Miracle-dz/UniFAR-FTUs huggingface dataset:
Restless Legs Syndrome in Parkinson’s Disease

Background: Restless legs syndrome (RLS) might worsen sleep quality and quality of life in people with Parkinson’s disease (PwPD). Objective: The main aim of the present study is to explore the associations between RLS and sleep, quality of life and other non-motor symptoms (NMS) in a sample of PwPD. Methods: We compared the clinical features of 131 PwPD with and without RLS, in a cross-sectional study. We used several validated scales for assessment: the International Restless Legs Syndrome Study Group rating scale (IRLS), Parkinson’s Disease Sleep Scale version 2 (PDSS-2), Parkinson’s Disease Questionnaire (PDQ-39), Non-Motor Symptoms Questionnaire (NMSQ) and International Parkinson and Movement Disorder Society Non-Motor Rating Scale (MDS-NMS). Results: Thirty-five patients (26.71%) out of the total PwPD met the RLS diagnostic criteria, without significant differences between male (57.14%) and female (42.87%) (p = 0.431). Higher total scores of PDSS-2 were recorded among PwPD + RLS (p < 0.001), suggesting worse sleep quality. Significant correlations were observed between the diagnosis of RLS and some types of pain (especially nocturnal pain), physical fatigue and probable sleep-disordered breathing, according to the MDS-NMSS assessment. Conclusions: RLS has a high frequency in PwPD and it requires proper management, considering its consequences on sleep and quality of life.
Electron impact cross-sections and rate coefficients of Si+2 ions relevant to collisional radiative modeling of silicon plasma

We report on the fine structure resolved electron impact excitation (EIE) cross-sections of Si+2 from its ground state 3s 2 (J= 0) to the 41 excited fine structure levels of the configurations 3s3p, 3p 2, 3s3d, 3s4s, 3s4p, 3s5s, 3s4d, 3s4f, 3s5p, 3s5d and 3s5f using relativistic distorted wave theory. The excitation cross-sections from the excited metastable levels (3P0,3P2) of the configuration 3s3p to higher excited levels as well as for some other dominant transitions relevant to plasma modeling are also obtained. In addition, the ionization cross-sections are evaluated from the ground and metastable levels to higher ionized state Si+3 (2S1/2). The calculated cross-sections are utilized to obtain the rate coefficients corresponding to EIE and ionization processes affecting the intensity of prominent Si+2 emission lines 379.61, 380.65, 456.78, and 457.48 nm recorded through optical emission spectroscopic measurements by Wang et al (2020 Phys. Plasmas 27 063513) on laser produced silicon plasma. Further, the rate coefficients corresponding to radiative, and three body recombination are also presented. The reported cross-sections and rate coefficients will be useful for developing rigorous collisional radiative model for the diagnostics of silicon plasma.
Maternal Embryonic Leucine Zipper Kinase is Associated with Metastasis in Triple-negative Breast Cancer

Abstract Triple-negative breast cancer (TNBC) has high relapse and metastasis rates and a high proportion of cancer stem-like cells (CSC), which possess self-renewal and tumor initiation capacity. MELK (maternal embryonic leucine zipper kinase), a protein kinase of the Snf1/AMPK kinase family, is known to promote CSC maintenance and malignant transformation. However, the role of MELK in TNBC metastasis is unknown; we sought to address this in the current study. We found that MELK mRNA levels were higher in TNBC tumors [8.11 (3.79–10.95)] than in HR+HER2− tumors [6.54 (2.90–9.26)]; P < 0.001]. In univariate analysis, patients with breast cancer with high-MELK–expressing tumors had worse overall survival (P < 0.001) and distant metastasis-free survival (P < 0.01) than patients with low-MELK–expressing tumors. In a multicovariate Cox regression model, high MELK expression was associated with shorter overall survival after adjusting for other baseline risk factors. MELK knockdown using siRNA or MELK inhibition using the MELK inhibitor MELK-In-17 significantly reduced invasiveness, reversed epithelial-to-mesenchymal transition, and reduced CSC self-renewal and maintenance in TNBC cells. Nude mice injected with CRISPR MELK-knockout MDA-MB-231 cells exhibited suppression of lung metastasis and improved overall survival compared with mice injected with control cells (P < 0.05). Furthermore, MELK-In-17 suppressed 4T1 tumor growth in syngeneic BALB/c mice (P < 0.001). Our findings indicate that MELK supports metastasis by promoting epithelial-to-mesenchymal transition and the CSC phenotype in TNBC. Significance: These findings indicate that MELK is a driver of aggressiveness and metastasis in TNBC.
AlphaFold2-based structure prediction and target study of PD-L1 protein

