Extensive Model Research Articles

A Multi-Scale Feature Fusion Based Lightweight Vehicle Target Detection Network on Aerial Optical Images

Vehicle detection with optical remote sensing images has become widely applied in recent years. However, the following challenges have remained unsolved during remote sensing vehicle target detection. These challenges include the dense and arbitrary angles at which vehicles are distributed and which make it difficult to detect them; the extensive model parameter (Param) that blocks real-time detection; the large differences between larger vehicles in terms of their features, which lead to a reduced detection precision; and the way in which the distribution in vehicle datasets is unbalanced and thus not conducive to training. First, this paper constructs a small dataset of vehicles, MiVehicle. This dataset includes 3000 corresponding infrared and visible image pairs, offering a more balanced distribution. In the infrared part of the dataset, the proportions of different vehicle types are as follows: cars, 48%; buses, 19%; trucks, 15%; freight, cars 10%; and vans, 8%. Second, we choose the rotated box mechanism for detection with the model and we build a new vehicle detector, ML-Det, with a novel multi-scale feature fusion triple cross-criss FPN (TCFPN), which can effectively capture the vehicle features in three different positions with an mAP improvement of 1.97%. Moreover, we propose LKC–INVO, which allows involution to couple the structure of multiple large kernel convolutions, resulting in an mAP increase of 2.86%. We also introduce a novel C2F_ContextGuided module with global context perception, which enhances the perception ability of the model in the global scope and minimizes model Params. Eventually, we propose an assemble–disperse attention module to aggregate local features so as to improve the performance. Overall, ML-Det achieved a 3.22% improvement in accuracy while keeping Params almost unchanged. In the self-built small MiVehicle dataset, we achieved 70.44% on visible images and 79.12% on infrared images with 20.1 GFLOPS, 78.8 FPS, and 7.91 M. Additionally, we trained and tested our model on the following public datasets: UAS-AOD and DOTA. ML-Det was found to be ahead of many other advanced target detection algorithms.

Integrating IoT and visual question answering in smart cities: Enhancing educational outcomes

Emerging as a paradigmatic shift in urban development, smart cities harness the potential of advanced information and communication technologies to seamlessly integrate urban functions, optimize resource allocation, and improve the effectiveness of city management. Within the domain of smart education, the imperative application of Visual Question Answering (VQA) technology encounters significant limitations at the prevailing stage, particularly the absence of a robust Internet of Things (IoT) framework and the inadequate incorporation of large pre-trained language models (LLMs) within contemporary smart education paradigms, especially in addressing zero-shot VQA scenarios, which pose considerable challenges. In response to these constraints, this paper introduces an IoT-based smart city framework that is designed to refine the functionality and efficacy of educational systems. This framework is delineated into four cardinal layers: the data collection layer, data transmission layer, data management layer, and application layer. Furthermore, we introduce the innovative TeachVQA methodology at the application layer, synergizing VQA technology with extensive pre-trained language models, thereby considerably enhancing the dissemination and assimilation of educational content. Evaluative metrics in the VQAv2 and OKVQA datasets substantiate that the TeachVQA methodology not only outperforms existing VQA approaches, but also underscores its profound potential and practical relevance in the educational sector.

Can altruistic factors motivate residents’ energy-saving behavior? An application of stimulus-organism-response theory in China

As China increasingly focuses on sustainable development, understanding and influencing urban residents’ energy-saving behaviors becomes vital for addressing high energy consumption issues. Thus, this research chooses the residents in China as its object, and studies the factors influencing residents’ habitual energy-saving behaviors. We establish a comprehensive theoretical model suitable for this study based on the Stimulus-Organism-Response (SOR) theory. Utilizing on-the-spot investigations, extensive data collection and model establishment, our research reveals that “altruism” – defined here as actions driven by concern for societal expectations – predominantly motivates daily energy-saving behaviors. In addition, external factors (cost of energy-saving measures) have a significant impact on residents’ energy-saving investment behaviors. Based on our findings, we propose specific policy recommendations focusing on publicity on environmental pollution, the platform for sharing energy conservation experiences and formulation of group-differentiated energy conservation policies. Therefore, this study provides policy implications for motivating residents to take energy-saving actions.

