Miracle-dz/UniFAR-FTUs huggingface dataset:
Surfactant Proteins SP-A and SP-D Ameliorate Pneumonia Severity and Intestinal Injury in a Murine Model of Staphylococcus Aureus Pneumonia

ABSTRACT Staphylococcus aureus pneumonia is an important cause of sepsis which causes gut injury, inflammation, and apoptosis. The surfactant proteins surfactant protein A (SP-A) and surfactant protein D (SP-D) bind bacterial pathogens and facilitate clearance of pathogens, apoptotic bodies, and modulate immune responses. SP-A and SP-D are expressed in both lung and gut epithelia. We hypothesize SP-A and SP-D regulate pneumonia severity and gut injury during pneumonia. Methods: Wild-type (WT) and SP-A and SP-D double knockout (SP-A/D KO) mice were subjected to S. aureus or sham pneumonia. Bronchoalveolar lavage and tissue harvest were performed 24 h later. Pneumonia severity, gut mucosal injury, inflammation, and apoptosis were measured using a combination of histology, immunohistochemistry, cytokine assay, TUNEL assay, quantitative real-time polymerase chain reaction, and Western blot analyses. Results: Pneumonia increased gut inflammation, apoptosis, and mucosal injury in both groups. Pneumonia histology and bacterial growth in bronchoalveolar lavage fluid demonstrate more severe infection in SP-A/D KO mice compared with WT controls. SP-A/D KO mice with pneumonia also demonstrate more severe histologic gut mucosal injury, increased gut apoptosis, elevated caspase-3 levels, and Bax/Bcl-2 mRNA expression compared with WT pneumonia mice. Nuclear factor &kgr;B (NF-&kgr;B) p65 expression and its nuclear translocation, gut levels of tumor necrosis factor &agr; and interleukin-1&bgr; were all increased in SP-A/D KO mice with pneumonia compared with WT controls. Conclusions: These data provide evidence SP-A and SP-D attenuate S. aureus pneumonia severity resulting in decreased intestinal mucosal injury, apoptosis, and inflammation. Improved pulmonary clearance of S. aureus decreased caspase-3 and Bax/Bcl-2 expressions and decreased activation of the NF-&kgr;B signaling pathway in intestine represent potential mechanisms for the effects of SP-A and SP-D on gut injury during pneumonia.
A protocol for secure and energy-aware reprogramming in WSN

Many protocols for reprogramming wireless sensor networks have been proposed, but most assume non-malicious nodes. Considering that this assumption is inadequate for many types of applications, we propose the protection of these networks by covering four security aspects: authentication, integrity, confidentiality and availability. Although similar proposals have already been presented, the problem we see with them is that while aiming for a complete and definitive solution they incur heavy resource consumption (e.g., energy). Since WSN are resource-constrained networks, we propose a pragmatic approach where we try to balance these two opposing design elements: security and resource consumption. We evaluate our proposal to show that such balance can be achieved, and state that this approach is more convenient in the real world than a "definitive" security solution that takes a heavy toll on resources.
Information fusion based on kernel entropy component analysis in discriminative canonical correlation space with application to audio emotion recognition

As an information fusion tool, Kernel Entropy Component Analysis (KECA) is realized by using descriptor of information entropy and optimized by entropy estimation. However, as an unsuper-vised method, it merely puts the information or features from different channels together without considering their intrinsic structures and relations. In this paper, we introduce an enhanced version of KECA for information fusion, KECA in Discriminative Canonical Correlation Space (DCCS). Not only the intrinsic structures and discriminative representations are considered, but also the natural representations of input data are revealed by entropy estimation, leading to improved recognition accuracy. The effectiveness of the proposed solution is evaluated through experiments on two audio emotion databases. Experimental results show that the proposed solution outperforms the existing methods based on similar principles.
Massive spatial query on the Kepler architecture

