Miracle-dz/UniFAR-FTUs huggingface dataset:
PERIOPERATIVE FEVER AND OUTCOME IN SURGICAL PATIENTS WITH ANEURYSMAL SUBARACHNOID HEMORRHAGE

OBJECTIVEWe examined the incidence of perioperative fever and its relationship to outcome among patients enrolled in the Intraoperative Hypothermia for Aneurysm Surgery Trial. METHODSOne thousand patients with initial World Federation of Neurological Surgeons grades of I to III undergoing clipping of intracranial aneurysms after subarachnoid hemorrhage were randomized to intraoperative normothermia (36°C–37°C) or hypothermia (32.5°C–33.5°C). Fever (≥38.5°C) and other complications (including infections) occurring between admission and discharge (or death) were recorded. Functional and neuropsychologic outcomes were assessed 3 months postoperatively. The primary outcome variable for the trial was dichotomized Glasgow Outcome Scale (good outcome versus all others). RESULTSFever was reported in 41% of patients. In 97% of these, fever occurred in the postoperative period. The median time from surgery to first fever was 3 days. All measures of outcome were worse in patients who developed fever, even in those without infections or who were World Federation of Neurological Surgeons grade I. Logistic regression analyses were performed to adjust for differences in preoperative factors (e.g., age, Fisher grade, initial neurological status). This demonstrated that fever continued to be significantly associated with most outcome measures, even when infection was added to the model. An alternative stepwise model selection process including all fever-related measures from the preoperative and intraoperative period (e.g., hydrocephalus, duration of surgery, intraoperative blood loss) resulted in the loss of significance for dichotomized Glasgow Outcome Scale, but significant associations between fever and several other outcome measures remained. After adding postoperative delayed ischemic neurological deficits to the model, only worsened National Institutes of Health Stroke Scale score, Barthel Activities of Daily Living index, and discharge destination (home versus other) remained independently associated with fever. CONCLUSIONThese findings suggest that fever is associated with worsened outcome in surgical subarachnoid hemorrhage patients, although, because the association between fever and the primary outcome measure for the trial is dependent on the covariates used in the analysis (particularly operative events and delayed ischemic neurological deficits), we cannot rule out the possibility that fever is a marker for other events. Only a formal trial of fever treatment or prevention can address this issue.
Inferring Cognitive Models from Data using Approximate Bayesian Computation

An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial complexity and variety in human behavioral strategies. We report an investigation into a new approach using approximate Bayesian computation (ABC) to condition model parameters to data and prior knowledge. As the case study we examine menu interaction, where we have click time data only to infer a cognitive model that implements a search behaviour with parameters such as fixation duration and recall probability. Our results demonstrate that ABC (i) improves estimates of model parameter values, (ii) enables meaningful comparisons between model variants, and (iii) supports fitting models to individual users. ABC provides ample opportunities for theoretical HCI research by allowing principled inference of model parameter values and their uncertainty.
Revisión sistemática de estudios relacionados con el uso de Internet en población universitaria = Systematic review of studies related to the use of Internet in university population.

5 1. Marco teórico. 6 1.1. ¿Qué es Internet? 6 1.2. Las adicciones conductuales 7 1.3. Internet como adicción conductual 9 1.4. Principales grupos de vulnerabilidad 11
Differentiable Kernels in Generalized Matrix Learning Vector Quantization

In the present paper we investigate the application of differentiable kernel for generalized matrix learning vector quantization as an alternative kernel-based classifier, which additionally provides classification dependent data visualization. We show that the concept of differentiable kernels allows a prototype description in the data space but equipped with the kernel metric. Moreover, using the visualization properties of the original matrix learning vector quantization we are able to optimize the class visualization by inherent visualization mapping learning also in this new kernel-metric data space.
Clinical laboratory practices for the isolation and identification of Campylobacter in Foodborne Diseases Active Surveillance Network (FoodNet) sites: baseline information for understanding changes in surveillance data.

