Miracle-dz/UniFAR-FTUs huggingface dataset:
JC virus antibody index in natalizumab-treated patients: correlations with John Cunningham virus DNA and C-reactive protein level

Natalizumab-treated patients have a higher risk of developing progressive multifocal leukoencephalopathy. Exposure to John Cunningham virus (JCV) is a prerequisite for PML (progressive multifocal leukoencephalopathy). To assess JCV exposure in multiple sclerosis patients, we performed a serological examination, obtained the antibody index, performed real-time polymerase chain reaction (PCR) to detect JCV DNA in plasma and urine, and investigated the role of ultrasensitive C-reactive protein (usCRP) as a possible biological marker of JCV reactivation. We retrospectively analyzed consecutive natalizumab-treated multiple sclerosis patients who underwent a JCV antibody test through a two-step enzyme-linked immunosorbent assay (STRATIFY test) to the measure of serum usCRP levels, and to perform blood and urine JCV PCR. The studied cohort included 97 relapsing–remitting patients (60 women). Fifty-two patients (53.6%) tested positive for anti-JCV antibodies. PCR showed JCV DNA in the urine of 30 out of 83 (36.1%) patients and 28 out of 44 seropositive patients (63.6%), with a 6.7% false-negative rate for the STRATIFY test. Normalized optical density values were higher in urinary JCV DNA-positive patients (P<0.0001). Interestingly, the level of usCRP was higher in urinary JCV DNA-positive patients and correlated to the number of DNA copies in urine (P=0.028). As expected, patients’ age correlated with JCV seropositivity and with JC viruria (P=0.02 and P=0.001, respectively). JC viruria was significantly correlated with a high JCV antibody index and high serum usCRP levels. We suggest that PCR and usCRP might be useful as markers of JCV reactivation, and that patients should be monitored between STRATIFY assessments.
Delay in Hospital Admission of Patients with Cerebral Vein and Dural Sinus Thrombosis

Factors influencing early hospital admission have been described for several stroke types but not for cerebral vein and dural sinus thrombosis (CVT). CVT is more difficult to diagnose than arterial stroke; delay in hospital admission may postpone CVT treatment. The purposes of this study were: (1) to describe the delay between the onset of symptoms and hospital admission of patients with CVT, and (2) to identify the variables that influence that delay. We registered the interval (days) between the onset of symptoms and hospital admission in 91 consecutive patients admitted to 20 Portuguese hospitals between June 1995 and June 1998. We also studied the impact of admission delay on treatments (prescription of anticoagulants and the number of days elapsed between the onset of symptoms and start of anticoagulation and admission). Median admission delay was 4 days. Twenty-two (25%) patients were admitted within 24 h. Two thirds of the patients were admitted within 7 days and 75% within 13 days. In multiple logistic regression analysis, admission within 24 h was positively associated with mental status disorder (delirium or abulia; OR = 4.59; 95% CI = 1.41–14.89) and negatively associated with headache (OR = 0.03; 95% CI = 0.00–0.32). Presentation as isolated intracranial hypertension was associated with admission delay of more than 4 days (OR = 2.63; 95% CI = 0.97–7.14). Papilloedema was associated with an admission delay of more than 13 days (OR = 4.69; 95% CI = 1.61–13.61). There was no association between admission delay and the proportion of anticoagulated patients. The interval between onset of symptoms and start of anticoagulation was shorter in patients admitted earlier (p = 0.0001, for either admission within 24 h, 4 or 13 days). There is a considerable delay until the clinical picture associated with CVT is recognised as justifying hospital admission, especially when patients present with symptoms identical to isolated intracranial hypertension syndrome.
A Note on k-support Norm Regularized Risk Minimization

The k-support norm has been recently introduced to perform correlated sparsity regularization. Although Argyriou et al. only reported experiments using squared loss, here we apply it to several other commonly used settings resulting in novel machine learning algorithms with interesting and familiar limit cases. Source code for the algorithms described here is available.
Aggregate Functions in DLV

