Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral analysis of all‐night human sleep EEG in narcoleptic patients and normal subjects

To investigate the pathophysiology of narcoleptic patients' sleep in detail, we analysed and compared the whole‐night polysomnograms of narcoleptic patients and normal human subjects. Eight drug‐naive narcoleptic patients and eight age‐matched normal volunteers underwent polysomnography (PSG) on two consecutive nights. In addition to conventional visual scoring of the polysomnograms, rapid eye movement (REM)‐density and electroencephalograph (EEG) power spectra analyses were also performed. Sleep onset REM periods and fragmented nocturnal sleep were observed as expected in our narcoleptic patients. In the narcoleptic patients, REM period duration across the night did not show the significant increasing trend that is usually observed in normal subjects. In all narcoleptic patient REM periods, eye movement densities were significantly increased. The power spectra of narcoleptic REM sleep significantly increased between 0.3 and 0.9 Hz and decreased between 1.0 and 5.4 Hz. Further analysis revealed that non‐rapid eye movement (NREM) period duration and the declining trend of delta power density in the narcoleptic patients were not significantly different from the normal subjects. Compared with normal subjects, the power spectra of narcoleptic NREM sleep increased in the 1.0–1.4 Hz and 11.0–11.9 Hz frequency bands, and decreased in a 24.0–26.9 Hz frequency band. Thus, increased EEG delta and decreased beta power densities were commonly observed in both the NREM and REM sleep of the narcoleptic patients, although the decrease in beta power during REM sleep was not statistically significant. Our visual analysis revealed fragmented nocturnal sleep and increased phasic REM components in the narcoleptic patients, which suggest the disturbance of sleep maintenance mechanism(s) and excessive effects of the mechanism(s) underlying eye movement activities during REM sleep in narcolepsy. Spectral analysis revealed significant increases in delta components and decreases in beta components, which suggest decreased activity in central arousal mechanisms. These characteristics lead us to hypothesize that two countervailing mechanisms underlie narcoleptic sleep pathology.
On Evaluating ECG Biometric Systems: Session-Dependence and Body Posture

This paper addresses the challenges of evaluating electrocardiogram (ECG) biometric recognition systems. While this new biometric modality has attracted significant interest, a majority of the prior art has approached it from the perspective of a typical biometric modality and has neglected the physiological factors that directly affect the behavior of the respective systems. In an effort to bring to the table the idiosyncratic properties of the ECG biometric modality, this paper presents the UofT ECG database and offers a comprehensive analysis of the underlying interindividual variability under a number of conditions, such as body posture, physical activity, and time lapse. The performance of various methodologies is reported under the above-mentioned conditions and a method based on template fusion is proposed to address these shortcomings.
c-fos Reduces Corticosterone-Mediated Effects on Neurotrophic Factor Expression in the Rat Hippocampal CA1 Region

The transcription of neurotrophic factors, i.e., basic fibroblast growth factor (bFGF) and brain-derived neurotrophic factor (BDNF) is regulated by glucocorticoid receptor (GR) and mineralocorticoid receptor (MR) activation despite the lack of a classical glucocorticoid response element in their promoter region. A time course for corticosterone (10 mg/kg, s.c.) in adrenalectomized rats revealed a peak hormone effect at the 4 hr time interval for bFGF (110–204% increase), BDNF (53–67% decrease), GR (53–64% decrease), and MR (34–56% decrease) mRNA levels in all hippocampal subregions using in situ hybridization. c-fos mRNA levels were affected exclusively in the dentate gyrus after 50 min to 2 hr (38–46% decrease). Furthermore, it was evaluated whether corticosterone regulation of these genes depends on interactions with the transcription factor complex activator protein-1. c-fos antisense oligodeoxynucleotides were injected into the dorsal hippocampus of adrenalectomized rats. Corticosterone was given 2 hr later, and the effects on gene expression were measured 4 hr later. In CA1, antisense treatment significantly and selectively enhanced the hormone action on the expression of bFGF (44% enhanced increase) and BDNF (38% enhanced decrease) versus control oligodeoxynucleotide treatment. In addition, an upregulation of c-fos expression (89% increase) was found. There were no effects of c-fos antisense on hippocampal GR and MR expression. Thus it seems that a tonic c-fos mechanism exists within CA1, which reduces GR- and MR-mediated effects on expression of bFGF and BDNF.
SDN Controlled Local Re-routing to Reduce Congestion in Cloud Data Center

