Calibration of calorimetric measurement in a liquid argon time projection chamber

The liquid argon time projection chamber provides high resolution event images and excellent calorimetric resolution for studying neutrino physics and searching for beyond standard model physics. In this article, we review the main physics processes that affect the detector response, including the electronics and field responses, space charge effects, electron attachment to impurities, diffusion and recombination. We describe methods to measure those effects, which are used to calibrate the detector response and convert the measured raw ADC counts to the original energy deposition.Comment: 15 pages, 8 figures, submitted to Instrument

A study of muon neutrino to electron neutrino oscillations in the MINOS experiment

The observation of neutrino oscillations (neutrino changing from one flavor to another) has provided compelling evidence that the neutrinos have non-zero masses and that leptons mix, which is not part of the original Standard Model of particle physics. The theoretical framework that describes neutrino oscillation involves two mass scales ({Delta}m{sub atm}{sup 2} and {Delta}m{sub sol}{sup 2}), three mixing angles ({theta}{sub 12}, {theta}{sub 23}, and {theta}{sub 13}) and one CP violating phase ({delta}{sub CP}). Both mass scales and two of the mixing angles ({theta}{sub 12} and {theta}{sub 23}) have been measured by many neutrino experiments. The mixing angle {theta}{sub 13}, which is believed to be very small, remains unknown. The current best limit on {theta}13 comes from the CHOOZ experiment: {theta}{sub 13} < 11{sup o} at 90% C.L. at the atmospheric mass scale. {delta}{sub CP} is also unknown today. MINOS, the Main Injector Neutrino Oscillation Search, is a long baseline neutrino experiment based at Fermi National Accelerator Laboratory. The experiment uses a muon neutrino beam, which is measured 1 km downstream from its origin in the Near Detector at Fermilab and then 735 km later in the Far Detector at the Soudan mine. By comparing these two measurements, MINOS can obtain parameters in the atmospheric sector of neutrino oscillations. MINOS has published results on the precise measurement of {Delta}m{sub atm}{sup 2} and {theta}{sub 23} through the disappearance of muon neutrinos in the Far Detector and on a search for sterile neutrinos by looking for a deficit in the number of neutral current interactions seen in the Far Detector. MINOS also has the potential to improve the limit on the neutrino mixing angle {theta}{sub 13} or make the first measurement of its value by searching for an electron neutrino appearance signal in the Far Detector. This is the focus of the study presented in this thesis. We developed a neural network based algorithm to distinguish the electron neutrino signal from background. The most important part of this measurement is the background estimation, which is done through extrapolation. The number of background events is measured at the Near Detector, then extrapolated to the Far Detector. Since different background sources extrapolate differently, some knowledge about the relative contribution from different background sources is necessary. We developed a method that can be used to obtain relative contributions of various background sources from comparison of background rates in the horn-on and horn-off configurations. We also described our effort to improve two aspects of the Monte Carlo simulation which are very important for the {nu}{sub e} appearance analysis: one is the hadronization model in the neutrino-nucleon interactions, the other is the modeling of PMT crosstalk. We performed a blind analysis and examined several sidebands before looking at the signal region. After we opened the box, we observed a 1.4 {sigma} excess of {nu}{sub e}-like events in the Far Detector compared with the number of predicted background events. The excess is well within the statistical fluctuation of the background events. If we interpret the excess as a {nu}{sub e} signal from {nu}{sub {mu}} {yields} {nu}{sub e} oscillation, the best fit sin{sup 2} 2{theta}{sub 13} value is consistent with the CHOOZ limit. However we want to emphasize that our result is consistent with {theta}{sub 13} = 0 at 90% C.L.

Increased gene copy number of DEFA1/DEFA3 worsens sepsis by inducing endothelial pyroptosis.

Sepsis claims an estimated 30 million episodes and 6 million deaths per year, and treatment options are rather limited. Human neutrophil peptides 1-3 (HNP1-3) are the most abundant neutrophil granule proteins but their neutrophil content varies because of unusually extensive gene copy number polymorphism. A genetic association study found that increased copy number of the HNP-encoding gene DEFA1/DEFA3 is a risk factor for organ dysfunction during sepsis development. However, direct experimental evidence demonstrating that these risk alleles are pathogenic for sepsis is lacking because the genes are present only in some primates and humans. Here, we generate DEFA1/DEFA3 transgenic mice with neutrophil-specific expression of the peptides. We show that mice with high copy number of DEFA1/DEFA3 genes have more severe sepsis-related vital organ damage and mortality than mice with low copy number of DEFA1/DEFA3 or wild-type mice, resulting from more severe endothelial barrier dysfunction and endothelial cell pyroptosis after sepsis challenge. Mechanistically, HNP-1 induces endothelial cell pyroptosis via P2X7 receptor-mediating canonical caspase-1 activation in a NLRP3 inflammasome-dependent manner. Based on these findings, we engineered a monoclonal antibody against HNP-1 to block the interaction with P2X7 and found that the blocking antibody protected mice carrying high copy number of DEFA1/DEFA3 from lethal sepsis. We thus demonstrate that DEFA1/DEFA3 copy number variation strongly modulates sepsis development in vivo and explore a paradigm for the precision treatment of sepsis tailored by individual genetic information

