Miracle-dz/UniFAR-FTUs huggingface dataset:
Analgesic effects of intravenous flunixin and intrafunicular lidocaine or their combination for castration of lambs

Objective To analyse the effectiveness of intrafunicular lidocaine and intravenous flunixin for reducing pain and signs of stress in lambs undergoing surgical castration. Design Randomised controlled trial. Setting One university teaching hospital in Italy. Participants 30 healthy male lambs, 9–12 weeks old. Intervention Allocation to five groups: a control group (C), undergoing general anaesthesia but not castration; a surgery group (S), undergoing orchiectomy without analgesic treatment; a surgery-lidocaine group (SL), undergoing orchiectomy and receiving intrafunicular 2 per cent lidocaine solution; a surgery-flunixin group (SF), undergoing orchiectomy and receiving intravenous flunixin; a surgery-flunixin-lidocaine group (SFL), undergoing orchiectomy and receiving both intrafunicular lidocaine and intravenous flunixin. Main outcome measures Nociception and stress were assessed through intraoperative indicators, serum cortisol concentration, glycaemia, behaviour, immune response and clinical evaluation of the heart rate (HR), respiratory rate and rectal temperature after surgery. Results Groups S and SL showed increased values of intraoperative HR, mean arterial pressure and postoperative cortisol concentration. In group SFL, cortisol values were similar to those of group C. No other difference could be detected. Conclusions The combination of intravenous flunixin and intrafunicular lidocaine reduced the pain and discomfort of lambs castrated under general anaesthesia. Intrafunicular lidocaine alone did not prevent pain or discomfort associated with castration. Trial registration number 30/2012/CEISA/COM.
Privacy-Preserving Spatial Crowdsourcing Based on Anonymous Credentials

In Spatial Crowdsourcing (SC), a set of spatio-temporal tasks are outsourced to a set of workers, i.e., individuals with mobile devices who physically travel to task locations. The process of matching workers to tasks is performed by a SC server. To perform matching, the SC server needs access to worker locations. However, the SC server may not be trustworthy. Current solutions for protecting locations of workers assume that a trusted cellular service provider (CSP) knows the identities and locations of workers and sanitizes locations before sharing them with the SC server. In practice, the CSP may not have the technical ability, nor the proper incentives to perform the sanitization task. Thus, location protection must be performed by a Location Privacy Provider (LPP). To prevent identity disclosure to the LPP, we propose a novel solution based on anonymous credentials which preserves worker privacy. Our solution allows registered workers to log on to the LPP and receive tasks from the SC-server anonymously. In addition, our solution assures the confidentiality and integrity of spatial tasks. Our implementation and experiments demonstrate that our solution is practical.
Expectancy-Based Strategic Processes Are Influenced by Spatial Working Memory Load and Individual Differences in Working Memory Capacity

The present research examined whether imposing a high (or low) working memory (WM) load in different types of non-verbal WM tasks could affect the implementation of expectancy-based strategic processes in a sequential verbal Stroop task. Participants had to identify a colored (green vs. red) target patch that was preceded by a prime word (GREEN or RED), which was either incongruent or congruent with the target color on 80% and 20% of the trials, respectively. Previous findings have shown that participants can strategically use this information to predict the upcoming target color, and avoid the standard Stroop interference effect. The Stroop task was combined with different types of non-verbal WM tasks. In Experiment 1, participants had to retain sets of four arrows that pointed either in the same (low WM load) or in different directions (high WM load). In Experiment 2, they had to remember the spatial locations of four dots which either formed a straight line (low load) or were randomly scattered in a square grid (high load). In addition, participants in the two experiments performed a change localization task to assess their WM capacity (WMC). The results in both experiments showed a reliable congruency by WM load interaction. When the Stroop task was performed under a high WM load, participants were unable to efficiently ignore the incongruence of the prime, as they consistently showed a standard Stroop effect, regardless of their WMC. Under a low WM load, however, a strategically dependent effect (reversed Stroop) emerged. This ability to ignore the incongruence of the prime was modulated by WMC, such that the reversed Stroop effect was mainly found in higher WMC participants. The findings that expectancy-based strategies on a verbal Stroop task are modulated by load on different types of spatial WM tasks point at a domain-general effect of WM on strategic processing. The present results also suggest that the impact of loading WM on expectancy-based strategies can be modulated by individual differences in WMC.
DC electrical tree initiation in silicone rubber under temperature gradient

