Bounds on large extra dimensions from the simulation of black hole events at the LHC

If large extra dimensions exist, the Planck scale may be as low as a TeV and microscopic black holes may be produced in high-energy particle collisions at this energy scale. We simulate microscopic black hole formation at the Large Hadron Collider and compare the simulation results with recent experimental data by the Compact Muon Solenoid collaboration. The absence of observed black hole events in the experimental data allows us to set lower bounds on the Planck scale and various parameters related to microscopic black hole formation for a number ($3-6$) of extra dimensions. Our analysis sets lower bounds on the fundamental Planck scale ranging from 0.6 TeV to 4.8 TeV for black holes fully decaying into Standard Model particles and 0.3 TeV to 2.8 TeV for black holes settling down to a remnant, depending on the minimum allowed black hole mass at formation. Formation of black holes with mass less than 5.2 TeV to 6.5 TeV (SM decay) and 2.2 TeV to 3.4 TeV (remnant) is excluded at 95\% C.L. Our analysis shows consistency with and difference from the CMS results.Comment: 15 pages, 5 figure

High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using Semantic Diffusion Model

Lung cancer has been one of the leading causes of cancer-related deaths worldwide for years. With the emergence of deep learning, computer-assisted diagnosis (CAD) models based on learning algorithms can accelerate the nodule screening process, providing valuable assistance to radiologists in their daily clinical workflows. However, developing such robust and accurate models often requires large-scale and diverse medical datasets with high-quality annotations. Generating synthetic data provides a pathway for augmenting datasets at a larger scale. Therefore, in this paper, we explore the use of Semantic Diffusion Mod- els (SDM) to generate high-fidelity pulmonary CT images from segmentation maps. We utilize annotation information from the LUNA16 dataset to create paired CT images and masks, and assess the quality of the generated images using the Frechet Inception Distance (FID), as well as on two common clinical downstream tasks: nodule detection and nodule localization. Achieving improvements of 3.96% for detection accuracy and 8.50% for AP50 in nodule localization task, respectively, demonstrates the feasibility of the approach.Comment: 4 pages, 1 figure, submitted to MIDL 202

The Intersection of the Criminal Justice, Education, and Mental Healthcare Systems and Its Influence on Boys and Young Men of Color

The authors provide a scan of the academic and gray literature on the intersection of the criminal justice, mental health, and education systems, and how it influences the lives of at-risk racial/ethnic minority youth (boys and young men of color). As well, the authors identify interventions that aim to improve outcomes for racial/ethnic minority at-risk youth at the intersection of these three structural systems

Relatively Prime Polynomials and Nonsingular Hankel Matrices over Finite Fields

The probability for two monic polynomials of a positive degree n with coefficients in the finite field F_q to be relatively prime turns out to be identical with the probability for an n x n Hankel matrix over F_q to be nonsingular. Motivated by this, we give an explicit map from pairs of coprime polynomials to nonsingular Hankel matrices that explains this connection. A basic tool used here is the classical notion of Bezoutian of two polynomials. Moreover, we give simpler and direct proofs of the general formulae for the number of m-tuples of relatively prime polynomials over F_q of given degrees and for the number of n x n Hankel matrices over F_q of a given rankComment: 10 pages; to appear in the Journal of Combinatorial Theory, Series

Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion

This paper aims to solve a fundamental problem in intensity-based 2D/3D registration, which concerns the limited capture range and need for very good initialization of state-of-the-art image registration methods. We propose a regression approach that learns to predict rotation and translations of arbitrary 2D image slices from 3D volumes, with respect to a learned canonical atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks (CNNs) to learn the highly complex regression function that maps 2D image slices into their correct position and orientation in 3D space. Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data. We extensively evaluate the effectiveness of our approach quantitatively on simulated MRI brain data with extreme random motion. We further demonstrate qualitative results on fetal MRI where our method is integrated into a full reconstruction and motion compensation pipeline. With our CNN regression approach we obtain an average prediction error of 7mm on simulated data, and convincing reconstruction quality of images of very young fetuses where previous methods fail. We further discuss applications to Computed Tomography and X-ray projections. Our approach is a general solution to the 2D/3D initialization problem. It is computationally efficient, with prediction times per slice of a few milliseconds, making it suitable for real-time scenarios.Comment: 8 pages, 4 figures, 6 pages supplemental material, currently under review for MICCAI 201

Subset of Heat-Shock Transcription Factors Required for the Early Response of Arabidopsis to Excess Light

Sunlight provides energy for photosynthesis and is essential for nearly all life on earth. However, too much or too little light or rapidly fluctuating light conditions cause stress to plants. Rapid changes in the amount of light are perceived as a change in the reduced/oxidized (redox) state of photosynthetic electron transport components in chloroplasts. However, how this generates a signal that is relayed to changes in nuclear gene expression is not well understood. We modified redox state in the reference plant, Arabidopsis thaliana, using either excess light or low light plus the herbicide DBMIB (2,5-dibromo-3-methyl-6-isopropyl-p-benzoquinone), a well-known inhibitor of photosynthetic electron transport. Modification of redox state caused a change in expression of a common set of about 750 genes, many of which are known stress-responsive genes. Among the most highly enriched promoter elements in the induced gene set were heat-shock elements (HSEs), known motifs that change gene expression in response to high temperature in many systems. We show that HSEs from the promoter of the ASCORBATE PEROXIDASE 2 (APX2) gene were necessary and sufficient for APX2 expression in conditions of excess light, or under low light plus the herbicide. We tested APX2 expression phenotypes in overexpression and loss-of-function mutants of 15 Arabidopsis A-type heat-shock transcription factors (HSFs), and identified HSFA1D, HSFA2, and HSFA3 as key factors regulating APX2 expression in diverse stress conditions. Excess light regulates both the subcellular location of HSFA1D and its biochemical properties, making it a key early component of the excess light stress network of plants