Pair Production of a 125 GeV Higgs Boson in MSSM and NMSSM at the LHC

In light of the recent LHC Higgs search data, we investigate the pair production of a SM-like Higgs boson around 125 GeV in the MSSM and NMSSM. We first scan the parameter space of each model by considering various experimental constraints, and then calculate the Higgs pair production rate in the allowed parameter space. We find that in most cases the dominant contribution to the Higgs pair production comes from the gluon fusion process and the production rate can be greatly enhanced, maximally 10 times larger than the SM prediction (even for a TeV-scale stop the production rate can still be enhanced by a factor of 1.3). We also calculate the chi-square value with the current Higgs data and find that in the most favored parameter region the production rate is enhanced by a factor of 1.45 in the MSSM, while in the NMSSM the production rate can be enhanced or suppressed (\sigma_{SUSY}/\sigma_{SM} varies from 0.7 to 2.4).Comment: 15 pages, 5 figure

Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest

The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer's disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly analyzing the multimodal data, we propose a novel method to construct fusion features and a classification method based on the random forest for identifying the important features. Specifically, we construct the fusion features using the gene sequence and subregions correlation to reduce the diversity in same group. Moreover, samples and features are selected randomly to construct a random forest, and genetic algorithm and clustering evolutionary are used to amplify the difference in initial decision trees and evolve the trees. The features in resulting decision trees that reach the peak classification are the important "subregion gene pairs". The findings verify that our method outperforms well in classification performance and generalization. Particularly, we identified some significant subregions and genes, such as hippocampus amygdala transition area (HATA), fimbria, parasubiculum and genes included RYR3 and PRKCE. These discoveries provide some new candidate genes for AD and demonstrate the contribution of hippocampal subregions and genes to AD

Excess Deaths of Gastrointestinal, Liver, and Pancreatic Diseases During the COVID-19 Pandemic in the United States

Objectives: To evaluate excess deaths of gastrointestinal, liver, and pancreatic diseases in the United States during the COVID-19 pandemic.Methods: We retrieved weekly death counts from National Vital Statistics System and fitted them with a quasi-Poisson regression model. Cause-specific excess deaths were calculated by the difference between observed and expected deaths with adjustment for temporal trend and seasonality. Demographic disparities and temporal-spatial patterns were evaluated for different diseases.Results: From March 2020 to September 2022, the increased mortality (measured by excess risks) for Clostridium difficile colitis, gastrointestinal hemorrhage, and acute pancreatitis were 35.9%; 24.8%; and 20.6% higher than the expected. For alcoholic liver disease, fibrosis/cirrhosis, and hepatic failure, the excess risks were 1.4–2.8 times higher among younger inhabitants than older inhabitants. The excess deaths of selected diseases were persistently observed across multiple epidemic waves with fluctuating trends for gastrointestinal hemorrhage and fibrosis/cirrhosis and an increasing trend for C. difficile colitis.Conclusion: The persistently observed excess deaths of digestive diseases highlights the importance for healthcare authorities to develop sustainable strategies in response to the long-term circulating of SARS-CoV-2 in the community

Development of a Remote Handling Robot for the Maintenance of an ITER-Like D-Shaped Vessel

Robotic operation is one of the major challenges in the remote maintenance of ITER vacuum vessel (VV) and future fusion reactors as inner operations of Tokamak have to be done by robots due to the internal adverse conditions. This paper introduces a novel remote handling robot (RHR) for the maintenance of ITER-like D-shaped vessel. The modular designed RHR, which is an important part of the remote handling system for ITER, consists of three parts: an omnidirectional transfer vehicle (OTV), a planar articulated arm (PAA), and an articulated teleoperated manipulator (ATM). The task of RHR is to carry processing tools, such as the viewing system, leakage detector, and electric screwdriver, to inspect and maintain the components installed inside the D-shaped vessel. The kinematics of the OTV, as well as the kinematic analyses of the PAA and ATM, is studied in this paper. Because of its special length and heavy payload, the dynamics of the PAA is also investigated through a dynamic simulation system based on robot technology middleware (RTM). The results of the path planning, workspace simulations, and dynamic simulation indicate that the RHR has good mobility together with satisfying kinematic and dynamic performances and can well accomplish its maintenance tasks in the ITER-like D-shaped vessel

Antigenic variation of influenza viruses and its impact on seasonal transmission of influenza : a multi-disciplinary approach in incorporating environmental and genetic measurements in an influenza epidemiology study in Hong Kong

