Miracle-dz/UniFAR-FTUs huggingface dataset:
Rifampin Stability in 7H9 Broth and Löwenstein-Jensen Medium

ABSTRACT The objectives of this study were to monitor the stability of rifampin (RIF) in Löwenstein-Jensen medium (L-J medium) and 7H9 broth, which are the media commonly used for drug susceptibility testing (DST) of Mycobacterium tuberculosis. Rifampin degradation in stock solution, 7H9 broth, and L-J medium and during the inspissation process for L-J medium preparation was serially monitored by high-performance liquid chromatography (HPLC). L-J medium-based DST was conducted to examine the effect of L-J medium storage on the DST outcome. The RIF stock solution was stable for at least 3 months when kept at either 4°C or −20°C; RIF in 7H9 broth and L-J medium was almost 50% decayed after 1 week of storage at 37°C, and rifampin could not be detected in 7H9 or L-J medium after 3 weeks or 6 weeks of storage at 37°C. Approximately half of the drug was decomposed after 4 months of storage at 4°C for both media, and after 6 months of storage at 4°C, RIF in L-J medium was undetectable, while 38% of RIF remained in 7H9 medium. Approximately 21, 24, 29, and 35% RIF degradations were detected when the L-J medium was coagulated at 75°C, 80°C, 85°C, and 90°C, respectively. The DST outcomes when using L-J medium stored for different periods of time were consistent with the RIF concentration monitoring data. Rifampin in stock solution is stable for at least 3 months at a reduced storage temperature. Media containing RIF should be prepared strictly according to validated standard operating procedures. RIF degradation is a possible reason for false resistance categorizations of M. tuberculosis isolates in the clinical laboratory.
A comparison of the accuracy of two sets of diagnostic criteria in the early detection of complex regional pain syndrome following surgical treatment of distal radial fractures

A total of 120 patients were examined for the presence of symptoms of complex regional pain syndrome after surgical treatment of a distal radial fracture. The patients were assessed at six weeks and 71 of them were also assessed at 12 weeks. The International Association for the Study of Pain criteria and the complex regional pain syndrome severity score were used to make the diagnosis. At six weeks, ten patients (8.3%) met the criteria of complex regional pain syndrome in both the International Association for the Study of Pain and complex regional pain syndrome scores. At 12 weeks six patients (8.4%) met International Association for the Study of Pain and two (2.8%) patients the complex regional pain syndrome severity score criteria. Only one of the patients diagnosed with complex regional pain syndrome required treatment for the complex regional pain syndrome. In all the other patients the features of complex regional pain syndrome settled spontaneously. Our results suggest that complex regional pain syndrome after distal radius fractures occurs less frequently than was previously reported. The International Association for the Study of Pain criteria and the complex regional pain syndrome severity scores showed similar sensitivity in early diagnosis of complex regional pain syndrome, but both are poor indicators of the need for treatment.
Use of Automated Urine Microscopy Analysis in Clinical Diagnosis of Urinary Tract Infection: Defining an Optimal Diagnostic Score in an Academic Medical Center Population

ABSTRACT A retrospective case record study was conducted that established a scoring tool based on clinical and iQ200 parameters, able to predict or rule out the clinical diagnosis of UTI in the majority of adult patients in an academic hospital. Automated standardized quantitative urine analysis, such as iQ200 analysis, is on the rise because of its high accuracy and efficiency compared to those of traditional urine analysis. Previous research on automated urinalysis focused mainly on predicting culture results but not on the clinical diagnosis of urinary tract infection (UTI). A retrospective analysis was conducted of consecutive urine samples sent in for culture because of suspected UTI. UTI was defined by expert opinion, based on reported symptoms, conventional urine sediment analysis, and urine cultures. Parameters of iQ200 analysis and clinical symptoms and signs were compared between cases and controls. Optimal cutoff values were determined for iQ200 parameters, and multivariate logistic regression analysis was used to identify the set of variables that best predicts the clinical diagnosis of UTI for development of a scoring tool. A total of 382 patients were included. Optimal cutoff values of iQ200 analysis were 74 white blood cells (WBC)/μl, 6,250 “all small particles” (ASP)/μl, and a bacterial score of 2 on an ordinal scale of 0 to 5. The scoring tool attributed 1 point for frequent micturition or increased urge, 2 points for dysuria, 1 point for a bacterial score of ≥2, 2 points for WBC/μl of ≥50, and an additional point for WBC/μl of ≥150. This score had a sensitivity of 86% and a specificity of 92% when using a threshold of <4 points. The combination of iQ200 analysis and a simple survey could predict or rule out UTIs in a majority of patients in an academic medical center.
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights

Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships. A predominant approach to collaborative filtering is neighborhood based ("k-nearest neighbors"), where a user-item preference rating is interpolated from ratings of similar items and/or users. We enhance the neighborhood-based approach leading to substantial improvement of prediction accuracy, without a meaningful increase in running time. First, we remove certain so-called "global effects" from the data to make the ratings more comparable, thereby improving interpolation accuracy. Second, we show how to simultaneously derive interpolation weights for all nearest neighbors, unlike previous approaches where each weight is computed separately. By globally solving a suitable optimization problem, this simultaneous interpolation accounts for the many interactions between neighbors leading to improved accuracy. Our method is very fast in practice, generating a prediction in about 0.2 milliseconds. Importantly, it does not require training many parameters or a lengthy preprocessing, making it very practical for large scale applications. Finally, we show how to apply these methods to the perceivably much slower user-oriented approach. To this end, we suggest a novel scheme for low dimensional embedding of the users. We evaluate these methods on the netflix dataset, where they deliver significantly better results than the commercial netflix cinematch recommender system.
Candida palmioleophila: Characterization of a Previously Overlooked Pathogen and Its Unique Susceptibility Profile in Comparison with Five Related Species

ABSTRACT Candida palmioleophila has previously been misidentified as C. famata or C. guilliermondii. We have investigated traditional and modern identification methods for the identification of this and related species. Forty-one clinical isolates previously identified as C. famata or C. guilliermondii and 8 reference strains were included. Color development on CHROMagar, growth temperature ranges, micromorphologies, carbon assimilation (ID32C), matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) profiles, and susceptibility profiles (mica- and anidulafungin and itra-, vori-, posa-, and fluconazole MICs were determined by EUCAST method EDef 7.1, and caspofungin MICs were determined by Etest) were determined, and results were compared to those of molecular identification (ITS1 and ITS2 sequencing). The following five different species were identified among the clinical isolates by sequencing, but no C. famata isolates were found: C. guilliermondii (22 isolates), C. palmioleophila (8 isolates), C. fermentati (6 isolates), C. lusitaniae (3 isolates), and C. intermedia (2 isolates). C. palmioleophila developed a distinct scintillating color of turquoise to rose, grew at 40°C, and failed to produce pseudohyphae within 14 days. The ID32C profile for 7/9 C. palmioleophila isolates was 5367352315, and all were unable to hydrolyze esculin (Esc). The six related species were well discriminated by MALDI-TOF MS. The susceptibility pattern for C. palmioleophila was unique, as the echinocandin MICs were low (range, 0.008 to 0.125 μg/ml) and fluconazole MICs were high (range, 8 to >16 μg/ml). Correct identification of C. palmioleophila is important due to its unique susceptibility profile. Identification is possible yet laborious with conventional techniques, whereas MALDI-TOF MS easily separated the related species.
Evolução tardia da operação de Cox para fibrilação atrial em valvopatia mitral

The "Maze" procedure for surgical treatment of chronic atrial fibrillation (AF) described by Cox was performed in 34 patients (pts) from July 91 to October 94; 26 were female and the ages ranged from 35 to 68y (50.2y). All pts had mitral valve disease (MVD) and chronic AF (> 1 year). Thirty one pts had surgical rheumatic MVD (mitral stenosis in 21; mitral double disfunction in 9) and 3 had mixomatous MVD with severe regurgitation. Twenty eight pts were in FC (NYHA) IV and 6 in FC III preoperatively. Left atrium diameter varied from 4.4 to 6.5 cm (5.5 cm). In 21 pts the surgical treatment was performed following the original "Maze" technique and in 13 pts with the modifications proposed by Cox. The surgery was completed with 17 mitral conservative procedures and 17 mitral valve replacements (bovine pericardium bioprostheses). Eight pts had left atrial thrombosis. There were 2 (5.8%) immediate deaths (low cardiac output and severe liver disfunction) and 1 patient died in the 48th day with infeccious complications. The first patient required reoperation for bleeding review. Atrial arrhythmias were observed in 6 pts and 14 required temporary pace-maker. Regular "sinus" rhythm was observed in all pts. No other major complications were observed. In 30 pts with more than 6 months of follow up, 27 (90%) were in regular rhythm (heart rate from 55 to 87bpm), with no antiarrhythmic drugs and atrial contraction demonstrated by ECHO. In conclusion, the "Maze" procedure showed effective to treat AF even in pts with big atrium and MVD, with acceptable immediate and late results. Longer observation is required to a definitive conclusion.
Improved Accuracy of Cefepime Susceptibility Testing for Extended-Spectrum-Beta-Lactamase-Producing Enterobacteriaceae with an On-Demand Digital Dispensing Method

