In this work we numerically re-examine the loop-induced WIMP-nucleon scattering cross section for the simplified dark matter models and the constraint set by the latest direct detection experiment. ...We consider a fermion, scalar or vector dark matter component from five simplified models with leptophobic spin-0 mediators coupled only to Standard Model quarks and dark matter particles. The tree-level WIMP-nucleon cross sections in these models are all momentum-suppressed. We calculate the non-suppressed spin-independent WIMP-nucleon cross sections from loop diagrams and investigate the constrained space of dark matter mass and mediator mass by Xenon1T. The constraints from indirect detection and collider search are also discussed.
A
bstract
The tau lepton plays important role in distinguishing neutrino mass patterns and determining the chirality nature in heavy scalar mediated neutrino mass models, in the light of the neutrino ...oscillation experiments and its polarization measurement. We investigate the lepton flavor signatures with tau lepton at LHC upgrades, i.e. HL-LHC, HE-LHC and FCC-hh, through leptonic processes from doubly charged Higgs in the Type II Seesaw. We find that for the channel with one tau lepton in final states, the accessible doubly charged Higgs mass at HL-LHC can reach 655 GeV and 695 GeV for the neutrino mass patterns of normal hierarchy (NH) and inverted hierarchy (IH) respectively, with the luminosity of 3000 fb
−1
. Higher masses, 975-1930 GeV for NH and 1035-2070 GeV for IH, can be achieved at HE-LHC and FCC-hh.
Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages. The fusion of multiple data modalities can provide a ...holistic view of AD staging analysis. Thus, we use deep learning (DL) to integrally analyze imaging (magnetic resonance imaging (MRI)), genetic (single nucleotide polymorphisms (SNPs)), and clinical test data to classify patients into AD, MCI, and controls (CN). We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data. We also develop a novel data interpretation method to identify top-performing features learned by the deep-models with clustering and perturbation analysis. Using Alzheimer's disease neuroimaging initiative (ADNI) dataset, we demonstrate that deep models outperform shallow models, including support vector machines, decision trees, random forests, and k-nearest neighbors. In addition, we demonstrate that integrating multi-modality data outperforms single modality models in terms of accuracy, precision, recall, and meanF1 scores. Our models have identified hippocampus, amygdala brain areas, and the Rey Auditory Verbal Learning Test (RAVLT) as top distinguished features, which are consistent with the known AD literature.
Abstract
The offshore oilfield microgrid can effectively integrate distributed power and hybrid energy storage, and its coordinated control can effectively ensure the safe and stable operation of the ...microgrid. In order to ensure the effect of coordinated control and improve the efficiency of coordinated control, a distributed coordinated control method for hybrid energy storage of offshore oil field microgrids in a short time scale is proposed. By setting two objective functions, namely, short time scale and optimal allocation of hybrid energy storage power of offshore oilfield microgrid, configuration capacity, and operation power constraints, a two-stage distributed coordinated control model for hybrid energy storage of offshore oilfield microgrid is constructed. The experimental results show that the proposed hybrid energy storage distributed coordination control method for offshore oilfield microgrids has a good effect and high efficiency, and can effectively ensure the stable operation of offshore oilfield microgrids. The experimental results show that the proposed distributed coordinated control method for hybrid energy storage of offshore oilfield microgrids has a good effect and high efficiency. When the number of iterations reaches 100, the coordinated control time of this method can reach a lower time. It effectively ensures the stable operation of the offshore oil field microgrid.
