Miracle-dz/UniFAR-FTUs huggingface dataset:
The tryptophan hydroxylase 1 (TPH1) gene, schizophrenia susceptibility, and suicidal behavior: A multi‐centre case–control study and meta‐analysis

Serotonin (5‐hydroxytryptamin; 5‐HT) alternations has since long been suspected in the pathophysiology of schizophrenia. Tryptophan hydroxylase (tryptophan 5‐monooxygenase; TPH) is the rate‐limiting enzyme in the biosynthesis of 5‐HT, and sequence variation in intron 6 of the TPH1 gene has been associated with schizophrenia. The minor allele (A) of this polymorphism (A218C) is also more frequent in patients who have attempted suicide and individuals who died by suicide, than in healthy control individuals. In an attempt to replicate previous findings, five single nucleotide polymorphisms (SNPs) were genotyped in 837 Scandinavian schizophrenia patients and 1,473 controls. Three SNPs spanning intron 6 and 7, including the A218C and A779C polymorphisms, were associated with schizophrenia susceptibility (P = 0.019). However there were no differences in allele frequencies of these loci between affected individuals having attempted suicide at least once and patients with no history of suicide attempts (P = 0.84). A systematic literature review and meta‐analysis support the A218C polymorphism as a susceptibility locus for schizophrenia (odds ratio 1.17, 95% confidence interval 1.07–1.29). Association studies on suicide attempts are however conflicting (heterogeneity index I2 = 0.54) and do not support the A218C/A779C polymorphisms being a susceptibility locus for suicidal behavior among individuals diagnosed with a psychiatric disorder (OR = 0.96 [0.80–1.16]). We conclude that the TPH1 A218/A779 locus increases the susceptibility of schizophrenia in Caucasian and Asian populations. In addition, the data at hand suggest that the locus contributes to the liability of psychiatric disorders characterized by elevated suicidal rates, rather than affecting suicidal behavior of individuals suffering from a psychiatric disorder. © 2009 Wiley‐Liss, Inc.
Path planning for autonomous underwater vehicles in realistic oceanic current fields: Application to gliders in the Western Mediterranean sea

Autonomous Underwater Vehicles (AUVs) usually operate in ocean environments characterized by complex spatial variability which can jeopardize their missions. To avoid this, planning safety routes with minimum energy cost is of primary importance. This work revisits the benefits, in terms of travelling time, of path planning in marine environments showing spatial variability. By means of a path planner presented in a previous paper, this work focuses on the application to a real environment of such techniques. Extensive computations have been carried out to calculate optimal paths on realistic ocean environments, based on autonomous underwater glider properties as the mobile platform. Unlike previous works, the more realistic and applied case of an autonomous underwater glider surveying the Western Mediterranean Sea is considered. Results indicate that substantial energy savings of planned paths compared to straight line trajectories are obtained when the current intensity and the vehicle speed are comparable. Conversely, the straight line path betwe en starting and ending points can be considered an optimum path when the current speed does not exceed half of the vehicle velocity. In both situations, benefits of path planning seem dependent also on the spatial structure of the current field.
Adjustment of the lateral and longitudinal size of scanned proton beam spots using a pre-absorber to optimize penumbrae and delivery efficiency

In scanned-beam proton therapy, the beam spot properties, such as the lateral and longitudinal size and the minimum achievable range, are influenced by beam optics, scattering media and drift spaces in the treatment unit. Currently available spot scanning systems offer few options for adjusting these properties. We investigated a method for adjusting the lateral and longitudinal spot size that utilizes downstream plastic pre-absorbers located near a water phantom. The spot size adjustment was characterized using Monte Carlo simulations of a modified commercial scanned-beam treatment head. Our results revealed that the pre-absorbers can be used to reduce the lateral full width at half maximum (FWHM) of dose spots in water by up to 14 mm, and to increase the longitudinal extent from about 1 mm to 5 mm at residual ranges of 4 cm and less. A large factor in manipulating the lateral spot sizes is the drift space between the pre-absorber and the water phantom. Increasing the drift space from 0 cm to 15 cm leads to an increase in the lateral FWHM from 2.15 cm to 2.87 cm, at a water-equivalent depth of 1 cm. These findings suggest that this spot adjustment method may improve the quality of spot-scanned proton treatments.
Neural activation differences in amputees during imitation of intact versus amputee movements

