The Application of Remote Sensing and GIS for Improving Modeling the Response of Wetland Vegetation Communities to Water Level Fluctuations at Long Point, Ontario

Coastal wetlands are complex and dynamic environments which are of high environmental, social, and economic importance. With the acceleration of climate change and global warming, it is necessary to monitor and protect dynamic coastal wetlands. Wetland ecosystem simulation modeling is one approach to help produce better wetland protection and management strategies. The application of remote sensing and Geographic Information System (GIS) in wetland ecosystem simulation models can help with better spatial modeling of wetland ecosystems. In addition, coastal topographic models can achieve digital representations of terrain surfaces and aquatic environments. This study applies remote sensing and GIS technologies for improving wetland vegetation simulation modeling. First, the study integrates multiple topographic data sources (i.e. Light Detection and Ranging data (LiDAR) and bathymetry data) to generate a coastal topographic model. Shoreline data are involved in the generation process. Second, a pre-existing wetland simulation model is updated to a new version to model the response of wetland vegetation communities to water level fluctuations at Long Point, Ontario. Third, different coastal topographic models have been employed to explore how a coastal topographic model affects the wetland simulation results. Model sensitivity analysis is conducted to explore the variation of model simulation results to different vegetation transition baselines parameter. Findings from this study suggest that a high accuracy coastal topographic model could yield a higher accuracy simulation result in a wetland ecosystem simulation model. Second, the application of remote sensing and the integration of multiple topographic data (e.g. LiDAR data and bathymetry data) could provide high accuracy and high density elevation information in coastal area, especially in land-water transitional areas. Finally, a narrower vegetation transition baseline increases the possibility for a wetland community shift to a wetter wetland community

Description of the newly observed Ωc∗\Omega^{*}_c states as molecular states

In this work, we study the strong decays of the newly observed $\Omega^{*}_c(3185)$ and $\Omega^{*}_c(3327)$ assuming that $\Omega^{*}_c(3185)$ and $\Omega^{*}_c(3327)$ as $S$-wave $D\Xi$ and $D^{*}\Xi$ molecular state, respectively. Since the $\Omega_c^{*}$ was observed in the $\Xi_c^{+}K^{-}$ invariant mass distributions, the partial decay width of $\Omega^{*}_c(3185)$ and $\Omega^{*}_c(3327)$ into $\Xi_c^{+}K^{-}$ through hadronic loops are evaluated with the help of the effective Lagrangians. Moreover, the decay channel of $\Xi_c^{'}\bar{K}$ is also included. The decay process is described by the $t$-channel $\Lambda$, $\Sigma$ baryons and $D_s$, $D_s^{*}$ mesons exchanges, respectively. By comparison with the LHCb observation, the current results support the $\Omega^{*}_c(3327)$ with$J^P=3/2^{-}$ as pure $D^{*}\Xi$ molecule while the $\Omega^{*}_c(3327)$ with $J^P=1/2^{-}$ can not be well reproduced in the molecular state picture. In addition, the spin-parity $J^P=1/2^{-}$ $D\Xi$ molecular assumptions for the $\Omega^{*}_c(3185)$ can't be conclusively determined. It may be a meson-baryon molecule with a big $D\Xi$ component. Although the decay width of the $\Omega_c^{*}\to{}\bar{K}\Xi_c^{'}$ is of the order several MeV, it can be well employed to test the molecule interpretations of $\Omega^{*}_c(3185)$ and $\Omega^{*}_c(3327)$

Patients with myasthenia gravis with acute onset of dyspnea: predictors of progression to myasthenic crisis and prognosis

Background: Life-threatening myasthenic crisis (MC) occurs in 10–20% of the patients with myasthenia gravis (MG). It is important to identify the predictors of progression to MC and prognosis in the patients with MG with acute exacerbations. Objective: This study aimed to explore the predictors of progression to MC in the patients with MG with acute onset of dyspnea and their short-term and long-term prognosis. Methods: This study is a retrospective cohort study. We collected and analyzed data on all the patients with MG with acute dyspnea over a 10-year period in a single center using the univariate and multivariate analysis. Results: Eighty-six patients with MG were included. In their first acute dyspnea episodes, 36 (41.9%) episodes eventually progressed to MC. A multivariate analysis showed that the early-onset MG (adjusted OR: 3.079, 95% CI 1.052–9.012) and respiratory infection as a trigger (adjusted OR: 3.926, 95% CI 1.141–13.510) were independent risk factors for the progression to MC, while intravenous immunoglobulin (IVIg) treatment prior to the mechanical ventilation (adjusted OR: 0.253, 95% CI 0.087–0.732) was a protective factor. The prognosis did not significantly differ between the patients with and without MC during the MG course, with a total of 45 (52.3%) patients reaching post-intervention status better than minimal manifestations at the last follow-up. Conclusion: When treating the patients with MG with acute dyspnea, the clinicians should be aware of the risk factors of progression to MC, such as early-onset MG and respiratory infection. IVIg is an effective treatment. With proper immunosuppressive therapy, this group of patients had an overall good long-term prognosis

Association between clinical factors and result of immune checkpoint inhibitor related myasthenia gravis: a single center experience and systematic review