In this paper, initiation behaviors of DC electrical tree in silicone rubber were studied under different voltage polarities and temperature gradients (temperature of needle and ground electrodes ranged from 20 to 120 °C). Electrical tree initiation probability, tree shape, and tree length were obtained via the statistics. The results show that polarity effects, where the positive DC voltage is easier for trees to initiate, appear evidently under various temperature situations. As for the temperature gradient, tree initiation voltage decreases first and then returns to a larger value with the increasing needle temperature when the ground electrode temperature is fixed at 20 °C. Both the probability of multiple-branch tree and corresponding tree length increase and subsequently decrease. However, tree initiation voltage decreases alone and tree length increases with the rise of ground electrode temperature when the needle temperature is fixed at 20 °C. Homo charge injection becomes easier with increasing needle temperature following the Schottky-Richardson approximation under ramped DC voltages. In addition, electric field strength at the tip decreases when the needle temperature is higher than ground temperature, since the conductivity is sensitive to the local temperature and electric field. It is believed that the needle temperature and the temperature gradient dominate tree initiation behaviors, where the former affects the collisional ionization and partial relaxation of silicone rubber chains, and the latter decides electric field distribution in the bulk.
Robust and Probabilistic Failure-Aware Placement

Motivated by the growing complexity and heterogeneity of modern data centers, and the prevalence of commodity component failures, this article studies the failure-aware placement problem of placing tasks of a parallel job on machines in the data center with the goal of increasing availability. We consider two models of failures: adversarial and probabilistic. In the adversarial model, each node has a weight (higher weight implying higher reliability) and the adversary can remove any subset of nodes of total weight at most a given bound W and our goal is to find a placement that incurs the least disruption against such an adversary. In the probabilistic model, each node has a probability of failure and we need to find a placement that maximizes the probability that at least K out of N tasks survive at any time. For adversarial failures, we first show that (i) the problems are in Σ2, the second level of the polynomial hierarchy; (ii) a variant of the problem that we call RobustFap (for Robust Failure-Aware Placement) is co-NP-hard; and (iii) an all-or-nothing version of RobustFap is Σ2-complete. We then give a polynomial-time approximation scheme (PTAS) for RobustFap, a key ingredient of which is a solution that we design for a fractional version of RobustFap. We then study HierRobustFap, which is the fractional RobustFap problem over a hierarchical network, in which failures can occur at any subset of nodes in the hierarchy, and a failure at a node can adversely impact all of its descendants in the hierarchy. To solve HierRobustFap, we introduce a notion of hierarchical max-min fairness and a novel Generalized Spreading algorithm, which is simultaneously optimal for every upper bound W on the total weight of nodes that an adversary can fail. These generalize the classical notion of max-min fairness to work with nodes of differing capacities, differing reliability weights, and hierarchical structures. Using randomized rounding, we extend this to give an algorithm for integral HierRobustFap. For the probabilistic version, we first give an algorithm that achieves an additive ε approximation in the failure probability for the single level version, called ProbFap, while giving up a (1 + ε) multiplicative factor in the number of failures. We then extend the result to the hierarchical version, HierProbFap, achieving an ε additive approximation in failure probability while giving up an (L + ε) multiplicative factor in the number of failures, where L is the number of levels in the hierarchy.
The C-terminal Phosphorylation Sites of eel Follicle-Stimulating Hormone Receptor are Important Role in the Signal Transduction