The mirror neuron system (MNS) has been attributed with increased activation in motor-related cortical areas upon viewing of another's actions. Recent work suggests that limb movements that are similar and dissimilar in appearance to that of the viewer equivalently activate the MNS. It is unclear if this result can be observed in the action encoding areas in amputees who use prosthetic devices. Intact subjects and upper extremity amputee prosthesis users were recruited to view video demonstrations of tools being used by an intact actor and a prosthetic device user. All subjects pantomimed the movements seen in the video while recording electroencephalography (EEG). Intact subjects showed equivalent left parietofrontal activity during imitation planning after watching the intact or prosthetic arm. Likewise, when prosthesis users imitated prosthesis demonstrations, typical left parietofrontal activation was observed. When prosthesis users imitated intact actors, an additional pattern was revealed which showed greater activity in right parietal and occipital regions that are associated with the mentalizing system. This change may be required for prosthesis users to plan imitation movements in which the limb states between the observed and the observer do not match. The finding that prosthesis users imitating other prosthesis users showed typical left parietofrontal activation suggests that these subjects engage normal planning related activity when they are able to imitate a limb matching their own. This result has significant implications on rehabilitation, as standard therapy involves training with an intact occupational therapist, which could necessitate atypical planning mechanisms in amputees when learning to use their prosthesis.
Predicting driver lane change intent using HCRF

Accurately predicting drivers intent in advance could help ADAS reduce false alarm rate and improve performance. In this paper, we propose a driver intent prediction approach base on Hidden Conditional Random Fields model. The work can substantially utilize multiple dynamic characteristics of the driving signals, such as the steering wheel angle, lateral position, and drivers gaze compared with other batch process algorithm like Support Vector Machine (SVM). Moreover, it is more discriminative than traditional methods based on Hidden Markov Model (HMM). The experiments were carried out in a driving simulator, and we designed a more complex driving environment compared with previous works. In our experiment, the curvature of the road was not constant and the subjects could make lane change decision on their own. The results show that the proposed method outperforms over SVM and HMM. The prediction accuracy is 99% in 0.5s before the lane change, and 85% in 2s before the maneuver.
Biological and Biochemical Characteristics of Cytoadhesion of Plasmodium falciparum-Infected Erythrocytes to Chondroitin-4-Sulfate

ABSTRACT The cytoadhesion of Plasmodium falciparum laboratory strains and clones to Saimiri brain microvascular endothelial cells (SBEC 17), with chondroitin-4-sulfate (CSA) as the only adhesion receptor, was tested. Only one strain had significant cytoadhesion. However, CSA-specific infected erythrocytes (IRBCs) were detected in all strains after selection of a CSA-specific subpopulation by culturing the few adherent IRBCs. This demonstrates the lack of sensitivity of cytoadhesion microassays for detecting small quantities of CSA-specific IRBCs in cultures or field isolates. Cytoadhesion to CSA is maximal at 24 h of the cycle and decreases with the onset of schizogony, reaching a minimum just before reinvasion. This fluctuation must be taken into account in comparisons of the cytoadhesion of different strains or isolates. The minimum size of CSA for active inhibition was 4 kDa, and a mass of 9 kDa was required for inhibition similar to that obtained with the 50-kDa CSA. In contrast to cytoadhesion to CSA, which is pH independent or maximal at physiological pH (depending on the target endothelial cells), adhesion to CD36 and intercellular adhesion molecule 1 was pH dependent, requiring acidic conditions to be maximal in all cases. Cytoadhesion to CSA may trigger the occlusion of microvessels and cause the acidosis necessary for the other receptors to be fully efficient. If this key role in the mechanisms of sequestration were to be confirmed in vivo, prevalence studies of the CSA cytoadhesion phenotype would have to be reevaluated, because simple cytoadhesion assays do not detect CSA-specific parasites present in very low numbers, and these parasites might then be undetected in the peripheral blood but present in organs in which sequestration occurs, such as the placenta (M. Fried and P. E. Duffy, Science 272:1502–1504, 1996).
Container Integrity and Condition Monitoring using RF Vibration Sensor Tags

