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Clustered miRNAs consist of two or more miRNAs transcribed together and may coordinately regulate gene expression. Differential expression of clustered miRNAs is found to be controlled by crosstalk of genetic or epigenetic mechanisms. It has been demonstrated that clustered miRNA expression patterns greatly impact cancer cell progression. With the CmirC initiative, we initially developed a comprehensive database to identify copy number variation (CNV) driven clustered miRNAs in cancer. Now, we extended the analysis and identified three miRNAs, mir-96, mir-183, and mir-21, were found to be significantly upregulated in 17 cancer types. Further, CmirC is now upgraded to determine the impact of changes in the DNA methylation status at clustered miRNAs by utilizing The Cancer Genomic Atlas (TCGA) cancer datasets. We examined specific methylation datasets from 9,639 samples, pinpointing 215,435 methylation sites and 27,949 CpG islands with miRNA cluster information. The integrated analysis identified 34 clusters exhibiting differentially methylated CpG sites across 14 cancer types. Furthermore, we determined that CpG islands in the promoter region of 20 miRNA clusters could play a regulatory role. Along with ensuring a straightforward and convenient user experience, CmirC has been updated with improved data browsing and analysis functionalities, as well as enabled hyperlinks to literature and miR-cancer databases. The enhanced version of CmirC is anticipated to play an important role in providing information on the regulation of clustered miRNA expression, and their targeted oncogenes and tumor suppressors. The newly updated version of CmirC is available at . [ABSTRACT FROM AUTHOR]

An increasing number of research projects and infrastructure services involve pooling data across different survey programs. Creating a homogenous integrated dataset from heterogeneous source data is the domain of ex-post harmonization. The harmonization process involves various considerations. However, chief among them is whether two survey measurement instruments have captured the same concept. This issue of conceptual comparability is a fundamental precondition for pooling different source variables to form a harmonized target variable. Our paper explores this issue with a mixed-methods approach. On the one hand, we use psychometric latent variable modeling by presenting several single-item wordings for social trust to respondents and then performing factor analytic procedures. On the other hand, we complement and contrast these quantitative findings with qualitative findings gained with an open-ended web probe. The combined approach gave valuable insights into the conceptual comparability of the eleven social-trust-related single-item wordings. For example, we find that negative, distrust-related wordings and positive, trust-related wordings should not be pooled into an integrated variable. However, the paper will also illustrate and discuss why it is easier to disprove conceptual comparability than fully prove it. [ABSTRACT FROM AUTHOR]

The accumulation of heavy impurities like tungsten in the plasma core of fusion devices can cause significant radiative power losses or even lead to a disruption. It is therefore crucial to monitor the tungsten impurity concentration. In this paper, we follow the integrated data analysis approach using Bayesian probability theory to jointly estimate tungsten concentration profiles and kinetic profiles from soft X-ray, interferometry and electron cyclotron emission measurements. As the full Bayesian inference using Markov chain Monte Carlo sampling is time-consuming, we also discuss emulation of the inference process using neural networks, with a view to real-time implementation. [ABSTRACT FROM AUTHOR]

Abstract Background Genomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth. Main A likely reason for this is that many wetlab-based researchers are not formally trained to apply bioinformatic tools and may therefore assume that they lack the necessary experience to do so themselves. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments. Conclusion Integrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq) can not only assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation but will also help establishing new hypotheses and pre-testing them in silico.

Background: Genomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth. Main: A likely reason for this is that many wetlab-based researchers are not formally trained to apply bioinformatic tools and may therefore assume that they lack the necessary experience to do so themselves. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments. Conclusion: Integrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq) can not only assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation but will also help establishing new hypotheses and pre-testing them in silico. [ABSTRACT FROM AUTHOR]

Plasma electron density is a crucial parameter in plasma studies. Accurately inverting the plasma electron density profile is vital for plasma control experiments and the investigation of plasma physical mechanisms. This paper proposes an integrated data analysis (IDA) method based on Bayesian inference, which integrates polarimetric interferometry, hydrogen cyanide laser interferometer, and microwave reflectometer diagnostics for inverting the plasma electron density profile. To enhance inversion accuracy, a Gaussian prior probability of the non-stationary hyperparameter is used. This prior probability effectively simulates situations where there is a large plasma electron density gradient in the pedestal, especially under the condition of high-confinement mode discharge. Compared to the use of Gaussian prior probability for the stationary hyperparameter, the proposed IDA method based on the non-stationary hyperparameter prior probability achieves higher inversion accuracy.

