Miracle-dz/UniFAR-FTUs huggingface dataset:
Sharp wound debridement in local anaesthesia using EMLA cream: 6 years' experience in 1084 patients

Sharp debridement is the most efficient method for clearing the woundbed in the exudation and granulation phase of wound healing. At our clinic the anaesthetic lidocaine–prilocaine cream, EMLA, has been used as an analgesic for sharp debridement since 1994. A review of patients' records was conducted, including ulcer size, dose of cream used, analgesic efficacy and complications. During a 6-year period a total of 1084 patients were treated for leg ulcers, decubitus ulcers, abscess revisions, anal and coccyx fistulae, postoperative wounds, diabetic ulcers and burns. Doses ranging from 3 to 150 g cream were applied for 45–60 min. In all patients except three the analgesia was adequate for debridement. We observed no allergic reactions, no clinical symptoms of local anaesthetic toxicity or methaemoglobinaemia. In 12 patients (1.1%) a burning sensation was reported directly after the application of EMLA cream to the ulcer, which, however, subsided within 15–20 min. In our experience, sharp debridement in percutaneous analgesia with EMLA is efficient, economical, safe, and tolerable for the patient.
High-Affinity Maltose Binding and Transport by the Thermophilic Anaerobe Thermoanaerobacter ethanolicus 39E

ABSTRACT Thermoanaerobacter ethanolicus is a gram-positive thermophile that produces considerable amounts of ethanol from soluble sugars and polymeric substrates, including starch. Growth on maltose, a product of starch hydrolysis, was associated with the production of a prominent membrane-associated protein that had an apparent molecular weight of 43,800 and was not detected in cells grown on xylose or glucose. Filter-binding assays revealed that cell membranes bound maltose with high affinity. Metabolic labeling ofT. ethanolicus maltose-grown cells with [14C]palmitic acid showed that this protein was posttranslationally acylated. A maltose-binding protein was purified by using an amylose resin affinity column, and the binding constant was 270 nM. Since maltase activity was found only in the cytosol of fractionated cells and unlabeled glucose did not compete with radiolabeled maltose for uptake in whole cells, it appeared that maltose was transported intact. In whole-cell transport assays, the affinity for maltose was approximately 40 nM. Maltotriose and α-trehalose competitively inhibited maltose uptake in transport assays, whereas glucose, cellobiose, and a range of disaccharides had little effect. Based on these results, it appears that T. ethanolicus possesses a high-affinity, ABC type transport system that is specific for maltose, maltotriose, and α-trehalose.
An improved outer bound for the multiterminal source-coding problem

We prove a new outer bound on the rate-distortion region for the multiterminal source-coding problem. This bound subsumes the best known bound in the literature and improves upon it strictly in some cases. The improved bound enables us to obtain a new, conclusive result for the binary-erasure instance of the "CEO problem. " The bound recovers many of the converse results that have been established for special cases of the problem, including the recent one for the Gaussian version of the CEO problem
TOA Estimation with DLC Receivers for IEEE 802.15.4a UWB Systems

In many circumstances, it is necessary for a node in a Wireless Sensor Network (WSN) to be aware of its location in the physical world. For example, tracking or event-detection functions are not useful if the WSN cannot provide any information where the event has happened. Some location procedures have been proposed in the IEEE 802.15. 4a standard, including the time of arrival (TOA) estimation, the location estimation, and network location algorithms. In this paper, we discuss an effective TOA estimation scheme with the DLC receiver [7], which employs impulse radio UWB signals compliant with the IEEE 802.15. 4a standard. By modifying the synchronization algorithm with the DLC receiver, the TOA can be obtained straightly.
Hyperspectral image segmentation with Markov chain model

The Hidden Markov Chain (HMC) model has been extended to take into consideration the multi-component representation of an hyperspectral data cube. Parameters estimation is performed using the general Iterative Conditional Estimation (ICE) method. The vectorial extension of the model is straightforward since the vectorial point of view joints the observation of each pixel as a spectral signature. Then, the segmentation procedure achieves an estimation of multi-dimensional correlated probability density functions (pdf). Multi-dimensional densities have been estimated by a set of 1D densities through a projection step that makes component independent and of reduced dimension. Classifications have been applied to an image from the CASI sensor including 17 bands (from 450 to 950 nm) representing an intensive agricultural region (Brittany, France). Since, the intrinsic dimensionality of the observation has been estimated to 4, the multi-component HMC model has been applied to the CASI image reduced to 4 bands through an adapted projection pursuit method.
Reverse white-coat effect as an independent risk for microalbuminuria in treated hypertensive patients.

