LLukas22/scidocs huggingface dataset:
Archetype-based conversion of EHR content models: pilot experience with a regional EHR system

BACKGROUND
Exchange of Electronic Health Record (EHR) data between systems from different suppliers is a major challenge. EHR communication based on archetype methodology has been developed by openEHR and CEN/ISO. The experience of using archetypes in deployed EHR systems is quite limited today. Currently deployed EHR systems with large user bases have their own proprietary way of representing clinical content using various models. This study was designed to investigate the feasibility of representing EHR content models from a regional EHR system as openEHR archetypes and inversely to convert archetypes to the proprietary format.


METHODS
The openEHR EHR Reference Model (RM) and Archetype Model (AM) specifications were used. The template model of the Cambio COSMIC, a regional EHR product from Sweden, was analyzed and compared to the openEHR RM and AM. This study was focused on the convertibility of the EHR semantic models. A semantic mapping between the openEHR RM/AM and the COSMIC template model was produced and used as the basis for developing prototype software that performs automated bi-directional conversion between openEHR archetypes and COSMIC templates.


RESULTS
Automated bi-directional conversion between openEHR archetype format and COSMIC template format has been achieved. Several archetypes from the openEHR Clinical Knowledge Repository have been imported into COSMIC, preserving most of the structural and terminology related constraints. COSMIC templates from a large regional installation were successfully converted into the openEHR archetype format. The conversion from the COSMIC templates into archetype format preserves nearly all structural and semantic definitions of the original content models. A strategy of gradually adding archetype support to legacy EHR systems was formulated in order to allow sharing of clinical content models defined using different formats.


CONCLUSION
The openEHR RM and AM are expressive enough to represent the existing clinical content models from the template based EHR system tested and legacy content models can automatically be converted to archetype format for sharing of knowledge. With some limitations, internationally available archetypes could be converted to the legacy EHR models. Archetype support can be added to legacy EHR systems in an incremental way allowing a migration path to interoperability based on standards.
Psychological treatments for chronic post-traumatic stress disorder. Systematic review and meta-analysis.

BACKGROUND
The relative efficacy of different psychological treatments for chronic post-traumatic stress disorder (PTSD) is unclear.


AIMS
To determine the efficacy of specific psychological treatments for chronic PTSD.


METHOD
In a systematic review of randomised controlled trials, eligible studies were assessed against methodological quality criteria and data were extracted and analysed.


RESULTS
Thirty-eight randomised controlled trials were included in the meta-analysis. Trauma-focused cognitive-behavioural therapy (TFCBT), eye movement desensitisation and reprocessing (EMDR), stress management and group cognitive-behavioural therapy improved PTSD symptoms more than waiting-list or usual care. There was inconclusive evidence regarding other therapies. There was no evidence of a difference in efficacy between TFCBT and EMDR but there was some evidence that TFCBT and EMDR were superior to stress management and other therapies, and that stress management was superior to other therapies.


CONCLUSIONS
The first-line psychological treatment for PTSD should be trauma-focused (TFCBT or EMDR).
Specificity of relations between adolescents' cognitive emotion regulation strategies and Internalizing and Externalizing psychopathology.

OBJECTIVE
Of the study was to examine the extent to which cognitive emotion regulation strategies were 'common determinants' of Internalizing and Externalizing problems and/or 'specific determinants' distinguishing one problem category from the other.


METHOD
The sample comprised 271 12- to 18-year-old secondary school students. Internalizing and Externalizing problems were measured by the Youth Self-Report (YSR) and Cognitive Emotion Regulation Strategies were measured by the Cognitive Emotion Regulation Questionnaire (CERQ), in a cross-sectional design.