Disjunctive Logic Programming (DLP) is a very expressive formalism: it allows for expressing every property of finite structures that is decidable in the complexity class . Despite this high expressiveness, there are some simple properties, often arising in real-world applications, which cannot be encoded in a simple and natural manner. Especially properties that require the use of arithmetic operators (like sum, count, or min) on a set of elements satisfying some conditions, cannot be naturally expressed in classic DLP. To overcome this deficiency, we extend DLP by aggregate functions. In contrast to a previous proposal, we also consider the case of unstratified aggregates. We formally define the semantics of the new language (called DLP ) by means of a generalization of the Gelfond-Lifschitz transformation, and illustrate the use of the new constructs on relevant knowledge-based problems. We analyze the computational complexity of DLP , showing that the addition of aggregates does not bring a higher cost in that respect. And we provide an implementation of DLP in DLV– the state-of-the-art DLP system – and report on experiments which confirm the usefulness of the proposed extension also for the efficiency of the computation.
Key Role of Local Regulation in Chemosensing Revealed by a New Molecular Interaction-Based Modeling Method

The signaling network underlying eukaryotic chemosensing is a complex combination of receptor-mediated transmembrane signals, lipid modifications, protein translocations, and differential activation/deactivation of membrane-bound and cytosolic components. As such, it provides particularly interesting challenges for a combined computational and experimental analysis. We developed a novel detailed molecular signaling model that, when used to simulate the response to the attractant cyclic adenosine monophosphate (cAMP), made nontrivial predictions about Dictyostelium chemosensing. These predictions, including the unexpected existence of spatially asymmetrical, multiphasic, cyclic adenosine monophosphate–induced PTEN translocation and phosphatidylinositol-(3,4,5)P3 generation, were experimentally verified by quantitative single-cell microscopy leading us to propose significant modifications to the current standard model for chemoattractant-induced biochemical polarization in this organism. Key to this successful modeling effort was the use of “Simmune,” a new software package that supports the facile development and testing of detailed computational representations of cellular behavior. An intuitive interface allows user definition of complex signaling networks based on the definition of specific molecular binding site interactions and the subcellular localization of molecules. It automatically translates such inputs into spatially resolved simulations and dynamic graphical representations of the resulting signaling network that can be explored in a manner that closely parallels wet lab experimental procedures. These features of Simmune were critical to the model development and analysis presented here and are likely to be useful in the computational investigation of many aspects of cell biology.
Minimal Cooling of Neutron Stars: A New Paradigm

A new classification of neutron star cooling scenarios, involving either "minimal" cooling or "enhanced" cooling, is proposed. The minimal cooling scenario replaces and extends the so-called standard cooling scenario to include neutrino emission from the Cooper pair breaking and formation process. This emission dominates that due to the modified Urca process for temperatures close to the critical temperature for superfluid pairing. Minimal cooling is distinguished from enhanced cooling by the absence of neutrino emission from any direct Urca process, due either to nucleons or to exotica such as hyperons, Bose condensates, or deconfined quarks. Within the minimal cooling scenario, theoretical cooling models can be considered to be a four parameter family involving the equation of state (including various compositional possibilities) of dense matter, superfluid properties of dense matter, the composition of the neutron star envelope, and the mass of the neutron star. The consequences of minimal cooling are explored through extensive variations of these parameters. The results are compared with the inferred properties of thermally emitting neutron stars in order to ascertain if enhanced cooling occurs in any of them. All stars for which thermal emissions have been clearly detected are at least marginally consistent with the lack of enhanced cooling, given the combined uncertainties in ages and temperatures or luminosities. The two pulsars PSR 0833-45 (Vela) and PSR 1706-44 would require enhanced cooling in case their ages and/or temperatures are on the lower side of their estimated values, whereas the four stars PSR 0656+14, PSR 1055-52, Geminga, and RX J0720.4-3125 may require some source of internal heating in case their age and/or luminosity are on the upper side of their estimated values. The new upper limits on the thermal luminosity of PSR J0205+6449 (in the supernova remnant 3C 58) and RX J0007.0+7302 (in CTA 1) are indicative of the occurrence of some enhanced neutrino emission beyond the minimal scenario.
A metric to detect fault-prone software modules using text filtering

