Sensitivity to millicharged particles in future proton-proton collisions at the LHC with the milliQan detector

We report on the expected sensitivity of dedicated scintillator-based detectors at the LHC for elementary particles with charges much smaller than the electron charge. The dataset provided by a prototype scintillator-based detector is used to characterise the performance of the detector and provide an accurate background projection. Detector designs, including a novel slab detector configuration, are considered for the data taking period of the LHC to start in 2022 (Run 3) and for the high luminosity LHC. With the Run 3 dataset, the existence of new particles with masses between 10 MeV and 45 GeV could be excluded at 95% confidence level for charges between 0.003e and 0.3e, depending on their mass. With the high luminosity LHC dataset, the expected limits would reach between 10 MeV and 80 GeV for charges between 0.0018e and 0.3e, depending on their mas

Clustering identifies endotypes of traumatic brain injury in an intensive care cohort: a CENTER-TBI study

Background While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as ‘mild’, ‘moderate’ or ‘severe’ based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights. Methods We developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (< 24 h) demographic, clinical, physiological, laboratory and imaging data to identify subgroups of TBI patients admitted to the intensive care unit in the CENTER-TBI dataset (N = 1,728). A cluster similarity index was used for robust determination of optimal cluster number. Mutual information was used to quantify feature importance and for cluster interpretation. Results Six stable endotypes were identified with distinct GCS and composite systemic metabolic stress profiles, distinguished by GCS, blood lactate, oxygen saturation, serum creatinine, glucose, base excess, pH, arterial partial pressure of carbon dioxide, and body temperature. Notably, a cluster with ‘moderate’ TBI (by traditional classification) and deranged metabolic profile, had a worse outcome than a cluster with ‘severe’ GCS and a normal metabolic profile. Addition of cluster labels significantly improved the prognostic precision of the IMPACT (International Mission for Prognosis and Analysis of Clinical trials in TBI) extended model, for prediction of both unfavourable outcome and mortality (both p < 0.001). Conclusions Six stable and clinically distinct TBI endotypes were identified by probabilistic unsupervised clustering. In addition to presenting neurology, a profile of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates refining current TBI classifications with factors describing metabolic stress. Such data-driven clusters suggest TBI endotypes that merit investigation to identify bespoke treatment strategies to improve care

Creating Carbon Offsets in Agriculture through No-till cultivation: A Meta-Analysis of Costs and Carbon Benefits

Carbon terrestrial sinks are often seen as a low-cost alternative to fuel switching and reduced fossil fuel use for lowering atmospheric CO2. To determine whether this is true for agriculture, one meta-regression analysis (52 studies, 536 observations) examines the costs of switching from conventional tillage to no-till, while another (51 studies, 374 observations) compares carbon accumulation under the two practices. Costs per ton of carbon uptake are determined by combining the two results. The viability of agricultural carbon sinks is found to vary by region and crop, with no-till representing a low-cost option in some regions (costs of less than $10 per tC), but a high-cost option in others (costs of 100–$400 per tC). A particularly important finding is that no-till cultivation may store no carbon at all if measurements are taken at sufficient depth. In some circumstances no-till cultivation may yield a lsquotriple dividendrdquo of carbon storage, increased returns and reduced soil erosion, but in many others creating carbon offset credits in agricultural soils is not cost effective because reduced tillage practices store little or no carbon

Benefits of organic farming to biodiversity vary among taxa

Habitat and biodiversity differences between matched pairs of organic and non-organic farms containing cereal crops in lowland England were assessed by a large-scale study of plants, invertebrates, birds and bats. Habitat extent, composition and management on organic farms was likely to favour higher levels of biodiversity and indeed organic farms tended to support higher numbers of species and overall abundance across most taxa. However, the magnitude of the response varied; plants showed larger and more consistent responses than other taxa. Variation in response across taxa may be partly a consequence of the small size and isolated context of many organic farms. Extension of organic farming could contribute to the restoration of biodiversity in agricultural landscapes

A management algorithm for patients with intracranial pressure monitoring: the Seattle International Severe Traumatic Brain Injury Consensus Conference (SIBICC).

Management algorithms for adult severe traumatic brain injury (sTBI) were omitted in later editions of the Brain Trauma Foundation's sTBI Management Guidelines, as they were not evidence-based. We used a Delphi-method-based consensus approach to address management of sTBI patients undergoing intracranial pressure (ICP) monitoring. Forty-two experienced, clinically active sTBI specialists from six continents comprised the panel. Eight surveys iterated queries and comments. An in-person meeting included whole- and small-group discussions and blinded voting. Consensus required 80% agreement. We developed heatmaps based on a traffic-light model where panelists' decision tendencies were the focus of recommendations. We provide comprehensive algorithms for ICP-monitor-based adult sTBI management. Consensus established 18 interventions as fundamental and ten treatments not to be used. We provide a three-tier algorithm for treating elevated ICP. Treatments within a tier are considered empirically equivalent. Higher tiers involve higher risk therapies. Tiers 1, 2, and 3 include 10, 4, and 3 interventions, respectively. We include inter-tier considerations, and recommendations for critical neuroworsening to assist the recognition and treatment of declining patients. Novel elements include guidance for autoregulation-based ICP treatment based on MAP Challenge results, and two heatmaps to guide (1) ICP-monitor removal and (2) consideration of sedation holidays for neurological examination. Our modern and comprehensive sTBI-management protocol is designed to assist clinicians managing sTBI patients monitored with ICP-monitors alone. Consensus-based (class III evidence), it provides management recommendations based on combined expert opinion. It reflects neither a standard-of-care nor a substitute for thoughtful individualized management

Multidimensional Analysis of Magnetic Resonance Imaging Predicts Early Impairment in Thoracic and Thoracolumbar Spinal Cord Injury

Literature examining magnetic resonance imaging (MRI) in acute spinal cord injury (SCI) has focused on cervical SCI. Reproducible systems have been developed for MRI-based grading; however, it is unclear how they apply to thoracic SCI. Our hypothesis is that MRI measures will group as coherent multivariate principal component (PC) ensembles, and that distinct PCs and individual variables will show discriminant validity for predicting early impairment in thoracic SCI. We undertook a retrospective cohort study of 25 patients with acute thoracic SCI who underwent MRI on admission and had American Spinal Injury Association Impairment Scale (AIS) assessment at hospital discharge. Imaging variables of axial grade, sagittal grade, length of injury, thoracolumbar injury classification system (TLICS), maximum canal compromise (MCC), and maximum spinal cord compression (MSCC) were collected. We performed an analytical workflow to detect multivariate PC patterns followed by explicit hypothesis testing to predict AIS at discharge. All imaging variables loaded positively on PC1 (64.3% of variance), which was highly related to AIS at discharge. MCC, MSCC, and TLICS also loaded positively on PC2 (22.7% of variance), while variables concerning cord signal abnormality loaded negatively on PC2. PC2 was highly related to the patient undergoing surgical decompression. Variables of signal abnormality were all negatively correlated with AIS at discharge with the highest level of correlation for axial grade as assessed with the Brain and Spinal Injury Center (BASIC) score. A multiple variable model identified BASIC as the only statistically significant predictor of AIS at discharge, signifying that BASIC best captured the variance in AIS within our study population. Our study provides evidence of convergent validity, construct validity, and clinical predictive validity for the sampled MRI measures of SCI when applied in acute thoracic and thoracolumbar SCI