Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of cardiac cycle on ultrasound assessment of endothelial function

To investigate the effects of non-ECG-triggered imaging during ultrasound studies of endothelial function, brachial artery diameters were measured throughout the cardiac cycle at rest, during reactive hyperemia, and after administration of nitroglycerin. R-wave-triggered imaging using a 7.5-MHz ultrasound transducer with acquisition every 41.7-66.7 ms was performed in 24 subjects. Cardiac cycle-related variation was computed as the maximum per cent change from the end-diastolic diameter. The range of possible errors in flow-mediated dilation (FMD) and nitroglycerin-mediated vasodilation that may result from ignoring cyclic variations in diameter was determined for each condition. True FMD, true nitroglycerin-mediated vasodilation, and the maximum and minimum values that could be erroneously calculated for FMD if timing was ignored all differed dramatically (p 0.05). The range of apparent FMD values that could be measured was nearly three times the true FMD value. Ignoring temporal position within the cardiac cycle artifactually increased calculated FMD into the normal range, despite truly impaired FMD. Peak arterial dilation occurred before end-systole and greater baseline vessel compliance was associated with greater FMD. Brachial arterial diameters vary significantly throughout the cardiac cycle. The magnitude of this variation is similar to the arterial dilation induced by reactive hyperemia and nitroglycerin, making ECG-triggered imaging mandatory for accurate and reproducible clinical and research measurements of artery diameters and FMD. Measurement of diameters at end-diastole may be preferred to other time-points in the cardiac cycle.
Embolization of spinal dural arteriovenous fistulas: importance of occlusion of the draining vein.

OBJECT The aim of this study was to assess whether glue-induced occlusion of the draining vein predicts permanent closure of the fistula following embolization of spinal dural arteriovenous fistulas (SDAVFs). METHODS Between 1994 and 2004, 36 consecutive patients with an SDAVF were treated at the authors' institution. Twelve patients underwent surgery and 24 glue-based embolization. In 12 of 24 embolization procedures the draining vein was occluded and no recurrence or persistent fistula was seen during the follow-up period. In the other 12 patients the glue had not reached the draining vein and in eight of these the fistula recurred, necessitating additional treatment. CONCLUSIONS In embolization of SDAVFs penetration of the glue into the draining vein predicts permanent closure of the fistula. When penetration of the glue into the draining vein can be expected, embolization is the preferred treatment option. In other cases surgery should be the treatment of choice.
Implementing Ultra Low Latency Data Center Services with Programmable Logic

Data centers require many low-level network services to implement high-level applications. Key-Value Store (KVS) is a critical service that associates values with keys and allows machines to share these associations over a network. Most existing KVS systems run in software and scale out by running parallel processes on multiple microprocessor cores to increase throughput. In this paper, we take an alternate approach by implementing an ultra-low-latency KVS in Field Programmable Gate Array (FPGA) logic. As with a software-based KVS, lookup transactions are sent over Ethernet to the machine that stores the value associated with that key. We find that the implementation in logic, however, scales up to provide much higher search throughput with much lower latency and power consumption than other implementations in software. As with other KVS systems like redis, memcached, and Aerospike, high-level applications store, replace, delete, and search keys using a standard Application Programming Interface (API). Our API hashes long keys into statistically unique identifiers and maps variable-length messages into a finite set of fixed-size values. These keys and values are then formatted into a compact, binary Open Compute Storage Message (OCSM) format and transported in User Data Protocol (UDP)/Internet Protocol (IP) Ethernet frames over 10 Gigabit/second Ethernet (10GE) or faster network links. When transporting OCSM over 10 GE and by processing the performing the key/value search in FPGA logic, a fiber-to-fiber lookup latency of under a half microsecond (0.5 μS) was achieved. Using four 10 GE interfaces, a single instance of the FPGA-accelerated search core achieves a throughput of 150 Million Searches Per Second1 (MSPS). Compared to traditional software, the FPGA KVS is 88 times faster while using 21× less power than socket I/O. Compared to software optimized with DPDK kernel bypass, the FPGA KVS was measured to process messages 10× faster while using 13× less energy.
Developing baxter

