Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Treatment-Associated Inflammatory Response on Diffusion-Weighted Magnetic Resonance Imaging and 2-[18F]-Fluoro-2-Deoxy-d-Glucose-Positron Emission Tomography Imaging Biomarkers

Purpose: Functional imaging biomarkers of cancer treatment response offer the potential for early determination of outcome through the assessment of biochemical, physiologic, and microenvironmental readouts. Cell death may result in an immunologic response, thus complicating the interpretation of biomarker readouts. This study evaluated the temporal effect of treatment-associated inflammatory activity on diffusion magnetic resonance imaging and 2-[18F]-fluoro-2-deoxy-d-glucose-positron emission tomography imaging (FDG-PET) biomarkers to delineate the effects of the inflammatory response on imaging readouts. Experimental Design: Rats with intracerebral 9L gliosarcomas were separated into four groups consisting of control, an immunosuppressive agent dexamethasone (Dex), 1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU), and BCNU+Dex. Animals were imaged using diffusion-weighted magnetic resonance imaging and FDG-PET at 0, 3, and 7 days posttreatment. Results: In the BCNU- and BCNU+Dex-treated animal groups, diffusion values increased progressively over the 7-day study period to ∼23% over baseline. The FDG percentage change of standard uptake value decreased at day 3 (−30.9%) but increased over baseline levels at day 7 (+20.1%). FDG-PET of BCNU+Dex-treated animals were found to have percentage of standard uptake value reductions of −31.4% and −24.7% at days 3 and 7, respectively, following treatment. Activated macrophages were observed on day 7 in the BCNU treatment group with much fewer found in the BCNU+Dex group. Conclusions: Results revealed that treatment-associated inflammatory response following tumor therapy resulted in the accentuation of tumor diffusion response along with a corresponding increase in tumor FDG uptake due to the presence of glucose-consuming activated macrophages. The dynamics and magnitude of potential inflammatory response should be considered when interpreting imaging biomarker results. Clin Cancer Res; 16(5); 1542–52
Classifiers Comparison For Attack Detection in Computer Networks

We can notice a large increase in the number of various anomalies in computer networks, leading to concern about the identification of these attacks. One method proposed in this study is the use of data mining in an attempt to support a correct identification of these anomalies. The use of Weka tool allows using a labelled database, an identification and analysis of anomalies in a computer network environment serving as a basis for improvements in that environment.
Recognition of IgG by Fcgamma receptor. The role of Fc glycosylation and the binding of peptide inhibitors.

Recently determined crystal structures of the complex between immunoglobulin constant regions (Fc) and their Fc-respective receptors (FcR) have revealed the detailed molecular interactions of this receptor-ligand pair. Of particular interest is the contribution of a glycosylation at Asn(297) of the C(H)2 domain of IgG to receptor recognition. The carbohydrate moieties are found outside the receptor.Fc interface in all receptor.Fc complex structures. To understand the role of glycosylation in FcR recognition, the receptor affinities of a deglycosylated IgG1 and its Fc fragment were determined by solution binding studies using surface plasmon resonance. The removal of carbohydrates resulted in a non-detectable receptor binding to the Fc alone and a 15- to 20-fold reduction of the receptor binding to IgG1, suggesting that the carbohydrates are important in the function of the FcgammaRIII. Structurally, the carbohydrates attached to Asn(297) fill the cavity between the C(H)2 domains of Fc functioning equivalently as a hydrophobic core. This may stabilize a favorable lower hinge conformation for the receptor binding. The structure of the complex also revealed the dominance of the lower hinge region in receptor.Fc recognition. To evaluate the potential of designing small molecular ligands to inhibit the receptor function, four lower hinge peptides were investigated for their ability to bind to the receptor FcgammaRIII. These peptides bind specifically to FcgammaRIII with affinities 20- to 100-fold lower than IgG1 and are able to compete with Fc in receptor binding. The results of peptide binding illustrate new ways of designing therapeutic compounds to block Fc receptor activation.
Learning Geometric Concepts via Gaussian Surface Area

