Miracle-dz/UniFAR-FTUs huggingface dataset:
Varied Responses to a High m.3243A>G Mutation Load and Respiratory Chain Dysfunction in Patient-Derived Cardiomyocytes

The m.3243A>G mutation in mitochondrial tRNA-Leu(UUR) is one of the most common pathogenic mitochondrial DNA mutations in humans. The clinical manifestations are highly heterogenous and the causes for the drastic clinical variability are unknown. Approximately one third of patients suffer from cardiac disease, which often increases mortality. Why only some patients develop cardiomyopathy is unknown. Here, we studied the molecular effects of a high m.3243A>G mutation load on cardiomyocyte functionality, using cells derived from induced pluripotent stem cells (iPSC-CM) of two different m.3243A>G patients, only one of them suffering from severe cardiomyopathy. While high mutation load impaired mitochondrial respiration in both patients’ iPSC-CMs, the downstream consequences varied. mtDNA mutant cells from a patient with no clinical heart disease showed increased glucose metabolism and retained cellular ATP levels, whereas cells from the cardiac disease patient showed reduced ATP levels. In this patient, the mutations also affected intracellular calcium signaling, while this was not true in the other patient’s cells. Our results reflect the clinical variability in mitochondrial disease patients and show that iPSC-CMs retain tissue specific features seen in patients.
Investigation on the causality relationship between environmental innovation and energy consumption: Empirical evidence from EU countries

Environmental innovations, investments, and expenses have been identified as an efficient and reliable way of addressing ecological issues. Nevertheless, how environmental innovations, investments, and expenses may influence the level of environmental pollution in European nations and whether the outcome may fluctuate among various environmental innovation indicators remain to be inspected. Therefore, this research is designed to empirically scrutinize the influence of environmental innovations, environmental investment, environmental expenditure, research and development (R&D) expenses, and foreign direct investment (FDI) on renewable and non-renewable energy in a sample of 15 European countries during the period from 2005 to 2018. To achieve this, we apply robust panel econometric estimation techniques. After testing the stationary property of the series, the findings of the Pedroni cointegration test disclose the presence of a long-run stable connection among the series. The empirical results from the ARDL, FMOLS and DOLS regression show that the impact of environmental innovations, environmental expenditure, R&D expenses, and FDI help to reduce overall non-renewable energy and promote renewable energy. In contrast, environmental investment significantly increases non-renewable energy and diminishes renewable energy usage. Moreover, the findings of Dumitrescu and Hurlin's tests discover a unidirectional causality running from non-renewables and renewables towards environmental investment, environmental expenditure, and FDI. Additionally, bi-directional causality is found between environmental innovations R&D, with both non-renewable and renewable energy utilization. Furthermore, we summarize by arguing that the efforts toward efficient and sustainable use of energy by reducing the combustion of non-renewable energy sources should support modern and innovative strategies by ensuring the transformation of non-renewables with renewable energy sources.
Effect of heavy metals on germination, biochemical, and L-DOPA content in Mucuna pruriens (L.) DC.

Mucuna pruriens (L.) DC. is a medicinal plant with a wide range of pharmacological properties that have been used in various medicinal preparations for centuries. M. pruriens is a rich source of levodopa (L-DOPA), mainly used to treat Parkinson’s disease. The present study investigates the impact of heavy metals such as cadmium (Cd), mercury (Hg), and lead (Pb) on the growth parameters and biochemical characteristics, including the L-DOPA content of M. pruriens . The seeds of M. pruriens were treated with different concentrations of Cd (0–250 ppm), Hg (0–250 ppm), and Pb (0–2000 ppm) for 21 days. On exposure to heavy metals, the germination %, the vegetative growth, and the biochemical characteristics such as the protein, carbohydrate, chlorophyll, total phenol, flavonoid, and proline content varied significantly in the heavy metal-treated plants when compared to control. It was also observed that the L-DOPA content increased with increased metal concentration and then decreased further with higher concentration of metals. The metal accumulation increased with the increase in the metal concentration. The seeds treated with 1000 ppm of Pb showed the highest L-DOPA content compared with control and other treatments.
The Role of Psychological Factors and Vaccine Conspiracy Beliefs in Influenza Vaccine Hesitancy and Uptake among Jordanian Healthcare Workers during the COVID-19 Pandemic

