Searching for a Pair-Produced Supersymmetric Top Partner Using Recursive Jigsaw Variables and Boosted Decision Trees with 139 fb⁻¹ of Data From the ATLAS Detector

The Standard Model of Particle Physics is the most comprehensive theory describing how fundamental particles and three of the four fundamental forces are related. However, the Standard Model is known to be an incomplete theory with several limitations. Supersymmetry is an extension of the Standard Model of Particle Physics, introducing supersymmetric partners to every fermion and boson in the Standard Model. Supersymmetry gives a diverse collection of theoretical models providing solutions to these phenomenological inconsistencies. It contains a mechanism for stabilizing the Higgs boson mass while predicting the existence of several new particles, including a suitable Dark Matter candidate. The Large Hadron Collider (LHC) is the world\u27s most powerful particle accelerator, located at the CERN laboratory near Geneva, Switzerland. In the Summer of 2012, the ATLAS and CMS experiments at CERN announced the discovery of a particle,which was later confirmed to be the Higgs boson. This was a massive accomplishment, the discovery of a particle hypothesized in 1964 that has remained elusive until now. However, this is not the end of the experimental effort. ATLAS and CMS are general purpose detectors performing a multitude of measurements, as well as carrying out many searches for Beyond the Standard Model (BSM) physics. In this dissertation, two searches are conducted for a pair-produced stop squark, the supersymmetric partner to the top quark. The stop can decay to a variety of final states, depending upon the hierarchy of the mass eigenstates formed from the linear superposition of the SUSY partners of the Higgs boson and electroweak gauge bosons. In this stop search, the relevant supersymmetric mass eigenstate is the neutralino. The searches for the stop in the 3-body decay channel presented here consist of a b-quark, W-boson, and a neutralino, with both W-bosons decaying to a lepton and a neutrino. In order to discriminate the signal from background two techniques are employed, a cut-and-count technique using recursive jigsaw variables and a technique using Boosted Decision Trees. The recursive jigsaw variables are derived using the Recursive Jigsaw Reconstruction technique, a method for decomposing measured properties event-by-event by approximating the rest frame of each intermediate particle state. These variables are powerful discriminators on their own, as shown in the cut-and-count analysis. Machine learning techniques are also utilized by training boosted decision trees, using the recursive jigsaw variables in tandem with other kinematic variables, to study whether we can enhance our discovery potential. These analyses use 139 inverse fb of 13 TeV data collected at the ATLAS experiment during Run-2 of the LHC from 2015 until 2018. No evidence of an excess beyond the SM background prediction is observed in the Recursive Jigsaw Reconstruction analysis, however, exclusion limits at 95% confidence levels are set far exceeding the previous limits. The potential for an improvement on these limits is demonstrated by training Boosted Decision Trees, a technique I hope is used in future BSM physics searches

Dark Matter Science Project

A Dark Matter Science Project is being developed in the context of the ESCAPE (European Science Cluster of Astronomy and Particle physics ESFRI research infrastructure) project as a collaboration between scientists in European Research Infrastructures and experiments seeking to explain the nature of dark matter (such as HL-LHC, KM3NeT, CTA, DarkSide). The goal of this ESCAPE Science Project is to highlight the synergies between different dark matter communities and experiments, by producing new scientific results as well as by making the necessary data and software tools fully available. As part of this Science Project, we use experimental data and software algorithms from selected direct detection, indirect detection, and particle collider experiments involved in ESCAPE as prototypes for end-to-end analysis pipelines on a Virtual Research Environment that is being prepared as one of the building blocks of the European Open Science Cloud (EOSC). This contribution focuses on the implementation of the workflows on the Virtual Research Environment using ESCAPE tools (such as the Data Lake and REANA), and on the prospects for data management, data analysis and computing in the EOSC-Future project

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.Postprint (published version

“I was trying to save the world”: delusion-like ideation and associated impacts reported by Western practitioners of Buddhist meditation