BACKGROUND The influence of the converse phenomenon of white-coat hypertension called 'reverse white-coat hypertension' or 'masked hypertension' on hypertensive target organ damage has not been fully elucidated. The present study assessed the hypothesis that this phenomenon may specifically associate with microalbuminuria, a marker of early renal damage, in treated hypertension. METHODS A total of 267 treated essential hypertensive patients (133 men and 134 women; mean age, 66 years) without renal insufficiency or macroalbuminuria were enrolled in this study. Patients were classified into three groups by the difference between office and day-time ambulatory systolic blood pressure (BP) levels; i.e. subjects with white-coat effect (W group: office--day-time systolic BP > or =20 mmHg, n = 48), with reverse white-coat effect (R group: office - day-time systolic BP < - 10 mmHg, n = 43) and without white-coat or reverse white-coat effect (N group: -10 mmHg < or = office--day-time systolic BP <20 mmHg, n = 176). The urinary albumin (U-Alb) level was measured as the albumin to creatinine excretion ratio in the urine. Microalbuminuria was defined as U-Alb of > or =30 and <300 mg/g Cr. RESULTS R group had a well-controlled office BP (130/77 mmHg), but their day-time BP (148/87 mmHg) was elevated compared with the other two groups. The levels of U-Alb excretion in N group, W group and R group were 12.3 (8.4, 25.6), 16.0 (10.5, 31.7) and 24.3 (10.2, 79.7) mg/g Cr [median (interquartile range)], respectively. Both U-Alb level and prevalence of microalbuminuria were significantly greater in R group than in N group. Multivariate analyses revealed that the presence of reverse white-coat effect, but not white-coat effect, was a significant predictor for microalbuminuria, independent of various clinical variables including ambulatory BP levels (odds ratio 2.63 vs N group, P = 0.02). CONCLUSION These findings suggest that the presence of reverse white-coat effect may be an independent risk for early renal damage in treated hypertensive patients.
Metabolic Signatures of Cultured Human Adipocytes from Metabolically Healthy versus Unhealthy Obese Individuals

Background and Aims Among obese subjects, metabolically healthy and unhealthy obesity (MHO/MUHO) can be differentiated: the latter is characterized by whole-body insulin resistance, hepatic steatosis, and subclinical inflammation. Aim of this study was, to identify adipocyte-specific metabolic signatures and functional biomarkers for MHO versus MUHO. Methods 10 insulin-resistant (IR) vs. 10 insulin-sensitive (IS) non-diabetic morbidly obese (BMI >40 kg/m2) Caucasians were matched for gender, age, BMI, and percentage of body fat. From subcutaneous fat biopsies, primary preadipocytes were isolated and differentiated to adipocytes in vitro. About 280 metabolites were investigated by a targeted metabolomic approach intracellularly, extracellularly, and in plasma. Results/Interpretation Among others, aspartate was reduced intracellularly to one third (p = 0.0039) in IR adipocytes, pointing to a relative depletion of citric acid cycle metabolites or reduced aspartate uptake in MUHO. Other amino acids, already known to correlate with diabetes and/or obesity, were identified to differ between MUHO's and MHO's adipocytes, namely glutamine, histidine, and spermidine. Most species of phosphatidylcholines (PCs) were lower in MUHO's extracellular milieu, though simultaneously elevated intracellularly, e.g., PC aa C32∶3, pointing to increased PC synthesis and/or reduced PC release. Furthermore, altered arachidonic acid (AA) metabolism was found: 15(S)-HETE (15-hydroxy-eicosatetraenoic acid; 0 vs. 120pM; p = 0.0014), AA (1.5-fold; p = 0.0055) and docosahexaenoic acid (DHA, C22∶6; 2-fold; p = 0.0033) were higher in MUHO. This emphasizes a direct contribution of adipocytes to local adipose tissue inflammation. Elevated DHA, as an inhibitor of prostaglandin synthesis, might be a hint for counter-regulatory mechanisms in MUHO. Conclusion/Interpretation We identified adipocyte-inherent metabolic alterations discriminating between MHO and MUHO.
Lactobacilli and azoreductase activity in the murine cecum

