Tetrachlorocarbonyliridates: Water-Soluble Carbon Monoxide Releasing Molecules Rate-Modulated by the Sixth Ligand

A new family of compounds is presented as potential carbon monoxide releasing molecules (CORMs). These compounds, based on tetrachlorocarbonyliridate(III) derivatives, were synthesized and fully characterized by X-ray diffraction, electrospray mass spectrometry, IR. NMR, and density functional theory calculations. The rate of CO release was studied via the myoglobin assay. The results showed that the rate depends on the nature of the sixth ligand, trans to CO, and that a significant modulation on the release rate can be produced by changing the ligand. The reported compounds are soluble in aqueous media, and the rates of CO release are comparable with those for known CORMs, releasing CO at a rate of 0.03-0.58 mu M min(-1) in a 10 mu M solution of myoglobin and 10 mu M of the complexes.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.

Groundwater favorability map using GIS multicriteria data analysis on crystalline terrain, Sao Paulo State, Brazil

This paper presents the groundwater favorability mapping on a fractured terrain in the eastern portion of Sao Paulo State, Brazil. Remote sensing, airborne geophysical data, photogeologic interpretation, geologic and geomorphologic maps and geographic information system (GIS) techniques have been used. The results of cross-tabulation between these maps and well yield data allowed groundwater prospective parameters in a fractured-bedrock aquifer. These prospective parameters are the base for the favorability analysis whose principle is based on the knowledge-driven method. The mutticriteria analysis (weighted linear combination) was carried out to give a groundwater favorabitity map, because the prospective parameters have different weights of importance and different classes of each parameter. The groundwater favorability map was tested by cross-tabulation with new well yield data and spring occurrence. The wells with the highest values of productivity, as well as all the springs occurrence are situated in the excellent and good favorabitity mapped areas. It shows good coherence between the prospective parameters and the well yield and the importance of GIS techniques for definition of target areas for detail study and wells location. (c) 2008 Elsevier B.V. All rights reserved.

Bayesian Estimation of the Logistic Positive Exponent IRT Model

A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric ICC treats both correct and incorrect answers symmetrically, which results in a logical contradiction in ordering examinees on the ability scale. A data set corresponding to a mathematical test applied in Peruvian public schools is analyzed, where comparisons with other parametric IRT models also are conducted. Several model comparison criteria are discussed and implemented. The main conclusion is that the LPE and RLPE IRT models are easy to implement and seem to provide the best fit to the data set considered.

Prediction in Multilevel Logistic Regression

The purpose of this article is to present a new method to predict the response variable of an observation in a new cluster for a multilevel logistic regression. The central idea is based on the empirical best estimator for the random effect. Two estimation methods for multilevel model are compared: penalized quasi-likelihood and Gauss-Hermite quadrature. The performance measures for the prediction of the probability for a new cluster observation of the multilevel logistic model in comparison with the usual logistic model are examined through simulations and an application.

Spacelike Surfaces in L(4) with Degenerate Gauss Map

In this paper we study and present a complete classification of spacelike surfaces with degenerate Gauss map in the Lorentz-Minkowski space L(4).

Thioredoxin-1 promotes survival in cells exposed to S-nitrosoglutathione: Correlation with reduction of intracellular levels of nitrosothiols and up-regulation of the ERK1/2 MAP Kinases

Accumulating evidence indicates that post-translational protein modifications by nitric oxide and its derived species are critical effectors of redox signaling in cells. These protein modifications are most likely controlled by intracellular reductants. Among them, the importance of the 12 kDa dithiol protein thioredoxin-1 (TRX-1) has been increasingly recognized. However, the effects of TRX-1 in cells exposed to exogenous nitrosothiols remain little understood. We investigated the levels of intracellular nitrosothiols and survival signaling in HeLa cells over-expressing TRX-1 and exposed to S-nitrosoglutahione (GSNO). A role for TRX-1 expression on GSNO catabolism and cell viability was demonstrated by the concentration-dependent effects of GSNO on decreasing TRX-1 expression, activation of capase-3, and increasing cell death. The over-expressaion of TRX-1 in HeLa cells partially attenuated caspase-3 activation and enhanced cell viability upon GSNO treatment. This was correlated with reduction of intracellular levels of nitrosothiols and increasing levels of nitrite and nitrotyrosine. The involvement of ERK, p38 and JNK pathways were investigated in parental cells treated with GSNO. Activation of ERK1/2 MAP kinases was shown to be critical for survival signaling. lit cells over-expressing TRX-1, basal phosphorylation levels of ERK1/2 MAP kinases were higher and further increased after GSNO treatment. These results indicate that the enhanced cell viability promoted by TRX-1 correlates with its capacity to regulate the levels of intracellular nitiosothiols and to up-regulate the survival signaling pathway mediated by the ERK1/2 MAP kinases.