Delusional ideation is characteristic of psychopathology (e.g., psychosis, bipolar disorder) and is also found among the general population. Contemporary case studies have documented delusional ideation as a feature of meditation-induced psychosis, and Buddhist literature on the side effects and adverse effects of meditation also includes discussion of transient experiences that could be considered delusional or delusion-like ideation. Drawing upon interviews with more than 100 Buddhist meditation practitioners and meditation experts (teachers and clinicians) in the West, this paper presents a mixed-methods study of delusion-like ideation (DLI) associated with meditation. We establish a typology of eight types of DLI and report their relative frequencies among the sample; we identify impacts and treatment outcomes associated with DLI; and we provide four case studies that illustrate the risk factors, trajectories, outcomes, and appraisals associated with DLI. We show how responses to DLI are shaped not only by the type of DLI but also by their duration, severity, and impact, as well as the associated appraisals made both by meditators and by meditation teachers and psychiatrists. In some cases, the phenomenology of DLI suggests influences from the lived context of Buddhist meditation cultures. Furthermore, although DLI are normalized in Buddhist meditation culture under certain circumstances, meditation experts also noted the potential severity of meditation-related DLI, with some identifying it as a “red flag” meriting close monitoring if not immediate intervention. Finally, we discuss various explanatory models that could account for the presence, content, and impacts of DLI among meditators, drawing upon the environmental conditions and social contexts of meditation retreats, the role of attention and sensory attenuation in meditation practice, and the ways in which meditation-related DLI can function as a cultural and spiritual “idiom of distress.

Ivermectin for COVID-19 in adults in the community (PRINCIPLE): an open, randomised, controlled, adaptive platform trial of short- and longer-term outcomes

Background The evidence for whether ivermectin impacts recovery, hospital admissions, and longer-term outcomes in COVID-19 is contested. The WHO recommends its use only in the context of clinical trials. Methods In this multicentre, open-label, multi-arm, adaptive platform randomised controlled trial, we included participants aged ≥18 years in the community, with a positive SARS-CoV-2 test, and symptoms lasting ≤14 days. Participants were randomised to usual care, usual care plus ivermectin tablets (target 300-400 μg/kg per dose, once daily for 3 days), or usual care plus other interventions. Co-primary endpoints were time to first self-reported recovery, and COVID-19 related hospitalisation/death within 28 days, analysed using Bayesian models. Recovery at 6 months was the primary, longer term outcome. Trial registration: ISRCTN86534580. Findings The primary analysis included 8811 SARS-CoV-2 positive participants (median symptom duration 5 days), randomised to ivermectin (n=2157), usual care (n=3256), and other treatments (n=3398) from June 23, 2021 to July 1, 2022. Time to self-reported recovery was shorter in the ivermectin group compared with usual care (hazard ratio 1·15 [95% Bayesian credible interval, 1·07 to 1·23], median decrease 2.06 days [1·00 to 3·06]), probability of meaningful effect (pre-specified hazard ratio ≥1.2) 0·192). COVID-19-related hospitalisations/deaths (odds ratio 1·02 [0·63 to 1·62]; estimated percentage difference 0% [-1% to 0·6%]), serious adverse events (three and five respectively), and the proportion feeling fully recovered were similar in both groups at 6 months (74·3% and 71·2% respectively (RR = 1·05, [1·02 to 1·08]) and also at 3 and 12 months.,. Interpretation Ivermectin for COVID-19 is unlikely to provide clinically meaningful improvement in recovery, hospital admissions, or longer-term outcomes. Further trials of ivermectin for SARS-Cov-2 infection in vaccinated community populations appear unwarranted. Funding UKRI / National Institute of Health Research (MC_PC_19079)

Quinoa phenotyping methodologies: An international consensus

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.Fil: Stanschewski, Clara S.. King Abdullah University of Science and Technology; Arabia SauditaFil: Rey, Elodie. King Abdullah University of Science and Technology; Arabia SauditaFil: Fiene, Gabriele. King Abdullah University of Science and Technology; Arabia SauditaFil: Craine, Evan B.. Washington State University; Estados UnidosFil: Wellman, Gordon. King Abdullah University of Science and Technology; Arabia SauditaFil: Melino, Vanessa J.. King Abdullah University of Science and Technology; Arabia SauditaFil: Patiranage, Dilan S. R.. King Abdullah University of Science and Technology; Arabia SauditaFil: Johansen, Kasper. King Abdullah University of Science and Technology; Arabia SauditaFil: Schmöckel, Sandra M.. King Abdullah University of Science and Technology; Arabia SauditaFil: Bertero, Hector Daniel. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Producción Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Oakey, Helena. University of Adelaide; AustraliaFil: Colque Little, Carla. Universidad de Copenhagen; DinamarcaFil: Afzal, Irfan. University of Agriculture; PakistánFil: Raubach, Sebastian. The James Hutton Institute; Reino UnidoFil: Miller, Nathan. University of Wisconsin; Estados UnidosFil: Streich, Jared. Oak Ridge National Laboratory; Estados UnidosFil: Amby, Daniel Buchvaldt. Universidad de Copenhagen; DinamarcaFil: Emrani, Nazgol. Christian-albrechts-universität Zu Kiel; AlemaniaFil: Warmington, Mark. Agriculture And Food; AustraliaFil: Mousa, Magdi A. A.. Assiut University; Arabia Saudita. King Abdullah University of Science and Technology; Arabia SauditaFil: Wu, David. Shanxi Jiaqi Agri-Tech Co.; ChinaFil: Jacobson, Daniel. Oak Ridge National Laboratory; Estados UnidosFil: Andreasen, Christian. Universidad de Copenhagen; DinamarcaFil: Jung, Christian. Christian-albrechts-universität Zu Kiel; AlemaniaFil: Murphy, Kevin. Washington State University; Estados UnidosFil: Bazile, Didier. Savoirs, Environnement, Sociétés; Francia. Universite Paul-valery Montpellier Iii; FranciaFil: Tester, Mark. King Abdullah University of Science and Technology; Arabia Saudit