This paper provides insight into the process that engineers at Rethink Robotics are using to develop a user-friendly experience for Baxter, a new industrial robot with common sense. For the purpose of this paper, the user experience centers around three core areas of development: the functionality of the robot, the intuitive UI and Use Cases being tested, along with the ensuing applications for the initial release of the robot's software. Personas aid UI designers in establishing accurate assumptions about users and Use Cases enable Software Quality Assurance engineers to test Baxter on real world applications to ensure that the robot successfully performs the types of tasks that are needed by U.S. Manufacturers. The paper also includes a number of customer applications that are well suited for Baxter.
Joint RNN model for argument component boundary detection

Argument Component Boundary Detection (ACBD) is an important sub-task in argumentation mining; it aims at identifying the word sequences that constitute argument components, and is usually considered as the first sub-task in the argumentation mining pipeline. Existing ACBD methods heavily depend on task-specific knowledge, and require considerable human efforts on feature-engineering. To tackle these problems, in this work, we formulate ACBD as a sequence labeling problem and propose a variety of Recurrent Neural Network (RNN) based methods, which do not use domain specific or handcrafted features beyond the relative position of the sentence in the document. In particular, we propose a novel joint RNN model that can predict whether sentences are argumentative or not, and use the predicted results to more precisely detect the argument component boundaries. We evaluate our techniques on two corpora from two different genres; results suggest that our joint RNN model obtain the state-of-the-art performance on both datasets.
DIKE: a system supporting the semi‐automatic construction of cooperative information systems from heterogeneous databases

In this paper we present DIKE, a system supporting the semi‐automatic construction of cooperative information systems from heterogeneous databases. The input of DIKE consists of the set of databases to belong to the cooperative system. First, DIKE constructs a data repository representing a structured, integrated and consistent description of the information stored in the input databases. The data repository thus constructed is then used as the core structure of a mediator‐like module supporting the user‐friendly integrated access to available data resources. The core of DIKE is the extraction and exploitation of the inter‐schema knowledge (in the form of inter‐schema properties) relative to the involved database schemas. Copyright © 2003 John Wiley & Sons, Ltd.
Glucan incorporated into a fruit drink effectively lowers serum LDL-cholesterol concentrations 1 – 4

Background: -Glucan can reduce serum concentrations of total and LDL cholesterol. The mechanism of this action is not clear, however, and it is difficult to predict the cholesterol-lowering effect of a food product enriched with -glucan. Objectives: We examined the effects of a -glucan–enriched fruit juice on serum lipids and lipoproteins and on markers of cholesterol absorption (serum concentrations of plant sterols) and synthesis (serum concentrations of lathosterol). In addition, we measured effects on lipid-soluble antioxidants. Design: After a 3-wk run-in period, healthy subjects consumed daily a fruit drink providing 5 g rice starch [placebo (control) group; n 22] or -glucan from oats (n 25) for 5 wk (parallel design). At the end of the run-in period and at the end of the intervention, blood samples were taken for analysis of lipids and lipoproteins, noncholesterol sterols, and fat-soluble antioxidants. Changes between the end of the run-in period and the end of the intervention were calculated for each subject. Differences in changes between the groups were analyzed statistically. Results: The differences between the control and -glucan groups in the change in serum concentrations of total and LDL cholesterol, respectively, were 4.8% (P 0.012) and 7.7% (P 0.005). The differences between the groups in the change in serum concentrations of lathosterol and sitosterol were 13% (P 0.023) and 11% (P 0.030), respectively. No significant effects were found on fat-soluble antioxidants. Conclusions: -Glucan lowers serum concentrations of total and LDL cholesterol when incorporated into a fruit drink. A reduced cholesterol absorption contributes to the cholesterol-lowering effect of -glucan without affecting plasma concentrations of lipid-soluble antioxidants. Am J Clin Nutr 2006;83:601–5.
Analysis of molecular expression patterns and integration with other knowledge bases using probabilistic Bayesian network models

