Miracle-dz/UniFAR-FTUs huggingface dataset:
Safinamide Differentially Modulates In Vivo Glutamate and GABA Release in the Rat Hippocampus and Basal Ganglia

Safinamide has been recently approved as an add-on to levodopa therapy for Parkinson disease. In addition to inhibiting monoamine oxidase type B, it blocks sodium channels and modulates glutamate (Glu) release in vitro. Since this property might contribute to the therapeutic action of the drug, we undertook the present study to investigate whether safinamide inhibits Glu release also in vivo and whether this effect is consistent across different brain areas and is selective for glutamatergic neurons. To this aim, in vivo microdialysis was used to monitor the spontaneous and veratridine-induced Glu and GABA release in the hippocampus and basal ganglia of naive, awake rats. Brain levels of safinamide were measured as well. To shed light on the mechanisms underlying the effect of safinamide, sodium currents were measured by patch-clamp recording in rat cortical neurons. Safinamide maximally inhibited the veratridine-induced Glu and GABA release in hippocampus at 15 mg/kg, which reached free brain concentrations of 1.89–1.37 µM. This dose attenuated veratridine-stimulated Glu (but not GABA) release in subthalamic nucleus, globus pallidus, and substantia nigra reticulata, but not in striatum. Safinamide was ineffective on spontaneous neurotransmitter release. In vitro, safinamide inhibited sodium channels, showing a greater affinity at depolarized (IC50 = 8 µM) than at resting (IC50 = 262 µM) potentials. We conclude that safinamide inhibits in vivo Glu release from stimulated nerve terminals, likely via blockade of sodium channels at subpopulations of neurons with specific firing patterns. These data are consistent with the anticonvulsant and antiparkinsonian actions of safinamide and provide support for the nondopaminergic mechanism of its action.
Dietary advice with or without pravastatin for the management of hypercholesterolaemia associated with protease inhibitor therapy

Background Therapy with a HIV protease inhibitor is associated with elevations in cholesterol and triglycerides. HMG-CoA reductase inhibitors (`statins') are the established therapy for persons with primary hypercholesterolaemia. Because of drug interactions, pravastatin may represent the preferred choice in those taking HIV protease inhibitors. Design A randomized, open-label comparative 24 week trial of dietary advice alone or with pravastatin in 31 male patients established on protease inhibitor-based regimens for greater than 12 weeks with viral load < 500 copies/ml and cholesterol > 6.5 mmol/l. Results There were no significant clinical or laboratory events and no patient discontinuation secondary to adverse effects. Viral rebound did not occur. Relative to baseline, total cholesterol at week 24 fell significantly in the pravastatin (1.2 mmol/l; 17.3%) (P < 0.05) but not in the dietary advice (0.3 mmol/l; 4%) group. The difference between the two groups approached significance at week 24 (P = 0.051). This fall was accounted for entirely by a reduction in low density lipoprotein [calculated change 1.24 mmol/l (19%) and 0.07 mmol (5.5%) in pravastatin and dietary advice groups, respectively] as high density lipoprotein rose non-significantly by 0.6 mmol/l in both groups. Weight, basal metabolic rate, fasting glucose and triglycerides did not change significantly in either group. Conclusions Dietary advice plus pravastatin significantly reduced total cholesterol in HIV-positive individuals taking protease inhibitors, without significant adverse effects. The inclusion of pravastatin substantially increases the magnitude of the change, which is comparable with changes achieved in endogenous hyperlipidaemia.
The Socialization Process for Women with Physical Disabilities: The Impact of Agents and Agencies in the Introduction to an Elite Sport