The Titanic quantitative dataset has long been used to teach statistics. However, combining the quantitative dataset with a qualitative dataset of survivor testimonies shows that the Titanic case is an even better example to teach mixed methods. This article offers practical tools to teach mixed methods to undergraduate or postgraduate students in the social sciences, using the Titanic datasets. Based on an empirical analysis of the survival probabilities on the Titanic, we show how mixed methods lead to superior explanations than mono-method strategies. This paper has two goals: 1) to introduce the freely available linked Titanic datasets; and 2) to present a three-hour step-by-step exercise with the Titanic datasets that can be used to learn and teach mixed methods.

The types of metabolites measured in metabolomics studies vary depending on many factors, including differences in methods. Centralizing the distributed raw data is also often difficult due to confidentiality issues. These difficulties prevent the integrated analysis of metabolomic data from multiple studies. In this study, we extend the data collaboration analysis, an integrated data analysis method, by sharing dimensionality-reduced intermediate representations instead of the raw data to allow it to be applied to distributed data where the samples are completely different, and features are partially common. We then evaluated the improvement in performance using non-common features in the data collaboration analysis. To perform this evaluation, we created the four artificial datasets and the two datasets generated from metabolomics public data where samples are completely different and features are partially common. For each of these datasets, we compared the classification performance including area under the curve in the receiver operating characteristic curve (ROC-AUC) with the following three cases: (i) a case where only local data were used for training, (ii) the data collaboration analysis with only the common features of the distributed datasets, and (iii) the data collaboration analysis with all the features including non-common features. In most cases, the data collaboration analysis using all features demonstrated better results compared to the data collaboration analysis only using common features (by 1.3–4.8 points ROC-AUC for each dataset on average) or that trained on only one of the datasets (by 1.8–2.9 points ROC-AUC for each dataset on average). It was confirmed that the data collaboration analysis could integrate and analyze distributed data where samples are completely different and features are partially common, which can improve the classification accuracy in machine learning without sharing the raw data.

The objective of the Fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to artificial intelligence for automatization and improving reliability of control processes.

The Titanic quantitative dataset has long been used to teach statistics. However, combining the quantitative dataset with a qualitative dataset of survivor testimonies shows that the Titanic case is an even better example to teach mixed methods. This article offers practical tools to teach mixed methods to undergraduate or postgraduate students in the social sciences, using the Titanic datasets. Based on an empirical analysis of the survival probabilities on the Titanic, we show how mixed methods lead to superior explanations than mono-method strategies. This paper has two goals: 1) to introduce the freely available linked Titanic datasets; and 2) to present a three-hour step-by-step exercise with the Titanic datasets that can be used to learn and teach mixed methods. [ABSTRACT FROM AUTHOR]

Despite tremendous interest in social games and game studies, the potential of game heuristics for the field of mixed methods remains unknown. This article introduces game heuristics to mixed methods research, showing how it was used in a specific study on the survival probabilities on the Titanic. Specifically, we describe how game heuristics was used to create the explanandum, code and interpret the qualitative material, and set up and interpret the quantitative model. Furthermore, we show and explicate how game heuristics was used to construct seven types of meta-inferences. The Titanic data set is especially interesting, since it is routinely used for statistical mono-method teaching; however, it can be shown that a mixed methods approach leads to a better explanation. [ABSTRACT FROM AUTHOR]

