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Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the data would lead to large changes in the chosen feature subset, then many selected features are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metrics (MSE or AUC) with proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications with continuous or binary outcomes. We conclude that Stability is a preferred feature selection criterion over model prediction metrics because it better quantifies the reproducibility of the feature selectionmethod.

Iron oxides are chemical compounds which have different polymorphic forms, including γ-Fe2O3 (maghemite), Fe3O4 (magnetite), and FeO (wustite). Among them, the most studied are γ-Fe2O3 and Fe3O4, as they possess extraordinary properties at the nanoscale (such as super paramagnetism, high specific surface area, biocompatible etc.), because at this size scale, the quantum effects affect matter behavior and optical, electrical and magnetic properties. Therefore, in the nanoscale, these materials become ideal for surface functionalization and modification in various applications such as separation techniques, magnetic sorting (cells and other biomolecules etc.), drug delivery, cancer hyperthermia, sensing etc., and also for increased surface area-to-volume ratio, which allows for excellent dispersibility in the solution form. The current methods used are partially and passively mixed reactants, and, thus, every reaction has a different proportion of all factors which causes further difficulties in reproducibility. Direct active and complete mixing and automated approaches could be solutions to this size- and shape-controlled synthesis, playing a key role in its exploitation for scientific or technological purposes. An ideal synthesis method should be able to allow reliable adjustment of parameters and control over the following: fluctuation in temperature; pH, stirring rate; particle distribution; size control; concentration; and control over nanoparticle shape and composition i.e., crystallinity, purity, and rapid screening. Iron oxide nanoparticle (IONP)-based available clinical applications are RNA/DNA extraction and detection of infectious bacteria and viruses. Such technologies are important at POC (point of care) diagnosis. IONPs can play a key role in these perspectives. Although there are various methods for synthesis of IONPs, one of the most crucial goals is to control size and properties with high reproducibility to accomplish successful applications. Using multiple characterization techniques to identify and confirm the oxide phase of iron can provide better characterization capability. It is very important to understand the in-depth IONP formation mechanism, enabling better control over parameters and overall reaction and, by extension, properties of IONPs. This work provides an in-depth overview of different properties, synthesis methods, and mechanisms of iron oxide nanoparticles (IONPs) formation, and the diverse range of their applications. Different characterization factors and strategies to confirm phase purity in the IONP synthesis field are reviewed. First, properties of IONPs and various synthesis routes with their merits and demerits are described. We also describe different synthesis strategies and formation mechanisms for IONPs such as for: wustite (FeO), hematite (α-Fe2O3), maghemite (ɤ-Fe2O3) and magnetite (Fe3O4). We also describe characterization of these nanoparticles and various applications in detail. In conclusion, we present a detailed overview on the properties, size-controlled synthesis, formation mechanisms and applications of IONPs.

This paper is a tutorial developed for the data analysis platform Galaxy. The purpose of Galaxy is to make high-throughput computational data analysis, such as molecular dynamics, a structured, reproducible and transparent process. In this tutorial we focus on 3 questions: How are protein-ligand systems parameterized for molecular dynamics simulation? What kind of analysis can be carried out on molecular trajectories? How can high-throughput MD be used to study multiple ligands? After finishing you will have learned about force-fields and MD parameterization, how to conduct MD simulation and analysis for a protein-ligand system, and understand how different molecular interactions contribute to the binding affinity of ligands to the Hsp90 protein.

The hollow polyaniline (PANI) micro/nanospheres are obtained through a simple monomer polymerization in alkaline solution with Triton X-100 Micelles as soft templates. The hollow PANI micro/nanospheres demonstrate rapid and effective removal ability for Chromium (VI) (Cr (VI)) in a wide pH range, and the maximum removal capacity can reach 127.88 mg/g at pH 3. After treated with acid, the used hollow PANI micro/nanospheres have about the similar removal capacity of Cr (VI) from wastewater.

A fully-worked example of decision-support-scale uncertainty quantification (UQ) and data assimilation (DA) is presented. The analyses are implemented for an existing groundwater flow model of the Edwards Aquifer, Texas, USA, and are completed in a script-based workflow that strives to be transparent and reproducible. High-dimensional parameter DA is used to history-match simulated outputs to corresponding state observations of spring discharge and groundwater level. Then a hindcast of a historic drought is made. Using available state observations recorded during drought conditions, the combined UQ and DA analyses are shown to yield an ensemble of model results that bracket the observed hydrologic responses. All files and scripts used for the analyses are placed in the public-domain to serve as a template for other practitioners who are interested in undertaking these types of analyses.