Global supply chain operations use hundreds of thousands of container trucks to transport valuable packages and items within and across continents. The recent developments in wireless sensing, including in the so-called RFID sensor tags, offer significant potential for continuous long-range integrity monitoring of containers at various phases of their shipment. In particular, the vibration patterns of a container and its contents can reveal significant information related to its operating environment and integrity during transport, handling and storage. The present work investigates the sensitivity of the patterns of container vibrations gathered as signals from wireless sensor tags to the following four major factors that define the container operating conditions: terrain type, speed of the vehicle, weight dimensions of the container items. Quantitative relationships are established between the vibration patterns and the salient factors through a series of experiments involving the use of a scaled model of a container truck and a wireless sensor that captures vibrations at 200 Hz sampling rates. The idea is to classify the operating conditions by analyzing the complex dynamics underlying vibration signals. Using nonlinear dynamic characterization, we find that the Lyapunov exponents of dynamics underlying signals are positive (0.01-0.02 for Stage 1 and 0.005 for Stage 2 experiments). The statistical and nonlinear dynamic features together are successfully mapped using a neural network to classify between the different operating conditions. The neural networks were able to identify the correct operating conditions from the vibration sensor features about 90% of the times. In real life, the research results can be applied to accurately capture the environmental and operating conditions during container transport. This will help proactively address possible serious integrity losses.
Access control via belnap logic: Intuitive, expressive, and analyzable policy composition

Access control to IT systems increasingly relies on the ability to compose policies. Hence there is benefit in any framework for policy composition that is intuitive, formal (and so “analyzable” and “implementable”), expressive, independent of specific application domains, and yet able to be extended to create domain-specific instances. Here we develop such a framework based on Belnap logic. An access-control policy is interpreted as a four-valued predicate that maps access requests to either grant, deny, conflict, or unspecified -- the four values of the Belnap bilattice. We define an expressive access-control policy language PBel, having composition operators based on the operators of Belnap logic. Natural orderings on policies are obtained by lifting the truth and information orderings of the Belnap bilattice. These orderings lead to a query language in which policy analyses, for example, conflict freedom, can be specified. Policy analysis is supported through a reduction of the validity of policy queries to the validity of propositional formulas on predicates over access requests. We evaluate our approach through firewall policy and RBAC policy examples, and discuss domain-specific and generic extensions of our policy language.
FAST detector on many-core computers

Many-core architectures are becoming increasingly popular due to advances in open programming environments. Typical architectures are Graphics Processing Units (GPUs) and Homogenous Computing Fabrics, offering high computing parallelism in order to decrease execution time of computationally intensive algorithms, for example in Computer Vision. In this paper, a low complexity interest point detector has been written in OpenCL language and executed on different desktop and embedded computing platforms. The algorithm performs interest point detection in an image, around which there is enough distinctive information to enable further description. The algorithm has been optimized on the targeted platforms and performance and accuracy comparison has been performed.
DECOUPLING THE DATA GEOMETRY FROM THE PARAMETER GEOMETRY FOR STOCHASTIC GRADIENTS

