Miracle-dz/UniFAR-FTUs huggingface dataset:
A systematic review and meta-analysis of the prevalence of left ventricular non-compaction in adults.

AIMS To assess the reported prevalence of left ventricular non-compaction (LVNC) in different adult cohorts, taking in to consideration the role of diagnostic criteria and imaging modalities used. METHODS AND RESULTS A systematic review and meta-analysis of studies reporting LVNC prevalence in adults. Studies were sourced from Pre-Medline, Medline, and Embase and assessed for eligibility according to inclusion criteria. Eligible studies provided a prevalence of LVNC in adult populations (≥12 years). Studies were assessed, and data extracted by two independent reviewers. Fifty-nine eligible studies documenting LVNC in 67 unique cohorts were included. The majority of studies were assessed as moderate or high risk of bias. The pooled prevalence estimates for LVNC were consistently higher amongst cohorts diagnosed on cardiac magnetic resonance (CMR) imaging (14.79%, n = 26; I2 = 99.45%) compared with echocardiogram (1.28%, n = 36; I2 = 98.17%). This finding was unchanged when analysis was restricted to studies at low or moderate risk of bias. The prevalence of LVNC varied between disease and population representative cohorts. Athletic cohorts demonstrated high pooled prevalence estimates on echocardiogram (3.16%, n = 5; I2 = 97.37%) and CMR imaging (27.29%, n = 2). CONCLUSION Left ventricular non-compaction in adult populations is a poorly defined entity which likely encompasses both physiological adaptation and pathological disease. There is a higher prevalence with the introduction of newer imaging technologies, specifically CMR imaging, which identify LVNC changes more readily. The clinical significance of these findings remains unclear; however, there is significant potential for overdiagnosis, overtreatment, and unnecessary follow-up.
Using Virtual Site Visits in the Clinical Evaluation of Nurse Practitioner Students

Supplemental digital content is available in the text. Background The use of telehealth technology to conduct virtual site visits is an innovative strategy for evaluating the performance of nurse practitioner (NP) students in remote settings. Although there is an abundance of studies on telehealth for the remote monitoring and assessment of patients, there are limited data on its use for evaluating NP students during clinical learning experiences. Purpose The purpose of this project was to understand the perspectives of NP students and faculty on the feasibility of using virtual site visits to evaluate the students' performance during clinical experiences. Methods Online surveys were used to collect student and faculty perspectives on the use of virtual technology during clinical site observations. Results Overall, students and faculty reported positive experiences with the virtual site visits. Conclusion Virtual site visits are feasible in most clinical settings.
Experimental Comparison of Online Anomaly Detection Algorithms

Anomaly detection methods abound and are used extensively in streaming settings in a wide variety of domains. But a strength can also be a weakness; given the vast number of methods, how can one select the best method for their application? Unfortunately, there is no one best way for all do- mains. Existing literature is focused on creating new anomaly detection methods or creating large frameworks for experi- menting with multiple methods at the same time. As the literature continues to grow, extensive evaluation of every avail- able anomaly detection method is not feasible. To reduce this evaluation burden, in this paper we present a framework to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays. We provide a comprehensive experimental validation of multiple anomaly detection methods over different time series charac- teristics to form guidelines. Applying our framework can save time and effort by surfacing the most promising anomaly de- tection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods.
Four seasons of Czech asthma study: asthma characteristics and management reality in the Czech Republic

