Miracle-dz/UniFAR-FTUs huggingface dataset:
The Emerging Resistance in Nosocomial Urinary Tract Infections: From the Pediatrics Perspective

Background Healthcare-associated infections results in increased health care costs and mortality. There are limited studies concerning the distribution of the etiologic agents and the resistance patterns of the microorganisms causing healthcare-associated urinary tract infections (HA-UTI) in pediatric settings. Objectives The aim of this study was to evaluate the distribution and antibiotic susceptibility patterns of pathogens causing HA-UTI in children. Material and Methods Isolates from 138 children with UTI who were hospitalized in pediatric, neonatal and pediatric surgery intensive care units were reviewed. Results Most common isolated organism was Klebsiella pneumoniae (34.1%) and Escherichia coli (26.8%). Among the Pseudomonas aeruginosa, Meropenem and imipenem resistance rates were 46.2% and 38.5%. Extended-spectrum beta-lactamase (ESBL) production was present in 48 Klebsiella species (82.8%). Among ESBL positive Klebsiella species, the rate of meropenem and imipenem resistance was 18.8%, and ertapenem resistance was 45.9%. Extended spectrum beta-lactamase production was present in 27 (72.9%) Escherichia coli species. Among ESBL positive E. coli, the rate of meropenem and imipenem resistance was 7.4%, and ertapenem resistance was 14.8% Conclusions Emerging meropenem resistance in P. aeruginosa, higher rates of ertapenem resistance in ESBL positive ones in E. coli and Klebsiella species in pediatric nosocomial UTI are important notifying signs for superbug infections.
Event Detection in Wireless Body Area Networks Using Kalman Filter and Power Divergence

The collected data by biomedical sensors must be analyzed for automatic detection of physiological changes. The early identification of an event in collected data is required to trigger an alarm upon detection of patient health degradation. Such alarms inform healthcare professionals and allow them to quickly react by taking appropriate actions. However, events result from physiological change or faulty measurements, and lead to false alarms and unnecessary medical intervention. In this paper, we propose a framework for automatic detection of events from collected data by biomedical sensors. The proposed approach is based on the Kalman filter to forecast the current measurement and to derive the baseline of the time series. The power divergence is used to measure the distance between the forecasted and measured values. When a change occurs, this metric significantly deviates from past values. To distinguish emergency events from faulty measurements, we exploit the spatial correlation between the monitored attributes. We conduct experiments on real physiological data set and our results show that our proposed framework achieves a good detection accuracy with a low false alarm rate. Its simplicity and processing speed make our proposed framework efficient and effective for real-world deployment.
Colorectal cancer screening program using FIT: quality of colonoscopy varies according to hospital type

Abstract Background and study aims To compare the quality of colonoscopy in a population-based coordinated program of colorectal cancer screening according to type of hospital (academic or non-academic). Patients and methods Consecutive patients undergoing colonoscopy after positive FIT (≥ 20 ug Hb/g feces) between January 2009 and September 2016 were prospectively included at five academic and seven non-academic public hospitals. Screening colonoscopy quality indicators considered were adenoma detection rate, cecal intubation rate, complications and bowel preparation quality. Results A total of 48,759 patients underwent colonoscopy, 34,616 (80 %) in academic hospitals and 14,143 in non-academic hospitals. Among these cases, 19,942 (37.1 %) advanced adenomas and 2,607 (5.3 %) colorectal cancers (CRCs) were detected, representing a total of 22,549 (46.2 %) cases of advanced neoplasia. The adenoma detection rate was 64 %, 63.1 % in academic hospitals and 66.4 % in non-academic hospitals (P < 0.001). Rates of advanced adenoma detection, cecal intubation and adequate colonic preparation were 45.8 %, 96.2 % and 88.3 %, respectively, and in all cases were lower (implying worse quality care) in academic hospitals (45.3 % vs 48.7 %; odds ratio [OR] 0.87, 95 % confidence interval [CI] 0.84 – 0.91; 95.9 % vs 97 %; OR 0.48, 95 % CI 0.38 – 0.69; and 86.4 % vs 93 %; OR 0.48, 95 % CI 0.45 – 0.5; respectively; P < 0.001 in all cases). In 13 patients, all in the academic hospital group, CRC was diagnosed after colonoscopy (0.26 cases × 1000 colonoscopies). Rates of CRC treated by endoscopy were similar in both types of hospital (30 %). The rate of severe complication was 1.2 % (602 patients), with no significant differences by hospital type: bleeding occurred in 1/147 colonoscopies and perforation in 1/329. One patient died within 30 days after screening colonoscopy. Conclusions The quality of colonoscopy was better in non-academic hospitals. The rate of detection of advanced neoplasia was higher in non-academic hospitals and correlated with the rate of post-colonoscopy CRC.
On the Beneficial Roles of Fading and Transmit Diversity in Wireless Power Transfer With Nonlinear Energy Harvesting