How can molecular expression experiments be interpreted with greater than ten to the fourth measurements per chip? How can one get the most quantitative information possible from the experimental data with good confidence? These are important questions whose solutions require an interdisciplinary combination of molecular and cellular biology, computer science, statistics, and complex systems analysis. The explosion of data from microarray techniques present the problem of interpreting the experiments. The availability of large-scale knowledge bases provide the opportunity to maximize the information extracted from these experiments. We have developed new methods of discovering biological function, metabolic pathways, and regulatory networks from these data and knowledge bases. These techniques are applicable to analyses for biomedical engineering, clinical, and fundamental cell and molecular biology studies. Our approach uses probabilistic, computational methods that give quantitative interpretations of data in a biological context. We have selected Bayesian statistical models with graphical network representations as a framework for our methods. As a first step, we use a nave Bayesian classifier to identify statistically significant patterns in gene expression data. We have developed methods which allow us to (a) characterize which genes or experiments distinguish each class from the others, (b) cross-index the resulting classes with other databases to assess biological meaning of the classes, and (c) display a gross overview of cellular dynamics. We have developed a number of visualization tools to convey the results. We report here our methods of classification and our first attempts at integrating the data and other knowledge bases together with new visualization tools. We demonstrate the utility of these methods and tools by analysis of a series of yeast cDNA microarray data and to a set of cancerous/normal sample data from colon cancer patients. We discuss extending our methods to inferring biological pathways and networks using more complex dynamic Bayesian networks.
Learning to Generate Genotypes with Neural Networks

Neural networks and evolutionary computation have a rich intertwined history. They most commonly appear together when an evolutionary algorithm optimises the parameters and topology of a neural network for reinforcement learning problems, or when a neural network is applied as a surrogate fitness function to aid the evolutionary optimisation of expensive fitness functions. In this paper we take a different approach, asking the question of whether a neural network can be used to provide a mutation distribution for an evolutionary algorithm, and what advantages this approach may offer? Two modern neural network models are investigated, a Denoising Autoencoder modified to produce stochastic outputs and the Neural Autoregressive Distribution Estimator. Results show that the neural network approach to learning genotypes is able to solve many difficult discrete problems, such as MaxSat and HIFF, and regularly outperforms other evolutionary techniques.
A discriminative approach for appearance based loop closing

The place recognition module is a fundamental component in SLAM systems, as incorrect loop closures may result in severe errors in trajectory estimation. In the case of appearance-based methods the bag-of-words approach is typically employed for recognizing locations. This paper introduces a novel algorithm for improving loop closures detection performance by adopting a set of visual words weights, learned offline accordingly to a discriminative criterion. The proposed weights learning approach, based on the large margin paradigm, can be used for generic similarity functions and relies on an efficient online leaning algorithm in the training phase. As the computed weights are usually very sparse, a gain in terms of computational cost at recognition time is also obtained. Our experiments, conducted on publicly available datasets, demonstrate that the discriminative weights lead to loop closures detection results that are more accurate than the traditional bag-of-words method and that our place recognition approach is competitive with state-of-the-art methods.
Evolution of arterial stiffness after kidney transplantation.

BACKGROUND Increased arterial stiffness (AS) is a major determinant of cardiovascular complications in end-stage renal disease (ESRD) patients. Little is known about AS evolution after kidney transplantation. The aim of the study was to characterize the evolution of AS after kidney transplantation in a population of ESRD patients, in comparison to those remaining in dialysis. METHODS Eighty-eight patients (age between 35 and 65) were recruited from the waiting list for kidney transplantation of the University Hospital of Nancy. Two vascular evaluations were performed at a 1-year interval. During this interval, 39 patients were transplanted and 49 remained in dialysis. RESULTS At inclusion, median pulse-wave velocity (PWV) was similar in transplanted patients and transplantation-pending patients, respectively, 9.2 (7.9-11.9) and 9.8 (7.7-12.1) m/s. No difference between the two groups was found at the 1-year interval. Median of time after transplantation was 6.3 (3.8-10.1) months. Median of blood pressure (MBP) decreased only in the transplanted patients [99 (93-112) versus 96 (90-101) mmHg, P < 0.01] Multivariate analysis showed that PWV changes depend on changes in MBP and baseline PWV. CONCLUSION Although no difference in the 1-year PWV evolution was found, the low MBP value in transplanted patients allow to expect a better long-term evolution of AS and a better cardiovascular prognosis after kidney transplantation than in transplantation-pending patients.
Theory of War

