Miracle-dz/UniFAR-FTUs huggingface dataset:
Safinamide as Add-On Therapy to Levodopa in Mid- to Late-Stage Parkinson’s Disease Fluctuating Patients: Post hoc Analysesof Studies 016 and SETTLE

Background: Studies 016 and SETTLE showed that safinamide was safe and effective as adjunct therapy in patients with advanced Parkinson’s disease (PD) and motor fluctuations. The addition of safinamide to a stable dose of levodopa alone or with other antiparkinsonian medications significantly increased ON time with no/non-troublesome dyskinesia, decreased OFF time and improved Parkinson’s symptoms. Objective: To evaluate the clinical effects of safinamide 100 mg/day on motor fluctuations and cardinal Parkinson’s symptoms in specific patient subgroups using pooled data from Studies 016 and SETTLE. Methods: Both studies were double blind, placebo-controlled, randomized, phase 3 trials which enrolled patients with mid- to late-stage PD experiencing motor fluctuations while receiving optimized and stable doses of levodopa, alone or with other dopaminergic treatments. The present post-hoc analyses assessed the change from baseline in ON time (with no or non-troublesome dyskinesia) and OFF time in subgroups of patients who were receiving only levodopa at baseline, who were classified as “mild fluctuators” (daily OFF time ≤4 h), and who were receiving concomitant dopaminergic therapy, with or without amantadine, and the effects of safinamide versus placebo on individual cardinal PD symptoms during ON time. Results: Safinamide significantly increased mean ON time (with no or non-troublesome dyskinesia) and reduced mean OFF time when used as first adjunct therapy in levodopa-treated patients and patients with mild motor fluctuations. Mean daily ON time (with no or non-troublesome dyskinesia) and OFF time were favorably changed, compared with placebo, to similar extents regardless of whether patients were receiving concomitant dopamine agonists, catechol-O-methyltransferase inhibitors and amantadine. Additionally, safinamide improved bradykinesia, rigidity, tremor and gait. Conclusions: Safinamide was a safe and effective first adjunct therapy in levodopa-treated patients and improved 4/5 cardinal symptoms of PD while providing benefits to mild and non-mild fluctuators and patients receiving other concomitant dopaminergic therapies.
The NPro Product of Bovine Viral Diarrhea Virus Inhibits DNA Binding by Interferon Regulatory Factor 3 and Targets It for Proteasomal Degradation

ABSTRACT Bovine viral diarrhea virus (BVDV) is a pestivirus that can establish a persistent infection in the developing fetus and has the ability to disable the production of type I interferon. In this report, we extend our previous observations that BVDV encodes a protein able to specifically block the activity of interferon regulatory factor 3 (IRF-3), a transcription factor essential for interferon promoter activation, by demonstrating that this is a property of the N-terminal protease fragment (NPro) of the BVDV polyprotein. Although BVDV infections cause relocalization of cellular IRF-3 from the cytoplasm to the nucleus early in infection, NPro blocks IRF-3 from binding to DNA. NPro has the additional property of targeting IRF-3 for polyubiquitination and subsequent destruction by cellular multicatalytic proteasomes. The autoprotease activity of NPro is not required for the inhibition of type I interferon induction or the targeting of IRF-3 for degradation.
Speech intelligibility improvement of cochlear implant using release of masking

The purpose of this paper is to propose a simple technique to increase speech intelligibility for cochlear implant user to perceive speech with a competing background noise (masker). In the first experiment we processed the target speech with more channels of a voice coder (vocoder) than those of the masker speech. The performance on recognizing conversational speech with fluctuating masker was improved. The results suggest that the cochlear implant user may have access to the salient acoustical cues due to release of masking. In the second experiment, we used several well-known signal enhancement techniques on the same stimuli. For 20 channels of the target and 8 channels on the masker, the results showed that the Boll's spectral subtraction outperforms the other enhancement techniques on segmental SNR (segSNR) 8.9 dB. We also conducted subjective listening test with mean opinion score (MOS) of 3.2/5.0 from 30 normal hearing participants. This improvement may due to the lower frequencies were enhanced while eliminating the higher frequencies.
Efficient Information-based Visual Robotic Mapping in Unstructured Environments