Vaccination to prevent influenza virus infection and to lessen its severity is recommended among healthcare workers (HCWs). Health professionals have a higher risk of exposure to viruses and could transmit the influenza virus to vulnerable patients who are prone to severe disease and mortality. The aim of the current study was to evaluate the levels of influenza vaccine acceptance and uptake as well as its determinants, among Jordanian HCWs over the last influenza season of 2021/2022. This study was based on a self-administered electronic survey that was distributed in March 2022. Psychological determinants of influenza vaccine acceptance and vaccine conspiracy beliefs were assessed using the previously validated 5C scale questionnaire (confidence, complacency, constraints, calculation and collective responsibility) and the vaccine conspiracy beliefs scale. The study sample comprised a total of 1218 HCWs: nurses (n = 412, 33.8%), physicians (n = 367, 30.1%), medical technicians (n = 182, 14.9%), pharmacists (n = 161, 13.2%) and dentists (n = 87, 7.1%), among others. About two-thirds of the study sample expressed willingness to receive influenza vaccination if provided free of charge (n = 807, 66.3%), whereas less than one-third were willing to pay for the vaccine (n = 388, 31.9%). The self-reported uptake of the influenza vaccine in the last influenza season was 62.8%. The following factors were significantly associated with higher acceptance of influenza vaccination if provided freely, as opposed to vaccine hesitancy/rejection: male sex; physicians and dentists among HCW categories; higher confidence and collective responsibility; and lower complacency, constraints and calculation. Higher influenza vaccine uptake was significantly correlated with nurses and physicians among HCW categories, older age, a higher monthly income, higher confidence and collective responsibility, lower complacency and constraints and lower embrace of general vaccine conspiracy beliefs. The results of the current study can provide helpful clues to improve influenza vaccine coverage among HCWs in Jordan. Consequently, this can help to protect vulnerable patient groups and reserve valuable resources in healthcare settings. Psychological determinants appeared to be the most significant factors for vaccine acceptance and uptake, whereas the embrace of general vaccine conspiracy beliefs was associated with lower rates of influenza vaccine uptake, which should be considered in educational and interventional measures aiming to promote influenza vaccination.
Adaptive Recurrent Forward Network for Dense Point Cloud Completion

Point cloud completion is an interesting and challenging task in 3D vision, which aims to recover complete shapes from sparse and incomplete point clouds. Existing completion networks often require a vast number of parameters and substantial computational costs to achieve a high performance level, which may limit their practical application. In this work, we propose a novel Adaptive efficient Recurrent Forward Network (ARFNet), which is composed of three parts: Recurrent Feature Extraction (RFE), Forward Dense Completion (FDC) and Raw Shape Protection (RSP). In an RFE, multiple short global features are extracted from incomplete point clouds, while a dense quantity of completed results are generated in a coarse-to-fine pipeline in the FDC. Finally, we propose the Adamerge module to preserve the details from the original models by merging the generated results with the original incomplete point clouds in the RSP. In addition, we introduce the Sampling Chamfer Distance to better capture the shapes of the models and the balanced expansion constraint to restrict the expansion distances from coarse to fine. According to the experiments on ShapeNet and KITTI, our network can achieve state-of-the-art completion performances on dense point clouds with fewer parameters, smaller model sizes, lower memory costs and a faster convergence.
Gated Attention Recurrent Neural Network: A Deeping Learning Approach for Radar-Based Precipitation Nowcasting