Background: Neurological immune-related adverse events (nirAEs) are rare toxicities of immune-checkpoint inhibitors (ICI). With the increase use of ICIs, incidence of nirAEs is growing, among which ICI related MG (irMG) is causing high fatality rate. Given the limited evidence, data from a large cohort of patients with irMG is needed to aid in recognition and management of this fatal complication. Objective: This study aimed to summarize clinical characteristics of irMG and explore predictors of irMG clinical outcome. Methods: We summarized our institution's patients who were diagnosed as irMG between Sep 2019 and Oct 2021. We systematically reviewed the literature through Oct 2021 to identify all similar reported patients who met inclusion criteria. As the control group, patients with idiopathic MG were used. We collected data on clinical features, management, and outcomes of both irMG and idioMG cases. Further statistical analysis was conducted. Results: Sixty three irMG patients and 380 idioMG patients were included in the final analysis. For irMG patients, six were from our institution while the rest 57 were from reported cases. The average age of irMG patients is 70.16 years old. Forty three were male. Average time from first ICI injection to symptom onset was 5.500 weeks. Eleven patients had a past history of MG. Higher MGFA classification and higher QMGS rates were observed in irMG patients compared to idioMG patients. For complication, more irMG patients had myositis or myocarditis overlapping compared to idioMG patients. The most commonly used treatment was corticosteroids for both idioMG and irMG. Twenty one patients (35%) with irMG had unfavorable disease outcome. Single variate and multivariate binary logistic regression proved that association with myocarditis, high MGFA classification or QMGS rates at first visit were negatively related to disease outcome in irMG patients. Conclusion: irMG is a life-threatening adverse event. irMG has unique clinical manifestations and clinical outcome compared to idioMG. When suspicious, early evaluation of MGFA classification, QMGS rates and myositis/myocarditis evaluation are recommended

Allylic oxidation of olefins with a manganese-based metal-organic framework

Selective oxidation of olefins to α,β-unsaturated ketones under mild reaction conditions have attracted considerable interest, since α,β-unsaturated ketones can serve to be synthetic precursors for various downstream chemical products. The major challenges inherently with this chemical oxidation are chem-, regio-selectivity as well as environmental concerns, i.e. catalyst recycle, safety and cost. Using atmospheric oxygen as an environmental friendly oxidant, we found that a metal-organic framework (MOF) constructed with Mn and tetrazolate ligand (CPF-5) showed good activity and selectivity for the allylic oxidation of olefins to α,β-unsaturated ketones. Under the optimized condition, we could achieve 98% conversion of cyclohexene and 87% selectivity toward cyclohexanone. The combination of a substoichiometric amount of TBHP (tert-butylhydroperoxide) and oxygen not only provides a cost effective oxidation system but significantly enhances the selectivity to α,β-unsaturated ketones, outperforming most reported oxidation methods. This catalytic system is heterogeneous in nature, and CPF-5 could be reused at least five times without a significant decrease in its catalytic activity and selectivity

Human connectome module pattern detection using a new multi-graph MinMax cut model

Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method

Long-term efficacy of non-steroid immunosuppressive agents in anti-muscle-specific kinase positive myasthenia gravis patients: a prospective study

Background and Purpose: Anti-muscle-specific kinase (MuSK) positive myasthenia gravis (MG) is characterized by a high relapsing rate, thus, choosing the appropriate oral drug regimen is a challenge. This study aimed to evaluate the efficacy of oral immunosuppressants (IS) in preventing relapse in MuSK-MG. Methods: This prospective cohort observational study included patients with MuSK-MG at Peking Union Medical College Hospital between January 1, 2018, and November 15, 2021. The patients were divided into 2 groups: those with (IS+) or without (IS-) non-steroid immunosuppressive agents. The primary outcome was relapsed at follow-up, and the log-rank test was used to compare the proportion of maintenance-free relapse between the groups; hazard ratio (HR) was calculated using the Cox proportional hazards models. Results: Fifty-three of 59 patients with MuSK-MG were included in the cohort, 14 were in the IS+ group, and 39 were in the IS- group. Twenty-four cases in the cohort experienced relapse at least once; the relapse rate was 2/14 (14.3%) in the IS+ group and 22/39 (56.4%) in the IS- group. At the end of follow-up, the proportion of maintenance-free relapse was significantly different between the two groups (log-rank χ2 = 4.94, P = 0.02). Of all the potential confounders, only the use of IS was associated with a reduced risk of relapse. The HR for relapse among patients in the IS+ group was 0.21 (95%CI 0.05–0.58) and was 0.23 (95%CI 0.05–0.93) in a model adjusted for age, sex, relapse history, highest Myasthenia Gravis Foundation of America (MGFA), and accumulated time of steroid therapy. Conclusions: This study provides evidence that oral non-steroid immunosuppressive agents may be beneficial in reducing relapse in patients with MuSK-MG

Predicting Interrelated Alzheimer's Disease Outcomes via New Self-Learned Structured Low-Rank Model

Alzheimer's disease (AD) is a progressive neurodegenerative disorder. As the prodromal stage of AD, Mild Cognitive Impairment (MCI) maintains a good chance of converting to AD. How to efficaciously detect this conversion from MCI to AD is significant in AD diagnosis. Different from standard classification problems where the distributions of classes are independent, the AD outcomes are usually interrelated (their distributions have certain overlaps). Most of existing methods failed to examine the interrelations among different classes, such as AD, MCI conversion and MCI non-conversion. In this paper, we proposed a novel self-learned low-rank structured learning model to automatically uncover the interrelations among different classes and utilized such interrelated structures to enhance classification. We conducted experiments on the ADNI cohort data. Empirical results demonstrated advantages of our model