Data center networks require densely interconnected topologies to provide high bandwidth for various cloud computing services. It is required to fully utilize the bandwidth resource in such networks with varying traffic patterns. Conventional tree network topologies and routing mechanisms with single shortest path routing will cause congestion along oversubscribed links. Thus, path selection in a load-balanced way is needed to alleviate congestion and improve application performance. Multi-path routing algorithms can distribute traffic over diverse paths optimally than simple solutions like ECMP. They, however, require considerable computations, i.e. Frequently updating the path cost tree and dynamically optimizing path selections, thus they do not adapt well for large scale data center networks. In this paper, we propose a local rerouting mechanism in software defined networking (SDN) based Data Center networks to effectively manage congestion in the event of link congestion or failure. Unlike the works that deal with congestion by notifying the source which then reacts by rate adjustment or rerouting flows from source, our rerouting approach will re-forward flows (at the point of congestion or one hop before) to other possible paths based on our flow classification scheme. We demonstrate the effectiveness of our proposal through the simulation results on various topologies.
Learning to use episodic memory Action

This paper brings together work in modeling episodic memory and reinforcement learning (RL). We demonstrate that is possible to learn to use episodic memory retrievals while simultaneously learning to act in an external environment. In a series of three experiments, we investigate using RL to learn what to retrieve from episodic memory and when to retrieve it, how to use temporal episodic memory retrievals, and how to build cues that are the conjunctions of multiple features. In these experiments, our empirical results demonstrate that it is computationally feasible to learn to use episodic memory; furthermore, learning to use internal episodic memory accomplishes tasks that reinforcement learning alone cannot. These experiments also expose some important interactions that arise between reinforcement learning and episodic memory. In a fourth experiment, we demonstrate that an agent endowed with a simple bit memory cannot learn to use it effectively. This indicates that mechanistic characteristics of episodic memory may be essential to learning to use it, and that these characteristics are not shared by simpler memory mechanisms. 2010 Published by Elsevier B.V.
Çocuk Hemşirelerinin İş Tanımlama Ve Doyum Düzeyleri

Arastirma, cocuk hemsirelerinin is tanimlama ve is doyum duzeylerinin degerlendirilmesi, aralarindaki iliskinin incelenmesi ve etkileyen faktorlerin belirlenmesi amaciyla yapilmistir. Tanimlayici nitelikte yapilan arastirma, Erzurum’da bir universite hastanesinde 1 Mayis- 30 Kasim 2012 tarihleri arasinda gerceklestirilmistir. Arastirmanin evrenini, universite hastanesi cocuk kliniklerinde calisan 60 cocuk hemsiresi olusturmustur. Orneklem secimine gidilmemis, butun hemsireler arastirmaya dahil edilmistir. Verilerin toplanmasinda, Kisisel Bilgi Formu, Is Tanimlamasi Olcegi ve Minnesota Is Doyum Olcegi kullanilmistir. Verilerin istatistiksel degerlendirilmesinde yuzdelik dagilimlar, ortalama, standart sapma, t testi, Alpha katsayi hesaplamasi ve korelasyon analizi kullanilarak degerlendirilmistir. Arastirmada, cocuk hemsirelerinin %91.7’sinin kadin, yas ortalamasinin 27.30±5.07 oldugu, %70’inin universite mezunu, %53.3’unun evli , %65’i bes yil ve alti hemsirelik deneyimine ve %78.3’unun bes yil ve alti cocuk hemsireligi deneyimine sahip oldugu, %55’inin yenidogan unitesinde calistigi, %88.3’unun klinik hemsiresi oldugu, %80’inin vardiya usulu ile calistigi ve %45’inin cocuk hemsireligini isteyerek tercih ettigi saptanmistir. Arastirmada, 5 yil ve alti hemsirelik deneyimi olan ve surekli gunduz calisanlarda, is tanimlama daha yuksek bulunmustur (p
Efficient parallel Recursive Gaussian SIFT algorithm based on multi-core DSP

