THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

animals used (i.e., species/strain, sex, and age/weight). Most of the papers surveyed did not report using randomisation (87%) or blinding (86%) to reduce bias in animal selection and outcome assessment. Only 70% of the publications that used statistical methods fully described them and presented the results with a measure of precision or variability [5]. These findings are a cause for concern and are consistent with reviews of many research areas, including clinical studies, published in recent years [2–22].

https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.1000412&type=printable

Improving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Gut commensal microbes shape the mucosal immune system by regulating the differentiation and expansion of several types of T cell. Clostridia, a dominant class of commensal microbe, can induce colonic regulatory T (Treg) cells, which have a central role in the suppression of inflammatory and allergic responses. However, the molecular mechanisms by which commensal microbes induce colonic Treg cells have been unclear. Here we show that a large bowel microbial fermentation product, butyrate, induces the differentiation of colonic Treg cells in mice. A comparative NMR-based metabolome analysis suggests that the luminal concentrations of short-chain fatty acids positively correlates with the number of Treg cells in the colon. Among short-chain fatty acids, butyrate induced the differentiation of Treg cells in vitro and in vivo, and ameliorated the development of colitis induced by adoptive transfer of CD4(+) CD45RB(hi) T cells in Rag1(-/-) mice. Treatment of naive T cells under the Treg-cell-polarizing conditions with butyrate enhanced histone H3 acetylation in the promoter and conserved non-coding sequence regions of the Foxp3 locus, suggesting a possible mechanism for how microbial-derived butyrate regulates the differentiation of Treg cells. Our findings provide new insight into the mechanisms by which host-microbe interactions establish immunological homeostasis in the gut.

Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msi-workgroups-feedback@lists.sourceforge.net. Further, community input related to this document can also be provided via this electronic forum.

/pdf/proposed-minimum-reporting-standards-for-chemical-analysis-4ohwb35hkf.pdf

Proposed minimum reporting standards for chemical analysis

Out-of-Distribution Example Detection in Deep Neural Networks using Distance to Modelled Embedding

La presente invention concerne un ensemble de genes qui, quand ils sont modifies dans des plantes, provoque une modification des proprietes de la lignine. L'invention concerne egalement une construction d'ADN, tel qu'un vecteur, convenant au procede de l'invention. L'invention concerne en outre une cellule de plante ou une descendance des plantes et le bois produit par les plantes selon l'invention. L'abaissement des teneurs en lignine aboutit a une meilleure qualite, d'une part de la saccharification en cas de bio-raffinage et de production d'ethanol, et d'autre part de la pulpe et du papier. L'augmentation des teneurs en lignine met en œuvre des proprietes de la lignine destinees a la production d'energie. Les genes et les constructions d'ADN peuvent etre utilises pour l'identification de plantes presentant des caracteristiques modifiees de leur lignine par comparaison aux types sauvages. Selon l'invention, les genes et les constructions d'ADN peuvent egalement etre utilises comme candidats genes dans la selection assistee par marqueurs.

Matière végétale, plantes et procédé de production d'une plante dont les propriétés de la lignine sont modifiées

Buildings are highly energy-consuming and therefore are largely accountable for environmental degradation. Detecting anomalous energy consumption is one of the effective ways to reduce energy consumption. Besides, it can contribute to the safety and robustness of building systems since anomalies in the energy data are usually the reflection of malfunctions in building systems. As the most flexible and applicable type of anomaly detection approach, unsupervised anomaly detection has been implemented in several studies for building energy data. However, no studies have investigated the joint influence of data structures and algorithms’ mechanisms on the performance of unsupervised anomaly detection for building energy data. Thus, we put forward a novel workflow based on two levels, data structure level and algorithm mechanism level, to effectively detect the imperceptible anomalies in the energy consumption profiles of buildings. The proposed workflow was implemented in a case study for identifying the anomalies in three real-world energy consumption datasets from two types of commercial buildings. Two aims were achieved through the case study. First, it precisely detected the contextual anomalies concealed beneath the time variation of the energy consumption profiles of the three buildings. The performance in terms of areas under the precision-recall curves (AUC_PR) for the three given datasets were 0.989, 0.941, and 0.957, respectively. Second, more broadly, the joint effect of the two levels was examined. On the data level, all four detectors on the contextualized data were superior to their counterparts on the original data. On the algorithm level, there was a consistent ranking of detectors regarding their detecting performances on the contextualized data. The consistent ranking suggests that local approaches outperform global approaches in the scenarios where the goal is to detect the instances deviating from their contextual neighbors rather than the rest of the entire data.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09737138.pdf

Toward Delicate Anomaly Detection of Energy Consumption for Buildings: Enhance the Performance From Two Levels

Discrimination between cell types in the co-culture environment with multiple cell lines can assist in examining the interaction between different cell populations. Identifying different cell cultures in addition to cell segmentation in co-culture is essential for understanding the cellular mechanisms associated with disease states. In drug development, biologists are more interested in co-culture models because they replicate the tumor environment in vivo better than the monoculture models. Additionally, they have a measurable effect on cancer cell response to treatment. Co-culture models are critical for designing a drug with maximum efficacy on cancer while minimizing harm to the rest of the body. In the past, there existed minimal progress related to cell-type aware segmentation in the monoculture and no development whatsoever for the co-culture. The introduction of the LIVECell dataset has allowed us to perform experiments for cell-type-aware segmentation. However, it is composed of microscopic images in a monoculture environment. This paper presents a framework for co-culture microscopic image data generation, where each image can contain multiple cell cultures. The framework also presents a pipeline for culture-dependent cell segmentation in co-culture microscopic images. The extensive evaluation revealed that it is possible to achieve cell-type aware segmentation in co-culture microscopic images with good precision.

DeepMuCS: A Framework for Co-culture Microscopic Image Analysis: From Generation to Segmentation

Plant cell walls are composed of a number of coextensive polysaccharide-rich networks (i.e., pectin, hemicellulose, protein). Polysaccharide-rich cell walls are important in a number of biological processes including fruit ripening, plant-pathogen interactions (e.g., pathogenic fungi), fermentations (e.g., winemaking), and tissue differentiation (e.g., secondary cell walls). Applying appropriate methods is necessary to assess biological roles as for example in putative plant gene functional characterization (e.g., experimental evaluation of transgenic plants). Obtaining datasets is relatively easy, using for example gas chromatography-mass spectrometry (GC-MS) methods for monosaccharide composition, Fourier transform infrared spectroscopy (FT-IR) and comprehensive microarray polymer profiling (CoMPP); however, analyzing the data requires implementing statistical tools for large-scale datasets. We have validated and implemented a range of multivariate data analysis methods on datasets from tobacco, grapevine, and wine polysaccharide studies. Here we present the workflow from processing samples to acquiring data to performing data analysis (particularly principal component analysis (PCA) and orthogonal projection to latent structure (OPLS) methods).

Johan Trygg

Papers

Out-of-Distribution Example Detection in Deep Neural Networks using Distance to Modelled Embedding

Matière végétale, plantes et procédé de production d'une plante dont les propriétés de la lignine sont modifiées

Toward Delicate Anomaly Detection of Energy Consumption for Buildings: Enhance the Performance From Two Levels

DeepMuCS: A Framework for Co-culture Microscopic Image Analysis: From Generation to Segmentation

Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods.