Miracle-dz/UniFAR-FTUs huggingface dataset:
Routine coagulation test abnormalities caused by rivaroxaban

Rationale: Rivaroxaban is a non-vitamin K antagonist oral anticoagulant. Current recommendations state that coagulation monitoring is not required, and neither the dose nor dosing interval requires adjustment in response to changes in coagulation parameters when rivaroxaban is used for approved indications. Guidelines mainly discuss the indications for rivaroxaban and non-vitamin K antagonist oral anticoagulants in general; they offer less guidance regarding how to use these medications in specific clinical situations to bridge the gulf between guidelines and clinical practice. Patient concerns: An 88-year-old man with a long history of atrial fibrillation presented to the hospital with worsening dyspnea and chest pain. Significantly, he had an estimated glomerular filtration rate of 46.7 mL/min. He was prescribed oral rivaroxaban 20 mg once daily. After 7 days, the patient complained of maroon colored stools. Diagnosis: Laboratory investigations revealed that the patient's prothrombin time (PT) and activated partial thromboplastin time (aPTT) were elevated. Rivaroxaban induced gastrointestinal bleeding was suspected. Interventions: Rivaroxaban was discontinued and routine coagulation tests were monitored daily. Outcomes: Two days following the discontinuation of the drug, the bleeding was controlled and hemoglobin was normal, but the PT and aPTT remained abnormal. On the third day after discontinuing rivaroxaban, the patient experienced sudden syncope and pulselessness and expired. Lessons: This case indicates that in real-world situations, a small number of patients may develop changes in both PT and aPTT during rivaroxaban therapy. Therefore, coagulation monitoring should be considered in patients with risk factors for bleeding, such as elderly patients with renal insufficiency.
Meta-Analysis of the Relationship between the APOE Gene and the Onset of Parkinson's Disease Dementia

Purpose To clarify the relationship between certain genotypes or alleles of the APOE gene and the onset risk of Parkinson's disease dementia (PDD). Methods The PubMed, Cochrane, Embase, CBM, CNKI, and Wanfang databases were searched to identify all case-control studies and cohort studies published before October 30, 2017, that investigated the association between the APOE gene and the onset of PDD. Manual information retrieval was also performed. All studies that met the quality requirements were included in a meta-analysis performed using RevMan 5.3 software. Results The meta-analysis included 17 studies, with a total of 820 patients in the PDD group and 1,922 in the non-PDD group. The influence of the APOE gene on PDD onset was analyzed from three aspects: five genotypes vs. ε3/3, ε2+/ε4+ vs. ε3/3, and ε4+ vs. ε4−. The risk factors for PDD may include the genotypes ε3/4 (OR 1.47, 95% CI 1.14–1.89) and ε4/4 (OR 2.93, 95% CI 1.20–7.14). In patients with PDD, there was no significant difference in the distribution of ε2+ vs. ε3/3 (OR 1.35, 95% CI 0.97–1.87, P=0.07). The risk of PDD was 1.61 times greater in ε4+ compared with ε3/3 (OR 1.61, 95% CI 1.24–2.08, P=0.0003). As the results indicated that ε2+ did not play a role as a risk factor or a protective factor, we divided the population into ε4+ and ε4− for the meta-analysis and found that, among patients with Parkinson's disease, the dementia risk of those with ε4+ was 1.72 times greater than that of those with ε4− (OR 1.72, 95% CI 1.41–2.10, P < 0.00001). Subgroup analysis in accordance with different geographical regions revealed that ε4+ was a risk factor for PDD in people from all regions. Conclusions Among the APOE genotypes, ε2+ is neither a risk factor nor a protective factor for PDD, while ε4+ is a risk factor for PDD. The present results are applicable to Asian, European, and American patients with Parkinson's disease. Regarding the single APOE genotypes, ε3/4 and ε4/4 may be risk factors for PDD; however, further studies with large sample sizes are needed to verify this.
Underdetermined Source Separation with Simultaneous DOA Estimation Without Initial Value Dependency

