Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Downregulation of the Metabotropic Glutamate Receptor Type 5 Dampens the Reactive and Neurotoxic Phenotype of Adult ALS Astrocytes

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive degeneration of motor neurons (MNs). Astrocytes display a toxic phenotype in ALS, which results in MN damage. Glutamate (Glu)-mediated excitotoxicity and group I metabotropic glutamate receptors (mGluRs) play a pathological role in the disease progression. We previously demonstrated that in vivo genetic ablation or pharmacological modulation of mGluR5 reduced astrocyte activation and MN death, prolonged survival and ameliorated the clinical progression in the SOD1G93A mouse model of ALS. This study aimed to investigate in vitro the effects of mGluR5 downregulation on the reactive spinal cord astrocytes cultured from adult late symptomatic SOD1G93A mice. We observed that mGluR5 downregulation in SOD1G93A astrocytes diminished the cytosolic Ca2+ overload under resting conditions and after mGluR5 simulation and reduced the expression of the reactive glial markers GFAP, S100β and vimentin. In vitro exposure to an anti-mGluR5 antisense oligonucleotide or to the negative allosteric modulator CTEP also ameliorated the altered reactive astrocyte phenotype. Downregulating mGluR5 in SOD1G93A mice reduced the synthesis and release of the pro-inflammatory cytokines IL-1β, IL-6 and TNF-α and ameliorated the cellular bioenergetic profile by improving the diminished oxygen consumption and ATP synthesis and by lowering the excessive lactate dehydrogenase activity. Most relevantly, mGluR5 downregulation hampered the neurotoxicity of SOD1G93A astrocytes co-cultured with spinal cord MNs. We conclude that selective reduction in mGluR5 expression in SOD1G93A astrocytes positively modulates the astrocyte reactive phenotype and neurotoxicity towards MNs, further supporting mGluR5 as a promising therapeutic target in ALS.
Evaluation of the Relationship between Osteoporosis Parameters in Plain Hip Radiography and DXA Results in 156 Patients at a Single Center in Turkey

Background: The aim of the current study is to determine the relationship between osteoporosis findings in plain X-ray and dual-energy X-ray absorptiometry (DXA) measurement results and to create an alternative diagnostic method for osteoporosis without DXA measurement when necessary. Methods: DXA values and hip radiographs of 156 patients were retrospectively analyzed. Singh index (SI), Dorr index (DI), cortical thickness index (CTI), and canal-to-calcar ratio (CCR) measurements from both plain hip radiographs were determined by two observers. The correlation of the DXA parameters (hip total T-score, femoral neck T-score, hip total Z-score, hip total bone mineral density [BMD], and femoral neck BMD) and osteoporosis markers on plain hip radiography (SI, DI, CTI, and CCR) was calculated. In addition, patients were evaluated by dividing them into three groups according to the level of their T-scores (normal, osteopenia, and osteoporosis). In addition, cut-off values were calculated for CTI and CCR. Results: The mean age was 68.27 ± 8.27 (50–85) years. There was a strong correlation between hip total T-score values and SI, DI, and CTI (r = 0.683, −0.667, and 0.632, respectively), and a moderate correlation (r = −0.495) with CCR. When both hips were compared, there were strong correlations between radiographic parameters (r = 0.942 for SI, 0.858 for DI, 0.957 for CTI, and 0.938 for CCR, all with p < 0.001). When patients divided into three groups according to the T-score level were compared in terms of SI, DI, CTI, and CCR, it was found to be directly related to osteoporosis level (all p < 0.001). In the differentiation of osteopenia and osteoporosis, the cut-off values were 50.4 degrees for CTI and 60.3 degrees for CCR. Conclusion: Good correlations between hip total T-score values and SI, DI, CTI, and CCR (r = 0.683, −0.667, 0.632, and −0.495, respectively) indicate that the presence of osteoporosis can be detected by hip radiography findings without DXA.
Multi-Walled Carbon Nanotube Array Modified Electrode with 3D Sensing Interface as Electrochemical DNA Biosensor for Multidrug-Resistant Gene Detection

