Examining the effectiveness of technology use in classrooms: A tertiary meta-analysis

Identifying effective literacy instruction programs has been a focal point for governments, educators and parents over the last few decades (Ontario Ministry of Education, 2004, 2006; Council of Ontario Directors of Education, 2011). Given the increasing use of computer technologies in the classroom and in the home, a variety of information communication technology (ICT) interventions for learning have been introduced. Meta-analyses comparing the impact of these programs on learning, however, have yielded inconsistent findings (Andrews, Freeman, Hou, McGuinn, Robinson, & Zhu, 2007; Slavin, Cheung, Groff, & Lake, 2008; Slavin, Lake, Chambers, Cheung, & Davis, 2009; Torgerson & Zhu, 2003). The present tertiary meta-analytic review re-assesses outcomes presented in three previous meta-analyses. Four moderator variables assessed the impact of the systematic review from which they were retrieved, training and support, implementation fidelity and who delivered the intervention (teacher versus researcher). Significant results were found when training and support was entered as a moderator variable with the small overall effectiveness of the ICTs (ES = 0.18), similar to those found in previous research, increasing significantly (ES = 0.57). These findings indicate the importance of including implementation factors such as training and support, when considering the relative effectiveness of ICT interventions

Clonal kinetics and single-cell transcriptional profiling of CAR-T cells in patients undergoing CD19 CAR-T immunotherapy

Chimeric antigen receptor (CAR) T-cell therapy has produced remarkable anti-tumor responses in patients with B-cell malignancies. However, clonal kinetics and transcriptional programs that regulate the fate of CAR-T cells after infusion remain poorly understood. Here we perform TCRB sequencing, integration site analysis, and single-cell RNA sequencing (scRNA-seq) to profile CD8+ CAR-T cells from infusion products (IPs) and blood of patients undergoing CD19 CAR-T immunotherapy. TCRB sequencing shows that clonal diversity of CAR-T cells is highest in the IPs and declines following infusion. We observe clones that display distinct patterns of clonal kinetics, making variable contributions to the CAR-T cell pool after infusion. Although integration site does not appear to be a key driver of clonal kinetics, scRNA-seq demonstrates that clones that expand after infusion mainly originate from infused clusters with higher expression of cytotoxicity and proliferation genes. Thus, we uncover transcriptional programs associated with CAR-T cell behavior after infusion.Published versio

Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data.

An important aspect of immune monitoring for vaccine development, clinical trials, and research is the detection, measurement, and comparison of antigen-specific T-cells from subject samples under different conditions. Antigen-specific T-cells compose a very small fraction of total T-cells. Developments in cytometry technology over the past five years have enabled the measurement of single-cells in a multivariate and high-throughput manner. This growth in both dimensionality and quantity of data continues to pose a challenge for effective identification and visualization of rare cell subsets, such as antigen-specific T-cells. Dimension reduction and feature extraction play pivotal role in both identifying and visualizing cell populations of interest in large, multi-dimensional cytometry datasets. However, the automated identification and visualization of rare, high-dimensional cell subsets remains challenging. Here we demonstrate how a systematic and integrated approach combining targeted feature extraction with dimension reduction can be used to identify and visualize biological differences in rare, antigen-specific cell populations. By using OpenCyto to perform semi-automated gating and features extraction of flow cytometry data, followed by dimensionality reduction with t-SNE we are able to identify polyfunctional subpopulations of antigen-specific T-cells and visualize treatment-specific differences between them

Identification and rejection of scattered neutrons in AGATA

Gamma rays and neutrons, emitted following spontaneous fission of 252Cf, were measured in an AGATA experiment performed at INFN Laboratori Nazionali di Legnaro in Italy. The setup consisted of four AGATA triple cluster detectors (12 36-fold segmented high-purity germanium crystals), placed at a distance of 50 cm from the source, and 16 HELENA BaF2 detectors. The aim of the experiment was to study the interaction of neutrons in the segmented high-purity germanium detectors of AGATA and to investigate the possibility to discriminate neutrons and gamma rays with the gamma-ray tracking technique. The BaF2 detectors were used for a time-of-flight measurement, which gave an independent discrimination of neutrons and gamma rays and which was used to optimise the gamma-ray tracking-based neutron rejection methods. It was found that standard gamma-ray tracking, without any additional neutron rejection features, eliminates effectively most of the interaction points due to recoiling Ge nuclei after elastic scattering of neutrons. Standard tracking rejects also a significant amount of the events due to inelastic scattering of neutrons in the germanium crystals. Further enhancements of the neutron rejection was obtained by setting conditions on the following quantities, which were evaluated for each event by the tracking algorithm: energy of the first and second interaction point, difference in the calculated incoming direction of the gamma ray, figure-of-merit value. The experimental results of tracking with neutron rejection agree rather well with Geant4 simulations

An approach for integrating multimodal omics data into sparse and interpretable models.

Using omics data, a common goal is to identify a concise set of variables that predict a clinical endpoint from an extensive pool. In a recent paper published in Nature Biotechnology, Hédou et al. <sup>1</sup> introduced Stabl, a computational method crafted to identify sparse yet robust signatures linked to endpoints

Measurement of the cross-section and charge asymmetry of WW bosons produced in proton-proton collisions at s=8\sqrt{s}=8 TeV with the ATLAS detector

This paper presents measurements of the $W^+ \rightarrow \mu^+\nu$ and $W^- \rightarrow \mu^-\nu$ cross-sections and the associated charge asymmetry as a function of the absolute pseudorapidity of the decay muon. The data were collected in proton--proton collisions at a centre-of-mass energy of 8 TeV with the ATLAS experiment at the LHC and correspond to a total integrated luminosity of 20.2~\mbox{fb^{-1}}. The precision of the cross-section measurements varies between 0.8% to 1.5% as a function of the pseudorapidity, excluding the 1.9% uncertainty on the integrated luminosity. The charge asymmetry is measured with an uncertainty between 0.002 and 0.003. The results are compared with predictions based on next-to-next-to-leading-order calculations with various parton distribution functions and have the sensitivity to discriminate between them.Comment: 38 pages in total, author list starting page 22, 5 figures, 4 tables, submitted to EPJC. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/STDM-2017-13

Search for chargino-neutralino production with mass splittings near the electroweak scale in three-lepton final states in √s=13 TeV pp collisions with the ATLAS detector

A search for supersymmetry through the pair production of electroweakinos with mass splittings near the electroweak scale and decaying via on-shell W and Z bosons is presented for a three-lepton final state. The analyzed proton-proton collision data taken at a center-of-mass energy of √s=13  TeV were collected between 2015 and 2018 by the ATLAS experiment at the Large Hadron Collider, corresponding to an integrated luminosity of 139  fb−1. A search, emulating the recursive jigsaw reconstruction technique with easily reproducible laboratory-frame variables, is performed. The two excesses observed in the 2015–2016 data recursive jigsaw analysis in the low-mass three-lepton phase space are reproduced. Results with the full data set are in agreement with the Standard Model expectations. They are interpreted to set exclusion limits at the 95% confidence level on simplified models of chargino-neutralino pair production for masses up to 345 GeV

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology