Dry-season length and runoff control annual variability in stream DOC dynamics in a small, shallowgroundwater-dominated agricultural watershed

International audienceAs a phenomenon integrating climate conditions and hydrological control of the connection betweenstreams and terrestrial dissolved organic carbon (DOC) sources, groundwater dynamics controlpatterns of stream DOC characteristics (concentrations and fluxes). Influence of intra-annualvariations in groundwater level, discharge and climatic factors on DOC concentrations and fluxeswere assessed over 13 years at the headwater watershed of Kervidy-Naizin (5 km²) in westernFrance. Four seasonal periods were delineated within each year according to groundwaterfluctuations (A: rewetting, B: high flow, C: recession, and D: drought). Annual and seasonal baseflow vs stormflow DOC concentrations were defined based on daily hydrograph readings. Highinter-annual variability of annual DOC fluxes (5.4-39.5 kg.ha-1.yr-1) indicates that several years ofdata are required to encompass variations in water flux to evaluate the actual DOC export capacity ofa watershed. Inter-annual variability of mean annual DOC concentrations was much lower (4.9-7.5mg C.l-1), with concentrations decreasing within each year from ca. 9.2 mg C.l-1 in A to ca. 3.0 mgC.l-1 in C. This indicates an intra-annual pattern of stream DOC concentrations controlled by DOCsource characteristics and groundwater dynamics very similar across years. Partial least squareregressions combined with multiple linear regressions showed that the dry season characteristics(length and drawdown) determine the mean annual DOC concentration while annual runoffdetermines the annual flux. Antagonistic mechanisms of production-accumulation and dilution depletioncombined with an unlimited DOC supply from riparian wetland soils can mitigate theresponse of stream concentrations to global changes and climatic variations

Construction of an Immigrant Integration Composite Indicator through the Partial Least Squares Structural Equation Model K-Means

Integration is a multidimensional process, which can take place in different ways and at different times in relation to each of the single economic, social, cultural, and political dimensions. Hence, examining every single dimension is important as well as building composite indexes simultaneously inclusive of all dimensions in order to obtain a complete description of a complex phenomenon and to convey a coherent set of information. In this paper, we aim at building an immigrant integration composite indicator (IICI), able to measure the different aspects related to integration such as employment, education, social inclusion, active citizenship, and on the basis of which to simultaneously classify territorial areas such as European regions. For this application, the data collected in 274 European regions from the European Social Survey (ESS), Round 8, on immigration have been used

MultiBaC: A strategy to remove batch effects between different omic data types

[EN] Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform-i.e. gene expression- is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is part of a research project that is totally funded by Conselleria d'Educacio, Cultura i Esport (Generalitat Valenciana) through PROMETEO grants program for excellence research groups.Ugidos, M.; Tarazona Campos, S.; Prats-Montalbán, JM.; Ferrer, A.; Conesa, A. (2020). MultiBaC: A strategy to remove batch effects between different omic data types. Statistical Methods in Medical Research. 29(10):2851-2864. https://doi.org/10.1177/0962280220907365S285128642910Kupfer, P., Guthke, R., Pohlers, D., Huber, R., Koczan, D., & Kinne, R. W. (2012). Batch correction of microarray data substantially improves the identification of genes differentially expressed in Rheumatoid Arthritis and Osteoarthritis. 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Sterile Testis Complementation with Spermatogonial Lines Restores Fertility to DAZL-Deficient Rats and Maximizes Donor Germline Transmission

Despite remarkable advances in assisted reproductive capabilities ∼4% of all couples remain involuntarily infertile. In almost half of these cases, a lack of conception can in some measure be attributed to the male partner, wherein de novo Y-chromosomal deletions of sperm-specific Deleted-in-Azoospermia (DAZ) genes are particularly prevalent. In the current study, long-term cultures of rat spermatogonial stem cells were evaluated after cryo-storage for their potential to restore fertility to rats deficient in the DAZ-like (DAZL) gene. Detailed histological analysis of DAZL-deficient rat testes revealed an apparently intact spermatogonial stem cell compartment, but clear failure to produce mature haploid gametes resulting in infertility. After proliferating >1 million-fold in cell number during culture post-thaw, as few as 50,000 donor spermatogonia transplanted into only a single testis/recipient effectively restored fecundity to DAZL-deficient rats, yielding 100% germline transmission to progeny by natural mating. Based on these results, the potency and efficacy of this donor stem cell line for restoring fertility to azoospermic rodents is currently unprecedented. Prospectively, similar successes in humans could be directly linked to the feasibility of obtaining enough fully functional spermatogonial stem cells from minimal testis biopsies to be therapeutically effective. Thus, regeneration of sperm production in this sterile recipient provides an advanced pre-clinical model for optimizing the efficacy of stem cell therapies to cure a paradoxically increasing number of azoospermic men. This includes males that are rendered infertile by cancer therapies, specific types of endocrine or developmental defects, and germline-specific de novo mutations; all of whom may harbor healthy sources of their own spermatogonial stem cells for treatment

Gene-based partial least-squares approaches for detecting rare variant associations with complex traits

Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis

Exploration of ecological factors related to the spatial heterogeneity of tuberculosis prevalence in P. R. China

