Miracle-dz/UniFAR-FTUs huggingface dataset:
Cerebrovascular dynamics after pediatric traumatic brain injury

Objective: We aimed to investigate model-based indices of cerebrovascular dynamics after pediatric traumatic brain injury (TBI) using transcranial Doppler ultrasound (TCD) integrated into multimodality neurologic monitoring (MMM). Methods: We performed a retrospective analysis of pediatric TBI patients undergoing TCD integrated into MMM. Classic TCD characteristics included pulsatility indices and systolic, diastolic and mean flow velocities of the bilateral middle cerebral arteries. Model-based indices of cerebrovascular dynamics included the mean velocity index (Mx), compliance of the cerebrovascular bed (Ca), compliance of the cerebrospinal space (Ci), arterial time constant (TAU), critical closing pressure (CrCP) and diastolic closing margin (DCM). Classic TCD characteristics and model-based indices of cerebrovascular dynamics were investigated in relation to functional outcomes and intracranial pressure (ICP) using generalized estimating equations with repeated measures. Functional outcomes were assessed using the Glasgow Outcome Scale–Extended Pediatrics score (GOSE-Peds) at 12 months, post-injury. Results: Seventy-two separate TCD studies were performed on twenty-five pediatric TBI patients. We identified that reduced Ci (estimate −5.986, p = 0.0309), increased CrCP (estimate 0.081, p < 0.0001) and reduced DCM (estimate −0.057, p = 0.0179) were associated with higher GOSE-Peds scores, suggestive of unfavorable outcome. We identified that increased CrCP (estimate 0.900, p < 0.001) and reduced DCM (estimate −0.549, p < 0.0001) were associated with increased ICP. Conclusion: In an exploratory analysis of pediatric TBI patients, increased CrCP and reduced DCM and Ci are associated with unfavorable outcomes, and increased CrCP and reduced DCM are associated with increased ICP. Prospective work with larger cohorts is needed to further validate the clinical utility of these features.
Model for the Electrical Behavior of a Microplasma

The complex current fluctuations observed in connection with microplasma breakdown can be explained by a simple model containing two constants: extrapolated breakdown voltage Vb and series resistance Rs; and two continuous probability functions: turnoff probability per unit time p10(I) as a function of pulse current I and turn‐on probability per unit time p01. Experimental methods allowing an accurate measurement of these four quantities are described. The new concept of an extrapolated breakdown voltage Vb is discussed based on two independent measurements: one of secondary multiplication and the other of instantaneous current, both as a function of voltage. Within the experimental accuracy of 20 mV both methods extrapolated to one and the same breakdown voltage. The turnoff probability p10(I) is determined by a new combination of experimental techniques to cover the current range from 5 to 70 μA with a variation of 11 decades for p10(I). The observation of a narrow turnoff interval is explained quantita.
Exergy analysis of a cogeneration system through Artificial Neural Network (ANN) method

The main objective of this study is to apply the Artificial Neural Network (ANN) method to a cogeneration system, located in Izmir, Turkey, for exergetic evaluation purposes. The data used are based on the actual operational conditions and the results obtained from this system, which was exergetically analysed by the authors. It consists of three turbines with a total capacity of 13 MW, six spray dryers and two heat exchangers. A comparison between the exergy destruction values obtained from exergy analysis calculations and the ANN method is made. Fast ANN (FANN) package (library) has been chosen as an ANN application to implement into the C+ + code named CogeNNExT, which has been written and developed by the authors. From the single output of the ANN (FANN) results, the main exergy destruction rate with 60.96 MW in the exergetic analysis is found to be 61.001 MW with an error of 0.075%. From the two outputs of another ANN result, the mean input and output exergy values are found with errors of 0.438% and 2.211%, respectively.
Differences in protein expression, at the basal state and at 2 h of insulin infusion, in muscle biopsies from healthy Arab men with high or low insulin sensitivity measured by hyperinsulinemic euglycemic clamp