The sources of people's leisure interests have been a topic of interest for many years. Many of the attitudes people develop towards leisure are the result of social learning and social agents. However, the leisure socialization process can be different for people with disabilities in that many of their social agents may be unaware of the leisure opportunities available to those with disabilities. The purpose of the study was to examine the socialization process of women with physical disabilities into elite wheelchair basketball. The study was guided by the social learning paradigm (Kenyon & McPherson, 1973). Face-to-face interviews were conducted with women who were members of the USA National Wheelchair Basketball Team. Major themes found in the research included that multiple agents can be influential in the socialization process, peers with disabilities are often considered important socializing agents, and agents and agencies often coexist in socializing roles. Another noteworthy finding was that many of the women indicated their social agents' unfamiliarity with disability sports, and the ramifications this had on their lives.
Entrepreneurs, Contracts, and the Failure of Young Firms

Although economic theory has emphasized that moral hazard and hold-up problems influence the design of contracts, very little is known about the process by which explicit contracts are established and the effect of contractual arrangements on firm performance. This paper attempts to demonstrate that firms are selected for survival on the basis of contracting efficiency. Based on a statistical analysis of 170 new franchise contracts and interviews with the founders of 16 of these new franchise systems, we show that new franchise chains that adopt exclusive territories are more likely to survive over time than chains that do not. Moreover, successful and failed entrepreneurs possess different information about how to design contracts. These entrepreneurs undertake "contractual experiments" based on the information they possess. Those whose experiments prove to be more consistent with economic theory are rewarded for their superior information with survival.
From Causal Theories to Successor State Axioms and STRIPS-Like Systems

We describe a system for specifying the efiects of actions. Unlike those commonly used in AI planning, our system uses an action description language that allows one to specify the efiects of actions using domain rules, which are state constraints that can entail new action efiects from old ones. Declaratively, an action domain in our language corresponds to a nonmonotonic causal theory in the situation calculus. Procedurally, such an action domain is compiled into a set of propositional theories, one for each action in the domain, from which fully instantiated successor state-like axioms and STRIPS-like systems are then generated. We expect the system to be a useful tool for knowledge engineers writing action speciflcations for classical AI planning systems, GOLOG systems, and other systems where formal speciflcations of actions are needed.
Differential G-protein-coupled Receptor Phosphorylation Provides Evidence for a Signaling Bar Code*

G-protein-coupled receptors are hyper-phosphorylated in a process that controls receptor coupling to downstream signaling pathways. The pattern of receptor phosphorylation has been proposed to generate a “bar code” that can be varied in a tissue-specific manner to direct physiologically relevant receptor signaling. If such a mechanism existed, receptors would be expected to be phosphorylated in a cell/tissue-specific manner. Using tryptic phosphopeptide maps, mass spectrometry, and phospho-specific antibodies, it was determined here that the prototypical Gq/11-coupled M3-muscarinic receptor was indeed differentially phosphorylated in various cell and tissue types supporting a role for differential receptor phosphorylation in directing tissue-specific signaling. Furthermore, the phosphorylation profile of the M3-muscarinic receptor was also dependent on the stimulus. Full and partial agonists to the M3-muscarinic receptor were observed to direct phosphorylation preferentially to specific sites. This hitherto unappreciated property of ligands raises the possibility that one mechanism underlying ligand bias/functional selectivity, a process where ligands direct receptors to preferred signaling pathways, may be centered on the capacity of ligands to promote receptor phosphorylation at specific sites.
Robust and structure exploiting optimisation algorithms: an integral quadratic constraint approach

We consider the problem of analysing and designing gradient-based discrete-time optimisation algorithms for a class of unconstrained optimisation problems having strongly convex objective functions with Lipschitz continuous gradient. By formulating the problem as a robustness analysis problem and making use of a suitable adaptation of the theory of integral quadratic constraints, we establish a framework that allows to analyse convergence rates and robustness properties of existing algorithms and enables the design of novel robust optimisation algorithms with prespecified guarantees capable of exploiting additional structure in the objective function.
Fault Tolerant Topology Control with Unreliable Failure Detectors

Wireless ad-hoc networking has emerged as its own discipline over the past decade and topology control is an important problem in such networks. The aim of topology control is to construct an appropriate topology to improve the efficiency and performance of the overlying algorithms (e.g., routing). Fault tolerance is a necessary feature of a topology to be applicable in realistic scenarios. This paper presents an analysis of the requirements to tolerate crash failures in the topology with the help of Failure detectors. We show that the class of perfect Failure detectors is strong enough to form a reliable underlay for routing and to avoid message loops.
A fast non-local based stereo matching algorithm using graph cuts