Large-scale learning problems require algorithms that scale benignly with respect to the size of the dataset and the number of parameters to be trained; leading numerous practitioners to favor the classic stochastic gradient descent (SGD [1, 2, 3]) over more sophisticated methods. Besides its fast convergence, SGD has been observed to sometimes lead to signi cantly better generalization performance than batch gradient descent. SGD is also quicker than batch methods in adapting to non-stationary data distributions. Its Achilles heel are the inherently sequential updates, making it very di cult to parallelize across many machines; which is clashing with the goals of large-scale learning. Our goals here are twofold. On the theoretical level, we want to gain a fuller understanding of how the dynamics of stochastic updates contrast with those of batch updates, and how they are a ected by the conditioning of energy surfaces, the presence of local optima, and the properties of the data distribution. On the practical level, we want to use this knowledge to design more e cient mini-batch SGD variants (which are parallelizable), together with robust settings for their hyper-parameters. The study of stochastic gradient methods dates back over six decades [1, 2, 3, 4, 5, 6, 7], but to our knowledge, the present questions remain understudied. The most similar viewpoint is found in approaches based on natural gradients and information geometry [8, 9, 10].
Monitoring User Pattern in School Information System Using Logfile Analysis

Analyzing user patterns in school information systems can be difficult as several methods (e.g. interviews, surveys, and observations) can be time-consuming. We propose logfile analysis as a method that offers several advantages, primarily non-reactive data capture. With logfiles from a school with over 100 teachers over a seven month period, we try to get a deeper insight about the system’s usage and the interactions between users. The results show that three user groups can be identified, classified by the intensity of usage. Network graphs helped us to visualize a complex system and helped us to identify important subjects and categories. Nevertheless, logfiles alone lack in providing information giving deeper insights about uses of the system like user goals and aims.
Intelligent Software Systems in Historical Context

The purpose of this chapter is to trace the evolution of intelligent software from data-centric applications that essentially encapsulate their data environment to ontology-based applications with automated reasoning capabilities. It is argued that a distinction may be drawn between human intelligence and component capabilities within a more general definition of intelligence, and that such component capabilities can be embedded in computer software. The primary vehicle in the quest for intelligent software has been the gradual recognition of the central role played by data and information, rather than the logic and functionality of the application. The three milestones in this evolution have been: the separation of data management from the internal domain of the application; the development of standard data exchange protocols such as the Extensible Markup Language (XML), which allows machine interpretable structure and meaning to be added to data exchange packages; and, the ability to build information models that are rich in relationships and are thereby capable of supporting the automated reasoning capabilities of software agents.
Novel cut wires metamaterial exhibiting negative refractive index

We present, design and analyze a novel planar Left-Handed (LH) metamaterial in the microwave region. This metamaterial is composed of only cut metallic wires and is used under normal-to-plane incidence. Using Finite Element Method (FEM) based simulations and microwave experiments, we have investigated the material properties of the structure. Simultaneous negative values are observed for the permittivity ε and permeability μ by the inversion method from the transmission and reflection responses. A negative index n is verified in a bulk prism engineered by stacking several layers of the metamaterial. Our work demonstrates the feasibility of a LH metamaterial composed of only cut wires.
A new lifetime distribution

Many if not most lifetime distributions are motivated only by mathematical interest. Here, a new three-parameter distribution motivated mainly by lifetime issues is introduced. Some properties of the new distribution including estimation procedures, univariate generalizations and bivariate generalizations are derived. A real data application is described to show its superior performance versus at least that of 15 of the known lifetime models.
Timing of present and future snowmelt from high elevations in northwest Montana