Azoreductase activity in the ceca of mice lacking lactobacilli as members of the normal microflora (reconstituted-lactobacillus-free [RLF] mice) was compared with that of RLF mice whose gastrointestinal tracts were colonized by strains of Lactobacillus delbrueckii and Lactobacillus fermentum. Azoreductase activity was 31% lower in the ceca of mice colonized by lactobacilli.
Reducing source-generated noise in shallow seismic data using linear and hyperbolic τ-p transformations

High‐resolution seismic reflection data recorded at many locations on the earth are plagued by the overwhelming effects of direct, refracted, guided, and surface waves. These different components of source‐generated noise may completely mask reflections at traveltimes <∼50–100 ms. Conventional processing methods that include the time‐consuming application of mute functions may lead to the misprocessing of source‐generated noise (especially guided waves) as reflected events and/or the unintentional removal of important shallow reflections. We introduce a combined linear and hyperbolic τ‐p processing scheme that results in the effective separation of reflections from source‐generated noise. After applying linear moveout terms that adjust the direct, refracted, and guided arrivals to appear horizontal to subhorizontal, the reduced traveltime shot gathers are transformed into the linear τ‐p domain. It is then straightforward to design a single τ‐p filter that eliminates most of the source‐generated noise thro.
◦ ◦ W ◦ ◦ W ◦ ◦ five ◦ ◦ minus ◦ ◦ two ◦ ◦ plus ◦ ◦ three

We investigate how neural networks can be used for hierarchical, compositional semantics. To this end, we define the simple but nontrivial artificial task of processing nested arithmetic expressions and study whether different types of neural networks can learn to add and subtract. We find that recursive neural networks can implement a generalising solution, and we visualise the intermediate steps: projection, summation and squashing. We also show that gated recurrent neural networks, which process the expressions incrementally, perform surprisingly well on this task: they learn to predict the outcome of the arithmetic expressions with reasonable accuracy, although performance deteriorates with increasing length. To analyse what strategy the recurrent network applies, visualisation techniques are less insightful. Therefore, we develop an approach where we formulate and test hypotheses on what strategies these networks might be following. For each hypothesis, we derive predictions about features of the hidden state representations at each time step, and train ’diagnostic classifiers’ to test those predictions. Our results indicate the networks follow a strategy similar to our hypothesised ’incremental strategy’.
Cross-Layered Design of Spectrum Sensing and MAC for Opportunistic Spectrum Access

In cognitive radio networks, the secondary users (SUs) are allowed to use the spectrum originally allocated to primary users (PUs) as long as the PUs are not using it temporarily. This operation is called opportunistic spectrum access (OSA), and it is assisted through spectrum sensing. In distributed OSA, the SUs sense the channel independently; once the channel is available, they contend for channel access on a frame-by-frame basis. In this paper, we study the random medium access control (MAC) in conjunction with the sensing protocol design. In particular, we are interested in the design of frame duration, sensing time and MAC random access to maximize the secondary network throughput performance while protecting the PUs from the interference of secondary users' operations. We formulate the nonlinear constrained optimization problems for the described system model with cross-layered and layered approaches. Simulations show that the cross-layered approach performs much better than layered approach especially when the frame duration is small.
Temporal Variability of Urinary Phthalate Metabolite Levels in Men of Reproductive Age