Background Skeletal muscle is the main site for insulin-dependent glucose disposal. The hyperinsulinemic euglycemic clamp (HIEC) is the gold standard for the assessment of insulin sensitivity (IS). We have previously shown that insulin sensitivity, measured by HIEC, varied widely among a group of 60 young healthy men with normoglycemia. The aim of this study was to correlate the proteomic profile of skeletal muscles to insulin sensitivity. Methods Muscle biopsies from 16 subjects having the highest (M ≥ 13; n = 8, HIS) and lowest (M ¾ 6, n = 8, LIS) IS were obtained at baseline and during insulin infusion after stabilization of the blood glucose level and glucose infusion rate at the end of the HIEC. The samples were processed using a quantitative proteomic analysis approach. Results At baseline, 924 proteins were identified in the HIS and LIS groups. Among the 924 proteins detected in both groups, three were suppressed and three were increased significantly in the LIS subjects compared with the HIS subjects. Following insulin infusion, 835 proteins were detected in both groups. Among the 835 proteins, two showed differential responsiveness to insulin; ATP5F1 protein was decreased, and MYLK2 was higher in the LIS group compared with that in the HIS group. Our data suggest that alteration in mitochondrial proteins and an increased number of proteins involved in fast-twitch fiber correlate to insulin sensitivity in healthy young Arab men. Conclusions These results suggest a change in a small number of differentially expressed proteins. A possible reason for this small change could be our study cohorts representing a homogeneous and healthy population. Additionally, we show differences in protein levels from skeletal muscle in low and high insulin sensitivity groups. Therefore, these differences may represent early events for the development of insulin resistance, pre-diabetes, and type 2 diabetes.
Flow Invariance Preserving Feedback Controllers for the Navier–Stokes Equation

Abstract In this paper we develop a concrete procedure for designing feedback controllers to ensure that the resultant dynamics of turbulence will preserve certain prescribed physical constraints. Examples of such constraints include, in particular, the level sets of well known invariants of the inviscid flow such as helicity. We also bring to light a certain m-accretivity property of suitable quantization of the nonlinearity in the Navier–Stokes equation and utilize the theory of nonlinear semigroups to resolve the controlled Navier–Stokes inclusion with a multi-valued feedback term.
An overview of radiolabeled amino acid tracers in oncologic imaging

Molecular imaging has witnessed a great progress in the field of oncology over the past few decades. Radiolabeled amino acid (AA) tracers are particularly helpful in the areas where the utility of 18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography with computed tomography imaging has been limited such as in evaluating brain tumors, neuroendocrine tumors (NETs), and prostate cancer. Radiolabeled AA tracers such as 6-[18F]-L-fluoro-L-3, 4-dihydroxyphenylalanine (18F-FDOPA), 18F-fluoro-ethyl-tyrosine (18F-FET), and 11C-methionine have found wide applications in brain tumors, which, unlike 18F-FDG, concentrate in the tumor tissue to a greater extent than that in normal brain tissue by providing accurate information about tumor volume and boundaries. 18F-FDOPA is also useful in evaluating NETs. Tracers such as 18F-FACBC (Fluciclovine) and anti-1-amino-2-[18F]fluorocyclopentyl-1-carboxylic acid (18F-FACPC) are used in imaging of prostate cancer and provide valuable information of locoregional, recurrent, and metastatic disease. This review highlights AA tracers and their major applications in imaging, viz., in evaluating brain tumors, NETs, and prostate cancer.
A genetic approach for network partitioning

A new partitioning algorithm, genetic partitioning, utilizing genetic algorithms is presented. The genetic partitioning has the abilities of climbing uphill with respect to the best solution, and of implicit parallel processing due to its population inherent in genetic algorithms. Two circuits are examined. The results of the first circuit are optimal in 85% of the cases when compared with those obtained using the exhaustive search approach. Genetic partitioning is competitive with simulated annealing in performance, as verified by the second circuit.
New tools for robustness analysis

The author provides a personal perspective on research concerning Kharitonov's theorem of robustness analysis, as well as extensions and generalizations of this theorem. He discusses only a selected subset of the published literature which he feels provides the uninitiated reader with a coherent path through the rapidly expanding diversity of results. After a statement of Kharitonov's theorem and its limitations, he considers such issues as polytope stability problems and the edge theorem, and more general coefficient dependencies on perturbations. <<ETX>>
The worked example effect, the generation effect, and element interactivity.

