Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Rapid-Blood-Culture-Identification Result Interpretation and Antibiotic Prescribing Practices

ABSTRACT Rapid pathogen identification can alter antibiotic prescribing practices if interpreted correctly. Microbiology reporting can be difficult to understand, and new technology has made it more challenging. Nebraska Medicine recently implemented the BioFire FilmArray blood culture identification panel (BCID) coupled with stewardship-based education on interpretation. Physician BCID result interpretation and prescribing were assessed via an electronic survey, with a response rate of 40.8% (156/382 surveys). Seven questions required respondents to interpret BCID results, identify the most likely pathogen, and then choose therapy based on the results. The tallied correct responses resulted in a knowledge score. General linear models evaluated the effect of role, specialty, and utilization of the BCID interpretation guide on the mean knowledge score. The specialties of the respondents included 55.7% internal medicine, 19.7% family medicine, and 24.6% other. Roles included 41.1% residents, 5.0% fellows, and 53.9% faculty. Most reported that they reviewed antimicrobial susceptibility results (89.4%) and adjusted therapy accordingly (81.6%), while only 60% stated that they adjusted therapy based on BCID results. The correct response rates ranged from 52 to 86% for the interpretation questions. The most common errors included misinterpretation of Enterobacteriaceae and Staphylococcus genus results. Neither role nor specialty was associated with total knowledge score in multivariate analysis (P = 0.13 and 0.47, respectively). In conclusion, physician interpretation of BCID results is suboptimal and can result in ineffective treatment or missed opportunity to narrow therapy. With the implementation of new technology, improved reporting practices of BCID results with clinical decision support tools providing interpretation guidance available at the point of care is recommended.
Cutaneous Infection Caused by Macrophomina phaseolina in a Child with Acute Myeloid Leukemia

ABSTRACT We report a case of Macrophomina phaseolina skin infection in an immunocompromised child with acute myeloid leukemia, which was treated successfully with posaconazole without recurrence after a hematopoietic stem cell transplant. The fungus was identified by DNA sequencing using both the internal transcribed spacer and D1/D2 region of the 28S ribosomal DNA gene.
Classification of Temporal and Sequential Data Using Bag-of-Subsequences Features

Classifying sequential data is an important problem in machine learning with applications in time series, sensor streams, and image analysis. The ordered structure of sequential data presents a difficulty for the standard classification models, which has motivated the task of generating features for vector-based discriminative models. Shapelet methods, which have been extensively studied in this topic, identify class-specific subsequence patterns and exploit dissimilarity from them. Their approach merits the classification performance and the visual interpretability of the discriminative patterns. In this paper, we aim to improve on the robustness of the pattern-based discriminative model by considering the sequential data instances a "bag" of characteristic patterns. We propose a data cleaning method for generating small clusters of class-specific patterns and a vector representation which features the distance between clustered patterns and the sequential instance seen as a bag-of-subsequences. We further employ max-margin learning and feature selection to extract few class-characterizing patterns. We present numerical experiments using time series and silhouette image data and compared the performances of the proposed methods with other time series classification models. Our visual analysis of the extracted features showed that the proposed method can extract characteristic subsequences of the data.
Learning a Secure Classifier against Evasion Attack

In security sensitive applications, there is a crafty adversary component which intends to mislead the detection system. The presence of an adversary component conflicts with the stationary data assumption that is a common assumption in most machine learning methods. Since machine learning methods are not inherently adversary-aware, it necessitates to investigate security evaluation of machine learning based detection systems in the adversarial environment. Research in adversarial environment mostly focused on modeling adversarial attacks and evaluating impact of them on learning algorithms, only few studies have devised learning algorithms with improved security. In this paper we propose a secure learning model against evasion attacks on the application of PDF malware detection. The experimental results acknowledge that the proposed method significantly improves the robustness of the learning system against manipulating data and evasion attempts at test time.
Vitamin C: Overview and Update

Vitamin C functions in enzyme activation, oxidative stress reduction, and immune function. There is considerable evidence that vitamin C protects against respiratory tract infections and reduces risk for cardiovascular disease and some cancers. Current trials are examining the efficacy of intravenous vitamin C as cancer therapy. Many experts believe that the recommended intakes for vitamin C (45 to 90 mg daily) are several orders of magnitude too low to support optimal vitamin C functionality. Also, there is a misperception that vitamin C deficiency disease (scurvy) is largely historical and rarely observed in developed nations. Physical symptoms of scurvy include swelling of the lower extremities, bleeding gums, fatigue, and hemorrhaging, as well as psychological problems, including depression, hysteria, and social introversion. The long-term safety of vitamin C supplementation seems evident as large investigations have noted reduced risk of mortality in vitamin C supplementing populations and in those with elevated plasma vitamin C concentrations.
Cognito: Automated Feature Engineering for Supervised Learning