In this paper, a new segment-based stereo matching algorithm (FastNL_Gc) is introduced. FastNL_Gc depends locally on the introduced Non-Local-Mean (NL-Mean) approach that provides a fast denoising technique with an edge preserving property in the initial disparity map estimation and globally on graph cuts for the disparity plane assignment using a new energy formulation of the stereo problem in segment domain. The methodology is tested on Middlebury stereo benchmark and the results indicate that the proposed FastNL_Gc method is compatible with the current state-of-the-art stereo matching algorithms in dealing with the conventionally difficult areas, such as textureless regions, disparity discontinuous boundaries and occluded portions.
SIC-GAN: A Self-Improving Collaborative GAN for Decoding Sketch RNNs

Variational RNNs are proposed to output “creative” sequences. Ideally, a collection of sequences produced by a variational RNN should be of both high quality and high variety. However, existing decoders for variational RNNs suffer from a trade-off between quality and variety. In this paper, we seek to learn a variational RNN that decodes high-quality and high-variety sequences. We propose the Self-Improving Collaborative GAN (SIC-GAN), where there are two generators (variational RNNs) collaborating with each other to output a sequence and aiming to trick the discriminator into believing the sequence is of good quality. By deliberately weakening one generator, we can make another stronger in balancing quality and variety. We conduct experiments using the QuickDraw dataset and the results demonstrate the effectiveness of SIC-GAN empirically.
Platform for ligand-based virtual screening integration

Ligand-based Virtual screening became a standard in-silico complement to the wet laboratory screening in small molecules discovery. It utilizes prior knowledge about molecules to rank a set of candidate molecules with yet unknown activity. Virtual screening software is often implemented as a specialized screening tool or as a part of a drug-discovery suite where ligand-based screening is one of the available tools. There exist many approaches to virtual screening, but although there exist several free-to-use screening tools, these tools usually provide only single approach to screening or do not provide user with aggregated analyses of multiple (preferably state-of-the-art) screening approaches. Such a functionality would be useful since different screening approaches yield different ranking of the candidate molecules. To the best of our knowledge there is no freely available, easy-to-use, tool that would allow to apply multiple screening approaches to a dataset and subsequently facilitate integration and interpretation of the results. Here, we present the first version of such a tool called ViSeT, available at https://github.com/skodapetr/viset under the MIT licence. ViSeT is a web-based, extensible, ligand-based virtual screening tool supporting multiple screening approaches and subsequent analysis of the results.
Functional Interactions of VirB11 Traffic ATPases with VirB4 and VirD4 Molecular Motors in Type IV Secretion Systems

ABSTRACT Pilus biogenesis and substrate transport by type IV secretion systems require energy, which is provided by three molecular motors localized at the base of the secretion channel. One of these motors, VirB11, belongs to the superfamily of traffic ATPases, which includes members of the type II secretion system and the type IV pilus and archaeal flagellar assembly apparatus. Here, we report the functional interactions between TrwD, the VirB11 homolog of the conjugative plasmid R388, and TrwK and TrwB, the motors involved in pilus biogenesis and DNA transport, respectively. Although these interactions remained standing upon replacement of the traffic ATPase by a homolog from a phylogenetically related conjugative system, namely, TraG of plasmid pKM101, this homolog could not replace the TrwD function for DNA transfer. This result suggests that VirB11 works as a switch between pilus biogenesis and DNA transport and reinforces a mechanistic model in which VirB11 proteins act as traffic ATPases by regulating both events in type IV secretion systems.
Shifts of Attention During Spatial Language Comprehension - A Computational Investigation