Open science principles that seek to improve science can effectively bridge the gap between researchers and environmental managers. However, widespread adoption has yet to gain traction for the development and application of bioassessment products. At the core of this philosophy is the concept that research should be reproducible and transparent, in addition to having long-term value through effective data preservation and sharing. In this article, we review core open science concepts that have recently been adopted in the ecological sciences and emphasize how adoption can benefit the field of bioassessment for both prescriptive condition assessments and proactive applications that inform environmental management. An example from the state of California demonstrates effective adoption of open science principles through data stewardship, reproducible research, and engagement of stakeholders with multimedia applications. We also discuss technical, sociocultural, and institutional challenges for adopting open science, including practical approaches for overcoming these hurdles in bioassessment applications.

We present a microfabricated spiral-coupled passive resonator sensor realized through integrated passive device (IPD) technology for the sensitive detection and characterization of water-ethanol solutions. In order to validate the performance of the proposed device, we explicitly measured and analyzed the radio frequency (RF) characteristics of various water-ethanol solution compositions. The measured results showed a drift in the resonance frequency from 1.16 GHz for deionized (DI) water to 1.68 GHz for the solution containing 50% ethanol, whereas the rejection level given by the reflection coefficient decreased from −29.74 dB to −14.81 dB. The obtained limit of detection was 3.82% volume composition of ethanol in solution. The derived loaded capacitance was 21.76 pF for DI water, which gradually decreased to 8.70 pF for the 50% ethanol solution, and the corresponding relative permittivity of the solution decreased from 80.14 to 47.79. The dissipation factor increased with the concentration of ethanol in the solution. We demonstrated the reproducibility of the proposed sensor through iterative measures of the samples and the study of surface morphology. Successive measurement of different samples had no overlapping and had very minimum bias between RF characteristics for each measured sample. The surface profile for bare sensors was retained after the sample test, resulting a root mean square (RMS) value of 11.416 nm as compared to 10.902 nm for the bare test. The proposed sensor was shown to be a viable alternative to existing sensors for highly sensitive water-ethanol concentration detection.

In the search for better or new methods/techniques to visualise fingermarks or to analyse them exploiting their chemical content, fingermarks inter-variability may hinder the assessment of the method effectiveness. Variability is due to changes in the chemical composition of the fingermarks between different donors and within the same donor, as well as to differential contact time, pressure and angle. When validating a method or comparing it with existing ones, it is not always possible to account for this type of variability. One way to compensate for these issues is to employ, in the early stages of the method development, a device generating reproducible fingermarks. Here the authors present their take on such device, as well as quantitatively describing its performance and benefits against the manual production of marks. Finally a short application is illustrated for the use of this device, at the method developmental stages, in an emerging area of fingerprinting research concerning the retrieval of chemical intelligence from fingermarks.

Guava (Psidium guajava L.) is an open-pollinated crop having 25-40% dissimilarity index which promotes heterozygosity and adds new cultivars. Morpho-genetic characterization of 37 guava accessions was carried out for genetic variability and structure of guava germplasm located in Punjab province, Pakistan. Principal Component analysis (PCA) was subjected to analyze the morphological diversity and for genetic analysis we applied cluster analysis, using the PyElph software. PCA distributed thirty one traits into six components and first two components accounted 39.5% of total variation. A dendrogram constructed on the basis of morphological traits which showed 34% dissimilarity index among thirty seven guava accessions and divided them into 6 groups. For genetic characterization 18 microsatellites were used, the size of reproducible and scorable bands ranged from 150 to 320 bp. The 18 primer pairs amplified 85 alleles with an average number of 4.7 alleles per locus and no more than two displayed bands (nuclear SSR loci). The phylogenetic tree based on molecular analysis showed 50% dissimilarity index among selected guava accessions and separated them into 4 groups.

Succinonitrile samples with a reproducible and stable substructure are obtained by compression creep of single crystals at room temperature. The succinonitrile single crystals are prepared using the Bridgman method. The crystalline perfection of the samples is controlled with Laue patterns obtained in a camera devised for low temperature diffraction. The material used for crystal growth contains 50 ppm of impurities, it has been purified by successive distillations followed by zone refining. The creep deformation used to introduce the substructure allows also to study the mechanical behaviour of succinonitrile. The experiments result in mechanical properties comparable to those of the other materials Preparation de polycristaux par fluage en compression a temperature ambiante d'eprouvettes monocristallines. Preparation des monocristaux par la methode de Bridgman a partir d'un materiau purifie par distillations successives et fusion de zone. Controle de la perfection cristalline par diffraction Laue et microscopie optique