Automated Interactive Visualization on Abstract Concepts in Computer Science

The paper presents CSVisFrame, a framework formaking visualizations, which solves the understanding difficultyon learning abstract concepts in computer science including datastructures and algorithms. With the framework, instructors anddevelopers can develop all varieties of interactive visualizations,with which students can learn and understand abstract conceptsin computer science more easily.CSVisFrame has been applied to both offline and onlinecomputer courses. Students from Sichuan Normal Universityhave enjoyed visualizations based on CSVisFrame in their AlgorithmDesign and Analysis course, and thousands of students ofJisuanke have benefitted from online CSVisFrame-based visualizedcomputer science courses. The effectiveness of CSVisFramebasedvisualizations has been demonstrated by our sample survey,which shows that visualizations are widely accepted, and almostall students can achieve a better learning. CSVisFrame is opensourced1,and demonstrations based on CSVisFrame are free2

Progresses and Perspectives of Anti-PD-1/PD-L1 Antibody Therapy in Head and Neck Cancers

Head and neck cancer is the 6th most common malignancy worldwide and urgently requires novel therapy methods to change the situation of low 5-years survival rate and poor prognosis. Targeted therapy provides more precision, higher efficiency while lower adverse effects than traditional treatments like surgery, radiotherapy, and chemotherapy. Blockade of PD-1 pathway with antibodies against PD-1 or PD-L1 is such a typical targeted therapy which reconstitutes anti-tumor activity of T cell in treatments of cancers, especially those highly expressing PD-L1, including head and neck cancers. There are many clinical trials all over the world and FDA has approved anti-PD-1/PD-L1 drugs for head and neck cancers. However, with the time going, the dark side of this therapy has emerged, including some serious side effects and drug resistance. Novel materials like nanoparticles and combination therapy have been developed to improve the efficacy. At the same time, standards for evaluation of activity and safety are to be established for this new therapy. Here we provide a systematic review with comprehensive depth on the application of anti-PD1/PD-L1 antibodies in head and neck cancer treatment: mechanism, drugs, clinical studies, influencing factors, adverse effects and managements, and the potential future developments

Extracting low energy signals from raw LArTPC waveforms using deep learning techniques -- A proof of concept

We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. We demonstrate that our approach overcomes the inherent shortcomings of traditional cut-based methods by extending sensitivity to signals with ADC values below their imposed thresholds. This approach exhibits great promise in enhancing the capabilities of future generation neutrino experiments like DUNE to carry out their low-energy neutrino physics programs

AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation

Recombinant adeno-associated virus (rAAV) vectors have revolutionized gene therapy, but their broad tropism and suboptimal transduction efficiency limit their clinical applications. To overcome these limitations, researchers have focused on designing and screening capsid libraries to identify improved vectors. However, the large sequence space and limited resources present challenges in identifying viable capsid variants. In this study, we propose an end-to-end diffusion model to generate capsid sequences with enhanced viability. Using publicly available AAV2 data, we generated 38,000 diverse AAV2 viral protein (VP) sequences, and evaluated 8,000 for viral selection. The results attested the superiority of our model compared to traditional methods. Additionally, in the absence of AAV9 capsid data, apart from one wild-type sequence, we used the same model to directly generate a number of viable sequences with up to 9 mutations. we transferred the remaining 30,000 samples to the AAV9 domain. Furthermore, we conducted mutagenesis on AAV9 VP hypervariable regions VI and V, contributing to the continuous improvement of the AAV9 VP sequence. This research represents a significant advancement in the design and functional validation of rAAV vectors, offering innovative solutions to enhance specificity and transduction efficiency in gene therapy applications

Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing

We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we attempt to reprocess the data by running several thousand concurrent grid jobs, a rate we expect to be typical of current and future neutrino physics experiments. We process most of the dataset with the GPU version of our processing algorithm and the remainder with the CPU version for timing comparisons. We find that a 100-GPU cloud-based server is able to easily meet the processing demand, and that using the GPU version of the event processing algorithm is two times faster than processing these data with the CPU version when comparing to the newest CPUs in our sample. The amount of data transferred to the inference server during the GPU runs can overwhelm even the highest-bandwidth network switches, however, unless care is taken to observe network facility limits or otherwise distribute the jobs to multiple sites. We discuss the lessons learned from this processing campaign and several avenues for future improvements.Comment: 13 pages, 9 figures, matches accepted versio

Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment

The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors. The present round of the assessment was based on the recent structures of dopamine D3 and CXCR4 chemokine receptors bound to small molecule antagonists and CXCR4 with a synthetic cyclopeptide. Thirty-five groups submitted their receptor-ligand complex structure predictions prior to the release of the crystallographic coordinates. With closely related homology modeling templates, as for dopamine D3 receptor, and with incorporation of biochemical and QSAR data, modern computational techniques predicted complex details with accuracy approaching experimental. In contrast, CXCR4 complexes that had less-characterized interactions and only distant homology to the known GPCR structures still remained very challenging. The assessment results provide guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the GPCR universe. © 2011 Elsevier Ltd. All rights reserved