Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of eye movements on emotional memories: using an objective measure of cognitive load

Background Eye movement desensitization and reprocessing (EMDR) is an effective treatment for posttraumatic stress disorder. The working memory (WM) theory explains its efficacy: recall of an aversive memory and making eye movements (EM) both produce cognitive load, and competition for the limited WM resources reduces the memory's vividness and emotionality. The present study tested several predictions from WM theory. Objective We hypothesized that 1) recall of an aversive autobiographical memory loads WM compared to no recall, and 2) recall with EM reduces the vividness, emotionality, and cognitive load of recalling the memory more than only recall or only cognitive effort (i.e., recall of an irrelevant memory with EM). Method Undergraduates (N=108) were randomly assigned to one of three conditions: 1) recall relevant memory with EM, 2) recall relevant memory without EM, and 3) recall irrelevant memory with EM. We used a random interval repetition task to measure the cognitive load of recalling the memory. Participants responded to randomly administered beeps, with or without recalling the memory. The degree to which participants slow down during recall provides an index of cognitive load. We measured the cognitive load and self-reported vividness and emotionality before, halfway through (8×24 s), and after (16×24 s) the intervention. Results Reaction times slowed down during memory recall compared to no recall. The recall relevant with EM condition showed a larger decrease in self-reported vividness and emotionality than the control conditions. The cognitive load of recalling the memory also decreased in this condition but not consistently more than in the control conditions. Conclusions Recall of an aversive memory loads WM, but drops in vividness and emotionality do not immediately reduce the cognitive load of recalling the memory. More research is needed to find objective measures that could capture changes in the quality of the memory. Highlights of the article Recall of an aversive autobiographical memory is a cognitive demanding task. The vividness and emotionality of an aversive memory decrease more after recall with eye movements than after only recall or only cognitive effort (i.e., recall of an irrelevant memory with eye movements). The cognitive load of recalling the memory does not immediately reduce after recall with eye movements compared to only recall or only cognitive effort. Intervention duration is positively related to memory effects.
Bacterial start site prediction.

With the growing number of completely sequenced bacterial genes, accurate gene prediction in bacterial genomes remains an important problem. Although the existing tools predict genes in bacterial genomes with high overall accuracy, their ability to pinpoint the translation start site remains unsatisfactory. In this paper, we present a novel approach to bacterial start site prediction that takes into account multiple features of a potential start site, viz., ribosome binding site (RBS) binding energy, distance of the RBS from the start codon, distance from the beginning of the maximal ORF to the start codon, the start codon itself and the coding/non-coding potential around the start site. Mixed integer programing was used to optimize the discriminatory system. The accuracy of this approach is up to 90%, compared to 70%, using the most common tools in fully automated mode (that is, without expert human post-processing of results). The approach is evaluated using Bacillus subtilis, Escherichia coli and Pyrococcus furiosus. These three genomes cover a broad spectrum of bacterial genomes, since B.subtilis is a Gram-positive bacterium, E.coli is a Gram-negative bacterium and P. furiosus is an archaebacterium. A significant problem is generating a set of 'true' start sites for algorithm training, in the absence of experimental work. We found that sequence conservation between P. furiosus and the related Pyrococcus horikoshii clearly delimited the gene start in many cases, providing a sufficient training set.
Enhancing Embedded Processors with Specific Instruction Set Extensions for Network Applications

The bandwidth explosion of last years and its dropping cost have resulted in bandwidth-hungry and computationally intensive applications. To effectively handle current and future applications, networks will need to support new protocols that should include differentiated services, security, and various network management functions. In order to satisfy the new requirements, an application specific instruction set processor (ASIP), derived from the extension of a popular MIPS embedded processor, is proposed in this paper. An efficient instruction set extended properly for network applications is introduced. Experimental results on Netbench, a benchmarking suite for network applications, prove that the proposed instruction set presents remarkable performance and power improvements over the ARM7TDMI and MIPS processors.
TYPifier: Inferring the type semantics of structured data