Precipitation nowcasting predicts the future rainfall intensity in local areas in a brief time that impacts directly on human life. In this paper, we express the precipitation nowcasting as a spatiotemporal sequence prediction problem. Predictive learning for a spatiotemporal sequence aims to construct a model of natural spatiotemporal processes to predict the future frames based on historical frames. The spatiotemporal process is an abstraction of some of the spatial things in nature that change with time, and they usually do not change very dramatically. To simplify the model and facilitate the training, we considered that the spatiotemporal process satisfies the generalized Markov properties. The natural spatiotemporal processes are nonlinear and non-stationary in many aspects. The processes are not satisfied with the first-order Markov properties when making predictions, such as the nonlinear movement, expansion, dissipation, and intensity enhancement of echoes. To describe such complex spatiotemporal variations, higher-order Markov models need to be used for the modeling. However, many of the previous models for spatiotemporal prediction constructed were based on first-order Markov properties, losing information on the higher-order variations. Thus, we propose a recurrent neural network which satisfies the multi-order Markov properties to create more accurate spatiotemporal predictions. In this network, the core component is the memory cell structure of the gated attention mechanism, which combines the current input information, extracts the historical state that best matches the existing input from the historical multi-period memory information, and then predicts the future. Through this principle of the gated attention, we could extract the historical state information that is richer and deeper to predict the future and more accurately describe the changing characteristics of motion. The experiments show that our GARNN network captures the spatiotemporal characteristics better and obtains excellent results in the precipitation forecasting with radar echoes.
Muscle Hypertrophy Response to Range of Motion in Strength Training: A Novel Approach to Understanding the Findings

ABSTRACT One resistance training variable that may be altered to achieve desired outcomes is the range of motion used in training. Generally, the strength and conditioning field has accepted that using a greater range of motion in strength training exercises results in more substantial muscle hypertrophy outcomes. However, this theory has proved to be inconsistently supported in the literature, and to date, no sufficient explanation exists to explain this phenomenon. This review article seeks to outline a novel approach for potentially describing the disparities seen in range of motion research with respect to hypertrophy outcomes by applying the unique length-tension curve of each muscle being examined. As will be discussed in the review, virtually all the results from range of motion studies in various muscles have corresponded to each muscle's length-tension curve; muscles that are active on the descending limb of the curve appear to garner greater hypertrophy from using larger ranges of motion. Conversely, muscles that are not active on the descending limb exhibit similar adaptations despite alterations in range of motion. A novel hypothesis for applying this information to resistance training programs will be presented and discussed.
A Spatio-Temporal Monitoring Method Based on Multi-Source Remote Sensing Data Applied to the Case of the Temi Landslide

It is challenging to monitor landslides due to their heavy concealment and the extreme destructiveness during the long development of landslides. Many landslide monitoring tools are somewhat onefold. In this paper, a comprehensive landslide monitoring method involving multiple factors from time-series multi-data sources is proposed. We focus on the changes in three aspects consisting of the vegetation condition, the surface deformation information and the landslide susceptibility. Firstly, the fractional vegetation cover of the landslide is extracted from optical remote sensing Gaofen-1 (GF-1) images using the dimidiate pixel model. Next, the surface deformation information of the landslide is derived from SAR remote sensing Sentinel-1A images applying the SBAS-InSAR method. Then, the landslide susceptibility based on GF-1, Sentinel-1A images and DEM data is computed using the analytic hierarchy process method. Finally, the spatio-temporal correlations of the vegetation condition, the surface deformation information and the landslide susceptibility are compared and interpreted. The Temi landslide is located along the Jinsha River and poses a high risk of blocking the river. Taking the Temi landslide as the study area, it is indicated from the results that the fractional vegetation cover, surface deformation information and landslide susceptibility reveal a consistency in the patterns of changes in spatial and temporal terms. As the surface deformation information improves, the status of the landslide vegetation also deteriorates and the landslide susceptibility becomes high, which indicates an increased probability of the creep and even the occurrence of landslides. In contrast, when the surface deformation information drops, the vegetation condition of the landslide becomes superior and the landslide becomes less susceptible, which means the likelihood of sliding declines. This study provides a new idea for a landslide monitoring method and potential way for natural disaster prevention and mitigation.
Separated by spaces: Undergraduate students re-sort along attitude divides when choosing whether to learn in spaces designed for active learning