ABSTRACT The large extracellular domain of glycoprotein hormone receptors is a unique feature within the G protein-coupled receptors (GPCRs) family. After interaction with the hormone, the receptor becomes coupled to Gs, which, in turn stimulates adenylyl cyclase and the production of cAMP. Potential phosphorylation sites exist in the C-terminal region of GPCRs. The experiments described herein represent attempts to determine the functions of the eel follicle-stimulating hormone receptor (eelFSHR). We constructed a mutant of eelFSHR, in which the C-terminal cytoplasmic tail was truncated at residue 614 (eelFSHR-t614). The eelFSHR-t614 lacked all potential phosphorylation sites present in the C-terminal region of eelFSHR. In order to obtain the eelFSHR ligand, we produced recombinant follicle-stimulating hormone (rec-eelFSHβ/α) in the CHO-suspension cells. The expression level was 2-3 times higher than that of the transient expression of eelFSH in attached CHO-K1 cells. The molecular weight of the rec-eelFSHβ/α protein was identified to be approximately 34 kDa. The cells expressing eelFSHR-t614 showed an increase in agonist-induced cAMP responsiveness. The maximal cAMP responses of cells expressing eelFSHR-t614 were lower than those of cells expressing eelFSHR-wild type (eelFSHR-WT). The EC50 following C-terminal deletion in CHO-K1 cells was approximately 60.4% of that of eelFSHR-WT. The maximal response in eelFSHR-t614 cells was also drastically lower than that of eelFSHR-WT. We also found similar results in PathHunter Parental cells expressing β-arrestin. Thus, these data provide evidence that the truncation of the C-terminal cytoplasmic tail phosphorylation sites in the eelFSHR greatly decreased cAMP responsiveness and maximal response in both CHO-K1 cells and PathHunter Parental cells expressing β-arrestin.
Can Artificial Intelligence Reliably Report Chest X-Rays?: Radiologist Validation of an Algorithm trained on 1.2 Million X-Rays

Background and Objectives: Chest x-rays are the most commonly performed, cost-effective diagnostic imaging tests ordered by physicians. A clinically validated, automated artificial intelligence system that can reliably separate normal from abnormal would be invaluable in addressing the problem of reporting backlogs and the lack of radiologists in low-resource settings. The aim of this study was to develop and validate a deep learning system to detect chest x-ray abnormalities. Methods: A deep learning system was trained on 1.2 million x-rays and their corresponding radiology reports to identify abnormal x-rays and the following specific abnormalities: blunted costophrenic angle, calcification, cardiomegaly, cavity, consolidation, fibrosis, hilar enlargement, opacity and pleural effusion. The system was tested versus a 3-radiologist majority on an independent, retrospectively collected de-identified set of 2000 x-rays. The primary accuracy measure was area under the ROC curve (AUC), estimated separately for each abnormality as well as for normal versus abnormal reports. Results: The deep learning system demonstrated an AUC of 0.93(CI 0.92-0.94) for detection of abnormal scans, and AUC(CI) of 0.94(0.92-0.97),0.88(0.85-0.91), 0.97(0.95-0.99), 0.92(0.82-1), 0.94(0.91-0.97), 0.92(0.88-0.95), 0.89(0.84-0.94), 0.93(0.92-0.95), 0.98(0.97-1), 0.93(0.0.87-0.99) for the detection of blunted CP angle, calcification, cardiomegaly, cavity, consolidation, fibrosis,hilar enlargement, opacity and pleural effusion respectively. Conclusions and Relevance: Our study shows that a deep learning algorithm trained on a large quantity of labelled data can accurately detect abnormalities on chest x-rays. As these systems further increase in accuracy, the feasibility of using artificial intelligence to extend the reach of chest x-ray interpretation and improve reporting efficiency will increase in tandem.
Concurrent Imitative Movement During Action Observation Facilitates Accuracy of Outcome Prediction in Less-Skilled Performers