In this paper, a sparsity-based method for solving an underde-termind source separation problem is proposed. The proposed method is formulated as a convex optimization problem with two kinds of sparsity priors: sparsity in time-frequency domain and direction-of-arrival (DOA). These priors enable simultaneous estimation of DOA and sound sources, while the estimation result does not depend on an initialization method thanks to the convexity. Experiments using 4 sound sources recorded by 2 microphones confirmed that every random initial value in the proposed method resulted in the same performance which was better than the conventional methods.
Comparative Analysis of MPPT Techniques for Negative Output Super-Lift Luo Converter and Hardware Implementation of NOSLC

The acute energy crisis and boundless availability of the renewable energy resources compared to the finite resources of fossil fuels have made the Photovoltaic power generation, the most rapidly emergent sources of electricity. However low conversion efficiency being the major hindrances to the PV power generationis overcome withNegative Output Super-Lift Luo converter (NOSLC) connected to the panel, whose duty ratio is controlled using various MPPT algorithms to trap maximum possible power available at the panel. This work thus represents a comparative analysis of Perturb and Observe(P&O) and Incremental conductance MPPT algorithms applied to NOSLC and the hardware implementation of the NOSLC.
CNN-LIDAR pedestrian classification: combining range and reflectance data

The use of multiple sensors in perception systems is becoming a consensus in the automotive and robotics industries. Camera is the most popular technology, however, radar and LIDAR are increasingly being adopted more often in protection and safety systems for object/obstacle detection. In this paper, we particularly explore the LIDAR sensor as an inter-modality technology which provides two types of data, range (distance) and reflectance (intensity return), and study the influence of high-resolution distance$/$depth (DM) and reflectance maps (RM) on pedestrian classification using a deep Convolutional Neural Network (CNN). Pedestrian protection is critical for advanced driver assistance system (ADAS) and autonomous driving, and it has regained particular attention recently for known reasons. In this work, CNN-LIDAR based pedestrian classification is studied in three distinct cases: (i) having a single modality as input in the CNN, (ii) by combining distance and reflectance measurements at the CNN input-level (early fusion), and (iii) combining outputs scores from two single-modal CNNs (late fusion). Distance and intensity (reflectance) raw data from LIDAR are transformed to high-resolution (dense) maps which allow a direct implementation on CNNs both as single or multi-channel inputs (early fusion approach). In terms of late-fusion, the outputs from individual CNNs are combined by means of non-learning rules, such as: minimum, maximum, average, product. Pedestrian classification is evaluated on a 'binary classification' dataset created from the KITTI Vision Benchmark Suite, and results are shown for the three cases.
Distress Intolerance Moderation of Neurophysiological Markers of Response Inhibition After Induced Stress: Relations With Cannabis Use Disorder

Cannabis use is prevalent but only a minority of regular users develop cannabis use disorder (CUD); thus, CUD risk identification among current cannabis users is vital for targeted intervention development. Existing data suggest that high distress intolerance (DI), an individual difference reflective of the ability to withstand negative affect, is linked to CUD, possibly via stress-elicited impairment of response inhibition but this has never been explicitly tested. Frequent cannabis users with high and low DI completed a go/no-go task during EEG recording before and after a laboratory stressor. Relations between DI, cannabis use-related problems, and behavioral as well as neurophysiological markers of response inhibition functioning were assessed. DI significantly moderated the effect of the stressor on the conflict-monitoring but not evaluative phase of response inhibition as measured by N2 and P3a amplitude, respectively. Unexpectedly, cannabis users with high DI demonstrated stressor-elicited enhancement rather than impairment of conflict-monitoring neural activity, which was related to faster reaction time (RT) and decreased past-month cannabis problems. Enhanced inhibition-related modulation of P3a amplitude was generally associated with increased cannabis problems regardless of acute stress. Results did not provide support for stress-elicited impairment in cognitive control as a mechanism linking high DI and CUD, though some support was found for the relevance of inhibition-related neural activity to CUD. Stress-elicited enhancement of conflict-monitoring neural activity during response inhibition may reflect an adaptive neural response among cannabis users with high DI that protects against CUD in this at-risk group.
Is Antibiotic Prophylaxis Necessary Before Dental Procedures in Patients Post Total Ankle Arthroplasty?