The aim of this study was to describe age-related changes in sexual risk and substance use using existing data from three HIV/sexually transmitted infection (STI) prevention trials conducted in Atlanta, Georgia, that enrolled young African American women. We used two novel analysis methods: integrative data analysis (IDA) and time-varying effect models (TVEM). Each trial collected self-reported behavioral data and vaginal swab specimens assayed for chlamydia, gonorrhea, and trichomonas. Baseline data from all participants and follow-up data from participants not randomized to an active intervention arm were integrated in a pooled dataset using an IDA approach. The pooled dataset included observations for 1974 individuals, aged 14-25 years for behavioral outcomes and 16-25 years for STI outcomes. We used TVEM to model age-related changes in sexual risk and substance use behaviors and STI acquisition, adjusting for trial as a fixed effect. Coital frequency and condomless sex increased through the early 20s while multiple partnerships remained fairly steady. Alcohol use and cigarette smoking increased through about age 24 years before declining. Marijuana use peaked at age 16-17 years and thereafter generally declined. STI acquisition was highest at age 16 years. This study demonstrates the feasibility and utility of innovative methodological techniques to address novel questions related to adolescent development using existing data from multiple trials. The results suggest that mid-adolescence and the early 20s may be periods of particular risk. The findings may be useful for timing culturally and developmentally relevant prevention interventions for young African American women. [ABSTRACT FROM AUTHOR]

Identification of genomic biomarkers is an important area of research in the context of drug discovery experiments. These experiments typically consist of several high dimensional datasets that contain information about a set of drugs (compounds) under development. This type of data structure introduces the challenge of multi-source data integration. High-Performance Computing (HPC) has become an important tool for everyday research tasks. In the context of drug discovery, high dimensional multi-source data needs to be analyzed to identify the biological pathways related to the new set of drugs under development. In order to process all information contained in the datasets, HPC techniques are required. Even though R packages for parallel computing are available, they are not optimized for a specific setting and data structure. In this article, we propose a new framework, for data analysis, to use R in a computer cluster. The proposed data analysis workflow is applied to a multi-source high dimensional drug discovery dataset and compared with a few existing R packages for parallel computing. [ABSTRACT FROM AUTHOR]

Cocaine overdose death rates among Black people are higher than that of any other racial/ethnic group, attributable to synthetic opioids in the cocaine supply. Understanding the most effective psychostimulant use treatment interventions for Black people is a high priority. While some interventions have proven effective for the general population, their comparative effectiveness among Black people remains unknown. To address this gap, our NIDA-funded Clinical Trials Network (CTN) study (0125), will use Integrative Data Analysis (IDA) to examine treatment effectiveness across 9 CTN studies. This manuscript describes the study protocol for CTN-0125. Of the 59 completed randomized clinical trials in the CTN with available datasets, nine met our inclusion criteria: 1) behavioral intervention, 2) targeted cocaine use or use disorder, 3) included sub-samples of participants who self-identified as Black and 4) included outcome measures of cocaine and psychostimulant use and consequences. We aim to 1) estimate scale scores of cocaine use severity while considering study-level measurement non-invariance, 2) compare the effectiveness of psychosocial treatments for psychostimulant use, and 3) explore individual (e.g., concomitant opioid use, age, sex, employment, pre-treatment psychiatric status) and study-level moderators (e.g., attendance/retention) to evaluate subgroup differences in treatment effectiveness. The NIDA CTN provides a unique collection of studies that can offer insight into what interventions are most efficacious for Black people. Findings from our CTN-0125 study have the potential to substantially inform treatment approaches specifically designed for Black people who use psychostimulants. • Opioid-involved overdose deaths among Black people who use cocaine are on the rise. • While interventions exist, the comparative effectiveness for Black people is unknown. • Integrative data analysis will be used to combine data across 9 NIDA CTN trials. • A protocol for examining comparative effectiveness is presented. [ABSTRACT FROM AUTHOR]

