Miracle-dz/UniFAR-FTUs huggingface dataset:
Decision Support and Navigation to Increase Colorectal Cancer Screening Among Hispanic Patients

Background: Effective strategies are needed to raise colorectal cancer screening rates among Hispanics. Methods: We surveyed and randomized 400 Hispanic primary care patients either to a Decision Support and Navigation Intervention (DSNI) Group (n = 197) or a Standard Intervention (SI) Group (n = 203). Both groups received a colorectal cancer screening kit [bilingual informational booklet, fecal immunochemical stool blood test (SBT), and colonoscopy screening instructions]. The DSNI Group received a telephone contact from a patient navigator. The navigator clarified screening test preference and likelihood of test performance, helped to develop a screening plan, and provided guidance through test performance. An endpoint telephone survey and medical chart review were completed. Multivariable analyses were conducted to assess 12-month screening adherence, change in decision stage, and knowledge and perceptions. Results: Screening adherence was significantly higher in the DSNI Group than the SI Group [OR, 4.8; 95% confidence interval (CI), 3.1–7.6]. The DSNI Group, compared with the SI Group, also displayed higher SBT screening [OR, 4.2; 95% CI, 2.6–6.7), higher colonoscopy screening (OR, 8.8; 95% CI, 4.1–18.7), and greater forward change in screening decision stage (OR, 4.9; 95% CI, 2.6–9.5). At endpoint, study groups did not differ in screening knowledge or perceptions. Conclusions: The DSNI had a greater positive impact on colorectal cancer screening outcomes than the SI. Impact: Health system implementation of DSNI strategies may help to reduce Hispanic colorectal cancer screening disparities in primary care.
An Epistemic Halpern-Shoham Logic for Gradable Justification

The Allen’s relations between temporal intervals found its logical representation in terms of the so-called Halpern-Shoham logic (HS). This system forms a modal-type logic interpreted in the interval-based Kripke semantics. Unfortunately, not all phenomena of temporal reasoning may be rendered by HS. In 2013, an epistemic extension of HS was proposed by A. Lomuscio. In this paper a new epistemic Halpern-Shoham logic – as a system of multi-valued logic – for gradable justification is proposed. This system is semantically interpreted in the so-called fibred semantics, enriched by some elements of the so-called behavioral semantics of Fagin. The subject basis of the analysis is determined by a unique epistemic version of Traveling Salesman Problem in this paper.
A comparison between ANN and SVM classifiers for Parkinson’s disease by using a model-free computer-assisted handwriting analysis based on biometric signals

Patients suffering from Parkinson’s Disease (PD) are characterized by an abnormal handwriting activity since they have difficulties in motor coordination and a decline in cognition. In this paper, we propose a model-free technique for differentiating PD patients from healthy subjects by using a handwriting analysis tool based on biometric signals (e.g., surface ElectroMyoGraphy, pen pressure, etc.) and an Artificial Intelligence-based classifier. Experimental tests have been carried on with both healthy and PD subjects to identify the most representative features, the best writing patterns and the best AI-based classification approach between Artificial Neural Network (ANN) and Support Vector Machine (SVM) in terms of accuracy and repeatability. Finally, the obtained results are reported and discussed to infer some important properties on writing patterns, classification approaches and the role of muscular activities on the handwriting analysis applied to neurodegenerative disease research.
Intervention Models for Increasing Access to Behavioral Health Services Among Youth: A Systematic Review

Objective: The purpose of this systematic literature review is to examine interventions designed to improve access to behavioral health services among youth in the United States and to identify how the concept of access to health care has been measured and conceptualized in these studies. Method: The review used a systematic search strategy to identify articles published in medical, nursing, and psychological literature. The search yielded 579 initial articles, of which 69 appeared to be candidates for inclusion in the review. In the end, 19 studies qualified for synthesis in the review. Studies were required to use an experimental or quasi-experimental design and include a comparison group. Results: Results indicated that access to behavioral health services can be improved by providing services in schools, using telehealth models, or delivering multicomponent interventions in clinical settings. Models that incorporated parental support, peer support from other parents, or other ways of engaging parents in the utilization process, as well as multicomponent and policy interventions, helped improve access to care. In the studies reviewed, access was operationalized primarily in terms of child behavioral health service utilization. Conclusion: Interventions delivered in schools, engaging parents, or utilizing telehealth technology show promise for increasing access for youth affected by behavioral health disorders.
Transmit Strategies for Massive Machine-Type Communications based on Mean Field Games