Skilled athletes can predict the outcome of actions performed by others, based on the kinematic information inherent in others’ actions, earlier and more accurately than less-skilled athletes. Activation of the motor cortex during action observation indicates motor simulation of other’s actions in one’s own motor system; this contributes to skilled outcome prediction. Thus, the present study investigated whether concurrent movements during action observation that affect motor simulation influence the accuracy of outcome prediction, namely, whether concurrent imitative movement and self-movement enhance and inhibit accuracy, respectively, based on skill level. Twelve male varsity basketball players (skilled group) and twelve male college students with no special training in basketball (less-skilled group) were required to predict the outcome of a basketball free throw by another player based on the action kinematics in the following four conditions: prediction without any action (observation), prediction with right-wrist volar flexion with maximum speed (incongruent-action), prediction with concurrent imitative movement during observation by right-wrist flexion as if imitating the model’s action (imitative-motion), or prediction with concurrent self-movement by right-wrist flexion as if shooting by oneself (self-motion). The results showed that the skilled group had degraded accuracy of outcome prediction in the self-motion condition compared to the observation condition. In contrast, accuracy in the less-skilled group was facilitated in the imitative-motion condition compared to the observation condition. The findings suggest that, at least in less-skilled participants, the appropriate motor simulation that relates to skilled prediction can be virtually induced by concurrent imitative movement during the prediction task, even if they have less experience of free throws. This effect in imitative movement is likely to occur by producing identical motor commands with observed action, thereby enabling the prediction of sensory consequences and outcome accurately via a forward model. We propose that traditional perceptual training with concurrent imitative movement is likely to be an effective way to develop visual- and motor-based hybrid outcome predictions that produce superior inferences in skilled athletes.
Etablissement d'un modèle régional de classification typologique des paysages (For a typological classification of landscapes)

Resume. - Les combinaisons d'elements constitutifs du paysage sont etudiees dans le Jura central. Elles sont mises en evidence par analyse factorielle et classification hierarchique. Les types definis peuvent etre appliques a d'autres zones. Le modele de classement s'enrichit ainsi par extension laterale et affinement categoriel.
Gene Expression Detection Assay for Cancer Clinical Use

Cancer is a genetic disease where genetic variations cause abnormally functioning genes that appear to alter expression. Proteins, the final products of gene expression, determine the phenotypes and biological processes. Therefore, detecting gene expression levels can be used for cancer diagnosis, prognosis, and treatment prediction in a clinical setting. In this review, we investigated six gene expression assay systems (qRT-PCR, DNA microarray, nCounter, RNA-Seq, FISH, and tissue microarray) that are currently being used in clinical cancer studies. Some of these methods are also commonly used in a modified way; for example, detection of DNA content or protein expression. Herein, we discuss their principles, sample preparation, design, quantification and sensitivity, data analysis, time for sample preparation and processing, and cost. We also compared these methods according to their sample selection, particularly for the feasibility of using formalin-fixed paraffin-embedded (FFPE) samples, which are routinely archived for clinical cancer studies. We intend to provide a guideline for choosing an assay method with respect to its oncological applications in a clinical setting.
Effects of the intestinal microbial metabolite butyrate on the development of colorectal cancer

Colorectal cancer (CRC) is one of the major health threats in developed countries. Changes in dietary components, such as more protein and lipid intake, can increase the risk of CRC. Diet affects CRC in many ways. They regulate the composition and function of gut microbiota, which have an amazing metabolic capacity and can produce short chain fatty acids (SCFAs), such as propionate, acetate, and butyrate. Butyrate is a principal energy source for colonic epithelial cells and plays an important role in maintaining the stability of gut microbiota and the integrity of intestinal epithelium. However, there are few studies reviewing the anti-CRC potentials of butyrate. This review summarizes the recent research progresses in the effect of gut microbiota imbalance and the decrease in intestinal microbial metabolite butyrate caused by unbalanced diet on CRC development, and discusses the mechanisms of butyrate-induced anti-CRC activities, which may guide people to prevent CRC by improving diet structures.
Maternal Overnutrition Programs Central Inflammation and Addiction-Like Behavior in Offspring

Obesity or maternal overnutrition during pregnancy and lactation might have long-term consequences in offspring health. Fetal programming is characterized by adaptive responses to specific environmental conditions during early life stages. Programming alters gene expression through epigenetic modifications leading to a transgenerational effect of behavioral phenotypes in the offspring. Maternal intake of hypercaloric diets during fetal development programs aberrant behaviors resembling addiction in offspring. Programming by hypercaloric surplus sets a gene expression pattern modulating axonal pruning, synaptic signaling, and synaptic plasticity in selective regions of the reward system. Likewise, fetal programming can promote an inflammatory phenotype in peripheral and central sites through different cell types such as microglia and T and B cells, which contribute to disrupted energy sensing and behavioral pathways. The molecular mechanism that regulates the central and peripheral immune cross-talk during fetal programming and its relevance on offspring's addictive behavior susceptibility is still unclear. Here, we review the most relevant scientific reports about the impact of hypercaloric nutritional fetal programming on central and peripheral inflammation and its effects on addictive behavior of the offspring.
A Strategy of MR Brain Tissue Images' Suggestive Annotation Based on Modified U-Net