In tropical and subtropical regions, influenza shows a less clear seasonal pattern than in temperate regions, with multiple peaks observed throughout the year. Understanding the mechanism of influenza outbreaks could facilitate the timely adoption of public health response and control measures. However the complicate seasonality of influenza in warm climate brings difficulty in modelling and forecasting the virus peaks. Previous studies have explored the impacts of meteorological factors and human behaviour on influenza seasonality, but there is no studies so far that have investigated the impact of another potential seasonal factor, antigenicity change of influenza viruses, likely due to the lack of consecutively collected genetic and antigenicity data. Besides seasonality, antigenic variation of influenza virus (AVI) has also been associated with disease burden for influenza, but evidence has also been limited. In my study I was trying to address the following research questions: 1) Is antigenic change associated with influenza disease burden? 2) Does antigenic change of influenza coincide with the surge of influenza seasonal activities? 3) Is antigenic change positively associated with virus transmission rates? To assess the association between annual disease burden and antigenic change, I first calculated annual antigenic changes of influenza for the seasonal dominant subtype A(H3N2). Then I used a Poisson regression model to calculate annual rates of excess mortality and hospitalization attributable to influenza in Hong Kong from 2001 to 2012. In the period 2001-12, antigenic changes of subtype A(H3N2) were found positively and highly correlated with both excess mortality and hospitalization rates (per 1,000,000 population) associated to A(H3N2) virus in cool seasons (October to March), with Spearman correlation coefficients from 0.655 to 0.809 for different disease. To answer the second question, I utilized a systematic sample of influenza A cases collected in Hong Kong to explore the temporal trend of antigenic changes. During 2013-14, a total of 2,115 samples were collected. Influenza A positive specimens were subsequently sub-typed into A(H3N2) and A(H1N1)pdm09 and were systematically selected for sequencing. Amino acid changes were found for both subtypes, but none of them were identified as antigenic drifted strains from the WHO vaccine composition strains in HI tests. No significant difference in antigenic variations was observed across age and gender, although further studies with longer and larger age-specific sequence and antigenicity data are warranted for better understanding the evolution of circulated influenza A virus particularly in tropical and subtropical regions. The association of antigenic changes with seasonal influenza transmission rates, which were measured by time-varying reproduction numbers Rt during 2013-14, was subsequently explored by comparing classical linear models with and without antigenic changes included as one of driving factors. The estimated reproduction numbers generally had a reverse linear relationship with the environmental factors for temperature and relative humidity in A(H1N1)pdm09 and for temperature in A(H3N2). My findings suggest that incorporation of antigenic variations and environmental factors could improve the performance of the mathematical model for influenza transmission rate, and potentially could contribute to a better prediction of seasonal influenza outbreaks.published_or_final_versionPublic HealthDoctoralDoctor of Philosoph

Latest insights in disease-modifying osteoarthritis drugs development

Osteoarthritis (OA) is a prevalent and severely debilitating disease with an unmet medical need. In order to alleviate OA symptoms or prevent structural progression of OA, new drugs, particularly disease-modifying osteoarthritis drugs (DMOADs), are required. Several drugs have been reported to attenuate cartilage loss or reduce subchondral bone lesions in OA and thus potentially be DMOADs. Most biologics (including interleukin-1 (IL-1) and tumor necrosis factor (TNF) inhibitors), sprifermin, and bisphosphonates failed to yield satisfactory results when treating OA. OA clinical heterogeneity is one of the primary reasons for the failure of these clinical trials, which can require different therapeutic approaches based on different phenotypes. This review describes the latest insights into the development of DMOADs. We summarize in this review the efficacy and safety profiles of various DMOADs targeting cartilage, synovitis, and subchondral bone endotypes in phase 2 and 3 clinical trials. To conclude, we summarize the reasons for clinical trial failures in OA and suggest possible solutions

The properties and formation mechanism of oat β-glucan mixed gels with different molecular weight composition induced by high-pressure processing.

High pressure, an emerging nonthermal technology has been widely applied in food product modifications. The effects of oat β-glucan concentration and pressure on the properties of mixed gels with the different ratios of varying molecular weight (MW) β-gulcan induced by HPP were investigated. The results showed that the lowest β-glucan concentration forming a gel was 15% at 200 MPa, while 8% β-glucan was required to form a gel at 500 MPa. The gel intensity and textural properties increased with elevating β-glucan total concentration and pressure. The characteristic compact and smooth mixed gel formed with 12% β-glucan at a ratio of 50:50 at 400 MPa for 30 min. Under this optimal parameters, the mixed solution showed a relatively lower particle size and turbidity, and the hydrogen bonding and electrostatic interaction played the main role during the gel formation process by high pressure. In addition, the core molecular structure of β-glucan was maintained in the mixed gel formed under the optimal parameters

Hazard Assessment of Debris-Flow along the Baicha River in Heshigten Banner, Inner Mongolia, China

This study focused on a cloud model approach for considering debris-flow hazard assessment, in which the cloud model provided a model for transforming the qualitative and quantitative expressions. Additionally, the entropy method and analytical hierarchy process were united for calculating the parameters weights. The weighting method avoids the disadvantages inherent in using subjective or objective methods alone. Based on the cloud model and component weighting method, a model was established for the analysis of debris-flow hazard assessment. There are 29 debris-flow catchments around the pumped storage power station in the study area located near Zhirui (Inner Mongolia, China). Field survey data and 3S technologies were used for data collection. The results of the cloud model calculation process showed that of the 29 catchments, 25 had low debris-flow hazard assessment, three had moderate hazard assessment, and one had high hazard assessment. The widely used extenics method and field geological surveys were used to validate the proposed approach. This approach shows high potential as a useful tool for debris-flow hazard assessment analysis. Compared with other prediction methods, it avoids the randomness and fuzziness in uncertainty problems, and its prediction results are considered reasonable