Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Targeted (STAR) Glycemic Control: Design, Safety, and Performance

Introduction: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach that directly accounts for intra- and interpatient variability with a stochastically derived maximum 5% risk of blood glucose (BG) below 72 mg/dl. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in virtual and clinical pilot trials. Methods: Clinically validated virtual trials using data from 370 patients in the SPRINT (Specialized Relative Insulin and Nutrition Titration) study were used to design the STAR protocol and test its safety, performance, and required clinical effort prior to clinical pilot trials. Insulin and nutrition interventions were given every 1–3 h as chosen by the nurse to allow them to manage workload. Interventions were designed to maximize the overlap of the model-predicted (5–95th percentile) range of BG outcomes with the 72–117 mg/dl band and thus provide a maximum 5% risk of BG <72 mg/dl. Interventions were calculated using clinically validated computer models of human metabolism and its variability in critical illness. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of the American College of Chest Physicians/Society of Critical Care Medicine (ACCP/SCCM) goal (25 kg/kcal/h). Insulin doses were limited (8 U/h maximum), with limited increases based on current rate (0.5–2.0 U/h). Initial clinical pilot trials involved 3 patients covering ~450 h. Approval was granted by the Upper South A Regional Ethics Committee. Results: Virtual trials indicate that STAR provides similar glycemic control performance to SPRINT with 2–3 h (maximum) measurement intervals. Time in the 72–126 mg/dl and 72–145 mg/dl bands was equivalent for all controllers, indicating that glycemic outcome differences between protocols were only shifted in this range. Safety from hypoglycemia was improved. Importantly, STAR using 2–3 h (maximum) intervention intervals reduced clinical burden up to 30%, which is clinically very significant. Initial clinical trials showed glycemic performance, safety, and management of inter- and intrapatient variability that matched or exceeded the virtual trial results. Conclusions: In virtual trials, STAR TGC provided tight control that maximized the likelihood of BG in a clinically specified glycemic band and reduced hypoglycemia with a maximum 5% (or lower) expected risk of light hypoglycemia (BG <72 mg/dl) via model-based management of intra- and interpatient variability. Clinical workload was self-managed and reduced up to 30% compared with SPRINT. Initial pilot clinical trials matched or exceeded these virtual results.
Easy predictions for the easy-hard-easy transition

We study the scaling properties of sequential and parallel versions of a local search algorithm, WalkSAT, in the easy regions of the easy-hard-easy phase transition (PT) in Random 3-SAT.In the underconstrained region, we study scaling of the sequential version of WalkSAT. We find linear scaling at fixed clause/variable ratio. We also study the case in which a parameter inspired by "finite-size scaling" is held constant. The scaling then also appears to be a simple power law. Combining these results gives a simple prediction for the performance of WalkSAT over most of the easy region. The experimental results suggest that WalkSAT is acting as a threshold algorithm, but with threshold below the satisfiability threshold.Performance of a parallel version of WalkSAT is studied in the over-constrained region. This is more difficult because it is an opumization rather than decision problem. We use the solution quality, the number of unsatisfied clauses, obtained by the sequential algorithm to set a target for its parallel version. We find that qualities obtained by the sequential search with O(<i>n</i>) steps, are achievable by the parallel version in O(<i>log(n)</i>) steps. Thus, the parallelization is efficient for these "easy MAXSAT" problems.
Competence Tracking and Automatic Training Design Simulation

Skills taught through computer-based simulation are the training solution of the 21 st century for the Knowledge Worker. This same approach is being applied by the authors to the design process of the training itself. An innovative approach to the definition of a Training Schedule is presented starting from a software based Competence Tracking. The idea is to map different training methodologies together with psychological and pedagogical aspects in a software system and to choose the correct mixture and weight of these according to the training objectives of a group of people. The competences of these people are traced and stored in the same system and the gap of competences is computed starting from “ideal profiles” and from the training objectives. A user modelling system is used to highlight potential differences in the training goals among the group and to obtain a learner oriented knowledge structure.
Scouter: A Stream Processing Web Analyzer to Contextualize Singularities

Anomaly detection is a key feature of applications processing singularities using IoT sensor measures. In a recent project, we highlighted that to guarantee high quality detections, metadata providing spatio-temporal contexts on sensor measures are needed. These metadata hence become an integral component of the anomaly detection computation and need to be processed using a streaming approach. In this paper, we introduce Scouter, a generic tool that helps in capturing, analyzing, scoring and storing the contextual information of a given application domain. The processing depends on a semantic-based approach that exploits ontologies to score the relevancy of contextual information. The paper provides details on the system’s architecture, describes lessons learned and introduces practical aspects.
A Strategy for Screening Monoclonal Antibodies for Arabidopsis Flowers

