Deep Learning to Improve the Sensitivity of Di-Higgs Searches in the 4b4b Channel

The study of di-Higgs events, both resonant and non-resonant, plays a crucial role in understanding the fundamental interactions of the Higgs boson. In this work we consider di-Higgs events decaying into four $b$-quarks and propose to improve the experimental sensitivity by utilizing a novel machine learning algorithm known as Symmetry Preserving Attention Network (\textsc{Spa-Net}) -- a neural network structure whose architecture is designed to incorporate the inherent symmetries in particle reconstruction tasks. We demonstrate that the \textsc{Spa-Net} can enhance the experimental reach over baseline methods such as the cut-based and the Deep Neural Networks (DNN)-based analyses. At the Large Hadron Collider, with a 14-TeV centre-of-mass energy and an integrated luminosity of 300 fb$^{-1}$, the \textsc{Spa-Net} allows us to establish 95\% C.L. upper limits in resonant production cross-sections that are 10\% to 45\% stronger than baseline methods. For non-resonant di-Higgs production, \textsc{Spa-Net} enables us to constrain the self-coupling that is 9\% more stringent than the baseline method

Two-dimensional photonic crystals with anisotropic unit cells imprinted from poly(dimethylsiloxane) membranes under elastic deformation

We study structural symmetries of two-dimensional (2D) photonic crystals with anisotropic unit cells, including square- and rectangular-lattices with orientationally modulated elliptic motifs, and a compound structure consisting of circles with sixfold rotational symmetry and elliptical lines with twofold symmetry, which are created through elastic deformation of a single elastomeric membrane with circular pores. We then investigate the photonic bandgap (PBG) properties of the corresponding 2D Si posts and their tolerance to the structural deviation. We find that in the compound structure the overall PBGs are dominated by the sublattice with a higher symmetry, while the total symmetry is determined by the one with a lower symmetry

Gallic Acid Induces a Reactive Oxygen Species-Provoked c-Jun NH 2

Idiopathic pulmonary fibrosis is a chronic lung disorder characterized by fibroblasts proliferation and extracellular matrix accumulation. Induction of fibroblast apoptosis therefore plays a crucial role in the resolution of this disease. Gallic acid (3,4,5-trihydroxybenzoic acid), a common botanic phenolic compound, has been reported to induce apoptosis in tumor cell lines and renal fibroblasts. The present study was undertaken to examine the role of mitogen-activated protein kinases (MAPKs) in lung fibroblasts apoptosis induced by gallic acid. We found that treatment with gallic acid resulted in activation of c-Jun NH2-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and protein kinase B (PKB, Akt), but not p38MAPK, in mouse lung fibroblasts. Inhibition of JNK using pharmacologic inhibitor (SP600125) and genetic knockdown (JNK specific siRNA) significantly inhibited p53 accumulation, reduced PUMA and Fas expression, and abolished apoptosis induced by gallic acid. Moreover, treatment with antioxidants (vitamin C, N-acetyl cysteine, and catalase) effectively diminished gallic acid-induced hydrogen peroxide production, JNK and p53 activation, and cell death. These observations imply that gallic acid-mediated hydrogen peroxide formation acts as an initiator of JNK signaling pathways, leading to p53 activation and apoptosis in mouse lung fibroblasts

Extended Symmetry Preserving Attention Networks for LHC Analysis

Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment of detector objects to partons. An approach based on a generalized attention mechanism, symmetry preserving attention networks (SPANet), has been previously applied to top quark pair decays at the Large Hadron Collider, which produce six hadronic jets. Here we extend the SPANet architecture to consider multiple input streams, such as leptons, as well as global event features, such as the missing transverse momentum. In addition, we provide regression and classification outputs to supplement the parton assignment. We explore the performance of the extended capability of SPANet in the context of semi-leptonic decays of top quark pairs as well as top quark pairs produced in association with a Higgs boson. We find significant improvements in the power of three representative studies: search for ttH, measurement of the top quark mass and a search for a heavy Z' decaying to top quark pairs. We present ablation studies to provide insight on what the network has learned in each case

Maintenance therapy of low-dose nivolumab, S-1, and leucovorin in metastatic pancreatic adenocarcinoma with a germline mutation of MSH6: A case report