ABSTRACT Extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae generally cannot be treated with penicillins and cephalosporins. However, some later-generation cephalosporins, including cefepime, are poorly hydrolyzed by specific ESBL enzymes, and certain strains demonstrate in vitro susceptibility to these agents, potentially affording additional treatment opportunities. Moreover, the ability to adjust both the dose and dosing interval of beta-lactam agents allows the treatment of strains with elevated MICs that were formerly classified in the intermediate range. The ability to treat strains with elevated cefepime MICs is codified in new susceptible dose-dependent (SDD) breakpoints promulgated by the Clinical and Laboratory Standards Institute. In the interest of validating and implementing new cefepime SDD criteria, we evaluated the performances of Vitek 2, disk diffusion, and a MicroScan panel compared to that of reference broth microdilution (BMD) during the testing of 64 strains enriched for presumptive ESBL phenotype (based on nonsusceptibility to ceftriaxone). Surprisingly, categorical agreement with BMD was only 47.6%, 57.1%, and 44.6% for the three methods, respectively. Given these findings, we tested the performance of the HP D300 inkjet-assisted broth microdilution digital dispensing method (DDM), which was previously described by our group as an at-will testing alternative. In contrast to commercial methods, DDM results correlated well with the reference method, with 86% categorical agreement, 91.1% evaluable essential agreement, and no major or very major errors. The reproducibility and accuracy of MIC determinations were statistically equivalent to BMD. Our results provide support for the use of the DDM as a BMD equivalent methodology that will enable hospital-based clinical laboratories to support cefepime MIC-based dosing strategies.
Point-of-Interest Recommender Systems: A Separate-Space Perspective

With the rapid development of Location-based Social Network (LBSN) services, a large number of Point-Of-Interests (POIs) have been available, which consequently raises a great demand of building personalized POI recommender systems. A personalized POI recommender system can significantly assist users to find their preferred POIs and help POI owners to attract more customers. However, it is very challenging to develop a personalized POI recommender system because a user's checkin decision making process is very complex and could be influenced by many factors such as social network and geographical distance. In the literature, a variety of methods have been proposed to tackle this problem. Most of these methods model user's preference for POIs with integrated approaches and consider all candidate POIs as a whole space. However, by carefully examining a longitudinal real-world checkin data, we find that the whole space of users' checkins actually consists of two parts: social friend space and user interest space. The social friend space denotes the set of POI candidates that users' friends have checked-in before and the user interest space refers to the set of POI candidates that are similar to users' historical checkins, but are not visited by their friends yet. Along this line, we develop separate models for the both spaces to recommend POIs. Specifically, in social friend space, we assume users would repeat their friends' historical POIs due to the preference propagation through social networks, and propose a new Social Friend Probabilistic Matrix Factorization (SFPMF) model. In user interest space, we propose a new User Interest Probabilistic Matrix Factorization (UIPMF) model to capture the correlations between a new POI and one user's historical POIs. To evaluate the proposed models, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on the real-world data set. The experimental results firmly demonstrate the effectiveness of our proposed models.
Immunotherapies for acute viral myocarditis in the pediatric patient

Acute viral myocarditis is an inflammatory cardiomyopathy associated with impaired ventricular function. Although it is likely that most of these cases are subclinical, a fulminant form in children may lead to circulatory collapse. Extracorporeal mechanical support or cardiac transplantation may be required to salvage these patients. The early and late outcomes range from spontaneous recovery with or without the development of dilated cardiomyopathy to death. In the past 15 yrs, several authors have reported varying success with immunotherapies in the early treatment of acute viral myocarditis (1–5). These treatments have included the infusion of immune globulin and the use of other immunosuppressive agents (i.e., prednisone, methylprednisolone, azathioprine, OKT3) at the time of presentation and during the early phase of the cardiomyopathy. The early form of acute viral myocarditis is thought to be due to an inflammatory response secondary to a viral pathogen. Therefore, immune globulin could potentially enhance viral clearance if it contains neutralizing antibodies to the virus (6). Likewise, immune globulin may help to reduce the inflammatory response triggered by proinflammatory cytokine release. Other immunosuppressive therapies may act to suppress cell-mediated inflammatory reactions within the myocardium during the subacute phase of myocarditis. An autoimmune response may play a role in chronic myocarditis, and immunosuppression may help to reduce this reaction (7). But is suppressing the body’s immune response to an acute viral illness a good thing to do? By inhibiting cell-mediated immunity and the production of antiviral neutralizing antibodies, viral clearance may be delayed. The safety of immunotherapy in acute viral myocarditis must be a consideration. The question of whether there is sufficient scientific evidence to support or discourage the use of these treatments is the basis of this review.
DeBot: Twitter Bot Detection via Warped Correlation