Regier and Carlson (2001) have investigated the processing of spatial prepositions and developed a cognitive model that formalizes how spatial prepositions are evaluated against depicted spatial relations between objects. In their Attentional Vector Sum (AVS) model, a population of vectors is weighted with visual attention, rooted at the reference object and pointing to the located object. The deviation of the vector sum from a reference direction is then used to evaluate the goodness-of-fit of the spatial preposition. Crucially, the AVS model assumes a shift of attention from the reference object to the located object. The direction of this shift has been challenged by recent psycholinguistic and neuroscientific findings. We propose a modified version of the AVS model (the rAVS model) that integrates these findings. In the rAVS model, attention shifts from the located object to the reference object in contrast to the attentional shift from the reference object to the located object implemented in the AVS model. Our model simulations show that the rAVS model accounts for both the data that inspired the AVS model and the most recent findings.
Acute naming deficits following dominant temporal lobectomy

Age at 1st risk for seizures may predict anomia following dominant anterior temporal lobectomy. We assessed confrontation naming before and 2 to 3 weeks after surgery in 45 right-handed patients grouped by side of focus and presence or absence of early (≤5 years) risk factors. After left lobectomy, 6 of 10 (60%) patients with no early risks demonstrated significant decline (≥25%) in naming, but none of the patients with early risks showed this decline. After right lobectomy, there was no change. Cerebral representation of naming may be atypical in patients with early risks.
AUTOMOCK: Automated Synthesis of a Mock Environment for Test Case Generation

During testing, there are several reasons to exclude some of the components used by the unit under test, such as: (1) the component affects the state of the world in an irreversible way; (2) the component is not accessible for testing purposes (e.g., a web service); (3) the component introduces a major performance degradation to the testing phase (e.g., due to long computations); (4) it is hard (i.e., statistically unlikely) to obtain the output required by the test from the component. In such cases, we replace the component with a mock one. In this paper, we integrate the synthesis of mock components with the generation of test cases for the current testing goal (e.g., coverage). To avoid the generation of meaningless data, which may lead to assertion violation not related to bugs, we include a weak mock postcondition. We consider ways to automatically synthesize such postcondition. We empirically evaluate the quality of the mocks generated by our approach, as well as the benefits mocks introduce in terms of improved coverage and improved performance of the test case generator.
A Jini-Based Solution for Electronic Prescriptions

In most countries today, handwritten, paper-based medical prescriptions are the norm. While efforts have been made in the past and are being made at present to migrate toward electronic dispensation of prescriptions, these have generally omitted to incorporate ubiquitous computing technology in their proposed solutions. In this paper, we focus on this issue and describe a Jini-based prototypical solution for electronic prescriptions, which allows for their wireless transmission to in-range pharmacies and the augmentation of the service levels rendered to the user, with, for instance, information about queue lengths and estimated waiting times being provided to the patients. Clinical and user evaluation revealed that there were high levels of agreement as regards the prototype's effectiveness, ease of use, and usefulness
Restricted Transfer learning for Text Categorization

In practice, machine learning systems deal with multiple datasets over time. When the feature spaces between these datasets overlap, it is possible to transfer information from one task to another. Typically in transfer learning, all labeled data from a source task is saved to be applied to a new target task thereby raising concerns of privacy, memory and scaling. To ameliorate such concerns, we present a semi-supervised algorithm for text categorization that transfers information across tasks without storing the data of the souce task. In particular, our technique learns a sparse low-dimensional projection from unlabeled and the source task data. In particular, our technique learns low-dimensional sparse word clusters-based features from the source task data and a massive amount of additional unlabeled data. Our algorithm is efficient, highly parallelizable, and outperforms competitive baselines by up to 9% on several difficult benchmark text categorization tasks.
A two-staged approach to vision-based pedestrian recognition using Haar and HOG features

This article presents a two-staged approach to recognize pedestrians in video sequences on board of a moving vehicle. The system combines the advantages of two feature families by splitting the recognition process into two stages: In the first stage, a fast search mechanism based on simple features is applied to detect interesting regions. The second stage uses a computationally more expensive, but also more accurate set of features on these regions to classify them into pedestrian and non-pedestrian. We compared various feature extraction configurations of different complexities regarding classification performance and speed. The complete system was evaluated on a number of labeled test videos taken from real-world drives and also compared against a publicly available pedestrian detector. This first system version analyzes only single image frames without using any temporal information like tracking. Still, it achieves good recognition performance at reasonable run time.
Dialogue Act Recognition in Synchronous and Asynchronous Conversations

