1,209 research outputs found
Age-related increase of kynurenic acid in human cerebrospinal fluid-IgG and beta(2)-microglobulin changes
Kynurenic acid (KYNA) is an endogenous metabolite in the kynurenine pathway of tryptophan degradation and is an antagonist at the glycine site of the N-methyl-D-aspartate as well as at the alpha 7 nicotinic cholinergic receptors. In the brain tissue KYNA is synthesised from L-kynurenine by kynurenine aminotransferases (KAT) I and II. A host of immune mediators influence tryptophan degradation. In the present study, the levels of KYNA in cerebrospinal fluid (CSF) and serum in a group of human subjects aged between 25 and 74 years were determined by using a high performance liquid chromatography method. In CSF and serum KAT I and II activities were investigated by radioenzymatic assay, and the levels of β2-microglobulin, a marker for cellular immune activation, were determined by ELISA. The correlations between neurochemical and biological parameters were evaluated. Two subject groups with significantly different ages, i.e. 50 years, p < 0.001, showed statistically significantly different CSF KYNA levels, i.e. 2.84 ± 0.16 fmol/μl vs. 4.09 ± 0.14 fmol/μl, p < 0.001, respectively; but this difference was not seen in serum samples. Interestingly, KYNA is synthesised in CSF principally by KAT I and not KAT II, however no relationship was found between enzyme activity and ageing. A positive relationship between CSF KYNA levels and age of subjects indicates a 95% probability of elevated CSF KYNA with ageing (R = 0.6639, p = 0.0001). KYNA levels significantly correlated with IgG and β2-microglobulin levels (R = 0.5244, p = 0.0049; R = 0.4253, p = 0.043, respectively). No correlation was found between other biological parameters in CSF or serum. In summary, a positive relationship between the CSF KYNA level and ageing was found, and the data would suggest age-dependent increase of kynurenine metabolism in the CNS. An enhancement of CSF IgG and β2-microglobulin levels would suggest an activation of the immune system during ageing. Increased KYNA metabolism may be involved in the hypofunction of the glutamatergic and/or nicotinic cholinergic neurotransmission in the ageing CNS
Evaluating the power security of the RF: methods and results
Доклад "Методы и результаты оценки энергетической безопасности регионов Российской Федерации" посвящен проблемам исследования энергетической безопасности регионов и субъектов Российской Федерации. В тексте кратко изложен разработанный авторами методический подход к анализу и диагностике энергетической безопасности территорий различного уровня, а также обсуждены результаты диагностики энергетической безопасности регионов Российской Федерации и территорий Уральского экономического район
Normative Evaluation of a Letter Cancellation Instrument for the Assessment of Sustained Attention: A Construct Validation Study
Cancellation tests are simple instruments that have traditionally been used to study sustained attention. Common formats follow a test pattern in which rows of letters symbols or numbers are randomly interspersed with designated targets. Test participants are generally asked to identify targets while ignoring similar non-target distracter items. In the current study we present normative data on a new cancellation instrument developed at SDSU. We present guidelines for administration, as well as normative data on omission errors, commission errors, mean target hit rates, processing speed performance, and test-retest reliability for 102 undergraduate participants in the 18-25 year old age range. Statistical analysis suggests that the NIMH-SDSU Letter Cancellation Protocol has high test-retest reliability, but is also susceptible to practice effects when subsequent administrations occur within 5 weeks
Reversible magnetomechanical collapse: virtual touching and detachment of rigid inclusions in a soft elastic matrix
Soft elastic composite materials containing particulate rigid inclusions in a
soft elastic matrix are candidates for developing soft actuators or tunable
damping devices. The possibility to reversibly drive the rigid inclusions
within such a composite together to a close-to-touching state by an external
stimulus would offer important benefits. Then, a significant tuning of the
mechanical properties could be achieved due to the resulting mechanical
hardening. For a long time, it has been argued whether a virtual touching of
the embedded magnetic particles with subsequent detachment can actually be
observed in real materials, and if so, whether the process is reversible. Here,
we present experimental results that demonstrate this phenomenon in reality.
Our system consists of two paramagnetic nickel particles embedded at finite
initial distance in a soft elastic polymeric gel matrix. Magnetization in an
external magnetic field tunes the magnetic attraction between the particles and
drives the process. We quantify the scenario by different theoretical tools,
i.e., explicit analytical calculations in the framework of linear elasticity
theory, a projection onto simplified dipole-spring models, as well as detailed
finite-element simulations. From these different approaches, we conclude that
in our case the cycle of virtual touching and detachment shows hysteretic
behavior due to the mutual magnetization between the paramagnetic particles.
