Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors predictive of the failure of medical treatment in patients with pleural infection

Background/Aims The clinical outcomes of some patients with pleural infection may be favorable with medical treatment alone, but in others, the disease progresses and requires additional surgical treatment. However, little is known about the factors affecting this difference. The aim of this study was to investigate the factors predictive of failure of medical treatment in patients with pleural infection. Methods A cohort of 127 consecutive patients who were admitted to the hospital with pleural infection was studied. Clinical manifestations and laboratory findings in patients in whom medical treatment succeeded or failed were reviewed. Results In univariate analysis, the significant factors associated with medical treatment outcome were age, smoking history, duration of chief complaint, serum albumin level, and pleural fluid glucose and lactate dehydrogenase levels (p < 0.05). Multivariate logistic regression analysis identified age and duration of chief complaint as independent predictive factors for failure of medical treatment, with odds ratios of 0.871 (p = 0.013) and 0.797 (p = 0.026), respectively. Receiver operating characteristic curve analysis determined cutoff values of 50.5 years for age and 4.5 days for duration of chief complaint. Conclusions We demonstrated that a younger age < 50.5 years and shorter duration of chief complaint < 4.5 days were independent predictive factors for the failure of medical treatment in patients with pleural infection. This suggests their role as evaluative criteria in setting indications for the optimal treatment in patients with pleural infection. A larger, prospective study is required to confirm these findings.
What Klein’s “Semantic Gradient” Does and Does Not Really Show: Decomposing Stroop Interference into Task and Informational Conflict Components

The present study suggests that the idea that Stroop interference originates from multiple components may gain theoretically from integrating two independent frameworks. The first framework is represented by the well-known notion of “semantic gradient” of interference and the second one is the distinction between two types of conflict – the task and the informational conflict – giving rise to the interference (MacLeod and MacDonald, 2000; Goldfarb and Henik, 2007). The proposed integration led to the conclusion that two (i.e., orthographic and lexical components) of the four theoretically distinct components represent task conflict, and the other two (i.e., indirect and direct informational conflict components) represent informational conflict. The four components were independently estimated in a series of experiments. The results confirmed the contribution of task conflict (estimated by a robust orthographic component) and of informational conflict (estimated by a strong direct informational conflict component) to Stroop interference. However, the performed critical review of the relevant literature (see General Discussion), as well as the results of the experiments reported, showed that the other two components expressing each type of conflict (i.e., the lexical component of task conflict and the indirect informational conflict) were small and unstable. The present analysis refines our knowledge of the origins of Stroop interference by providing evidence that each type of conflict has its major and minor contributions. The implications for cognitive control of an automatic reading process are also discussed.
A multi-source satellite data approach for modelling Lake Turkana water level: calibration and validation using satellite altimetry data

Lake Turkana is one of the largest desert lakes in the world and is characterized by high degrees of inter- and intra-annual fluctuations. The hydrology and water bal- ance of this lake have not been well understood due to its remote location and unavailability of reliable ground truth datasets. Managing surface water resources is a great chal- lenge in areas where in-situ data are either limited or unavail- able. In this study, multi-source satellite-driven data such as satellite-based rainfall estimates, modelled runoff, evap- otranspiration, and a digital elevation dataset were used to model Lake Turkana water levels from 1998 to 2009. Due to the unavailability of reliable lake level data, an approach is presented to calibrate and validate the water balance model of Lake Turkana using a composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altime- try data. Model validation results showed that the satellite- driven water balance model can satisfactorily capture the pat- terns and seasonal variations of the Lake Turkana water level fluctuations with a Pearson's correlation coefficient of 0.90 and a Nash-Sutcliffe Coefficient of Efficiency (NSCE) of 0.80 during the validation period (2004-2009). Model er- ror estimates were within 10 % of the natural variability of the lake. Our analysis indicated that fluctuations in Lake Turkana water levels are mainly driven by lake inflows and over-the-lake evaporation. Over-the-lake rainfall contributes only up to 30 % of lake evaporative demand. During the modelling time period, Lake Turkana showed seasonal vari- ations of 1-2 m. The lake level fluctuated in the range up to 4 m between the years 1998-2009. This study demon- strated the usefulness of satellite altimetry data to calibrate and validate the satellite-driven hydrological model for Lake Turkana without using any in-situ data. Furthermore, for Lake Turkana, we identified and outlined opportunities and challenges of using a calibrated satellite-driven water balance model for (i) quantitative assessment of the impact of basin developmental activities on lake levels and for (ii) forecast- ing lake level changes and their impact on fisheries. From this study, we suggest that globally available satellite altime- try data provide a unique opportunity for calibration and val- idation of hydrologic models in ungauged basins.
Debiasing crowdsourced quantitative characteristics in local businesses and services