In field environments it is often not possible to provide robot teams with detailed a priori environment and task models. In such unstructured environments, robots will need to create a dimensionally accurate three-dimensional geometric model of its surroundings by performing appropriate sensor actions. However, uncertainties in robot locations and sensing limitations/occlusions make this difficult. A new algorithm, based on iterative sensor planning and sensor redundancy, is proposed to build a geometrically consistent dimensional map of the environment for mobile robots that have articulated sensors. The aim is to acquire new information that leads to more detailed and complete knowledge of the environment. The robot(s) is controlled to maximize geometric knowledge gained of its environment using an evaluation function based on Shannon’s information theory. Using the measured and Markovian predictions of the unknown environment, an information theory based metric is maximized to determine a robotic agent’s next best view (NBV) of the environment. Data collected at this NBV pose are fused using a Kalman filter statistical uncertainty model to the measured environment map. The process continues until the environment mapping process is complete. The work is unique in the application of information theory to enhance the performance of environment sensing robot agents. It may be used by multiple distributed and decentralized sensing agents for efficient and accurate cooperative environment modeling. The algorithm makes no assumptions of the environment structure. Hence, it is robust to robot failure since the environment model being built is not dependent on any single agent frame, but is set in an absolute reference frame. It accounts for sensing uncertainty, robot motion uncertainty, environment model uncertainty and other critical parameters. It allows for regions of higher interest receiving greater attention by the agents. This algorithm is particularly well suited to unstructured environments, where sensor uncertainty and occlusions are significant. Simulations and experiments show the effectiveness of this algorithm.
A Datalog Engine for Iterative Graph Algorithms on Large Clusters

Distributed computations on graphs gained importance with the emergence of large graphs, e.g., in the web or social networks. Frameworks like Hadoop, Giraph and Spark are used for their processing. Yet, they require advanced programming techniques to minimize skew and data shuffling. Declarative, query-like, but at the same time efficient solutions like Pig for general purpose analytics are lacking. In this paper we promote the use of declarative datalog with aggregation for large graph processing. We presents an implementation which extends tApache Spark with the capability of executing datalog queries. This approach makes it possible to express graph algorithms in a well studied declarative query language and execute them on an existing and mature infrastructure for distributed computation. At the same time the data processed with datalog queries is fully integrated with the caching mechanism of Spark and can be part of a larger iterative algorithm.
An Incremental Neural Network Construction Algorithm for Training Multilayer Perceptrons

The problem of determining the architecture of a multilayer perceptron together with the disadvantages of the standard backpropagation algorithm, directed the research towards algorithms that determine not only the weights but also the structure of the network necessary for learning the data. We propose a Constructive Algorithm with Multiple Operators using Statistical Test (MOST) for determining the architecture. The networks that are constructed by MOST can have multiple hidden layers with multiple hidden units in each layer. The algorithm uses node removal, addition and layer addition and determines the number of nodes in layers by heuristics. It applies a statistical test to compare different architectures. The results are promising and near optimal.
A hierarchical Bayesian model to estimate the unobservable predation rate on sawfly cocoons by small mammals

Predation by small mammals has been reported as an important mortality factor for the cocoons of sawfly species. However, it is difficult to provide an accurate estimate of newly spun cocoons and subsequent predation rates by small mammals for several reasons. First, all larvae do not spin cocoons at the same time. Second, cocoons are exposed to small mammal predation immediately after being spun. Third, the cocoons of the current generation are indistinguishable from those of the previous generation. We developed a hierarchical Bayesian model to estimate these values from annual one-time soil sampling datasets. To apply this model to an actual data set, field surveys were conducted in eight stands of larch plantations in central Hokkaido (Japan) from 2009 to 2012. Ten 0.04-m2 soil samples were annually collected from each site in mid-October. The abundance of unopened cocoons (I), cocoons emptied by small-mammal predation (M), and empty cocoons caused by something other than small-mammal predation (H) were determined. The abundance of newly spun cocoons, the predation rate by small mammals before and after cocoon sampling, and the annual rate of empty cocoons that remained were estimated. A posterior predictive check yielded Bayesian P-values of 0.54, 0.48, and 0.07 for I, M, and H, respectively. Estimated predation rates showed a significant positive correlation with the number of trap captures of small mammals. Estimates of the number of newly spun cocoons had a significant positive correlation with defoliation intensity. These results indicate that our model showed an acceptable fit, with reasonable estimates. Our model is expected to be widely applicable to all hymenopteran and lepidopteran insects that spin cocoons in soil.
Performance validation of fault-tolerance software: a compositional approach