Feature engineering involves constructing novel features from given data with the goal of improving predictive learning performance. Feature engineering is predominantly a human-intensive and time consuming step that is central to the data science workflow. In this paper, we present a novel system called "Cognito", that performs automatic feature engineering on a given dataset for supervised learning. The system explores various feature construction choices in a hierarchical and non-exhaustive manner, while progressively maximizing the accuracy of the model through a greedy exploration strategy. Additionally, the system allows users to specify domain or data specific choices to prioritize the exploration. Cognito is capable of handling large datasets through sampling and built-in parallelism, and integrates well with a state-of-the-art model selection strategy. We present the design and operation of Cognito, along with experimental results on eight real datasets to demonstrate its efficacy.
Deep learning for posture analysis in fall detection

We propose a novel computer vision based fall detection system using deep learning methods to analyse the postures in a smart home environment for detecting fall activities. Firstly, background subtraction is employed to extract the foreground human body. Then the binary human body images form the input to the classifier. Two deep learning approaches based on a Boltzmann machine and deep belief network are compared with a support vector machine approach. The final decision on the occurrence of a fall is made on the basis of combining the classifier output with certain contextual rules. Evaluations are performed on recordings from a real home care environment, in which 15 people create 2904 postures.
Rapid Differentiation of Mixed Influenza A/H1N1 Virus Infections with Seasonal and Pandemic Variants by Multitemperature Single-Stranded Conformational Polymorphism Analysis

ABSTRACT Mixed infections of a single host with different variants of influenza A virus are the main source of reassortants which may have unpredictable properties when they establish themselves in the human population. In this report we describe a method for rapid detection of mixed influenza virus infections with the seasonal A/H1N1 human strain and the pandemic A/H1N1/v strain which emerged in 2009 in Mexico and the United States. The influenza virus A/H1N1 variants were characterized by the multitemperature single-stranded conformational polymorphism (MSSCP) method. The MSSCP gel patterns of hemagglutinin gene fragments of pandemic A/H1N1/v and different seasonal A/H1N1 strains were easily distinguishable 2 h after completion of reverse transcription-PCR (RT-PCR). Using the MSSCP-based genotyping approach, coinfections with seasonal and pandemic variants of the A/H1N1 subtype were identified in 4 out of 23 primary samples obtained from patients that presented with influenza-like symptoms to hospitals across Poland during the 2009-2010 epidemic season. Pandemic influenza virus strain presence was confirmed in all these primary samples by real-time RT-PCR. The sensitivity level of the MSSCP-based minor genetic variant detection was 0.1%, as determined on a mixture of DNA fragments obtained from amplification of the hemagglutinin gene of seasonal and pandemic strains. The high sensitivity of the method suggests its applicability for characterization of new viral variants long before they become dominant.
Evaluation of the Aspergillus Western Blot IgG Kit for Diagnosis of Chronic Aspergillosis

ABSTRACT Immunoprecipitin detection (IPD) is the current reference confirmatory technique for anti-Aspergillus antibody detection; however, the lack of standardization is a critical drawback of this assay. In this study, we evaluated the performance of the Aspergillus Western blot (Asp-WB) IgG kit (LDBio Diagnostics, Lyon, France), a recently commercialized immunoblot assay for the diagnosis of various clinical presentations of chronic aspergillosis. Three hundred eight serum samples from 158 patients with aspergillosis sensu lato (s.l.) were analyzed. More specifically, 267 serum samples were derived from patients with Aspergillus disease, including 89 cases of chronic pulmonary aspergillosis, 10 of aspergilloma, and 32 of allergic bronchopulmonary aspergillosis, while 41 samples were from patients with Aspergillus colonization, including 15 cystic fibrosis (CF) and 12 non-CF patients. For blood donor controls, the Asp-WB specificity was 94%, while the kit displayed a sensitivity for the aspergillosis s.l. diagnosis of 88.6%, with a diagnostic odds ratio (DOR) of 119 (95% confidence interval [CI], 57 to 251). The DOR values were 185.22 (95% CI,78.79 to 435.45) and 43.74 (95% CI, 15.65 to 122.20) for the diagnosis of Aspergillus disease and Aspergillus colonization, respectively. Among the patients, the sensitivities of the Asp-WB in the diagnosis of Aspergillus colonization were 100% and 41.7% in CF and non-CF patients, respectively. The Asp-WB yielded fewer false-negative results than did IPD. In conclusion, the Asp-WB kit performed well for the diagnosis of various clinical presentations of aspergillosis in nonimmunocompromised patients, with an enhanced standardization and a higher sensitivity than with IPD, which is the current reference method.
Textural Features for Image Classification

Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
Selective detection of endotoxin using an impedance aptasensor with electrochemically deposited gold nanoparticles

Using a single-stranded DNA (ssDNA) aptamer exhibiting high binding affinity (Kd = 12 nM) to endotoxin as a probe, an impedance sensor where aptamer-conjugated gold nanoparticles (AuNPs) were electrochemically deposited on a gold electrode was fabricated and its performance in regard to endotoxin detection assessed. AuNPs have been employed widely as biosensors because of their unique physical and chemical properties. In order to maximize the performance of the impedance aptasensor on endotoxin detection, some critical factors affecting aptamer conjugation to AuNPs and target recognition ability (i.e. concentrations of aptamer coupled with AuNPs, pH, ion strength and cation effect at the time of aptamer–endotoxin interaction) were optimized. Electrochemical impendence spectroscopy, cyclic voltametry, atomic force microscope, scanning electron microscope and quartz crystal microbalance were employed to characterize all the modification/detection procedures during the sensor fabrication. The developed aptasensor showed a broad linear dynamic detection range (0.01–10.24 ng/ml) with a very low detection limit for endotoxin (0.005 ng/ml), despite the presence of several biomolecules (e.g. plasmid DNA, RNA, serum albumin, Glc and sucrose) known to interfere with other endotoxin assays. The demonstrated aptasensor required a detection time of only 10 min, providing a simple and fast analytical method to specifically detect endotoxin from complex biological liqors.
Diagnostic Accuracy of Presepsin (sCD14-ST) for Prediction of Bacterial Infection in Cerebrospinal Fluid Samples from Children with Suspected Bacterial Meningitis or Ventriculitis

ABSTRACT Children with temporary external ventricular drains (EVD) are prone to nosocomial infections. Diagnosis of bacterial meningitis and ventriculitis in these children is challenging due to frequent blood contamination of cerebrospinal fluid (CSF) and the presence of chemical ventriculitis. The aim of this study was to compare diagnostic accuracy of presepsin (sCD14-ST), a novel biomarker of bacterial infection in CSF, to predict bacterial infection in comparison to the accuracy of established biomarkers like those demonstrated in biochemical analysis of CSF. We conducted a prospective study with 18 children with suspected bacterial meningitis or ventriculitis who had 66 episodes of disease. CSF samples were taken from external ventricular drainage. We measured presepsin in CSF, as well as CSF leukocyte count, glucose, and proteins. CSF was also taken to prove bacterial infection with culture methods or with 16S rRNA gene broad-range PCR (SepsiTest; Molzym, Germany). Infection was clinically confirmed in 57 (86%) episodes of suspected meningitis or ventriculitis. Chemical ventriculitis was diagnosed in 9 (14%) episodes of suspected meningitis or ventriculitis. Diagnostic accuracies presented as area under the curve (AUC) for sCD14-ST, leukocytes, and proteins measured in CSF were 0.877 (95% confidence interval [CI], 0.793 to 0.961), 0.798 (95% CI, 0.677 to 0.920), and 0.857 (95% CI, 0.749 to 0.964), respectively. With CSF culture, we detected bacteria in 17 samples, compared to 37 detected with broad-range PCR. It was found that presepsin was present at a significantly higher level in children with clinically proven ventriculitis than in those without meningitis or ventriculitis. Diagnostic accuracies of presepsin were superior to those of leukocytes or proteins in CSF. Presepsin-guided 16S rRNA gene PCR could be used in everyday clinical practice to improve etiological diagnosis of meningitis and ventriculitis and to prescribe more appropriate antibiotics.
Interference of silica nanoparticles with the traditional Limulus amebocyte lysate gel clot assay

Endotoxin contaminations of engineered nanomaterials can be responsible for observed biological responses, especially for misleading results in in vitro test systems, as well as in vivo studies. Therefore, endotoxin testing of nanomaterials is necessary to benchmark their influence on cells. Here, we tested the traditional Limulus amebocyte lysate gel clot assay for the detection of endotoxins in nanoparticle suspensions with a focus on possible interference of the particles with the test system. We systematically investigated the effects of nanomaterials made of, or covered by, the same material. Different types of bare or PEGylated silica nanoparticles, as well as iron oxide–silica core shell nanoparticles, were tested. Detailed inhibition/enhancement controls revealed enhanced activity in the Limulus coagulation cascade for all particles with bare silica surface. In comparison, PEGylation led to a lower degree of enhancement. These results indicate that the protein–particle interactions are the basis for the observed inhibition and enhancement effects. The enhancement activity of a particle type was positively related to the calculated particle surface area. For most silica particles tested, a dilution of the sample within the maximum valid dilution was sufficient to overcome non-valid enhancement, enabling semi-quantification of the endotoxin contamination.
Linear Control System Analysis and Design

