Miracle-dz/UniFAR-FTUs huggingface dataset:
PARTIAL BODY WEIGHT-SUPPORTED TREADMILL TRAINING FOR GAIT RESTORATION IN HEMIPLEGIC PATIENTS: EVALUATION OF USING MOTOR-EVOKED POTENTIALS

Background: Partial body weight support treadmill training (PBWST) is a task-oriented gait rehabilitation protocol for hemiplegic/paretic patients with proven clinical efficiency. At the neurophysiologic level, motor-evoked potentials (MEP) induced by transcranial magnetic stimulation (TMS) and recorded from target lower limb muscles may be used to assess the post-rehabilitation effects. Objective: Assessment of the effects of PBWST on the MEPs recorded from target lower limb muscles in chronic poststroke hemiparetics. Methodology: Thirty patients with post-stroke chronic hemiparesis were randomly allocated to one of 2 groups; PBWST group and conventional rehabilitation group. Initial evaluation included recording the MEP parameters from the quadriceps, tibialis anterior and gastrocnemius muscles. In addition, functional assessment tools included Fugel-Meyer assessment (FMA), functional ambulation category as well as Standing Balance Test. Endpoint evaluation using the same methods were obtained by the completion of 8 weeks of rehabilitation at a frequency of 3 times a week. Results: Both study groups have shown significant post rehabilitation improvement of most of the recorded MEP
Extraction of parameters of surface states from experimental test structures

We present a procedure for extracting the parameters of a simple model for localized states at the exposed surfaces of GaAs/AlGaAs heterostructures. Such a procedure is based on a comparison between experimental data on test structures and results from the solution of the Poisson-Schrodinger equation in the growth direction of the heterostructure. We show that a model of surface states based on just two parameters is sufficient for obtaining good agreement with experiments and significant improvement with respect to the assumption of Fermi level pinning. In addition, the procedure proposed allows the extraction of the donor concentration in the doped layer and the unintentional doping in the substrate.
Influence of Commonly Used Primer Systems on Automated Ribosomal Intergenic Spacer Analysis of Bacterial Communities in Environmental Samples

Due to the high diversity of bacteria in many ecosystems, their slow generation times, specific but mostly unknown nutrient requirements and syntrophic interactions, isolation based approaches in microbial ecology mostly fail to describe microbial community structure. Thus, cultivation independent techniques, which rely on directly extracted nucleic acids from the environment, are a well-used alternative. For example, bacterial automated ribosomal intergenic spacer analysis (B-ARISA) is one of the widely used methods for fingerprinting bacterial communities after PCR-based amplification of selected regions of the operon coding for rRNA genes using community DNA. However, B-ARISA alone does not provide any taxonomic information and the results may be severely biased in relation to the primer set selection. Furthermore, amplified DNA stemming from mitochondrial or chloroplast templates might strongly bias the obtained fingerprints. In this study, we determined the applicability of three different B-ARISA primer sets to the study of bacterial communities. The results from in silico analysis harnessing publicly available sequence databases showed that all three primer sets tested are specific to bacteria but only two primers sets assure high bacterial taxa coverage (1406f/23Sr and ITSF/ITSReub). Considering the study of bacteria in a plant interface, the primer set ITSF/ITSReub was found to amplify (in silico) sequences of some important crop species such as Sorghum bicolor and Zea mays. Bacterial genera and plant species potentially amplified by different primer sets are given. These data were confirmed when DNA extracted from soil and plant samples were analyzed. The presented information could be useful when interpreting existing B-ARISA results and planning B-ARISA experiments, especially when plant DNA can be expected.
A Veriiable Secure Distributed System a Veriiable Secure Distributed System

This paper presents a design for a veriiable secure distributed system. Based on the secure distributed system of Rushby and Randell, the desgin is presented as a hierarchy of levels implementing a set of virtual machines, where each level can be separately veriied for correctness with respect to the speciication and security policy. The heart of the system is based on Bevier's veriied kernel, KIT, which is modiied to provide a base for a secure distributed computing system. We reason about this kernel in the context of the Rushby{Rnadell secure distributed system.
AP-1 subunits: quarrel and harmony among siblings

The AP-1 transcription factor is mainly composed of Jun, Fos and ATF protein dimers. It mediates gene regulation in response to a plethora of physiological and pathological stimuli, including cytokines, growth factors, stress signals, bacterial and viral infections, as well as oncogenic stimuli. Studies in genetically modified mice and cells have highlighted a crucial role for AP-1 in a variety of cellular events involved in normal development or neoplastic transformation causing cancer. However, emerging evidence indicates that the contribution of AP-1 to determination of cell fates critically depends on the relative abundance of AP-1 subunits, the composition of AP-1 dimers, the quality of stimulus, the cell type and the cellular environment. Therefore, AP-1-mediated regulation of processes such as proliferation, differentiation, apoptosis and transformation should be considered within the context of a complex dynamic network of signalling pathways and other nuclear factors that respond simultaneously.
The Effect of Treatment History on Therapeutic Outcome: Psychological and Neurobiological Underpinnings