Background: The need for dental antibiotic prophylaxis after orthopedic surgery remains unclear. Current recommendations are based on patients with total hip and total knee arthroplasties. We investigated available evidence regarding the need for dental antibiotic prophylaxis in patients post foot and ankle surgery, specifically total ankle arthroplasty. Additionally, we examined the microbiology behind the risk of transient bacteremia from dental procedures and whether this leads to an increased risk for postoperative infection in foot and ankle surgery. Methods: We performed a MEDLINE literature review of English articles between 1980 and 2018 on patients with prosthetic joints undergoing dental work, and studies evaluating hematogenous prosthetic joint infection (PJI) and dental antibiotic prophylaxis. We additionally included articles on PJI post total ankle arthroplasty, as well as committee guidelines. Results: There is no literature at present that evaluates transient bacteremia with dental procedures in patients following foot and ankle surgery. The data on this topic are isolated to PJI rates in the context of hip and total knee arthroplasty. This is of particular interest as rates of total ankle arthroplasty PJI have been reported to be 2- to 4-fold higher than in hip and total knee arthroplasty. Conclusion: The concern for postoperative infection due to transient bacteremia without dental antibiotic prophylaxis can be extrapolated to patients undergoing foot and ankle surgeries. Some data suggest that oral cavity bacteria can seed a prosthetic joint, though no clear relationship has been demonstrated. Similar risk factors have been identified between hip and knee PJI and total ankle arthroplasty. In light of the absence of scrutiny of and guidelines on this topic in foot and ankle surgery, it may be advisable to apply similar principles to decision-making in patients after foot and ankle surgery. Level of Evidence: Level III, systematic review.
The Psychological Risk of Minimal Risk Activities: A Pre- and Posttest Study Using the Self-Assessment Manikin

It is difficult to apply U.S. Federal Code of Regulation’s criterion for “minimal risk,” because benchmarks of minimal risk have not been quantified. Our goal was to examine the psychological risks of several day-to-day activities. Using the Self-Assessment Manikin (SAM), we assessed the state valence and arousal of 432 patients and employees at a large Midwestern Department of Veterans Affairs medical facility before and after they had their blood drawn, saw their primary care physician or mental health provider, or took part in an exercise class. Exercise was associated with near-large to large salutary effects (Cohen’s d = 0.76-1.17); other effects were small or moderate in positive directions (Cohen’s d = 0.02-0.51). Our findings are a promising start toward establishing benchmarks and quantifying the psychological harms of minimal risk activities. Estimates such as these may help researchers determine whether their own research exceeds minimal risk.
Elderly patients’ (≥65 years) experiences associated with discharge; Development, validity and reliability of the Discharge Care Experiences Survey

Background A review of the literature reveals a lack of validated instruments that particularly measure quality in the hospital discharge process. This study aims to develop and validate a survey instrument feasible for measuring quality (≥65 years) related to the discharge process based on elderly patients’ experiences. Methods Construction of the Discharge Care Patient Experience Survey (DICARES) was based on 16 items identified by literature reviews. Intraclass correlation for test–retest was applied to assess consistency of the survey. Explorative factors analysis was applied to identify and validate the factor structures of the DICARES. Cronbach’s α was used to assess internal reliability. To evaluate the external validity of the final DICARES questionnaire the patients’ scores were correlated with scores obtained from the three other questionnaires; the Nordic Patient Experiences Questionnaire, the 12-Item Short-Form Health Survey and Subjective Health Complaints. The DICARES association with readmissions was examined. Results A total of 270 patients responded (64.4%). The mean age of participants was 77.1 years and 57.8% were men. The exploratory factor analysis resulted in a 10-item instrument consisting of three factors explaining 63.5% of the total variance. The Cronbach’s α were satisfactory (≥70). Overall intraclass correlation was 0.76. A moderate Spearman correlation (rho = 0.54, p <0.01) was found between the total mean DICARES score and total mean score of the Nordic Patient Experiences Questionnaire. The total mean DICARES score was inversely associated with the quality indicator based on readmissions (OR 0.62, CI 95: 0.41–0.95, p = 0.028) Conclusion We have developed a 10-item questionnaire consisting of three factors which may be a feasible instrument for measuring quality of the discharge process in elderly patients. Further testing in a wider population should be carried out before implementation in health care settings.
Indian Banknote Recognition using Convolutional Neural Network