RESULTS
First, adolescents with Internalizing problems, Externalizing problems, comorbid Internalizing and Externalizing problems and a control group were compared on their specific cognitive emotion regulation strategies. Results showed that adolescents with Internalizing problems (both pure and comorbid) scored significantly higher on the cognitive emotion regulation strategies of self-blame and rumination than those with Externalizing (pure) problems or the control group. Unique relationships between the separate cognitive strategies and Internalizing and Externalizing problems were tested by means of Multiple Regression Analyses. Specific relationships were found between Internalizing problems and self-blame, rumination and positive reappraisal and between Externalizing problems and positive refocusing. No 'common' correlates were found.


CONCLUSIONS
Theoretical models designed for the prediction of Internalizing problems might not simply be used for the prediction of Externalizing problems. Different (cognitive) intervention strategies should be used for adolescents with Internalizing problems and Externalizing problems.
Prevalence and distribution of demarcated opacities in permanent 1st molars and incisors in 6 to 8-year-old Danish children.

OBJECTIVE
To estimate the prevalence and describe the distribution of demarcated opacities and possible consequences of this condition in permanent 1st molars and incisors in Danish children.


MATERIAL AND METHODS
Among all 6 to 8-year-old children in two municipalities, 745 (83.6%) were clinically examined for the occurrence of creamy-white or yellowish-brown demarcated opacities, posteruptive breakdown of tooth substance in relation to the opacities, atypical restorations, and extractions, i.e. restorations or extractions as a result of the occurrence of demarcated opacities.


RESULTS
In 647 children with four fully erupted permanent 1st molars, the prevalence of demarcated opacities and of lesions with loss of tooth substance due to demarcated opacities in any 1st molar was 37.3% (95% confidence interval (95% CI) 33.6-41.0%) and 6.3% (95% CI 4.7-8.5%), respectively. Permanent incisors were 2.5 times more frequently affected among children with one or more affected permanent 1st molar than among children with no such teeth. Demarcated creamy-white opacities were the type of lesion found most frequently, and the most frequently affected tooth types were the upper central incisors followed by 1st molars.


CONCLUSIONS
Nearly half of the examined 6 to 8-year-old children had permanent 1st molars or incisors with demarcated opacities. More than 6% of the children had macroscopic loss of tooth substance due to demarcated opacities.
A multi-agent architecture for dynamic scheduling of steel hot rolling

Steel production is a complex process and finding c oherent schedules for the wide variety of productio n steps in a dynamic environment where disturbances frequently occur is a challenging task. Steel production involves a ran ge of processes: continuous caster, hot strip mill, furnaces. The sc heduling systems of the caster and the hot strip mi ll have very different objectives and constraints. It is important that th e schedules for these two processes are able to rea ct to the presence of real time information concerning production failures and customer requests. In this paper we propose a multi-agent architecture fo integrated dynamic scheduling of the Hot Strip Mill and the Caster. Each process in the steel production environment is assigned to an agent which performs, independently , its dynamic scheduling at a local level by using a Tabu search meta-heuristic optimisation method. In this archite cture, we have elaborated the dynamic scheduling of the Hot Strip Mill Agent. The other agents in the architectures s imulate the production of the coil orders and the real-time events concern ing the non-availability of coils. The dynamic sche duling of the Hot Strip Mill agent takes into account local objectives, rea l time information and information received from ot her agents. To address the problem of dynamic scheduling of the Hot Strip Mill, schedule repair and complete rescheduling str ategies are presented and their performance is assessed with respect to m easures of utility, stability and robustness. We re po t experiments which have been carried out to evaluate the performance o f these strategies.
Blind Estimation of Motion Blur Parameters for Image Deconvolution

This paper describes an approach to estimate the parameters of a motion blur (direction and length) directly form the observed image. The motion blur estimate can then be used in a standard nonblind deconvolution algorithm, thus yielding a blind motion deblurring scheme. The estimation criterion is based on recent results about the general spectral behavior of natural images. Experimental results show that the proposed approach is able to accurately estimate both the length and orientation of motion blur kernels, even for small lengths which are traditionally difficult.
Detecting Surface Features During Locomotion using Optic Flow