Massive Machine Type Communications are one of the three main type of communication applications in upcoming 5G wireless networks. In this type of communication, the network is required to handle a huge number of devices transmitting information to the same base station receiver in an uncoordinated manner. In this setting, the problem of minimizing energy usage while achieving QoS requirements is a very complex stochastic control problem with a very large number of optimizing agents. In this paper, we propose a Mean Field Games model for this problem that reduces the complexity by a great deal and is thus amenable to numerical solution. Our model is general enough to include generic rate functions, arbitrary energy and QoS requirements per user, different channel fading models, and design knobs for determining the importance of different performance goals. We provide details of the proposed numerical solution and present numerical results that illustrate the characteristics of the obtained control policy.
A Machine Learning Approach to Malicious JavaScript Detection using Fixed Length Vector Representation

To add more functionality and enhance usability of web applications, JavaScript (JS) is frequently used. Even with many advantages and usefulness of JS, an annoying fact is that many recent cyberattacks such as drive-by-download attacks exploit vulnerability of JS codes. In general, malicious JS codes are not easy to detect, because they sneakily exploit vulnerabilities of browsers and plugin software, and attack visitors of a web site unknowingly. To protect users from such threads, the development of an accurate detection system for malicious JS is soliciting. Conventional approaches often employ signature and heuristic-based methods, which are prone to suffer from zero-day attacks, i.e., causing many false negatives and/or false positives. For this problem, this paper adopts a machine-learning approach to feature learning called Doc2Vec, which is a neural network model that can learn context information of texts. The extracted features are given to a classifier model (e.g., SVM and neural networks) and it judges the maliciousness of a JS code. In the performance evaluation, we use the D3M Dataset (Drive-by-Download Data by Marionette) for malicious JS codes and JSUPACK for benign ones for both training and test purposes. We then compare the performance to other feature learning methods. Our experimental results show that the proposed Doc2Vec features provide better accuracy and fast classification in malicious JS code detection compared to conventional approaches.
Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy

Image guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications. Needle tip and trajectory were annotated on 583 T2-weighted intra-procedural MRI scans acquired after needle insertion for 71 patients who underwent transperineal MRI-targeted biopsy procedure at our institution. The images were divided into two independent training–validation and test sets at the patient level. A deep 3-D fully convolutional neural network model was developed, trained, and deployed on these samples. The accuracy of the proposed method, as tested on previously unseen data, was 2.80-mm average in needle tip detection and 0.98° in needle trajectory angle. An observer study was designed in which independent annotations by a second observer, blinded to the original observer, were compared with the output of the proposed method. The resultant error was comparable to the measured inter-observer concordance, reinforcing the clinical acceptability of the proposed method. The proposed system has the potential for deployment in clinical routine.
Alterations in regional kidney oxygenation during expansion of extracellular fluid volume in conscious healthy sheep.

Expansion of extracellular fluid volume with crystalloid solutions is a common medical intervention, but its effects on renal cortical and medullary oxygenation are poorly understood. Therefore, we instrumented sheep under general anesthesia to enable continuous measurement of systemic and renal hemodynamics, global renal oxygen delivery and consumption, and intrarenal tissue perfusion and oxygen tension (Po2) in conscious animals ( n = 7). The effects of three sequential intermittent infusions of 500 ml of compound sodium lactate solution, administered at hourly intervals, were determined. Volume expansion induced transient increases in mean arterial pressure (+7 ± 2%), central venous pressure (+50 ± 19%), and cardiac output (+15 ± 3%). There were sustained increases in renal medullary tissue Po2 (+35 ± 10%) despite increases in global renal oxygen consumption (+66 ± 18%) and renal oxygen extraction (+64 ± 8%). Volume expansion did not significantly alter renal blood flow, renal oxygen delivery, or medullary perfusion. The sustained increase in medullary Po2 was paralleled by increased bladder urine Po2 (34 ± 4%). Cortical perfusion and Po2 did not change significantly. Our findings indicate that extracellular fluid volume expansion can increase renal medullary oxygenation, providing a potential mechanistic basis for its use as prophylaxis against iatrogenic acute kidney injury. They also indicate that continuous measurement of bladder urine Po2 could be used to monitor the effects of volume expansion on medullary oxygenation. However, the mechanisms mediating increased medullary oxygenation during volume expansion remain to be determined.
A Centerness Peak Based Clustering Algorithm

