Miracle-dz/UniFAR-FTUs huggingface dataset:
Siçanlarda Psikolojik Strese Bağli Oksidan Doku Hasarinda Over Hormonlarinin Rolü Role of Ovarian Hormones in Psychological Stress-induced Oxidative Organ Damage in Rats

Objective: Stress response varies with respect to gender via the hypothalamic–pituitary–gonadal axis. We aimed to investigate the effect of ovarian hormone deficiency on psychological stress response and oxidative damage. Methods: Female Sprague Dawley rats (250–300 g, n=56) were divided as control, sham, and ovariectomy (OVX) groups. Sham operation or surgical OVX were conducted under anesthesia. After 60 days, the rats were placed in a special chamber to induce psychological stress by electric shock and were kept in the same chamber for 30 min on the following 3 days. Glucocorticoid receptor antagonist RU-486 (10 mg/kg), oxytocin receptor antagonist atosiban (1 mg/kg), or saline was intraperitoneally administered 10 min before stress exposure. After the hole-board anxiety test, the rats were decapitated on the 4th day; tissue and blood samples were obtained. Results: Psychological stress increased cortisol levels in the RU-486-administered group, while cortisol levels were decreased in the atosiban-administered group. Serum interleukin (IL)-1β levels, but not TNF-α levels, were increased by inducing stress. Stress increased oxidative damage in the stomach, colon, and brain of ovariectomized rats (p<0.05–0.001), while atosiban partially reversed and RU-486 exaggerated oxidative damage. GSH levels that were depleted because of stress were partially replenished by administering atosiban; however, RU-486 had no effect on GSH levels. Conclusion: Although the absence of ovarian hormones during psychological stress had no effect on cortisol or anxiety levels, changes in cytokine levels and oxidative tissue damage were observed.
Evaluating Asymmetric Multicore Systems-on-Chip using Iso-Metrics

The end of Dennard scaling has pushed power consumption into a first order concern for current systems, on par with performance. As a result, near-threshold voltage computing (NTVC) has been proposed as a potential means to tackle the limited cooling capacity of CMOS technology. Hardware operating in NTV consumes significantly less power, at the cost of lower frequency, and thus reduced performance, as well as increased error rates. In this paper, we investigate if a low-power systems-on-chip, consisting of ARM's asymmetric big.LITTLE technology, can be an alternative to conventional high performance multicore processors in terms of power/energy in an unreliable scenario. For our study, we use the Conjugate Gradient solver, an algorithm representative of the computations performed by a large range of scientific and engineering codes.
Smart terrain causality chains for adventure-game puzzle generation

Adventure videogames have the player assume the role of protagonist in an interactive story, which is primarily driven by exploration and puzzle-solving. A major drawback with this genre is minimal replayability, since the player has already seen what there is to explore, and knows how to solve the puzzles. We propose a technique to generate variations on puzzles that fit in the same location in the original story, and therefore don't require fully procedural story generation. We keep a database of smart terrain items, which can have effects on other items. Puzzles are generated by taking advantage of a duality between puzzle-solving and generation. Once we build smart terrain causality chains (STCCs) of puzzle solutions, a puzzle known to be solvable can be generated by simply inserting the items contained in a causality chain into the environment. We demonstrate this technique in an experimental videogame, Space Dust, which shows that even a very short adventure game can produce multiple interesting playthroughs when STCC-based puzzle generation is added.
Optimizing graph queries with graph joins and Sprinkle SPARQL

Big data problems are often more akin to sparse graphs rather than relational tables. As such we argue that graph-based physical representations provide advantages in terms of both size and speed for executing queries. Drawing from research in sparse matrices, we use a compressed sparse row (CSR) format to model graph-oriented data. We also present two novel mechanisms for exploiting the CSR format that both find optimal join strategies and also prune variable bindings before expensive join operations occur. The first tactic we call Sprinkle SPARQL, which takes triple patterns of SPARQL queries and performs low-cost, linear-time set intersections to produce a constrained list of variable bindings for each variable in a query. Besides constrained lists of variable bindings, Sprinkle SPARQL also produces metrics that are consumed by the join algorithm to select an optimal execution path. The second tactic, graph joins, utilizes the CSR data structure as an index to efficiently join two variables expressed in a triple pattern together. We evaluate our approach on two data sets with over a billion edges: LUBM(8000) and an R-MAT graph generated with Graph5001 parameters and extended to have edge labels.
FPGA Implementation of CORDIC-Based Square Root Operation for Parameter Extraction of Digital Pre-Distortion for Power Amplifiers