The sensitivity of snowmelt‐driven water supply to climate variability and change is difficult to assess in the mountain west, where strong climatic gradients coupled with complex topography are sampled by sparse ground measurements. We developed a model which ingests daily satellite imagery and meteorological data and is suitable for areas >1000 km2, yet captures spatial variability of snow accumulation and melt in steep mountain terrain. We applied the model for the years 2000–2008 to a 2900 km2 snowmelt‐dominated watershed in NW Montana. We found that >25% of the basin's snow water equivalent (SWE) accumulates above the highest measurement station and >70% accumulates above the mean elevation of surrounding SNOTEL stations. Consequently, scaling point measurements of SWE to describe basin conditions could lead to significant misrepresentation of basin snow. Simulations imply that present‐day temperature variability causes measures of snowmelt timing to vary by over 4 weeks from year‐to‐year. Temperature variability causes a larger spread in snowmelt timing in a warmer climate. On average, snowmelt timing occurs 3 weeks earlier in late 21st century projections, with about 25% of future conditions observed today.
D I a B E T E S & M E T a B O L I S M J O U R N a L the Association of Self-reported Coronary Heart Disease with Diabetes Duration in Korea

which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: This study aimed to investigate the association of diabetes duration with self-reported coronary heart disease (CHD) in Korea. Methods: Among data from 34,145 persons compiled in the third Korean National Health and Nutrition Examination Survey in 2005, laboratory test and nutritional survey data from 5,531 persons were examined. The participants were asked to recall a physician's diagnosis of CHD (angina or myocardial infarction). Results: Age-and sex-adjusted relative risk for CHD was 1.51 (95% confidence interval [CI], 0.64 to 3.59; not significant) for diabetes with duration of <1 year, 2. for diabetes with a duration >5 years, compared with non-diabetes as a control. Even after adjusting for age, sex, current smoking status, waist circumference, hypertension, triglycerides, high density lipoprotein cholesterol, and fasting plasma glucose, relative risk for CHD was 2. in diabetes with duration of >10 years with non-diabetes as a control. Conclusion: CHD prevalence increased with an increase in diabetes duration in Korean men and women. Recently detected diabetes (duration <1 year) was not significantly associated with CHD prevalence compared to non-diabetes. However, diabetes of a duration of >5 years was associated with an increase in CHD compared to non-diabetics after adjusting for several CHD risk factors.
Retinal Photoreceptor Expresses Toll-Like Receptors (TLRs) and Elicits Innate Responses Following TLR Ligand and Bacterial Challenge

Toll-like receptors (TLRs) play an important role in host defense against microbial pathogens. Our previous studies have shown that TLRs are expressed on various retinal cells (Microglia and Müller glia) and orchestrate retinal innate responses in bacterial endophthalmitis. In this study, we used a well-characterized mouse cone photoreceptor cell line (661W); and demonstrated that these cells express all known TLRs. Although the stimulation of 661W cells with TLR ligands (Pam3Cys, PolyI:C, LPS, Flagellin, Poly DT, and ODN) did not alter TLR expression, downstream TLR-signaling pathways (NF-κB, p38, and ERK) are activated. Moreover, TLR-activated 661W cells secreted significant amounts of inflammatory mediators (IL-6, IL-1β, MIP-2, and KC) in their culture supernatant, as assessed by ELISA. A similar trend was observed in 661W cells challenged with live bacteria (Staphylococcus aureus). Interestingly, the neutralization of TLR2, a major receptor for S. aureus recognition, did not significantly attenuate bacterial-induced inflammatory mediators, suggesting the existence of TLR2-independent mechanisms in photoreceptor cells. Together, these results indicate that photoreceptors constitutively express functional TLRs and possess the ability to initiate innate responses following pathogen challenge, implicating their role in retinal innate immunity.
Regularized Fitted Q-Iteration for planning in continuous-space Markovian decision problems

Reinforcement learning with linear and non-linear function approximation has been studied extensively in the last decade. However, as opposed to other fields of machine learning such as supervised learning, the effect of finite sample has not been thoroughly addressed within the reinforcement learning framework. In this paper we propose to use L2 regularization to control the complexity of the value function in reinforcement learning and planning problems. We consider the Regularized Fitted Q-Iteration algorithm and provide generalization bounds that account for small sample sizes. Finally, a realistic visual-servoing problem is used to illustrate the benefits of using the regularization procedure.