Miracle-dz/UniFAR-FTUs huggingface dataset:
Which dimensions of food-related lifestyle are likely to be associated with obesity in Italy?

Abstract Objective To compare obese v. non-obese consumers in terms of their general food-related lifestyles and to explore possible food-related factors affecting obesity in Italy. Design The data were collected using a self-completion questionnaire. Data included sociodemographic information, measures of the Food-Related Lifestyle scale (FRL) and self-reported weight and height. A logistic regression model was fitted for the sample with obesity as the dependent variable and sociodemographic characteristics and FRL dimensions as the independent variables. Setting The survey was carried out in Italy between October and November 2010. Subjects One thousand respondents were recruited with equal quotas for men v. women. The sample was representative of the Italian population in term of age groups and geographic areas. The participants were over 18 years of age and were solely or jointly responsible for the family's food shopping. Results Our analyses revealed that sociodemographic, economic and cultural variables affect the increasing rate of obesity in Italy. Obese respondents appeared to find more enjoyment from shopping and interest in cooking compared with non-obese ones. Moreover, they were more likely to find self-fulfilment in foods. However, obese respondents appeared to be less interested in the nutritional content of foods, suggesting their weak involvement in nutritional aspects when they eat. In fact, the obese respondents gave preference to snacks over meals. Conclusions The outcome of the study would suggest that in targeted interventions for public health purposes in order to address obesity, attention should be placed on the role that food plays in obese consumers’ lives.
Channeling 5 Minute Photospheric Oscillations into the Solar Outer Atmosphere through Small-Scale Vertical Magnetic Flux Tubes

We report two-dimensional MHD simulations which demonstrate that photospheric 5 minute oscillations can leak into the chromosphere inside small-scale vertical magnetic flux tubes. The results of our numerical experiments are compatible with those inferred from simultaneous spectropolarimetric observations of the photosphere and chromosphere obtained with the Tenerife Infrared Polarimeter (TIP) at 10830 Å. We conclude that the efficiency of energy exchange by radiation in the solar photosphere can lead to a significant reduction of the cutoff frequency and may allow for the propagation of the 5 minute waves vertically into the chromosphere.
An Integrated Framework for Dependable and Revivable Architectures Using Multicore Processors

This paper presents a high-availability system architecture called INDRA $an integrated framework for dependable and revivable architecture that enhances a multicore processor (or CMP) with novel security and fault recovery mechanisms. INDRA represents the first effort to create remote attack immune, self-healing network services using the emerging multicore processors. By exploring the property of a tightly-coupled multicore system, INDRA pioneers several concepts. It creates a hardware insulation, establishes finegrained fault monitoring, exploits monitoring/backup concurrency, and facilitates fast recovery services with minimal performance impact. In addition, INDRA's fault/exploit monitoring is implemented in software rather than in hardware logic, thereby providing better flexibility and upgradability. To provide efficient service recovery and thus improve service availability, we propose a novel delta state backup and recovery on-demand mechanism in INDRA that substantially outperforms conventional checkpointing schemes. We demonstrate and evaluate INDRA's capability and performance using real network services and a cycle-level architecture simulator. As indicated by our performance results, INDRA is highly effective in establishing a more dependable system with high service availability using emerging multicore processors
Echo-Guided Mitral Repair

Background—Echocardiography is available in the most basic healthcare environments. Mitral repair is potentially curative and, when possible, recommended over replacement. The efficacy of echo-guided repair has not been established. Methods and Results—We developed a succinct set of precisely defined images observed to be highly concordant with intraoperative findings. These images guided intervention on 237 consecutive patients. None were lost to follow-up, and serial echocardiography was obtained on all repairs. This analysis includes 2037 echocardiograms. The intent to repair or replace was documented preoperatively in 98.7%. Concordance was associated with successful repair (97.8% versus 57.1%; P=0.001). Three-dimensional concordance was higher than 2-dimensional (100% versus 94.4%; P=0.05). Echocardiography guided a graduated surgical approach for degenerative and myopathic repairs by quantifying segmental prolapse, anterior leaflet closing angles, and tenting for integration of secondary chord lysis (P<0.001) and commissural width (P<0.01). Repair rates increased from 46.5% to 77.6% (P<0.001). Concomitant Society of Thoracic Surgeons rates were 46.6% (versus unguided 46.5%; P=0.99) and 54.9% (versus echo guided 77.6%; P<0.001). Repair was successful in 91.5% of isolated echo-guided mitral operations (versus concomitant Society of Thoracic Surgeons 70.0%; P<0.001). Echo-guided repair rates for degenerative, myopathic, and inflammatory diseases were 99.0%, 97.1%, and 84.2% with linearized annual recurrent regurgitation of 0.63%, 2.19%, and 4.37%, respectively. Conclusions—Echocardiography can reliably identify repairable mitral disease and guide intervention. Echo-guided repair is associated with a higher rate of initial success than unguided historical and concomitant national controls. Three-dimensional echo improves concordance. Secondary chord lysis is associated with durable repair and may prevent ventricular remodeling.
Electrical conductivity and physiological comfort of silver coated cotton fabrics