Perpendicularly self-oriented and shape-controlled L1(0)-FePt nanorods directly synthesized by a temperature-modulated process

The synthesis and self-assembly of tetragonal phase-containing L1(0)-Fe(55)Pt(45) nanorods with high coercive field is described. The experimental procedure resulted in a tetragonal/cubic phase ratio close to 1:1 for the as-synthesized nanoparticles. Using different surfactant/solvent proportions in the process allowed control of particle morphology from nanospheres to nanowires. Monodisperse nanorods with lengths of 60 +/- 5 nm and diameters of 2-3 nm were self-assembled in a perpendicular oriented array onto a substrate surface using hexadecylamine as organic spacer. Magnetic alignment and properties assigned, respectively, to the shape anisotropy and the tetragonal phase suggest that the self-assembled materials are a strong candidate to solve the problem of random magnetic alignment observed in FePt nanospheres leading to applications in ultrahigh magnetic recording (UHMR) systems capable of achieving a performance of the order of terabits/in(2).

Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

Computerized identification of motor complications in Parkinson's disease

Objective: To investigate whether spirography-based objective measures are able to effectively characterize the severity of unwanted symptom states (Off and dyskinesia) and discriminate them from motor state of healthy elderly subjects. Background: Sixty-five patients with advanced Parkinson’s disease (PD) and 10 healthy elderly (HE) subjects performed repeated assessments of spirography, using a touch screen telemetry device in their home environments. On inclusion, the patients were either treated with levodopa-carbidopa intestinal gel or were candidates for switching to this treatment. On each test occasion, the subjects were asked trace a pre-drawn Archimedes spiral shown on the screen, using an ergonomic pen stylus. The test was repeated three times and was performed using dominant hand. A clinician used a web interface which animated the spiral drawings, allowing him to observe different kinematic features, like accelerations and spatial changes, during the drawing process and to rate different motor impairments. Initially, the motor impairments of drawing speed, irregularity and hesitation were rated on a 0 (normal) to 4 (extremely severe) scales followed by marking the momentary motor state of the patient into 2 categories that is Off and Dyskinesia. A sample of spirals drawn by HE subjects was randomly selected and used in subsequent analysis. Methods: The raw spiral data, consisting of stylus position and timestamp, were processed using time series analysis techniques like discrete wavelet transform, approximate entropy and dynamic time warping in order to extract 13 quantitative measures for representing meaningful motor impairment information. A principal component analysis (PCA) was used to reduce the dimensions of the quantitative measures into 4 principal components (PC). In order to classify the motor states into 3 categories that is Off, HE and dyskinesia, a logistic regression model was used as a classifier to map the 4 PCs to the corresponding clinically assigned motor state categories. A stratified 10-fold cross-validation (also known as rotation estimation) was applied to assess the generalization ability of the logistic regression classifier to future independent data sets. To investigate mean differences of the 4 PCs across the three categories, a one-way ANOVA test followed by Tukey multiple comparisons was used. Results: The agreements between computed and clinician ratings were very good with a weighted area under the receiver operating characteristic curve (AUC) coefficient of 0.91. The mean PC scores were different across the three motor state categories, only at different levels. The first 2 PCs were good at discriminating between the motor states whereas the PC3 was good at discriminating between HE subjects and PD patients. The mean scores of PC4 showed a trend across the three states but without significant differences. The Spearman’s rank correlations between the first 2 PCs and clinically assessed motor impairments were as follows: drawing speed (PC1, 0.34; PC2, 0.83), irregularity (PC1, 0.17; PC2, 0.17), and hesitation (PC1, 0.27; PC2, 0.77). Conclusions: These findings suggest that spirography-based objective measures are valid measures of spatial- and time-dependent deficits and can be used to distinguish drug-related motor dysfunctions between Off and dyskinesia in PD. These measures can be potentially useful during clinical evaluation of individualized drug-related complications such as over- and under-medications thus maximizing the amount of time the patients spend in the On state.