Institutions of higher education are seeking to support more active learning among faculty, and that support includes the creation of active learning spaces to support more student-centered course activities. However, incremental development of these learning spaces leads to a sorting of students between active and passive learning environments. This study collected repeat-measure data from undergraduate students enrolled in a two-semester course sequence to evaluate factors affecting student enrollment decisions and student attitudes toward learning. We integrated this combination of research tools using a QuantCrit analytic framework, which revealed student attitudes and decision-making processes that were generally stable over the study period for individuals, but very different when examining student enrollment groups over time. Limited access to active learning classrooms forced students to self-sort based on either their social networks or their attitudes toward learning. That, in turn, may create a marginalizing force that pushes out some students—most often women—from undergraduate programs.
Criticality In Cellular Membranes And The Information Geometry Of Simple Models

1 We present a minimal model of plasma membrane heterogeneity that combines criticality with connectivity to cortical cytoskeleton. Our model is motivated by recent observations of micron-sized critical fluctuations in plasma membrane vesicles that are detached from their cortical cytoskeleton. We incorporate criticality using a conserved order parameter Ising model coupled to a simple actin cytoskeleton interacting through point-like pinning sites. Using this minimal model, we recapitulate several experimental observations of plasma membrane raft heterogeneity. Small (r ∼ 20nm) and dynamic fluctuations at physiological temperatures arise from criticality. Including connectivity to cortical cytoskeleton disrupts large fluctuations, prevents macroscopic phase separation at low temperatures (T ≤ 22oC), and provides a template for long lived fluctuations at physiological temperature (T = 37oC). Cytoskeleton-stabilized fluctuations produce significant barriers to the diffusion of some membrane components in a manner that is weakly dependent on the number of pinning sites and strongly dependent on criticality. More generally, we demonstrate that critical fluctuations provide a physical mechanism to organize and spatially segregate membrane components by providing channels for interaction over large distances. 1This chapter has been published in The Biophysical Journal with co-authors Stefanos Papanikolaou, James P. Sethna and Sarah L. Veatch [93]
Analysis and design of a linear voltage controlled ring oscillator with rail-to-rail tuning range and zero static power

ABSTRACT This paper analyses and designs a linear voltage controlled ring oscillator (VCRO) with rail-to-rail tuning range and zero static power. The rail-to-rail tuning range is achieved via adding a PMOS tuning transistor and corresponding current mirror into the conventional current-starved inverter with only a NMOS tuning transistor. The static power is eliminated via introducing a control-pulse-generating auxiliary circuit to guarantee only a dynamic power during the transition times. The linearity, i.e. global tuning gain over the rail-to-rail tuning range is obtained via doing a theoretical tuning gain analysis in the piecewise ranges to determine the ratios of the transistors. Besides, the phase noise is predicted by transplanting an analytical method of the flicker noise. Finally, the proposed VCRO is verified by Cadence Spectre-Virtuoso in the CSMC CMOS 180 nm process.
Inter-Ligand STD NMR: An Efficient 1D NMR Approach to Probe Relative Orientation of Ligands in a Multi-Subsite Protein Binding Pocket