Square root operation is a critical operation in the parameter extraction algorithm of the digital pre-distortion for power amplifiers. Accuracy and speed of square root operation are important factors in the digital pre-distortion parameter extraction process. But square root operation is difficult to realize with normal method for achieving high throughput. In this paper, a CORDIC algorithm, which is realized with pipeline structure on FPGA, is introduced to realize the square root operation. The calculation precision can be improved by increasing the iteration numbers, optimizing and selecting the parameters related to iterations. The CORDIC algorithm is implemented in FPGA and can be called through the Nios II Custom Instruction in Altera Stratix II development platform. The validation results demonstrate that the proposed method simplifies the digital pre-distortion parameter extraction algorithm and speeds up the computation.
Drawing Road Networks with Focus Regions

Mobile users of maps typically need detailed information about their surroundings plus some context information about remote places. In order to avoid that the map partly gets too dense, cartographers have designed mapping functions that enlarge a user-defined focus region - such functions are sometimes called fish-eye projections. The extra map space occupied by the enlarged focus region is compensated by distorting other parts of the map. We argue that, in a map showing a network of roads relevant to the user, distortion should preferably take place in those areas where the network is sparse. Therefore, we do not apply a predefined mapping function. Instead, we consider the road network as a graph whose edges are the road segments. We compute a new spatial mapping with a graph-based optimization approach, minimizing the square sum of distortions at edges. Our optimization method is based on a convex quadratic program (CQP); CQPs can be solved in polynomial time. Important requirements on the output map are expressed as linear inequalities. In particular, we show how to forbid edge crossings. We have implemented our method in a prototype tool. For instances of different sizes, our method generated output maps that were far less distorted than those generated with a predefined fish-eye projection. Future work is needed to automate the selection of roads relevant to the user. Furthermore, we aim at fast heuristics for application in real-time systems.
Nutrition-Related Conditions among Children of Migrant and Seasonal Farmworkers in the United States: Causes and Solutions

The purpose of this literature review is to determine the prevalence of nutrition-related conditions among children of migrant and seasonal farmworkers (MSFW) compared to national averages, as well as effectiveness of strategies for minimizing any health discrepancies. MSFW live under harsh conditions, engage in long hours of physically demanding labor, and receive insufficient income. Compounding demographic characteristics create insurmountable barriers to quality health care and nutrition education resulting in disproportionately unhealthy children within the MSFW population as compared to the greater US population. The prevalence of overweight, obesity, diabetes mellitus, cardiovascular disease, and numerous infections is higher among MSFW children. Specialized health clinics have been developed to meet the unique needs of MSFW and their families. Mobility of health care professionals has allowed MSFW to receive care within a comfortable and familiar context and at an appropriate time of day. Community partnerships have created unity and momentum to promote change. Empowering and educating community members as translators and advocates has improved interactions between health care professionals and MSFW. Despite efforts of existing programs, most MSFW still receive no health care or nutrition education. Implementation of university students as respite care in MSFW clinics will increase the capacity of such clinics during the busiest times of the year. Further research is needed to better understand current strategies and their effectiveness as well as the MSFW population itself, especially on the West Coast.
Adjectives and Adverbs as Indicators of Affective Language for Automatic Genre Detection

We report the results of a systematic study of the feasibility of automatically classifying documents by genre using adjectives and adverbs as indicators of affective language. In addition to the class of adjectives and adverbs, we focus on two specific subsets of adjectives and adverbs: (1) trait adjectives, used by psychologists to assess human personality traits, and (2) speaker-oriented adverbs, studied by linguists as markers of narrator attitude. We report the results of our machine learning experiments using Accuracy Gain, a measure more rigorous than the standard measure of Accuracy. We find that it is possible to classify documents automatically by genre using only these subsets of adjectives and adverbs as discriminating features. In many cases results are superior to using the count of (a) nouns, verbs, or punctuation, or (b) adjectives and adverbs in general. In addition, we find that relatively few speaker-oriented adverbs are needed in the discriminant models. We conclude that at least in these two cases, the psychological and linguistic literature leads to identification of features that are quite useful for genre detection and for other applications in which identification of style and other non-topical characteristics of documents is important.
UBVRI night sky brightness during sunspot maximum at ESO-Paranal

In this paper we present and discuss for the first time a large data set of UBVRI night sky brightness measurements collected at ESO-Paranal from April 2000 to September 2001. A total of about 3900 images obtained on 174 different nights with FORS1 were analysed using an automatic algorithm specifically designed for this purpose. This led to the construction of an unprecedented database that allowed us to study in detail a number of effects such as differential zodiacal light contamination, airmass dependency, daily solar activity and moonlight contribution. Particular care was devoted to the investigation of short time scale variations and micro-auroral events. The typical dark time night sky brightness values found for Paranal are similar to those reported for other astronomical dark sites at a similar solar cycle phase. The zenith-corrected values averaged over the whole period are 22.3, 22.6, 21.6 20.9 and 19.7 mag arcsec -2 in $U, B, V, R$ and I respectively. In particular, there is no evidence of light pollution either in the broadband photometry or in the high-airmass spectra we have analysed. Finally, possible applications for the exposure time calculators are discussed.
Cognitive and neural processes involved in non-clinical auditory hallucinations.