The worked example effect indicates that examples providing full guidance on how to solve a problem result in better test performance than a problem-solving condition with no guidance. The generation effect occurs when learners generating responses demonstrate better test performance than learners in a presentation condition that provides an answer. This contradiction may be resolved by the suggestion that the worked example effect occurs for complex, high-element interactivity materials that impose a heavy working memory load whereas the generation effect is applicable for low-element interactivity materials. Two experiments tested this hypothesis in the area of geometry instruction using students with different levels of prior knowledge in geometry. The results of Experiment 1 indicated a worked example effect obtained for materials high in element interactivity and a generation effect for materials low in element interactivity. As levels of expertise increased in Experiment 2, thus reducing effective complexity, this interaction was replaced by a generation effect for all materials. These results suggest that when students need to learn low-element interactivity material, learning will be enhanced if they generate rather than study responses but if students need to learn high-element interactivity material, study may be preferable to generating responses.
Insight into the bZIP gene family in Lagenaria siceraria: Genome and transcriptome analysis to understand gene diversification in Cucurbitaceae and the roles of LsbZIP gene expression and function under cold stress

The basic leucine zipper (bZIP) as a well-known transcription factor family, figures prominently in diverse biological and developmental processes and response to abiotic/biotic stresses. However, no knowledge of the bZIP family is available for the important edible Cucurbitaceae crop bottle gourd. Herein, we identified 65 putative LsbZIP genes and characterized their gene structure, phylogenetic and orthologous relationships, gene expression profiles in different tissues and cultivars, and responsive genes under cold stress. The phylogenetic tree of 16 released Cucurbitaceae plant genomes revealed the evolutionary convergence and divergence of bZIP family. Based on the specific domains, LsbZIP family were classified into 12 clades (A–K, S) with similar motifs and exon-intron distribution. 65 LsbZIP genes have undergone 19 segmental and two tandem duplication events with purifying selection. The expression profiling of LsbZIP genes showed tissue-specific but no cultivar-specific pattern. The cold stress-responsive candidate LsbZIP genes were analyzed and validated by RNA-Seq and RT-PCR, providing new insights of transcriptional regulation of bZIP family genes in bottle gourd and their potential functions in cold-tolerant variety breeding.
MODELING OF A PIEZO‐ACTUATED POSITIONING STAGE BASED ON A HYSTERESIS OBSERVER

Piezo‐actuated positioning stages contain two parts: a Piezo Electric Actuator (PEA) and a positioning mechanism. The tracking control accuracy of the piezo‐actuated positioning stage is limited due to the hysteretic nonlinearity of the PEA and the friction behavior of the positioning mechanism. This loss in precision restricts the use of the piezo‐actuated stage in an ultra‐high‐precision optical system. This paper presents an inversion‐based approach to reduce the nonlinearity of the PEA by using a proposed hysteresis observer. To reduce the degredation in precision due to the friction behavior of the positioning mechanism, a PI feedback controller with a feed‐forward controller based on a hysteresis observer is proposed to solve tracking problems with modelling uncertainties and external disturbances.
The Zebrafish (Danio rerio) as a Model for Studying Voluntary Physical Exercise and its Effects on Behavior and Metabolism

: The objective of this study was to develop a viable and low-cost model of voluntary physical exercise that could be applied to studies on metabolism and behavior. 40 male zebrafish (Danio rerio) were studied, divided into two groups: control and voluntary physical exercise. The model consisted of two aquariums connected by a translucent tube and a video camera on the side to measure physical exercise parameters of the animals. The animals showed higher acceleration and maximum speed and had a higher frequency of activity in the light period. In this model of voluntary physical exercise, we observed better performance in locomotor assessment tests, which was not accompanied by increased anxiety or changes in biochemical parameters related to lipid metabolism. Zebrafish responded positively to voluntary physical exercise and this model appears to be a good option for further studies.
Bayesian Analysis for Sociologists