The flower is one of the most complex structures of angiosperms and is essential for sexual reproduction. Current studies using molecular genetic tools have made great advances in understanding flower development. Due to the lack of available antibodies, studies investigating the localization of proteins required for flower development have been restricted to use commercial antibodies against known antigens such as GFP, YFP, and FLAG. Thus, knowledge about cellular structures in the floral organs is limited due to the scarcity of antibodies that can label cellular components. To generate monoclonal antibodies that can facilitate molecular studies of the flower, we constructed a library of monoclonal antibodies against antigenic proteins from Arabidopsis inflorescences and identified 61 monoclonal antibodies. Twenty-four of these monoclonal antibodies displayed a unique band in a western blot assay in at least one of the examined tissues. Distinct cellular distribution patterns of epitopes were detected by these 24 antibodies by immunofluorescence microscopy in a flower section. Subsequently, a combination of immunoprecipitation and mass spectrometry analysis identified potential targets for three of these antibodies. These results provide evidence for the generation of an antibody library using the total plant proteins as antigens. Using this method, the present study identified 61 monoclonal antibodies and 24 of them were efficiently detecting epitopes in both western blot experiments and immunofluorescence microscopy. These antibodies can be applied as informative cellular markers to study the biological mechanisms underlying floral development in plants.
Multiple concurrent updates

There has been a lot of work published in recent literature regarding, large database design and application, especially in the area of simultaneous access and multiple concurrent updates. The various published solutions deal generally with either deadlock prevention, or detection and recovery techniques. In contrast, this paper presents considerations for the joint solution of four problems: 1) integrity, (assuring one process does not void the updates of another), 2) deadlock, 3) restartibility, (avoiding the construction of phantom structure), and 4) high efficiency buffering.
Representative Template Set Generation Method for Pedestrian Detection

Template matching is an effective approach for pedestrian detection. In order to achieve real-time and accurate detection, how to obtain a suitable representative template set is still an open problem due to the large variety of pedestrian shape. This paper introduced a representative template generation method for a template matching based pedestrian detection system (PDS). Based on nonlinear manifold learning and clustering, the new approach can generate a suitable representative template subset from a large amount of original templates. First, an improved nonlinear dimensionality reduction method was proposed to map original templates to feature vectors (points) in the low-dimensional embedding space; second, representative points were generated in the embedding space by clustering; at the end, corresponding representative template set were synthesized by mapping inversely the newly generated points from the embedding space to the visual input space.The experimental results showed that the template generation method speeds up detection procedure without considerable loss of performance.
[Study of inequalities in hospital mortality using the Charlson comorbidity index].

OBJECTIVE The evaluation of quality and equity in healthcare attendance requires adequate study methods and information systems. Thus, this study was performed with the objective of comparing mortality among elderly patients attended within the private network and within the Brazilian national health system (SUS). METHOD An information system that recorded causes of hospitalization and associated diseases (comorbidity) in relation to public and private hospital admissions was utilized. The hospitalization of 21,695 patients in Ribeirão Preto, State of Sao Paulo, in 1998 and 1999 was studied. These patients had diseases of the circulatory and respiratory systems. Analysis was done via the methodology put forward by Charlson, in which comorbidities are scored to give the Charlson comorbidity index and age over 50 years (per decade) is scored to give the Charlson comorbidity-age index. The patients were stratified according to comorbidity and decade of age over 50 years, with separation of hospitalizations via SUS from those via the private network (non-SUS). The coefficient of hospital mortality was calculated for each stratum. RESULTS It was observed that the risk of death increased almost sixfold when the number of associated diseases increased. The risk of death for SUS patients was more than twice the risk for non-SUS patients (relative risk: 2.12). Significant differences between SUS and non-SUS patients were found by associating the patient's comorbidity with decade of age. When the risk of death was very low or very high, there were no statistical differences between SUS and non-SUS patients. In other, intermediate situations, precisely where the attendance might make a difference, the mortality among SUS patients was more than twice as great (relative risk: 2.14). CONCLUSIONS The difference in mortality between SUS and non-SUS patients, according to Charlson's criteria, is significant among patients of intermediate risk, for whom the care is most important. The Charlson comorbidity index correlates with hospital mortality.
Detection of Correct and Incorrect Measurements in Real-Time Continuous Glucose Monitoring Systems by Applying a Postprocessing Support Vector Machine

Support vector machines (SVMs) are an attractive option for detecting correct and incorrect measurements in real-time continuous glucose monitoring systems (RTCGMSs), because their learning mechanism can introduce a postprocessing strategy for imbalanced datasets. The proposed SVM considers the geometric mean to obtain a more balanced performance between sensitivity and specificity. To test this approach, 23 critically ill patients receiving insulin therapy were monitored over 72 h using an RTCGMS, and a dataset of 537 samples, classified according to International Standards Organization (ISO) criteria (372 correct and 165 incorrect measurements), was obtained. The results obtained were promising for patients with septic shock or with sepsis, for which the proposed system can be considered as reliable. However, this approach cannot be considered suitable for patients without sepsis.
Computational Studies of Snake Venom Toxins