Immune checkpoint inhibitors (ICIs) provide substantial benefits to a small subset of patients with advanced cancer with mismatch repair deficiency (MMRD) or microsatellite instability (MSI), including patients with pancreatic ductal adenocarcinoma (PDAC). However, the long duration of ICI treatment presents a considerable financial burden. We present the case of a 63-year-old woman with metastatic PDAC refractory to conventional chemotherapy. Genetic analyses identified an MSH6 germline mutation and a high tumor mutation burden (TMB). Complete response (CR) was achieved after a short course of low-dose nivolumab (20 mg once every 2 weeks) with chemotherapy. CR was maintained for over 1 year with low-dose nivolumab and de-escalated chemotherapy without any immune-related adverse events. This case supports the further exploration of low-dose, affordable ICI-containing regimens in patients with advanced MSI-high/TMB-high cancer

Enteric bacterial loads are associated with interleukin-6 levels in systemic inflammatory response syndrome patients

AbstractBackgroundLoss of intestinal integrity is a critical contributor to excessive inflammation following severe trauma or major surgery. In the case of enterocyte damage, intestinal fatty acid-binding protein (IFABP) is released into the extracellular space. Excessive production of interleukin (IL)-6 can induce systemic inflammatory response syndrome (SIRS). However, the correlation of IL-6 with gut barrier failure and bacterial translocation in critically ill patients has not been well characterized.PurposesTo define the relationship between enteric bacterial loads and IL-6 levels in patients with SIRS.MethodsVariables related to prognosis and treatment were measured in 85 patients with SIRS upon admission to the emergency room. IL-6 and IFABP were measured using an enzyme-linked immunosorbent assay. Enteric bacterial loads in blood were measured through quantitative real-time polymerase chain reaction with primers specific for enteric bacteria.ResultsMultivariate analysis revealed a positive correlation between enteric bacterial loads and IL-6 levels in blood. Elevated IFABP concentration was associated with low blood pressure, high respiration rate, hyperglycemia, and high Sequential Organ Failure Assessment score. Elevated C-reactive protein concentrations were associated with higher soluble CD14 levels in blood.ConclusionEnterocyte damage is associated with hypotension and tachypnia in patients with SIRS. Gut function failure may permit enteric bacteria to enter the blood, thereby elevating IL-6 levels and inducing a systemic inflammatory response, resulting in multiple organ failure

FPGA Deployment of LFADS for Real-time Neuroscience Experiments

Large-scale recordings of neural activity are providing new opportunities to study neural population dynamics. A powerful method for analyzing such high-dimensional measurements is to deploy an algorithm to learn the low-dimensional latent dynamics. LFADS (Latent Factor Analysis via Dynamical Systems) is a deep learning method for inferring latent dynamics from high-dimensional neural spiking data recorded simultaneously in single trials. This method has shown a remarkable performance in modeling complex brain signals with an average inference latency in milliseconds. As our capacity of simultaneously recording many neurons is increasing exponentially, it is becoming crucial to build capacity for deploying low-latency inference of the computing algorithms. To improve the real-time processing ability of LFADS, we introduce an efficient implementation of the LFADS models onto Field Programmable Gate Arrays (FPGA). Our implementation shows an inference latency of 41.97 $\mu$s for processing the data in a single trial on a Xilinx U55C.Comment: 6 pages, 8 figure

Clinical and pathological correlates of severity classifications in trigger fingers based on computer-aided image analysis

BACKGROUND: The treatment of trigger finger so far has heavily relied on clinicians’ evaluations for the severity of patients’ symptoms and the functionality of affected fingers. However, there is still a lack of pathological evidence supporting the criteria of clinical evaluations. This study’s aim was to correlate clinical classification and pathological changes for trigger finger based on the tissue abnormality observed from microscopic images. METHODS: Tissue samples were acquired, and microscopic images were randomly selected and then graded by three pathologists and two physicians, respectively. Moreover, the acquired images were automatically analyzed to derive two quantitative parameters, the size ratio of the abnormal tissue region and the number ratio of the abnormal nuclei, which can reflect tissue abnormality caused by trigger finger. A self-developed image analysis system was used to avoid human subjectivity during the quantification process. Finally, correlations between the quantitative image parameters, pathological grading, and clinical severity classification were assessed. RESULTS: One-way ANOVA tests revealed significant correlations between the image quantification and pathological grading as well as between the image quantification and clinical severity classification. The Cohen’s kappa coefficient test also depicted good consistency between pathological grading and clinical severity classification. CONCLUSIONS: The criteria of clinical classification were found to be highly associated with the pathological changes of affected tissues. The correlations serve as explicit evidence supporting clinicians in making a treatment strategy of trigger finger. In addition, our proposed computer-aided image analysis system was considered to be a promising and objective approach to determining trigger finger severity at the microscopic level