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease

Parametric Identification of Delay Systems from Empirical Stability Information

The work presented in this document alleviates challenges associated with applying academically proven analysis techniques to real-world dynamical systems. Specifically, by providing improved parameter identification methods, the contributions of this work help bridge the gap between theoretical predictions and experimentally-observed behavior. This work is primarily motivated by the existence of powerful predictive methods in the literature of manufacturing research, specifically pertaining to the stability of subtractive manufacturing methods like milling and turning, which are not currently implemented in industry due to the prohibitive effort required by available methods for identifying system properties covering the range of cutting conditions that may be encountered. The motivating technique, temporal finite element analysis (TFEA), is first detailed and demonstrated to be sensitive to system parameters. The remainder of this document, subsequently, focuses on improving or developing approaches for identifying parameters influencing or quantifying the level of stability of dynamical systems, offering a connection between theory and experiment.First, an improvement to the fundamental technique of logarithmic decrement damping estimation is provided. Specifically, an analytical expression for the optimal number of periods between samples is derived. This expression, obtained from an uncertainty analysis of the method's principal equation, is shown to be a function of only one system parameter: the damping ratio. This suggests that for linear systems with viscous damping there is a unique, damping-dependent period choice corresponding to minimum uncertainty in the estimated damping ratio. This result led to the discovery of a constant optimal amplitude ratio offering a straightforward guideline that can be applied by the experimenter to obtain damping ratio estimates with minimum uncertainty. The derived expressions are applied to a series of numerical and experimental systems to confirm their validity. Next, focus is placed on systems with periodic steady-state behavior. This work applies empirical Floquet theory to extract characteristic multipliers from time series data and builds upon prior works by significantly reducing the influence of experimental noise. Characteristic multipliers (CMs), which are the eigenvalues of the transition matrix governing the evolution of transient solutions over one period of motion, quantify the local stability of periodic orbits and can be estimated experimentally without knowledge of the system model. Traditionally, empirical CM estimates were obtained from the transient dynamics observed by subtracting measured steady-state behavior from a recorded perturbed response. The subtraction of two experimentally measured quantities amplifies noise, producing inaccurate CM estimates. By applying a moving integral to isolate the transient dynamics, this work provides an approach to reduce, rather than amplify, the influence of noise on empirical CM results. This approach is applied to the reconstructed phase space of a numerical example and an experimental system to demonstrate the improvements. This work culminates in a study identifying parameters of milling operations. In this work, a heuristic optimization routine is developed which, given information gleaned from vibration data collected during cutting, identifies modal parameters and cutting coefficients of the model governing interrupted cutting. Characteristic multipliers estimated from the transient induced by the onset of cutting forces provide information regarding the true stability characteristics of the system. Model parameters are fit to the collected empirical CMs with a genetic algorithm, which is well-suited to navigate the numerous local minima of the objective function. New insight is provided into how control parameters and modal properties affect stability, demonstrating that results can be influenced through careful selection of cutting tests. The approach is applied to single-degree-of-freedom and two-degree-of-freedom milling systems, where accurate estimates of parameters are achieved.</p

The ATLAS Tile Calorimeter Phase-II Upgrade Demonstrator Data Acquisition and Software

The Phase-II upgrades will prepare the ATLAS experiment for the High Luminosity LHC (HL-LHC), planned to begin operation in 2026. The HL-LHC is expected to deliver more than ten times the integrated luminosity of LHC Runs 1-3 combined. To achieve this in a reasonable amount of time, an increase in instantaneous luminosity corresponding to up to 200 simultaneous $pp$ interactions per bunch crossing is required. This large luminosisty increase presents significant challenges to the detector, trigger, and data acquisition systems in the form of increased trigger rates and detector occupancy. The results from the tests with beam performed at CERN, as well as the latest results on the development of the on- and off-detector electronics, firmware, data processing, and simulation components of the Tile Calorimeter Demonstrator readout system are presented