns observed at regional and inter‐continental scales are consistent with the expectation that large populations are less prone to extinction than small ones, resulting in a strong extinction–area relationship coherent over a large spatio‐temporal scale. Our study is the first multi‐scale quantitative assessment of background extinction patterns for freshwater fishes.     

Prediction of protein submitochondria locations based on data fusion of various features of sequences

In this study, the predictors are developed for protein submitochondria locations based on various features of sequences. Information about the submitochondria location for a mitochondria protein can provide much better understanding about its function. We use ten representative models of protein samples such as pseudo amino acid composition, dipeptide composition, functional domain composition, the combining discrete model based on prediction of solvent accessibility and secondary structure elements, the discrete model of pairwise sequence similarity, etc. We construct a predictor based on support vector machines (SVMs) for each representative model. The overall prediction accuracy by the leave-one-out cross validation test obtained by the predictor which is based on the discrete model of pairwise sequence similarity is 1% better than the best computational system that exists for this problem. Moreover, we develop a method based on ordered weighted averaging (OWA) which is one of the fusion data operators. Therefore, OWA is applied on the 11 best SVM-based classifiers that are constructed based on various features of sequence. This method is called Mito-Loc. The overall leave-one-out cross validation accuracy obtained by Mito-Loc is about 95%. This indicates that our proposed approach (Mito-Loc) is superior to the result of the best existing approach which has already been reported.     

INDI: a computational framework for inferring drug interactions and their associated recommendations

Inferring drug–drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP‐related DDIs (along with their associated CYPs) and pharmacodynamic, non‐CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver‐operating characteristic curve)?0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co‐administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/～bnet/software/INDI , facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike.     

A brief review of computational gene prediction methods

With the development of genome sequencing for many organisms, more and more raw sequences need to be annotated. Gene prediction by computational methods for finding the location of protein coding regions is one of the essential issues in bioinformatics. Two classes of methods are generally adopted: similarity based searches and ab initio prediction. Here, we review the development of gene prediction methods, summarize the measures for evaluating predictor quality, highlight open problems in this area, and discuss future research directions.     

惠州西湖外来鱼类入侵与鱼类群落状况研究

【目的】摸清惠州西湖鱼类群落组成和外来鱼类入侵现状。【方法】2023年2月,在惠州西湖主湖区开展鱼类资源调查,运用Jaccard相似性指数、相似性分析、非度量多维尺度排序法等方法分析外来鱼类物种组成、丰度差异、分布格局、群聚结构和群落相似性。【结果】共捕获鱼类21种,包括鲤、鲫和鲢等16种土著鱼类以及豹纹翼甲鲇、齐氏罗非鱼、尼罗罗非鱼、伽利略罗非鱼和露斯塔野鲮5种外来鱼类。外来鱼类质量和数量分别占总渔获物的45.36%和44.18%。豹纹翼甲鲇和齐氏罗非鱼在5种外来鱼类中质量和数量占比相对较高,表明外来种群资源量具有一定规模;相似性分析结果表明,西湖平均Jaccard相似性指数处于相似等级(数值为0.62),表明惠州西湖鱼类群落空间分布相似度高;聚类分析结果表明,在78.73%的相似性水平时,惠州西湖鱼类群落可分为4组,但它们之间的差异未达到生态学意义上的差异显著(P>0.05)。【结论】惠州西湖各站点鱼类群落较为相似,豹纹翼甲鲇和齐氏罗非鱼在各个站点均有出现,入侵较为严重,亟需采取相应的防控措施。     

An improved alignment-free model for dna sequence similarity metric

Background

DNA Clustering is an important technology to automatically find the inherent relationships on a large scale of DNA sequences. But the DNA clustering quality can still be improved greatly. The DNA sequences similarity metric is one of the key points of clustering. The alignment-free methodology is a very popular way to calculate DNA sequence similarity. It normally converts a sequence into a feature space based on words’ probability distribution rather than directly matches strings. Existing alignment-free models, e.g. k-tuple, merely employ word frequency information and ignore many types of useful information contained in the DNA sequence, such as classifications of nucleotide bases, position and the like. It is believed that the better data mining results can be achieved with compounded information. Therefore, we present a new alignment-free model that employs compounded information to improve the DNA clustering quality.

Results

This paper proposes a Category-Position-Frequency (CPF) model, which utilizes the word frequency, position and classification information of nucleotide bases from DNA sequences. The CPF model converts a DNA sequence into three sequences according to the categories of nucleotide bases, and then yields a 12-dimension feature vector. The feature values are computed by an entropy based model that takes both local word frequency and position information into account. We conduct DNA clustering experiments on several datasets and compare with some mainstream alignment-free models for evaluation, including k-tuple, DMk, TSM, AMI and CV. The experiments show that CPF model is superior to other models in terms of the clustering results and optimal settings.

Conclusions

The following conclusions can be drawn from the experiments. (1) The hybrid information model is better than the model based on word frequency only. (2) For DNA sequences no more than 5000 characters, the preferred size of sliding windows for CPF is two which provides a great advantage to promote system performance. (3) The CPF model is able to obtain an efficient stable performance and broad generalization.     

河西走廊沙漠人工植被区土壤种子库特征

土壤种子库作为地上植被更新的重要的种源储备库,在植被自然恢复和演替过程以及生态系统建设中起着重要作用。以河西走廊不同区域沙漠人工植被为研究对象,研究了土壤种子库物种组成、时空分布和数量变化特征。结果表明：河西走廊沙漠人工林土壤种子库共出现27种植物,分属8科22属,以藜科植物最多,生活型以草本植物种子比例最高,占到90.6%—95.06%;土壤种子库密度介于19.29粒/m~2—858.57粒/m~2之间,从东到西呈水平地带性分布,土壤种子库分布主要集中在0—2cm土层中,不同样地土壤种子库密度均呈明显的垂直分布,在0—10cm土层内,随着土层深度增加,种子库密度先减小后增大;土壤种子库多样性(Simpson指数)在河西走廊东段沙漠人工植被区最高,在0.671—0.812之间,河西走廊中段沙漠人工植被区为0.417—0.809之间,河西走廊西段沙漠人工植被区为0.256—0.707之间,从东到西呈下降趋势,Shannon-Wiener指数、Margalett丰富度指数、Peilow均匀度指数也表现出相同的趋势,说明由于生境的植被的异质性程度高,使土壤种子库之间的差异性显著;河西走廊沙漠...