The density peak based clustering algorithm is a recently proposed density based clustering approach. This algorithm treats the data corresponding to local density peaks as cluster centers and groups non-center data based on the density relationship among neighboring data. While being simple, this algorithm is shown to be effective and computationally efficient. In the density peak based algorithm, the data with the largest local density are selected as cluster centers. On one hand, the real cluster centers may not have the largest local density in clusters. On the other hand, this practice may discriminate against the clusters of small density. In this paper we propose to measure the centerness of data and treat the centerness peaks as cluster centers. The centerness of one data is evaluated by the distribution of nearest neighbors in the neighborhood, and it measures to which degree one data is surrounded by its nearest neighbors. We present a histogram based method to calculate the centerness, and show that the centerness measure solves the problem resulted from density difference among clusters in the density peak based algorithm. In addition, the cluster centers identified by centerness peaks are more consistent with human observation. Experiments on various datasets and comparisons with other algorithms illustrate the effectiveness of our algorithm.
Phylogenetic analysis of the cytochrome P450 (CYP450) nucleotide sequences of the horse and predicted CYP450s of the white rhinoceros (Ceratotherium simum) and other mammalian species

Background The plight of the white rhinoceros (Ceratotherium simum) and the increasing need of treatment options for injured poaching victims led to the necessity to expand the knowledge on applicable drugs in this endangered species. With very little information available on drug pharmacokinetics in rhino, veterinarians have to rely on information generated from other species. The horse being a closely related species, has served as the model for dose extrapolations. However, from recent research on enrofloxacin and carprofen, the white rhino showed considerable differences in the pharmacokinetic properties of these drugs in comparison to the horse. While the reason for the differences is unknown, a likely cause may be a difference in present cytochrome P450 (CYP450), which may result in the rhino being genetically deficient in certain enzyme families. Methods For this paper we assess the degree of similarity of the CYP genome sequences across the different species, using BLAT (BLAST-like alignment tool) for the alignment of the nucleotide sequences of the equine CYP450 with potential homologous nucleotide sequences of the published database from white rhinos and other mammalian species (cow, pig, dog, sheep, elephant, mouse and human). Results The white rhino nucleotide sequences were 90.74% identical to the equine sequences. This was higher than the degree of similarity between any of the other evaluated species sequences. While no specific CYP family were found to be deficient in the published rhino genome, the horse genome contained additional genetic sequence for a larger number of iso-enzymes that were not present in the rhino. Discussion In pharmacokinetic study, it is well known that absence of a metabolic enzyme will result in constraints in drug metabolism and drug elimination. While this was our speculation, comparison to the horse and other mammalian species indicate that all the described CYP genes required for metabolism are present within the rhino genome. These results leave functional differences in enzyme activity and a lack of isoenzymes as the likely reason for the constraint in drug metabolism. Despite a more than 90% similarity of the equine and rhino gene sequences, seemingly small differences can have major effects on drug metabolism. Thus, in spite of the close anatomical relationship, the rhino should not simply be treated like a big horse.
Bioactive Glass Granules Inhibit Mature Bacterial Biofilms on the Surfaces of Cochlear Implants.

HYPOTHESIS Biofilm formation on cochlear implant (CI) surfaces differs between bacterial species and can be reduced by the application of S53P4 bioactive glass. BACKGROUND The formation of bacterial biofilms on medical devices, such as cochlear implants, can lead to chronic infections resulting in the need for implant removal. In this study, various surfaces of three CI implant kits from different manufacturers were examined for bacterial biofilm formation and reduction of a pre-existing biofilm by the application of bioactive glass. METHODS Biofilm formations of 4 bacterial species causing implant-related infections were tested on 17 different surfaces: Pseudomonas aeruginosa (ATCC9027), Staphylococcus aureus (ATCC6538), Staphylococcus epidermidis (ATCC12228), and Streptococcus pyogenes (ATCC19615). For P. aeruginosa and S. aureus biofilm reduction after application of S53P4 bioactive glass was evaluated. RESULTS All tested microbial species formed biofilms on the examined CI surfaces in a strain-dependent manner. For S. aureus, a significantly higher biofilm formation on metal components compared with silicone was found whereas the other strains did not show a material specific biofilm formation. Application of S53P4 bioactive glass resulted in a significant reduction of P. aeruginosa and S. aureus mature biofilm. CONCLUSION The four bacteria species displayed biofilm formation on the CI surfaces in a species- and material-specific manner. The results show that bioactive glass can reduce biofilm formation on CI materials in vitro. Future studies are necessary to confirm the results in vivo.
Decoding music-induced experienced emotions using functional magnetic resonance imaging - Preliminary results

Music is known to be a powerful elicitor of emotions. Here, we examined the possibility of decoding musicinduced experienced emotions – affective responses induced in the listener by music stimuli – using functional magnetic resonance imaging (fMRI) data collected while participants listened to various movie soundtrack excerpts. Using a machine learning based classification framework, preliminary results indicate that it is possible to, under certain conditions, decode the arousal (High/Low) and valence (Positive/Negative) of the emotional state experienced during listening. Future work will be aimed at improving methodological issues in order to establish whether these results extend to larger populations and more general types of music stimuli.
SegmentedFusion: 3D Human Body Reconstruction Using Stitched Bounding Boxes