Abstract The objective of present study was to develop multifunctional and wearable electrically conductive fabrics with acceptable comfort properties by in-situ deposition of silver particles. The effect of silver nitrate concentration and number of dips was investigated for change in electrical conductivity, EMI shielding, and antimicrobial properties of coated fabrics. The dynamic light scattering and SEM analysis were employed to study the morphology of deposited silver particles. The EMI shielding was found to increase with increase in concentration of silver particles. Later, the comfort and mechanical properties were studied. No significant decrease in air permeability and water vapor permeability was observed due to partial coverage of fabric pores by coating of silver particles. Moreover, the coated fabrics also showed promising behavior toward antimicrobial properties. When the durability of coated fabrics was examined against washing, the application of binder provided good retention of silver particles without loss of electrical conductivity of coated fabrics.
Ligands act as pharmacological chaperones and increase the efficiency of δ opioid receptor maturation

The endoplasmic reticulum (ER) is recognized as an important site for regulating cell surface expression of membrane proteins. We recently reported that only a fraction of newly synthesized δ opioid receptors could leave the ER and reach the cell surface, the rest being degraded by proteasomes. Here, we demonstrate that membrane‐permeable opioid ligands facilitate maturation and ER export of the receptor, thus acting as pharmacological chaperones. We propose that these ligands stabilize the newly synthesized receptor in the native or intermediate state of its folding pathway, possibly by inducing stabilizing conformational constrains within the hydrophobic core of the protein. The receptor precursors that are retained in the ER thus represent fully competent folding intermediates that can be targets for pharmacological intervention aimed at regulating receptor expression and cellular responsiveness. The pharmacological chaperone action is independent of the intrinsic signaling efficacy of the ligand, since both agonists and antagonists were found to promote receptor maturation. This novel property of G protein‐coupled receptor ligands may have important implications when considering their effects on cellular responsiveness during therapeutic treatments.
177Lu-EC0800 Combined with the Antifolate Pemetrexed: Preclinical Pilot Study of Folate Receptor Targeted Radionuclide Tumor Therapy

Targeted radionuclide therapy has shown impressive results for the palliative treatment of several types of cancer diseases. The folate receptor has been identified as specifically associated with a variety of frequent tumor types. Therefore, it is an attractive target for the development of new radionuclide therapies using folate-based radioconjugates. Previously, we found that pemetrexed (PMX) has a favorable effect in reducing undesired renal uptake of radiofolates. Moreover, PMX also acts as a chemotherapeutic and radiosensitizing agent on tumors. Thus, the aim of our study was to investigate the combined application of PMX and the therapeutic radiofolate 177Lu-EC0800. Determination of the combination index (CI) revealed a synergistic inhibitory effect of 177Lu-EC0800 and PMX on the viability of folate receptor–positive cervical (KB) and ovarian (IGROV-1) cancer cells in vitro (CI < 0.8). In an in vivo study, tumor-bearing mice were treated with 177Lu-EC0800 (20 MBq) and a subtherapeutic (0.4 mg) or therapeutic amount (1.6 mg) of PMX. Application of 177Lu-EC0800 with PMXther resulted in a two- to four-fold enhanced tumor growth delay and a prolonged survival of KB and IGROV-1 tumor-bearing mice, as compared to the combination with PMXsubther or untreated control mice. PMXsubther protected the kidneys from undesired side effects of 177Lu-EC0800 (20 MBq) by reducing the absorbed radiation dose. Intact kidney function was shown by determination of plasma parameters and quantitative single-photon emission computed tomography using 99mTc-DMSA. Our results confirmed the anticipated dual role of PMX. Its unique features resulted in an improved antitumor effect of folate-based radionuclide therapy and prevented undesired radio-nephrotoxicity. Mol Cancer Ther; 12(11); 2436–45. ©2013 AACR.
Visual Shape Perception as Bayesian Inference of 3D Object-Centered Shape Representations

