Miracle-dz/UniFAR-FTUs huggingface dataset:
Staphylococcus aureus Myocarditis with Associated Left Ventricular Apical Thrombus

Staphylococcus aureus myocarditis is a rare diagnosis with a high mortality rate, usually seen in people who are immunocompromised. Here, we report a case of a 44-year-old man on methotrexate for rheumatoid arthritis who presented in septic shock and was diagnosed with staphylococcus aureus myocarditis. The myocarditis was associated with a left ventricular apical thrombus, with normal systolic function. The myocarditis and associated thrombus were characterised on transthoracic echocardiogram and subsequently on cardiac magnetic resonance imaging. Cardiac magnetic resonance (CMR) imaging showed oedema in the endomyocardium, consistent with acute myocarditis, associated with an apical mural thrombus. Repeat CMR 3 weeks following discharge from hospital showed marked improvement in endomyocardial oedema and complete resolution of the apical mural thrombus. He was treated with a 12-week course of antibiotics and anticoagulated with apixaban. The patient was successfully managed with intravenous antibiotics and anticoagulation with complete recovery.
A Possible Mechanism: Vildagliptin Prevents Aortic Dysfunction through Paraoxonase and Angiopoietin-Like 3

The collected data have revealed the beneficial effects of dipeptidyl peptidase-4 (DPP-4) inhibitors on the vascular endothelium, including vildagliptin. However, the involved mechanisms are not yet clear. In this study, Sprague-Dawley rats were randomly divided into the following four groups: control, diabetic, diabetic + low-dose vildagliptin (10 mg/kg/d), and diabetic + high-dose vildagliptin (20 mg/kg/d). The diabetic model was created by feeding a high-fat diet for four weeks and injection of streptozotocin. Then, vildagliptin groups were given oral vildagliptin for twelve weeks, and the control and diabetic groups were given the same volume of saline. The metabolic parameters, endothelial function, and whole genome expression in the aorta were examined. After 12 weeks of treatment, vildagliptin groups showed significantly reduced blood glucose, blood total cholesterol, and attenuated endothelial dysfunction. Notably, vildagliptin may inhibit angiopoietin-like 3 (Angptl3) and betaine-homocysteine S-methyltransferase (Bhmt) expression and activated paraoxonase-1 (Pon1) in the aorta of diabetic rats. These findings may demonstrate the vasoprotective pathway of vildagliptin in vivo.
On learning linear functions from subset and its applications in quantum computing

Let $\mathbb{F}_q$ be the finite field of size $q$ and let $\ell: \mathbb{F}_q^n \to \mathbb{F}_q$ be a linear function. We introduce the {\em Learning From Subset} problem LFS$(q,n,d)$ of learning $\ell$, given samples $u \in \mathbb{F}_q^n$ from a special distribution depending on $\ell$: the probability of sampling $u$ is a function of $\ell(u)$ and is non zero for at most $d$ values of $\ell(u)$. We provide a randomized algorithm for LFS$(q,n,d)$ with sample complexity $(n+d)^{O(d)}$ and running time polynomial in $\log q$ and $(n+d)^{O(d)}$. Our algorithm generalizes and improves upon previous results \cite{Friedl, Ivanyos} that had provided algorithms for LFS$(q,n,q-1)$ with running time $(n+q)^{O(q)}$. We further present applications of our result to the {\em Hidden Multiple Shift} problem HMS$(q,n,r)$ in quantum computation where the goal is to determine the hidden shift $s$ given oracle access to $r$ shifted copies of an injective function $f: \mathbb{Z}_q^n \to \{0, 1\}^l$, that is we can make queries of the form $f_s(x,h) = f(x-hs)$ where $h$ can assume $r$ possible values. We reduce HMS$(q,n,r)$ to LFS$(q,n, q-r+1)$ to obtain a polynomial time algorithm for HMS$(q,n,r)$ when $q=n^{O(1)}$ is prime and $q-r=O(1)$. The best known algorithms \cite{CD07, Friedl} for HMS$(q,n,r)$ with these parameters require exponential time.
Fitness Measures Among Boy Scouts Completing the Personal Fitness Merit Badge