BACKGROUND Campylobacter is a leading cause of foodborne illness in the United States. Understanding laboratory practices is essential to interpreting incidence and trends in reported campylobacteriosis over time and provides a baseline for evaluating the increasing use of culture-independent diagnostic methods for Campylobacter infection. METHODS The Foodborne Diseases Active Surveillance Network (FoodNet) conducts surveillance for laboratory-confirmed Campylobacter infections. In 2005, FoodNet conducted a survey of clinical laboratories to describe routine practices used for isolation and identification of Campylobacter. A profile was assigned to laboratories based on complete responses to key survey questions that could impact the recovery and isolation of Campylobacter from stool specimens. RESULTS Of 411 laboratories testing on-site for Campylobacter, 97% used only culture methods. Among those responding to the individual questions, nearly all used transport medium (97%) and incubated at 42°C (94%); however, most deviated from existing guidelines in other areas: 68% held specimens in transport medium at room temperature before plating, 51% used Campy blood agar plate medium, 52% read plates at <72 hours of incubation, and 14% batched plates before placing them in a microaerobic environment. In all, there were 106 testing algorithms among 214 laboratories with a complete profile; only 16 laboratories were fully adherent to existing guidelines. CONCLUSIONS Although most laboratories used culture-based methods, procedures differed widely and most did not adhere to existing guidelines, likely resulting in underdiagnosis. Given the availability of new culture-independent testing methods, these data highlight a clear need to develop best practice recommendations for Campylobacter infection diagnostic testing.
Training-based super-resolution algorithm using k-means clustering and detail enhancement

This paper presents a computationally efficient learning-based super-resolution algorithm using k-means clustering and detail enhancement. Conventional learning-based super-resolution requires a huge size of dictionary for reliable performance, which brings about a tremendous memory cost as well as a burdensome matching computation. In order to overcome this problem, the proposed algorithm significantly reduces the size of the trained dictionary by properly clustering similar patches at the learning phase. Simulation results show that the proposed algorithm provides superior visual quality to the conventional algorithms, while needing much less computational complexity.
Characterization and Crystallization of the Lumen Side Domain of the Chloroplast Rieske Iron-Sulfur Protein*

A soluble, 139-residue COOH-terminal polypeptide fragment of the Rieske iron-sulfur protein of the cytochrome b6f complex from spinach chloroplasts was obtained by limited proteolysis of the complex and a two-step chromatography purification protocol. The purified Rieske iron-sulfur protein fragment was characterized by: (i) a single NH2-terminal sequence, NH2-Phe-Val-Pro-Pro-Gly-Gly, starting with residue 41 of the intact Rieske protein; (ii) a single molecular weight species determined by mass spectrometry with a molecular weight of 14,620 ± 2 without the [2Fe-2S] cluster; (iii) an optical absorbance spectrum with redox- and pH-dependent maxima and minima; and (iv) a reduced-oxidized optical difference spectrum characterized by ΔεmM = 3.8 mM−1 cm−1 for ΔA at 394 versus 409 nm, which was used to determine the midpoint oxidation-reduction potential, which is +359 ± 7 mV at 25°C from pH 5.5-6.5, and +319 ± 2 mV at pH 7, with an apparent pKox = 6.5 ± 0.2 for the oxidized protein. The EPR spectrum measured at 17 K was characterized by the g values, gz = 2.03 and gy = 1.90, and a broad band centered at gx ≈ 1.74, very similar or identical to those of the Rieske cluster in the b6f complex, implying that the environment of the [2Fe-2S] cluster is similar to that in the complex. Midpoint potential determination by low temperature EPR yielded a redox midpoint potential (Em) of +365-375 mV of the soluble Rieske fragment at pH 6 and 7 and an Em of +295-300 mV of the Rieske cluster in the cytochrome b6f complex at pH 6 and 7. The Em difference implies that the environment of the cluster in the soluble Rieske fragment is slightly more polar than that of the cluster in the intact complex. Single crystals of the Rieske polypeptide were obtained that are capable of x-ray diffraction to atomic resolution (<2.5 Å), contain one molecule per asymmetric unit, a solvent content of approximately 30%, and belong to the triclinic space group P1 with cell dimensions, a = 29.1 Å, b = 31.9 Å, c = 35.8 Å, α = 95.6°, β = 107.1°, γ = 117.3°.
Identification of Probabilities of Languages