THIS book tries to combine the classical methods of analysis and synthesis of linear control systems and the so-called modern methods. While it gives a fairly complete coverage of the classical methods leaning on the authors' well-known previous book (Feedback Control System Analysis and Synthesis), it deals with the modern methods in a less systematic way, leaving open some important problems, such as some aspects of optimal control theory, stability of digital control systems, and their analysis and synthesis in the time domain. It is understandable that such a book cannot be complete, however, the main problems should be dealt with instead of other less important ones. After a short motivating introduction, the book deals with writing the system equations for different types of systems, such as electrical, mechanical, thermal, hydraulic ones using physical laws, and gives a short description of the Lagrange method. The solution of the system equations and determining the steadystate response using classical methods, state equations and Laplace transformation is dealt with in the next two chapters. System representation is explained using block diagrams, simulation diagrams, signal flow graphs, state transition signal flow graphs and transformation to diagonal and companion forms. The determination of system types, steady-state error coefficients, stability using Routh criteria, root locus frequency response representation using Bode, Polar and Nichols plots, stability using Nyquist criteria and the correlation between time response, root locus and frequency response, and compensation using root locus and frequency response plots is explained in detail in Chapters 7-11, where a great part is based on the old book. From Chapters 12 to 18 modern control theory is introduced in the form of state variable feedback, controllability and observability, observers, Lyapunov second method, algebraic Riccati equation, determination of the weighting matrices in the
Coinfection of Fusobacterium nucleatum and Actinomyces israelii in Mastoiditis Diagnosed by Next-Generation DNA Sequencing

ABSTRACT Some bacterial infections involve potentially complex mixtures of species that can now be distinguished using next-generation DNA sequencing. We present a case of mastoiditis where Gram stain, culture, and molecular diagnosis were nondiagnostic or discrepant. Next-generation sequencing implicated coinfection of Fusobacterium nucleatum and Actinomyces israelii, resolving these diagnostic discrepancies.
Molecular Profiling of Escherichia coli O157:H7 and Non-O157 Strains Isolated from Humans and Cattle in Alberta, Canada

ABSTRACT Virulence markers in Shiga toxin-producing Escherichia coli (STEC) and their association with diseases remain largely unknown. This study determines the importance of 44 genetic markers for STEC (O157 and non-O157) from human clinical cases and their correlation to disease outcome. STEC isolated from a cattle surveillance program were also included. The virulence genes tested were present in almost all O157:H7 isolates but highly variable in non-O157 STEC isolates. Patient age was a significant determinant of clinical outcome.
Managing Procurement Spend Using Advanced Compliance Analytics

Often the processes for purchasing commodities and services within a business enterprise are centralized into a procurement organization. These purchases are often sourced from one or more suppliers, or vendors, based on contract terms and conditions (such as price, payment terms etc.), availability, and quality or legacy habit of purchasing service with known vendors. We have found that many organizations lack appropriate processes and disciplines to drive demand to preferred suppliers. Thus these enterprises are unable to leverage the value of the pre-negotiated contracts due to lack of process education, approval process steps or appropriate purchasing tools that could result in significant amounts of spending that would be considered not compliant (not being sourced through preferred suppliers). Depending upon the size of the organization, such transactions range from several million dollars to billions of dollars. Manually sifting or employing typical query tools to review large amounts of spend transaction data with multiple attributes to identify the level of non compliant spend and identify areas to take action is a daunting task. In this paper, we discuss a software solution for spend compliance analytics that includes measurements of cost savings due to increased compliance and identification of areas where spend tends to be non compliant. We have developed a web enabled advanced analytical solution called Compliance Analytics Tool (CAT) that embeds a two phase methodology for compliance management. In the first phase, we use advanced data mining techniques to segment a large amount of historical spend transactions to quickly identify promising areas of improvement, exploiting a multitude of purchasing attributes such as business unit, procurement category, suppliers, etc. The second phase employs portfolio optimization techniques to further focus on specific segments that provide maximum benefit based on desired compliance targets or available budget. We also discuss the solution architecture that integrates business analytics along with business intelligence tools, dashboards, and data warehousing.