Objectives:The nosological status of isolated non-clinical auditory hallucinations and their link to schizophrenia is unclear. We aimed to investigate the functional neural basis of these hallucinatory experiences.Methods:After selection from 1206 people, 68 participants of high, medium and low hallucinations proneness completed a task designed to elicit verbal hallucinatory phenomena under conditions of stimulus degradation. Eight subjects who reported hearing a voice when none was present repeated the task during functional imaging.Results:During the signal detection task, the high hallucination-prone subgroup reported a voice to be present when it was not (false alarms) significantly more often than the average or low subgroups (p<0.03, df=2). On fMRI, patterns of activation during these false alarms showed activation in the superior and middle temporal cortex (p<0.001). Conclusions:Auditory hallucinatory experiences reported in non-clinical samples appear to be mediated by similar patterns of cerebral activation as found during hallucinations in schizophrenia.
Paulistine—The Functional Duality of a Wasp Venom Peptide Toxin

It has been reported that Paulistine in the venom of the wasp Polybia paulista co-exists as two different forms: an oxidized form presenting a compact structure due to the presence of a disulfide bridge, which causes inflammation through an apparent interaction with receptors in the 5-lipoxygenase pathway, and a naturally reduced form (without the disulfide bridge) that exists in a linear conformation and which also causes hyperalgesia and acts in the cyclooxygenase type II pathway. The reduced peptide was acetamidomethylated (Acm-Paulistine) to stabilize this form, and it still maintained its typical inflammatory activity. Oxidized Paulistine docks onto PGHS2 (COX-2) molecules, blocking the access of oxygen to the heme group and inhibiting the inflammatory activity of Acm-Paulistine in the cyclooxygenase type II pathway. Docking simulations revealed that the site of the docking of Paulistine within the PGHS2 molecule is unusual among commercial inhibitors of the enzyme, with an affinity potentially much higher than those observed for traditional anti-inflammatory drugs. Therefore, Paulistine causes inflammatory activity at the level of the 5-lipooxygenase pathway and, in parallel, it competes with its reduced form in relation to the activation of the cyclooxygenase pathway. Thus, while the reduced Paulistine causes inflammation, its oxidized form is a potent inhibitor of this activity.
Inflammatory Cytokine Profile Associated with Metabolic Syndrome in Adult Patients with Type 1 Diabetes

Objective. To compare the serum concentration of IL-6, IL-10, TNF, IL-8, resistin, and adiponectin in type 1 diabetic patients with and without metabolic syndrome and to determine the cut-off point of the estimated glucose disposal rate that accurately differentiated these groups. Design. We conducted a cross-sectional evaluation of all patients in our type 1 diabetes clinic from January 2012 to January 2013. Patients were considered to have metabolic syndrome when they fulfilled the joint statement criteria and were evaluated for clinical, biochemical, and immunological features. Methods. We determined serum IL-6, IL-8, IL-10, and TNF with flow cytometry and adiponectin and resistin concentrations with enzyme linked immunosorbent assay in patients with and without metabolic syndrome. We also compared estimated glucose disposal rate between groups. Results. We tested 140 patients. Forty-four percent fulfilled the metabolic syndrome criteria (n = 61), 54% had central obesity, 30% had hypertriglyceridemia, 29% had hypoalphalipoproteinemia, and 19% had hypertension. We observed that resistin concentrations were higher in patients with MS. Conclusion. We found a high prevalence of MS in Mexican patients with T1D. The increased level of resistin may be related to the increased fat mass and could be involved in the development of insulin resistance.
Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection

We present a block-wise approach to detect stationary objects based on spatio-temporal change detection. First, block candidates are extracted by filtering out consecutive blocks containing moving objects. Then, an online clustering approach groups similar blocks at each spatial location over time via statistical variation of pixel ratios. The stability changes are identified by analyzing the relationships between the most repeated clusters at regular sampling instants. Finally, stationary objects are detected as those stability changes that exceed an alarm time and have not been visualized before. Unlike previous approaches making use of Background Subtraction, the proposed approach does not require foreground segmentation and provides robustness to illumination changes, crowds and intermittent object motion. The experiments over an heterogeneous dataset demonstrate the ability of the proposed approach for short- and long-term operation while overcoming challenging issues.
Extracting features for a brain-computer interface by self-organising fuzzy neural network-based time series prediction