Abstract Objective: To describe the characteristics and management of asthma in clinical practice in the Czech Republic in the context of international guidelines and clinical realities. Methods: Data were collected over four seasons from summer 2016 to spring 2017 and are mostly presented using descriptive statistics. Results: We obtained valid data for 4557 adult patients with asthma, including detailed phenotyping (71% eosinophilic allergic, 10% eosinophilic non-allergic, 19% non-eosinophilic non-allergic asthma) from 58 allergologists and 56 pulmonologists. The average time to diagnosis was 3 years. In more than half of the subjects, bronchodilator testing (BDT) results were available at primary diagnosis. More than 10% of physicians did not test for mold allergy. Occupational asthma was diagnosed in 0.7% of subjects. According to the attending physician, 68% of patients had well-controlled and 10% had uncontrolled asthma. Ninety-four percent of patients were on preventive treatment, with 91% using an inhaled corticosteroids (ICS) at an average dose of 705 µg/day budesonide equivalent. Approximately 75% of patients were on an ICS/LABA, with 91% using fixed combinations. Among patients using ICS/formoterol, a maintenance and reliever therapy regime was prescribed in 67%. Conclusions: The quality of asthma management in the Czech Republic is comparable to that of other developed countries and better in some respects (frequent BDT, phenotyping, and use of preventive treatment). Nevertheless, there is unnecessary delay in diagnosis and lack of research on possible environmental causes (workplace, molds). Pharmacotherapy shows good adherence to guidelines. Although 10% of patients show poor control, there is concurrently a trend for overtreatment.
The Association between End-of-Life Care and the Time Interval between Provision of a Do-Not-Resuscitate Consent and Death in Cancer Patients in Korea

Purpose We explored the relationship between the use of each medical intervention and the length of time between do-not-resuscitate (DNR) consent and death in Korea. Materials and Methods A total of 295 terminal cancer patients participated in this retrospective study. Invasive interventions (e.g., cardiopulmonary resuscitation, intubation, and hemodialysis), less invasive interventions (e.g., transfusion, antibiotic use, inotropic use, and laboratory tests), and the time interval between the DNR order and death were evaluated. The subjects were divided into three groups based on the amount of time between DNR consent and death (G1, time interval ≤ 1 day; G2, time interval > 1 day to ≤ 3 days; and G3, time interval > 3 days). Results In general, there were fewer transfusions and laboratory tests near death. Invasive interventions tended to be implemented only in the G1 group. There was also less inotrope use and fewer laboratory tests in the G3 group than G1 and G2. Moreover, the G3 group received fewer less invasive interventions than those in G1 (odds ratio [OR], 0.16; 95% confidence interval [CI], 0.03 to 0.84; 3 days before death, and OR, 0.16; 95% CI, 0.04 to 0.59; the day before death). The frequency of less invasive interventions both 1 and 3 days before death was significantly lower for the G3 group than the G1 (p ≤ 0.001) and G2 group compared to G1 (p=0.001). Conclusion Earlier attainment of DNR permission was associated with reduced use of medical intervention. Thus, physicians should discuss death with terminal cancer patients at the earliest practical time to prevent unnecessary and uncomfortable procedures and reduce health care costs.
Automatic Adaptation to Sensor Replacements

Many software systems run on long-lifespan platforms that operate in diverse and dynamic environments. If these software systems could automatically adapt to hardware changes, it would signiﬁcantly reduce the maintenance cost and enable rapid upgrade. In this paper, we study the problem of how to automatically adapt to sensor changes, as an important step towards building such long-lived, survivable software systems. We address the adaptation scenarios where a set of sensors are replaced by new sensors. Our approach reconstructs sensor values of replaced sensors by preserving distributions of sensor values before and after the sensor change, thereby not warranting a change in higher-layer software. Compared to existing work, our approach has the following advantages: a) exploiting new sensors without requiring an overlapping period of time between new sensors and old ones; b) providing an estimation of adaptation quality; and c) scaling to a large number of sensors. Experiments on weather data and Unmanned Undersea Vehicle (UUV) data demonstrate that our approach can automatically adapt to sensor changes with higher accuracy compared to baseline methods.
A Case of Rheumatoid Arthritis and Paget Disease of Bone