To solve the problem of high computational complexity and real-time poor of the SIFT(Scale Invariant Feature Transform) algorithm, a parallel data streams RGF-SIFT(Recursive Gaussian Filter-SIFT) algorithm based on DSP multi-core processor is proposed. The proposed algorithm uses the forth-order recursive Gaussian filter to replace the linear Gaussian filtering of the SIFT algorithm. Then the four modules of RGF-SIFT computing tasks are assigned to multiple DSP core for parallel processing, and implemented synchronization for multicore processor through inter-processor communication (IPC) and other technologies. Experimental results show that the parallel RGF-SIFT algorithm detects feature point more than the algorithm of SIFT, and the repetition rate of the correct feature point is very high. At execution time, the parallel RGF-SIFT algorithm has higher speedup ratio.
Breakout Algorithm for Solving Distributed Constraint Satisfaction Problems

This paper presents a new algorithm for solving distributed constraint satisfaction problems (distributed CSPs) called the distributed breakout algorithm, which is inspired by the breakout algorithm for solving centralized CSPs. In this algorithm, each agent tries to optimize its evaluation value (the number of constraint violations) by exchanging its current value and the possible amount of its improvement among neighboring agents. Instead of detecting the fact that agents as a whole are trapped in a local-minimum, each agent detects whether it is in a quasi-local-minimum, which is a weaker condition than a local-minimum, and changes the weights of constraint violations to escape from the quasi-local-minimum. Experimental evaluations show this algorithm to be much more efcient than existing algorithms for critically di cult problem instances of distributed graph-coloring problems.
Understanding Maternal Mentalizing Capacity and Attachment Representations of Children With Reactive Attachment Disorder: Two Case Illustrations

Deprivation and neglect during the early care of a child is known to possibly lead to the development of reactive attachment disorder (RAD). However, subtler influences within the relationship between mother and child have been less explored and defined. This article discusses the relevance of understanding the relationship between the mother’s mentalizing capacity and the child’s attachment representations. Two mother–child dyads of school-age children with RAD inhibited and disinhibited types are used to describe the children’s attachment representations and the mothers’ capacity to mentalize. The 2 cases were drawn from a larger study at the Université de Sherbrooke in collaboration with the Child Psychiatry Department of a general hospital in the Montreal metropolitan area. The mothers’ capacity to mentalize and the children’s attachment representations were analyzed using quantitative and qualitative data. The children’s attachment representations were assessed using the Attachment Focused Coding System (Reiner & Splaun, 2008), as applied to the Attachment Story Completion Task (Bretherton, Ridgeway, & Cassidy, 1990). The mothers’ capacity to mentalize was measured by the Addendum to Reflective Functioning Scoring Manual (Fonagy, Steele, Steele, & Target, 1998) for use with the Parent Development Interview-Revised (Slade, Aber, Berger, Bresgi, & Kaplan, 2005). The relationship between the mother’s mentalizing capacity and the children’s attachment representations are discussed using mentalization theory.
The impact of Freedson bout vs. non-Freedson bout physical activity on metabolic syndrome risk in college students