We test the hypothesis that: (1) Optic flow can be used to detect significant environmental features during locomotion in a biped, even given significant up and down movement and jarring of the robot during locomotion. (2) Reliable detection is only possible if a prediction of the expected optic flow field is made at each instance. This prediction should be driven by the phase of the robot's gait as well as other information about the state of the robot. (3) This prediction can be accomplished in a distributed, biologically plausible framework. Our results using a walking biped mechanism strongly support this hypothesis and indicate that optic flow is a viable strategy and that the prediction of optic flow is a critical component in this behavior. We believe this is the first time that this approach has been used and demonstrated successfully on a walking machine. For walking robots to realize their full potential, distal environment sensing must be tightly integrated with the walking cycle. Distal sensing is crucial to allow anticipatory gait adjustment to accommodate varying terrain. Close coupling of the visual and locomotor cycle can lead to rapid, adaptive adjustment of the robot. Biological systems have a tightly integrated action perception cycle, unlike that seen in most legged robots. In the 1950's, Gibson pointed out the importance of the flow of the 'optic array' (i.e. optic flow), a visual cue arising from relative movement of the environment, in the control of human locomotion [1]. Relative motion of the environment can reveal environment structure. His pioneering ideas have influenced generations of experimentalists in the brain and behavioral sciences. In robotics, the route to using optic flow has come principally from studies of insects. Insect motion processing pathways are relatively well understood. There have been a number of robotic implementations that use optic flow as its primary input for visual navigation. For a flavor of that work, see [2-9]. Vision is a particularly elegant, multifunctional distal sense. It is not surprising that biological systems have relied so heavily on vision to guide locomotion. However, there has been little or no work using optic flow to control legged locomotion although there has been interest in other visual techniques for the control of walking machines (visual servoing, color, stereopsis, terrain reconstruction using laser ranging). Given the strong belief in the biological community that optic flow is a key component of visuomotor coordination (e.g. [10-12]), …
The Formula for Ammonia Monohydrate

The reality of NH4OH was argued in J. Chem. Educ. and elsewhere a decade ago. Further evidence is now available.
A self adaptive differential harmony search based optimized extreme learning machine for financial time series prediction

This paper proposes a hybrid learning framework called Self Adaptive Differential Harmony Search Based Optimized Extreme Learning Machine (SADHS-OELM) for single hidden layer feed forward neural network (SLFN). The new learning paradigm seeks to take advantage of the generalization ability of extreme learning machines (ELM) along with the global learning capability of a self adaptive differential harmony search technique in order to optimize the fitting performance of SLFNs. SADHS is a variant of harmony search technique that uses the current to best mutation scheme of DE in the pitch adjustment operation for harmony improvisation process. SADHS has been used for optimal selection of the hidden layer parameters, the bias of neurons of the hidden-layer, and the regularization factor of robust least squares, whereas ELM has been applied to obtain the output weights analytically using a robust least squares solution. The proposed learning algorithm is applied on two SLFNs i.e. RBF and a low complexity Functional link Artificial Neural Networks (CEFLANN) for prediction of closing price and volatility of five different stock indices. The proposed learning scheme is also compared with other learning schemes like ELM, DE-OELM, DE, SADHS and two other variants of harmony search algorithm. Performance comparison of CEFLANN and RBF with different learning schemes clearly reveals that CEFLANN model trained with SADHS-OELM outperforms other learning methods and also the RBF model for both stock index and volatility prediction. & 2014 Elsevier B.V. All rights reserved.
Optic neuritis: findings on MRI, CSF examination and HLA class II typing in 60 patients and results of a short-term follow-up