PD-L1 is an immune protein in human body that can play an important role in cancer immunotherapy. By binding to antibodies, the binding activity of PD-L1 and PD-1 is blocked, which in turn inhibits cancer cells. Thus the structure of PD-L1 is very important in studying the binding of antibodies to it. However, experimental methods to solve the structures of PD- L1 and numerous complexes are expensive and consuming. Thus, it is essential to exploit computational methods to help biologists figure out the structures and the underlying mechanisms. In this paper, we explore whether AlphaFold2 is able to accurately predict the structure of PD-L1 and whether we can use AlphaFold2 to capture the binding sites of PD-L1 when binding to different antibodies. Our results show that AlphaFold2 has high confident scores and accuracy in predicting the structure of PD-L1 and the binding sites with atezolizumab and durvalumab. For the interaction between PD-L1 and the antibodies, AlphaFold2 can capture most of the hydrogen bonds as well as the salt bridges. Our work suggests that AlphaFold2 can not only be used as a tool to predict the structure of proteins, but also serves as a useful tool for antibody discovery, e.g. providing high-quality predicted structures for downstreaming docking, which brings new hope for drug discovery.
Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing

The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance.
Same concerns, same responses? A Bayesian quantile regression analysis of the determinants for supporting nuclear power generation in Japan

Using the Internet survey data from 6500 individuals, this study examines the determinants for supporting the restart of nuclear power plants operation in Japan. The variable of interest is the level of support that is measured as a categorical and ordered variable, for which ordered logit or probit is commonly estimated. This study departs from the literature using Bayesian ordinal quantile regression (Rahman 2015, Bayesian Anal. doi:10.1214/15-BA939) to address whether covariates have differential effects at various conditional quantiles of the latent response variable. This approach allows us to explore, for example, whether three otherwise identical individuals, the first with an average unobserved preference for the restart, the second with a low unobserved preference, and the third with a high unobserved preference, respond similarly or differently to a change in a covariate. The results show that for most of the covariates examined, including concerns about meltdowns and concerns about global warming, the effects differ across conditional quantiles of the latent response variable. In other words, the covariate effects depend crucially on individuals’ unobserved preferences for the restart (conditional on observables). The results also show that there are considerable gender differences in response to changes in covariates.
Recent Advances and Challenges in Long Wavelength Sensitive Cationic Photoinitiating Systems

With the advantages offered by cationic photopolymerization (CP) such as broad wavelength activation, tolerance to oxygen, low shrinkage and the possibility of “dark cure”, it has attracted extensive attention in photoresist, deep curing and other fields in recent years. The applied photoinitiating systems (PIS) play a crucial role as they can affect the speed and type of the polymerization and properties of the materials formed. In the past few decades, much effort has been invested into developing cationic photoinitiating systems (CPISs) that can be activated at long wavelengths and overcome technical problems and challenges faced. In this article, the latest developments in the long wavelength sensitive CPIS under ultraviolet (UV)/visible light-emitting diodes (LED) lights are reviewed. The objective is, furthermore, to show differences as well as parallels between different PIS and future perspectives.
Disturbances produced by an electric potential probe on MHD flows in rectangular ducts

Experimental data for local velocity in liquid metal duct flows exposed to an external magnetic field can be obtained from measurements of electric potential differences recorded by probes that are moved along the channel width. These instruments, known as conduction anernometers or Liquid-metal Electromagnetic Velocity Instruments (LEVI), have been used in the past preferentially for investigating almost fully developed magnetohydrodynamic (MHD) flows in poorly conducting ducts and flows with smooth variations along the channel axis. For such applications, where electric current density is negligible, the probe was assumed to give reliable results and the potential gradient signal was directly interpreted as a velocity measure. If the flow varies along its path on very short length scales, like in ducts with abrupt change of cross section or in manifolds, non-negligible 3D electric currents occur so that the LEVI readings may become inaccurate. Moreover, the presence of the probe itself may perturb significantly the flow field due to the formation of internal layers that develop along magnetic field lines tangential to the shaft and the insulating body of the instrument. Experiments showed an asymmetry in the distribution of the measured transverse potential gradient and its underestimation compared with the one expected from flow rate measurements and from theoretical predictions for fully developed MHD flows. A numerical analysis of MHD flows around a probe inserted in a rectangular duct has been performed to support the physical interpretation of potential measurements and to study and quantify the influence of the instrument itself on the readings. A calibration procedure is suggested, which allows using measurements of potential differences to get reliable data for velocities in the duct.
Automated systems for monitoring microclimate parameters and electricity metering of an experimental building

The Arctic is a strategically important region for Russia. In the process of searching for new solutions for the rapid construction of energy-efficient and safe buildings in the Arctic, it is necessary to develop systems for monitoring and maintaining indoor microclimate, ensuring optimal use of resources and implementing building management systems. Continuous automatic monitoring will help to increase comfort and safety in the room, as well as reduce energy consumption. The possibility of remote data transmission will allow you to quickly respond to emergencies and take actions to prevent the development of accidents. The article presents the results of the development and testing of a software and hardware complex based on a model object on the territory of the Murmansk State Technical University. The development and research of systems for automated monitoring of microclimate parameters and electricity metering of a model object in Murmansk has been carried out. The results of the study of the parameters of the deviation of temperature indicators are presented. The parameters of the deviation of the indicators of the humidity control system from the sensors are determined. A comparative analysis of the total electricity consumption of the model object for the main consumers of the log part and the frame part was carried out.
Improving semantic coverage of data-to-text generation model using dynamic memory networks

This paper proposes a sequence-to-sequence model for data-to-text generation, called DM-NLG, to generate a natural language text from structured nonlinguistic input. Specifically, by adding a dynamic memory module to the attention-based sequence-to-sequence model, it can store the information that leads to generate previous output words and use it to generate the next word. In this way, the decoder part of the model is aware of all previous decisions, and as a result, the generation of duplicate words or incomplete semantic concepts is prevented. To improve the generated sentences quality by the DM-NLG decoder, a postprocessing step is performed using the pretrained language models. To prove the effectiveness of the DM-NLG model, we performed experiments on five different datasets and observed that our proposed model is able to reduce the slot error rate rate by 50% and improve the BLEU by 10%, compared to the state-of-the-art models.
Analysis of Errors and Misconceptions in the Learning of Calculus by Undergraduate Students.

This paper is going to analyse errors and misconceptions in an undergraduate course in Calculus. The study will be based on a group of 10 BEd. Mathematics students at Great Zimbabwe University. Data is gathered through use of two exercises on Calculus 1&2.The analysis of the results from the tests showed that a majority of the errors were due to knowledge gaps in basic algebra. We also noted that errors and misconceptions in calculus were related to learners' lack of advanced mathematical thinking since concepts in calculus are intertwined. Also in this study we highlight some common errors/ mistakes which can be done by lecturers during the teaching process.Students studying calculus often make the same mistakes and similarly lecturers teaching calculus have patterns of mistakes. This paper is derived from practical situations hence it is open to updating and can be adapted by other calculus teachers in different setups.
The influence of RBF differentiation on the accuracy of the alternative LBIE method for solving the Navier-Stokes equations

In this paper, the local boundary integral equation (LBIE) method employing the simplified approach for imposing the boundary conditions is used to solve the lid driven cavity problem described by the velocity-vorticity formulations of the Navier-Stokes equations. The objective of this study is to investigate how the differentiation of the radial basis functions carried out to approximate the spatial gradients in the simplified approach affects the accuracy of the numerical solutions in problems where knowledge of the spatial gradients is of significance. The numerical results were compared with benchmark solutions and with those obtained using the radial basis integral equation method; an integral equation based meshless method that does not involve the differentiation of the RBF to approximate the spatial gradients.
Emerging Frontiers in Nanotechnology for Precision Agriculture: Advancements, Hurdles and Prospects

This review article provides an extensive overview of the emerging frontiers of nanotechnology in precision agriculture, highlighting recent advancements, hurdles, and prospects. The benefits of nanotechnology in this field include the development of advanced nanomaterials for enhanced seed germination and micronutrient supply, along with the alleviation of biotic and abiotic stress. Further, nanotechnology-based fertilizers and pesticides can be delivered in lower dosages, which reduces environmental impacts and human health hazards. Another significant advantage lies in introducing cutting-edge nanodiagnostic systems and nanobiosensors that monitor soil quality parameters, plant diseases, and stress, all of which are critical for precision agriculture. Additionally, this technology has demonstrated potential in reducing agro-waste, synthesizing high-value products, and using methods and devices for tagging, monitoring, and tracking agroproducts. Alongside these developments, cloud computing and smartphone-based biosensors have emerged as crucial data collection and analysis tools. Finally, this review delves into the economic, legal, social, and risk implications of nanotechnology in agriculture, which must be thoroughly examined for the technology’s widespread adoption.
An extended lifetime measure for telecommunication network

A new measure for network performance evaluation called topology lifetime was introduced in [4, 5]. This measure is based on the notion of unexpected traffic growth and can be used for comparison of topologies. We discuss some advantages and disadvantages of the approach of [4] and suggest some modifications to this approach. In particular we discuss how to evaluate the influence of a subgraph to the lifetime measure and introduce the notion of the order of a path. This notion is useful if we consider a possible extension to the set of working paths in order to support the traffic for the time that is needed for installation of new facilities.
A Survey on Image Steganography and Steganalysis