In recent years, Saturation Transfer Difference NMR (STD NMR) has been proven to be a powerful and versatile ligand-based NMR technique to elucidate crucial aspects in the investigation of protein-ligand complexes. Novel STD NMR approaches relying on “multi-frequency” irradiation have enabled us to even elucidate specific ligand-amino acid interactions and explore the binding of fragments in previously unknown binding subsites. Exploring multi-subsite protein binding pockets is especially important in Fragment Based Drug Discovery (FBDD) to design leads of increased specificity and efficacy. We hereby propose a novel multi-frequency STD NMR approach based on direct irradiation of one of the ligands in a multi-ligand binding process, to probe the vicinity and explore the relative orientation of fragments in adjacent binding sub-sites, which we called Inter-Ligand STD NMR (IL-STD NMR). We proved its applicability on (i) a standard protein-ligand system commonly used for ligand-observed NMR benchmarking: Naproxen as bound to Bovine Serum Albumin, and (ii) the biologically relevant system of Cholera Toxin Subunit B and two inhibitors adjacently bound within the GM1 binding site. Relative to Inter-Ligand NOE (ILOE), the current state-of-the-art methodology to probe relative orientations of adjacent ligands, IL-STD NMR requires about one tenth of the experimental time and protein consumption, making it a competitive methodology with the potential to be applied in the pharmaceutical industries.
CALIBRATION AND VALIDATION OF THE SWAT HYDROLOGICAL MODEL FOR THE MUCURI RIVER BASIN

Hydrological models are becoming more and more widespread, mainly due to their capacity to simulate the impact of environmental changes on water resources. In this way, the aim of this study was to calibrate and validate the SWAT model for the soil and climatic conditions of the Mucuri River Basin, located in the Northeast region of the States of Minas Gerais, Brazil. The SWAT-CUP software module SUFI2 was used to analyze the sensitivity, calibration and validation of the model. The calibration was performed in an intermediate fluviometric station and the validation in five other located: three upstream, one downstream and one independent from the calibration point. It was evidenced for the study area that the parameters referring to the basic flow processes were more sensitive. The model obtained a good adjustment with an overestimate tendency of 15%. In general, the SWAT model, using SWAT-CUP was good and adequate in terms of its calibration performance and validation of the flow simulation in the Mucuri River Basin by the determination coefficients, Nash-Suttclife efficiency and percentage of trend.
Design and Service Effect Evaluation of Agricultural Social Service Platform Based on 5G and Cloud Computing

With the continuous development of information technology, various industries continue to use information technology to promote industrial upgrading and optimize the industrial structure. Agricultural informatization, as the only way for future agricultural development, will become an inevitable direction for the rejuvenation of traditional industries. The proposal of the development concept of “Mass Entrepreneurship, Mass Innovation” marks that my country has gradually entered an innovative society, and the development of public services and service products will become a powerful guarantee for promoting the construction of the agricultural informatization industry chain. This paper starts from the application of cloud computing and 5G in agriculture, considers the impact of big data on agriculture, and combines the business requirements and functional requirements of agricultural social services. The design of agricultural socialization service platform is realized, and finally, the service effect of the platform is evaluated by neural network. The following is a summary of the work done in this paper: (1) The research background and significance of the development concept of agricultural informatization services are expounded. Finally, the research content and method of this paper are discussed. (2) The technical principle and optimization algorithm of BPNN are introduced, and the evaluation index of the service effect of agricultural information platform is designed. (3) Select the appropriate parameters to build the experimental model and then train it, then compare the experimental effects of the traditional BP algorithm and the optimized three algorithms, and comprehensively evaluate the LM-BP model with the best performance.
INVESTIGATION ON THE MALYSIAN DUABANGA MOLUCCANA CROSS SECTIONS AS SOUND ABSORBING FUNCTIONAL MATERIALS

This study focused on Duabanga moluccana, which is a fast-growing tropical tree native to Southeast Asia. Duabanga moluccana cross sectional specimens were analyzed to gas permeability, pore size, and open-pore porosity and also measured its sound absorption coefficients with an impedance tube. Duabanga moluccanashowed a larger pore size and greater open-pore porosity than other hardwood species, and its sound absorbing performance was superior to that of the gypsum board currently used as a commercial ceiling material. Although this was a laboratory-scale investigation, we demonstrate that Duabanga moluccanacross sections have potential as a natural sound absorbing functional building material.