In this work, we study the effectiveness of state-of-the-art, sophisticated supervised learning algorithms for dialogue act modeling across a comprehensive set of different spoken and written conversations including: emails, forums, meetings, and phone conversations. To this aim, we compare the results of SVM-multiclass and two structured predictors namely SVMhmm and CRF algorithms. Extensive empirical results, across different conversational modalities, demonstrate the effectiveness of our SVM-hmm model for dialogue act recognition in conversations.
ON "THE RETURN OF THE SOLDIER"

The soldier returns from the front to the three women who love him. His wife, Kitty, with her cold, moonlight beauty, and his devoted cousin Jenny wait in their exquisite home on the crest of the Harrow-weald. Margaret Allington, his first and long-forgotten love, is nearby in the dreary suburb of Wealdstone. But the soldier is shell-shocked and can only remember the Margaret he loved fifteen years before, when he was a young man and she an inn-keeper's daughter. His cousin he remembers only as a childhood playmate; his wife he remembers not at all. The women have a choice - to leave him where he wishes to be, or to 'cure' him. It is Margaret who reveals a love so great that she can make the final sacrifice.
Minimal Hepatic Encephalopathy: A Vehicle for Accidents and Traffic Violations

OBJECTIVES: MHE patients have impairment on driving tests. However, it is unclear whether this impairment is restricted to the testing environment or is associated with increased traffic violations and/or motor vehicle accidents.METHODS: An anonymous driving history and driving behavior questionnaire (DBQ: self-scored, best score 104), coded according to MHE status, was sent to 200 cirrhotics without overt hepatic encephalopathy and 100 age/education-matched controls. The questionnaire inquired about demographics, alcohol/illegal drug use, and violations/accidents within 1 and 5 yr. The cirrhotics had been divided into those with MHE (MHE+), without MHE (MHE−), and those not tested for MHE because of psychoactive drug use, on a previous study.RESULTS: Cirrhotics versus controls had similar driving duration, alcohol/illegal drug use but significantly higher percentage with violations within both 1 and 5 yr (25% vs 4% [5 yr]), 13% vs 2% [(1 yr]), accidents (17% vs 4% [5 yr]), 9% vs 1% [1 yr]), and both (34% vs 7% [5 yr], 18% vs 3% [1 yr]). MHE+ cirrhotics had significantly higher percentage with violations (36% [5 yr], 21% [1 yr]), accidents (33% [5 yr]), 17% [1 yr]), and both (53% [5 yr], 33% [1 yr]) versus other cirrhotics. DBQ score was significantly lower in cirrhotics than controls (92 vs 99). Within cirrhotics, DBQ score was highest in MHE– versus other groups. MHE+ status was the only risk factor (odds ratios: 4.2–7.6) for violations and for accidents on multivariate logistic regression.CONCLUSIONS: Cirrhotics have a higher self-reported occurrence of violations and accidents compared to controls. MHE+ a is strong predictor for violations and accidents. Prospective studies investigating the effect of MHE treatment on violations and accidents are warranted.
Reoptimization of Motif Finding Problem

One of the approaches in solving NP-hard problems is through reoptimization. In this technique, we solve a locally modified instance of a problem by making use of known solution to its original instance instead of obtaining a solution from scratch. In this paper, we present a reoptimization of motif finding problem. Since the problem is showed to be self-reducible, we can use a self-reduction method to solve the reoptimization variant of the problem. Using the method, we have improved the approximation ratio of the algorithm solving the reoptimized version as compared to the non-reoptimized counterpart. Moreover, we showed that if a certain problem is self-reducible, any problem that obtains a polynomial-time reduction to it is also self-reducible.