Despite decades of research, little is known about how people visually perceive object shape. We hypothesize that a promising approach to shape perception is provided by a “visual perception as Bayesian inference” framework which augments an emphasis on visual representation with an emphasis on the idea that shape perception is a form of statistical inference. Our hypothesis claims that shape perception of unfamiliar objects can be characterized as statistical inference of 3D shape in an object-centered coordinate system. We describe a computational model based on our theoretical framework, and provide evidence for the model along two lines. First, we show that, counterintuitively, the model accounts for viewpoint-dependency of object recognition, traditionally regarded as evidence against people’s use of 3D object-centered shape representations. Second, we report the results of an experiment using a shape similarity task, and present an extensive evaluation of existing models’ abilities to account for the experimental data. We find that our shape inference model captures subjects’ behaviors better than competing models. Taken as a whole, our experimental and computational results illustrate the promise of our approach and suggest that people’s shape representations of unfamiliar objects are probabilistic, 3D, and object-centered.
Classifying sentiment in microblogs: is brevity an advantage?

Microblogs as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. However, this short length coupled with their noisy nature can pose difficulties for standard machine learning document representations. In this work we examine the hypothesis that it is easier to classify the sentiment in these short form documents than in longer form documents. Surprisingly, we find classifying sentiment in microblogs easier than in blogs and make a number of observations pertaining to the challenge of supervised learning for sentiment analysis in microblogs.
Somatic Hypermutation and Selection of B Cells in Thymic Germinal Centers Responding to Acetylcholine Receptor in Myasthenia Gravis1

The muscle weakness in myasthenia gravis (MG) is mediated by autoantibodies against the nicotinic acetylcholine receptor (AChR) at the neuromuscular junction. Production of these pathogenic autoantibodies is believed to be associated with germinal centers (GC) and anti-AChR-secreting plasma cells in the hyperplastic thymus of patients with early onset MG (EOMG). Here, we describe the repertoire of rearranged heavy chain V genes and their clonal origins in GC from a typical EOMG patient. Three hundred fifteen rearranged Ig VH genes were amplified, cloned, and sequenced from sections of four thymic GC containing AChR-specific B cells. We found that thymic GC contain a remarkably heterogeneous population of B cells. Both naive and circulating memory B cells undergo Ag-driven clonal proliferation, somatic hypermutation, and selection. Numerous B cell clones were present, with no individual clone dominating the response. Comparisons of B cell clonal sequences from different GC and known anti-AChR Abs from other patients showed convergent mutations in the complementarity determining regions. These results are consistent with AChR driving an ongoing GC response in the thymus of EOMG patients. This is the first detailed analysis of B cell clones in human GC responding to a defined protein Ag, and the response we observed may reflect the effects of chronic stimulation by autoantigen.
Time-domain simulation of electromagnetic fields based on frequency-domain reduced-order models including Debye materials

This contribution presents a new technique for constructing time-domain models from finite-element based simulation data in the frequency-domain, including frequency-dependent materials of Debye type. In order to reduce computational costs, projection-based model-order reduction techniques are applied. Not only do they provide an accurate input/output description of the system but also allow for reconstructing the electromagnetic fields. The resulting reduced-order model is proven to be passive, which is crucial for preserving causality in time-domain simulations.
Quantifying patterns of endocranial heat distribution: Brain geometry and thermoregulation

The mechanisms involved in brain thermoregulation are still poorly known, and many disagreements still exist concerning the selective cooling capacity of the brain volume. This issue has also been discussed in human evolution and paleoneurology, speculating on possible changes associated with hominid encephalization. Although the vascular system is supposed to be the main component responsible for thermoregulation, brain geometry also plays an important role in the pattern of heat distribution.
Mindboggling morphometry of human brains

Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains every conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, and more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle’s algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available. Author Summary Brains vary in many ways, including their shape. Analysing differences in shape between brains or changes in brain shape over time has been used to characterize morphology of diseased brains, but these analyses conventionally rely on simple volumetric shape measures. We believe that access to a greater variety of shape measures could provide greater sensitivity and specificity to morphological disturbances, and could aid in diagnosis, tracking, and prediction of the progression of mental health disorders. Mindboggle is open source software that provides neuroscientists (and indeed, anyone interested in computing shapes) tools for computing a variety of shape measures, including area, volume, thickness, curvature, geodesic depth, travel depth, Laplace-Beltrami spectra, and Zernike moments. In addition to algorithmic contributions, we conducted evaluations and applied Mindboggle to conduct the most detailed shape analysis of human brains.
Spontaneous and therapeutic prognostic factors in adult hemispheric World Health Organization Grade II gliomas: a series of 1097 cases: clinical article.