Purpose The aim of this study is to examine moderate to vigorous physical activity in Freedson bouts compared to non-Freedson bouts and their association with Metabolic Syndrome risk factors in college students. Methods 72 subjects aged 18-26 were recruited from James Madison University. Subjects height, weight, waist circumference, blood pressure, and body composition were assessed on visit 1. Blood pressure, fasted blood glucose, and lipid profile were assessed on visit 2. Subjects wore an Actigraph GT3X+ accelerometer, which measured physical activity and sleep for 7 days and nights. Univariate Pearson correlation analyses were performed to determine the relationship physical activity variables and MetS risk factors. Variables determined to have the greatest correlation for each risk factor were used as independent variables in a step-wise, multiple linear regression to determine the best predictor for each MetS risk factor. Variables established as having the greatest correlation with MetS were evaluated as covariates. Statistical significance was set a priori at p<0.05. Results Correlational analyses suggest the strongest predictors of MetS were daily average time in moderate activity, daily average MVPA, total number of Freedson bouts, total minutes in Freedson bouts, total MVPA, and total steps. Step count was the only significant predictor of waist circumference and systolic blood pressure (R= 0.07; p<0.05; R= 0.14; p<0.01, respectively). Total Freedson bouts was the only significant predictor for HDL (R= 0.062; p<0.05). Waist circumference was significantly higher in
Building a Fine-Grained Entity Typing System Overnight for a New X (X = Language, Domain, Genre)

Recent research has shown great progress on fine-grained entity typing. Most existing methods require pre-defining a set of types and training a multi-class classifier from a large labeled data set based on multi-level linguistic features. They are thus limited to certain domains, genres and languages. In this paper, we propose a novel unsupervised entity typing framework by combining symbolic and distributional semantics. We start from learning general embeddings for each entity mention, compose the embeddings of specific contexts using linguistic structures, link the mention to knowledge bases and learn its related knowledge representations. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework doesn't rely on any annotated data, predefined typing schema, or hand-crafted features, therefore it can be quickly adapted to a new domain, genre and language. Furthermore, it has great flexibility at incorporating linguistic structures (e.g., Abstract Meaning Representation (AMR), dependency relations) to improve specific context representation. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.
A Distributed Algorithm for Constrained Multi-Robot Task Assignment for Grouped Tasks

In this paper, we present provably-good distributed task allocation (assignment) algorithms for a heterogeneous multi-robot system where the tasks form disjoint groups and there are constraints on the number of tasks a robot can do (both within the overall mission and within each task group). Our problem is motivated by applications where multiple robots with heterogeneous capabilities have to work together to accomplish tasks. Thus, for our purposes, a task group is a compound task composed of more than one atomic tasks where one robot is required for each atomic task. Since robots have limited battery life, we assume that the number of (atomic) tasks that a robot can do within a mission has an upper bound. Futhermore, each robot has a constraint on the number of tasks it can do from each group (this models the fact that multiple robots may be needed to simultaneously perform the atomic tasks that make up the compound task). Each robot obtains a payoff (or incurs a cost) for each task and the overall objective for task allocation is to maximize the total payoff (or minimize the total cost) of all the robots. In general, existing (centralized or distributed) algorithms for task allocation either assume that (atomic) tasks are independent, or do not provide performance guarantee for the situation where task constraints exist. We show that our problem can be solved in polynomial time by a centralized algorithm by reducing it to a minimum cost network flow problem. We then present a decentralized algorithm (that extends the auction algorithm of Bertsekas for linear assignment problems [1]) to provide an almost optimal solution. We prove that our solution is within a factor of O(nte) of the optimal solution, where nt is the total number of tasks and e is a parameter that we choose (the guarantees are the same as that of the original auction algorithm for unconstrained tasks). The decentralized algorithm assumes a shared memory model of computation that may be unrealistic for many multi-robot deployments. Therefore, we show that by using a maximum consensus algorithm along with our algorithm, we can design a totally distributed algorithm for task allocation with group constraints. The key aspect of our distributed algorithm is that the overall objective is (nearly) maximized by each robot maximizing its own objective iteratively (using a modified payoff function based on an auxiliary variable, called price of a task). Our algorithm is polynomial in the number of tasks as well as the number of robots.
What Does Research Tell Us About Educating Mainstream Teachers to Work With ELLs?