Drug resistance in cancer is associated with overexpression of the multidrug resistance (MDR1) gene, leading to the failure of cancer chemotherapy treatment. Therefore, the establishment of an effective method for the detection of the MDR1 gene is extremely crucial in cancer clinical therapy. Here, we report a novel DNA biosensor based on an aligned multi-walled carbon nanotube (MWCNT) array modified electrode with 3D nanostructure for the determination of the MDR1 gene. The microstructure of the modified electrode was observed by an atomic force microscope (AFM), which demonstrated that the electrode interface was arranged in orderly needle-shaped protrusion arrays. The electrochemical properties of the biosensor were characterized by cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). Chronocoulometry (CC) was used for the quantitative detection of the MDR1 gene. Taking advantage of the good conductivity and large electrode area of the MWCNT arrays, this electrochemical DNA sensor achieved a dynamic range from 1.0 × 10−12 M to 1.0 × 10−8 M with a minimal detection limit of 6.4 × 10−13 M. In addition, this proposed DNA biosensor exhibited high sensitivity, selectivity, and stability, which may be useful for the trace analysis of the MDR1 gene in complex samples.
Effect of Number of Lanes on Traffic Characteristics of Reinforcement Learning Based Autonomous Driving

Traffic characteristics such as signalized intersections and high vehicle density combined with low vehicle speeds account for 12-55% increase in commute time. Increasing the number of lanes in the highway infrastructure can help increase the highway capacity and consequently reduce the associated commute delay. However, due to human-related features such as driver behavior and vehicle interaction as well as induced demand, it is recommended to limit the number of highway lanes to four. Recently, in view of the rising discussion regarding the deployment of Autonomous Vehicles (AVs), it is important to study the effect of the number of highway lanes on traffic characteristics with respect to AVs. This will provide insights into the full potential of AVs in terms of reducing commute time as well as provide crucial insights into the design of future road networks. Therefore, in this study, RL-based AV frameworks are developed to investigate the effect of the number of highway lanes on traffic characteristics. Specifically, we study the effect of the number of lanes on traffic characteristics in terms of travel speed, collision, and driving on the right-most lane. Results with two well-known RL-based AV frameworks simulated on highways with lanes ranging from 2 to 8 and increasing vehicle count respectively show improved traffic characteristics as the number of lanes increases. However, improvement depends on the RL algorithm employed.
Land Cover Classification Assessment Using Decision Trees and Maximum Likelihood Classification Algorithms on Landsat 8 Data

Classification technique on remote sensing images is an effort taken to identify the class of each pixel based on the spectral characteristics of various channels. Traditional classifications such as Maximum Likelihood are based on statistical parameters such as standard deviation and mean, which have a probability model of each pixel in each class. While the object-based classification method, one of which is the Decision Trees, is based on rules for each class with mathematical functions. This study compares the Decision Trees and Maximum Likelihood algorithms for land cover classification in the Surabaya and Bangkalan areas using Landsat 8 data. This research begins with creating Regions of Interest (ROIs) and Rules on images with greater than and less than functions for Decision Trees. The ROIs test was carried out using the Separability Index and matching each class using the Confusion Matrix. The experimental results show that the accuracy value resulting from the Confusion Matrix calculation is 90.48%, with a Kappa Coefficient Value of 0.87. The Decision Trees method produces land cover nigher to the actual condition than the Maximum Likelihood method. The difference in the class distribution of the two ways is not significant. This study is limited because the validation uses manual interpretation results. Future research is expected to use the large-scale classification results from the relevant agencies to verify the classification results and use field data, larger samples of ROIs, and the use of high-resolution imagery in order to improve the classification results.
Bioactive Compounds and Antioxidant Activity in Seeds of Bred Lines of Common Bean Developed from Interspecific Crosses