Structured data representing entity descriptions often lacks precise type information. That is, it is not known to which type an entity belongs to, or the type is too general to be useful. In this work, we propose to deal with this novel problem of inferring the type semantics of structured data, called typification. We formulate it as a clustering problem and discuss the features needed to obtain several solutions based on existing clustering solutions. Because schema features perform best, but are not abundantly available, we propose an approach to automatically derive them from data. Optimized for the use of schema features, we present TYPifier, a novel clustering algorithm that in experiments, yields better typification results than the baseline clustering solutions.
Plasmonics and its Applications

Plasmonics is a quickly developing subject that combines fundamental research and applications ranging from areas such as physics to engineering, chemistry, biology, medicine, food sciences, and the environmental sciences. Plasmonics appeared in the 1950s with the discovery of surface plasmon polaritons. Then, plasmonics went through a novel impulsion in mid-1970s when the surface-enhanced Raman scattering was discovered. Nevertheless, it is in this last decade that a very significant explosion of plasmonics and its applications has occurred. Thus, this special issue reports a snapshot of current advances in these various areas of plasmonics and its applications presented in the format of several articles and reviews written by worldwide researchers of this topic.
Immunocapture of virions with virus-specific antibodies prior to high-throughput sequencing effectively enriches for virus-specific sequences

Virus discovery based on high-throughput sequencing relies on enrichment for virus sequences prior to library preparation to achieve a sufficient number of viral reads. In general, preparations of double-stranded RNA or total RNA preparations treated to remove rRNA are used for sequence enrichment. We used virus-specific antibodies to immunocapture virions from plant sap to conduct cDNA synthesis, followed by library preparation and HTS. For the four potato viruses PLRV, PVY, PVA and PYV, template preparation by virion immunocapture provided a simpler and less expensive method than the enrichment of total RNA by ribosomal depletion. Specific enrichment of viral sequences without an intermediate amplification step was achieved, and this high coverage of sequences across the viral genomes was important to identify rare sequence variations. Using this approach, the first complete genome sequence of a potato yellowing virus isolate (PYV, DSMZ PV-0706) was determined in this study. PYV can be confidently assigned as a distinct species in the genus Ilarvirus.
A Study of Semantic Search in SemSearch 2011

describe the joint efforts of University of Delaware and HP Labs China in the participation of both the Entity Search Track and List Search Track in SemSearch Challenge 2011. In the Entity Search track we focus on how to apply existing Information Retrieval techniques to retrieve the related en- tities from semi-structured RDF corpus and how to improve the performance using some heuristics. In the List Search track we studied how to apply Natural Language Process- ing techniques to analyze the query and extract the type requirement which can be used to filter out the irrelevant entities from initial retrieval.
Clustering-Based Stratified Seed Sampling for Semi-Supervised Relation Classification

Seed sampling is critical in semi-supervised learning. This paper proposes a clustering-based stratified seed sampling approach to semi-supervised learning. First, various clustering algorithms are explored to partition the unlabeled instances into different strata with each stratum represented by a center. Then, diversity-motivated intra-stratum sampling is adopted to choose the center and additional instances from each stratum to form the unlabeled seed set for an oracle to annotate. Finally, the labeled seed set is fed into a bootstrapping procedure as the initial labeled data. We systematically evaluate our stratified bootstrapping approach in the semantic relation classification subtask of the ACE RDC (Relation Detection and Classification) task. In particular, we compare various clustering algorithms on the stratified bootstrapping performance. Experimental results on the ACE RDC 2004 corpus show that our clustering-based stratified bootstrapping approach achieves the best F1-score of 75.9 on the sub-task of semantic relation classification, approaching the one with golden clustering.
Automatic Identification of Round Diatom