Introduction A formative framework for positive lifestyle and health behaviors is established during adolescence. The Boy Scouts of America (BSA) is one organization that promotes healthy lifestyles among adolescent males and includes earning the Personal Fitness Merit Badge (PFMB). Despite the BSA Oath “to keep myself physically strong”, there is a paucity of evidence describing fitness test (FT) outcomes or correlations between FT and variables including age or body mass index (BMI) among Boy Scouts. Therefore, the purpose of this study is to describe and correlate FT to age and BMI among adolescent Boy Scouts. Methods A prospective study design recruited Boy Scouts aged 11-17 years from Southeast Michigan using a sample of convenience. After securing physician clearance to participate, FT measures were performed utilizing previously established methodology. Third year doctor of physical therapy students trained in the measurement protocol performed the FT measures. Measures encompassed those required for the PFMB: BMI, pull-ups, push-ups, sit and reach flexibility test (SRF), sit-ups, and the one mile run (1MR). Data was analyzed using descriptive statistics, a Kruskal-Wallis test examined relationships between BMI and FT, and a nonparametric Spearman correlation examined correlations between FT performance and both age and BMI with statistical significance set at less than 0.05. Results Ninety-nine Boy Scouts, whose mean age was 12.6 years (SD 1.4), met the inclusion criteria. The mean BMI was 21.5 (kg/height (cm)) 2 (SD 5.4) with eight scouts meeting criteria of underweight, 56 normal weight, 14 overweight, and 21 obese. Fitness test results were as follows: pull-ups 1.75 (SD 2.7), push-ups 18.0 (SD 10.6), SRF 21.2 cm (SD 10.5), sit-ups 28.9 (SD 8.7), and the 1MR run 616.5 seconds (SD 156.8). When comparing normal weight scouts to overweight and obese scouts, a significantly higher frequency of pull-ups (p=0.002, p=.001), push-ups (p=0.02, p=0.03), sit-ups (p=0.01, p=0.003,), and decreased time for completion on the 1MR (p=0.001, p=0.001) was identified, respectively. Furthermore, while no correlations were identified by age, a negative correlation was identified between increased sit-up frequency (r=-0.36, p=0.001) and decreased 1MR time performance (r=0.39, p=0.001) and a higher BMI. Discussion While prior evidence suggests improved FT scores in adolescent males with advancing age, this was not observed. Less favorable PFMB required FT performance with an increased BMI among Boy Scouts is in congruence with prior reports for adolescent males. Conclusion Variations in FT performance levels were observed among Boy Scouts completing the initial FT requirements of the PFMB. However, correlations between higher BMI and less favorable FT performance were detected.
Entanglement-Based Feature Extraction by Tensor Network Machine Learning

It is a hot topic how entanglement, a quantity from quantum information theory, can assist machine learning. In this work, we implement numerical experiments to classify patterns/images by representing the classifiers as matrix product states (MPS). We show how entanglement can interpret machine learning by characterizing the importance of data and propose a feature extraction algorithm. We show on the MNIST dataset that when reducing the number of the retained pixels to 1/10 of the original number, the decrease of the ten-class testing accuracy is only O (10–3), which significantly improves the efficiency of the MPS machine learning. Our work improves machine learning’s interpretability and efficiency under the MPS representation by using the properties of MPS representing entanglement.
ncdDetect2: improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation

Motivation: Understanding the mutational processes that act during cancer development is a key topic of cancer biology. Nevertheless, much remains to be learned, as a complex interplay of processes with dependencies on a range of genomic features creates highly heterogeneous cancer genomes. Accurate driver detection relies on unbiased models of the mutation rate that also capture rate variation from uncharacterized sources. Results: Here, we analyse patterns of observed‐to‐expected mutation counts across 505 whole cancer genomes, and find that genomic features missing from our mutation‐rate model likely operate on a megabase length scale. We extend our site‐specific model of the mutation rate to include the additional variance from these sources, which leads to robust significance evaluation of candidate cancer drivers. We thus present ncdDetect v.2, with greatly improved cancer driver detection specificity. Finally, we show that ranking candidates by their posterior mean value of their effect sizes offers an equivalent and more computationally efficient alternative to ranking by their P‐values. Availability and implementation: ncdDetect v.2 is implemented as an R‐package and is freely available at http://github.com/TobiasMadsen/ncdDetect2 Supplementary information: Supplementary data are available at Bioinformatics online.
A Systematic Review on Non-mammalian Models in Epilepsy Research