Information about quantitative characteristics in local businesses and services, such as the number of people waiting in line in a cafe and the number of available fitness machines in a gym, is important for informed decision, crowd management and event detection. In this paper, we investigate the potential of leveraging crowds as sensors to report such quantitative characteristics and investigate how to recover the true quantity values from noisy crowdsourced information. Through experiments, we find that crowd sensors have both bias and variance in quantity sensing, and task difficulties impact the sensing accuracy. Based on these findings, we propose an unsupervised probabilistic model to jointly assess task difficulties, ability of crowd sensors and true quantity values. Our model differs from existing categorical truth finding models as ours is specifically designed to tackle quantitative truth. In addition to devising an efficient model inference algorithm in a batch mode, we also design an even faster online version for handling streaming data. Experimental results in various scenarios demonstrate the effectiveness of our model.
An insight- and task-based methodology for evaluating spatiotemporal visual analytics

We present a method for evaluating visualizations using both tasks and exploration, and demonstrate this method in a study of spatiotemporal network designs for a visual analytics system. The method is well suited for studying visual analytics applications in which users perform both targeted data searches and analyses of broader patterns. In such applications, an effective visualization design is one that helps users complete tasks accurately and efficiently, and supports hypothesis generation during open-ended exploration. To evaluate both of these aims in a single study, we developed an approach called layered insight- and task-based evaluation (LITE) that interposes several prompts for observations about the data model between sequences of predefined search tasks. We demonstrate the evaluation method in a user study of four network visualizations for spatiotemporal data in a visual analytics application. Results include findings that might have been difficult to obtain in a single experiment using a different methodology. For example, with one dataset we studied, we found that on average participants were faster on search tasks using a force-directed layout than using our other designs; at the same time, participants found this design least helpful in understanding the data. Our contributions include a novel evaluation method that combines well-defined tasks with exploration and observation, an evaluation of network visualization designs for spatiotemporal visual analytics, and guidelines for using this evaluation method.
The City Browser: Utilizing Massive Call Data to Infer City Mobility Dynamics

This paper presents the City Browser, a tool developed to analyze the complexities underlying human mobility at the city scale. The tool uses data generated from mobile phones as a proxy to provide several insights with regards to the commuting patterns of the population within the bounds of a city. The three major components of the browser are the data warehouse, modules and algorithm, and the visualization interface. The modules and algorithm component utilizes Call Detail Records (CDRs) stored within the data warehouse to infer mobility patterns that are then communicated through the visualization interface. The modules and algorithm component consists of four modules: the spatial-temporal decomposition module, the home/work capturing module, the community detection module, and the flow estimation module. The visualization interface manages the output of each module to provide a comprehensive view of a city’s mobility dynamics over varying time scales. A case study is presented on the city of Riyadh in Saudi Arabia, where the browser was developed to better understand city mobility patterns.
Regulation of Ventral Surface Chemoreceptors by the Central Respiratory Pattern Generator

The rat retrotrapezoid nucleus (RTN) contains neurons described as central chemoreceptors in the adult and respiratory rhythm-generating pacemakers in neonates [parafacial respiratory group (pfRG)]. Here we test the hypothesis that both RTN and pfRG neurons are intrinsically chemosensitive and tonically firing neurons whose respiratory rhythmicity is caused by a synaptic feedback from the central respiratory pattern generator (CPG). In halothane-anesthetized adults, RTN neurons were silent below 4.5% end-expiratory (e-exp) CO2. Their activity increased linearly (3.2 Hz/1% CO2) up to 6.5% (CPG threshold) and then more slowly to peak ∼10 Hz at 10% CO2. Respiratory modulation of RTN neurons was absent below CPG threshold, gradually stronger beyond, and, like pfRG neurons, typically (42%) characterized by twin periods of reduced activity near phrenic inspiration. After CPG inactivation with kynurenate (KYN), RTN neurons discharged linearly as a function of e-exp CO2 (slope, +1.7 Hz/1% CO2) and arterial pH (threshold, 7.48; slope, 39 Hz/pH unit). In coronal brain slices (postnatal days 7–12), RTN chemosensitive neurons were silent at pH 7.55. Their activity increased linearly with acidification up to pH 7.2 (17 Hz/pH unit at 35°C) and was always tonic. In conclusion, consistent with their postulated central chemoreceptor role, RTN/pfRG neurons encode pH linearly and discharge tonically when disconnected from the rest of the respiratory centers in vivo (KYN treatment) and in vitro. In vivo, RTN neurons receive respiratory synchronous inhibitory inputs that may serve as feedback and impart these neurons with their characteristic respiratory modulation.
Tracking the Emergence of a New Breed Using 49,034 SNP in Sheep