In this work, a method for automatic identification of round diatoms based on image texture features is presented. This method combines segmentation adjustment by curve fitting and texture measures based on the spectrum features. The classification accuracy was tested using leave on out methods. With classification carried out using a BP neural network we attained 96.2% accuracy from a set of image containing six species of round diatom. The result is an effective attempt of round diatom identification based on texture character.
An Immunity-based Ant Colony Optimization Topology Control Algorithm for 3D Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been attracting the attention of both academic and industrial research communities for the past few years. To find an optimal topology control strategy in 3-dimensional (3D) WSNs is a new challenge. In this paper, we discuss issues related to the topology control in 3D WSNs, review a 2D model for WSNs, and introduce a 3D model for WSNs. An Immunity-Based Ant Colony Optimization Topology Control Algorithm for 3D WSNs is introduced, which has better performance with the nature of feedback and paradigm of ant colony and immunity algorithm reducing the redundancy iteration. The advantage of both the Ant Colony Optimization algorithm (ACO) and Connected Dominating Set (CDS) are fully taken in this algorithm. Simulation results show that the CDS-ACO has the better performance in WSNs. Copyright © 2013 IFSA.
An accelerated optimization algorithm for distributed model predictive control

We regard a network of systems with coupled dynamics or constraints that we want to controloptimally. The costfunction is assumed to be separable and convex. We show that the corresponding centralized model predictive control (MPC) problem can be recast as a separable convex optimization problem. Furthermore, we present a dual-based decomposition method, called here the proximal center method, to solve separable convex problems. In Necoara and Suykens (2008) we have provided convergence proofs and eficiency estimates for the proximal center method which improves with one order of magnitude the bounds on the number of iterations of the classical dual subgradient method. The new distributed optimization method is suitable for application to distributed MPC since it is highly parallelizable, each subsystem uses local information and the coordination between the local MPC controllers is performed via the Lagrange multipliers corresponding to the coupled dynamics. Simulation results are also included.
Vectisol Formulation Enhances Solubility of Resveratrol and Brings Its Benefits to Kidney Transplantation in a Preclinical Porcine Model

Current organ shortages have led centers to extend the acceptance criteria for organs, increasing the risk for adverse outcomes. Current preservation protocols have not been adapted so as to efficiently protect these organs. Herein, we target oxidative stress, the key mechanism of ischemia reperfusion injury. Vectisol® is a novel antioxidant strategy based on the encapsulation of resveratrol into a cyclodextrin, increasing its bioavailability. We tested this compound as an additive to the most popular static preservation solutions and machine perfusion (LifePort) in a preclinical pig model of kidney autotransplantation. In regard to static preservation, supplementation improved glomerular filtration and proximal tubular function early recovery. Extended follow-up confirmed the higher level of protection, slowing chronic loss of function (creatininemia and proteinuria) and the onset of histological lesions. Regarding machine perfusion, the use of Vectisol® decreased oxidative stress and apoptosis at the onset of reperfusion (30 min post declamping). Improved quality was confirmed with decreased early levels of circulating SOD (Superoxide Dismutase) and ASAT (asparagine amino transferase). Supplementation slowed the onset of chronic loss of function, as well as interstitial fibrosis and tubular atrophy. The simple addition of Vectisol® to the preservation solution significantly improved the performance of organ preservation, with long-term effects on the outcome. This strategy is thus a key player for future multi-drug therapy aimed at ischemia reperfusion in transplantation.
Impact of the new MELD‐based allocation system on waiting list and post‐transplant survival—a cohort analysis using the French national CRISTAL database