Epilepsy is a common neurological disorder characterized by seizures which result in distinctive neurobiological and behavioral impairments. Not much is known about the causes of epilepsy, making it difficult to devise an effective cure for epilepsy. Moreover, clinical studies involving epileptogenesis and ictogenesis cannot be conducted in humans due to ethical reasons. As a result, animal models play a crucial role in the replication of epileptic seizures. In recent years, non-mammalian models have been given a primary focus in epilepsy research due to their advantages. This systematic review aims to summarize the importance of non-mammalian models in epilepsy research, such as in the screening of anti-convulsive compounds. The reason for this review is to integrate currently available information on the use and importance of non-mammalian models in epilepsy testing to aid in the planning of future studies as well as to provide an overview of the current state of this field. A PRISMA model was utilized and PubMed, Springer, ScienceDirect and SCOPUS were searched for articles published between January 2007 and November 2017. Fifty-one articles were finalized based on the inclusion/exclusion criteria and were discussed in this review. The results of this review demonstrated the current use of non-mammalian models in epilepsy research and reaffirmed their potential to supplement the typical rodent models of epilepsy in future research into both epileptogenesis and the treatment of epilepsy. This review also revealed a preference for zebrafish and fruit flies in lieu of other non-mammalian models, which is a shortcoming that should be corrected in future studies due to the great potential of these underutilized animal models.
Use of Multiple Imputation to Estimate the Proportion of Respiratory Virus Detections Among Patients Hospitalized With Community-Acquired Pneumonia

Abstract Background Real-time polymerase chain reaction (PCR) on respiratory specimens and serology on paired blood specimens are used to determine the etiology of respiratory illnesses for research studies. However, convalescent serology is often not collected. We used multiple imputation to assign values for missing serology results to estimate virus-specific prevalence among pediatric and adult community-acquired pneumonia hospitalizations using data from an active population-based surveillance study. Methods Presence of adenoviruses, human metapneumovirus, influenza viruses, parainfluenza virus types 1–3, and respiratory syncytial virus was defined by positive PCR on nasopharyngeal/oropharyngeal specimens or a 4-fold rise in paired serology. We performed multiple imputation by developing a multivariable regression model for each virus using data from patients with available serology results. We calculated absolute and relative differences in the proportion of each virus detected comparing the imputed to observed (nonimputed) results. Results Among 2222 children and 2259 adults, 98.8% and 99.5% had nasopharyngeal/oropharyngeal specimens and 43.2% and 37.5% had paired serum specimens, respectively. Imputed results increased viral etiology assignments by an absolute difference of 1.6%–4.4% and 0.8%–2.8% in children and adults, respectively; relative differences were 1.1–3.0 times higher. Conclusions Multiple imputation can be used when serology results are missing, to refine virus-specific prevalence estimates, and these will likely increase estimates.
A Tensor Based Data Model for Polystore: An Application to Social Networks Data

In this article, we show how the mathematical object tensor can be used to build a multi-paradigm model for the storage of social data in data warehouses. From an architectural point of view, our approach allows to link different storage systems (polystore) and limits the impact of ETL tools performing model transformations required to feed different analysis algorithms. Therefore, systems can take advantage of multiple data models both in terms of query execution performance and the semantic expressiveness of data representation. The proposed model allows to reach the logical independence between data and programs implementing analysis algorithms. With a concrete case study on message virality on Twitter during the French presidential election of 2017, we highlight some of the contributions of our model.
From Kinect skeleton data to hand gesture recognition with radar

