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 $\Omega^{*}_c(3327)$

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

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

Optimization of “Deoxidation Alloying” Batching Scheme

In this paper, a mathematical model was established to predict the deoxidation alloying and to optimize the type and quantity of input alloys. Firstly, the GCA method was used to obtain the main factors affecting the alloy yield of carbon and manganese based on the historical data. Secondly, the alloy yield was predicted by the stepwise MRA, the BP neural network and the regression SVM models, respectively. The conclusion is that the regression SVM model has the highest prediction accuracy and the maximum deviation between the test set prediction result and the real value was only 0.0682 and 0.0554. Thirdly, in order to reduce the manufacturer's production cost, the genetic algorithm was used to calculate the production cost mathematical programming model. Finally, sensitivity analysis was performed on the prediction model and the cost optimization model. The unit price of 20% of the alloy raw materials was increased by 20%, and the total cost change rate was 0.7155%, the lowest was -0.4297%, which proved that the mathematical model established presented strong robustness and could be certain reference value for the current production of iron and steel enterprises

Recombinant TAT–gelonin fusion toxin: Synthesis and characterization of heparin/protamine‐regulated cell transduction

Protein toxins, such as gelonin, are highly desirable anti‐cancer drug candidates due to their unparalleled potency and repetitive reaction mechanism in inhibiting protein translation. However, for its potential application in cancer therapy, there remains the cell membrane barrier that allows permeation of only small molecules, which must be overcome. To address this challenge, we conjugated gelonin with a protein transduction domain (PTD), the TAT peptide, via genetic recombination. The chimeric TAT–gelonin fusion protein (TAT‐Gel) retained equipotent N ‐glycosidase activity yet displayed greater cell uptake than unmodified recombinant gelonin (rGel), thereby yielding a significantly augmented cytotoxic activity. Remarkably, TAT‐Gel displayed up to 177‐fold lower IC 50 (avg. 54.3 n M ) than rGel (avg. IC 50 : 3640 n M ) in tested cell lines. This enhanced cytotoxicity, however, also raised potential toxicity concerns due to the non‐selectivity of PTD in its mediated cell transduction. To solve this problem, we investigated the plausibility of regulating the cell transduction of TAT‐Gel via a reversible masking using heparin and protamine. Here, we demonstrated, both in vitro and in vivo , that the cell transduction of TAT‐Gel can be completely curbed with heparin and yet this heparin block can be efficiently reversed by the addition of protamine. This reversible tight regulation of the cell transduction of TAT‐Gel by heparin and protamine sheds light of possible application of TAT‐Gel in achieving a highly effective yet safe drug therapy for the treatment of tumors. © 2014 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 103A: 409–419, 2015.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109572/1/jbma35188.pd

Network-guided sparse learning for predicting cognitive outcomes from MRI measures

Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from MRI measures. In particular, sparse models have been proposed to identify the optimal imaging markers with high prediction power. However, the complex relationship among imaging markers are often overlooked or simplified in the existing methods. To address this issue, we present a new sparse learning method by introducing a novel network term to more flexibly model the relationship among imaging markers. The proposed algorithm is applied to the ADNI study for predicting cognitive outcomes using MRI scans. The effectiveness of our method is demonstrated by its improved prediction performance over several state-of-the-art competing methods and accurate identification of cognition-relevant imaging markers that are biologically meaningful