We study the effect of channel fading in wireless power transfer (WPT) and show that fading enhances the RF-to-DC conversion efficiency of nonlinear RF energy harvesters. We then develop a new form of signal design for WPT, denoted as transmit diversity, that relies on multiple dumb antennas at the transmitter to induce fast fluctuations of the wireless channel. Those fluctuations boost the RF-to-DC conversion efficiency thanks to the energy harvester nonlinearity. In contrast with (energy) beamforming, transmit diversity does not rely on channel state information at the transmitter (CSIT) and does not increase the average power at the energy harvester input, even though it still enhances the overall end-to-end power transfer efficiency. Transmit diversity is also combined with recently developed (energy) waveform and modulation to provide further enhancements. The efficacy of the scheme is analyzed using physics-based and curve fitting-based nonlinear models of the energy harvester and demonstrated using circuit simulations, prototyping and experimentation. Measurements with two transmit antennas reveal gains of 50% in harvested DC power over a single transmit antenna setup. The work (again) highlights the crucial role played by the harvester nonlinearity and demonstrates that multiple transmit antennas can be beneficial to WPT even in the absence of CSIT.
Diagnostic Accuracy of BD Phoenix CPO Detect for Carbapenemase Production in 190 Enterobacteriaceae Isolates

The rapid and accurate detection of carbapenemase-producing Enterobacteriaceae (CPE) is necessary for patient management and infection control measures. We compared the performance of the BD Phoenix CPO Detect with that of a homemade Carba NP assay and a modified carbapenem inactivation method (mCIM) by challenging all 3 assays with 190 isolates of Enterobacteriaceae with meropenem MICs of >0.125 mg/liter. ABSTRACT The rapid and accurate detection of carbapenemase-producing Enterobacteriaceae (CPE) is necessary for patient management and infection control measures. We compared the performance of the BD Phoenix CPO Detect with that of a homemade Carba NP assay and a modified carbapenem inactivation method (mCIM) by challenging all 3 assays with 190 isolates of Enterobacteriaceae with meropenem MICs of >0.125 mg/liter. A total of 160 isolates produced KPC-, IMI-1-, NDM-, IMP-, and OXA-type carbapenemases, while 30 isolates were negative for carbapenemase production. The sensitivity and specificity were 90.6% (95% confidence interval [CI], 85.0% to 94.7%) and 100.0% (95% CI, 88.4% to 100.0%), respectively, for the Carba NP; 100.0% (95% CI, 97.7% to 100.0%) and 96.7% (95% CI, 82.7% to 99.9%), respectively, for the mCIM; and 89.4% (95% CI, 83.5% to 93.7%) and 66.7% (95% CI, 47.2% to 82.7%), respectively, for the BD Phoenix CPO Detect. In particular, the BD CPO Detect failed to detect a significant number of CPE with IMI-1. While the BD Phoenix CPO Detect is able to classify carbapenemases and is built into routine susceptibility testing with the potential to reduce the time to CPE detection, its low specificity means that a positive result will need confirmatory testing by another method.
Discovering Public Health Nurse–Specific Family Home Visiting Intervention Patterns Using Visualization Techniques

Visualization is a Big Data method for detecting and validating previously unknown and hidden patterns within large data sets. This study used visualization techniques to discover and test novel patterns in public health nurse (PHN)–client–risk–intervention–outcome relationships. To understand the mechanism underlying risk reduction among high risk mothers, data representing complex social interventions were visualized in a series of three steps, and analyzed with other important contextual factors using standard descriptive and inferential statistics. Overall, client risk decreased after clients received personally tailored PHN services. Clinically important and unique PHN–client–risk–intervention–outcome patterns were discovered through pattern detection using streamgraphs, heat maps, and parallel coordinates techniques. Statistical evaluation validated that PHN intervention tailoring leads to improved client outcomes. The study demonstrates the importance of exploring data to discover ways to improve care quality and client outcomes. Further research is needed to examine additional factors that may influence PHN–client–risk–intervention–outcome patterns, and to test these methods with other data sets.
Endobronchial coil therapy in severe emphysema: 6-month outcomes from a Swiss National Registry.