Domestic animals are unique in that they have been organised into managed populations called breeds. The strength of genetic divergence between breeds may vary dependent on the age of the breed, the scenario under which it emerged and the strength of reproductive isolation it has from other breeds. In this study, we investigated the Gulf Coast Native breed of sheep to determine if it contains lines of animals that are sufficiently divergent to be considered separate breeds. Allele sharing and principal component analysis (PCA) using nearly 50,000 SNP loci revealed a clear genetic division that corresponded with membership of either the Florida or Louisiana Native lines. Subsequent analysis aimed to determine if the strength of the divergence exceeded that found between recognised breed pairs. Genotypes from 14 breeds sampled from Europe and Asia were used to obtain estimates of pair-wise population divergence measured as F ST. The divergence separating the Florida and Louisiana Native (F ST = 6.2%) was approximately 50% higher than the average divergence separating breeds developed within the same region of Europe (F ST = 4.2%). This strongly indicated that the two Gulf Coast Native lines are sufficiently different to be considered separate breeds. PCA using small SNP sets successfully distinguished between the Florida and Louisiana Native animals, suggesting that allele frequency differences have accumulated across the genome. This is consistent with a population history involving geographic separation and genetic drift. Suggestive evidence was detected for divergence at the poll locus on sheep chromosome 10; however drift at neutral markers has been the largest contributor to the genetic separation observed. These results document the emergence of populations that can be considered separate breeds, with practical consequences for bio-conservation priorities, animal registration and the establishment of separate breed societies.
A numerical study of brown dwarf formation via encounters of protostellar discs

The formation of brown dwarfs (BDs) due to the fragmentation of early-stage protostellar discs undergoing pairwise encounters was investigated. High resolution allowed the use of realistic, flared initial disc models where both the vertical structure and the local Jeans mass were resolved, preventing numerical enhancement or suppression of fragmentation. The results show that objects with masses ranging from giant planets to low-mass stars can form during such encounters from discs that were stable before the encounter. The parameter space of initial spin–orbit orientations and the azimuthal angles for each disc were explored with a series of 48 simulations. For the types of interactions studied, an upper limit on the initial Toomre Q value of ∼2 was found for fragmentation to occur. Depending on the initial configuration, shocks, tidal-tail structures and mass inflows were responsible for the condensation of disc gas into self-gravitating objects. In general, retrograde discs were more likely to fragment. It was also found that in fast encounters, where the interaction time-scale was significantly shorter than the discs' dynamical time-scales, the protostellar discs tended to be truncated without forming objects. The newly formed objects had masses ranging from 0.9 to 127 MJ, with the majority in the BD regime. These often resided in star–BD multiples and in some cases also formed hierarchical orbiting systems. Most of them had large angular momenta and highly flattened, disc-like shapes. With the inclusion of the appropriate physics, these could reasonably be expected to evolve into proto-BD discs with jets and subsequent planet formation around the BD. The objects had radii ranging from 0.1 to 10 au at the time of formation. The disc gas was assumed to be locally isothermal, appropriate for the short cooling times in extended protostellar discs but not for condensed objects. An additional case with explicit cooling that reduced to zero for optically thick gas was simulated to test the extremes of cooling effectiveness and it was still possible to form objects in this case. Detailed radiative transfer (not studied here) is expected to lengthen the internal evolution time-scale for these objects so that they spend considerable time as puffed-up, prolate ellipsoids, but not to alter the basic result that proto-BD discs can form during protostellar encounters.
Second and Third Generation Voltage-Sensitive Fluorescent Proteins for Monitoring Membrane Potential

Over the last decade, optical neuroimaging methods have been enriched by engineered biosensors derived from fluorescent protein (FP) reporters fused to protein detectors that convert physiological signals into changes of intrinsic FP fluorescence. These FP-based indicators are genetically encoded, and hence targetable to specific cell populations within networks of heterologous cell types. Among this class of biosensors, the development of optical probes for membrane potential is both highly desirable and challenging. A suitable FP voltage sensor would indeed be a valuable tool for monitoring the activity of thousands of individual neurons simultaneously in a non-invasive manner. Previous prototypic genetically-encoded FP voltage indicators achieved a proof of principle but also highlighted several difficulties such as poor cell surface targeting and slow kinetics. Recently, we developed a new series of FRET-based Voltage-Sensitive Fluorescent Proteins (VSFPs), referred to as VSFP2s, with efficient targeting to the plasma membrane and high responsiveness to membrane potential signaling in excitable cells. In addition to these FRET-based voltage sensors, we also generated a third series of probes consisting of single FPs with response kinetics suitable for the optical imaging of fast neuronal signals. These newly available genetically-encoded reporters for membrane potential will be instrumental for future experimental approaches directed toward the understanding of neuronal network dynamics and information processing in the brain. Here, we review the development and current status of these novel fluorescent probes.
1 THE TRILLION DOLLAR CONUNDRUM : COMPLEMENTARITIES AND HEALTH INFORMATION TECHNOLOGY

