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We introduce a class of multi-Higgs doublet extensions of the Standard Model that solve the strong $CP$problem with profound consequences for the flavor sector. The Yukawa matrices are constrained to have many zero entries by a “Higgs-flavor” symmetry, $G_{\mathrm{HF}}$, that acts on Higgs and quark fields. The violation of both $CP$and $G_{\mathrm{HF}}$occurs in the Higgs mass matrix so that, for certain choices of $G_{\mathrm{HF}}$charges, the strong $CP$parameter $\overline{\theta }$is zero at tree level. Radiative corrections to $\overline{\theta }$are computed in this class of theories. They vanish in realistic two-Higgs doublet models with $G_{\mathrm{HF}}={\mathbb{Z}}_3$. We also construct realistic three-Higgs models with $G_{\mathrm{HF}}=\mathrm{U}\left(1\right)$, where the one-loop results for $\overline{\theta }$are model-dependent. Requiring $\overline{\theta }<{10}^{-10}$has important implications for the flavor problem by constraining the Yukawa coupling and Higgs mass matrices. Contributions to $\overline{\theta }$from higher-dimension operators are computed at one loop and can also be sufficiently small, although the hierarchy problem of this class of theories is worse than in the Standard Model. 

A<sc>bstract</sc> The strong CP problem is solved in Parity symmetric theories, with the electroweak gauge group containing SU(2)_L× SU(2)_Rbroken by the minimal set of Higgs fields. Neutrino masses may be explained by adding the same number of gauge singlet fermions as the number of generations. The neutrino masses vanish at tree-level and are only radiatively generated, leading to larger couplings of right-handed neutrinos to Standard Model particles than with the tree-level seesaw mechanism. We compute these radiative corrections and the mixing angles between left- and right-handed neutrinos. We discuss sensitivities to these right-handed neutrinos from a variety of future experiments that search for heavy neutral leptons with masses from tens of MeV to the multi-TeV scale. 

Across basic research studies, cell counting requires significant human time and expertise. Trained experts use thin focal plane scanning to count (click) cells in stained biological tissue. This computer-assisted process (optical disector) requires a well-trained human to select a unique best z-plane of focus for counting cells of interest. Though accurate, this approach typically requires an hour per case and is prone to inter-and intra-rater errors. Our group has previously proposed deep learning (DL)-based methods to automate these counts using cell segmentation at high magnification. Here we propose a novel You Only Look Once (YOLO) model that performs cell detection on multi-channel z-plane images (disector stack). This automated Multiple Input Multiple Output (MIMO) version of the optical disector method uses an entire z-stack of microscopy images as its input, and outputs cell detections (counts) with a bounding box of each cell and class corresponding to the z-plane where the cell appears in best focus. Compared to the previous segmentation methods, the proposed method does not require time-and labor-intensive ground truth segmentation masks for training, while producing comparable accuracy to current segmentation-based automatic counts. The MIMO-YOLO method was evaluated on systematic-random samples of NeuN-stained tissue sections through the neocortex of mouse brains (n=7). Using a cross validation scheme, this method showed the ability to correctly count total neuron numbers with accuracy close to human experts and with 100% repeatability (Test-Retest). 

Automatic cell quantification in microscopy images can accelerate biomedical research. There has been significant progress in the 3D segmentation of neurons in fluorescence microscopy. However, it remains a challenge in bright-field microscopy due to the low Signal-to-Noise Ratio and signals from out-of-focus neurons. Automatic neuron counting in bright-field z-stacks is often performed on Extended Depth of Field images or on only one thick focal plane image. However, resolving overlapping cells that are located at different z-depths is a challenge. The overlap can be resolved by counting every neuron in its best focus z-plane because of their separation on the z-axis. Unbiased stereology is the state-of-the-art for total cell number estimation. The segmentation boundary for cells is required in order to incorporate the unbiased counting rule for stereology application. Hence, we perform counting via segmentation. We propose to achieve neuron segmentation in the optimal focal plane by posing the binary segmentation task as a multi-class multi-label task. Also, we propose to efficiently use a 2D U-Net for inter-image feature learning in a Multiple Input Multiple Output system that poses a binary segmentation task as a multi-class multi-label segmentation task. We demonstrate the accuracy and efficiency of the MIMO approach using a bright-field microscopy z-stack dataset locally prepared by an expert. The proposed MIMO approach is also validated on a dataset from the Cell Tracking Challenge achieving comparable results to a compared method equipped with memory units. Our z-stack dataset is available at 