Automatic and objective assessment of alternating tapping performance in Parkinson’s disease

This paper presents the development and evaluation of a method for enabling quantitative and automatic scoring of alternating tapping performance of patients with Parkinson’s disease (PD). Ten healthy elderly subjects and 95 patients in different clinical stages of PD have utilized a touch-pad handheld computer to perform alternate tapping tests in their home environments. First, a neurologist used a web-based system to visually assess impairments in four tapping dimensions (‘speed’, ‘accuracy’, ‘fatigue’ and ‘arrhythmia’) and a global tapping severity (GTS). Second, tapping signals were processed with time series analysis and statistical methods to derive 24 quantitative parameters. Third, principal component analysis was used to reduce the dimensions of these parameters and to obtain scores for the four dimensions. Finally, a logistic regression classifier was trained using a 10-fold stratified cross-validation to map the reduced parameters to the corresponding visually assessed GTS scores. Results showed that the computed scores correlated well to visually assessed scores and were significantly different across Unified Parkinson’s Disease Rating Scale scores of upper limb motor performance. In addition, they had good internal consistency, had good ability to discriminate between healthy elderly and patients in different disease stages, had good sensitivity to treatment interventions and could reflect the natural disease progression over time. In conclusion, the automatic method can be useful to objectively assess the tapping performance of PD patients and can be included in telemedicine tools for remote monitoring of tapping.

A Map of Impoundments and Their Primary Purpose on Maine Rivers

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DETAILED MAP AND RECREATIONAL SUITABILITY ASSESSMENT OF THE COLBY TRAIL SYSTEM

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Pressure Perturbation Calorimetry and Guest-Host Chemistry of NDMG and N-MAP with p-Sulfonatocalix[6]arene

The aim of this project is to provide an explanation for recently obtained binding constants for two similar guest molecules, NDMG and N-MAP, with a p-sulfonatocalix[6]arene host in ammonium acetate buffer. This work was done primarily using pressure perturbation calorimetry, which is a technique that determines the coefficient of thermal expansion, α, which is in turn related to the solute molecule's effect on the order of the surrounding water molecules. A series of experiments were designed to test the effects of suspected confounding variables on the validity of PPC data. PPC was then used to study NDMG and N-MAP in ammonium acetate buffer. NDMG exhibited a minimum in α as function of temperature, while N-MAP did not. This difference was theorized to be due to the formation of an intramolecular hydrogen bond in monocationic NDMG that would lower the heat capacity of the molecule and better distribute the molecule's charge. Computational work and nuclear magnetic resonance spectroscopy confirmed that monocationic, ring-closed NDMG has less concentrated charge and more constrained motion than monocationic, ring-open NDMG. This evidence supports the theory that monocationic NDMG forms an intramolecular hydrogen bond and that this may be responsible for the minimum in α. This difference may explain the differences in binding constants between NDMG and N-MAP.

Detailed Map and Recreational Suitability Assessment of the Colby Trail System

This map is designed as a resource for students and the public to use and develop a better understanding of the trails system on the Colby Campus. I used a Garmin GPSmap 60CS to chart all the trails on Runnals Hill and in the Arboretum. Then, using ArcGIS, I compiled the tracked trails and laid them over an aerial photo of the campus. Because many of the trails are hard to find, I took digital photos of each trail entry to help the user locate them. Then, by taking note of the grade and width of the trail, I decided which trails were suitable for certain activities. This gives users an idea of where to go for walking, running, mountain biking, cross-country skiing, and snowshoeing.