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

FEANN{}^\textbf{ANN} −- 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${}^\text{ANN}$ 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

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

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

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

Institutionalizing Undergraduate Research - 2012 Progress Report

The goal of the Undergraduate Research Initiative is to make undergraduate research a priority at Georgia College and a key element of its culture. This 2012 report was crafted by the Undergraduate Research Initiative (URI) Committee. The URI committee was charged in 2010 to study, investigate, and implement practices and policies that lead to institutionalized best practices in faculty-student collaborations through undergraduate research and creative activity. The successes, challenges, opportunities and recommendations highlighted herein are faculty-driven and faculty-led. They respond to the critical need to bring attention to undergraduate research as a high impact pedagogy that has the potential to transform the intellectual climate of Georgia College