Machine learning approaches have been widely used for fault-prone module detection. Introduction of machine learning approaches induces development of new software metrics for fault-prone module detection. We have proposed an approach to detect fault-prone modules using the spam-filtering technique. To use our approach in the conventional fault-prone module prediction approaches, we construct a metric from the output of spam-filtering based approach. Using our new metric, we conducted an experiment to show the effect of new metric. The result suggested that use of new metric as well as conventional metrics is effective for accuracy of fault-prone module prediction.
Personality and Psychiatric Disorders in Women Affected by Polycystic Ovary Syndrome

Background: Polycystic ovary syndrome (PCOS) is the most prevalent endocrine disorder among fertile women. Studies show reduced quality of life, anxiety, depression, body dissatisfaction, eating disorder, and sexual dysfunction, but the etiology of these disturbs remains still debated. The aim of our study is to verify whether this hyperandrogenic syndrome characterizes a strong psycho(patho)logical personality. Method: Sixty PCOS subjects (mean age 25.8 ± 4.7 years) were evaluated by anthropometric, metabolic, hormonal, clinical, and psychological parameters. After the certainty of the diagnosis of PCOS, the Rorschach test, according to Exner’s comprehensive system (CS) and the Millon Clinical Multiaxial Inventory-III (MCMI-III) were administered to each patient. The control group, on which the comparison was carried out, was composed by 40 healthy and aged compared women who were exclusively administered the Rorschach test according to CS. Results: MCMI-III evidenced axis II DSM-IV personality disorders [4.1% schizoid, depressive, sadistic, negativistic (passive–aggressive), and masochistic, 6.1% avoiding, 12.2% dependent, 20.4% histrionic, 16.3% narcissistic, 2.0% obsessive–compulsive], and axis I DSM-IV psychiatric disorders: 10.2% anxiety, 2.0% somatoform disorder and bipolar disorder, 16.3% major depressive disorder. Finally, we found 44.9% delusional disorder and 4.1% thought disorder. Rorschach test’s results show 53.1% reduced coping abilities and social skills, 55.1% depression, 30.6% perceptual distortion and cognitive slippage, 24.5% constantly alert and worry, 8.1% at risk for suicide, and finally about 50% of our patients had chronic stress. Conclusion: PCOS women have relevant personality and psychiatric disorders, when compared with normal subjects.
A Probabilistic Strategy for Understanding Action Selection

Brain regions involved in transforming sensory signals into movement commands are the likely sites where decisions are formed. Once formed, a decision must be read out from the activity of populations of neurons to produce a choice of action. How this occurs remains unresolved. We recorded from four superior colliculus neurons simultaneously while monkeys performed a target selection task. We implemented three models to gain insight into the computational principles underlying population coding of action selection. We compared the population vector average (PVA)/optimal linear estimator (OLE) and winner-takes-all (WTA) models and a Bayesian model, maximum a posteriori estimate (MAP), to determine which predicted choices most often. The probabilistic model predicted more trials correctly than both the WTA and the PVA. The MAP model predicted 81.88%, whereas WTA predicted 71.11% and PVA/OLE predicted the least number of trials at 55.71 and 69.47%. Recovering MAP estimates using simulated, nonuniform priors that correlated with monkeys' choice performance, improved the accuracy of the model by 2.88%. A dynamic analysis revealed that the MAP estimate evolved over time and the posterior probability of the saccade choice reached a maximum at the time of the saccade. MAP estimates also scaled with choice performance accuracy. Although there was overlap in the prediction abilities of all the models, we conclude that movement choice from populations of neurons may be best understood by considering frameworks based on probability.
A Two-Microphone Noise Reduction System for Cochlear Implant Users with Nearby Microphones—Part II: Performance Evaluation