The story of a young boy sold into white slavery in post-Civil War America, who suffers at the hands of a cruel master. He escapes to a new life as a railroadman, then an itinerant preacher, but within him lies the need for revenge against his former master's son - the object of an enduring hatred.
The Divisive Coverage Effect

The media often depict public opinion as divided by gender, race, or political affiliation. Can this type of reporting help create the group differences portrayed? Two experiments investigated whether exposure to divisive coverage could widen the gulf between real groups. Participants in Experiment 1, some of whom were affiliated with pan-Hellenic (Greek) organizations, viewed a videotape depicting the tribunal of a fraternity member accused of vandalism. Interviews with other students ostensibly from the same university preceded the tribunal scene and depicted either (a) all Greek interviewees supporting the fraternity member and all non-Greek interviewees opposing him (divisive coverage) or (b) Greek and non-Greek interviewees holding equally mixed opinions (nondivisive coverage). Only the divisive coverage condition led to significant differences between Greek and non-Greek participants, with Greek participants judging the defendant less harshly. Experiment 2 replicated the divisive coverage effect using the issue of support for affirmative action among male and female participants.
Finite-state transducer based modeling of morphosyntax with applications to Hungarian LVCSR

This article introduces a novel approach to model morphosyntax in morpheme unit based speech recognizers. The proposed method is evaluated in our recent Hungarian large vocabulary continuous speech recognition (LVCSR) system. The architecture of the recognition system is based on the weighted finite state transducer (WFST) paradigm. The task domain is the recognition of fluently read sentences selected from a major daily newspaper. The vocabulary units used in the system are morpheme based in order to provide sufficient coverage of the large number of word-forms resulting from affixation and compounding. Besides the standard morpheme N-gram language model we evaluate the novel stochastic morphosyntactic language model (SMLM) that describes the valid word-forms (morpheme combinations) of the language. Thanks to the flexible transducer-based architecture of the system the morphosyntactic component is integrated seamlessly with the basic modules with no need to modify the decoder itself. The proposed stochastic morphosyntactic language model decreases the error rate by 17.9% relatively compared to the baseline trigram system. The morpheme error rate of the best configuration is 14.75% in a 1350 morpheme Hungarian dictation task.
From Amino Acid to Glucosinolate Biosynthesis: Protein Sequence Changes in the Evolution of Methylthioalkylmalate Synthase in Arabidopsis[W][OA]

This work examines changes at the protein level during evolutionary recruitment of an enzyme from primary to secondary metabolism. It shows that the recruitment of an enzyme from Leu biosynthesis to the glucosinolate pathway likely involved the loss of the regulatory domain and several other amino acid exchanges. Methylthioalkylmalate synthase (MAM) catalyzes the committed step in the side chain elongation of Met, yielding important precursors for glucosinolate biosynthesis in Arabidopsis thaliana and other Brassicaceae species. MAM is believed to have evolved from isopropylmalate synthase (IPMS), an enzyme involved in Leu biosynthesis, based on phylogenetic analyses and an overlap of catalytic abilities. Here, we investigated the changes in protein structure that have occurred during the recruitment of IPMS from amino acid to glucosinolate metabolism. The major sequence difference between IPMS and MAM is the absence of 120 amino acids at the C-terminal end of MAM that constitute a regulatory domain for Leu-mediated feedback inhibition. Truncation of this domain in Arabidopsis IPMS2 results in loss of Leu feedback inhibition and quaternary structure, two features common to MAM enzymes, plus an 8.4-fold increase in the kcat/Km for a MAM substrate. Additional exchange of two amino acids in the active site resulted in a MAM-like enzyme that had little residual IPMS activity. Hence, combination of the loss of the regulatory domain and a few additional amino acid exchanges can explain the evolution of MAM from IPMS during its recruitment from primary to secondary metabolism.
Motives for Choosing a Career in Teaching: A South African Study