We introduce a strategy for creating virtual control groups-cases generated through computer algorithms that, when aggregated, may serve as experimental comparators where live controls are difficult to recruit, such as when programs are widely disseminated and randomization is not feasible. We integrated and harmonized data from eight archived longitudinal adolescent-focused data sets spanning the decades from 1980 to 2010. Collectively, these studies examined numerous psychosocial variables and assessed past 30-day alcohol, cigarette, and marijuana use. Additional treatment and control group data from two archived randomized control trials were used to test the virtual control algorithm. Both randomized controlled trials (RCTs) assessed intentions, normative beliefs, and values as well as past 30-day alcohol, cigarette, and marijuana use. We developed an algorithm that used percentile scores from the integrated data set to create age- and gender-specific latent psychosocial scores. The algorithm matched treatment case observed psychosocial scores at pretest to create a virtual control case that figuratively "matured" based on age-related changes, holding the virtual case's percentile constant. Virtual controls matched treatment case occurrence, eliminating differential attrition as a threat to validity. Virtual case substance use was estimated from the virtual case's latent psychosocial score using logistic regression coefficients derived from analyzing the treatment group. Averaging across virtual cases created group estimates of prevalence. Two criteria were established to evaluate the adequacy of virtual control cases: (1) virtual control group pretest drug prevalence rates should match those of the treatment group and (2) virtual control group patterns of drug prevalence over time should match live controls. The algorithm successfully matched pretest prevalence for both RCTs. Increases in prevalence were observed, although there were discrepancies between live and virtual control outcomes. This study provides an initial framework for creating virtual controls using a step-by-step procedure that can now be revised and validated using other prevention trial data. [ABSTRACT FROM AUTHOR]

Introduction: Hereditary transthyretin (TTR) amyloidosis with polyneuropathy (hATTR-PN) is a rare, autosomal dominant amyloidosis characterized primarily by progressive ascending sensorimotor neuropathy often associated with  autonomic involvement. hATTR-PN is caused by a mutation in the TTR gene leading to protein misfolding and amyloid accumulation in peripheral nerves and vital organs. The latest global prevalence estimates point to 10,000 cases worldwide, with an upper end of about 40,000. Tafamidis has been approved in over 40 countries for delaying neurologic disease progression in early-stage hATTR-PN. Multiple observational studies have examined clinical outcomes in hATTR-PN patients treated with tafamidis in the routine clinical setting. Integrative data analysis (IDA) is a technique for optimally constructing synthetic treatment and control cohorts from multiple independent studies, which allows meta-analysis of patient-level data. Herein, we provide a proof of concept for the application of IDA to real-world and natural history hATTR-PN data. IDA permits increased understanding of outcomes in tafamidis-treated and untreated persons with hATTR-PN by optimally pooling all available information.Methods: Summary statistics corresponding to the Neuropathy Impairment Score-Lower Limb (NIS-LL) from five published studies were pooled, converted to change from baseline means and variances, and analyzed using IDA. IDA-based synthetic cohorts were generated by averaging across studies stratified on treatment versus control cohort. Trends in change from baseline in each study and the corresponding synthetic cohorts were plotted. Patient-level data were simulated from the synthetic cohort trends in a Monte Carlo simulation to highlight the ability to contrast synthetic cohort trends using the mixed model for repeated measures (MMRM).Results: The average sample size among the five studies was 71 (37-128) patients. The average NIS-LL trends indicated that tafamidis-treated patients experienced slower progression in neuropathy compared to untreated patients. Synthetic cohort trends reflected the trends observed in the contributing studies, while simultaneously shrinking the width of corresponding confidence bands. Monte Carlo simulation results demonstrated precise recovery of the synthetic cohort and time-dependent simulated NIS-LL means by the MMRM.Discussion: This proof of concept demonstrates the utility of IDA-based synthetic cohorts for increased precision in characterizing and testing hypotheses about treatment outcomes and prognosis in hATTR-PN.Funding: Pfizer.Plain Language Summary: Plain language summary available for this article. Hereditary transthyretin (TTR) amyloidosis with polyneuropathy (hATTR-PN) is a rare inherited disease. It is caused by genetic mutations that change the structure of TTR proteins, causing an abnormal buildup of amyloid protein deposits (amyloidosis) in your body’s nerves and organs. This damages your nerves and organs and causes weakness, numbness, and pain. The drug tafamidis stabilizes TTR proteins and slows the disease’s progression. Tafamidis has been approved in over 40 countries based on clinical trial results. However, researchers and doctors are still studying how it works for patients in the real world. Over the past few years, researchers have published multiple real-world studies about tafamidis, but it is difficult to get a full, uniform picture of how well tafamidis works, because the studies are too different. hATTR-PN is a rare disease, so the number of patients per study is small; small sample sizes can make it more difficult to tell true effects from statistical noise, whereas large samples can be more precise. Integrative data analysis (IDA) is a technique that allows researchers to combine the results of multiple studies into a large pool of data and analyze the larger data set (or “synthetic cohort”) instead. This makes optimal use of each study’s information and creates a fuller picture of the treatment’s real-world results. This manuscript is a “proof of concept” to demonstrate how the IDA statistical method can be used to build synthetic cohorts from real-world hATTR-PN data, improving our understanding of hATTR-PN patients’ outcomes. [ABSTRACT FROM AUTHOR]