Accurate segmentation of MR brain tissue is a crucial step for diagnosis,surgical planning, and treatment of brain abnormalities. However,it is a time-consuming task to be performed by medical experts. So, automatic and reliable segmentation methods are required. How to choose appropriate training dataset from limited labeled dataset rather than the whole also has great significance in saving training time. In addition, medical data labeled is too rare and expensive to obtain extensively, so choosing appropriate unlabeled dataset instead of all the datasets to annotate, which can attain at least same performance, is also very meaningful. To solve the problem above, we design an automatic segmentation method based on U-shaped deep convolutional network and obtain excellent result with average DSC metric of 0.8610, 0.9131, 0.9003 for Cerebrospinal Fluid (CSF), Gray Matter (GM) and White Matter (WM) respectively on the well-known IBSR18 dataset. We use bootstrapping algorithm for selecting the most effective training data and get more state-of-the-art segmentation performance by using only 50% of training data. Moreover, we propose a strategy of MR brain tissue images' suggestive annotation for unlabeled medical data based on the modified U-net. The proposed method performs fast and can be used in clinical.
Heparan Sulfate Proteoglycans in Human Colorectal Cancer

Colorectal cancer is the third most common cancer worldwide, accounting for more than 610,000 mortalities every year. Prognosis of patients is highly dependent on the disease stage at diagnosis. Therefore, it is crucial to investigate molecules involved in colorectal cancer tumorigenesis, with possible use as tumor markers. Heparan sulfate proteoglycans are complex molecules present in the cell membrane and extracellular matrix, which play vital roles in cell adhesion, migration, proliferation, and signaling pathways. In colorectal cancer, the cell surface proteoglycan syndecan-2 is upregulated and increases cell migration. Moreover, expression of syndecan-1 and syndecan-4, generally antitumor molecules, is reduced. Levels of glypicans and perlecan are also altered in colorectal cancer; however, their role in tumor progression is not fully understood. In addition, studies have reported increased heparan sulfate remodeling enzymes, as the endosulfatases. Therefore, heparan sulfate proteoglycans are candidate molecules to clarify colorectal cancer tumorigenesis, as well as important targets to therapy and diagnosis.
Efficacy and safety of Ginkgo biloba extract as an “add-on” treatment to metformin for patients with metabolic syndrome: a pilot clinical study

Background and aim Ginkgo biloba (GKB) extract has shown to be beneficial in experimental models of metabolic and inflammatory disorders such as diabetes and metabolic syndrome (MTS). The objective of this pilot clinical study was to evaluate the effects of GKB extract as an “add-on” treatment with metformin (Met) in MTS patients. Patients and methods We performed a randomized, placebo-controlled, double-blinded clinical study in subjects with MTS. Forty patients completed the 90-day clinical trial and were randomly allocated to administer either GKB extract (120 mg capsule/day) or placebo (120 mg starch/day) as an add-on treatment with their currently used doses of Met for 90 days. During the study, body mass index (BMI), waist circumference (WC), serum leptin, glycated hemoglobin (HbA1c), fasting serum glucose (FSG), insulin, insulin resistance (IR), visceral adiposity index (VAI), lipid profile, and the inflammatory markers high sensitive C-reactive protein (hsCRP), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) were evaluated. Results GKB extract significantly decreases HbA1c, FSG and insulin levels, IR, BMI, WC, VAI, serum leptin, and the inflammatory markers compared to baseline values. Simultaneously, GKB did not negatively affect the functions of the liver, kidney, and hematopoietic system. Conclusion The use of GKB extract as an adjuvant with Met was effective in improving the outcome of patients with MTS.