In this paper, we present an optimized framework that can efficiently perform massive spatial queries on the current GPUs. To benefit the widely adopted filter-and-verify paradigm from GPUs, the skewed workloads are first associated with certain cells in a scaled spatial grid, such that the following range verification cost against the massive spatial objects can be significantly reduced. Particularly on the Kepler architecture, we highlight a two-level scheduling method to exploit good data localities by developing a novel dynamic scheduling method. Based on this virtual warp-based scheduling method, groups of threads can compete for the unbalanced tasks to ensure good load balance. We conduct various of skewed workloads with different object positions and query distributions, to evaluate our optimized methods. Experimental results show that, as compared to the existing fixed-size allocation methods, the proposed adaptive scheduling strategies improve the query throughput by one order of magnitude.
Design and implication of a rule based weight sparsity module in HTM spatial pooler

Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulates the working principles of neocortex useful for solving image classification problems. Image pixels are transformed to binary HTM feature vectors following a set of weighted synaptic operations that combines the principles of sparsity, hierarchy, and modularity. The sparsity of input pixel selection and feature encoding is introduced by setting initial weights as random following a uniform distribution. In contrast to this approach, we propose that sparsity can be achieved by a deterministic approach of setting the input weights based on local mean intensity of pixels as a window operation. The initial binary weights are selected and are then multiplied by the original image pixels to either suppress inputs with low potential or preserve input pixels with high potential. Feature selection is then performed by selecting high potential pixels, which are preserved and are greater than the mean of neighbouring pixels, and suppressing the rest. The proposed design was verified on a face image recognition problem. Results show that proposed approach not only allow to preserve feature sparsity, but also to increase recognition accuracy for image classification with HTM.
A Wait-Free Multi-word Atomic (1,N) Register for Large-Scale Data Sharing on Multi-core Machines

We present a multi-word atomic (1,N) register for multi-core machines exploiting Read-Modify-Write (RMW) instructions to coordinate the writer and the readers in a wait-free manner. Our proposal, called Anonymous Readers Counting (ARC), enables large-scale data sharing by admitting up to 2^{32}-2 concurrent readers on off-the-shelf 64-bit machines, as opposed to the most advanced RMW-based approach which is limited to 58 readers. Further, ARC avoids multiple copies of the register content while accessing it—this affects classical register's algorithms based on atomic read/write operations on single words. Thus, ARC allows for higher scalability with respect to the register size.
Teamwork and Adherence to Recommendations Explain the Effect of a Care Pathway on Reduced 30-day Readmission for Patients with a COPD Exacerbation

ABSTRACT This study aimed to increase our understanding of processes that underlie the effect of care pathway implementation on reduced 30-day readmission rate. Adherence to evidence-based recommendations, teamwork and burnout have previously been identified as potential mechanisms in this association. We conducted a secondary data analysis of 257 patients admitted with chronic obstructive pulmonary disease exacerbation and 284 team members caring for these patients in 19 Belgian, Italian and Portuguese hospitals. Clinical measures included 30-day readmission and adherence to a specific set of five care activities. Teamwork measures included team climate for innovation, level of organized care and burnout (emotional exhaustion, level of competence and mental detachment). Care pathway implementation was significantly associated with better adherence and reduced 30-day readmission. Better adherence and higher level of competence were also related to reduced 30-day readmission. Only better adherence fully mediated the association between care pathway implementation and reduced 30-day readmission. Better team climate for innovation and level of organized care, although both improved after care pathway implementation, did not show any explanatory mechanisms in the association between care pathway implementation and reduced 30-day readmission. Implementation of a care pathway had an impact on clinical and team indicators. To reduce 30-day readmission rates, in the development and implementation of a care pathway, hospitals should measure adherence to evidence-based recommendations during the whole process, as this can give information regarding the success of implementation
Impaired renal function is associated with brain atrophy and poststroke cognitive decline

