Embryonic Stem Cell-Derived L1 Overexpressing Neural Aggregates Enhance Recovery after Spinal Cord Injury in Mice

An obstacle to early stem cell transplantation into the acutely injured spinal cord is poor survival of transplanted cells. Transplantation of embryonic stem cells as substrate adherent embryonic stem cell-derived neural aggregates (SENAs) consisting mainly of neurons and radial glial cells has been shown to enhance survival of grafted cells in the injured mouse brain. In the attempt to promote the beneficial function of these SENAs, murine embryonic stem cells constitutively overexpressing the neural cell adhesion molecule L1 which favors axonal growth and survival of grafted and imperiled cells in the inhibitory environment of the adult mammalian central nervous system were differentiated into SENAs and transplanted into the spinal cord three days after compression lesion. Mice transplanted with L1 overexpressing SENAs showed improved locomotor function when compared to mice injected with wild-type SENAs. L1 overexpressing SENAs showed an increased number of surviving cells, enhanced neuronal differentiation and reduced glial differentiation after transplantation when compared to SENAs not engineered to overexpress L1. Furthermore, L1 overexpressing SENAs rescued imperiled host motoneurons and parvalbumin-positive interneurons and increased numbers of catecholaminergic nerve fibers distal to the lesion. In addition to encouraging the use of embryonic stem cells for early therapy after spinal cord injury L1 overexpression in the microenvironment of the lesioned spinal cord is a novel finding in its functions that would make it more attractive for pre-clinical studies in spinal cord regeneration and most likely other diseases of the nervous system

Gene Expression Profiling of Two Distinct Neuronal Populations in the Rodent Spinal Cord

BACKGROUND: In the field of neuroscience microarray gene expression profiles on anatomically defined brain structures are being used increasingly to study both normal brain functions as well as pathological states. Fluorescent tracing techniques in brain tissue that identifies distinct neuronal populations can in combination with global gene expression profiling potentially increase the resolution and specificity of such studies to shed new light on neuronal functions at the cellular level. METHODOLOGY/PRINCIPAL FINDINGS: We examine the microarray gene expression profiles of two distinct neuronal populations in the spinal cord of the neonatal rat, the principal motor neurons and specific interneurons involved in motor control. The gene expression profiles of the respective cell populations were obtained from amplified mRNA originating from 50-250 fluorescently identified and laser microdissected cells. In the data analysis we combine a new microarray normalization procedure with a conglomerate measure of significant differential gene expression. Using our methodology we find 32 genes to be more expressed in the interneurons compared to the motor neurons that all except one have not previously been associated with this neuronal population. As a validation of our method we find 17 genes to be more expressed in the motor neurons than in the interneurons and of these only one had not previously been described in this population. CONCLUSIONS/SIGNIFICANCE: We provide an optimized experimental protocol that allows isolation of gene transcripts from fluorescent retrogradely labeled cell populations in fresh tissue, which can be used to generate amplified aRNA for microarray hybridization from as few as 50 laser microdissected cells. Using this optimized experimental protocol in combination with our microarray analysis methodology we find 49 differentially expressed genes between the motor neurons and the interneurons that reflect the functional differences between these two cell populations in generating and transmitting the motor output in the rodent spinal cord

2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

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Improving PBL in Empowering Meta cognitive Skill of Students

Abstract Objective: Problem-Based Learning (PBL) is a potential constructivist learning strategy that empowers students’ Meta cognitive skill. PBL focuses on problem, involves thinking activity to solve problems, and correlates to cognitive function of students. Methods: The implementation of PBL reveals various benefits, but there are also some weaknesses in this learning strategy. Thus, it is necessary to implement a certain learning strategy that can cover the PBL weaknesses, such as Reading, Questioning, and Answering (RQA) learning strategy. RQA is a new learning strategy developed based on a fact that almost all students do not read the next lecture materials, causing failure of learning strategy planned and finally the students’ comprehension becomes low. RQA is also potential to empower students’ Meta cognitive skill. Findings: The integration of RQA and PBL learning strategy is called PBL-RQA learning strategy. This study was a quasi-experimental study designed to compare the effect of PBL, RQA, and PBL-RQA learning strategies on the students’ Meta cognitive skill of Faculty of Mathematics and Science, State University of Makassar. Application: The results of the study showed that the potency of PBL learning strategy in empowering the students’ Meta cognitive skill has been increased by integrating it to RQA learning strategy. The meta cognitive skill mean score of the students taught by PBL-RQA learning strategy was 21% higher than that of the students taught by PBL and 26.9% higher than that of the students taught by RQA learning strategy. Keywords: Answering, Meta Cognitive Skill, Problem-Based Learning, Questioning, Reading, PBL-RQ

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion

Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p

Combined measurement of the Higgs boson mass from the H → γγ and H → ZZ∗ → 4ℓ decay channels with the ATLAS detector using √s = 7, 8, and 13 TeV pp collision data

A measurement of the mass of the Higgs boson combining the H → Z Z ∗ → 4 ℓ and H → γ γ decay channels is presented. The result is based on 140     fb − 1 of proton-proton collision data collected by the ATLAS detector during LHC run 2 at a center-of-mass energy of 13 TeV combined with the run 1 ATLAS mass measurement, performed at center-of-mass energies of 7 and 8 TeV, yielding a Higgs boson mass of 125.11 ± 0.09 ( stat ) ± 0.06 ( syst ) = 125.11 ± 0.11     GeV . This corresponds to a 0.09% precision achieved on this fundamental parameter of the Standard Model of particle physics