Knowledge is limited about the level of bioactive compounds and antioxidant activity of seeds from bred lines of common beans developed from interspecific crosses using four different Phaseolus species (P. vulgaris L., P. coccineus L., P. acutifolius A. Gray. Gray., and P. dumosus). In this study, differences in the nutritional quality of seeds among 112 bean genotypes were evaluated by measuring the levels of phenolic compounds, pigments, antioxidant activity, and sugars. The bean genotypes were grown under high temperatures and acid soil conditions in the Amazon region of Colombia. Five typology groups of bean genotypes were identified based on the level of bioactive compounds and their functional capacity: (1) highly bioactive and functional (HBF); (2) moderately bioactive and functional (MBF); (3) moderate antioxidant content with pigment influence (MACP); (4) moderately antinutritional with limited antioxidant potential (MALAP); and (5) antinutritional, low bioactive, and functional (ALBF). We developed a nutritional quality index (NQI) with values ranging from 0 to 1 based on the nutritional and anti-nutritional balance of each genotype and the higher values of the NQI of a genotype indicating greater nutritional quality. We found three interspecific bred lines (SER 212, SER 213, and RRA 81), with NQI values higher than 0.8. These three lines belong to the typology group of HBF. The superior nutritional quality of these three interspecific bred lines is attributed to a greater level of bioactive compounds and antioxidant capacity. These three bred lines may serve as useful parents to develop nutritionally superior and stress-resilient beans from bean breeding programs. Further research is needed to explore the role of testa color in improving the nutritional quality of seeds of common bean genotypes grown under different climatic conditions.
Drag force on a liquid domain moving inside a membrane sheet surrounded by aqueous medium

We compute the drag on a circular and liquid microdomain diffusing in a two-dimensional fluid lipid bilayer membrane surrounded by a fluid above and below. Under the assumptions that the liquids are incompressible and the flow is of low Reynolds number, Stokes’ equations describe the flow in the two-dimensional membrane as well as in the surrounding three-dimensional fluid. The expression for the drag force on the liquid domain involves Fredholm integral equations of the second kind, which we numerically solve using discrete collocation method based on Chebyshev polynomials. We observe that when the domain is more viscous than the surrounding membrane (including the rigid domain case), the drag force is almost independent of the viscosity contrast between the domain and the surrounding membrane, as also observed earlier in experiments by other researchers. The mobility also varies logarithmically with Boussinesq number ${\it\beta}$ for large ${\it\beta}$ . On the other hand, for a less viscous domain the dimensionless drag force reduces with increasing viscosity contrast, and a significant change in the drag force, from that when there is no viscosity contrast or when the domain is rigid, has been observed. Further, the logarithmic behaviour of the mobility no longer holds for less viscous domains. Our method of computing the drag force and diffusion coefficient is valid for arbitrary viscosity contrast between the domain and membrane and any domain size (subject to ${\it\beta}\geqslant 5$ ).
Arabidopsis RAD16 Homologues Are Involved in UV Tolerance and Growth

In plants, prolonged exposure to ultraviolet (UV) radiation causes harmful DNA lesions. Nucleotide excision repair (NER) is an important DNA repair mechanism that operates via two pathways: transcription coupled repair (TC-NER) and global genomic repair (GG-NER). In plants and mammals, TC-NER is initiated by the Cockayne Syndrome A and B (CSA/CSB) complex, whereas GG-NER is initiated by the Damaged DNA Binding protein 1/2 (DDB1/2) complex. In the yeast Saccharomyces cerevisiae (S. cerevisiae), GG-NER is initiated by the Radiation Sensitive 7 and 16, (RAD7/16) complex. Arabidopsis thaliana has two homologues of yeast RAD16, At1g05120 and At1g02670, which we named AtRAD16 and AtRAD16b, respectively. In this study, we characterized the roles of AtRAD16 and AtRAD16b. Arabidopsis rad16 and rad16b null mutants exhibited increased UV sensitivity. Moreover, AtRAD16 overexpression increased plant UV tolerance. Thus, AtRAD16 and AtRAD16b contribute to plant UV tolerance and growth. Additionally, we found physical interaction between AtRAD16 and AtRAD7. Thus, the Arabidopsis RAD7/16 complex is functional in plant NER. Furthermore, AtRAD16 makes a significant contribution to Arabidopsis UV tolerance compared to the DDB1/2 and the CSB pathways. This is the first time the role and interaction of DDB1/2, RAD7/16, and CSA/CSB components in a single system have been studied.
A Complete Guide to Extraction Methods of Microplastics from Complex Environmental Matrices