Background: The current prevalence of tuberculosis (TB) in the People's Republic of China (P. R. China) demonstrates geographical heterogeneities, which show that the TB prevalence in the remote areas of Western China is more serious than that in the coastal plain of Eastern China. Although a lot of ecological studies have been applied in the exploration on the regional difference of disease risks, there is still a paucity of ecological studies on TB prevalence in P. R. China. Objective: To understand the underlying factors contributing to the regional inequity of TB burden in P. R. China by using an ecological approach and, thus, aiming to provide a basis to eliminate the TB spatial heterogeneity in the near future. Design: Latent ecological variables were identified by using exploratory factor analysis from data obtained from four sources, i.e. the databases of the National TB Control Programme (2001–2010) in P. R. China, the China Health Statistical Yearbook during 2002–2011, the China Statistical Yearbook during 2002–2011, and the provincial government websites in 2013. Partial least squares path modelling was chosen to construct the structural equation model to evaluate the relationship between TB prevalence and ecological variables. Furthermore, a geographically weighted regression model was used to explore the local spatial heterogeneity in the relationships. Results: The latent ecological variables in terms of ‘TB prevalence’, ‘TB investment’, ‘TB service’, ‘health investment’, ‘health level’, ‘economic level’, ‘air quality’, ‘climatic factor’ and ‘geographic factor’ were identified. With the exception of TB service and health levels, other ecological factors had explicit and significant impacts on TB prevalence to varying degrees. Additionally, each ecological factor had different impacts on TB prevalence in different regions significantly. Conclusion: Ecological factors that were found predictive of TB prevalence in P. R. China are essential to take into account in the formulation of locally comprehensive strategies and interventions aiming to tailor the TB control and prevention programme into local settings in each ecozone

Taiwanese Preservice Teachers’ Science, Technology, Engineering, and Mathematics Teaching Intention

This study applies the theory of planned behavior as a basis for exploring the impact of knowledge, values, subjective norms, perceived behavioral controls, and attitudes on the behavioral intention toward science, technology, engineering, and mathematics (STEM) education among Taiwanese preservice science teachers. Questionnaires (N = 139) collected information on the behavioral intention of preservice science teachers engaging in STEM education. Data were analyzed using descriptive statistics, path analysis, and analysis of variance. Results revealed that, in terms of direct effects, higher perceived behavioral control and subjective norms were associated with stronger STEM teaching intention. More positive attitude and greater knowledge were indirectly associated with higher subjective norms and perceived behavioral control, which resulted in stronger STEM teaching intention. Additionally, gender did not affect preservice teachers’ intention to adopt STEM teaching approaches. However, preservice teachers whose specialization was in different fields tended to influence their knowledge and perceived behavioral control; these issues require further investigation

A cross-sectional testing of The Iowa Personality Disorder Screen in a psychiatric outpatient setting

<p>Abstract</p> <p>Background</p> <p>Patients suspected of personality disorders (PDs) by general practitioners are frequently referred to psychiatric outpatient clinics (POCs). In that setting an effective screening instrument for PDs would be helpful due to resource constraints. This study evaluates the properties of The Iowa Personality Disorder Screen (IPDS) as a screening instrument for PDs at a POC.</p> <p>Methods</p> <p>In a cross-sectional design 145 patients filled in the IPDS and were examined with the SCID-II interview as reference. Various case-findings properties were tested, interference of socio-demographic and other psychopathology were investigated by logistic regression and relationships of the IPDS and the concept of PDs were studied by a latent variable path analysis.</p> <p>Results</p> <p>We found that socio-demographic and psychopathological factors hardly disturbed the IPDS as screening instrument. With a cut-off ≥4 the 11 items IPDS version had sensitivity 0.77 and specificity 0.71. A brief 5 items version showed sensitivity 0.82 and specificity 0.74 with cut-off ≥ 2. With exception for one item, the IPDS variables loaded adequately on their respective first order variables, and the five first order variables loaded in general adequately on their second order variable.</p> <p>Conclusion</p> <p>Our results support the IPDS as a useful screening instrument for PDs present or absent in the POC setting.</p

Alternative methods to analyse the impact of HIV mutations on virological response to antiviral therapy

<p>Abstract</p> <p>Background</p> <p>Principal component analysis (PCA) and partial least square (PLS) regression may be useful to summarize the HIV genotypic information. Without pre-selection each mutation presented in at least one patient is considered with a different weight. We compared these two strategies with the construction of a usual genotypic score.</p> <p>Methods</p> <p>We used data from the ANRS-CO3 Aquitaine Cohort Zephir sub-study. We used a subset of 87 patients with a complete baseline genotype and plasma HIV-1 RNA available at baseline and at week 12. PCA and PLS components were determined with all mutations that had prevalences >0. For the genotypic score, mutations were selected in two steps: 1) p-value < 0.01 in univariable analysis and prevalences between 10% and 90% and 2) backwards selection procedure based on the Cochran-Armitage Test. The predictive performances were compared by means of the cross-validated area under the receiver operating curve (AUC).</p> <p>Results</p> <p>Virological failure was observed in 46 (53%) patients at week 12. Principal components and PLS components showed a good performance for the prediction of virological response in HIV infected patients. The cross-validated AUCs for the PCA, PLS and genotypic score were 0.880, 0.868 and 0.863, respectively. The strength of the effect of each mutation could be considered through PCA and PLS components. In contrast, each selected mutation contributes with the same weight for the calculation of the genotypic score. Furthermore, PCA and PLS regression helped to describe mutation clusters (e.g. 10, 46, 90).</p> <p>Conclusion</p> <p>In this dataset, PCA and PLS showed a good performance but their predictive ability was not clinically superior to that of the genotypic score.</p