Background Endobronchial coils have been demonstrated in three randomized, controlled trials to improve quality of life, exercise tolerance, and lung function in patients with severe emphysema. This therapy is CE-marked and commercially available in Switzerland. Coil treated patients are followed in a post-market Swiss registry to collect safety and effectiveness data in routine clinical practice. Methods The Swiss coil registry was initiated in October 2013. At the end of November 2016, an interim analysis of all 64 patients treated in five centers was performed to evaluate safety and effectiveness at six months post treatment. Results patients had completed bilateral treatment with 6-month follow up at the time of data analysis. Patients had very severe, symptomatic emphysema and hyperinflation [38% male, mean age 66 years, BMI 24, FEV1 30% pred. , residual volume (RV) 247% pred. , 6-minute walking distance (6-MWD) 272 m, St. George Respiratory Questionnaire (SGRQ) 57 points]. Up to 6 months following treatment, seven serious adverse events (SAE) were reported in 6/29 patients. No device removals were necessary. At 6 months, responder rates [% achieving the minimal clinically important difference (MCID)] were as follows: RV (-0.35 L) 76%; FEV1 (+10%) 57%; SGRQ (-4 points) 87%; 6MWD (+26 m) 60%. Conclusions Endobronchial coil therapy performed in expert centers in Switzerland yields high 6-month responder rates across all relevant outcome.
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically through clever construction of a search space paired with simple learning algorithms. Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks. An open question is the degree to which such methods may generalize to new domains. In this work we explore the construction of meta-learning techniques for dense image prediction focused on the tasks of scene parsing, person-part segmentation, and semantic image segmentation. Constructing viable search spaces in this domain is challenging because of the multi-scale representation of visual information and the necessity to operate on high resolution imagery. Based on a survey of techniques in dense image prediction, we construct a recursive search space and demonstrate that even with efficient random search, we can identify architectures that outperform human-invented architectures and achieve state-of-the-art performance on three dense prediction tasks including 82.7% on Cityscapes (street scene parsing), 71.3% on PASCAL-Person-Part (person-part segmentation), and 87.9% on PASCAL VOC 2012 (semantic image segmentation). Additionally, the resulting architecture is more computationally efficient, requiring half the parameters and half the computational cost as previous state of the art systems.
Process Mining and Robotic Process Automation: A Perfect Match

Robotic process automation (RPA) provides a virtual workforce to automatize manual, repetitive and error-prone tasks. However, successful process automation requires knowledge about the potential for automation, effective training of the robots and continuous monitoring of their performance. Within this paper, we illustrate how process mining supports organizations throughout the lifecycle of RPA initiatives. Our process mining application provides a visual and fact-based proof for automation capabilities and enables prioritization of activities. The application further supports the user by monitoring and benchmarking robots to ensure sustainable benefits. These insights are especially valuable for process managers and miners with a special interest in process automation.
Simultaneous Cascaded Regression

This paper addresses the problem of localizing facial landmarks with deformable face models using cascaded regression strategies. Recently, these methods have become quite popular, standing out as simple and efficient approaches to optimize nonlinear objective functions. In this paper, we target the well-known Lucas and Kanade (LK) image alignment formulation and introduce the Simultaneous Cascaded Regression (SCR) technique, which can be considered as a cascaded regression extension of the Simultaneous Forwards Additive / Inverse Composition approaches. In contrast to previous LK techniques (Newton based optimizations) which require to recompute Jacobian and Hessians matrices at each iteration, our approach learns (offline) a sequence of descent directions, effectively behaving as averaged steepest descent matrices. Under this revised technique, we propose a part-based generative model (with a linear warp function), that accounts with the underlying shape and appearance structure embedded into regression process itself. Our method is validated on a number of experiments on several datasets (LFPW, LFW, HELEN, 300W), demonstrating a noticeable gain in accuracy/fitting performance when compared with other face alignment solutions.
Hypothalamic CNTF volume transmission shapes cortical noradrenergic excitability upon acute stress

Stress‐induced cortical alertness is maintained by a heightened excitability of noradrenergic neurons innervating, notably, the prefrontal cortex. However, neither the signaling axis linking hypothalamic activation to delayed and lasting noradrenergic excitability nor the molecular cascade gating noradrenaline synthesis is defined. Here, we show that hypothalamic corticotropin‐releasing hormone‐releasing neurons innervate ependymal cells of the 3rd ventricle to induce ciliary neurotrophic factor (CNTF) release for transport through the brain's aqueductal system. CNTF binding to its cognate receptors on norepinephrinergic neurons in the locus coeruleus then initiates sequential phosphorylation of extracellular signal‐regulated kinase 1 and tyrosine hydroxylase with the Ca2+‐sensor secretagogin ensuring activity dependence in both rodent and human brains. Both CNTF and secretagogin ablation occlude stress‐induced cortical norepinephrine synthesis, ensuing neuronal excitation and behavioral stereotypes. Cumulatively, we identify a multimodal pathway that is rate‐limited by CNTF volume transmission and poised to directly convert hypothalamic activation into long‐lasting cortical excitability following acute stress.
A Low Early High-density Lipoprotein Cholesterol Level Is an Independent Predictor of In-hospital Death in Patients with Acute Coronary Syndrome

