Melon/cowpea intercropping pattern influenced the N and C soil cycling and the abundance of soil rare bacterial taxa.

The high use of pesticides, herbicides, and unsustainable farming practices resulted in losses of soil quality. Sustainable farming practices such as intercropping could be a good alternative to traditional monocrop, especially using legumes such as cowpea (Vigna unguiculata L. Walp). In this study, different melon and cowpea intercropping patterns (melon mixed with cowpea in the same row (MC1); alternating one melon row and one cowpea row (MC2); alternating two melon rows and one cowpea row (MC3)) were assayed to study the intercropping effect on soil bacterial community through 16S rRNA region in a 3-year experiment. The results indicated that intercropping showed high content of total organic carbon, total nitrogen and ammonium, melon yield, and bacterial diversity as well as higher levels of beneficial soil microorganisms such a Pseudomonas, Aeromicrobium, Niastella, or Sphingomonas which can promote plant growth and plant defense against pathogens. Furthermore, intercropping showed a higher rare taxa diversity in two (MC1 and MC2) out of the three intercropping systems. In addition, N-cycling genes such as nirB, nosZ, and amoA were more abundant in MC1 and MC2 whereas the narG predicted gene was far more abundant in the intercropping systems than in the monocrop at the end of the 3-year experiment. This research fills a gap in knowledge about the importance of soil bacteria in an intercropping melon/cowpea pattern, showing the benefits to yield and soil quality with a decrease in N fertilization.

An automated process for supporting decisions in clustering-based data analysis.

BACKGROUND AND OBJECTIVE: Metrics are commonly used by biomedical researchers and practitioners to measure and evaluate properties of individuals, instruments, models, methods, or datasets. Due to the lack of a standardized validation procedure for a metric, it is assumed that if a metric is appropriate for analyzing a dataset in a certain domain, then it will be appropriate for other datasets in the same domain. However, such generalizability cannot be taken for granted, since the behavior of a metric can vary in different scenarios. The study of such behavior of a metric is the objective of this paper, since it would allow for assessing its reliability before drawing any conclusion about biomedical datasets. METHODS: We present a method to support in evaluating the behavior of quantitative metrics on datasets. Our approach assesses a metric by using clustering-based data analysis, and enhancing the decision-making process in the optimal classification. Our method assesses the metrics by applying two important criteria of the unsupervised classification validation that are calculated on the clusterings generated by the metric, namely stability and goodness of the clusters. The application of our method is facilitated to biomedical researchers by our evaluomeR tool. RESULTS: The analytical power of our methods is shown in the results of the application of our method to analyze (1) the behavior of the impact factor metric for a series of journal categories; (2) which structural metrics provide a better partitioning of the content of a repository of biomedical ontologies, and (3) the heterogeneity sources in effect size metrics of biomedical primary studies. CONCLUSIONS: The use of statistical properties such as stability and goodness of classifications allows for a useful analysis of the behavior of quantitative metrics, which can be used for supporting decisions about which metrics to apply on a certain dataset.

Evaluation of ontology structural metrics based on public repository data.

The development and application of biological ontologies have increased significantly in recent years. These ontologies can be retrieved from different repositories, which do not provide much information about quality aspects of the ontologies. In the past years, some ontology structural metrics have been proposed, but their validity as measurement instrument has not been sufficiently studied to date. In this work, we evaluate a set of reproducible and objective ontology structural metrics. Given the lack of standard methods for this purpose, we have applied an evaluation method based on the stability and goodness of the classifications of ontologies produced by each metric on an ontology corpus. The evaluation has been done using ontology repositories as corpora. More concretely, we have used 119 ontologies from the OBO Foundry repository and 78 ontologies from AgroPortal. First, we study the correlations between the metrics. Second, we study whether the clusters for a given metric are stable and have a good structure. The results show that the existing correlations are not biasing the evaluation, there are no metrics generating unstable clusterings and all the metrics evaluated provide at least reasonable clustering structure. Furthermore, our work permits to review and suggest the most reliable ontology structural metrics in terms of stability and goodness of their classifications. Availability: http://sele.inf.um.es/ontology-metrics.