In an era where man-machine interaction increasingly uses remote sensing, gesture recognition through use of the micro-Doppler (mD) effect is an emerging application which has attracted great interest. It is a sensible solution and here the authors show its potential for detecting aperiodic human movements. In this study, the authors classify ten hand gestures with a set of handcrafted features using simulated mD signatures generated from Kinect skeleton data. Data augmentation in the form of synthetic minority oversampling technique has been applied to create synthetic samples and classified with the support vector machine and K-nearest neighbour classifier with classification rate of 71.1 and 51% achieved. Finally, using weights generated by an action pair based one vs. one classification layer improves classification accuracy by 24.7 and 28.4%.
Hedgehog-GLI signaling in Foxd1-positive stromal cells promotes murine nephrogenesis via TGFβ signaling

ABSTRACT Normal kidney function depends on the proper development of the nephron: the functional unit of the kidney. Reciprocal signaling interactions between the stroma and nephron progenitor compartment have been proposed to control nephron development. Here, we show that removal of hedgehog intracellular effector smoothened (Smo-deficient mutants) in the cortical stroma results in an abnormal renal capsule, and an expanded nephron progenitor domain with an accompanying decrease in nephron number via a block in epithelialization. We show that stromal-hedgehog-Smo signaling acts through a GLI3 repressor. Whole-kidney RNA sequencing and analysis of FACS-isolated stromal cells identified impaired TGFβ2 signaling in Smo-deficient mutants. We show that neutralization and knockdown of TGFβ2 in explants inhibited nephrogenesis. In addition, we demonstrate that concurrent deletion of Tgfbr2 in stromal and nephrogenic cells in vivo results in decreased nephron formation and an expanded nephrogenic precursor domain similar to that observed in Smo-deficient mutant mice. Together, our data suggest a mechanism whereby a stromal hedgehog-TGFβ2 signaling axis acts to control nephrogenesis. Summary: Development of the embryonic kidney involves interactions between hedgehog, smoothened and GLI3 in cortical stromal cells that control nephron formation via TGFβ signaling.
Validation of the Zero-Heat-Flux thermometer (SpotOn®) in major gynecological surgery to monitor intraoperative core temperature: a comparative study with esophageal core temperature.

BACKGROUND The Zero-Heat-Flux technology of the SpotOn® sensor to measure core temperature has been validated in vascular and cardiac surgery. Our aim was to assess its validity in gynecological surgery in terms of time of measurement, thermic status (hypo-, normo-, or hyperthermia) and the use of different warming devices. METHODS Prospective study in patients scheduled for major gynecological surgery using three warming systems (Snuggle Warm®, Lithotomy Underbody Blanket® and Warmtac Pad®). Intraoperative temperature was simultaneously monitored using the SpotOn® sensor and a Dräger® esophageal probe; measurements were taken after induction (T0), between 0 and 60 min (T1), between 60 and 120 minutes (T2) and after 120 minutes (T3). We calculated the level of correlation between pairs of measurements both overall and in subgroups with an acceptable difference of less than 0.5 °C. We built Bland-Altman plots, and calculated the Pearson correlation (PC) and intraclass correlation coefficient (ICC) to test our data. RESULTS We recruited 70 patients and included 66 for analysis, giving a total of 401 paired measurements. Overall, the PC and ICC for paired temperature readings were 0.82 and 0.90, respectively. The lowest levels of correlation were observed after intubation, when a convective blanket was placed under the patient, and in hypothermic patients. The highest correlations were observed at T2, and in normothermic patients. CONCLUSIONS We found a high overall correlation between the SpotOn® and the esophageal probe, with negligible differences and almost perfect agreement. The warming system used, time of measurement, and thermic status appear to influence the level of correlation, but these differences were not clinically significant.
Learning-based Feedback Controller for Deformable Object Manipulation

