Point of Reference: A Multisited Exploration of African Migration and Fertility in France

A considerable literature explores whether the fertility of migrants from high-fertility contexts converges with that of women in lower fertility destinations. Nonetheless, much of this research compares migrants’ reproductive outcomes to those of native-born women in destination countries. Drawing on research emphasizing the importance of transnational perspectives, we standardize and integrate data collected in France (the destination) and in six high-fertility African countries (the senders). We show that African migrants in our sample had higher children ever born (CEB) than native French women but lower CEB than women in corresponding origin countries. These findings suggest that socialization into pronatalist norms is an incomplete explanation for migrant fertility in the first generation, an insight that is overlooked when analyzing destination settings only. Next, we conduct multivariate analyses that weight migrants’ background characteristics to resemble women in both origin and destination countries. Findings indicate that observed differences between African migrants in France and women in African origin countries help explain differences in CEB between the two groups, which supports selection. We also demonstrate that African migrants in France had delayed transitions into first, second, and third births and lower completed fertility compared to women in origin countries, thus disputing the disruption hypothesis. Finally, we show that observed differences between African migrants in France and native French women explain differences in CEB between the two groups, which supports adaptation. These multifaceted findings on selection, disruption, and adaptation would be obscured by analyzing destination settings only, thus validating a multisited approach to migrant fertility.

ROmodel: modeling robust optimization problems in Pyomo

This paper introduces ROmodel, an open source Python package extending the modeling capabilities of the algebraic modeling language Pyomo to robust optimization problems. ROmodel helps practitioners transition from deterministic to robust optimization through modeling objects which allow formulating robust models in close analogy to their mathematical formulation. ROmodel contains a library of commonly used uncertainty sets which can be generated using their matrix representations, but it also allows users to define custom uncertainty sets using Pyomo constraints. ROmodel supports adjustable variables via linear decision rules. The resulting models can be solved using ROmodels solvers which implement both the robust reformulation and cutting plane approach. ROmodel is a platform to implement and compare custom uncertainty sets and reformulations. We demonstrate ROmodel’s capabilities by applying it to six case studies. We implement custom uncertainty sets based on (warped) Gaussian processes to show how ROmodel can integrate data-driven models with optimization.

Data Integration Project in Robologee

Robologee is a sub-unit of PT. Bangun Satya Wacana is part of Kompas Gramedia which is focused in Education section for ages 7 to 12 years. Robologee is a diversification of the existing sub-units in PT. Bangun Satya Wacana. Robologee has branches located at Gramedia World so it is expected that it will have an impact on Gramedia traffic. Currently, Robologee is transforming in order to integrate data that will be stored in the cloud by Amazon Web Service.The goal of this project is that data can be accessed by various users and stored in one platform. In the analysis of the digital transformation project, 15 respondents have been determined who are parents as external customers. Based on the indicators used in DMM. It was found that Robologee's current condition is at the Advancing level. Based on the Roadmap this project is implemented for 1 year and consists of four stages. In the Budgeting analysis, Robologee has payback period of 1.7 years with an IRR of 7.512% greater than the expected return of 5% by the company. Then the NVP is in a positive number, so this project is feasible to implement.

On the Use of Gradient Boosting Methods to Improve the Estimation with Data Obtained with Self-Selection Procedures

In the last years, web surveys have established themselves as one of the main methods in empirical research. However, the effect of coverage and selection bias in such surveys has undercut their utility for statistical inference in finite populations. To compensate for these biases, researchers have employed a variety of statistical techniques to adjust nonprobability samples so that they more closely match the population. In this study, we test the potential of the XGBoost algorithm in the most important methods for estimation that integrate data from a probability survey and a nonprobability survey. At the same time, a comparison is made of the effectiveness of these methods for the elimination of biases. The results show that the four proposed estimators based on gradient boosting frameworks can improve survey representativity with respect to other classic prediction methods. The proposed methodology is also used to analyze a real nonprobability survey sample on the social effects of COVID-19.

Digital twin predicting diet response before and after long-term fasting

Today, there is great interest in diets proposing new combinations of macronutrient compositions and fasting schedules. Unfortunately, there is little consensus regarding the impact of these different diets, since available studies measure different sets of variables in different populations, thus only providing partial, non-connected insights. We lack an approach for integrating all such partial insights into a useful and interconnected big picture. Herein, we present such an integrating tool. The tool uses a novel mathematical model that describes mechanisms regulating diet-response and fasting metabolic fluxes, both for organ-organ crosstalk, and inside the liver. The tool can mechanistically explain and integrate data from several clinical studies, and correctly predict new independent data, including data from a new clinical study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, and we can quantify personalized expected differences in outcome for any diet. This constitutes a new digital twin technology.

Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor

Background: The majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. Results: We collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in the Bioconductor Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within the Bioconductor ecosystem of hundreds of packages for single-cell and multimodal data. Conclusions: We provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease.

More Than Just a Weed: An Exploration of the Antimicrobial Activity of Rumex crispus using a Multivariate Data Analysis Approach

Plants have a long history of use for their medicinal properties. The complexity of botanical extracts presents unique challenges and necessitates the application of innovative approaches to correctly identify and quantify bioactive compounds. For this study, we used untargeted metabolomics to explore the antimicrobial activity of Rumex crispus (yellow dock), a member of the Polygonaceae family used as an herbal remedy for bacterial infections. Ultra-performance liquid chromatography coupled with high resolution mass-spectrometry (UPLC-MS) was used to identify and quantify the known antimicrobial compound emodin. In addition, we used biochemometric approaches to integrate data measuring antimicrobial activity from R. crispus root starting material and fractions against methicillin-resistant Staphylococcus aureus (MRSA) with UPLC-MS data. Our results support the hypothesis that multiple constituents, including the anthraquinone emodin, contribute to the antimicrobial activity of R. crispus against MRSA.

SMILE: Mutual Information Learning for Integration of Single-cell Omics Data

Motivation Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single cell omics data to be integrated across sources, types, and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). Results Using a unique cell-pairing design, SMILE successfully integrates multi-source single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also integrate data from two or more modalities, such as joint profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C, and ChIP data. When paired cells are known, SMILE can integrate data with unmatched feature, such as genes for RNA-seq and genome wide peaks for ATAC-seq. Integrated representations learned from joint profiling technologies can then be used as a framework for comparing independent single source data. Supplementary information Supplementary data are available at Bioinformatics online. The source code of SMILE including analyses of key results in the study can be found at: https://github.com/rpmccordlab/SMILE.