Discusses the lessons learned in the modeling of a software fault tolerance solution built by a consortium of universities and industrial companies for an Esprit project called TIRAN (TaIlorable fault-toleRANce framework for embedded applications). The requirements of high flexibility and modularity for the software have lead to a modeling approach that is strongly based on compositionality. Since the interest was in assessing both the correctness and the performance of the proposed solution, we have cared for these two aspects at the same time, and, by means of an example, we show how this was a central aspect of our analysis.
Face Recognition Using Matrix Decomposition Technique Eigenvectors and SVD

Principle Component Analysis (PCA) is an important and well-known technique of face recognition, where eigenvectors are used. In this paper, we propose a face recognition technique, which combines Eigenvectors with Singular Value Decomposition (SVD) techniques to reduce size of the Eigen-matrix. The detailed theoretical derivation and analysis are presented and a simulation results on Olivetti Research Laboratory (ORL) face database has given. The simulation result indicates that the proposed approach is superior to conventional PCA with lower database size and recognition performance.
Labelling Maps using Multi-Objective Evolutionary Algorithms

Map labelling is the problem of arranging place names on maps such that labels do not overlap and are clear to a reader. Determining the optimal label arrangement is combinatorially difficult and has so far defied total automation. As such, map making remains a costly process. Today’s map labelling techniques generally consider only the number of overlaps during optimisation and while they do succeed at minimising this objective, there are other objectives that contribute to the overall quality of a map. We will discuss a multi-objective evolutionary algorithm designed to automate map labelling when assessing overall map quality by several criteria such as label overlaps, clarity, font size and aesthetics. This process allows a user to select their optimal compromise between each of the quality criteria. As the best compromise between these objectives is highly subjective and dependant on the map being labelled, our algorithm is superior to others in that it simultaneously finds a set of trade-off solutions rather than applying a set evaluation function that predefines the way that these quality constraints should be optimised. We have demonstrated several example real world maps to prove that this technique is able to produce maps of higher quality than competing techniques. We have also selected several final labelled maps which illuminate the compromises between quality criteria.
A Programmed Switch from IL-15- to IL-2-Dependent Activation in Human NK Cells1

IL-2 and IL-15 differentially control the development, activation and proliferation of human NK cells, although they share common signal-transducing receptor chains CD122 and common γ. To explore this issue, we analyzed in detail the kinetics of cytokine receptor expression, cytokine binding, and signaling responses in human NK cells treated with common γ-chain family cytokines. We provide evidence for the sequential expression of IL-15Rα and IL-2Rα at the surface of cytokine-stimulated human NK cells, independent of the cytokine used for stimulation (IL-2, IL-15, or IL-7). Binding experiments confirmed the switch of high-affinity receptor from IL-15R to IL-2R between 18 and 48 h after stimulation. Consequently, phospho-STAT5 signaling responses to IL-15 were efficient in human NK cells pretreated with cytokines for 18 h, but were abolished at 48 h. Functional NK cell responses to IL-15, including IFN-γ secretion and CD107a expression, followed a similar pattern, indicating the physiological relevance of the cytokine receptor switch. Importantly, IL-15 complexed to soluble IL-15Rα preserved the capacity to activate cytokine-stimulated human NK cells at 48 h, suggesting that human NK cells remained competent for IL-15 trans-presentation, while they had become refractory to free diffusible IL-15. These findings define a common cytokine receptor expression program, which increases human NK cell sensitivity to free IL-15 in early activation and redirects responses toward IL-2 and trans-presented IL-15 at later stages. Such a program may prevent excessive human NK cell activation by effectors of innate immunity and regulate the transition between the innate and adaptive stages of immune responses.
Principal component analysis neural network for textual document categorization and dimension reduction

This manuscript presents the study and application of the method of principal component analysis (PCA) in the field of text mining. We began by studying the theoretical basis behind this method and we have focused on two of its variants namely the neural PCA and kernel PCA. We used neural PCA for automatic categorization of text documents through an extraction of semantic concepts. The second contribution of our work is the use of PCA (neuronal and kernel) for the dimension reduction of textual documents through the automatic classification.
Human emotional stress analysis through time domain electromyogram features