We consider the problem of inferring the probability distribution associated with a language, given data consisting of an infinite sequence of elements of the languge. We do this under two assumptions on the algorithms concerned: (i) like a real-life algorothm it has round-off errors, and (ii) it has no round-off errors. Assuming (i) we (a) consider a probability mass function of the elements of the language if the data are drawn independent identically distributed (i.i.d.), provided the probability mass function is computable and has a finite expectation. We give an effective procedure to almost surely identify in the limit the target probability mass function using the Strong Law of Large Numbers. Second (b) we treat the case of possibly incomputable probabilistic mass functions in the above setting. In this case we can only pointswize converge to the target probability mass function almost surely. Third (c) we consider the case where the data are dependent assuming they are typical for at least one computable measure and the language is finite. There is an effective procedure to identify by infinite recurrence a nonempty subset of the computable measures according to which the data is typical. Here we use the theory of Kolmogorov complexity. Assuming (ii) we obtain the weaker result for (a) that the target distribution is identified by infinite recurrence almost surely; (b) stays the same as under assumption (i). We consider the associated predictions.
Transcript Expression Patterns Illuminate the Mechanistic Background of Hormesis in Caenorhabditis Elegans Maupas

The animal model Caenorhabditis elegans was employed to study polyphenol- and humic substances-induced hormetic changes in lifespan. A detailed insight into the underlying mechanism of hormesis was uncovered by applying whole genome DNA microarray experimentation over a range of quercetin (Q), tannic acid (TA), and humic substances (HuminFeed®, HF) concentrations. The transcriptional response to all exposures followed a non-linear mode which highlighted differential signaling and metabolic pathways. While low Q concentrations regulated processes improving the health of the nematodes, higher concentrations extended lifespan and modulated substantially the global transcriptional response. Over-represented transcripts were notably part of the biotransformation process: enhanced catabolism of toxic intermediates possibly contributes to the lifespan extension. The regulation of transcription, Dauer entry, and nucleosome suggests the presence of distinct exposure dependent differences in transcription and signaling pathways. TA- and HF-mediated transcript expression patterns were overall similar to each other, but changed across the concentration range indicating that their transcriptional dynamics are complex and cannot be attributed to a simple adaptive response. In contrast, Q-mediated hormesis was well aligned to fit the definition of an adaptive response. Simple molecules are more likely to induce an adaptive response than more complex molecules.
Revamping Question Answering with a Semantic Approach over World Knowledge

Classic textual question answering (QA) approaches that rely on statistical keyword relevance scoring without exploiting semantic content are useful to a certain extent, but are limited to questions answered by a small text excerpt. With the maturation of Wikipedia and with upcoming projects like DBpedia, we feel that nowadays QA can adopt a deeper, semantic approach to the task, where answers can be inferred using knowledge bases to overcome the limitations of textual QA approaches. In GikiCLEF, a QA-flavoured evaluation task, the best performing systems followed a semantic approach. In this paper, we present our motivations for preferring semantic approaches to QA over textual approaches, with Wikipedia serving as a raw knowledge source.
Muddy water animation with different details

Muddy water is an example of suspension, which is a mixture containing particles that separate into distinct layers if left undisturbed. When stirred, however, the mud substance flows like a liquid and again begins settling out. Mud is composed of various sized particles, and they produce different effects when it is blended with water. This paper classifies the mud particles into three types and proposes different simulation methods for the types. The experimental results demonstrate that the proposed methods effectively produce visually plausible muddy water effects. Copyright © 2015 John Wiley & Sons, Ltd.
Should Firms Spend More on Research and Development and Advertising during Recessions?