Abstract Leaves have a central role in plant energy capture and carbon conversion and therefore must continuously adapt their development to prevailing environmental conditions. To reveal the dynamic systems behaviour of leaf development, we profiled Arabidopsis leaf number six in depth at four different growth stages, at both the end‐of‐day and end‐of‐night, in plants growing in two controlled experimental conditions: short‐day conditions with optimal soil water content and constant reduced soil water conditions. We found that the lower soil water potential led to reduced, but prolonged, growth and an adaptation at the molecular level without a drought stress response. Clustering of the protein and transcript data using a decision tree revealed different patterns in abundance changes across the growth stages and between end‐of‐day and end‐of‐night that are linked to specific biological functions. Correlations between protein and transcript levels depend on the time‐of‐day and also on protein localisation and function. Surprisingly, only very few of >1700 quantified proteins showed diurnal abundance fluctuations, despite strong fluctuations at the transcript level.

Background: As new technologies allow investigators to collect multiple forms of molecular data (genomic, epigenomic, transcriptomic, etc) and multiple endpoints on a clinical trial cohort, it will become necessary to effectively integrate all these data in a way that reliably identifies biologically important genes. Methods: We introduce CC-PROMISE as an integrated data analysis method that combines components of canonical correlation (CC) and projection onto the most interesting evidence (PROMISE). For each gene, CC-PROMISE first uses CC to compute scores that represent the association of two forms of molecular data with each other. Next, these scores are substituted into PROMISE to evaluate the statistical evidence that the molecular data show a biologically meaningful relationship with the endpoints. Results: CC-PROMISE shows outstanding performance in simulation studies and an example application involving pediatric leukemia. In simulation studies, CC-PROMISE controls the type I error (misleading significance) rate very near the nominal level across 100 distinct null settings in which no molecular-endpoint association exists. Also, CC-PROMISE has better statistical power than three other methods that control type I error in 396 of 400 (99 %) alternative settings for which a molecular-endpoint association is present; the power advantage of CC-PROMISE exceeds 30 % in 127 of the 400 (32 %) alternative settings. These advantages of CC-PROMISE are also observed in an example application. Conclusion: CC-PROMISE very effectively identifies genes for which some form of molecular data shows a biologically meaningful association with multiple related endpoints. Availability: The R package CCPROMISE is currently available from www.stjuderesearch.org/site/depts/biostats/ software. [ABSTRACT FROM AUTHOR]

Many investigators recognize the importance of data sharing; however, they lack the capability to share data. Research efforts could be vastly expanded if Alzheimer disease data from around the world was linked by a global infrastructure that would enable scientists to access and utilize a secure network of data with thousands of study participants at risk for or already suffering from the disease. We discuss the benefits of data sharing, impediments today, and solutions to achieving this on a global scale. We introduce the Global Alzheimer's Association Interactive Network (GAAIN), a novel approach to create a global network of Alzheimer disease data, researchers, analytical tools, and computational resources to better our understanding of this debilitating condition. GAAIN has addressed the key impediments to Alzheimer disease data sharing with its model and approach. It presents practical, promising, yet, data owner-sensitive data-sharing solutions. [ABSTRACT FROM AUTHOR]