Emotional stress is induced on any person who is subjected to experience negative emotions (disgust, anger, fear and sadness) over a prolonged duration. Physiological signals (electrocardiogram (ECG), electromyogram (EMG), galvanic skin resistance (GSR), etc) effectively reflect the behavior of emotional stress compared to other modalities. In this work, the proposed experimental protocol induces emotional stress through audio-visual stimuli (video clips) and simultaneously acquired the EMG signals. EMG signals are preprocessed using IIR Notch filter, Elliptic filter and Discrete Wavelet Transform (DWT). Time domain features such as simple square integral (SSI), integrated EMG (IEMG), waveform length (WL) and difference of absolute standard deviation value (DASDV) are extracted from EMG signal and one-way ANOVA is used to compute the significance of each feature. In the classification stage, the subjective emotions are classifiers into negative (sadness, disgust, fear and anger) and positive (happiness and surprise) emotions. Thereafter, the negative emotions are further classified into emotional stress and non emotional stress. Two non-linear classifiers such as K Nearest Neighbor (KNN) fuzzy K Nearest Neighbor (fKNN) are used on this work. WL is founded as the most prominent feature among all four extracted features on emotional stress classification. The maximum mean classification rate of 70.85 % is achieved using KNN. According to the sensitivity values, KNN is better in identifying negative emotions and also the subject emotional stress.
Multiclass Classification of XSS Web Page Attack using Machine Learning Techniques

Web applications are most widely used technique for providing an access to online services. At the same time web applications are easiest way for vulnerable acts. When a security mechanism is failed then the user may download malicious code from a trusted web site. In this case, the malicious script is contracted to full access with all assets belonging to that legitimate web site. These types of attacks are called Cross-Site Scripting (XSS) attacks. Cross Site Scripting (XSS) attacks are the most common type of attack against web application, which allows hackers to inject the malicious script code for stealing the user‟s confidential information. Recent studies show that malicious code detection has become the most frequent vulnerability. In web browsers, the malicious script codes are executed and used to transfer the sensitive data to the third party (or hackers) domain. Currently, most research areas are attempted to prevent XSS on both the client and server side. In this paper, we present a machine learning technique to classify the malicious web pages. This work focus some of the possible ways to detect the XSS script on client side based on the features extracted from the web document content and the URL to scan the web pages for check the malicious scripts.
Registration-free laparoscope augmentation for intra-operative liver resection planning

In recent years, an increasing number of liver tumor indications were treated by minimally invasive laparoscopic resection. Besides the restricted view, a major issue in laparoscopic liver resection is the enhanced visualization of (hidden) vessels, which supply the tumorous liver segment and thus need to be divided prior to the resection. To navigate the surgeon to these vessels, pre-operative abdominal imaging data can hardly be used due to intraoperative organ deformations mainly caused by appliance of carbon dioxide pneumoperitoneum and respiratory motion. While regular respiratory motion can be gated and synchronized intra-operatively, motion caused by pneumoperitoneum is individual for every patient and difficult to estimate. Therefore, we propose to use an optically tracked mobile C-arm providing cone-beam CT imaging capability intraoperatively. The C-arm is able to visualize soft tissue by means of its new flat panel detector and is calibrated offline to relate its current position and orientation to the coordinate system of a reconstructed volume. Also the laparoscope is optically tracked and calibrated offline, so both laparoscope and C-arm are registered in the same tracking coordinate system. Intra-operatively, after patient positioning, port placement, and carbon dioxide insufflation, the liver vessels are contrasted and scanned during patient exhalation. Immediately, a three-dimensional volume is reconstructed. Without any further need for patient registration, the volume can be directly augmented on the live laparoscope video, visualizing the contrasted vessels. This augmentation provides the surgeon with advanced visual aid for the localization of veins, arteries, and bile ducts to be divided or sealed.
Weld penetration identification for VPPAW based on keyhole features and extreme learning machine

Variable polarity plasma arc welding, as an advanced manufacturing technology, has been successfully used in industrial production due to high energy density. The need for the control of the weld penetration remains of a long term interest in VPPAW process. In this study, a simple-flexible vision system was established to acquire a series of keyhole images, and the geometrical appearance of keyhole including the keyhole width and area are extracted based on part-based tree model. Then the acquired keyhole features are used to predict the weld penetration by using a novel extreme learning machine model. The research shows that ELM model can predict the penetration state of variable polarity plasma arc welding credibly and achieve real time monitoring for welding quality.
Empathizing With Nature: The Effects of Perspective Taking on Concern for Environmental Issues

In this article, I propose that concern for environmental problems is fundamentally linked to the degree to which people view themselves as part of the natural environment. Two studies are reported that test aspects of this theory. The first study describes the structure of people’s concern for environmental problems. Results from a confirmatory factor analysis showed a clear three-factor structure, which I labeled egoistic, altruistic