Patient: Male, 74 Final Diagnosis: Rheumatoid arthritis (RA) Symptoms: Joint pain • joint swelling Medication: — Clinical Procedure: — Specialty: Rheumatology Objective: Rare co-existance of disease or pathology Background: Paget disease is a primary bone disease with adjacent joint involvement that commonly presents with degenerative arthritis. Rheumatoid arthritis (RA) is mainly an articular disease with symmetric synovitis of the peripheral joints and usually does not involve the lumbar spine. The coexistence of Rheumatoid Arthritis and Paget disease of bone is infrequently reported in the literature. Case Report: A 74-year-old African-American man was referred for the investigation of symmetrical polyarthritis, left upper arm joint deformity, and low back pain. X-ray imaging of the hands demonstrated bilateral erosions and deformities of the carpal bones, and the left arm showed mixed lytic and sclerotic bone lesions. Laboratory tests showed high levels of rheumatoid factor and alkaline phosphatase. Analysis of the synovial fluid from the left knee was consistent with inflammatory joint disease. Conclusions: The purpose of this article is to present the diagnostic challenges associated with the rare coexistence of Rheumatoid Arthritis and Paget disease of bone in the same patient. The correct diagnosis of both diseases has important consequences in preventing treatment delay.
Exploring the Effects of Vitamin D Supplementation on Cognitive Functions and Mental Health Status in Subjects Under Methadone Maintenance Treatment.

OBJECTIVES Vitamin D deficiency may be linked to several mental complications including cognitive deficits, depression, and anxiety in patients under methadone maintenance treatment (MMT). This study was designed to explore the effect of vitamin D supplementation on cognitive functions and mental health parameters in subjects under MMT. METHODS This randomized, double-blinded, placebo-controlled clinical trial was carried out among 64 patients under MMT. Participants were randomly allocated to receive either 50,000 IU vitamin D supplements (n = 32) or placebo (n = 32) every 2 weeks for 24 weeks. Cognitive functions and mental health parameters were taken at baseline and posttreatment to evaluate relevant variables. RESULTS After the 24-week intervention, compared with the placebo, serum 25(OH) vitamin D levels significantly increased in participants who received vitamin D supplements (β 14.50; 95% confidence interval [CI], 13.17-15.83; P < 0.001). In addition, compared with the placebo, subjects who received vitamin D had a significant reduction in Iowa Gambling Task (β -6.25; 95% CI, -8.60 to -3.90; P < 0.001), and significant increases in Verbal Fluency Test (β 2.82; 95% CI, 0.78-4.86; P = 0.007), Immediate Logic Memory (β 1. 32; 95% CI, 0.27-2.37; P = 0.01), Reverse Digit Span (β 2.06; 95% CI, 1.18-2.94; P < 0.001) and visual working memory (β 0.75; 95% CI, 0.33-1.16; P = 0.001). Also, vitamin D supplementation significantly improved BDI (β -2.76; 95% CI, -3.97 to -1.55; P < 0.001) compared with the placebo. When we applied Bonferroni correction, LM-Immediate (P = 0.07) became nonsignificant, and other mental health parameters did not alter. CONCLUSIONS Overall, taking 50,000 IU vitamin D supplements every 2 weeks for 24 weeks by patients under MMT had beneficial effects on cognitive functions and some mental health parameters. Further studies are needed to confirm our findings.
Evaluation of Exclusion Netting for Spotted-Wing Drosophila (Diptera: Drosophilidae) Management in Minnesota Wine Grapes

Abstract Spotted-wing drosophila, Drosophila suzukii (Matsumura), an economically damaging invasive species of numerous fruit crops, was first detected in Minnesota in 2012. High fecundity, and short generation times facilitated a rapid rise in the global pest status of D. suzukii, particularly in North America and Europe. To date, the majority of crop injury research has focused on fruit crops such as blueberries, raspberries, and cherries. However, little is known regarding the impact of D. suzukii on the wine grape industry in the upper Midwest region of the United States. Field trials were conducted in Minnesota during the summers of 2017–2018 to examine season-long phenology of D. suzukii in wine grape vineyards and wineries, and to assess the efficacy of exclusion netting for control of D. suzukii. Four treatments were evaluated, 1) open plot check (control), 2) open plot treated with an insecticide, 3) exclusion netting, and 4) exclusion netting, with artificial infestations of D. suzukii adults. Exclusion netting was applied at véraison and removed at harvest. On each sample date, 20 berries (10 intact and 10 injured) were collected from each plot for dissection. The number of larvae and adults were recorded for each berry to determine infestation levels. As shown by mean larval infestations and injured berries across treatments, exclusion netting provided a significant reduction in the level of D. suzukii infested berries when compared with the untreated check. These results indicate that exclusion netting could provide an effective alternative management strategy for D. suzukii in wine grapes.
Interaction graphs: visual analysis of eye movement data from interactive stimuli