We study the learnability of sets in Ropf<sup>n</sup> under the Gaussian distribution, taking Gaussian surface area as the "complexity measure" of the sets being learned. Let C<sub>S</sub> denote the class of all (measurable) sets with surface area at most S. We first show that the class C<sub>S</sub> is learnable to any constant accuracy in time n<sup>O(S</sup> <sup>2</sup> <sup>)</sup>, even in the arbitrary noise ("agnostic'') model. Complementing this, we also show that any learning algorithm for C<sub>S</sub> information-theoretically requires 2<sup>Omega(S</sup> <sup>2</sup> <sup>)</sup> examples for learning to constant accuracy. These results together show that Gaussian surface area essentially characterizes the computational complexity of learning under the Gaussian distribution. Our approach yields several new learning results, including the following (all bounds are for learning to any constant accuracy): The class of all convex sets can be agnostically learned in time 2<sup>O</sup> <sup>~</sup> <sup>(radicn)</sup> (and we prove a 2<sup>Omega(radicn)</sup> lower bound for noise-free learning). This is the first subexponential time algorithm for learning general convex sets even in the noise-free (PAC) model. Intersections of k halfspaces can be agnostically learned in time n<sup>O(log</sup> <sup>k)</sup> (cf. Vempala's n<sup>O(k)</sup> time algorithm for learning in the noise-free model). Cones (with apex centered at the origin), and spheres witharbitrary radius and center, can be agnostically learned in time poly(n).
Assessing Children’s Time-Use in Relation to Physical Fitness and Risks of Obesity and Diabetes: Development of a New Physical Activity Self-Report Instrument

Background This study introduces a novel self-report instrument to measure children’s time-use in physical and sedentary activities and examines the relationships between children’s time-use and physical fitness and risks of obesity and diabetes. Methods The new instrument utilizes a series of timelines, each representing an activity type. 188 children (53% girls) aged 10 to 14 year-old participated in the study. Their time-use data for two weekdays and one weekend day were collected. Anthropometrics and cardiovascular fitness were measured and children’s BMI z-score and PACER z-score were computed. One-time blood draw for fasting glucose and insulin were used to calculate insulin resistance using homeostasis model assessment for insulin resistance (HOMAIR). Results The reliability assessment of this instrument indicated a moderately reproducible procedure (ICC > 0.6) for six activity types. The validity correlation for motorized travel time was high (r = 0.226, P < 0.05) between self-report instrument and GPS tracks. PACER z-score was positively correlated with time-uses of play (r = 0.159, P < 0.05), and organized sports (r = 0.198, P < 0.05); and was highly inversely correlated with BMI z-score (r = −0.441, P < 0.0001) and HOMAIR (r = −0.472, P < 0.0001). Overall, only 14% of the children had physical activity for more than 60 minutes daily over three observation days. Conclusions This instrument is particularly useful in assessing children’s activity patterns, especially for specific physical activities. The new instrument provides a reproducible measure of children’s perception of their activities. Our results emphasize the temporal context which is critical to formulating effective interventions targeting physical activity increase in children. Further efforts are needed to understand the differences between activity time obtained by the new self-report instrument and GPS tracks.
Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images

Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system.
Automated Storage Tiering Using Markov Chain Correlation Based Clustering

In this paper, we develop an automated and adaptive framework that aims to move active data to high performance storage tiers and inactive data to low cost/high capacity storage tiers by learning patterns of the storage workloads. The framework proposed is designed using efficient Markov chain correlation based clustering method (MCC), which can quickly predict or detect any changes in the current workload based on what the system has experienced before. The workload data is first normalized and Markov chains are constructed from the dynamics of the IO loads of the data storage units. Based on the correlation of one-step Markov chain transition probabilities k-means method is employed to group the storage units that have similar behavior at each point. Such framework can then easily be incorporated in various resource management policies that aim at enhancing performance, reliability, availability. The predictive nature of the model, particularly makes a storage system both faster and lower-cost at the same time, because it only uses high performance tiers when needed, and uses low cost/high capacity tiers when possible.
Nonsynchronized-State Estimation of Multichannel Networked Nonlinear Systems With Multiple Packet Dropouts Via T–S Fuzzy-Affine Dynamic Models

This paper investigates the problem of robust H∞ state estimation for a class of multichannel networked nonlinear systems with multiple packet dropouts. The nonlinear plant is represented by Takagi-Sugeno (T-S) fuzzy-affine dynamic models with norm-bounded uncertainties, and stochastic variables with general probability distributions are adopted to characterize the data missing phenomenon in output channels. The objective is to design an admissible state estimator guaranteeing the stochastic stability of the resulting estimation-error system with a prescribed H∞ disturbance attenuation level. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the estimator implementation with state-space partition may not be synchronized with the state trajectories of the plant. Based on a piecewise-quadratic Lyapunov function combined with S -procedure and some matrix-inequality-convexifying techniques, two different approaches are developed to robust filtering design for the underlying T-S fuzzy-affine systems with unreliable communication links. All the solutions to the problem are formulated in the form of linear-matrix inequalities (LMIs). Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.
Solar-Driven High-Temperature Electrolysis Reactor for Fuel Processing