The study investigated motives for teaching career entry by students (n= 232, males= 103, females= 129, mean age 23) at a large South African university. Data were collected using a career motives survey differentiating extrinsic, intrinsic and altruistic motivators. Extrinsic motivators scored higher than intrinsic or altruistic motivators. The motives advanced for teaching were in alignment with those reported in similar research carried out in metropolitan and developing countries.
Modification of hemiplegic compensatory gait pattern by symmetry-based motion controller of HAL

As one of several characteristics of hemiplegic patients after stroke, compensatory gait caused by affected limb is often seen. The purpose of this research is to apply a symmetry-based controller of a wearable type lower limb robot, Hybrid Assistive Limb (HAL) to hemiplegic patients with compensatory gait, and to investigate improvement of gait symmetry. The controller is designed respectively for swing phase and support phase according to characteristics of hemiplegic gait pattern. The controller during swing phase stores the motion of the unaffected limb and then provides motion support on the affected limb during the subsequent swing using the stored pattern to realize symmetric gait based on spontaneous limb swing. Moreover, the controller during support phase provides motion to extend hip and knee joints to support wearer's body. Clinical tests were conducted in order to assess the modification of gait symmetry. Our case study involved participation of one chronic stroke patient who performs abnormally-compensatory gait for both of the affected and unaffected limbs. As a result, the patient's gait symmetry was improved by providing motion support during the swing phase on the affected side and motion constraint during the support phase on the unaffected side. The study showed promising basis for the effectiveness of the controller for the future clinical study.
Alcohol, cocaine, and brain stimulation-reward in C57Bl6/J and DBA2/J mice.

BACKGROUND Pleasure and reward are critical features of alcohol drinking that are difficult to measure in animal studies. Intracranial self-stimulation (ICSS) is a behavioral method for studying the effects of drugs directly on the neural circuitry that underlies brain reward. These experiments had 2 objectives: first, to establish the effects of alcohol on ICSS responding in the C57Bl6/J (C57) and DBA2/J (DBA) mouse strains; and second, to compare these effects to those of the psychostimulant cocaine. METHODS Male C57 and DBA mice were implanted with unipolar stimulating electrodes in the lateral hypothalamus and conditioned to spin a wheel for reinforcement by the delivery of rewarding electrical stimulation (i.e., brain stimulation-reward or BSR). Using the curve-shift method, the BSR threshold (theta(0)) was determined immediately before and after oral gavage with alcohol (0.3, 0.6, 1.0, 1.7 g/kg) or water. Blood alcohol concentration (BAC) was measured to determine the influence of alcohol metabolism on BSR threshold. Separately, mice were administered cocaine (1.0, 3.0, 10.0, 30.0 mg/kg) or saline intraperitoneally. RESULTS In C57 mice, the 0.6 g/kg dose of alcohol lowered BSR thresholds by about 20%, during the rising (up to 40 mg/dl), but not falling, phase of BAC. When given to the DBA mice, alcohol lowered BSR thresholds over the entire dose range; the largest reduction was by about 50%. Cocaine lowered BSR thresholds in both strains. However, cocaine was more potent in DBA mice than in C57 mice as revealed by a leftward shift in the cocaine dose-response curve. For both alcohol and cocaine, effects on BSR threshold were dissociable from effects on operant response rates. CONCLUSIONS In C57 and DBA mice, reductions in BSR threshold reflect the ability of alcohol to potentiate the neural mechanisms of brain reward. The DBA mice are more sensitive to the reward-potentiating effects of both alcohol and cocaine, suggesting that there are mouse strain differences in the neural mechanisms of brain reward that can be measured with the ICSS technique.