Stereology-based methods provide the current state-of-the-art approaches for accurate quantification of numbers and other morphometric parameters of biological objects in stained tissue sections. The advent of artificial intelligence (AI)-based deep learning (DL) offers the possibility of improving throughput by automating the collection of stereology data. We have recently shown that DL can effectively achieve comparable accuracy to manual stereology but with higher repeatability, improved throughput, and less variation due to human factors by quantifying the total number of immunostained cells at their maximal profile of focus in extended depth of field (EDF) images. In the first of two novel contributions in this work, we propose a semi-automatic approach using a handcrafted Adaptive Segmentation Algorithm (ASA) to automatically generate ground truth on EDF images for training our deep learning (DL) models to automatically count cells using unbiased stereology methods. This update increases the amount of training data, thereby improving the accuracy and efficiency of automatic cell counting methods, without a requirement for extra expert time. The second contribution of this work is a Multi-channel Input and Multi-channel Output (MIMO) method using a U-Net deep learning architecture for automatic cell counting in a stack of z-axis images (also known as disector stacks). This DL-based digital automation of the ordinary optical fractionator ensures accurate counts through spatial separation of stained cells in the z-plane, thereby avoiding false negatives from overlapping cells in EDF images without the shortcomings of 3D and recurrent DL models. The contribution overcomes the issue of under-counting errors with EDF images due to overlapping cells in the z-plane (masking). We demonstrate the practical applications of these advances with automatic disector-based estimates of the total number of NeuN-immunostained neurons in a mouse neocortex. In summary, this work provides the first demonstration of automatic estimation of a total cell number in tissue sections using a combination of deep learning and the disector-based optical fractionator method. 

A bstract Rotations of an axion field in field space provide a natural origin for an era of kination domination, where the energy density is dominated by the kinetic term of the axion field, preceded by an early era of matter domination. Remarkably, no entropy is produced at the end of matter domination and hence these eras of matter and kination domination may occur even after Big Bang Nucleosynthesis. We derive constraints on these eras from both the cosmic microwave background and Big Bang Nucleosynthesis. We investigate how this cosmological scenario affects the spectrum of possible primordial gravitational waves and find that the spectrum features a triangular peak. We discuss how future observations of gravitational waves can probe the viable parameter space, including regions that produce axion dark matter by the kinetic misalignment mechanism or the baryon asymmetry by axiogenesis. For QCD axion dark matter produced by the kinetic misalignment mechanism, a modification to the inflationary gravitational wave spectrum occurs above 0.01 Hz and, for high values of the energy scale of inflation, the prospects for discovery are good. We briefly comment on implications for structure formation of the universe. 

The detection and segmentation of stained cells and nuclei are essential prerequisites for subsequent quantitative research for many diseases. Recently, deep learning has shown strong performance in many computer vision problems, including solutions for medical image analysis. Furthermore, accurate stereological quantification of microscopic structures in stained tissue sections plays a critical role in understanding human diseases and developing safe and effective treatments. In this article, we review the most recent deep learning approaches for cell (nuclei) detection and segmentation in cancer and Alzheimer's disease with an emphasis on deep learning approaches combined with unbiased stereology. Major challenges include accurate and reproducible cell detection and segmentation of microscopic images from stained sections. Finally, we discuss potential improvements and future trends in deep learning applied to cell detection and segmentation. 

A bstract The vanishing of the Higgs quartic coupling at a high energy scale may be explained by Intermediate Scale Supersymmetry, where supersymmetry breaks at (10 9 -10 12 ) GeV. The possible range of supersymmetry breaking scales can be narrowed down by precise measurements of the top quark mass and the strong coupling constant. On the other hand, nuclear recoil experiments can probe Higgsino or sneutrino dark matter up to a mass of 10 12 GeV. We derive the correlation between the dark matter mass and precision measurements of standard model parameters, including supersymmetric threshold corrections. The dark matter mass is bounded from above as a function of the top quark mass and the strong coupling constant. The top quark mass and the strong coupling constant are bounded from above and below respectively for a given dark matter mass. We also discuss how the observed dark matter abundance can be explained by freeze-out or freeze-in during a matter-dominated era after inflation, with the inflaton condensate being dissipated by thermal effects.