Concerns related to equity and efficacy of our previous center‐based allocation system have led us to introduce a patient‐based allocation system called the “Liver Score” that incorporates the model for end‐stage liver disease (MELD) score. The main objective of this study was to compare waitlist and post‐transplant survivals before and after implementation of the “Liver Score” using the French transplant registry (period before: 2004–2006 and period after: 2007–2012). Patients transplanted during the second period were sicker and had a higher MELD. One‐year waitlist survival (74% vs. 76%; P = 0.8) and 1‐year post‐transplant survival (86.3% vs. 85.7%; P = 0.5) were similar between the 2 periods. Cirrhotic recipients with MELD >35 had lower 1‐year post‐transplant survival compared to those with MELD <35 (74.8% vs. 86.3%; P < 0.01), mainly explained by their higher intubation and renal failure rates. The MELD showed a poor discriminative capacity. In cirrhotic recipients with MELD >35, patients presenting 2 or 3 risk factors (dialysis, intubation, or infection) had a lower 1‐year survival compared to those with none of these risk factors (61.2% vs. 92%; P < 0.01). The implementation of the MELD‐based allocation system has led to transplant sicker patients with no impact on waitlist and post‐transplant survivals. Nevertheless, selection of patients with MELD >35 should be completed to allow safe transplantation.
Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net.

PURPOSE Transrectal ultrasound (TRUS) is a versatile and real-time imaging modality that is commonly used in image-guided prostate cancer interventions (e.g., biopsy and brachytherapy). Accurate segmentation of the prostate is key to biopsy needle placement, brachytherapy treatment planning, and motion management. Manual segmentation during these interventions is time-consuming and subject to inter- and intraobserver variation. To address these drawbacks, we aimed to develop a deep learning-based method which integrates deep supervision into a three-dimensional (3D) patch-based V-Net for prostate segmentation. METHODS AND MATERIALS We developed a multidirectional deep-learning-based method to automatically segment the prostate for ultrasound-guided radiation therapy. A 3D supervision mechanism is integrated into the V-Net stages to deal with the optimization difficulties when training a deep network with limited training data. We combine a binary cross-entropy (BCE) loss and a batch-based Dice loss into the stage-wise hybrid loss function for a deep supervision training. During the segmentation stage, the patches are extracted from the newly acquired ultrasound image as the input of the well-trained network and the well-trained network adaptively labels the prostate tissue. The final segmented prostate volume is reconstructed using patch fusion and further refined through a contour refinement processing. RESULTS Forty-four patients' TRUS images were used to test our segmentation method. Our segmentation results were compared with the manually segmented contours (ground truth). The mean prostate volume Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and residual mean surface distance (RMSD) were 0.92 ± 0.03, 3.94 ± 1.55, 0.60 ± 0.23, and 0.90 ± 0.38 mm, respectively. CONCLUSION We developed a novel deeply supervised deep learning-based approach with reliable contour refinement to automatically segment the TRUS prostate, demonstrated its clinical feasibility, and validated its accuracy compared to manual segmentation. The proposed technique could be a useful tool for diagnostic and therapeutic applications in prostate cancer.
Estimating Loss Given Default from CDS Under Weak Identification

This paper combines a term structure model of credit default swaps (CDS) with weak-identification robust methods to jointly estimate the probability of default and the loss given default of the underlying firm. The model is not globally identified because it forgoes parametric time series restrictions that have aided identification in previous studies, but that are also difficult to verify in the data. The empirical results show that informative (small) confidence sets for loss given default are estimated for half of the firm-months in the sample, and most of these are much lower than and do not include the conventional value of 0.60. This also implies that risk-neutral default probabilities, and hence risk premia on default probabilities, are underestimated when loss given default is exogenously fixed at the conventional value instead of estimated from the data.
Toward understanding the causes of blood pH irregularities and the roles of newly described binuclear neurons of carotid bodies on blood pH regulation during subarachnoid hemorrhage: Experimental study