This paper presents a deep learning-based method for identification of denominations of Indian Currency Rupee notes from their color images. A classification framework has been implemented using the concept of transfer learning where a large convolutional neural network pre-trained on millions of natural images is employed for classification of images from new classes. An image dataset of four banknote denominations is prepared by preprocessing and augmentation of real-bank note images acquired in different viewpoints and lighting conditions via smartphone camera. A new top layer upon the convolutional base of a pre-trained MobileNet model is trained for a few epochs upon a portion of the dataset to achieve an agreeable accuracy upon validation subset. With no hassle of feature engineering or extensive preprocessing tasks, the retrained lightweight model achieves an accuracy of 96.6 % on a held out testing subset. Experimental results prove it to be employable for development of dedicated portable systems for identification of banknote denominations.
Analysis of the East Africa Power Pool (EAPP) with Renewable Energy Penetration and Proposed Asynchronous Tie Lines

Integration of Renewable Energy (RE) sources in the interconnected power systems has affected the conventional ways of formulating and solving such multi area problems. In this paper, the Eastern Africa Power Pool (EAPP) with high Renewable Energy (RE) penetration is considered with application of Multi Area Multi Objective Dynamic Economic Dispatch (MAMODED) and Asynchronous Interconnections. A hybrid of Modified Firefly Algorithm with Levy Flights and Derived Mutations (MFA-LF-DM) and Scenario-Based Method (SBM) is proposed for the NP Hard problem. Simulated results reveal that for a MAMODED-based EAPP with high RE penetration, asynchronous tie lines (MTDC in this case) are preferred due to reduced pooling costs and emissions. Further, hybrid approaches need to be applied for its solution as compared to the pure methods.
Modeling Contextual Modulation of Memory Associations in the Hippocampus

We present a computational model of how memories can be contextually acquired and recalled in the hippocampus. Our adaptive contextual memory model comprises the lateral entorhinal cortex (LEC), the dentate gyrus (DG) and areas CA3 and CA1 in the hippocampus, and assumes external inputs about context that originate in the prefrontal cortex (PFC). Specifically, we propose that there is a top-down bias on the excitability of cells in the DG of the hippocampus that recruits a sub-population of cells to differentiate contexts, independent of experienced stimuli, expanding the “pattern separation” role typically attributed to the DG. It has been demonstrated in rats that if PFC is inactivated, both acquisition and recall of memory associations are impaired. However, PFC inactivation during acquisition of one set of memory associations surprisingly leads to subsequent facilitation of the acquisition of a conflicting set of memory associations in the same context under normal PFC operation. We provide here the first computational and algorithmic account of how the absence or presence of the top-down contextual biases on the excitability of DG cells during different learning phases of these experiments explains these data. Our model simulates PFC inactivation as the loss of inhibitory control on DG, which leads to full or partial activation of DG cells related to conflicting memory associations previously acquired in different contexts. This causes context-inappropriate memory traces to become active in the CA3 recurrent network and thereby the output CA1 area within the hippocampus. We show that these incongruous memory patterns proactively interfere with and slow the acquisition of new memory associations. Further, we demonstrate that pattern completion within CA3 in response to a partial cue for the recall of previously acquired memories is also impaired by PFC inactivation for the same reason. Pre-training the model with interfering memories in contexts different from those used in the experiments, simulating a lifetime of experiences, was crucial to reproduce the rat behavioral data. Finally, we made several testable predictions based on the model that suggest future experiments to deepen our understanding of brain-wide memory processes.
On Modeling the STFT Phase of Audio Signals with the Von Mises Distribution