This article provides an applied introduction to Bayesian statistics for sociologists. Unlike frequentist statistics, which attaches repeated-sampling frequencies to test statistics, Bayesian statistics directly describes uncertainty about unknown statistical parameters with a probability distribution. With this foundation, much of Bayesian statistics follows from basic rules of probability theory. Three areas of Bayesian statistics are especially relevant for sociologists. First, hierarchical regression models allow several levels of uncertainty into an analysis. Second, Bayes factors provide a useful approach to the problems of model selection, model averaging, and posterior inference about model indexes. Third, recent breakthroughs in estimation methods offer valuable new tools for analysis of Bayesian models that were previously intractable.
The 1985 May superoutburst of the dwarf nova OY Carinae. II - IUE and Exosat observations

Ultraviolet (IUE) and X-ray (Exosat) observations of the eclipsing dwarf nova OY Carinae during the superoutburst of May 1985 are presented. From the lack of X-ray eclipse and UV behavior, it is deduced that the X-ray flux originates in an optically thin corona comparable in size to the Roche lobe, and not directly from the white dwarf or boundary layer. The asymmetric UV line emission originates partly in the accretion disk and partly in a wind. There is a strong modulation of the UV continuum flux that is thought to be caused by extended vertical disk structure shadowing the inner regions.
Enhanced Chimp Optimization-Based Feature Selection with Fuzzy Logic-Based Intrusion Detection System in Cloud Environment

Cloud computing (CC) refers to an Internet-based computing technology in which shared resources, such as storage, software, information, and platform, are offered to users on demand. CC is a technology through which virtualized and dynamically scalable resources are presented to users on the Internet. Security is highly significant in this on-demand CC. Therefore, this paper presents improved metaheuristics with a fuzzy logic-based intrusion detection system for the cloud security (IMFL-IDSCS) technique. The IMFL-IDSCS technique can identify intrusions in the distributed CC platform and secure it from probable threats. An individual sample of IDS is deployed for every client, and it utilizes an individual controller for data management. In addition, the IMFL-IDSCS technique uses an enhanced chimp optimization algorithm-based feature selection (ECOA-FS) method for choosing optimal features, followed by an adaptive neuro-fuzzy inference system (ANFIS) model enforced to recognize intrusions. Finally, the hybrid jaya shark smell optimization (JSSO) algorithm is used to optimize the membership functions (MFs). A widespread simulation analysis is performed to examine the enhanced outcomes of the IMFL-IDSCS technique. The extensive comparison study reported the enhanced outcomes of the IMFL-IDSCS model with maximum detection efficiency with accuracy of 99.31%, precision of 92.03%, recall of 78.25%, and F-score of 81.80%.
Single-mode behavior of a circular grating for potential disk-shaped DFB lasers

The reflectivity and the resonance condition of a circular grating for a disk-shaped distributed-feedback (DFB) laser are calculated. The periodicity and the position of the grating are so chosen that all of the reflections from each refractive index step are superimposed in-phase so as to be consistent with the resonant behavior of the fundamental mode wave. The calculated wave impedance (E/H) is almost purely imaginary in the central region of the grating for the higher modes. >
An edge-preserving high-resolution image reconstruction

In many visual problems, line variables, associated to the discontinuities present in the ideal image, are used to obtain better results. In this paper we propose to extend the use of line variables to the high-resolution reconstruction problem. Moreover, we impose that parallel discontinuities should not be close. To minimize the energy function related to this problem we use a CATILED (convex approximation technique for interacting line elements deblurring) algorithm. Experimental results confirm the goodness of this technique.
An Overview of Ovarian Cancer: The Role of Cancer Stem Cells in Chemoresistance and a Precision Medicine Approach Targeting the Wnt Pathway with the Antagonist sFRP4

