Vacuum stability in stau-neutralino coannihilation in MSSM

The stau-neutralino coannihilation provides a feasible way to accommodate the observed cosmological dark matter (DM) relic density in the minimal supersymmetric standard model (MSSM). In such a coannihilation mechanism the stau mass usually has an upper bound since its annihilation rate becomes small with the increase of DM mass. Inspired by this observation, we examine the upper limit of stau mass in the parameter space with a large mixing of staus. We find that the stau pair may dominantly annihilate into dibosons and hence the upper bound on the stau mass ($\sim400$ GeV) obtained from the $f\bar{f}$ final states can be relaxed. Imposing the DM relic density constraint and requiring a long lifetime of the present vacuum, we find that the lighter stau mass can be as heavy as about 1.4 TeV for the stau maximum mixing. However, if requiring the present vacuum to survive during the thermal history of the universe, this mass limit will reduce to about 0.9 TeV. We also discuss the complementarity of vacuum stability and direct detections in probing this stau coannihilation scenario.Comment: 12 pages, 6 figure

A minimal U(1)′U(1)^\prime extension of MSSM in light of the B decay anomaly

Motivated by the $R_K$ and $R_{K^*}$ anomalies from B decays, we extend the minimal supersymmetric model with a non-universal anomaly-free $U(1)^\prime$ gauge symmetry, coupling non-universally to the lepton sector as well as the quark sector. In particular, only the third generation quarks are charged under this $U(1)^\prime$, which can easily evade the dilepton bound from the LHC searches. An extra singlet is introduced to break this $U(1)^\prime$ symmetry allowing for the $\mu$-term to be generated dynamically. The relevant constraints of $B_s-\bar{B}_s$ mixing, $D^0-\bar{D}^0$ mixing and the LHC dilepton searches are considered. We find that in the allowed parameter space this $U(1)^\prime$ gauge interaction can accommodate the $R_K$ and $R_{K^*}$ anomalies and weaken considerably the $Z^\prime$ mass limits while remaining perturbative up to the Planck scale.Comment: 12 pages,2 figure

The DNA-sensing AIM2 inflammasome controls radiation-induced cell death and tissue injury

Acute exposure to ionizing radiation induces massive cell death and severe damage to tissues containing actively proliferating cells, including bone marrow and the gastrointestinal tract. However, the cellular and molecular mechanisms underlying this pathology remain controversial. Here, we show that mice deficient in the double-stranded DNA sensor AIM2 are protected from both subtotal body irradiation-induced gastrointestinal syndrome and total body irradiation-induced hematopoietic failure. AIM2 mediates the caspase-1-dependent death of intestinal epithelial cells and bone marrow cells in response to double-strand DNA breaks caused by ionizing radiation and chemotherapeutic agents. Mechanistically, we found that AIM2 senses radiation-induced DNA damage in the nucleus to mediate inflammasome activation and cell death. Our results suggest that AIM2 may be a new therapeutic target for ionizing radiation exposure

TRPA1 Activation-Induced Myelin Degradation Plays a Key Role in Motor Dysfunction After Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) is a devastating disease that is characterized by high morbidity and high mortality. ICH has an annual incidence of 10–30/100,000 people and accounts for approximately 10%–30% of all types of stroke. ICH mostly occurs at the basal ganglia, which is rich in nerve fibers; thus, hemiplegia is quite common in ICH patients with partial sensory disturbance and ectopic blindness. In the clinic, those symptoms are considered to originate from the white matter injury in the area, but the exact mechanisms are unknown, and currently, no effective drug treatments are available to improve the prognosis. Clarifying the mechanisms will contribute to the development of new treatment methods for patients. The transient receptor potential ankyrin 1 (TRPA1) channel is a non-selective cation channel that plays a role in inflammatory pain sensation and nociception and may be a potential regulator in emotion, cognition and social behavior. Here, we report that TRPA1 is involved in myelin damage and oxidative stress injury in a mouse ICH model. Intervention with the TRPA1 channel may be a new method to improve the motor function of patients in the early stage of ICH

Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

Review on Deep Learning Research and Applications in Wind and Wave Energy

Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification

Wave Power Density Hotspot Distribution and Correlation Pattern Exploration in the Gulf of Mexico

Wave energy has been studied and explored because of its enormous potential to supply electricity for human activities. However, the uncertainty of its spatial and temporal variations increases the difficulty of harvesting wave energy commercially. There are no large-scale wave converters in commercial operation yet. A thorough understanding of wave energy dynamic behaviors will definitely contribute to the acceleration of wave energy harvesting. In this paper, about 40 years of meteorological data from the Gulf of Mexico were obtained, visualized, and analyzed to reveal the wave power density hotspot distribution pattern, and its correlation with ocean surface water temperatures and salinities. The collected geospatial data were first visualized in MATLAB. The visualized data were analyzed using the deep learning method to identify the wave power density hotspots in the Gulf of Mexico. By adjusting the temporal and spatial resolutions of the different datasets, the correlations between the number of hotspots and their strength levels and the surface temperatures and salinities are revealed. The R value of the correlation between the wave power density hotspots and the salinity changes from −0.371 to −0.885 in a negative direction, and from 0.219 to 0.771 in a positive direction. For the sea surface temperatures, the R values range from −0.474 to 0.393. Certain areas within the Gulf of Mexico show relatively strong correlations, which may be useful for predicting the wave energy behavior and change patterns

Distributed Schemes for Crowdsourcing-Based Sensing Task Assignment in Cognitive Radio Networks

Spectrum sensing is an important issue in cognitive radio networks. The unlicensed users can access the licensed wireless spectrum only when the licensed wireless spectrum is sensed to be idle. Since mobile terminals such as smartphones and tablets are popular among people, spectrum sensing can be assigned to these mobile intelligent terminals, which is called crowdsourcing method. Based on the crowdsourcing method, this paper studies the distributed scheme to assign spectrum sensing task to mobile terminals such as smartphones and tablets. Considering the fact that mobile terminals’ positions may influence the sensing results, a precise sensing effect function is designed for the crowdsourcing-based sensing task assignment. We aim to maximize the sensing effect function and cast this optimization problem to address crowdsensing task assignment in cognitive radio networks. This problem is difficult to be solved because the complexity of this problem increases exponentially with the growth in mobile terminals. To assign crowdsensing task, we propose four distributed algorithms with different transition probabilities and use a Markov chain to analyze the approximation gap of our proposed schemes. Simulation results evaluate the average performance of our proposed algorithms and validate the algorithm’s convergence

Investigating Dominant Trip Distance for Intercity Passenger Transport Mode Using Large-Scale Location-Based Service Data

Intercity transport systems have been plagued by low efficiency and overutilization for a long time, due to unhealthy competition among multi-transport modes. Hence, this study aims to estimate the dominant trip distance of intercity passenger transport modes to optimize the allocation of intercity passenger transport resources and improve the efficiency of intercity transport systems. Dominant trip distance was classified into two types: Absolute dominant trip distance and relative dominant trip distance; and their respective models were developed using passenger transport mode share functions and fitting curves. Particularly, the big data of intercity passenger transport mode share rate of more than 360 cities in China was obtained using a network crawler and each passenger transport mode share function and their curves were proposed. Furthermore, the dominant trip distances estimation models of intercity passenger transport were developed and solved. The results show that there are significant differences in dominant trip distance between the transport modes. For example, the absolute and relative dominant trip distances of highway are 8–119 km and 8–463 km, respectively, while those of airway are 1594–3000 km and 2477–3000 km, respectively