Solar-driven high-temperature electrolysis (HTE) and thermochemical cycles (TCC) are two promising pathways for fuel processing and energy storage, which are considered in this thesis. The goals of this thesis are: i) offering engineering guidelines at system level via thermodynamic modeling frameworks for both technologies to evaluate and maximize their solar-to-fuel (STF) efficiencies and economic competitiveness, ii) the modeling, optimization, and experimental demonstration of a tubular solar reactor for efficient STF generation, and iii) investigation of a fully integrated solar reactor, which uses a tubular high-temperature electrolyzer as the solar absorber to reduce transmission heat losses and hence to improve the STF efficiency. The techno-economic analysis of HTE systems is performed leading to the identification of the hybrid approach (utilizing concentrated solar heating and photovoltaics) as the optimal choice generating hydrogen at a high efficiency and low costs. This supports the competitiveness of the hybrid approach for scaled solar hydrogen generation. A thermodynamic modeling framework for a two-step ceria-based TCC is developed analyzing different routes for oxygen partial pressure reduction. This allows for performance comparison between HTE and TCC. Compared with TCC systems, HTE systems work at significantly lower temperatures and the less stringent requirement for heat recovery and low oxygen partial pressure, while equivalent STF efficiency can be achieved indicating that HTE can be a more promising technology for scaled-up solar fuel plants. The design and optimization of a tubular solar receiver, a key component in HTE system, are conducted based on an experimentally validated coupled heat and mass transfer model for the concurrent direct steam and CO2 generation. This integrated numerical model is composed of a detailed 1D two-phase flow model in the receiver tubes, which is then incorporated into a coupled 3D heat transfer model for tubular reactor cavity. Based on the model, design and demonstration of a 1 kWth solar reactor are presented. The demonstrator employs a direct steam generation solar absorber (two parallel helical tubes) directly connected to a 250 Wel solid oxide electrolyzer stack forming a compact design to reduce the transmission heat losses of high-temperature fluids. A solar-to-thermal efficiency of 77.8% (at a fluid temperature of 700 K) and STF efficiency of 5.3% (with a 15% solar-to-electricity efficiency) are achieved. The proof-of-concept demonstration leads to a promising pathway for highly efficient STF
Frequency of Severe Hypoglycemia in Patients With Type I Diabetes With Impaired Awareness of Hypoglycemia

OBJECTIVE To determine the frequency of hypoglycemia in patients with type I diabetes and impaired awareness of hypoglycemia by prospective assessment. RESEARCH DESIGN AND METHODS A prospective study was undertaken for 12 months in 60 patients with type I diabetes: 29 had impaired awareness of hypoglycemia and 31 retained normal awareness of hypoglycemia. The two groups of patients were matched for age, age at onset of diabetes, duration of diabetes, and glycemic control. Episodes of severe hypoglycemia were recorded within 24 h of the event and verified where possible by witnesses. RESULTS During the 12 months, 19 (66%) of the patients with impaired awareness had one or more episodes of severe hypoglycemia with an overall incidence of 2.8 episodes · patient–1 ·year–1. By comparison, 8 (26%) of the patients with normal awareness experienced severe hypoglycemia (P < 0.01) with an annual incidence of 0.5 episode · patient–1 · year–1 (P < 0.001). Severe hypoglycemia occurred at different times of the day in the two groups: patients with impaired awareness experienced a greater proportion of episodes during the evening (P = 0.03), and patients with normal awareness experienced a greater proportion in the early morning (P = 0.05). An assessment of fear of hypoglycemia revealed that patients with impaired awareness of hypoglycemia worried more about hypoglycemia than did patients with normal awareness (P = 0.008), but did not modify their behavior accordingly. CONCLUSIONS This prospective evaluation demonstrated that impaired awareness of hypoglycemia predisposes to a sixfold increase in the frequency of severe hypoglycemia, much of which occurred at home during waking hours.
Performance Analysis of High Speed Double Tail Dynamic Comparator

In this paper, we present a performance comparison of existing clocked dynamic comparators. As delay is directly correlated with the submicron scaling, we investigate the performance of the above comparators in terms of delay and Power-Delay Product (PDP). PDP gives the average energy dissipated by the comparator for a single comparison. Simulation results using Mentor Graphics revealed better performance of High Speed Dynamic Comparator (HSDC) compared to conventional clocked comparators in 180nm, 250nm and 350nm technologies. Implementation results reveal that high speed dynamic comparator has energy dissipation of 25.14% less compared to the best of the designs used for comparison when operated at 50 MHz.
IDADIGE : Procédé de traitement des images de gels d'électrophorèse bidimensionnelle différentielle dans le contexte de la recherche de marqueurs protéiques. (IDADIGE: A process for 2D-gel images treatment and analysis in the context of the search for protein biomarkers)