It is increasingly recognized that the efficacy of medical treatments is determined in critical part by the therapeutic context in which it is delivered. An important characteristic of that context is treatment history. We recently reported first evidence for a carry-over of treatment experience to subsequent treatment response across different treatment approaches. Here we expand on these findings by exploring the psychological and neurobiological underpinnings of the effect of treatment experience on future treatment response in an experimental model of placebo analgesia with a conditioning procedure. In a combined behavioral and neuroimaging study we experimentally induced positive or negative experiences with an analgesic treatment in two groups of healthy human subjects. Subsequently we compared responses to a second, different analgesic treatment between both groups. We found that participants with an experimentally induced negative experience with the first treatment showed a substantially reduced response to a second analgesic treatment. Intriguingly, several psychological trait variables including anxiety, depression and locus of control modulate the susceptibility for the effects of prior treatment experiences on future treatment outcome. These behavioral effects were supported by neuroimaging data which showed significant differences in brain regions encoding pain and analgesia between groups. These differences in activation patterns were present not only during the pain phase, but also already prior to painful stimulation and scaled with the individual treatment response. Our data provide behavioral and neurobiological evidence showing that the influence of treatment history transfers over time and over therapeutic approaches. Our experimental findings emphasize the careful consideration of treatment history and a strictly systematic treatment approach to avoid negative carry-over effects.
When the bubble bursts: psychology or fundamentals?

The prices of stocks, bonds, and other assets frequently fluctuate, and sometimes these fluctuations are quite large. Such price shifts have important economic implications, including the possibility that asset prices have predictive power for the business cycle. In this article, Lee Ohanian analyzes the volatility of security prices and discusses whether movements in asset prices reflect changes in the fundamental value of the asset or whether extreme price changes may be associated with changes in market psychology
The Clustering of Active Galactic Nuclei in the Sloan Digital Sky Survey

We present the two-point correlation function (2PCF) of narrow-line active galactic nuclei (AGNs) selected within the First Data Release of the Sloan Digital Sky Survey. Using a sample of 13,605 AGNs in the redshift range 0.055 < z < 0.2, we find that the AGN autocorrelation function is consistent with the observed galaxy autocorrelation function on scales from 0.2 to greater than 100 h-1 Mpc. The AGN hosts trace an intermediate population of galaxies and are not detected in either the bluest (youngest) disk-dominated galaxies or many of the reddest (oldest) galaxies. We show that the AGN 2PCF is dependent on the luminosity of the narrow [O III] emission line (L), with low L AGNs having a higher clustering amplitude than high L AGNs. This is consistent with lower activity AGNs residing in more massive galaxies than higher activity AGNs, and L providing a good indicator of the fueling rate. Using a model relating halo mass to black hole mass in cosmological simulations, we show that AGNs hosted by ~1012 M☉ dark matter halos have a 2PCF that matches that of the observed sample. This mass scale implies a mean black hole mass for the sample of MBH ~ 108 M☉.
New approaches to public management : the case of Michigan

As the nation has debated the devolution of more authority to state governments, for programs ranging from welfare to job training, a central question has arisen: do the states have the capacity to take on the job? Despite the remarkable transformation of public management in the states over the last generation, most have been viewed as " laboratories of democracy, " the proving ground for new ideas, rather than as robust and muscular entities fully capable on their own of tackling the nations big issues. In the report, John M. Kost argues from his experience in Michigan that the states can be even more responsive and more capable than the federal government in delivering public services. That capacity, he says, rests critically on the task of growing the leadership needed to rethink how government works and to reengineer how it does its job. Kost, more than anything else, makes the case that building capacity depends on finding energetic officials and getting them elected and appointed to lead. With his case for leadership, Kost moves past the procedural and structural reforms and also demonstrates the remarkably bipartisan nature of public management reform. Democrats and Republicans alike, at all levels of government, have seized on the potential for galvanizing governance to produce better results. Kost inventories basic approaches that belong in every government reformers toolbox. He describes the standards, like customer service, privatization, procurement reform, and reengineering. He also explores often-missed issues, like information technology and civil service reform. Finally, he makes a very strong case indeed that these reforms have transformed the operations ofMichigans state government.
Altered Functional Connectivity of Fusiform Gyrus in Subjects with Amnestic Mild Cognitive Impairment: A Resting-State fMRI Study