Glycosylation and related processes play important roles in cancer development and progression, including metastasis. Several studies have shown that N-glycans have potential diagnostic value as cancer serum biomarkers. We have explored the significance of the abundance of particular serum N-glycan structures as important features of breast tumour biology by studying the serum glycome and tumour transcriptome (mRNA and miRNA) of 104 breast cancer patients. Integration of these types of molecular data allows us to study the relationship between serum glycans and transcripts representing functional pathways, such as metabolic pathways or DNA damage response. We identified tri antennary trigalactosylated trisialylated glycans in serum as being associated with lower levels of tumour transcripts involved in focal adhesion and integrin-mediated cell adhesion. These glycan structures were also linked to poor prognosis in patients with ER negative tumours. High abundance of simple monoantennary glycan structures were associated with increased survival, particularly in the basal-like subgroup. The presence of circulating tumour cells was found to be significantly associated with several serum glycome structures like bi and triantennary, di- and trigalactosylated, di- and trisialylated. The link between tumour miRNA expression levels and N-glycan production is also examined. [ABSTRACT FROM AUTHOR]

Given the steady increase in breast cancer rates in both the developed and developing world, there has been a concerted research effort undertaken worldwide to understand the molecular mechanisms underpinning the disease. The data generated from numerous clinical trials and experimental studies shed light on different aspects of the disease. We present a new version of the ROCK database (rock.icr.ac.uk), which integrates such diverse data types allowing unique analyses of published breast cancer experimental data. We have added several new data types and analysis modules to ROCK, which allow the user to interactively query and research the huge amounts of available experimental data and perform complex correlations across studies and data types such as gene expression, genomic copy number aberrations, micro RNA expression, RNA interference, survival analysis, clinical annotation and signalling protein networks. We present the recent and major functional updates and enhancements to the ROCK resource, including new analysis modules and microRNA and NGS data integration, and illustrate how ROCK can be used to confirm known experimental results as well as generate novel leads and new experimental hypotheses using the Wnt signalling cell surface receptor FZD7 and the Myc oncogene. ROCK provides a unique breast cancer analysis platform of integrated experimental datasets at the genomic, transcriptomic and proteomic level. This paper presents how ROCK has transitioned from being simply a database to an interactive resource useful to the broader breast cancer research community in our effort to facilitate research into the underlying molecular mechanisms of breast cancer. [ABSTRACT FROM AUTHOR]

This special issue presents reflections of international scholars on selected core issues of qualitative inquiry. They were produced in the context of an initiative to promote qualitative research in Switzerland, notably to build a network among qualitative researchers, to reflect on possibilities for a consensus on quality standards and teaching requirements, and to explore the viability of an archive and resource center for qualitative research. In the first part on the why and how of qualitative methods the reasons and objectives of the initiative are delineated, and the process of resurgence, legitimation, and institutionalization of qualitative methods is described. Then three major conceptual breaks in the field of qualitative inquiry are identified, and quality concerns in qualitative methods are discussed. In the second part on the why and how of archiving qualitative data, the situation in different European countries is presented. While France is just starting to collect qualitative data for archiving, the QualiData archive in Great Britain has already existed for more than a decade. In Germany, the Bremen Life Course Archive has the longest experience with systematic archiving of qualitative data. Crossing national borders, the Internet brings new challenges and potentials of providing resources and services on-line. It is also argued that the qualitative-quantitative divide is not very helpful, neglecting the complementarity and proximity of the two groups of methods in research, education, and archiving. It also prevents integrated data analysis and integrated styles of research, creating different archives and different types of software. Finally, crucial experiences of the QualiData archive in the area of user support are described and future measures discussed which should be taken. URN: urn:nbn:de:0114-fqs0502303

Leaves have a central role in plant energy capture and carbon conversion and therefore must continuously adapt their development to prevailing environmental conditions. To reveal the dynamic systems behaviour of leaf development, we profiled Arabidopsis leaf number six in depth at four different growth stages, at both the end‐of‐day and end‐of‐night, in plants growing in two controlled experimental conditions: short‐day conditions with optimal soil water content and constant reduced soil water conditions. We found that the lower soil water potential led to reduced, but prolonged, growth and an adaptation at the molecular level without a drought stress response. Clustering of the protein and transcript data using a decision tree revealed different patterns in abundance changes across the growth stages and between end‐of‐day and end‐of‐night that are linked to specific biological functions. Correlations between protein and transcript levels depend on the time‐of‐day and also on protein localisation and function. Surprisingly, only very few of >1700 quantified proteins showed diurnal abundance fluctuations, despite strong fluctuations at the transcript level. [ABSTRACT FROM AUTHOR]