OBJECT The spontaneous prognostic factors and optimal therapeutic strategy for WHO Grade II gliomas (GIIGs) have yet to be unanimously defined. Specifically, the role of resection is still debated, most notably because the actual amount of resection has seldom been assessed. METHODS Cases of GIIGs treated before December 2007 were extracted from a multicenter database retrospectively collected since January 1985 and prospectively collected since 1996. Inclusion criteria were a patient age ≥ 18 years at diagnosis, histological diagnosis of WHO GIIG, and MRI evaluation of tumor volume at diagnosis and after initial surgery. One thousand ninety-seven lesions were included in the analysis. The mean follow-up was 7.4 years since radiological diagnosis. Factors significant in a univariate analysis (with a p value ≤ 0.1) were included in the multivariate Cox proportional hazard regression model analysis. RESULTS At the time of radiological diagnosis, independent spontaneous factors of a poor prognosis were an age ≥ 55 years, an impaired functional status, a tumor location in a nonfrontal area, and, most of all, a larger tumor size. When the study starting point was set at the time of first treatment, independent favorable prognostic factors were limited to a smaller tumor size, an epileptic symptomatology, and a greater extent of resection. CONCLUSIONS This large series with its volumetric assessment refines the prognostic value of previously stressed clinical and radiological parameters and highlights the importance of tumor size and location. The results support additional arguments in favor of the predominant role of resection, in accordance with recently reported experiences.
Statistical Learning, Inductive Bias, and Bayesian Inference in Language Acquisition

Language acquisition is a problem of induction: the child learner is faced with a set of specific linguistic examples and must infer some abstract linguistic knowledge that allows the child to generalize beyond the observed data, i.e., to both understand and generate new examples. Many different generalizations are logically possible given any particular set of input data, yet different children within a linguistic community end up with the same adult grammars. This fact suggests that children are biased towards making certain kinds of generalizations rather than others. The nature and extent of children's inductive bias for language is highly controversial, with some researchers assuming that it is detailed and domain-specific (e.g., Chomsky 1973, Baker 1978, Chomsky 1981, Huang 1982, Fodor 1983, Bickerton 1984, Lasnik & Saito 1984, Gleitman & Newport 1995) and others claiming that domain-general constraints on memory and processing are sufficient to explain the consistent acquisition of language (e.g., Elman, Bates, Johnson, Karmiloff-Smith, Parisi, & Plunkett 1996, Sampson 2005). In this chapter, we discuss the contribution of an emerging theoretical framework called Bayesian learning that can be used to investigate the inductive bias needed for language acquisition. In the Bayesian view of learning, inductive bias consists of a combination of hard and soft constraints. Hard constraints make certain grammars impossible for any human to acquire; in the language of Bayesian modeling, these impossible grammars are outside the learner's hypothesis space. Grammars inside the hypothesis space are learnable given the right input data, but they may not all be equally easy to learn. Soft constraints, implemented in the form of a probability distribution over the hypothesis space, mean that the learner will be biased towards certain of these grammars more than others. A "difficult" (low-probability) grammar can be learned, but will require more evidence (input data favoring this grammar) in order to be learned. In the absence of such evidence, the child will instead acquire a high-probability grammar that is also compatible with the input. Under this view of learning, the central question of language acquisition is to determine what the hard and soft constraints are. A key assumption is that learners have access to domain-general statistical learning mechanisms that closely approximate the rules of probability theory. Given a particular set of input data, these probabilistic learning mechanisms then allow learners to converge on a grammar that is both compatible with the data and has high probability in the hypothesis space. In this sense, the grammar is the optimal choice, given the data. This notion of optimization arises from the ties between Bayesian modeling and the tradition of rational analysis in cognitive science (Chater & Oaksford, 1999), which focuses on the adaptation of the
Total antioxidant and total oxidant states, and serum paraoxonase‐1 in neonatal sepsis

Paraoxonase‐1 (PON‐1) is an enzyme with a glycoprotein structure that depends on calcium and which is located in serum high‐density lipoprotein (HDL). The aim of this study was to evaluate PON‐1, and oxidant/antioxidant state, before and after treatment for neonatal sepsis, and to determine the usability of PON‐1 in neonatal sepsis treatment.