The Large Hadron Electron Collider

An overview is given on key physics, detector and accelerator aspects of the LHeC, including its further development, with emphasis to its role as the cleanest microscope of parton dynamics and a precision Higgs facility.Comment: 13 pages, 4 figure

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Identification of a BRCA2-Specific modifier locus at 6p24 related to breast cancer risk

Common genetic variants contribute to the observed variation in breast cancer risk for BRCA2 mutation carriers; those known to date have all been found through population-based genome-wide association studies (GWAS). To comprehensively identify breast cancer risk modifying loci for BRCA2 mutation carriers, we conducted a deep replication of an ongoing GWAS discovery study. Using the ranked P-values of the breast cancer associations with the imputed genotype of 1.4 M SNPs, 19,029 SNPs were selected and designed for inclusion on a custom Illumina array that included a total of 211,155 SNPs as part of a multi-consortial project. DNA samples from 3,881 breast cancer affected and 4,330 unaffected BRCA2 mutation carriers from 47 studies belonging to the Consortium of Investigators of Modifiers of BRCA1/2 were genotyped and available for analysis. We replicated previously reported breast cancer susceptibility alleles in these BRCA2 mutation carriers and for several regions (including FGFR2, MAP3K1, CDKN2A/B, and PTHLH) identified SNPs that have stronger evidence of association than those previously published. We also identified a novel susceptibility allele at 6p24 that was inversely associated with risk in BRCA2 mutation carriers (rs9348512; per allele HR = 0.85, 95% CI 0.80-0.90, P = 3.9×10−8). This SNP was not associated with breast cancer risk either in the general population or in BRCA1 mutation carriers. The locus lies within a region containing TFAP2A, which encodes a transcriptional activation protein that interacts with several tumor suppressor genes. This report identifies the first breast cancer risk locus specific to a BRCA2 mutation background. This comprehensive update of novel and previously reported breast cancer susceptibility loci contributes to the establishment of a panel of SNPs that modify breast cancer risk in BRCA2 mutation carriers. This panel may have clinical utility for women with BRCA2 mutations weighing options for medical prevention of breast cancer

Energy frontier DIS at CERN: the LHeC and the FCC-eh, PERLE

Energy-frontier Deep Inelastic Scattering (DIS) can be realised at CERN through an energy recovery linac (ERL) that would produce 60 GeV electrons to collide with the High Luminosity (HL)-Large Hadron Collider (LHC) or, eventually, with the High Energy (HE)-LHC or the Future Circular Collider (FCC) hadron beams. It would deliver lepton-proton (nucleus) collisions with centre of mass energies in the range 0.8-3.5 TeV per nucleon, and luminosities exceeding 1034 (5×10^32) cm^−2s^−1 in electron-proton (electron-lead ion) beam operation. Such machine would provide a huge physics program, with the highest resolution microscope for hadron structure, rich Higgs, top and precision Electro-Weak (EW) physics, large possibilities for Beyond Standard Model (BSM) searches, and a unique top-energy nuclear physics facility with eventual access to a new regime of QCD at high partonic densities. All these aspects have strong complementarities with the respective, concurrent pp and AA programs. In this paper we review the LHeC, HE-LHeC and FCC-eh proposals at CERN, with emphasis on the accelerator and infrastructure aspects. We also review the project of an ERL demonstrator, PERLE, under consideration to be built at LAL Orsay

Exploring the energy frontier with deep inelastic scattering at the LHC

A proposal is described for complementing the intense, high energy proton and ion beams of the Large Hadron Collider (LHC) with a novel electron energy recovery linac (ERL) to provide a next generation electron-hadron collider facility of more than 1 TeV center-of-mass energy. This Large Hadron Electron Collider (LHeC) will substantially extend the kinematic range covered by the first ep collider, HERA, and surpass its luminosity by a factor of several hundreds. This configuration will be a microscope for the substructure of matter with unprecedentedly clean resolution and a facility for new physics in the Higgs, strong interaction and electroweak sector with a strong discovery potential. The paper also describes the accelerator design and its main characteristics. As such it had been submitted to the deliberations on the future of European particle physics

Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH): uncertainty quantification of geometric rupture risk parameters

