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Methods for the Behavioral, Educational, and Social Sciences (MBESS; Kelley, 2007b) is an open source package for R (R Development Core Team, 2007b), an open source statistical programming language and environment. MBESS implements methods that are not widely available elsewhere, yet are especially helpful for the idiosyncratic techniques used within the behavioral, educational, and social sciences. The major categories of functions are those that relate to confidence interval formation for noncentral t, F, and chi2 parameters, confidence intervals for standardized effect sizes (which require noncentral distributions), and sample size planning issues from the power analytic and accuracy in parameter estimation perspectives. In addition, MBESS contains collections of other functions that should be helpful to substantive researchers and methodologists. MBESS is a long-term project that will continue to be updated and expanded so that important methods can continue to be made available to researchers in the behavioral, educational, and social sciences.

During childhood and adolescence, a game could be an effective educational tool to learn healthy eating habits. We developed Kaledo, a new board game, to promote nutrition education and to improve dietary behavior. A two-group design with one pre-treatment assessment and two post-treatment assessments was employed. A total of 3,110 subjects (9–19 years old) from 20 schools in Campania, Italy, were included in the trial. In the treated group, the game was introduced each week over 20 consecutive weeks. Control group did not receive any intervention. The primary outcomes were (i) score on the “Adolescent Food Habits Checklist” (AFHC), (ii) scores on a dietary questionnaire, and (iii) BMI z -score. At the first post-assessment (6 months), the treated group obtained significantly higher scores than the control group on the AFHC (14.4 (95 % confidence interval (CI) 14.0 to 14.8) vs 10.9 (95 % CI 10.6 to −11.2); F(1,20) = 72.677; p  < 0.001) and on four sections of the dietary questionnaire: “nutrition knowledge” (6.5 (6.4 to 6.6) vs 4.6 (4.5 to 4.7); F(1,16) = 78.763; p  < 0.001), “healthy and unhealthy diet and food” (11.2 (11.0 to 11.4) vs 10.4 (10.3 to 10.6); F(1,32) = 21.324; p  < 0.001), “food habits” (32.4 (32.0 to 32.8) vs 27.64 (27.3 to 28.0); F(1,26) = 195.039; p  < 0.001), and “physical activity” (13.4 (13.2 to 13.7) vs 12.0 (11.8 to 12.6); F(1,20) = 20.765; p  < 0.001). Moreover, the treated group had significantly lower BMI z -score with respect to the controls at the first (0.44 (0.42 to 0.46) vs 0.58 (0.56 to 0.59), F(1,18) = 16.584, p  = 0.001) and at the second (18 months) (0.34 (0.30 to 0.38) vs 0.58 (0.54 to 0.62), F(1,13) = 7.577; p  = 0.017) post-assessments. Conclusion : Kaledo improved nutrition knowledge and dietary behavior over 6 months and had a sustained effect on the BMI z -score. Therefore, it may be used as an effective tool in childhood and adolescence obesity prevention programs.

Studying naturalistic animal behavior remains a difficult objective. Recent machine learning advances have enabled limb localization; however, extracting behaviors requires ascertaining the spatiotemporal patterns of these positions. To provide a link from poses to actions and their kinematics, we developed B-SOiD - an open-source, unsupervised algorithm that identifies behavior without user bias. By training a machine classifier on pose pattern statistics clustered using new methods, our approach achieves greatly improved processing speed and the ability to generalize across subjects or labs. Using a frameshift alignment paradigm, B-SOiD overcomes previous temporal resolution barriers. Using only a single, off-the-shelf camera, B-SOiD provides categories of sub-action for trained behaviors and kinematic measures of individual limb trajectories in any animal model. These behavioral and kinematic measures are difficult but critical to obtain, particularly in the study of rodent and other models of pain, OCD, and movement disorders. The study of naturalistic behaviour using video tracking is challenging. Here the authors develop a system, B-SOiD which allows automated behavioural tracking and segmentation of video of movements tested in mice, flies and humans.

Clinical metagenomic next-generation sequencing (mNGS), the comprehensive analysis of microbial and host genetic material (DNA and RNA) in samples from patients, is rapidly moving from research to clinical laboratories. This emerging approach is changing how physicians diagnose and treat infectious disease, with applications spanning a wide range of areas, including antimicrobial resistance, the microbiome, human host gene expression (transcriptomics) and oncology. Here, we focus on the challenges of implementing mNGS in the clinical laboratory and address potential solutions for maximizing its impact on patient care and public health.Clinical metagenomic next-generation sequencing (mNGS) is rapidly moving from bench to bedside. This Review discusses the clinical applications of mNGS, including infectious disease diagnostics, microbiome analyses, host response analyses and oncology applications. Moreover, the authors review the challenges that need to be overcome for mNGS to be successfully implemented in the clinical laboratory and propose solutions to maximize the benefits of clinical mNGS for patients.

We present sample transfer instrumentation and integrated protocols for the preparation and atom probe characterization of environmentally-sensitive materials. Ultra-high vacuum cryogenic suitcases allow specimen transfer between preparation, processing and several imaging platforms without exposure to atmospheric contamination. For expedient transfers, we installed a fast-docking station equipped with a cryogenic pump upon three systems; two atom probes, a scanning electron microscope / Xe-plasma focused ion beam and a N2-atmosphere glovebox. We also installed a plasma FIB with a solid-state cooling stage to reduce beam damage and contamination, through reducing chemical activity and with the cryogenic components as passive cryogenic traps. We demonstrate the efficacy of the new laboratory protocols by the successful preparation and transfer of two highly contamination- and temperature-sensitive samples-water and ice. Analysing pure magnesium atom probe data, we show that surface oxidation can be effectively suppressed using an entirely cryogenic protocol (during specimen preparation and during transfer). Starting with the cryogenically-cooled plasma FIB, we also prepared and transferred frozen ice samples while avoiding significant melting or sublimation, suggesting that we may be able to measure the nanostructure of other normally-liquid or soft materials. Isolated cryogenic protocols within the N2 glove box demonstrate the absence of ice condensation suggesting that environmental control can commence from fabrication until atom probe analysis.

Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle shower data at the calorimeter-cell level, and show significant improvements for simulation and reconstruction when using these networks compared to methods which rely on currently-used state-of-the-art algorithms. We define two models: an end-to-end reconstruction network which performs simultaneous particle identification and energy regression of particles when given calorimeter shower data, and a generative network which can provide reasonable modeling of calorimeter showers for different particle types at specified angles and energies. We investigate the optimization of our models with hyperparameter scans. Furthermore, we demonstrate the applicability of the reconstruction model to shower inputs from other detector geometries, specifically ATLAS-like and CMS-like geometries. These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.

Granger causality methods were developed to analyze the flow of information between time series. These methods have become more widely applied in neuroscience. Frequency-domain causality measures, such as those of Geweke, as well as multivariate methods, have particular appeal in neuroscience due to the prevalence of oscillatory phenomena and highly multivariate experimental recordings. Despite its widespread application in many fields, there are ongoing concerns regarding the applicability of Granger causality methods in neuroscience. When are these methods appropriate? How reliably do they recover the system structure underlying the observed data? What do frequency-domain causality measures tell us about the functional properties of oscillatory neural systems? In this paper, we analyze fundamental properties of Granger–Geweke (GG) causality, both computational and conceptual. Specifically, we show that (i) GG causality estimates can be either severely biased or of high variance, both leading to spurious results; (ii) even if estimated correctly, GG causality estimates alone are not interpretable without examining the component behaviors of the system model; and (iii) GG causality ignores critical components of a system’s dynamics. Based on this analysis, we find that the notion of causality quantified is incompatible with the objectives of many neuroscience investigations, leading to highly counterintuitive and potentially misleading results. Through the analysis of these problems, we provide important conceptual clarification of GG causality, with implications for other related causality approaches and for the role of causality analyses in neuroscience as a whole.

The ICARUS collaboration employed the 760-ton T600 detector in a successful 3-year physics run at the underground LNGS laboratory, performing a sensitive search for LSND-like anomalous ν e appearance in the CERN Neutrino to Gran Sasso beam, which contributed to the constraints on the allowed neutrino oscillation parameters to a narrow region around 1 eV 2 . After a significant overhaul at CERN, the T600 detector has been installed at Fermilab. In 2020 the cryogenic commissioning began with detector cool down, liquid argon filling and recirculation. ICARUS then started its operations collecting the first neutrino events from the booster neutrino beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis, which were used to test the ICARUS event selection, reconstruction and analysis algorithms. ICARUS successfully completed its commissioning phase in June 2022. The first goal of the ICARUS data taking will be a study to either confirm or refute the claim by Neutrino-4 short-baseline reactor experiment. ICARUS will also perform measurement of neutrino cross sections with the NuMI beam and several Beyond Standard Model searches. After the first year of operations, ICARUS will search for evidence of sterile neutrinos jointly with the Short-Baseline Near Detector, within the Short-Baseline Neutrino program. In this paper, the main activities carried out during the overhauling and installation phases are highlighted. Preliminary technical results from the ICARUS commissioning data with the BNB and NuMI beams are presented both in terms of performance of all ICARUS subsystems and of capability to select and reconstruct neutrino events.

Many substances of forensic interest are chiral and available either as racemates or pure enantiomers. Application of chiral analysis in biological samples can be useful for the determination of legal or illicit drugs consumption or interpretation of unexpected toxicological effects. Chiral substances can also be found in environmental samples and revealed to be useful for determination of community drug usage (sewage epidemiology), identification of illicit drug manufacturing locations, illegal discharge of sewage and in environmental risk assessment. Thus, the purpose of this paper is to provide an overview of the application of chiral analysis in biological and environmental samples and their relevance in the forensic field. Most frequently analytical methods used to quantify the enantiomers are liquid and gas chromatography using both indirect, with enantiomerically pure derivatizing reagents, and direct methods recurring to chiral stationary phases. © 2018 by the authors.

Optically controlled nongenetic neuromodulation represents a promising approach for the fundamental study of neural circuits and the clinical treatment of neurological disorders. Among the existing material candidates that can transduce light energy into biologically relevant cues, silicon (Si) is particularly advantageous due to its highly tunable electrical and optical properties, ease of fabrication into multiple forms, ability to absorb a broad spectrum of light, and biocompatibility. This protocol describes a rational design principle for Si-based structures, general procedures for material synthesis and device fabrication, a universal method for evaluating material photoresponses, detailed illustrations of all instrumentation used, and demonstrations of optically controlled nongenetic modulation of cellular calcium dynamics, neuronal excitability, neurotransmitter release from mouse brain slices, and brain activity in the mouse brain in vivo using the aforementioned Si materials. The entire procedure takes ~4–8 d in the hands of an experienced graduate student, depending on the specific biological targets. We anticipate that our approach can also be adapted in the future to study other systems, such as cardiovascular tissues and microbial communities. This protocol describes how to fabricate and apply silicon-based structures for optically controlled neuromodulation. The structures can be used for nongenetic neuronal excitation in cultured neurons, brain slices, and in vivo applications.