Phthalates are a family of multifunctional chemicals widely used in personal care and other consumer products. The ubiquitous use of phthalates results in human exposure through multiple sources and routes, including dietary ingestion, dermal absorption, inhalation, and parenteral exposure from medical devices containing phthalates. We explored the temporal variability over 3 months in urinary phthalate metabolite levels among 11 men who collected up to nine urine samples each during this time period. Eight phthalate metabolites were measured by solid-phase extraction–high-performance liquid chromatography–tandem mass spectrometry. Statistical analyses were performed to determine the between- and within-subject variance apportionment, and the sensitivity and specificity of a single urine sample to classify a subject’s 3-month average exposure. Five of the eight phthalates were frequently detected. Monoethyl phthalate (MEP) was detected in 100% of samples; monobutyl phthalate, monobenzyl phthalate, mono-2-ethylhexyl phthalate (MEHP), and monomethyl phthalate were detected in > 90% of samples. Although we found both substantial day-to-day and month-to-month variability in each individual’s urinary phthalate metabolite levels, a single urine sample was moderately predictive of each subject’s exposure over 3 months. The sensitivities ranged from 0.56 to 0.74. Both the degree of between- and within-subject variance and the predictive ability of a single urine sample differed among phthalate metabolites. In particular, a single urine sample was most predictive for MEP and least predictive for MEHP. These results suggest that the most efficient exposure assessment strategy for a particular study may depend on the phthalates of interest.
Exploring Checkpointing and Closed Nesting in Distributed Transactional Memory

Checkpointing and closed nesting are mechanisms typically used for implementing partial roll-back in transactional systems. Closed nesting limits the amount of work to redo on an abort by allowing sub-transactions to abort and retry independently from their parents. Checkpointing goes further and allows a transaction to be rolled back to any previous point where a checkpoint was saved. Checkpointing thus enables very fine-grained rollbacks. In this paper we focus on understanding the performance considerations of closed nesting and checkpointing in Distributed Transactional Memory (DTM). We extend an existing DTM algorithm, TFA, with support for the two partial rollback models, and implement it in the Hyflow2 opensource DTM framework for the JVM. We then perform a thorough evaluation to determine their behavior, implementation overheads, and favorable conditions.
Spelling Correction as a Foreign Language

In this paper, we reformulated the spell correction problem as a machine translation task under the encoder-decoder framework. This reformulation enabled us to use a single model for solving the problem that is traditionally formulated as learning a language model and an error model. This model employs multi-layer recurrent neural networks as an encoder and a decoder. We demonstrate the effectiveness of this model using an internal dataset, where the training data is automatically obtained from user logs. The model offers competitive performance as compared to the state of the art methods but does not require any feature engineering nor hand tuning between models.
Lattice‐BGK simulation of steady flow through vascular tubes with double constrictions

Purpose – The aim of the present study is to evaluate the accuracy and efficiency of Lattice‐BGK (LBGK) method application in simulation of the 3D flow through complex geometry. On the other hand, the steady flows through vascular tube with Reynolds number 10‐150 and different constriction spacing ratios are simulated.Design/methodology/approach – The numerical method is based on the LGBK method with an incompressible D3Q19 model. To treat the curved boundary, the “bounce back” scheme combined with spatial interpolation of second order is applied.Findings – The highly axisymmetric property in the direct 3D tube flow simulation is observed. Solutions obtained from LBGK method are quite consistent with that of finite volume method (FVM). The overall order of accuracy of these LBGK solutions is about 1.89. The LBGK incompressible D3Q19 model with the curved boundary treatment can handle the problems of 3D steady flow through complex geometry.Research limitations/implications – Investigating the flow in const.
An Ontology Enrichment Approach by Using DBpedia

Over the past decade, an increasing number of methods have been proposed for (semi-) automatic generation of ontology from text. However, the ontology generated by these methods usually does not meet the needs of many reasoning-based applications in different domains since most of these methods aim at generating inexpressive ontologies e.g. bare taxonomies. In this paper, a new ontology enrichment approach is proposed in which Web of Linked Data (in particular, DBpedia as one of the huge Linked Data datasets) is used as background knowledge beside text in order to recognize new ontological relations, specifically object properties, for ontology enrichment. In other words, this enrichment approach can be considered as a post-processing step for the "Relations" layer (i.e. the fifth layer) in Ontology Learning Stack, aiming at recommending new object properties to the ontology engineers enabling them to create much more expressive ontologies. This is actually a complementary approach to our recent approach towards adding Linked Data to ontology learning layers where we aimed at improving the functions associated to the "Synonyms" layer, the "Concept Formation" layer and the "Concept Hierarchy" layer of ontology learning stack. In order to evaluate the approach, a customized experimental design is introduced called the "Pseudo Gold Standard based Ontology Evaluation" in which the results obtained by a human expert are compared against those obtained automatically. Finally, the experimental results showed a satisfactory improvement in learning object properties.
Efficient mining of partial periodic patterns in time series database