Steganography and steganalysis are important topics in information hiding. Steganography refers to the technology of hiding data into digital media without drawing any suspicion, while steganalysis is the art of detecting the presence of steganography. This paper provides a survey on steganography and steganalysis for digital images, mainly covering the fundamental concepts, the progress of steganographic methods for images in spatial representation and in JPEG format, and the development of the corresponding steganalytic schemes. Some commonly used strategies for improving steganographic se- curity and enhancing steganalytic capability are summarized and possible research trends are discussed.
Multiple RNA Profiling Reveal Epigenetic Toxicity Effects of Oxidative Stress by Graphene Oxide Silver Nanoparticles in-vitro

Introduction The increasing industrial and biomedical utilization of graphene oxide silver nanoparticles (GO-AgNPs) raises the concern of nanosafety: exposure to the AgNPs or GO-AgNPs increases the generation of reactive oxygen species (ROS), causes DNA damage and alters the expression of whole transcriptome including mRNA, miRNA, tRNA, lncRNA, circRNA and others. Although the roles of different RNAs in epigenetic toxicity are being studied during the last decade, but still we have little knowledge about the role of circle RNAs (circRNAs) in epigenetic toxicity. Methods Rabbit fetal fibroblast cells (RFFCs) were treated with 0, 8, 16, 24, 32 and 48 μg/mL GO-AgNPs to test the cell viability and 24 μg/mL GO-AgNPs was selected as the experimental dose. After 24 h treatment with 24 μg/mL GO-AgNPs, the level of ROS, malondialdehyde (MDA), superoxide dismutase (SOD), intracellular ATP, glutathione peroxidase (GPx), and glutathione reductase (Gr) were measured in the RFFCs. High-throughput whole transcriptome sequencing was performed to compare the expression of circRNAs, long non-coding RNAs (lncRNA) and mRNA between 24 μg/mL GO-AgNPs-treated RFFCs and control cells. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis was performed to validate the accuracy of circRNA sequencing data. Bioinformatics analyses were performed to reveal the potential functional roles and related pathways of differentially expressed circRNAs, lncRNA and mRNA and to construct a circRNA–miRNA–mRNA interaction network. Results We found that 57 circRNAs, 75 lncRNAs, and 444 mRNAs were upregulated while 35 circRNAs, 21 lncRNAs, and 186 mRNAs were downregulated. These differentially expressed genes are mainly involved in the transcriptional mis-regulation of cancer through several pathways: MAPK signaling pathway (circRNAs), non-homologous end-joining (lncRNAs), as well as PPAR and TGF-beta signaling pathways (mRNAs). Conclusion These data revealed the potential roles of circRNAs in the GO-AgNPs induced toxicity through oxidative damage, which would be the basis for further research to determine their roles in the regulation of different biological processes.
Research on magnetic encoder based on temperature compensation

As a type of sensor intended for the real-time feedback and dynamic monitoring of angular velocity and angular displacement, encoder is widely used in electric vehicles, servo closed-loop control systems, household appliances, cameras and many other fields. In order to reduce the temperature drift caused by ambient temperature to the magnetic encoder, a self-tuning temperature compensation algorithm is proposed in this paper based on the calibration principle. The rationale of this algorithm is that error function is designed to calculate the error at different temperature and room temperature, and the partial derivative is calculated to update the weight coefficient. Finally, the least square model is applied to establish the relationship between Hall output amplitude and temperature coefficient, thus correcting the temperature drift of the encoder. According to the experimental results, the self-tuning temperature compensation algorithm is applicable to compensate for and correct the temperature drift caused by the amplitude of Hall signal at different ambient temperatures. The ultimate accuracy of temperature drift is improved by 0.2° for the encoder.
Temperature–air velocity association, experimental and modeling study of stevia leaves solar drying

Sugar is a natural high-calorie sweetener. Its excessive consumption is associated with health problems such as obesity, diabetes, heart disease, and degenerative issues. The stevia plant is a great natural substitute. It provides no calories and has medicinal properties such as oral antibacterial, hypoglycemic, and anti-hypertensive with a high nutritional value. In this work, the effects of temperature and air velocity in drying kinetics of stevia leaves were studied using a convective drier operating with air velocities (2, 3, and 4 m/s) and temperatures controlled (45 °C, 55 °C, and 65 °C). The highest drying rate was obtained at 65 °C and 4 m/s air velocity at 0.05 kg water/kg dry matter min. The experimental data were fitted to theoretical drying models to determine the best approach. It is found that the Page model gives a good fit for all experiments, with correlation coefficients (R2) > 0.9994. The equations to correlate this model's variables with air and temperature were determined. The results show that ΔE and glucoside values increase when temperature and air velocity increase.