Objective: To evaluate the interrelationship among impaired renal function, brain pathology on imaging, and cognitive decline in a longitudinal poststroke cohort. Methods: The Tel Aviv Brain Acute Stroke Cohort study is a prospective cohort of mild-moderate ischemic stroke/TIA survivors without dementia who underwent a 3T MRI and were cognitively assessed at admission and for 24 months following stroke. Renal function was evaluated at admission by creatinine clearance (CCl) estimation. The volumes of ischemic lesions and preexisting white matter hyperintensities (WMH), brain atrophy, and microstructural changes of the normal-appearing white matter tissue were measured using previously validated methods. Results: Baseline data were available for 431 participants. Participants with a CCl <60 mL/min at baseline performed significantly worse in all cognitive tests over time (p = 0.001) than those with a CCl ≥60 mL/min and had larger WMH volume and cortical atrophy and smaller hippocampal volume (all p < 0.001). After 2 years, 15.5% of the participants were diagnosed with cognitive impairment. Multiple logistic regression analysis, controlling for traditional risk factors, suggested CCl <60 mL/min at baseline as a significant predictor for the development of cognitive impairment 2 years after the index stroke (odds ratio 2.01 [95% confidence interval 1.03–3.92], p = 0.041). Conclusions: Impaired renal function is associated with increased WMH volume and cortical atrophy, known biomarkers of the aging brain, and is a predictor for cognitive decline 2 years after stroke/TIA. Decreased renal function may be associated with cerebral small vessel disease underlying poststroke cognitive decline, suggesting a new target for early intervention.
Development and validation of an HIV risk scorecard model

This research paper covers the development of an HIV risk scorecard using SAS Enterprise MinerTM. The HIV risk scorecard was developed using the 2007 South African annual antenatal HIV and syphilis seroprevalence data. Antenatal data contains various demographic characteristics for each pregnant woman, such as pregnant woman's age, male sexual partner's age, race, level of education, gravidity, parity, HIV and syphilis status. The purpose of this research was to use a scorecard to rank the effects of the demographic characteristics on influencing an individual's risk of acquiring an HIV infection, not the probability of being sick. The project encompassed the selection of the data sample, classing, selection of demographic characteristics, fitting of a regression model, generation of weights-of-evidence (WOE), calculation of information values (IVs), creation and validation of an HIV risk scorecard. The educational level and syphilis status of the pregnant women produced information values below 0.05 and were rejected from inclusion in the final HIV risk scorecard. Based on their respective information values, the following four demographic characteristics of the pregnant women were found to be of medium predictive strength and thus included in the final HIV risk scorecard; age, age of male sexual partner, gravidity and parity. The age of the pregnant woman had the highest information value and Gini coefficient. The HIV risk scorecard showed that the risk of contracting an HIV infection increased gradually up to the age of 30 years for females and 34 years old for their male sexual partners. Thereafter, the risk decreased gradually towards the age of 45.
Real-time data compression for thermal-controlled three-dimensional DRAM systems

In this work, we propose a half-size frequent pattern compression algorithm (HSFPC) for the 3D DRAM system. In the 3D DRAM system, the access data compressed by HSFPC can achieve better compression rate than by the traditional compression method, frequent pattern compression (FPC). In our experiments, the 3D DRAM system using HSFPC can reduce the peak temperature by 0.5 °C ~ 4.5 °C compared with the one using FPC.
Inactivated antithombin as anticoagulant reversal in a rat model of cardiopulmonary bypass: a potent and potentially safer alternative to protamine