Whenever a recession occurs, there is a heated dialog among marketing academics and practitioners about the appropriate levels of marketing spending. In this article, the authors investigate whether firms should spend more on research and development (R&D) and advertising in recessions. They propose that the effects of changes in firms’ R&D and advertising spending in recessions on profits and stock returns are contingent on their market share, financial leverage, and product-market profile (i.e., business-to-consumer goods, business-to-business services, business-to-business goods, or business-to-consumer services). They estimate the model using a panel of more than 10,000 firm-years of publicly listed U.S. firms from 1969 to 2008, during which there were seven recessions. Their results support the contingency approach. The authors compute the marginal effects, which show how the effects of changes in R&D and advertising spending in recessions vary across firms. The marginal effects provide evidence of inadequate spending (e.g., 98% of business-to-consumer goods firms underspend on R&D), proactivity (e.g., 96% of business-to-business services firms are at approximately the right levels on advertising). and excess spending (e.g., 92% of business-to-consumer services firms overspend on advertising). Using the authors’ approach and publicly available data, managers can estimate the effects of their firms’ and competitors’ R&D and advertising spending on profits and stock returns in recessions.
Image processing on high-performance RISC systems

The recent progress of RISC technology has led to the feeling that a significant percentage of image processing applications, which in the past required the use of special purpose computer architectures or of ad hoc hardware, can now be implemented in software on low cost general purpose platforms. We decided to undertake the study described in this paper to understand the extent to which this feeling corresponds to reality. We selected a set of reference RISC-based systems to represent RISC technology, and identified a set of basic image processing tasks to represent the image processing domain. We measured the performance and studied the behavior of the reference systems in the execution of the basic image processing tasks by running a number of experiments based on different program organizations. The results of these experiments are summarized in a table, which can be used by image processing application designers to evaluate whether RISC-based platforms are able to deliver the computing power required for a specific application.
Finding the k-best Equivalence Classes of Bayesian Network Structures for Model Averaging

In this paper we develop an algorithm to find the k-best equivalence classes of Bayesian networks. Our algorithm is capable of finding much more best DAGs than the previous algorithm that directly finds the k-best DAGs (Tian, He and Ram 2010). We demonstrate our algorithm in the task of Bayesian model averaging. Empirical results show that our algorithm significantly outperforms the k-best DAG algorithm in both time and space to achieve the same quality of approximation. Our algorithm goes beyond the maximum-a-posteriori (MAP) model by listing the most likely network structures and their relative likelihood and therefore has important applications in causal structure discovery.
Effectiveness of Salmon Carcass Analogs as a Form of Nutrient Enhancement for Juvenile Coho Salmon (Oncorhynchus kisutch) in Three Lower Columbia Watersheds

iv Acknowledgements.vi List of Tables.viii List of Figures.x Introduction.1 Methods.7 Results.22 Discussion.40 Conclusions.60 Tables.61 Figures.81 Literature Cited.116
Surface Grammatical Analysis for the Extraction of Terminological Noun Phrases

LEXTER is a software package for extracting terminology. A corpus of French language texts on any subject field is fed in, and LEXTER produces a list of likely terminological units to be submitted to an expert to be validated. To identify the terminological units, LEXTER takes their form into account and proceeds in two main stages: analysis, parsing. In the first stage, LEXTER uses a base of rules designed to indentify frontier markers in view to analysing the texts and extracting maximal-length noun phrases. In the second stage, LEXTER parses these maximal-length noun phrases to extract subgroups which by virtue of their grammatical structure and their place in the maximal-length noun phrases are likely to be terminological units. In this article, the type of analysis used (surface grammatical analysis) is highlighted, as the methodological approach adopted to adapt the rules (experimental approach).
Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection

Magnetic resonance spectroscopy (MRS) provides a non‐invasive measurement of the biochemistry of living tissue. However, signal variation due to tissue heterogeneity causes considerable mixing between different disease categories, making accurate class assignments difficult. This paper compares a systematic methodology for classifier design using multivariate bayesian variable selection (MBVS), with one based on feature extraction using independent component analysis (ICA). We illustrate the methodology and assess the classification performance using a data set comprising 41 magnetic resonance spectra acquired in vivo from two grades of brain tumour, namely low‐ and medium‐grade astrocytic tumours, labelled astrocytomas (AST), and high‐grade gliomas and glioblastomas labelled glioblastomas (GL). The aim of this study is threefold. First, to describe the application of the alternative methodologies to MRS, then to benchmark their classification performance, and finally to interpret the classification models in terms of biologically relevant signals derived from the spectra. The classification performance is assessed using the bootstrap method and by application to a test sample in a retrospective study. Copyright © 2003 John Wiley & Sons, Ltd.
Observed changes in temperature extremes over China–Pakistan Economic Corridor during 1980–2016

This study uses the daily maximum and minimum temperature data sets of 48 meteorological stations to assess the observed changes in temperature extremes over China–Pakistan Economic Corridor (CPEC) during 1980–2016. The nonparametric Mann–Kendall, Sen's slope estimator, least squares method, and two‐tailed simple t‐test methods were used to assess the trend in nine temperature extreme indices. The results of the study indicated that the trends in annual mean anomalies of cold nights, cold days, and frost days significantly decreased at the rates of −1.09, −1.58, and −1.05 day/decade, respectively. The number of warm nights, warm days, and summer days exhibited significant positive trends at the rates of 1.49, 1.04, and 4.32 day/decade, respectively. In addition, the trends of the warmest day and coldest day also increased significantly at the rates of 0.45 and 0.51 °C/decade, respectively. The trend of diurnal temperature range decreased significantly with −0.35 °C/decade. The spatial distribution of temperature extreme indices indicated that the number of cold nights, cold days, and diurnal temperature range have been decreased, while the trends of warm nights, warm days, summer days, warmest day, and coldest day have been increased over the whole country. Large‐scale atmospheric circulation changes derived from ERA‐Interim reanalysis show that a weak (strong) wind pressure, increasing (decreasing) geopotential height, and rapid warming (cooling) over the northern (southern) region have contributed to the changes in temperature extremes in Pakistan to some extent. With respect to elevation‐dependent warming (EDW), the trends of cold nights, warm days, summer days, warmest day, and diurnal temperature range showed significant positive correlations with increasing elevation. However, the trends of cold days, warm nights, frost days, and coldest day showed significant negative relationships with increasing elevation. The findings of this study will be helpful in the adaptation of climate change and mitigation of climatological disasters in the region. The study recommends that future studies should investigate the natural and anthropogenic drivers of temperature extremes and the possible mechanism of EDW in the CPEC region.
The Logical Foundations of Discourse Interpretation : A Guide to Various Possible Semantics for SDRT

A central but still unsettled question in formal theories about discourse interpretation is: What are the key theoretical structures on which discourse interpretation should depend? If we take our cue from theories that analyze the meanings of individual sentences, the meaning of the discourse's parts should determine the meaning of the whole; some sort principle of compositionality of meaning must hold at the level of discourse interpretation. So a theory of discourse interpretation must develop from an account of discourse structure.
"Feed the Fish": an affect-aware game

In this paper we report on an affective gaming interface and a user study which evaluates user response to affective gaming. "Feed the Fish" is an affect-aware game system which takes a player's facial expressions as input and dynamically responds to the player by changing the game elements. The goal of this system is to use human expressions to build a communication channel between the game and players so playing the game can be more enjoyable. We describe the implementation of the game system and discuss the result of the user study we have conducted with 22 participants. Participants enjoyed the game with the affect-aware system more than a non affective version of the game, and they felt it was more exciting since the game was more challenging and dynamic.