Visual cognition such as face recognition requests a high degree of functional integration between distributed brain areas of a network. It has been reported that the fusiform gyrus (FG) is an important brain area involved in facial cognition; altered connectivity of FG to some other regions may lead to a deficit in visual cognition especially face recognition. However, whether functional connectivity between the FG and other brain areas changes remains unclear in the resting state in amnestic mild cognitive impairment (aMCI) subjects. Here, we employed a resting-state functional MRI (fMRI) to examine alterations in functional connectivity of left/right FG comparing aMCI patients with age-matched control subjects. Forty-eight aMCI and 38 control subjects from the Alzheimer’s disease Neuroimaging Initiative were analyzed. We concentrated on the correlation between low frequency fMRI time courses in the FG and those in all other brain regions. Relative to the control group, we found some discrepant regions in the aMCI group which presented increased or decreased connectivity with the left/right FG including the left precuneus, left lingual gyrus, right thalamus, supramarginal gyrus, left supplementary motor area, left inferior temporal gyrus, and left parahippocampus. More importantly, we also obtained that both left and right FG have increased functional connections with the left middle occipital gyrus (MOG) and right anterior cingulate gyrus (ACC) in aMCI patients. That was not a coincidence and might imply that the MOG and ACC also play a critical role in visual cognition, especially face recognition. These findings in a large part supported our hypothesis and provided a new insight in understanding the important subtype of MCI.
Symbol Spotting using Full Visibility Graph Representation

In this paper, a method for matching symbols in line-drawings is presented. Facing both segmentation and recognition of symbols is a difficult challenge. Starting from the results of a vectorization proce- dure, a visibility graph is built to enhance the main geometric constraints which were specified during the construction of the initial document. The cliques detection, which correspond to a perceptual grouping of primi- tives, is used in the system to detect regions of particular interest. Both opened and perceptually closed curves are identified from aggregation of cliques. Finally, the recognition stage uses an attributed edit distance technique to match approximated curves within the host attributed re- lation graph and the ones from a collection of symbols.
Multi-Frame Estimation of Planar Motion

Traditional plane alignment techniques are typically performed between pairs of frames. We present a method for extending existing two-frame planar motion estimation techniques into a simultaneous multi-frame estimation, by exploiting multi-frame subspace constraints of planar surfaces. The paper has three main contributions: 1) we show that when the camera calibration does not change, the collection of all parametric image motions of a planar surface in the scene across multiple frames is embedded in a low dimensional linear subspace; 2) we show that the relative image motion of multiple planar surfaces across multiple frames is embedded in a yet lower dimensional linear subspace, even with varying camera calibration; and 3) we show how these multi-frame constraints can be incorporated into simultaneous multi-frame estimation of planar motion, without explicitly recovering any 3D information, or camera calibration. The resulting multi-frame estimation process is more constrained than the individual two-frame estimations, leading to more accurate alignment, even when applied to small image regions.
The Joint Labour Decisions Of Farm Couples: A Censored Response Analysis Of On-Farm And Off-Farm Work

Farm couples' labour market responses are partly the qualitative choice of entering the ff-farm labour market and partly the continuous choice of the number of on-farm and off-farm working hours, given entry. Such a setting is interesting when examining the increasing occurrence of multiple job-holdings among farmers in western economies. This paper presents a unified framework for handling such discrete/continuous choices, involving optimisation of farm production, household consumption, and multiple job-holdings for both husband and wife. A two-equation censoring panel data model is implemented and estimated from Norwegian panel data for 342 farms observed over a ten-year period.
Efficient Inverse Cholesky Factorization for Alamouti Matrices in G-STBC and Alamouti-Like Matrices in OMP

We apply the efficient inverse Cholesky factorization to Alamouti matrices in Groupwise Space-time Block Code (G-STBC) and Alamouti-like matrices in Orthogonal Matching Pursuit (OMP) for the sub-Nyquist sampling system. By utilizing some good properties of Alamouti or Alamouti-like matrices, we save about half the complexity. Then we propose the whole square-root algorithms for G-STBC and OMP, respectively. The proposed square-root G-STBC algorithm has an average speedup of 2.96~3.6 with respect to the sub-optimal G-STBC algorithm. On the other hand, when comparing the complexities of all the steps except the projection step, the complexity for the proposed square-root OMP algorithm is about 30% of that for the fast OMP algorithm by matrix inverse update.
A Distributed Approach to Hyper-Spectral Image Analysis Using Support Vectors