In this paper, we study statistical models for the phase of the short-time Fourier transform (STFT) of audio signals. STFT phase globally appears as uniformly distributed, which has led researchers in this field to model it as a uniform random variable. However, some information about the phase can be obtained from a sinusoidal model, which reveals its local structure. Therefore, we propose to model the phase with a von Mises (VM) random variable, which enables us to favor the sinusoidal model-based phase value. We estimate the distribution parameters and we validate this model on real audio data. In particular, we observe that both models (uniform and VM) are relevant from a statistical perspective but they convey different information about the phase (global vs. local). We also apply this VM model to an audio source separation task, where it outperforms previous approaches.
Fashionable Modelling with Flux

Machine learning as a discipline has seen an incredible surge of interest in recent years due in large part to a perfect storm of new theory, superior tooling, renewed interest in its capabilities. We present in this paper a framework named Flux that shows how further refinement of the core ideas of machine learning, built upon the foundation of the Julia programming language, can yield an environment that is simple, easily modifiable, and performant. We detail the fundamental principles of Flux as a framework for differentiable programming, give examples of models that are implemented within Flux to display many of the language and framework-level features that contribute to its ease of use and high productivity, display internal compiler techniques used to enable the acceleration and performance that lies at the heart of Flux, and finally give an overview of the larger ecosystem that Flux fits inside of.
Inspect Road Quality by Using Anomaly Detection Approach

Road quality can be representative of a country's development status, affect transportation speeds and traveler safety. However, there is no standard or set of rules to protect people from the dangers of damaged roads. This study proposes a method for evaluating road quality, which has been made possible by the recent rapid development of information technology and machine learning algorithms, and the popularity and widespread use of smartphones. The proposed inspection method is a road quality inspection APP which collects raw data from smartphone sensors, including GPS and accelerator sensors. Once the data is collected, the server side of the proposed system runs an anomaly detection algorithm to interpret the recorded oscillating amplitude of a specific section of road. The calculated results are then added to the Google Maps app, and abnormal road sections are marked in different colors.
Single-cell RNA-Seq analysis identifies a noncoding interleukin 4 (IL-4) RNA that post-transcriptionally up-regulates IL-4 production in T helper cells

High-throughput sequencing has revealed a tremendous complexity of cellular transcriptomes, which is partly due to the generation of multiple alternative transcripts from a single gene locus. Because alternative transcripts often have low abundance in bulk cells, the functions of most of these transcripts and their relationship with their canonical counterparts remain unclear. Here we applied single-cell RNA-Seq to analyze the transcriptome complexity of in vitro–differentiated, murine type 2 T helper (Th2) cells. We found that cytokine gene transcripts contribute most of the intercellular heterogeneity, with a group of universal cytokines, including interleukins 1a, 2, 3, and 16, being bimodally expressed. At the single-cell level, use of alternative promoters prevalently generated alternative transcripts. For instance, although undetectable in bulk cells, a noncoding RNA isoform of IL-4 (IL4nc), which was driven by an intronic promoter in the IL-4 locus, was predominantly expressed in a subset of Th2 cells. IL4nc displayed distinct temporal expression patterns compared with the canonical IL-4 mRNA and post-transcriptionally promoted the production of IL-4 protein in Th2 cells. In conclusion, our findings reveal a mechanism whereby minor noncanonical transcripts post-transcriptionally regulate expression of their cognate canonical genes.
Quantum Semantic Correlations in Hate and Non-Hate Speeches

This paper aims to apply the notions of quantum geometry and correlation to the typification of semantic relations between couples of keywords in different documents. In particular we analysed texts classified as hate / non hate speeches, containing the keywords "women", "white", and "black". The paper compares this approach to cosine similarity, a classical methodology, to cast light on the notion of "similar meaning".