Optic neuritis (ON) is a common first manifestation of multiple sclerosis (MS), and examination of patients with ON provides opportunities to study the early clinical stages of MS. This prospective study compares results of brain magnetic resonance imaging (MRI), cerebrospinal fluid (CSF) examinations and HLA-Dw2 phenotyping in 60 consecutive patients with ON. At a median of 17 days after the onset of ON, 69% had oligoclonal IgG bands, and at a median on 79 days after onset, 53% had multiple (≥ 3) white matter lesions on MRI. Subgroup analyses revealed that MRI abnormalities and oligoclonal IgG bands were equally common in patients examined early or late after the onset of ON. Strong correlations were found between the presence of MRI abnormalities and oligoclonal IgG bands. The HLA-Dw2 phenotype was significantly increased in ON patients compared with controls, but also significantly different from a group of MS patients from the same geographical area. A significant relation was found between Dw2 phenotype and oligoclonal IgG bands. During a mean follow-up time of about 2 years, the diagnosis in 17 of the patients changed to clinically definite MS. Initially, 16 of them had oligoclonal IgG bands and 12 had three or more MRI lesions. Both MRI and CSF studies are important diagnostic tools in the work-up of ON patients.
STROKENET: A NEURAL PAINTING ENVIRONMENT

We’ve seen tremendous success of image generating models these years. Generating images through a neural network is usually pixel-based, which is fundamentally different from how humans create artwork using brushes. To imitate human drawing, interactions between the environment and the agent is required to allow trials. However, the environment is usually non-differentiable, leading to slow convergence and massive computation. In this paper we try to address the discrete nature of software environment with an intermediate, differentiable simulation. We present StrokeNet, a novel model where the agent is trained upon a wellcrafted neural approximation of the painting environment. With this approach, our agent was able to learn to write characters such as MNIST digits faster than reinforcement learning approaches in an unsupervised manner. Our primary contribution is the neural simulation of a real-world environment. Furthermore, the agent trained with the emulated environment is able to directly transfer its skills to real-world software. 1
Efficacy of breast shielding during CT of the head

In light of increasing frequency of CT examinations in the past decades, the aims of this prospective study were to investigate scatter radiation breast exposure in head CT and its dependence upon body constitution, and to assess the efficacy of lead shielding as a means of breast dose reduction. In 49 women referred to head CT for objective medical reasons one breast was covered with lead apron during CT scanning. Radiation doses were measured by use of thermoluminescent dosimeters, at skin of both breasts and over the apron. The doses were then compared as well as correlated to body mass index and meatus acusticus externus-to-dosimeter distance, respectively. Average exposure at the skin of the unshielded breast was 0.28 mGy (range 0.15–0.41 mGy), compared with 0.13 mGy (range 0.05–0.29 mGy) at the shielded breast. The doses showed a mean reduction by 57% due to lead shielding. At least half of breast exposure was imparted to the breast from outside, whereas the remainder results from internal scatter. The higher the body mass index, the higher the percentage of internal scatter in total breast dose. Although the level of scatter radiation to the breast is generally low during head CT examination, the use of lead cover enables recognizable further reduction of the exposure, and is recommended as a feasible and effective procedure of breast protection during CT of the head.
Mechanisms of Disease: astrocytes in neurodegenerative disease

The term neurodegenerative disease refers to the principal pathology associated with disorders such as amyotrophic lateral sclerosis, Alzheimer's disease, Huntington's disease and Parkinson's disease, and it is presumed that neurodegeneration results in the clinical findings seen in patients with these diseases. Decades of pathological and physiological studies have focused on neuronal abnormalities in these disorders, but it is becoming increasingly evident that astrocytes are also important players in these and other neurological disorders. Our understanding of the normative biology of astrocytes has been aided by the development of animal models in which astrocyte-specific proteins and pathways have been manipulated, and mouse models of neurodegenerative diseases have also revealed astrocyte-specific pathologies that contribute to neurodegeneration. These models have led to the development of targeted therapies for pathways in which astrocytes participate, and this research should ultimately influence the clinical treatment of neurodegenerative disorders.
Computational Neuroscience Offers Hints for More General Machine Learning