Cluster Analysis of Microarray Data.

The cluster analysis has been widely applied by researchers from several scientific fields over the last decades. Advances in knowledge of biological phenomena have revived a great interest in cluster analysis due in part to the large amount of microarray data. Traditional clustering algorithms show, apart from the need of user-defined parameters, clear limitations to handle microarray data owing to its inherent characteristics: high-dimensional-low-sample-sized, highly redundant, and noisy. That has motivated the study of clustering algorithms tailored to the task of analyzing microarray data, which currently continue being developed and adapted. The present chapter is devoted to review clustering methods with different cluster analysis approaches in the challenging context of microarray data. Furthermore, the validation of the clustering results is briefly discussed by means of validity indexes used to assess the goodness of the number of clusters and the induced cluster assignments.

Analysis of physiological traits in the response of Chenopodiaceae, Amaranthaceae, and Brassicaceae plants to salinity stress.

Soil salinity is one of the main factors affecting plant growth. Dissection of plant response to salinity into physiological traits may result a simple approximation than the overall response that may influence many aspects of the plant. In the present study two factors were considered to evaluate the correlation of different physiological variables in the plant response to salinity. The first factor was the species, with four levels (Atriplex halimus, Salicornia fruticosa, Cakile maritima, and Brassica rapa), and the second was the salinity (0, 100, 200, and 300 mM NaCl). Thus, the interrelationships of distinct physiological traits - leaf succulence, minerals (micronutrients and macronutrients), plant water relations (osmotic potential, water potential, and hydraulic conductivity), protein content, catalase, and unsaturated fatty acids - were analyzed by Discriminant Canonical Analysis (DCA). Additional information supplied by the interaction between the variables provided a multivariate response pattern in which the two factors (species x salinity) influenced the relationship between responses rather than affecting a single response. Such analysis allows to establish whether the selected trait was associated to each other for helping to define the best set of parameters in relation to the response of new genotypes to salinity. Thus, plant growth was influenced by leaf succulence adaptation to salt stress whereas it was not determined by water relations. The Na ion prevailed over K as the element with the highest variability in the response to salinity in A. halimus and S. fruticosa, whereas in C. maritima and B. rapa, Ca, S, and P stood out more. Patterns of ion accumulation together with the protein and unsaturated fatty acid ratios could be used in discriminating plant response to salt stress may be positioned in interrelated groups. The results highlight new evidences in the response to salt stress associated to a specific interrelationship of a set of physiological parameters.

Contextual Factors Related to Alcohol Abuse Among Intimate Partner Violence Offenders.

BACKGROUND: The association between alcohol consumption and intimate partner violence (IPV) has been reiterated in numerous studies. Some authors have found higher levels of risk factors in intimate partner violence offenders (IPVOs) with alcohol problems than in IPVOs without such problems. OBJECTIVE: The aim of this study is to analyze the relationship of contextual variables with harmful alcohol use in a sample of IPVOs. METHOD: This cross-sectional research analyzes data from 231 IPVOs. In addition to demographic data, information was collected on alcohol use, ethnicity, accumulation of stressful life events and perceived social support and rejection. The sample was divided into hazardous and nonhazardous alcohol users, according to the AUDIT test scale. RESULTS: No differences were found between groups on demographic variables. The results of a hierarchical logistic regression analysis supplemented with ROC curves revealed that Latin American immigrants as opposed to Spanish nationality, accumulating stressful life events, and perceiving low social support significantly increased the likelihood of alcohol abuse, with adequate predictive power. CONCLUSION: Contextual variables such as ethnicity, accumulation of stressful life events, and lack of social support may explain harmful alcohol consumption. These variables should be taken into account in batterer intervention programs in order to reduce one of the most relevant risk factors of IPV: alcohol abuse.