Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Spatial transcriptomics in cancer research and potential clinical impact: a narrative review

Spatial transcriptomics (ST) provides novel insights into the tumor microenvironment (TME). ST allows the quantification and illustration of gene expression profiles in the spatial context of tissues, including both the cancer cells and the microenvironment in which they are found. In cancer research, ST has already provided novel insights into cancer metastasis, prognosis, and immunotherapy responsiveness. The clinical precision oncology application of next-generation sequencing (NGS) and RNA profiling of tumors relies on bulk methods that lack spatial context. The ability to preserve spatial information is now possible, as it allows us to capture tumor heterogeneity and multifocality. In this narrative review, we summarize precision oncology, discuss tumor sequencing in the clinic, and review the available ST research methods, including seqFISH, MERFISH (Vizgen), CosMx SMI (NanoString), Xenium (10x), Visium (10x), Stereo-seq (STOmics), and GeoMx DSP (NanoString). We then review the current ST literature with a focus on solid tumors organized by tumor type. Finally, we conclude by addressing an important question: how will spatial transcriptomics ultimately help patients with cancer?Michael A. Cilento, Christopher J. Sweeney, Lisa M. Butle

Prolonged oral coenzyme Q10-β-cyclodextrin supplementation increases skeletal muscle complex I+III activity in young Thoroughbreds

Coenzyme Q10 (CoQ10) is an essential component of the mitochondrial electron transport chain (ETC). Decreased skeletal muscle CoQ10 content may result in decreased ETC activity and energy production. This study tested the hypotheses that supplementation with oral CoQ10 will increase plasma CoQ10 concentrations and that prolonged supplementation will increase skeletal muscle CoQ10 content in young, healthy untrained Thoroughbreds. Nineteen Thoroughbreds (27.5±9.7 months old; 11 males, eight females) from one farm and maintained on a grass pasture with one grain meal per day were supplemented daily with 1.5 mg/kg body weight of an oral CoQ10-β-cyclodextrin inclusion complex. Whole-blood and skeletal muscle biopsies were collected before (T0) and after (T1) nine weeks of supplementation. Plasma CoQ10 concentrations were determined via high-performance liquid chromatography. Skeletal muscle mitochondrial ETC combined complex I+III enzyme activity (indirect measurement of CoQ10 content) was assessed spectrophotometrically and normalised to mitochondrial abundance. Horses accepted supplementation with no adverse effects. Plasma CoQ10 concentration increased in all horses following supplementation, with mean plasma CoQ10 concentration significantly increasing from T0 to T1 (0.13±0.02 vs 0.25±0.03 μg/ml; mean difference 0.12±0.03; P=0.004). However, variability in absorbance resulted in a 58% response rate (i.e. doubling of T1 above T0 values). The mean skeletal muscle complex I+III activity significantly increased from T0 to T1 (0.36±0.04 vs 0.59±0.05 pmol/min/mg of muscle, mean difference 0.23±0.05; P=0.0004), although T1 values for three out of 19 horses decreased on average by 23% below T0 values. In conclusion, oral supplementation with CoQ10 in the diet of young, healthy untrained Thoroughbreds increased mean plasma CoQ10 concentration by 99% with prolonged daily supplementation increasing mean skeletal muscle complex I+III activity by 65%. Additional research is warranted investigating training and exercise effects on skeletal muscle CoQ10 content in CoQ10 supplemented and un-supplemented Thoroughbreds