This paper presents SegmentedFusion, a method possessing the capability of reconstructing non-rigid 3D models of a human body by using a single depth camera with skeleton information. Our method estimates a dense volumetric 6D motion field that warps the integrated model into the live frame by segmenting a human body into different parts and building a canonical space for each part. The key feature of this work is that a deformed and connected canonical volume for each part is created, and it is used to integrate data. The dense volumetric warp field of one volume is represented efficiently by blending a few rigid transformations. Overall, SegmentedFusion is able to scan a non-rigidly deformed human surface as well as to estimate the dense motion field by using a consumer-grade depth camera. The experimental results demonstrate that SegmentedFusion is robust against fast inter-frame motion and topological changes. Since our method does not require prior assumption, SegmentedFusion can be applied to a wide range of human motions.
[Full-term low birth weight and its relationship with the socioeconomic conditions of municipalities in Antioquia: Spatio-temporal analysis].

INTRODUCTION Low birth weight is one of the main health predictors for childhood and adulthood. It is highly prevalent in developing countries and in children from poorer social classes. OBJECTIVE To analyze the geographical distribution and the trends of full-term low birth weight in the 125 municipalities of the department of Antioquia and their association with the socioeconomic conditions of the municipalities between 2000 and 2014. MATERIALS AND METHODS We conducted a longitudinal ecological study. The dependent variable was the prevalence of full-term low birth weight in 125 municipalities. The socioeconomic conditions of the municipalities were evaluated using the Human Development Index and the unsatisfied basic needs index. RESULTS Between 2000 and 2014, the prevalence of full-term low birth weight in Antioquia was 2.7 per 100 live births; this prevalence decreased steadily over time. This trend was also observed between regions, with the exception of the Occidente and Urabá regions, which showed increases of approximately 20.0%. The results also showed a reduction in geographical differences between the 125 municipalities, with the coefficient of variation decreasing from 20.6% to 13.72% over the 14-yearperiod. Finally, we found a positive social gradient between the full-term low birth weight prevalence and the level of development in the municipalities. CONCLUSION Full-term low birth weight prevalence decreased between 2000 and 2014, with some regions showing greater reductions than others. Paradoxically, less developed and poorer areas had a lower prevalence, which may be a result of underreporting of birth weight information in these municipalities.
Altered Eye-Movement Patterns During Text Reading in Obsessive–Compulsive Disorder and Internet Gaming Disorder

Obsessive-compulsive disorder (OCD) and internet gaming disorder (IGD), which are similar in that both involve repetitive behaviors and related with cognitive dysfunctions, frequently begin in early adolescence, which is a critical period for learning. Although the deterioration in cognitive functioning caused by these conditions may have adverse effects on information processing, such as text reading, there has been no comprehensive research on the objective indicators of altered reading patterns in these patients. Therefore, we evaluated eye-movement patterns during text reading in patients with OCD or IGD. In total, 20 patients with OCD, 28 patients with IGD and 24 healthy controls (HCs) participated in the reading task using an eye tracker. We compared the fixation durations (FDs), saccade amplitudes and eye-movement regressions of the three groups during reading. We explored relationships between the parameters reflecting altered reading patterns and those reflecting the severity of clinical symptoms. The average FDs and forward saccade amplitudes did not differ significantly among the groups. There were more eye-movement regressions in patients with OCD than in patients with IGD and HCs. No correlation was found between altered eye-movement patterns during reading and the severity of clinical symptoms in any of the patient groups. The significantly increased number of regressions (NRs) in the OCD group during reading may reflect these patients’ difficulties with inferential information processing, whereas the reading pattern in the IGD group is relatively intact. These findings suggest that patients with OCD and patients with IGD have different eye-movement patterns during reading reflecting distinct cognitive impairments in the two patient groups.
Gradient Boosting Decision Trees for Echocardiogram Images

High-throughput medical imaging procedures, such as echocardiogram, motivate the development of new computational tools to support specialists in decision making. This paper introduces a method to detect the posterior wall of the left ventricle from echocardiogram images, considering the PLAX view. Using a Light Gradient Boosting Machine, we evaluated a classification problem and compared results with Classification and Regression Trees using k-fold cross-validation regarding five quality measures. Experimental analysis shows that the proposed approach achieves relevant median scores of 61% for Dice, 67% for Sensitivity and 98% for Specificity on a dataset of only 69 images from three different sources. With some post-processing, the median Dice score increased to 78%. Although further improvements must be considered before deploying such a tool for clinical utilization, the results presented in this paper show a promising path towards the automation of echocardiogram analysis and segmentation of the posterior wall of the left ventricle.