Sustainable development is a big global challenge for the 21st century. In recent years, a class of emerging contaminants known as microplastics (MPs) has been identified as a significant pollutant with the potential to harm ecosystems. These small plastic particles have been found in every compartment of the planet, with aquatic habitats serving as the ultimate sink. The challenge to extract MPs from different environmental matrices is a tangible and imperative issue. One of the primary specialties of research in environmental chemistry is the development of simple, rapid, low-cost, sensitive, and selective analytical methods for the extraction and identification of MPs in the environment. The present review describes the developments in MP extraction methods from complex environmental matrices. All existing methodologies (new, old, and proof-of-concept) are discussed and evaluated for their potential usefulness to extract MPs from various biotic and abiotic matrices for the sake of progress and innovation. This study concludes by addressing the current challenges and outlining future research objectives aimed at combating MP pollution. Additionally, a set of recommendations is provided to assist researchers in selecting appropriate analytical techniques for obtaining accurate results. To facilitate this process, a proposed roadmap for MP extraction is presented, considering the specific environmental compartments under investigation. By following this roadmap, researchers can enhance their understanding of MP pollution and contribute to effective mitigation strategies.
Learning Rapid-Temporal Adaptations

A hallmark of human intelligence and cognition is its flexibility. One of the long-standing goals in AI research is to replicate this flexibility in a learning machine. In this work we describe a mechanism by which artificial neural networks can learn rapid-temporal adaptation - the ability to adapt quickly to new environments or tasks - that we call adaptive neurons. Adaptive neurons modify their activations with task-specific values retrieved from a working memory. On standard metalearning and few-shot learning benchmarks in both vision and language domains, models augmented with adaptive neurons achieve state-of-the-art results.
Three-Dimensional Cell Cultures: The Bridge between In Vitro and In Vivo Models

Although historically, the traditional bidimensional in vitro cell system has been widely used in research, providing much fundamental information regarding cellular functions and signaling pathways as well as nuclear activities, the simplicity of this system does not fully reflect the heterogeneity and complexity of the in vivo systems. From this arises the need to use animals for experimental research and in vivo testing. Nevertheless, animal use in experimentation presents various aspects of complexity, such as ethical issues, which led Russell and Burch in 1959 to formulate the 3R (Replacement, Reduction, and Refinement) principle, underlying the urgent need to introduce non-animal-based methods in research. Considering this, three-dimensional (3D) models emerged in the scientific community as a bridge between in vitro and in vivo models, allowing for the achievement of cell differentiation and complexity while avoiding the use of animals in experimental research. The purpose of this review is to provide a general overview of the most common methods to establish 3D cell culture and to discuss their promising applications. Three-dimensional cell cultures have been employed as models to study both organ physiology and diseases; moreover, they represent a valuable tool for studying many aspects of cancer. Finally, the possibility of using 3D models for drug screening and regenerative medicine paves the way for the development of new therapeutic opportunities for many diseases.
Service quality affecting student satisfaction in higher education institutions in Ghana

Abstract Higher education stakeholders have re-strategized to identify the unique competitive causes of comfortability among students’ satisfaction. Student satisfaction has been largely observed in the literature as a significant strategy and competitive factor for higher education providers. This has compelled institutions to implement quality measures to attract and retain both potential and current students. Therefore, this research is to explore the determinants of student satisfaction. Cross-sectional data was generated from a purposive sample of 400 students across higher education institutions in Ghana. The data was analysed using multiple regression. Expectation theory is the theory that unpins this study. The results reveal that administrative services, academic services, and physical evidence are significant components of service quality. The outcomes of the study show there is a positive and significant relationship between satisfaction, academic services, and administrative services; physical evidence influence student satisfaction.
Scalable Hamiltonian Monte Carlo via Surrogate Methods