Abstract Background Geometric parameters have been proposed for prediction of cerebral aneurysm rupture risk. Predicting the rupture risk for incidentally detected unruptured aneurysms could help clinicians in their treatment decision. However, assessment of geometric parameters depends on several factors, including the spatial resolution of the imaging modality used and the chosen reconstruction procedure. The aim of this study was to investigate the uncertainty of a variety of previously proposed geometric parameters for rupture risk assessment, caused by variability of reconstruction procedures. Materials 26 research groups provided segmentations and surface reconstructions of five cerebral aneurysms as part of the Multiple Aneurysms AnaTomy CHallenge (MATCH) 2018. 40 dimensional and non-dimensional geometric parameters, describing aneurysm size, neck size, and irregularity of aneurysm shape, were computed. The medians as well as the absolute and relative uncertainties of the parameters were calculated. Additionally, linear regression analysis was performed on the absolute uncertainties and the median parameter values. Results A large variability of relative uncertainties in the range between 3.9 and 179.8% was found. Linear regression analysis indicates that some parameters capture similar geometric aspects. The lowest uncertainties  80% were found for some curvature parameters. Conclusions Uncertainty analysis is essential on the road to clinical translation and use of rupture risk prediction models. Uncertainty quantification of geometric rupture risk parameters provided by this study may help support development of future rupture risk prediction models

GLP-1 and hunger modulate incentive motivation depending on insulin sensitivity in humans

Objective: To regulate food intake, our brain constantly integrates external cues, such as the incentive value of a potential food reward, with internal state signals, such as hunger feelings. Incentive motivation refers to the processes that translate an expected reward into the effort spent to obtain the reward; the magnitude and probability of a reward involved in prompting motivated behaviour are encoded by the dopaminergic (DA) midbrain and its mesoaccumbens DA projections. This type of reward circuity is particularly sensitive to the metabolic state signalled by peripheral mediators, such as insulin or glucagon-like peptide 1 (GLP-1). While in rodents the modulatory effect of metabolic state signals on motivated behaviour is well documented, evidence of state-dependent modulation and the role of incentive motivation underlying overeating in humans is lacking. Methods: In a randomised, placebo-controlled, crossover design, 21 lean (body mass index [BMI] < 25 kg/m(2)) and 16 obese (BMI3 30 kg/m(2)) volunteer participants received either liraglutide as a GLP-1 analogue or placebo on two separate testing days. Incentive motivation was measured using a behavioural task in which participants were required to exert physical effort using a handgrip to win different amounts of food and monetary rewards. Hunger levels were measured using visual analogue scales; insulin, glucose, and systemic insulin resistance as assessed by the homeostasis model assessment of insulin resistance (HOMA-IR) were quantified at baseline. Results: In this report, we demonstrate that incentive motivation increases with hunger in lean humans (F-(1,F-42) = 5.31, p = 0.026, beta = 0.19) independently of incentive type (food and non-food reward). This effect of hunger is not evident in obese humans (F-(1,F-62) = 1.93, p = 0.17, beta = -0.12). Motivational drive related to hunger is affected by peripheral insulin sensitivity (two-way interaction, F-(1,F- (35)) = 6.23, p = 0.017, beta = -0.281). In humans with higher insulin sensitivity, hunger increases motivation, while poorer insulin sensitivity dampens the motivational effect of hunger. The GLP-1 analogue application blunts the interaction effect of hunger on motivation depending on insulin sensitivity (three-way interaction, F-(1,F- 127) = 5.11, p = 0.026); no difference in motivated behaviour could be found between humans with normal or impaired insulin sensitivity under GLP-1 administration. Conclusion: We report a differential effect of hunger on motivation depending on insulin sensitivity. We further revealed the modulatory role of GLP-1 in adaptive, motivated behaviour in humans and its interaction with peripheral insulin sensitivity and hunger. Our results suggest that GLP-1 might restore dysregulated processes of midbrain DA function and hence motivational behaviour in insulin-resistant humans. (C) 2021 The Author(s). Published by Elsevier GmbH

Liraglutide restores impaired associative learning in individuals with obesity

Survival under selective pressure is driven by the ability of our brain to use sensory information to our advantage to control physiological needs. To that end, neural circuits receive and integrate external environmental cues and internal metabolic signals to form learned sensory associations, consequently motivating and adapting our behaviour. The dopaminergic midbrain plays a crucial role in learning adaptive behaviour and is particularly sensitive to peripheral metabolic signals, including intestinal peptides, such as glucagon-like peptide 1 (GLP-1). In a single-blinded, randomized, controlled, crossover basic human functional magnetic resonance imaging study relying on a computational model of the adaptive learning process underlying behavioural responses, we show that adaptive learning is reduced when metabolic sensing is impaired in obesity, as indexed by reduced insulin sensitivity (participants: N = 30 with normal insulin sensitivity; N = 24 with impaired insulin sensitivity). Treatment with the GLP-1 receptor agonist liraglutide normalizes impaired learning of sensory associations in men and women with obesity. Collectively, our findings reveal that GLP-1 receptor activation modulates associative learning in people with obesity via its central effects within the mesoaccumbens pathway. These findings provide evidence for how metabolic signals can act as neuromodulators to adapt our behaviour to our body’s internal state and how GLP-1 receptor agonists work in clinics.ISSN:2522-581