In this paper, we present a general learning-based framework to automatically visual-servo control the position and shape of a deformable object with unknown deformation parameters. The servo-control is accomplished by learning a feedback controller that determines the robotic end-effector's movement according to the deformable object's current status. This status encodes the object's deformation behavior by using a set of observed visual features, which are either manually designed or automatically extracted from the robot's sensor stream. A feedback control policy is then optimized to push the object toward a desired featured status efficiently. The feedback policy can be learned either online or offline. Our online policy learning is based on the Gaussian Process Regression (GPR), which can achieve fast and accurate manipulation and is robust to small perturbations. An offline imitation learning framework is also proposed to achieve a control policy that is robust to large perturbations in the human-robot interaction. We validate the performance of our controller on a set of deformable object manipulation tasks and demonstrate that our method can achieve effective and accurate servo-control for general deformable objects with a wide variety of goal settings.
Edinburgh Research Explorer Learning from demonstration of trajectory preferences through causal modeling and inference

—Learning from demonstration is associated with acquiring a solution to a task by mimicking a teacher demonstrator. Understanding the underlying reasons and in turn preferences that lead to a demonstration can yield better task comprehension. We present a generative model that describes a table-top task in terms of a causal model with respect to known concepts (e.g., the notion of a fork). Causal reasoning in the latent space of this generative model fully describes the meaning of the demonstration, e.g., that we would like to move far away from the fork. We show that by sampling from the model latent space, we can learn a solution to the problem that deﬁnes the task being demonstrated. We use a simulated kitchen table-top environment to show changes in the underlying trajectory preference of demonstrations for different objects. The ability to generate additional data through introspection of the latent space allows us to conﬁrm the causal model for the problem.
Causal Inference for Early Detection of Pathogenic Social Media Accounts

Pathogenic social media accounts such as terrorist supporters exploit communities of supporters for conducting attacks on social media. Early detection of PSM accounts is crucial as they are likely to be key users in making a harmful message "viral". This paper overviews my recent doctoral work on utilizing causal inference to identify PSM accounts within a short time frame around their activity. The proposed scheme (1) assigns time-decay causality scores to users, (2) applies a community detection-based algorithm to group of users sharing similar causality scores and finally (3) deploys a classification algorithm to classify accounts. Unlike existing techniques that require network structure, cascade path, or content, our scheme relies solely on action log of users.
Effect of the PIVO Device on the Procedure of Phlebotomy from Peripheral IV Catheters

Short peripheral catheters (SPC) are an existing conduit into many patients' veins and line draws from SPC are a desired method of routine blood collection especially in difficult venous access patients. The PIVO device facilitates blood collection through SPC and is being used clinically in a number of hospitals. This study aimed to determine the appropriate wait time following a flush and the minimum waste volume required to obtain an undiluted blood sample when using the PIVO device and how that differed from current guidelines from SPC line draws. A clinical study was conducted examining the analyte results of samples drawn with PIVO through a SPC at varying wait times following a saline flush. Both an initial waste volume and a postwaste sample were compared to a venipuncture control. The resulting samples showed no saline dilution as measured by sodium and creatinine results at all studied wait times. These findings suggest that blood collections using the PIVO device can produce a clinically valid sample with a 30-second wait following a SPC flush and no waste volume prior to sample collection.
Network-wide routing-oblivious heavy hitters

The recent introduction of SDN allows deploying new centralized network algorithms that dramatically improve the network operation. Many of these solutions rely on the assumption that the centralized controller merges data from different Network Monitoring Points (NMP) to obtain a network-wide view. This is far from trivial when the same packet may traverse through several NMPs. Therefore, existing solutions either assume that each packet is measured at exactly one NMP or that the routing of each packet is known. Another approach is to mark the sampled packets so that other NMPs are aware that the packet was already considered. We suggest the first network-wide and routing oblivious algorithms for three fundamental network monitoring problems. The suggested algorithms allow flexible NMP placement, require no control over edge routers, and are indifferent to network topology and routing. Moreover, they are based on passive measurements without modifying the traffic in any way. Formally, we provide a general, constant time framework that solves the distributed versions of the volume estimation, frequency estimation and heavy-hitters problems with provable guarantees. The evaluation of our scheme on real packet traces shows that we can achieve very accurate results using a very reasonable amount of memory. For example, using less than 60KB memory in each monitoring point we get a root square error of less than 0.01% of the packets for frequency estimation.