Author(s): Zhang, Cheng | Advisor(s): Zhao, Hongkai | Abstract: Markov chain Monte Carlo (MCMC) methods have been widely used in Bayesian inference involving intractable probabilistic models. However, simple MCMC algorithms (e.g., random walk Metropolis and Gibbs sampling) are notorious for their lack of computational efficiency in complex, high-dimensional models and poor scaling to large data sets. In recent years, many advanced MCMC methods (e.g., Hamiltonian Monte Carlo and Riemannian Manifold Hamiltonian Monte Carlo) have been proposed that utilize geometrical and statistical quantities from the model in order to explore the target distribution more effectively. The gain in the efficacy of exploration, however, often comes at a significant computational cost which hinders their application to problems with large data sets or complex likelihoods. In practice, it remains challenging to design scalable MCMC algorithms that can balance computational complexity and exploration efficacy well. To address this issue, some recent algorithms rely on stochastic gradient methods by approximating full data gradients using mini-batches of data. In contrast, this thesis focuses on accelerating the computation of MCMC samplers based on various surrogate methods via exploring the regularity of the target distribution.We start with a precomputing strategy that can be used to build efficient surrogates in relatively low-dimension parameter spaces. We then propose a random network surrogate architecture which can effectively capture the collective properties of large data sets or complex models with scalability, flexibility and efficiency. Finally, we provide a variational perspective for our random network surrogate methods and propose an approximate inference framework that combines the advantages of both variational Bayes and Markov chain Monte Carlo methods. The properties and efficiency of our proposed methods are demonstrated on a variety of synthetic and real-world data problems.
Case influence diagnostics for the significance of the linear regression model

In this paper we propose inﬂuence measures for two basic goodness-of-ﬁt statistics, the coe ﬃ cient of determination R 2 and test statistic F in the linear regression model using the deletion method. Some useful lemmas are provided. We also express the inﬂuence measures in terms of basic building blocks such as residual, leverage, and deviation that showed them as increasing function of residuals and a decreasing function of deviation. Further, the proposed measure reduces computational burden from O ( n ) to O (1). As illustrative examples, we applied the proposed measures to the stackloss data sets. We veriﬁed that deletion of one or few inﬂuential observations may result in big change in R 2 and F -statistic.
A MIMO approach for longitudinal sensing and control noise projections of Advanced Virgo gravitational wave detector

So far, the sensitivity of gravitational-wave (GW) detectors, in the low-frequency and mid-frequency regions of its bandwidth, has been limited by technical noises. The re-injection of sensing and control noises can be one of the main limitations. After the end of the third observing run O3, in preparation for the fourth observing run O4, an upgrade phase started among all the km-scale GW detectors, namely LIGO, Virgo and KAGRA, with the aim of improving their sensitivity. In particular, for the case of Advanced Virgo, one of the most significant upgrades is the installation of a signal recycling (SR) mirror, introducing the SR cavity. The main target of this SR mirror is to shape the sensitivity curve of the detector. The installation of a SR mirror adds an extra optical cavity and, thus, extra DoFs (longitudinal and angular), that should be controlled to keep its working point, ultimately increasing the complexity of the whole control strategy. In order to have an accurate description of the interferometer, we have implemented a multiple-input multiple-output (MIMO) model in the frequency domain. The target of this paper, after showing the Advanced Virgo configuration for the next observing run, is to describe the control scheme used for the main longitudinal degrees of freedom using a MIMO approach. In particular, we detail a useful matrix representation for the modeled system. Finally, we use the implemented model to project the sensing and control noises on the sensitivity curve. Following the obtained results, we propose noise subtraction filters to achieve the low control noise target in the low-frequency region of the sensitivity curve. Additionally, using this model, we have implemented the core of a noise budget tool, which will allow to estimate the contribution of all the known sources of noise on the measured sensitivity.