Despite numerous inherent merits of metal–organic frameworks (MOFs), structural fragility has imposed great restrictions on their wider involvement in many applications, such as in catalysis. Herein, ...a strategy for enhancing stability and enabling functionality in a labile Zr(IV)‐MOF has been proposed by in situ porphyrin substitution. A size‐ and geometry‐matched robust linear porphyrin ligand 4,4′‐(porphyrin‐5,15‐diyl)dibenzolate (DCPP2−) is selected to replace the 4,4′‐(1,3,6,8‐tetraoxobenzolmn3,8phenanthroline‐2,7(1H,3H,6H,8H)‐diyl)dibenzoate (NDIDB2−) ligand in the synthesis of BUT‐109(Zr), affording BUT‐110 with varied porphyrin contents. Compared to BUT‐109(Zr), the chemical stability of BUT‐110 series is greatly improved. Metalloporphyrin incorporation endows BUT‐110 MOFs with high catalytic activity in the photoreduction of CO2, in the absence of photosensitizers. By tuning the metal species and porphyrin contents in BUT‐110, the resulting BUT‐110‐50%‐Co is demonstrated to be a good photocatalyst for selective CO2‐to‐CO reduction, via balancing the chemical stability, photocatalytic efficiency, and synthetic cost. This work highlights the advantages of in situ ligand substitution for MOF modification, by which uniform distribution and high content of the incoming ligand are accessible in the resulting MOFs. More importantly, it provides a promising approach to convert unstable MOFs, which mainly constitute the vast MOF database but have always been neglected, into robust functional materials.
The in situ porphyrin substitution strategy is developed for modifying labile interpenetrated BUT‐109(Zr), affording BUT‐110 with enhanced chemical stability and photocatalytic activity. By tuning the species and contents of metalloporphyrin in BUT‐110, some of the BUT‐110 MOFs may serve as potential photocatalysts for selective CO2‐to‐CO reduction, in the absence of photosensitizer.
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bstract
We perform an analysis of the simplified dark matter models in the light of cosmic ray observables by AMS-02 and Fermi-LAT. We assume fermion, scalar or vector dark matter particle with a ...leptophobic spin-0 mediator that couples only to Standard Model quarks and dark matter via scalar and/or pseudo-scalar bilinear. The propagation and injection parameters of cosmic rays are determined by the observed fluxes of nuclei from AMS-02. We find that the AMS-02 observations are consistent with the dark matter framework within the uncertainties. The AMS-02 antiproton data prefer 30 (50) GeV - 5 TeV dark matter mass and require an effective annihilation cross section in the region of 4 × 10
−27
(7 × 10
−27
) - 4 × 10
−24
cm
3
/s for the simplified fermion (scalar and vector) dark matter models. The cross sections below 2 × 10
−26
cm
3
/s can evade the constraint from Fermi-LAT dwarf galaxies for about 100 GeV dark matter mass.
A
bstract
In this work we perform an analysis of the recent AMS-02 antiproton flux and the antiproton-to-proton ratio in the framework of simplified dark matter models. To predict the AMS-02 ...observables we adopt the propagation and injection parameters determined by the observed fluxes of nuclei. We assume that the dark matter particle is a Dirac fermionic dark matter, with leptophobic pseudoscalar or axialvector mediator that couples only to Standard Model quarks and dark matter particles. We find that the AMS-02 observations are consistent with the dark matter framework within the uncertainties. The antiproton data prefer a dark matter (mediator) mass in the 700 GeV-5 TeV region for the annihilation with pseudoscalar mediator and greater than 700 GeV (200 GeV-1 TeV) for the annihilation with axialvector mediator, respectively, at about 68% confidence level. The AMS-02 data require an effective dark matter annihilation cross section in the region of 1×10
−25
−1×10
−24
(1×10
−25
−4×10
−24
) cm
3
/sforthesimplifiedmodelwithpseudoscalar (axialvector) mediator. The constraints from the LHC and Fermi-LAT are also discussed.