Most snake venom toxins are proteins, and participate to envenomation through a diverse array of bioactivities, such as bleeding, inflammation, and pain, cytotoxic, cardiotoxic or neurotoxic effects. The venom of a single snake species contains hundreds of toxins, and the venoms of the 725 species of venomous snakes represent a large pool of potentially bioactive proteins. Despite considerable discovery efforts, most of the snake venom toxins are still uncharacterized. Modern bioinformatics tools have been recently developed to mine snake venoms, helping focus experimental research on the most potentially interesting toxins. Some computational techniques predict toxin molecular targets, and the binding mode to these targets. This review gives an overview of current knowledge on the ~2200 sequences, and more than 400 three-dimensional structures of snake toxins deposited in public repositories, as well as of molecular modeling studies of the interaction between these toxins and their molecular targets. We also describe how modern bioinformatics have been used to study the snake venom protein phospholipase A2, the small basic myotoxin Crotamine, and the three-finger peptide Mambalgin.
Robust Localization Using Time Difference of Arrivals

We investigate a localization problem using time-difference-of-arrival measurements with unknown and bounded measurement errors. Different from most existing algorithms, we consider the minimization of the worst-case position estimation error to improve the robustness of the algorithm. The localization problem is formulated as a nonconvex optimization problem. We adopt semidefinite relaxation to relax the original problem into a convex optimization problem, which can be solved using existing semidefinite program solvers. Simulation results show that our proposed algorithm has lower worst-case position estimation error than other existing algorithms.
Digital Quantification of Human Eye Color Highlights Genetic Association of Three New Loci

Previous studies have successfully identified genetic variants in several genes associated with human iris (eye) color; however, they all used simplified categorical trait information. Here, we quantified continuous eye color variation into hue and saturation values using high-resolution digital full-eye photographs and conducted a genome-wide association study on 5,951 Dutch Europeans from the Rotterdam Study. Three new regions, 1q42.3, 17q25.3, and 21q22.13, were highlighted meeting the criterion for genome-wide statistically significant association. The latter two loci were replicated in 2,261 individuals from the UK and in 1,282 from Australia. The LYST gene at 1q42.3 and the DSCR9 gene at 21q22.13 serve as promising functional candidates. A model for predicting quantitative eye colors explained over 50% of trait variance in the Rotterdam Study. Over all our data exemplify that fine phenotyping is a useful strategy for finding genes involved in human complex traits.
Estimating the Directed Information and Testing for Causality

The problem of estimating the directed information rate between two discrete processes (Xn) and (Yn) via the plug-in (or maximum-likelihood) estimator is considered. When the joint process ((Xn, Yn)) is a Markov chain of a given memory length, the plug-in estimator is shown to be asymptotically Gaussian and to converge at the optimal rate O(1/√n) under appropriate conditions; this is the first estimator that has been shown to achieve this rate. An important connection is drawn between the problem of estimating the directed information rate and that of performing a hypothesis test for the presence of causal influence between the two processes. Under fairly general conditions, the null hypothesis, which corresponds to the absence of causal influence, is equivalent to the requirement that the directed information rate be equal to zero. In that case, a finer result is established, showing that the plug-in converges at the faster rate O(1/n) and that it is asymptotically χ2-distributed. This is proved by showing that this estimator is equal to (a scalar multiple of) the classical likelihood ratio statistic for the above hypothesis test. Finally, it is noted that these results facilitate the design of an actual likelihood ratio test for the presence or absence of causal influence.
Recognizing Named Entities in Tweets

The challenges of Named Entities Recognition (NER) for tweets lie in the insufficient information in a tweet and the unavailability of training data. We propose to combine a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random Fields (CRF) model under a semi-supervised learning framework to tackle these challenges. The KNN based classifier conducts pre-labeling to collect global coarse evidence across tweets while the CRF model conducts sequential labeling to capture fine-grained information encoded in a tweet. The semi-supervised learning plus the gazetteers alleviate the lack of training data. Extensive experiments show the advantages of our method over the baselines as well as the effectiveness of KNN and semi-supervised learning.
Biosurfactant production in sugar beet molasses by some Pseudomonas spp.