Eye tracking studies have been conducted to understand the visual attention in different scenarios like, for example, how people read text, which graphical elements in a visualization are frequently attended, how they drive a car, or how they behave during a shopping task. All of these scenarios - either static or dynamic - show a visual stimulus in which the spectators are not able to change the visual content they see. This is different if interaction is allowed like in (graphical) user interfaces (UIs), integrated development environments (IDEs), dynamic web pages (with different user-defined states), or interactive displays in general as in human-computer interaction, which gives a viewer the opportunity to actively change the stimulus content. Typically, for the analysis and visualization of time-varying visual attention paid to a web page, there is a big difference for the analytics and visualization approaches - algorithmically as well as visually - if the presented web page stimulus is static or dynamic, i.e. time-varying, or dynamic in the sense that user interaction is allowed. In this paper we discuss the challenges for visual analysis concepts in order to analyze the recorded data, in particular, with the goal to improve interactive stimuli, i.e., the layout of a web page, but also the interaction concept. We describe a data model which leads to interaction graphs, a possible way to analyze and visualize this kind of eye movement data.
Cordycepin‐induced unfolded protein response‐dependent cell death, and AKT/MAPK‐mediated drug resistance in mouse testicular tumor cells

Testicular cancer is the most commonly diagnosed cancer in men at 15‐44 years of age, and radical orchidectomy combined with chemotherapy is currently considered as the standard treatment. However, drugs resistance and side effects that impact the quality of life for patients with testicular cancer have not been markedly improved in recent decades. In this study, we characterized the pharmacological exacerbation of the unfolded protein response (UPR), which is an effective approach to kill testicular cancer cells, by carrying out a clustering analysis of mRNA expression profiles and the immunobloting examination of cordycepin‐treated MA‐10 cells. The UPR is executed in response to endoplasmic reticulum stress to complement by an apoptotic response if the defect cannot be resolved. Results showed that cordycepin significantly modulated FoxO/P15/P27, PERK‐eIF2α (apoptotic), and the IRE1‐XBP1 (adaptive) UPR pathways. Interestingly, a fraction of MA‐10 cells survived after cordycepin treatment, the AKT, LC3 I/II, and MAPK signaling pathways were highly induced in attached cells as compared to the suspended cells, illustrating the drug resistance to cordycepin via activating AKT and MAPK pathways in MA‐10 cells. In summary, PERK‐eIF2α signaling pathway is required for pro‐apoptotic UPR in MA‐10 cell death following cordycepin treatment, suggesting a potential therapeutic application in treating testicular cancer. However, activation of AKT and MAPK pathways could possibly result in drug resistance to cordycepin in MA‐10 cells.
Exploiting Markov Random Fields to Enhance Retrieval in Case-Based Reasoning

The similarity assumption in Case-Based Reasoning (similar problems have similar solutions) has been questioned by several researchers. If knowledge about the adaptability of solutions is available, it can be exploited in order to guide retrieval. Several approaches have been proposed in this context, often assuming a similarity or cost measure deﬁned over the solution space. In this paper, we propose a novel approach where the adaptability of the solutions is captured inside a metric Markov Random Field (MRF). Each case is represented as a node in the MRF, and edges connect cases whose solutions are close in the solution space. States of the nodes represent the adaptability effort with respect to the query. Po-tentals are deﬁned to enforce connected nodes to share the same state; this models the fact that cases having similar solutions should have the same adaptability effort with respect to the query. The main goal is to enlarge the set of potentially adaptable cases that are retrieved (the recall) without signiﬁ-cantly sacriﬁcing the precision of retrieval. We will report on some experiments concerning a retrieval architecture where a simple kNN retrieval is followed by a further retrieval step based on MRF inference.
Transverse Urea‐Gradient Gel Electrophoresis