Esophageal squamous cell carcinoma (ESCC) is one of the deadliest cancers. We performed exome sequencing on 113 tumor-normal pairs, yielding a mean of 82 non-silent mutations per tumor, and 8 cell ...lines. The mutational profile of ESCC closely resembles those of squamous cell carcinomas of other tissues but differs from that of esophageal adenocarcinoma. Genes involved in cell cycle and apoptosis regulation were mutated in 99% of cases by somatic alterations of TP53 (93%), CCND1 (33%), CDKN2A (20%), NFE2L2 (10%) and RB1 (9%). Histone modifier genes were frequently mutated, including KMT2D (also called MLL2; 19%), KMT2C (MLL3; 6%), KDM6A (7%), EP300 (10%) and CREBBP (6%). EP300 mutations were associated with poor survival. The Hippo and Notch pathways were dysregulated by mutations in FAT1, FAT2, FAT3 or FAT4 (27%) or AJUBA (JUB; 7%) and NOTCH1, NOTCH2 or NOTCH3 (22%) or FBXW7 (5%), respectively. These results define the mutational landscape of ESCC and highlight mutations in epigenetic modulators with prognostic and potentially therapeutic implications.
Osteoporosis is a chronic bone metabolism disorder affecting millions of the world population. The RANKL/RANK/OPG signaling pathway has been confirmed to be the main regulator of osteoporosis. It is ...of great interest to identify appropriate therapeutic agents that can regulate the RANKL/RANK/OPG pathway. Baicalin (BA) is a well-known traditional Chinese medicine formula against various inflammatory diseases with a proven role of the RANKL/RANK/OPG pathway regulation. However, the potential effect of BA on osteoporosis and the mechanisms underlying this remain unclear. In the present study, we aimed to evaluate the efficacy of BA in the prevention of dexamethasone (DEX)-induced osteoporosis in zebrafish.
In this study, growth and development changes of zebrafish and calcein staining were assessed with a micrograph. The expression levels of RANKL and OPG and transcription factors in response to DEX induction and BA administration were evaluated by Western blotting and qRT-PCR. In addition, the intermolecular interactions of BA and RANKL were investigated by molecular docking.
Results show that BA enhances the growth and development of dexamethasone (DEX)-induced osteoporosis in zebrafish larvae. Calcein staining and calcium and phosphorus determination revealed that BA ameliorates mineralization of DEX-induced osteoporosis zebrafish larvae. BA also regulates the expression of RANKL and OPG and hampers the changes in gene expression related to bone formation and resorption under the induction of DEX in zebrafish. It can be inferred by molecular docking that BA may interact directly with the extracellular domain of RANKL.
The findings, herein, reveal that BA ameliorates DEX-induced osteoporosis by regulation of the RANK/RANKL/OPG signaling pathway.
•Deep learning based semantic segmentation method with fully convolutional network is proposed.•Defect images are captured via a self-developed image acquisition equipment (MTI-200a).•The proposed ...method can rapidly and accurately recognize defects for metro shield tunnels.
The performance of traditional visual inspection by handcrafted features for crack and leakage defects of metro shield tunnel is hardly satisfactory nowadays because it is low-efficient to distinguish defects from some interference such as segmental joints, bolt holes, cables and manual marks. Based on deep learning (DL), this paper proposes a novel image recognition algorithm for semantic segmentation of crack and leakage defects of metro shield tunnel using hierarchies of features extracted by fully convolutional network (FCN). The defect images in training dataset and testing dataset are captured via a self-developed image acquisition equipment named Moving Tunnel Inspection (MTI-200a). After the establishment of image datasets, FCN models of crack and leakage are separately trained through several iterations of forward inference and backward learning. Semantic segmentation of defect images is implemented via the corresponding FCN models using two-stream algorithm, i.e. one stream is used to recognize the crack by sliding-window-assembling operation and the other is adopted for the leakage by resizing-interpolation operation. Compared with two frequently-used traditional methods, i.e. region growing algorithm (RGA) and adaptive thresholding algorithm (ATA), great superiority of the proposed method in terms of recognition results, inference time and error rates is shown based on four typical types of defect images which are crack-only image, leakage-only image, two-defect-nonoverlapping (TDN) image, two-defect-overlapping (TDO) image. The proposed method using DL can be employed to rapidly and accurately recognize defects for structure health monitoring and maintenance of metro shield tunnels.
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