In this study rhamnolipid biosurfactant production was investigated in eighteen strains of Pseudomonas spp. Rhamnolipid by these strains was determined by a spectrophotometric method in nutrient broth medium (NB). From the 18 strains screened, two Pseudomonas strains (Pseudomonas luteola B17 and Pseudomonas putida B12) which had produced the highest percentage yield of rhamnolipid were examined for rhamnolipid production at different incubation times (24, 48 and 72 hr) and different sugar beet molasses concentrations [1-5% w/v concentration (1-5 g molasses/100 ml water)]. The rhamnolipid production increased with the increase in the concentration of molasses and maximum production occurred when 5 % (w/v) of molasses were used. At the same time, maximum rhamnolipid production occurred after 72 hr of incubation. When the amount of rhamnolipid produced at different incubation times (24, 48 and 72 hr) and with different concentrations of molasses [1-5 % w/v concentration (1-5 g molasses/100 ml water)] by Pseudomonas spp. ; was compared, no significant difference in amount of production was seen. These studies show that the waste product from sugar industry may be suggested for important biotechnological processes such as rhamnolipid production.
Computerized Analysis of Speech and Language to Identify Psycholinguistic Correlates of Frontotemporal Lobar Degeneration

ObjectiveTo evaluate the use of a semiautomated computerized system for measuring speech and language characteristics in patients with frontotemporal lobar degeneration (FTLD). BackgroundFTLD is a heterogeneous disorder comprising at least 3 variants. Computerized assessment of spontaneous verbal descriptions by patients with FTLD offers a detailed and reproducible view of the underlying cognitive deficits. MethodsAudiorecorded speech samples of 38 patients from 3 participating medical centers were elicited using the Cookie Theft stimulus. Each patient underwent a battery of neuropsychologic tests. The audio was analyzed by the computerized system to measure 15 speech and language variables. Analysis of variance was used to identify characteristics with significant differences in means between FTLD variants. Factor analysis was used to examine the implicit relations between subsets of the variables. ResultsSemiautomated measurements of pause-to-word ratio and pronoun-to-noun ratio were able to discriminate between some of the FTLD variants. Principal component analysis of all 14 variables suggested 4 subjectively defined components (length, hesitancy, empty content, grammaticality) corresponding to the phenomenology of FTLD variants. ConclusionSemiautomated language and speech analysis is a promising novel approach to neuropsychologic assessment that offers a valuable contribution to the toolbox of researchers in dementia and other neurodegenerative disorders.
Immunogenicity testing in human papillomavirus virus-like-particle vaccine trials.

The recently introduced human papillomavirus (HPV) prophylactic vaccines are the first widely approved vaccines specifically designed to prevent a sexually transmitted infection and are only the second (after the hepatitis B virus vaccine) designed to prevent human cancer, in this case cervical cancer and several other anogenital and head and neck cancers. The antigens in the HPV vaccines are naked icosohedral virus-like particles (VLPs) composed of the major HPV virion protein L1. These subunit vaccines were independently developed and tested by 2 companies, GlaxoSmithKline (GSK) and Merck [1]. The GSK vaccine, Cervarix, is bivalent—it contains the VLPs of HPV-16 and -18, the types that cause ~70% of cervical cancers worldwide—and is produced in insect cells. Cervarix also contains the proprietary adjuvant AS04, which is composed of monophyosphoryl lipid A and an aluminum salt. The Merck vaccine, Gardasil, is tetravalent—it contains the HPV-16 and -18 VLPs and the VLPs of HPV-6 and -11, which cause 80%–90% of genital warts—and is produced in yeast. Gardasil is adjuvanted with a simple aluminum salt. The vaccines are delivered by intramuscular injection in 3 doses over 6 months. Both have been remarkably effective in phase 3 trials conducted in young women, providing nearly complete protection against persistent genital tract infection and premalignant neoplastic disease end points caused by the HPV types targeted by the respective vaccines [2]. Both vaccines have been licensed in >50 countries, starting with Merck’s in 2006 and GSK’s in 2007, and millions of doses have been sold. However, despite the rapid and successful introduction of these 2 vaccines, they can reasonably be viewed as introductory products that will likely be followed by second-generation vaccines that will target more types and/or be less expensive to produce and deliver. This article will focus on one aspect of HPV VLP vaccines, the assessment of immunogenicity.
Dual-Polarized Massive MIMO Systems Under Multi-Cell Pilot Contamination

This paper considers a multi-cell network with base stations (BSs) equipped with large dual-polarized antenna arrays. We derive closed-form expressions of network performance in terms of user signal-to-interference-and-noise ratio both with perfect channel state information and in the presence of pilot contamination. In general, a dual-polarized system enjoys the reduction in pilot contamination and multiuser interference due to orthogonal polarizations, while the available BS array dimension is reduced in each polarized direction compared with a monopole antenna setup. The derived results, however, reveal that the performance of a dual polarization system can be optimized by properly setting receive antenna polarizations, and fortunately, the optimal performance behaves identically to that of a monopole-antenna one in massive MIMO. We finally verify the observations by numerical simulations.