Machine Learning has traditionally focused on narrow artificial intelligence solutions for specific problems. Despite this, we observe two trends in the state-of-the-art: One, increasing architectural homogeneity in algorithms and models. Two, algorithms having more general application: New techniques often beat many benchmarks simultaneously. We review the changes responsible for these trends and look to computational neuroscience literature to anticipate future progress.
Commercialize quantum technologies in five years

Masoud Mohseni, Peter Read, Hartmut Neven and colleagues at Google’s Quantum AI Laboratory set out investment opportunities on the road to the ultimate quantum machines. From aspects of quantum entanglement to chemical reactions with large molecules, many features of the world cannot be described efficiently with conventional computers based on binary logic. The solution, as physicist Richard Feynman realized three decades ago, is to use quantum processors that adopt a blend of classical states simultaneously, as matter does. Many technical hurdles must be overcome for such quantum machines to be practical, however. These include noise control and improving the fidelity of operations acting on the quantum states that encode the information. The quantum-computing community is channelling most of its efforts towards building the ultimate machine: a digital quantum computer that tolerates noise and errors, and that in principle can be applied to any problem. In theory, such a machine — which will need large processors comprising many quantum bits, or qubits — should be able to calculate faster than a conventional computer. Such capability is at least a decade away. Correcting for errors requires redundancy, and the number of qubits needed quickly mounts. For example, factorizing a 2,000-bit number in one day, a task believed to be intractable using classical computers, would take 100 million qubits, even if individual quantum operations failed just once in every 10,000 operations. We have yet to assemble digital quantum processors with tens of qubits. This conservative view of quantum computing gives the impression that investors will benefit only in the long term. We contend that short-term returns are possible with the small devices that will emerge within the next five years, even though these will lack full error correction. A lack of theoretical guarantees need not preclude success. Heuristic ‘hybrid’ methods that blend quantum and classical approaches could be the foundation for powerful future applications. The recent success of neural networks in machine learning is a good example. In the 1990s, when the computing power required to train deep neural networks was unavailable, it was fashionable in the field to focus on ‘convex’ methods (based on functions with a clear minimum solution) that had a strong theoretical basis. Today, these methods are no match for deep learning. The underlying algorithms of neural networks Google’s cryostats reach temperatures of 10 millikelvin to run its quantum processors. ER IK L U C ER O
Sr{sub 1.98}Eu{sub 0.02}SiO{sub 4} luminescence whisker based on vapor-phase deposition: Facile synthesis, uniform morphology and enhanced luminescence properties

Highlights: • For the first time, it is possible to obtain Sr{sub 1.98}Eu{sub 0.02}SiO{sub 4} whisker. • The whiskers are smooth and uniform with L/D ratio over 50. • Durability and thermal stability of the whisker are enhanced. - Abstract: A high performance strontium silicate phosphor has been successfully synthesized though a facile vapor-phase deposition method. The product consists of single crystal whiskers which are smooth and uniform, and with a sectional equivalent diameter of around 5 μm; the aspect ratio is over 50 and no agglomeration can be observed. X-ray diffraction result confirmed that the crystal structure of the whisker was α’-Sr{sub 2}SiO{sub 4}. The exact chemical composition was Sr{sub 1.98}Eu{sub 0.02}SiO{sub 4} which was analyzed by energy dispersive spectrometer and inductively coupled plasma-mass spectrometer. The whisker shows broad green emission with peak at 523 nm ranging from 470 to 600 nm (excited at 370 nm). Compared with traditional Sr{sub 2}SiO{sub 4}:Eu phosphor, durability (at 85% humidity and 85 °C) and thermal stability of the whisker are obviously improved. Moreover, growth mechanism of the Sr{sub 1.98}Eu{sub 0.02}SiO{sub 4} whiskers is Vapor–Liquid–Solid. On a macro-scale, the product is still powder which makes it suitable for the current packaging process ofmore » WLEDs.« less