Users of cochlear implants (auditory aids, which stimulate the auditory nerve electrically at the inner ear) often suffer from poor speech understanding in noise. We evaluate a small (intermicrophone distance 7 mm) and computationally inexpensive adaptive noise reduction system suitable for behind-the-ear cochlear implant speech processors. The system is evaluated in simulated and real, anechoic and reverberant environments. Results from simulations show improvements of 3.4 to 9.3 dB in signal to noise ratio for rooms with realistic reverberation and more than 18 dB under anechoic conditions. Speech understanding in noise is measured in 6 adult cochlear implant users in a reverberant room, showing average improvements of 7.9–9.6 dB, when compared to a single omnidirectional microphone or 1.3–5.6 dB, when compared to a simple directional two-microphone device. Subjective evaluation in a cafeteria at lunchtime shows a preference of the cochlear implant users for the evaluated device in terms of speech understanding and sound quality.
Relationship between Theory of Mind and Executive Function in Schizophrenia: A Systematic Review

Background: There is good agreement that Theory of Mind (ToM) and executive function are impaired to some degree in many patients with schizophrenia. However, few studies have set out to investigate as a primary aim the relationship between these 2 cognitive domains in the disorder. Methods: A systematic review was carried out to identify all published studies in which tests of ToM and executive function were administered to individuals with schizophrenia, and in which the relation between scores was reported. Results: The review revealed 17 relevant studies, of which 8 reported the relation between ToM and executive function using multivariate statistics. These all concurred in showing that ToM ability continued to predict an individual having schizophrenia (rather than being a control participant) once executive function was controlled for. Conclusions: There is very good agreement that ToM and executive function impairments in schizophrenia are independent of one another. Implications are discussed for the putative brain systems involved in the disorder and for its cognitive rehabilitation.
PrivBasis: Frequent Itemset Mining with Differential Privacy

The discovery of frequent itemsets can serve valuable economic and research purposes. Releasing discovered frequent itemsets, however, presents privacy challenges. In this paper, we study the problem of how to perform frequent itemset mining on transaction databases while satisfying differential privacy. We propose an approach, called PrivBasis, which leverages a novel notion called basis sets. A θ-basis set has the property that any itemset with frequency higher than θ is a subset of some basis. We introduce algorithms for privately constructing a basis set and then using it to find the most frequent itemsets. Experiments show that our approach greatly outperforms the current state of the art.
Regulatory T Cells Specific for the Same Framework 3 Region of the Vβ8.2 Chain Are Involved in the Control of Collagen II–induced Arthritis and Experimental Autoimmune Encephalomyelitis

Recent evidence indicates that chronic autoimmune disease can result from breakdown of regulation and subsequent activation of self-reactive T cells. In many murine autoimmune disease systems and in the Lewis rat, antigen-specific T cells utilizing the T cell receptor (TCR) Vβ8. 2 gene segment play a major role. In the myelin basic protein–induced experimental autoimmune encephalomyelitis (EAE) model in H-2u mice, we had shown that T cells recognizing a peptide determinant within the framework 3 region of the Vβ8. 2 chain have a critical role in influencing the course of the disease. Here, we report experiments in another disease system, collagen II (CII)–induced arthritis (CIA) in DBA/1LacJ (H-2q) mice, indicating a remarkably parallel control circuit to that found for EAE. A critical role is played by CII-specific Vβ8. 2bearing T cells in the CIA system, which we have confirmed. Animals treated with the superantigen SEB before CII administration are significantly protected from CIA. Next, we tested the ability of peptides encompassing the entire Vβ8. 2 chain to induce proliferative responses. Only TCR peptide B5 (amino acids 76–101), a regulatory peptide in EAE, induced proliferation. B5 was then used to vaccinate DBA/1LacJ mice and was shown to reduce greatly the severity and incidence of CIA as measured by joint inflammation or histology. Furthermore, similar protection was found when B5 was administered after CII immunization. It was shown that there is physiological induction of a proliferative response to B5 during CIA and that the determinant within B5 is produced from a single chain TCR construct containing the entire Vβ8. 2 chain. Finally, the regulation of CIA is discussed in the context of other experimental autoimmune diseases, especially EAE, with emphasis on what appear to be strikingly common mechanisms.
Quantitative Assessment of Changes in Cerebral Arteriovenous Malformation Hemodynamics After Embolization