This paper presents a novel feature extraction procedure (FEP) for extracting features from the electroencephalogram (EEG) recorded from subjects producing right and left motor imagery. Four self-organizing fuzzy neural networks (SOFNNs) are coalesced to perform one-step-ahead predictions for the EEG time series data. Features are derived from the mean squared error (MSE) in prediction or the mean squared of the predicted signals (MSY). Classification is performed using linear discriminant analysis (LDA). This novel FEP is tested on three subjects offline and classification accuracy (CA) rates approach 94% with information transfer (IT) rates >10 bits/min. Minimum subject specific data analysis is required and the approach shows good potential for online feature extraction and autonomous system adaptation.
Corpus-based Extraction of Modals in Consecutive Sentences

This work presents a method of learning modal auxiliaries. In our method, two consecutive uses of modals are given as example sentences. These are beneficial for learners to recognize the flow of thought in context. We also present a method of extracting example sentences from a corpus. Here sentences in the corpus are parsed and analyzed to extract only the main clauses, which are essential parts to convey the flow of ideas. Extracted sentences are simplified and examples can be retrieved in many different situations, therefore, examples can be useful for both teachers and learners alike.
Interpolating Depth Panoramas Captured With Kinect for the Oculus Rift CIS

As high quality, low-cost image sensors have proliferated in the consumer market, significant new frontiers in computer vision have become accessible. Similar advances in display technology have also made low-cost, practical virtual reality tools available to consumers. However, these developments have not yet been paired together in meaningful ways. We present our novel, low-cost virtual reality system, Oculorama, which is capable of capturing real environments and reproducing them in a virtual reality scene. Oculorama uses a Microsoft Kinect version 2 (V2) [5] to capture rich images containing both color (RGB) data and depth (D) data which are then aligned to produce a single RGB-D image. Our system combines these individual images into panoramic images, then constructs, renders, and displays 3D models in real time on an Oculus Rift Developer Kit 2 (DK2) [9]. This allows a user to move through the virtual space and see an accurate and convincing representation of the originallycaptured world. The main contributions include a real time algorithm for the continuous interpolation of perspective representations from discrete depth and color panoramas and display and interface via Oculus DK2. In addition, we design and implement a robotically automated data collection process using the Kinect V2, entirely built from inexpensive, consumergrade hardware. The end result is an end-to-end virtualization system that can be reproduced at a very low cost.
Studying the impact of ubiquitous monitoring technology on office worker behaviours: The value of sharing research data

Pervasive computing is a continually, and rapidly, growing field, although still remains in relative infancy. The possible applications for the technology are numerous, and stand to fundamentally change the way users interact with technology. However, alongside these are equally numerous potential undesirable effects and risks. The lack of empirical naturalistic data in the real world makes studying the true impacts of this technology difficult. This paper describes how two independent research projects shared such valuable empirical data on the relationship between pervasive technologies and users. Each project had different aims and adopted different methods, but successfully used the same data and arrived at the same conclusions. This paper demonstrates the benefit of sharing research data in multidisciplinary pervasive computing research where real world implementations are not widely available.
Application of Particle Image Velocimetry to Analyse Thermal Performance of LPG Cooking Burners

Thermal performance of three different types of liquefied petroleum gas (LPG) domestic cooking burners, i.e. a conventional radial flow, a vertical flow and a swirling flow were analyzed by means of Particle Image Velocimetry (PIV). PI V diagnostics can explain the burners' performance from their corresponding flow fields at an impingement area. With the difference of flow patterns, larger attacked area to the impingement surface increased the heat transfer time i.e. higher thermal efficiency. Dynamics properties i.e. velocity magnitude and turbulent intensity at the in vicinity of the pan's bottom well correlated with thermal efficiency of the three burners. Even for the same type of burner with difficulty in distinguishing their difference by the naked-eye, with a powerful of this technique, heat transfer characteristics are clearly explained. The main objective of this work is to firstly apply a PIV technique as a tool for analyzing thermal performance of LPG cooking burners.
Analysis of the magnetic torque on a tilted permanent magnet for drug delivery in capsule robots

In this paper, we present the analysis of the torque transmitted to a tilted permanent magnet that is to be embedded in a capsule robot to achieve targeted drug delivery. This analysis is carried out by using an analytical model and experimental results for a small cubic permanent magnet that is driven by an external magnetic system made of an array of arc-shaped permanent magnets (ASMs). Our experimental results, which are in agreement with the analytical results, show that the cubic permanent magnet can safely be actuated for inclinations lower than 75° without having to make positional adjustments in the external magnetic system. We have found that with further inclinations, the cubic permanent magnet to be embedded in a drug delivery mechanism may stall. When it stalls, the external magnetic system's position and orientation would have to be adjusted to actuate the cubic permanent magnet and the drug release mechanism. This analysis of the transmitted torque is helpful for the development of real-time control strategies for magnetically articulated devices.