This special issue presents reflections of international scholars on selected core issues of qualitative inquiry. They were produced in the context of an initiative to promote qualitative research in Switzerland, notably to build a network among qualitative researchers, to reflect on possibilities for a consensus on quality standards and teaching requirements, and to explore the viability of an archive and resource center for qualitative research. In the first part on the why and how of qualitative methods the reasons and objectives of the initiative are delineated, and the process of resurgence, legitimation, and institutionalization of qualitative methods is described. Then three major conceptual breaks in the field of qualitative inquiry are identified, and quality concerns in qualitative methods are discussed. In the second part on the why and how of archiving qualitative data, the situation in different European countries is presented. While France is just starting to collect qualitative data for archiving, the QualiData archive in Great Britain has already existed for more than a decade. In Germany, the Bremen Life Course Archive has the longest experience with systematic archiving of qualitative data. Crossing national borders, the Internet brings new challenges and potentials of providing resources and services on-line. It is also argued that the qualitative-quantitative divide is not very helpful, neglecting the complementarity and proximity of the two groups of methods in research, education, and archiving. It also prevents integrated data analysis and integrated styles of research, creating different archives and different types of software. Finally, crucial experiences of the QualiData archive in the area of user support are described and future measures discussed which should be taken. [ABSTRACT FROM AUTHOR]

Online delivery of parenting support is steadily increasing, yet the factors that influence program engagement and efficacy are still understudied. This study used an integrated data analysis approach to identify family and program-related factors that influence outcomes. We combined individual data from seven published efficacy trials of the web-based version of the Triple P-Positive Parenting Program. Data were analyzed for 985 families with children aged between 2 and 12 years (M = 4.87; SD = 2.14) using a Latent Change Score approach. At post-intervention, sociodemographic factors were not predictive of changes in child behavior problems, while parents of boys and those with higher education showed greater improvements in dysfunctional parenting. Parents who were initially more confident in their parenting showed more overall gains while parents with more initial adjustment difficulties showed less improvement. Only the effect of baseline child behavior problems on changes in dysfunctional parenting was moderated by treatment condition. At follow-up, program variant and completion were the primary outcome predictors, with completion found to be related to initial parenting confidence, internet usage and program variant. The implications of these findings for reaching and retaining parents in online programs across all phases of the engagement process are discussed., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Despite its long production history (since 1958) and extensive reservoir stimulation by water injection, the Mauddud reservoir of the Sabiriyah field (SAMA) in northern Kuwait has been plagued by relatively disappointing oil production increase, low oil-by-water replacement ratios, and early water-breakthrough observations. The work described in this paper indicates that reservoir complexity and associated permeability heterogeneity are at the root cause of this and are the result of the interplay between sedimentology, diagenesis and the structural evolution of the area. A multi-disciplinary study involving structural geology, sedimentology, diagenesis, reservoir engineering and geomechanics identified the geological processes responsible for the development of principal high-permeability (High-K) elements in SAMA. Their 3D distribution has been characterized and reservoir property ranges have been established, based on integrated evaluation of static (e.g., seismic, core, borehole image, wireline logs, and drilling information such as losses) and dynamic information (e.g., production and injection logging tools, well test information, pressure data, historical production data and tracer information). Feedback loops between geological realizations and dynamic simulations allowed the testing of various concepts and property ranges with learnings implemented in updates of the geological realizations. This approach increases the confidence level in the conceptual geological models. Three principal plumbing elements contribute to the reservoir and property heterogeneity in the SAMA reservoir. Firstly, relatively thin (one to several decimeters) cemented zones characterized by low matrix porosity but significant secondary vuggy and fracture porosity with high permeability occur in two distinct stratigraphic intervals in the reservoir. These intervals occur near sequence boundaries when the carbonates likely experienced brief periods of exposure. They are interpreted to be cemented cycle tops formed during meteoric diagenesis and represent key flow zones for the initial hydrocarbon production as well as for later high-rate production of formation water and injected water, as identified by production/injection logging tools. Faults and associated fault (damage) zones with abundant fractures are the second key plumbing element. Core recovery and observations from wells located closely to a fault zone identified from detailed seismic interpretation highlight the occurrence of abundant fractures. Tracer information clearly identifies faults as preferential migration pathways between injector and producer wells. Thirdly, induced fracture growth near injectors is an important process in the context of High K development. Hall plots identified that injectivity often increases near injectors after a certain period of injection. Geomechanical analysis suggests that induced fracture growth is plausible due to reduction in total horizontal stress due to the combined effect of depletion and water-injection-induced cooling of the reservoir. Reservoir simulations validate the requirement of presence of these three key elements in the plumbing diagram. Yet, dynamic sensitivity testing shows that the three processes each have a variable impact on flow in the reservoir, depending on local factors such as the nearby presence of faults and the development of the cemented cycle tops. Dynamic simulations further indicate that (1) porosity in the cemented cycle tops is likely low, in line with core observations and out of sync with upscaled porosities and (2) lateral extent of individual cemented cycle tops is in the order of few hundred meters, but final connectivity is enhanced by the stacking of different cemented cycle tops and their concentration in two distinct zones in the Mauddud. Armed with the insight from core analyses and dynamic model results, recommendations are made to improve oil production in SAMA: (1) inject away from the zones and faults, and (2) reduce the injection pressure, thus limiting the likelihood of "new" induced fractures and faults forming that may connect to "old" geologic fracture and faults and cause early-water-breakthrough and thus ineffective oil sweep. Reservoir stimulation via matrix injection, rather than under fracturing conditions, may reduce the initial oil production boost but will most likely lead to significantly better ultimate recovery over the long life of SAMA. • Strong integration between static and dynamic data and different subsurface disciplines for reservoir characterization. • The iterative use of model generation, reservoir simulation and sensitivity testing to narrow down subsurface uncertainty. • Plumbing diagram for conceptualization of flow in complex carbonates and communication amongst subsurface disciplines. [ABSTRACT FROM AUTHOR]