Our results are important for the design and construction of reversibly tunable
mechanical damping devices. Moreover, our projection on dipole-spring models
allows the formal connection of our description to various related systems,
e.g., magnetosome filaments in magnetotactic bacteria.Comment: 14 pages, 7 figure
FE An efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining
Herein, we present a new data-driven multiscale framework called
FE which is based on two main keystones: the usage of
physics-constrained artificial neural networks (ANNs) as macroscopic surrogate
models and an autonomous data mining process. Our approach allows the efficient
simulation of materials with complex underlying microstructures which reveal an
overall anisotropic and nonlinear behavior on the macroscale. Thereby, we
restrict ourselves to finite strain hyperelasticity problems for now. By using
a set of problem specific invariants as the input of the ANN and the Helmholtz
free energy density as the output, several physical principles, e.g.,
objectivity, material symmetry, compatibility with the balance of angular
momentum and thermodynamic consistency are fulfilled a priori. The necessary
data for the training of the ANN-based surrogate model, i.e., macroscopic
deformations and corresponding stresses, are collected via computational
homogenization of representative volume elements (RVEs). Thereby, the core
feature of the approach is given by a completely autonomous mining of the
required data set within an overall loop. In each iteration of the loop, new
data are generated by gathering the macroscopic deformation states from the
macroscopic finite element (FE) simulation and a subsequently sorting by using
the anisotropy class of the considered material. Finally, all unknown
deformations are prescribed in the RVE simulation to get the corresponding
stresses and thus to extend the data set. The proposed framework consequently
allows to reduce the number of time-consuming microscale simulations to a
minimum. It is exemplarily applied to several descriptive examples, where a
fiber reinforced composite with a highly nonlinear Ogden-type behavior of the
individual components is considered
A comparative study on different neural network architectures to model inelasticity
The mathematical formulation of constitutive models to describe the
path-dependent, i.e., inelastic, behavior of materials is a challenging task
and has been a focus in mechanics research for several decades. There have been
increased efforts to facilitate or automate this task through data-driven
techniques, impelled in particular by the recent revival of neural networks
(NNs) in computational mechanics. However, it seems questionable to simply not
consider fundamental findings of constitutive modeling originating from the
last decades research within NN-based approaches. Herein, we propose a
comparative study on different feedforward and recurrent neural network
architectures to model inelasticity. Within this study, we divide the models
into three basic classes: black box NNs, NNs enforcing physics in a weak form,
and NNs enforcing physics in a strong form. Thereby, the first class of
networks can learn constitutive relations from data while the underlying
physics are completely ignored, whereas the latter two are constructed such
that they can account for fundamental physics, where special attention is paid
to the second law of thermodynamics in this work. Conventional linear and
nonlinear viscoelastic as well as elastoplastic models are used for training
data generation and, later on, as reference. After training with random walk
time sequences containing information on stress, strain, and, for some models,
internal variables, the NN-based models are compared to the reference solution,
whereby interpolation and extrapolation are considered. Besides the quality of
the stress prediction, the related free energy and dissipation rate are
analyzed to evaluate the models. Overall, the presented study enables a clear
recording of the advantages and disadvantages of different NN architectures to
model inelasticity and gives guidance on how to train and apply these models
Detecting Misinformation with LLM-Predicted Credibility Signals and Weak Supervision
Credibility signals represent a wide range of heuristics that are typically
used by journalists and fact-checkers to assess the veracity of online content.
Automating the task of credibility signal extraction, however, is very
challenging as it requires high-accuracy signal-specific extractors to be
trained, while there are currently no sufficiently large datasets annotated
with all credibility signals. This paper investigates whether large language
models (LLMs) can be prompted effectively with a set of 18 credibility signals
to produce weak labels for each signal. We then aggregate these potentially
noisy labels using weak supervision in order to predict content veracity. We
demonstrate that our approach, which combines zero-shot LLM credibility signal
labeling and weak supervision, outperforms state-of-the-art classifiers on two
misinformation datasets without using any ground-truth labels for training. We
also analyse the contribution of the individual credibility signals towards
predicting content veracity, which provides new valuable insights into their
role in misinformation detection
Inverse design of spinodoid structures using Bayesian optimization
Tailoring materials to achieve a desired behavior in specific applications is
of significant scientific and industrial interest as design of materials is a
key driver to innovation. Overcoming the rather slow and expertise-bound
traditional forward approaches of trial and error, inverse design is attracting
substantial attention. Targeting a property, the design model proposes a
candidate structure with the desired property. This concept can be particularly
well applied to the field of architected materials as their structures can be
directly tuned. The bone-like spinodoid materials are a specific class of
architected materials. They are of considerable interest thanks to their
non-periodicity, smoothness, and low-dimensional statistical description.
Previous work successfully employed machine learning (ML) models for inverse
design. The amount of data necessary for most ML approaches poses a severe
obstacle for broader application, especially in the context of inelasticity.
That is why we propose an inverse-design approach based on Bayesian
optimization to operate in the small-data regime. Necessitating substantially
less data, a small initial data set is iteratively augmented by in silico
generated data until a structure with the targeted properties is found. The
application to the inverse design of spinodoid structures of desired elastic
properties demonstrates the framework's potential for paving the way for
advance in inverse design
Development of a measure of receptivity to instructional feedback and examination of its links to personality
CRediT authorship contribution statement: Anastasiya A. Lipnevich: Conceptualization, Methodology, Writing - original draft. Kalina Gjicali: Formal analysis, Writing - review & editing. Mustafa Asil: Formal analysis. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Peer reviewedPostprin
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