Abstract This commentary highlights findings from two reviews of research on the preparation and continuing development of regular classroom teachers to teach English language learners. Since both reviews use my “central tasks of teacher learning” framework, the commentary also assesses the framework’s usefulness in highlighting areas where research exists and where it is lacking. Looking across the reviews reveals that the area where most preservice studies cluster has the least number of inservice studies and vice versa.
Detection of Incorrect Case Assignments in Automatically Generated Paraphrases

This paper addresses the issue of correcting transfer errors in paraphrasing. Our previous investigation into transfer errors occurring in lexical and structural paraphrasing of Japanese sentences revealed that case assignment tends to be incorrect, irrespective of the types of transfer (Fujita and Inui, 2003). Motivated by this observation, we propose an empirical method to detect incorrect case assignment. Our error detection model combines two error detection models. They are separately trained on a large collection of positive examples and a small collection of manually labeled negative examples. Experimental results show that our combined model significantly enhances the baseline model which is trained only on positive examples. We also propose a selective sampling scheme to reduce the cost of collecting negative examples, and confirm the effectiveness for the error detection task.
Electrospun gelatin/PCL and collagen/PLCL scaffolds for vascular tissue engineering

Electrospun hybrid nanofibers prepared using combinations of natural and synthetic polymers have been widely investigated in cardiovascular tissue engineering. In this study, electrospun gelatin/polycaprolactone (PCL) and collagen/poly(l-lactic acid-co-ε-caprolactone) (PLCL) scaffolds were successfully produced. Scanning electron micrographs showed that fibers of both membranes were smooth and homogeneous. Water contact angle measurements further demonstrated that both scaffolds were hydrophilic. To determine cell attachment and migration on the scaffolds, both hybrid scaffolds were seeded with human umbilical arterial smooth muscle cells. Scanning electron micrographs and MTT assays showed that the cells grew and proliferated well on both hybrid scaffolds. Gross observation of the transplanted scaffolds revealed that the engineered collagen/PLCL scaffolds were smoother and brighter than the gelatin/PCL scaffolds. Hematoxylin and eosin staining showed that the engineered blood vessels constructed by collagen/PLCL electrospun membranes formed relatively homogenous vessel-like tissues. Interestingly, Young’s modulus for the engineered collagen/PLCL scaffolds was greater than for the gelatin/PCL scaffolds. Together, these results indicate that nanofibrous collagen/PLCL membranes with favorable mechanical and biological properties may be a desirable scaffold for vascular tissue engineering.
Tangshen formula attenuates diabetic renal injuries by upregulating autophagy via inhibition of PLZF expression

The Chinese herbal granule Tangshen Formula (TSF) has been proven to decrease proteinuria and improve estimated glomerular filtration rate (eGFR) in diabetic kidney disease (DKD) patients. However, the underlying mechanism of TSF on treatment of diabetic nephropathy (DN) remains unclear. The present study aimed to identify the therapeutic target of TSF in diabetic renal injuries through microarray-based gene expression profiling and establish its underlying mechanism. TSF treatment significantly attenuated diabetic renal injuries by inhibiting urinary excretion of albumin and renal histological injuries in diabetic (db/db) mice. We found that PLZF might be the molecular target of TSF in DN. In vivo, the db/db mice showed a significant increase in renal protein expression of PLZF and collagen III, and decrease in renal autophagy levels (downregulated LC3 II and upregulated p62/SQSTM1) compared to db/m mice. The application of TSF resulted in the downregulation of PLZF and collagen III and upregulation of autophagy level in the kidneys of db/db mice. In vitro, TSF reduced high glucose (HG)-induced cell proliferation for NRK52E cells. Further studies indicated that the exposure of NRK52E cells to high levels of glucose resulted in the downregulation of cellular autophagy and upregulation of collagen III protein, which was reversed by TSF treatment by decreasing PLZF expression. In conclusion, TSF might have induced cellular autophagy by inhibiting PLZF expression, which in turn resulted in an increase in autophagic degradation of collagen III that attenuated diabetic renal injuries.