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. IDADIGE : Procédé de traitement des images de gels d’électrophorèse bidimensionnelle différentielle dans le contexte de la recherche de marqueurs protéiques. Bernard Fabien
Fast two-level Viterbi search algorithm for unconstrained handwriting recognition

This paper describes a fast two-level Viterbi search algorithm for recognizing handwritten words as a sequence of characters concatenated according to a lexicon. The algorithm is based on hidden Markov model (HMM) representations of characters and it breaks up the computation of word likelihood scores into two levels: state level and character level. This enables the reuse of likelihood scores of characters to decode all words in the lexicon, avoiding repeated computation of state sequences. Experimental results with an 85,000-word vocabulary indicate that the computational cost of an off-line handwritten word recognition system may be reduced by more than a factor of 20 while not introducing search errors.
Selection criteria for patients with chronic ankle instability in controlled research: a position statement of the International Ankle Consortium

While research on chronic ankle instability (CAI) and awareness of its impact on society and health care systems has grown substantially in the last 2 decades, the inconsistency in participant/patient selection criteria across studies presents a potential obstacle to addressing the problem properly. This major gap within the literature limits the ability to generalise this evidence to the target patient population. Therefore, there is a need to provide standards for patient/participant selection criteria in research focused on CAI with justifications using the best available evidence. The International Ankle Consortium provides this position paper to present and discuss an endorsed set of selection criteria for patients with CAI based on the best available evidence to be used in future research and study designs. These recommendations will enhance the validity of research conducted in this clinical population with the end goal of bringing the research evidence to the clinician and patient.
Protection from MPTP‐induced neurotoxicity in differentiating mouse N2a neuroblastoma cells

We have shown previously that subcytotoxic concentrations of MPTP (1‐methyl‐4‐phenyl‐1,2,3,6‐tetrahydropyridine) inhibit axon outgrowth and are associated with increased neurofilament heavy chain (NF‐H) phosphorylation in differentiating mouse N2a neuroblastoma cells while higher doses (> 100 µm) cause cell death. In this work we assessed the ability of potential neuroprotective agents to alleviate both MPTP‐induced cell death (cytotoxicity) and MPTP‐induced NF‐H phosphorylation/reduction in axon outgrowth (neurotoxicity) in N2a cells induced to differentiate by dbcAMP. The neurotoxic effects of MPTP occurred in the absence of significant alterations in energy status or mitochondrial membrane potential. The hormone oestradiol (100 µm) reduced the cytotoxic effect of MPTP, but blocked di‐butyryl cyclic AMP (dbcAMP)‐induced differentiation, i.e. axon outgrowth. Both the cytotoxic and neurotoxic effects of MPTP were reduced by the monoamine osidase (MAO) inhibitors deprenyl and, to a lesser extent, clorgyline. Alleviation of both neurotoxicity and cytotoxicity was also achieved by conditioned medium derived from rat C6 glioma cells. In contrast, whilst the p38 MAP kinase inhibitor, SB202190, protected cells against MPTP‐induced neurotoxicity, it could not maintain cell viability at high MPTP exposures. In each case neuroprotection involved maintenance of the differentiating phenotype linked with attenuation of NF‐H hyper‐phosphorylation; the latter may represent a mechanism by which neuronal cells can moderate MPTP‐induced neurotoxicity. The use of a simplified neuronal cell model, which expresses subtle biochemical changes following neurotoxic insult, could therefore provide a valuable tool for the identification of potential neuroprotective agents.
A Grassmann manifold-based domain adaptation approach

Domain adaptation algorithms that handle shifts in the distribution between training and testing data are receiving much attention in computer vision. Recently, a Grassmann manifold-based domain adaptation algorithm that models the domain shift using intermediate subspaces along the geodesic connecting the source and target domains was presented in [6]. We build upon this work and propose replacing the step of concatenating feature projections on a very few sampled intermediate subspaces by directly integrating the distance between feature projections along the geodesic. The proposed approach considers all the intermediate subspaces along the geodesic. Thus, it is a more principled way of quantifying the cross-domain distance. We present the results of experiments on two standard datasets and show that the proposed algorithm yields favorable performance over previous approaches.
License plate detection using point of interest detectors and descriptors

A license plate detection task (LPD) is an important stage in the License plate recognition (LPR) implementations for automated transport systems. This paper is focused on a theory behind various interest points (IPs) detectors and region descriptors often used in applications of image processing including the LPR apps among others. We have carried out wide analysis on behaviour of several selected detectors and descriptors listed here: Harris, Shi&Tomasi, MSER, FAST, SURF and BRISK. These algorithms have been implemented, tested and compared each other from a point of view of their stability and repeatability. Our experiments were performed on a license plate localization task and carried out over an extensive gallery of real vehicle images.