Heparin anticoagulation followed by protamine reversal is commonly used in cardiopulmonary bypass (CPB). As an alternative to protamine, a recombinant inactive antithrombin (riAT) was designed as an antidote to heparin and was previously shown to be as potent as protamine in‐vitro. In the present study, riAT was assessed for its ability to neutralize heparin after CPB in a rat model. After 60 min of CPB under heparin, rats received 5 mg/kg protamine, 37.5 mg/kg riAT or phosphate buffered saline (PBS) as placebo. Residual anticoagulant activity was assessed using the activated partial thromboplastin time assay before, and 10–30 min after reversion. Haemodynamic monitoring was performed and plasma histamine concentration was also measured. In this model, riAT appeared to be as efficient as protamine in neutralizing heparin. Ten minutes after injection, riAT and protamine both decreased heparin activity, to 1.8 ± 1.3 and 4.5 ± 1.4 u/ml, respectively (23.1 ± 5.1 u/ml in placebo group). Furthermore, evolution of mean carotid arterial pressure, heart rate and plasma histamine levels was comparable in rats treated with PBS or riAT, while protamine exhibited haemodynamic side effects and increased histamine plasma concentration. Thus, riAT could represent an advantage over protamine in CPB because it efficiently reverses heparin activity without negative effects on haemodynamic parameters and plasma histamine level.
Motor Adaptation Deficits in Ideomotor Apraxia

Abstract Objectives: The cardinal motor deficits seen in ideomotor limb apraxia are thought to arise from damage to internal representations for actions developed through learning and experience. However, whether apraxic patients learn to develop new representations with training is not well understood. We studied the capacity of apraxic patients for motor adaptation, a process associated with the development of a new internal representation of the relationship between movements and their sensory effects. Methods: Thirteen healthy adults and 23 patients with left hemisphere stroke (12 apraxic, 11 nonapraxic) adapted to a 30-degree visuomotor rotation. Results: While healthy and nonapraxic participants successfully adapted, apraxics did not. Rather, they showed a rapid decrease in error early but no further improvement thereafter, suggesting a deficit in the slow, but not the fast component of a dual-process model of adaptation. The magnitude of this late learning deficit was predicted by the degree of apraxia, and was correlated with the volume of damage in parietal cortex. Apraxics also demonstrated an initial after-effect similar to the other groups likely reflecting the early learning, but this after-effect was not sustained and performance returned to baseline levels more rapidly, consistent with a disrupted slow learning process. Conclusions: These findings suggest that the early phase of learning may be intact in apraxia, but this leads to the development of a fragile representation that is rapidly forgotten. The association between this deficit and left parietal damage points to a key role for this region in learning to form stable internal representations. (JINS, 2017, 23, 139–149)
Organizing for agility: A complex adaptive systems perspective on agile software development process

Agile software development has caught the attention of both practitioners and academics in recent years. In spite of many anecdotes and papers describing lessons learnt the theoretical foundation of agile software development has not been systematically articulated. This paper proposes a conceptual framework to study agile software development based on the theory of complex adaptive systems. The framework is illustrated by a case study of an agile software development team. Several agile practices are identified and reflected on from the perspective of complex adaptive systems.
A Visual Analytics Framework for Exploring Theme Park Dynamics

In 2015, the top 10 largest amusement park corporations saw a combined annual attendance of over 400 million visitors. Daily average attendance in some of the most popular theme parks in the world can average 44,000 visitors per day. These visitors ride attractions, shop for souvenirs, and dine at local establishments; however, a critical component of their visit is the overall park experience. This experience depends on the wait time for rides, the crowd flow in the park, and various other factors linked to the crowd dynamics and human behavior. As such, better insight into visitor behavior can help theme parks devise competitive strategies for improved customer experience. Research into the use of attractions, facilities, and exhibits can be studied, and as behavior profiles emerge, park operators can also identify anomalous behaviors of visitors which can improve safety and operations. In this article, we present a visual analytics framework for analyzing crowd dynamics in theme parks. Our proposed framework is designed to support behavioral analysis by summarizing patterns and detecting anomalies. We provide methodologies to link visitor movement data, communication data, and park infrastructure data. This combination of data sources enables a semantic analysis of who, what, when, and where, enabling analysts to explore visitor-visitor interactions and visitor-infrastructure interactions. Analysts can identify behaviors at the macro level through semantic trajectory clustering views for group behavior dynamics, as well as at the micro level using trajectory traces and a novel visitor network analysis view. We demonstrate the efficacy of our framework through two case studies of simulated theme park visitors.
Compact nonlinear thermal modeling of packaged integrated systems