Monitoring the cooperative unfolding transition induced when a protein is exposed to elevated temperature or a chemical denaturant is an important strategy for characterizing the conformational properties of a globular protein. This transition may be analyzed quantitatively by a variety of spectroscopic techniques, but a simpler alternative is described in this unit: urea‐gradient gel electrophoresis. The pattern produced in the resulting gel can be used to estimate both the free energy change for unfolding and the rate of the unfolding transition. In addition, the technique can help identify either covalent or conformational heterogeneity in a protein sample. Because urea‐gradient gel patterns are sensitive to several parameters, including hydrodynamic volume, net charge, and conformational stability, the technique can be particularly useful for comparing two forms of a protein, e.g., a natural form and the product of recombinant bacteria.
Validation of Polymer-Based Screen-Printed Textile Electrodes for Surface EMG Detection

In recent years, the variety of textile electrodes developed for electrophysiological signal detection has increased rapidly. Among the applications that could benefit from this advancement, those based on surface electromyography (sEMG) are particularly relevant in rehabilitation, training, and muscle function assessment. In this work, we validate the performance of polymer-based screen-printed textile electrodes for sEMG signal detection. We obtained these electrodes by depositing poly-3,4-ethylenedioxythiophene doped with poly(styrene sulfonate) (PEDOT:PSS) onto cotton fabric, and then selectively changing the physical properties of the textile substrate. The manufacturing costs are low and this process meets the requirements of textile-industry production lines. The validation of these electrodes was based on their functional and electrical characteristics, assessed for two different electrode sizes and three skin-interface conditions (dry, solid hydrogel, or saline solution), and compared to those of conventional disposable gelled electrodes. Results show high similarity in terms of noise amplitude and electrode-skin impedance between the conventional and textile electrodes with the addition of solid hydrogel or saline solution. Furthermore, we compared the shape of the electrically induced sEMG, as detected by conventional and textile electrodes from tibialis anterior. The comparison yielded an $\text{R}^{\textsf {2}}$ value higher than 97% for all measurement conditions. Preliminary tests in dynamic conditions (walking) revealed the exploitability of the proposed electrode technology with saline application for the monitoring of sEMG for up to 35 min of activity. These results suggest that the proposed screen-printed textile electrodes may be an effective alternative to the conventional gelled electrodes for sEMG acquisition, thereby providing new opportunities in clinical and wellness fields.
Identifying the Focus of Negation Using Discourse Structure

This paper presents experimental results showing that discourse structure is a useful element in identifying the focus of negation. We deﬁne features extracted from RST-like discourse trees. We experiment with the largest publicly available corpus and an off-the-shelf discourse parser. Results show that discourse structure is especially beneﬁcial when predicting the focus of negation in long sentences.
Different Flavors of Attention Networks for Argument Mining

Argument mining is a rising area of Natural Language Processing (NLP) concerned with the automatic recognition and interpretation of argument components and their relations. Neural models are by now mature technologies to be exploited for automating the argument mining tasks, despite the issue of data sparseness. This could ease much of the manual effort involved in these tasks, taking into account heterogeneous types of texts and topics. In this work, we evaluate different attention mechanisms applied over a state-of-the-art architecture for sequence labeling. We assess the impact of different flavors of attention in the task of argument component detection over two datasets: essays and legal domain. We show that attention not only models the problem better but also supports interpretability.
Implicit Discourse Relation Classification with Syntax-Aware Contextualized Word Representations

Automatically identifying implicit discourse relations requires an in-depth semantic understanding of the text fragments involved in such relations. While early work investigated the usefulness of different classes of input features, current state-of-the-art models mostly rely on standard pre-trained word embeddings to model the arguments of a discourse relation. In this paper, we introduce a method to compute contextualized representations of words, leveraging information from the sentence dependency parse, to improve argument representation. The resulting token embeddings encode the structure of the sentence from a dependency point of view in their representations. Experimental results show that the proposed representations achieve state-of-the-art results when input to standard neural network architectures, surpassing complex models that use additional data and consider the interaction between arguments.