Acidosis is the most dangerous complication of subarachnoid hemorrhage (SAH). Although the carotid bodies (CBs) network is essential for pH regulation, neither binuclear neurons (BNN) nor their functions have been mentioned so far in the literature. The aim of this study was to investigate the crucial roles of mononuclear (MNN) or BNN in CBs on acidosis following SAH. Twenty‐five hybrid rabbits were used. Five rabbits were used as a control group, six for sham, and the remaining 14 rabbits were used as the study group by injection of 1 mL of autologous arterial blood into the cisterna magna to produce SAH. Normal and degenerated MNN/BNN densities of CBs were counted by stereological methods. The mean blood pH values were: 7.362 ± 0.041 in the control group; 7.324 ± 0.064 in sham, 7.272 ± 0.062 in the SAH group. The degenerated MNN and BNN values were 5 ± 1/mm3 and 9 ± 3/mm3 in the control group; 15 ± 5/mm3 and 22 ± 6/mm3 in sham, 965 ± 113/mm3 and 1532 ± 176/mm3 in the SAH group. Mean pH values were under 7.212 ± 0.130 in animals with prominent degenerated BNN. The differences between MNN/pH changes were significant between the SAH and control groups (P < 0.005); whereas BNN/pH values were significant between the SAH and sham groups (pH < 0.005), SAH and control (P < 0.0001). BNN degeneration could result in more severe acidosis than MNN following SAH which has not been described so far.
Treatment profiles and costs of patients with chronic pain in the population setting

Background The purpose of this study was to gather information about analgesic drug therapy in patients with chronic pain and perform cost estimates to guide future cost-effectiveness research in the area. Methods Data from patients aged 44 years and over suffering from any chronic condition and receiving regular analgesic drug therapy (for ≥6 months) who attended health care facilities within the area of Badalona during 2008 were collected in a retrospective study. Morbidity profiles were defined according to treatment setting (pain unit, hospital), World Health Organization analgesic step (1–2 versus 3), and a raw cost model based on resource use and work absenteeism was applied. Patients attending the pain unit or the hospital were considered undertreated if they were on step 1–2 analgesics. Multiple regression was used to compare costs between undertreated and non-undertreated patients among those attending the pain unit or the hospital. Results Only 410 of 18,157 patients ascertained (2.3%) were on step 3 analgesics. Their direct costs were greater than those of patients on step 1–2 analgesics, although the opposite was true regarding indirect costs. Of patients seen in the pain unit and in the hospital, 2.3% and 20.1%, respectively, were considered undertreated. Regression analyses revealed even greater costs in the subgroup of undertreated patients. Conclusion Step 3 analgesics are barely used. Up to one-fifth of patients may be undertreated, generating greater costs than those considered to be properly treated. Regression analyses did not clarify the proportion of their cost excess that was attributable to undertreatment.
Clock Synchronization Over IEEE 802.11—A Survey of Methodologies and Protocols

Just like Ethernet before, IEEE 802.11 is now transcending the borders of its usage from the office environment toward real-time communication on the factory floor. However, similar to Ethernet, the availability of synchronized clocks to coordinate and control communication and distributed real-time services is not a built-in feature in WLAN. Over the years, this has led to the design and use of a wide variety of customized protocols with varying complexity and precision, both for wired and wireless networks, in accordance with the increasingly demanding requirements from real-time applications. This survey looks into the details of synchronization over IEEE 802.11 with a particular focus on the infrastructure mode which is most relevant for industrial use cases. It highlights the different parameters which affect the performance of clock synchronization over WLAN and compares the performance of existing synchronization methods to analyze their shortcomings. Finally, it identifies new trends and directions for future research as well as features for wireless clock synchronization which will be required by the applications in the near future.
Transdisciplinary Engineering Education and Research Model

Since the medievel times the critical leading role of science has been a) to challenge the entrenched conceptions of human reason and b) to introduce new methods of investigation for deeper understanding of nature of things. Once again a scientists, critical thinkers, and engineers are indtroducing new notions in the transdisciplinary arena from which new generation of intellectual and physical analysis and synthesis tools and techniques will be produced.These tools and techniques will take the form in which science, engineering, and management notions would be fused into a unified transdisciplinary entities. These developments in integrated design and process science would in turn, once again, transform the human scene beyond recognition.