Abstract: Software development for a large project like Wendelstein 7-X (W7-X) requires an appropriate development process to provide a reliable environment supporting the coordinated work of scientific and engineering stakeholders. The first step to achieve this goal was the installation of a central source code management system (SCM). Besides being a code base it provides functions allowing the specification of a release procedure by introducing levels of acceptance specified by appropriate quality criteria. The SCM system serves as a starting point for automatic generation of standard code documentation and for performing automatic testing procedures on standardized integration platforms. In order to provide a universal documentation platform for the multi programming language environment at W7-X the DocSys system has been developed which allows to employ any (language specific) document generator producing html-pages for presentation via internet. An automatic integration testing environment is still being prepared and not yet in standard use. [Copyright &y& Elsevier]

Background: As new technologies allow investigators to collect multiple forms of molecular data (genomic, epigenomic, transcriptomic, etc) and multiple endpoints on a clinical trial cohort, it will become necessary to effectively integrate all these data in a way that reliably identifies biologically important genes., Methods: We introduce CC-PROMISE as an integrated data analysis method that combines components of canonical correlation (CC) and projection onto the most interesting evidence (PROMISE). For each gene, CC-PROMISE first uses CC to compute scores that represent the association of two forms of molecular data with each other. Next, these scores are substituted into PROMISE to evaluate the statistical evidence that the molecular data show a biologically meaningful relationship with the endpoints., Results: CC-PROMISE shows outstanding performance in simulation studies and an example application involving pediatric leukemia. In simulation studies, CC-PROMISE controls the type I error (misleading significance) rate very near the nominal level across 100 distinct null settings in which no molecular-endpoint association exists. Also, CC-PROMISE has better statistical power than three other methods that control type I error in 396 of 400 (99 %) alternative settings for which a molecular-endpoint association is present; the power advantage of CC-PROMISE exceeds 30 % in 127 of the 400 (32 %) alternative settings. These advantages of CC-PROMISE are also observed in an example application., Conclusion: CC-PROMISE very effectively identifies genes for which some form of molecular data shows a biologically meaningful association with multiple related endpoints., Availability: The R package CCPROMISE is currently available from www.stjuderesearch.org/site/depts/biostats/software .