We examine the heterogeneous relationship between the adoption of EMR and hospital operating costs at thousands of U.S. hospitals between 1996 and 2009. We first document a previously-identified puzzle: Adoption of EMR is associated with a slight cost increase. Drawing on the literature on IT and productivity, we analyze why this average effect arises. We find that: (1) EMR adoption is initially associated with a rise in costs; (2) EMR adoption at hospitals in IT-intensive locations leads to a decrease in costs after three years; and (3) Hospitals in other locations experience an increase in costs even after six years. (JEL I10, L30, O33)
Déficit de atención selectiva en la esquizofrenia

Actualmente la perspectiva neurocognitiva constituye una de las aproximaciones más influyentes para abordar el estudio de los déficit cognitivos en general y los atencionales en particular, que subyacen a los signos y síntomas relacionados con la esquizofrenia. Esta perspectiva neurocognitiva recoge e integra las aportaciones de la Psicología cognitiva, la Neuropsicología cognitiva y las Neurociencias. A continuación, señalaremos brevemente cuáles han sido las aportaciones más importantes y cómo se produce tal integración. Durante los últimos 40 años, los psicólogos cognitivos han tratado de comprender cómo las personas realizamos actividades de la vida cotidiana tan esenciales como leer, escribir, imaginar, reconocer. Para ello, han propuesto diversos modelos de cómo funciona la percepción, la memoria, el aprendizaje y la atención, procesos que la Psicología cognitiva ha entendido que son necesarios para la realización de dichas actividades. Una característica importante de dichos modelos es que contemplan las actividades cognitivas como un conjunto de operaciones elementales que realizan un trabajo orquestado para resolver con éxito la tarea que el sujeto lleva a cabo. De esta forma, cada operación elemental cumple una función cognitiva específica. Sin embargo, no es suficiente determinar las operaciones elementales en las que puede descomponerse una actividad mental compleja, sino que desde el enfoque neurocognitivo se asume que dichas operaciones se localizan en el cerebro. CONGRESO VIRTUAL DE NEUROPSICOLOGÍA. NEUROPSICOLOGÍA BÁSICA
The Child Problematic Traits Inventory in a Dutch General Population Sample of 3- to 7-Year-Old Children

The Child Problematic Traits Inventory (CPTI) is a new teacher rated instrument to assess psychopathic personality traits in children. This is the first study to test the psychometric properties of the Dutch CPTI. Teachers completed the CPTI for 287 3- to 7-year old-children. Results from confirmatory factor analysis supported the proposed 3-factor structure of the CPTI. The CPTI total score and three factor scores were internally consistent and showed the expected correlations with external criterion measures that have been linked to psychopathic personality, including conduct problems, proactive and reactive aggression, and temperament. This study also provides novel evidence that CPTI factor scores were positively related to an alternative measure of callous-unemotional traits, thereby supporting the criterion validity of the CPTI scores. In conclusion, this study replicates and extends prior tests of the CPTI in Sweden and suggests that the Dutch version of the CPTI holds promise as a teacher rated tool for assessing psychopathic traits in childhood.
A Sparsity-Based Model of Bounded Rationality

This paper defines and analyzes a "sparse max" operator, which is a less than fully attentive and rational version of the traditional max operator. The agent builds (as economists do) a simplified model of the world which is sparse, considering only the variables of first-order importance. His stylized model and his resulting choices both derive from constrained optimization. Still, the sparse max remains tractable to compute. Moreover, the induced outcomes reflect basic psychological forces governing limited attention. The sparse max yields a behavioral version of two basic chapters of the microeconomics textbook: consumer demand and competitive equilibrium. We obtain a behavioral version of Marshallian and Hicksian demand, the Slutsky matrix, the Edgeworth box, Roy's identity etc. The Slutsky matrix is no longer symmetric: non-salient prices are associated with anomalously small demand elasticities. Because the consumer exhibits nominal illusion, in the Edgeworth box, the offer curve is a two-dimensional surface rather than a one-dimensional curve. As a result, different aggregate price levels correspond to materially distinct competitive equilibria, in a similar spirit to a Phillips curve. This framework provides a way to assess which parts of basic microeconomics are robust, and which are not, to the assumption of perfect maximization.