This paper proposes a new thermal nonlinear modeling technique for packaged integrated systems. Thermal behavior of complicated systems like packaged electronic systems may exhibit nonlinear and temperature dependent properties. As a result, it is difficult to use a low order linear model to approximate the thermal behavior of the packaged integrated systems without accuracy loss. In this paper, we try to mitigate this problem by using piecewise linear (PWL) approach to characterizing the thermal behavior of those systems. The new method (called ThermSubPWL), which is the first proposed approach to nonlinear thermal modeling problem, identifies the linear local models for different temperature ranges using the subspace identification method. A linear transformation method is proposed to transform all the identified linear local models to the common state basis to build the continuous piecewise linear model. Experimental results validate the proposed method on a realistic packaged integrated system modeled via the multi-domain/physics commercial tool, COMSOL, under practical power signal inputs. The new piecewise models can lead to much smaller model order without accuracy loss, which translates to significant savings in both the simulation time and the time required to identify the reduced models compared to applying the high order models.
Automatic Generation of Benchmarks for Entity Recognition and Linking

The velocity dimension of Big Data plays an increasingly important role in processing unstructured data. Heretofore, no large-scale benchmarks were available to evaluate the performance of named entity recognition and entity linking solutions. This unavailability was due to the creation of gold standards for named entity recognition and entity linking being a time-intensive, costly and error-prone task. We hence investigate the automatic generation of benchmark texts with entity annotations for named entity recognition and linking from Linked Data. The main advantage of automatically constructed benchmarks is that they can be readily generated at any time, and are cost-effective while being guaranteed to achieve gold-standard quality. We compare the performance of 11 tools on the benchmarks we generate with their performance on 16 benchmarks that were created manually. Our results suggest that our automatic benchmark generation approach can create varied benchmarks that have characteristics similar to those of existing benchmarks. In addition, we perform a large-scale runtime evaluation of entity recognition and linking solutions for the first time in literature. Our experimental results are available at this http URL
Association Between Physical Fitness and Academic Achievement in a Cohort of Danish School Pupils.

BACKGROUND Time spent on physical activity in elementary school has been altered to improve core academics. However, little is known about the relationship between physical fitness and academic achievement. We examined the association between physical fitness and academic achievement and investigated the influence of parental socioeconomic status and ethnicity. METHODS Participants were 542 girls and 577 boys aged 13 to 15 residing in the Danish municipality of Aalborg. A watt-max cycle ergometer test was completed to evaluate physical fitness as represented by VO2 max (mL·kg(-1) ·min(-1) ). Academic achievement was measured 1 school year later through a series of mandatory exams within the humanities, sciences, and all obligatory defined exams. Parental income and education were drawn from nationwide registers. Linear regression models were used to investigate the association. RESULTS Adjusting for ethnicity and parental socioeconomic status, the effect size of the humanities was 0.08 grad/VO2 max (95% Cl: 0.05 to 0.11) for girls and 0.06 grad/VO2 max (95% Cl:0.03 to 0.08) for boys. The effect size of the sciences was 0.09 grad/VO2 max (95% Cl:0.05 to 0.13) for girls and 0.06 grad/VO2 max (95% Cl:0.03 to 0.09) for boys. The effect size of the defined exams was 0.09 grad/VO2 max (95% Cl:0.06 to 0.11) for girls and 0.06 grad/VO2 max (95% Cl:0.03 to 0.08) for boys. CONCLUSION We found a statistically significant positive association between physical fitness and academic achievement after adjusting for ethnicity and parental socioeconomic status.