Objective In patients with acute coronary syndrome (ACS), low high-density lipoprotein cholesterol (HDL-C) levels in samples collected after an overnight fast are diagnostic indicators and well-established predictors of adverse outcomes. However, the relationship between the HDL-C levels in samples collected just after arrival (early HDL-C) and in-hospital mortality remains unknown. The purposes of the present ACS study were to (1) evaluate the association between the early HDL-C levels of patients and in-hospital mortality and (2) compare the early HDL-C level with other well-known determinants associated with in-hospital mortality. Methods This retrospective study surveyed 638 consecutive ACS patients and then assessed the possible determinants of in-hospital mortality. All initial blood samples, including that for early HDL-C, were drawn within one hour of arrival. Results In the present study, the overall in-hospital mortality was 5.9%. A multivariable analysis showed that a low early HDL-C [odds ratio (OR) 2.53, 95% confidence interval (CI) 1.14-5.62], elevated troponin T (OR 4.40, 95% CI 1.26-15.29) and high Killip class (OR 15.41, 95% CI 7.29-32.59) were independent predictors of in-hospital mortality. A Kaplan-Meier survival analysis indicated that there the in-hospital outcome for the low early HDL-C group was significantly worse than that for the high early HDL-C group (age- and gender-adjusted hazard ratio 2.40, 95% CI 1.15-5.00, p=0.02). Conclusion ACS patients with low early HDL-C levels had higher in-hospital mortalities than those who did not have low early HDL-C levels. In addition to the already well-known determinants, low early HDL-C should also be considered as an independent predictor of in-hospital mortality in ACS patients who present to a cardiac care unit.
An Innovative Framework for Supporting Cognitive-Based Big Data Analytics for Frequent Pattern Mining

The increasing size of modern applications and services produces huge volumes of a wide variety of valuable data of different veracity at a high velocity, which in turn leads to a new challenge to big data analytics. Researchers often use these 5V’s (volume, variety, value, veracity, and velocity) to describe the features of big data. The interest of discovering patterns from a large collection of data has risen in business for transforming goods into services. Rich sources of big data include complex sensing-centered service systems. Embedded in these big data are useful information and knowledge. In this paper, we present an innovative framework for supporting cognitive-based big data analytics for frequent pattern mining. Evaluation results show the applicability of our framework to support cognitive computing for big data analytics of frequent patterns.
Pop-Out Tetris: An Implementation of a Tablet FTVR Game

In this paper, we apply a Fish Tank Virtual Reality technique to a commodity tablet. Our first goal is to develop a tablet-based FTVR system that does not require additional enhancement to a tablet because it would allow tablet user to use the technology quickly and easily. To address this goal, we adopt a FTVR technique based on previous works and use it to develop our tablet FTVR prototype. It uses a camera-based face tracking and the anaglyph 3D display techniques. Our second goal is to determine that whether it is feasible to use tablet-based FTVR for entertainment and other mobile applications in daily life. To address the second goal, we implemented a game for our prototype called “pop-out Tetris Then we assess our prototype and the game using heuristic evaluation technique. We describe the technique that we used to develop our tablet FTVR prototype. The technique is based on previous works; it uses a camera-based face tracking and the anaglyph 3D display techniques. Therefore, it does not require any additional enhancement to a tablet. In addition to the prototype, we also implemented a game for tablet FTVR called “pop-out Tetris we describe our design of the application. Then we assess our prototype and the game using heuristic evaluation technique. In the end, we concluded that the technique is not ready for day-to-day use case scenario. We underline key points that affect the adoption of tablet-based FTVR technology and provide recommendations for future research to consider when developing a mobile FTVR system.
Improving Network Capacity Scaling Law in Ultra-Dense Small Cell Networks

In this paper, we investigate the limitation of multi-user multiple-input multiple-output (MIMO) in ultra-dense networks (UDNs) and investigate how to overcome the limitation by designing efficient interference management strategies. Specifically, it is shown that the area spectral efficiency (ASE), an indicator to network capacity, would approach zero with over-deployed base stations (BSs) even when multi-user MIMO is applied. Worse still, it manifests that the multi-user gain of MIMO cannot be harvested in UDN due to the overwhelming interference. In particular, the maximal ASE is shown to be degraded by increasing the number of served users in each cell. To alleviate the bottleneck brought by interference, we have designed and optimized two simple but efficient BS activation policies. It is shown that the application of the optimized BS activation policy could improve network capacity scaling law and boost the potential of multi-user MIMO in UDN. Remarkably, the ASE is shown to increase with BS density <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> even when <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> and user density are sufficiently large. Moreover, the maximal ASE in the <inline-formula> <tex-math notation="LaTeX">$\lambda \rightarrow \infty $ </tex-math></inline-formula> regime is shown to increase with the number of served users in each cell, which contradicts with the all-BS-on case.