Simple Summary Ovarian cancer is one of the deadliest cancers in women and is unfortunately detected only in its later stages, by which time it will have metastasized to different organs. One of the major determinants of the rapid proliferation and spread of cancer is the presence of cancer stem cells (CSCs) within tumors. In this review, we discuss the emerging markers of ovarian cancer with respect to CSCs and circulating tumor cells. Cancer stem cell population is regulated by the Wnt signaling pathway, which is described in detail. This is relevant to comprehend the significance of Wnt in regulating the stem cell-like properties of cancer. Cancer stem cells are also responsible for enhancing chemoresistant properties and metastatic potential. Therefore, for a successful treatment regime, it is important to factor in drugs that are specific to CSCs. By targeting the promoters of CSCs, such as the Wnt signaling pathway, it is possible to suppress these cells specifically. Herein, we describe an inhibitor of Wnt, secreted frizzled related protein 4, which could be used to successfully destroy the cancer stem cells of ovarian cancer. Abstract Ovarian cancer is one of the most prevalent gynecological cancers, having a relatively high fatality rate with a low five-year chance of survival when detected in late stages. The early detection, treatment and prevention of metastasis is pertinent and a pressing research priority as many patients are diagnosed only in stage three of ovarian cancer. Despite surgical interventions, targeted immunotherapy and adjuvant chemotherapy, relapses are significantly higher than other cancers, suggesting the dire need to identify the root cause of metastasis and relapse and present more precise therapeutic options. In this review, we first describe types of ovarian cancers, the existing markers and treatment modalities. As ovarian cancer is driven and sustained by an elusive and highly chemoresistant population of cancer stem cells (CSCs), their role and the associated signature markers are exhaustively discussed. Non-invasive diagnostic markers, which can be identified early in the disease using circulating tumor cells (CTCs), are also described. The mechanism of the self-renewal, chemoresistance and metastasis of ovarian CSCs is regulated by the Wnt signaling pathway. Thus, its role in ovarian cancer in promoting stemness and metastasis is delineated. Based on our findings, we propose a novel strategy of Wnt inhibition using a well-known Wnt antagonist, secreted frizzled related protein 4 (sFRP4), wherein short micropeptides derived from the whole protein can be used as powerful inhibitors. The latest approaches to early diagnosis and novel treatment strategies emphasized in this review will help design precision medicine approaches for an effective capture and destruction of highly aggressive ovarian cancer.
QUANTUM STATE OF WORMHOLES AND TOPOLOGICAL ARROW OF TIME

This paper studies the time-symmetry problem in quantum gravity. The issue depends critically on the choice of the quantum state and has been considered in this paper by restricting it to the case of quantum wormholes. It is seen that pure states represented by a wave functional are time symmetric. However, a maximal analytic extension of the wormhole manifold is found that corresponds to a mixed state describable by a nondegenerate density-matrix functional that involves an extra quantum uncertainty for the three-metric, and is free from the divergences encountered so far in statistical states formulated in quantum gravity. It is then argued that, relative to one asymptotic region, the statistical quantum state of single Euclidean wormholes in semiclassical approximation is time-asymmetric and gives rise to a topological arrow of time which will reflect in the set of all quantum fields at low energies of the asymptotically flat region.
Diagnostics in Linear Discriminant Analysis

Abstract Some new diagnostic measures in discriminant analysis are proposed. They can be expressed in terms of the two fundamental influence statistics in discriminant analysis: d i 2 and ψ i . A theorem on the asymptotic distributions of the fundamental statistics is derived. Based on the theorem, the proposed measures can be shown to be asymptotically distributed as functions of independent chi-squared and standard normal random variables. Critical values and expected quantiles of the measures can then be constructed. Hence influential observations are detected using Q-Q plots and significance tests. Two measures have analogous forms in regression. The theorem is also useful for getting the asymptotic distributions of existing measures that are functions of d i 2 and ψ i . A comparison of the diagnostics in linear discriminant analysis, linear regression, and linear logistic regression (discriminant) analysis is made. Although discriminant coefficients can be determined under a regression model, regress.