We develop a warped correlation finder to identify correlated user accounts in social media websites such as Twitter. The key observation is that humans cannot be highly synchronous for a long duration, thus, highly synchronous user accounts are most likely bots. Existing bot detection methods are mostly supervised, which requires a large amount of labeled data to train, and do not consider cross-user features. In contrast, our bot detection system works on activity correlation without requiring labeled data. We develop a novel lag-sensitive hashing technique to cluster user accounts into correlated sets in near real-time. Our method, named DeBot, detects thousands of bots per day with a 94% precision and generates reports online everyday. In September 2016, DeBot has accumulated about 544,868 unique bots in the previous one year. We compare our detection technique with per-user techniques and with Twitter's suspension system. We observe that some bots can avoid Twitter's suspension mechanism and remain active for months, and, more alarmingly, we show that DeBot detects bots at a rate higher than the rate Twitter is suspending them.
Three-Phase State Estimation Using Hybrid Particle Swarm Optimization

This paper proposes a method for three-phase state estimation (SE) in power distribution network including on-load tap changers (OLTC) for voltage control. The OLTC tap positions are essentially discrete variables from the SE point of view. Estimation of these variables in SE presents a formidable challenge. The proposed methodology combines discrete and continuous state variables (voltage magnitudes, angles, and tap positions). A hybrid particle swarm optimization (HPSO) is applied to obtain the solution. The method is tested on standard IEEE 13- and 123-bus unbalanced test system models. The proposed algorithm accurately estimates the network bus voltage magnitudes and angles, and discrete tap values. The HPSO-based tap estimation provides a more accurate estimation of losses in the network, which helps in fair allocation of cost of losses in arriving at overall cost of electricity.
Assessing Satisfaction with Selected Student Services Using SERVQUAL, a Market-Driven Model of Service Quality.

The author demonstrates how the use of SERVQUAL, a market-driven assessment model adapted from business, can be used to study student satisfaction with four areas of support services hypothetically related to enrollment managment (academic records, admissions, career services, and financial aid). The sample included 748 students enrolled in general education courses at ten different private institutions.
A Hierarchical Electricity Market Structure for the Smart Grid Paradigm

This paper proposed a hierarchical structure for the electricity market to facilitate the coordination of energy markets in distribution and transmission networks. The proposed market structure enables the integration of microgrids, which provide energy and ancillary services in distribution networks. In the proposed hierarchical structure, microgrids participate in the energy market at the distribution networks settled by the distribution network operator (DNO), and load aggregators (LAs) interact with microgrids and generation companies (GENCOs) to import/export energy to/from the distribution network electricity markets from/to the wholesale electricity market. The proposed approach addressed the synergy of energy markets by introducing dynamic game with complete information for GENCOs, microgrids, and LAs. The proposed hierarchical competition is composed of bi-level optimization problems in which the respective upper-level problems maximize the individual market participants’ payoff, and the lower-level problems represent the market settlement accomplished by the DNO or the independent system operator. The bi-level problems are solved by developing sensitivity functions for market participants’ payoff with respect to their bidding strategies. A case study is employed to illustrate the effectiveness of the proposed approach.
Synchronized Measurements-Based Algorithm for Short Transmission Line Fault Analysis

A new numerical algorithm for the analysis of single line to ground faults on short overhead transmission lines is presented in this paper. It is based upon synchronized sampling at two line ports, and an accurate fault model including the arcing phenomena and tower footing resistance at the fault point. The core of the algorithm is an efficient nonrecursive parameter estimation method. A dynamic arc model is included in the fault model to represent the arc's interaction with the external power network. The algorithm accurately estimates the arc voltage amplitude, tower footing resistance, and the fault location, simultaneously. The algorithm is derived in the time domain, and is based on the synchronized acquisition of currents and voltages at both line terminals. Case studies based on simulated data are presented to demonstrate the effectiveness of the new method. In particular, the sensitivity of the proposed algorithm to synchronization errors, harmonics, line capacitance, and fault resistance were explored with lumped, distributed, transposed, and untransposed line models.
A Dynamic Game for Electricity Load Management in Neighborhood Area Networks

This paper develops a novel energy trading system with a community energy storage (CES) device for demand-side load management within a neighborhood area network. The energy users in the proposed system that have their own photovoltaic power generation are allowed to trade energy from their personal surplus with the grid and the CES device. We adopt a dynamic noncooperative repeated game with Pareto-efficient pure strategies as the decentralized approach for the users to determine optimal energy trading amounts for the next day. This decentralized model needs minimal information exchange or communication between users. Simulation results show that our system is able to provide peak load leveling for the grid, while providing financial benefits to users. Moreover, the performance benefits of our system are robust to both inaccuracy in day-ahead power forecasts and CES battery inefficiencies.