Partial periodicity search, i.e., search for partial periodic patterns in time-series databases, is an interesting data mining problem. Previous studies on periodicity search mainly consider finding full periodic patterns, where every point in time contributes (precisely or approximately) to the periodicity. However, partial periodicity is very common in practice since it is more likely that only some of the time episodes may exhibit periodic patterns. We present several algorithms for efficient mining of partial periodic patterns, by exploring some interesting properties related to partial periodicity such as the Apriori property and the max-subpattern hit set property, and by shared mining of multiple periods. The max-subpattern hit set property is a vital new property which allows us to derive the counts of all frequent patterns from a relatively small subset of patterns existing in the time series. We show that mining partial periodicity needs only two scans over the time series database, even for mining multiple periods. The performance study shows our proposed methods are very efficient in mining long periodic patterns.
Priority-Based Task Management in a GPGPU Megakernel

In this paper, we examine the challenges of implementing priority-based task management with respect to userdefined preferential attributes on a graphics processing unit (GPU). Previous approaches usually rely on constant synchronization with the CPU to determine the proper chronological sequence for execution. We transfer the responsibility for evaluating and arranging planned tasks to the GPU where they are continuously processed in a persistent kernel. By implementing a dynamic work queue with segments of variable size, we introduce possibilities for gradually improving the overall order and identify necessary meta data required to avoid write-read conflicts in a massively parallel environment. We employ an autonomous controller module to allow queue management to run concurrently with task execution. We revise Batcher’s bitonic merge sort and show its eligibility for sorting partitioned work queues. The performance of the algorithm for tasks with constant execution time is evaluated with respect to multiple setups and priority types. An implementation of a Monte Carlo Ray-Tracing engine is presented in which we use appropriate priority functions to direct the available resources of the GPU to areas of interest. The results show increased precision for the desired features at early rendering stages, demonstrating the selective preference established by our management system.
Surface-Functionalized Hyperbranched Poly(Amido Acid) Magnetic Nanocarriers for Covalent Immobilization of a Bacterial γ-Glutamyltranspeptidase

In this study, we synthesized water-soluble hyperbranched poly(amido acid)s (HBPAAs) featuring multiple terminal CO2H units and internal tertiary amino and amido moieties and then used them in conjunction with an in situ Fe2+/Fe3+ co-precipitation process to prepare organic/magnetic nanocarriers comprising uniformly small magnetic iron oxide nanoparticles (NP) incorporated within the globular HBPAAs. Transmission electron microscopy revealed that the HBPAA-γ-Fe2O3 NPs had dimensions of 6–11 nm, significantly smaller than those of the pristine γ-Fe2O3 (20–30 nm). Subsequently, we covalently immobilized a bacterial γ-glutamyltranspeptidase (BlGGT) upon the HBPAA-γ-Fe2O3 nanocarriers through the formation of amide linkages in the presence of a coupling agent. Magnetization curves of the HBPAA-γ-Fe2O3/BlGGT composites measured at 300 K suggested superparamagnetic characteristics, with a saturation magnetization of 52 emu g−1. The loading capacity of BlGGT on the HBPAA-γ-Fe2O3 nanocarriers was 16 mg g−1 support; this sample provided a 48% recovery of the initial activity. The immobilized enzyme could be recycled 10 times with 32% retention of the initial activity; it had stability comparable with that of the free enzyme during a storage period of 63 days. The covalent immobilization and stability of the enzyme and the magnetization provided by the HBPAA-γ-Fe2O3 NPs suggests that this approach could be an economical means of depositing bioactive enzymes upon nanocarriers for BlGGT-mediated bio-catalysis.