This paper presents a detailed analysis of a new distributed algorithm designed for hyper-spectral image analysis. Hyper-spectral imaging is a valuable technique for detection and classification of materials and objects on the Earth's surface. The conventional approach to hyper-spectral image analysis is based on dimensionality reduction using Principal Component Analysis (PCA). But the results contain more details of the frequently occurred objects compared to the minor objects in the scene. To resolve this, a new algorithm for hyper-spectral image analysis based on Support Vector Clustering (SVC) and Spectral Angle Mapping (SAM) is proposed in this work. The method is found to generate good results, but the calculation of Support Vectors, Spectral Angles and Principal Components are very time-consuming processes and a bulk of data is to be processed to analyse the hyper-spectral images. So the algorithm is designed in a distributed manner and a distributed environment based on Java/RMI is developed to implement it. The algorithm is tested with two Hyper-spectral image datasets of 210 bands each, which are taken with HYper-spectral Digital Imagery Collection Experiment (HYDICE) air-borne sensors. A performance analysis of the distributed environment is also carried out.
Constructing Optimal Clustering Architecture for Maximizing Lifetime in Large Scale Wireless Sensor Networks

In large scale wireless sensor networks, clustering is an effective technique for the purpose of improving the utilization of limited energy and prolonging the network lifetime. However, the problem of unbalanced energy dissipation exists in the multi-hop clustering model, where the cluster heads closer to the sink have to relay heavier traffic and consume more energy than farther nodes. Unequal clustering has proved to be able to achieve more uniform energy dissipation among cluster heads; however, how to quantify the size for the aim of scalability is a challenging problem. In this paper, we investigate the theoretical aspects of the unequal clustering strategy for minimizing and balancing the energy consumption of cluster heads in large uniform distributed sensor networks. Based on the proposed optimal clustering architecture, we design simple energy-aware head rotation and routing protocols to further balance the energy consumption among all nodes. Extensive simulations show that the proposed architecture can maximize the network lifetime defined from different aspects, and therefore, are suitable for the design and deployment of large scale sensor networks.
Automated Clinical Assessment from Smart home-based Behavior Data

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and wellbeing assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behaviour in the home and predicting standard clinical assessment scores of the residents. To accomplish this goal, we propose a Clinical Assessment using Activity Behavior (CAAB) approach to model a smart home resident’s daily behavior and predict the corresponding standard clinical assessment scores. CAAB uses statistical features that describe characteristics of a resident’s daily activity performance to train machine learning algorithms that predict the clinical assessment scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years using prediction and classification-based experiments. In the prediction-based experiments, we obtain a statistically significant correlation (r = 0.72) between CAABpredicted and clinician-provided cognitive assessment scores and a statistically significant correlation (r = 0.45) between CAABpredicted and clinician-provided mobility scores. Similarly, for the classification-based experiments, we find CAAB has a classification accuracy of 72% while classifying cognitive assessment scores and 76% while classifying mobility scores. These prediction and classification results suggest that it is feasible to predict standard clinical scores using smart home sensor data and learning-based data analysis.
Aspect-Oriented Solutions to Feature Interaction Concerns using AspectJ

In this paper, we propose a two-level architecture for feature driven software development, consisting of a base layer for a feature’s core behaviour and a meta- layer for resolution modules that provide solutions to feature interaction problems. Whilst a standard programming language is used at the base level, e.g. an object- oriented language such as Java, we propose the use of an aspect-oriented programming language for the inherent cross-cutting concerns that exist at the meta- level. We evaluate the use of AspectJ for the implementation of interaction resolution modules at the meta-level. This evaluation is carried out through an in- depth study of an email system. We conclude that aspect-oriented approaches are highly suited for this split-level architecture and that the architecture has many benefits for feature driven software development. Finally, we also highlight a number of problems with AspectJ for our intended use, but discuss how the selection of an alternative aspect-oriented technique would avoid these problems.
Detecting changes in nonisotropic images

If the noise component of image data is nonisotropic, i.e., if it has nonconstant smoothness or effective point spread function, then theoretical results for the P value of local maxima and the size of suprathreshold clusters of a statistical parametric map (SPM) based on random field theory are not valid. This assumption is reasonable for PET or smoothed fMRI data, but not if these data are projected onto an unfolded, inflated, or flattened 2D cortical surface. Anatomical data such as structure masks, surface displacements, and deformation vectors are also highly nonisotropic. The solution offered here is to suppose that the image can be warped or flattened (in a statistical sense) into a space where the data are isotropic. The subsequent corrected P values do not depend on finding this warping; it is sufficient only to know that such a warping exists. Hum. Brain Mapping 8:98–101, 1999. © 1999 Wiley‐Liss, Inc.
Analysis of electrochemical impedance spectroscopy in proton exchange membrane fuel cells

A literature review of electrochemical impedance spectroscopy (EIS) analysis of proton exchange membrane fuel cells (PEMFCs) is presented. Emphasis is placed on the papers that analyse the impedance response of the cathode and anode half‐cells of the PEMFCs based on a continuum‐mechanics approach. The other type of analysis, which is based on the equivalent‐circuits approach, is addressed for comparison. The relative advantages and disadvantages of the two approaches are discussed. Papers dealing with continuum‐mechanics‐based EIS modelling of general electrochemical systems are briefly reviewed. Copyright © 2005 John Wiley & Sons, Ltd.