Background and Purpose— Embolization reduces flow in arteriovenous malformations (AVMs) before surgical resection, but achievement of this goal is determined subjectively from angiograms. Here, we quantify effects of embolization on AVM flow. Methods— Records of patients who underwent AVM embolization at our institution between 2007 and 2013 and had flow rates obtained pre- and postembolization using quantitative magnetic resonance angiography were retrospectively reviewed. Total flow was estimated as aggregate flow within primary arterial feeders or flow in single draining veins. Results— Twenty-one patients were included (mean age 35 years, 24% hemorrhagic presentation) with Spetzler–Martin grades 1 to 4. Fifty-four total embolization sessions were performed. The mean AVM flow was 403.4±262.4 mL/min at baseline, 285.3±246.4 mL/min after single session (29% drop, P<0.001), and 102.0±103.3 mL/min after all sessions of embolization (75% drop, P<0.001). Total number of pedicles embolized (P<0.001) and embolization of an intranidal fistula during any session (P=0.002) were significantly associated with total decreased flow postembolization. On multivariate analysis, total pedicles embolized was predictive of total flow drop (P<0.001). However, pedicles embolized per session did not correlate with flow drop related to that session (P=0.44). Conclusions— AVM flow changes after embolization can be measured using quantitative magnetic resonance angiography. The total number of pedicles embolized after multiple embolization sessions was predictive of final flow, indicating this parameter is the best angiographic marker of a hemodynamically successful intervention. The number of pedicles embolized per session, however, did not correlate with flow drop in that session, likely because of flow redistribution after partial embolization.
The Effect of Attractive Polymer-Nanoparticle Interactions on the Local Segmental Dynamics of Polymer Nanocomposites

Considerable progress has been made in understanding the miscibility and morphology of polymer nanocomposites (PNCs). However, to date, there is little understood concerning the modification of segmental mobility at the polymernanoparticle interface, which due to prevalence of interfaces in PNCs, will predominately control the viscoelastic and mechanical properties of these materials. In this dissertation, static and dynamic experimental techniques are combined to identify the specific parameters controlling the modification of segmental dynamics at the polymer-nanoparticle interface in the model system of poly(2vinyl pyridine)/silica nanocomposites. In general, the experimental results clearly demonstrate that the segmental dynamics at the polymer-nanoparticle interface are suppressed relative to the bulk-like polymer and moreover that this effect is dependent on the molecular weight (MW) of the polymer matrix. Additionally, the density of the polymer-nanoparticle interface was found to unexpectedly decrease with increasing MW. These unexpected results indicate a competition between a suppression due to adsorption and a speeding up due to the reduction in density–which suggests that the chain packing at the polymer-nanoparticle interface becomes frustrated at higher MWs. Lastly, by comparing the strength of the polymer-nanoparticle interaction and molecular weight, it is proposed that the orientational anisotropy of the chain conformations primarily controls the magnitude of the dynamic suppression at the polymer-nanoparticle interface and suggests a new way to tailor the segmental dynamics on the nanoscale.
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations

Recent years have witnessed the unprecedented success of deep convolutional neural networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based SISR methods mostly assume that a low-resolution (LR) image is bicubicly downsampled from a high-resolution (HR) image, thus inevitably giving rise to poor performance when the true degradation does not follow this assumption. Moreover, they lack scalability in learning a single model to nonblindly deal with multiple degradations. To address these issues, we propose a general framework with dimensionality stretching strategy that enables a single convolutional super-resolution network to take two key factors of the SISR degradation process, i.e., blur kernel and noise level, as input. Consequently, the super-resolver can handle multiple and even spatially variant degradations, which significantly improves the practicability. Extensive experimental results on synthetic and real LR images show that the proposed convolutional super-resolution network not only can produce favorable results on multiple degradations but also is computationally efficient, providing a highly effective and scalable solution to practical SISR applications.