Model based optimization for refractive index mismatched light sheet imaging

Selective plane illumination microscopy (SPIM) is an optical sectioning imaging approach based on orthogonal light pathways for excitation and detection. The excitation pathway has an inverse relation between the optical sectioning strength and the effective field of view (FOV). Multiple approaches exist to extend the effective FOV, and here we focus on remote focusing to axially scan the light sheet, synchronized with a CMOS camera’s rolling shutter. A typical axially scanned SPIM configuration for imaging large samples utilizes a tunable optic for remote focusing, paired with air objectives focused into higher refractive index media. To quantitatively explore the effect of remote focus choices and sample space refractive index mismatch on light sheet intensity distributions, we developed an open-source computational approach for integrating ray tracing and field propagation. We validate our model’s performance against experimental light sheet profiles for various SPIM configurations. Our findings indicate that optimizing the position of the sample chamber relative to the excitation optics can enhance image quality by balancing aberrations induced by refractive index mismatch. We validate this prediction using a home-built, large sample axially scanned SPIM configuration and calibration samples. Our open-source, extensible modeling software can easily extend to explore optimal imaging configurations in diverse light sheet imaging settings.

Predicting protein interactions of the kinase Lck critical to Tcell modulation.

Protein-protein interactions (PPIs) play pivotal roles in directing Tcell fate. One key player is the non-receptor tyrosine protein kinase Lck that helps to transduce Tcell activation signals. Lck is mediated by other proteins via interactions that are inadequately understood. Here, we use the deep learning method AF2Complex to predict PPIs involving Lck, by screening it against ∼1,000 proteins implicated in immune responses, followed by extensive structural modeling for selected interactions. Remarkably, we describe how Lck may be specifically targeted by a palmitoyltransferase using a phosphotyrosine motif. We uncover "hotspot" interactions between Lck and the tyrosine phosphatase CD45, leading to a significant conformational shift of Lck for activation. Lastly, we present intriguing interactions between the phosphotyrosine-binding domain of Lck and the cytoplasmic tail of the immune checkpoint LAG3 and propose a molecular mechanism for its inhibitory role. Together, this multifaceted study provides valuable insights into Tcell regulation and signaling.

Diagnosis of ophthalmologic diseases in canines based on images using neural networks for image segmentation

The primary challenge in diagnosing ocular diseases in canines based on images lies in developing an accurate and reliable machine learning method capable of effectively segmenting and diagnosing these conditions through image analysis. Addressing this challenge, the study focuses on developing and rigorously evaluating a machine learning model for diagnosing ocular diseases in canines, employing the U-Net neural network architecture as a foundational element of this investigation.Through this extensive evaluation, the authors identified a model that exhibited good reliability, achieving prediction scores with an Intersection over Union (IoU) exceeding 80 %, as measured by the Jaccard index. The research methodology encompassed a systematic exploration of various neural network backbones (VGG, ResNet, Inception, EfficientNet) and the U-Net model, combined with an extensive model selection process and an in-depth analysis of a custom training dataset consisting of historical images of different medical symptoms and diseases in dog eyes.The results indicate a fairly high degree of accuracy in the segmentation and diagnosis of ocular diseases in canines, demonstrating the model's effectiveness in real-world applications. In conclusion, this potentially makes a significant contribution to the field by utilizing advanced machine-learning techniques to develop image-based diagnostic routines in veterinary ophthalmology. This model's successful development and validation offer a promising new tool for veterinarians and pet owners, enhancing early disease detection and improving health outcomes for canine patients.

Predicting Main Characteristics of Reinforced Concrete Buildings Using Machine Learning

This paper presents a comprehensive study of five machine learning (ML) algorithms for predicting key characteristics of Reinforced Concrete (RC) structural systems. A novel dataset, ModRes, consisting of 9723 examples derived from modal and response spectrum analyses on masonry-infilled three-dimensional RC buildings, was created for ML applications. The primary objective is to develop an ML model using five distinct algorithms from the literature, capable of concurrently predicting torsional irregularity, modal participating mass ratio (MPMR), and the fundamental period in a 3D environment, while accounting for the influence of infill walls. Additionally, the study aims to determine the applicability of pushover analysis (POA) without the need for extensive numerical modeling and analysis. This approach optimizes the preliminary design process with minimal computational effort, providing valuable insights into dynamic and torsional responses during seismic events. The Categorical Boosting algorithm demonstrated outstanding performance, achieving R2 values of 0.977 for torsional irregularity, 0.997 for the fundamental period, and 0.923 for MPMR on the test dataset. It also successfully predicted POA applicability with an error rate of only 1.36%. This study highlights the practical application of ML algorithms, underscoring their effectiveness in structural engineering.

Sheep Head Cadaveric Model for the Transmeatal Extensions of the Retrosigmoid Approach

AbstractThe transmeatal extension of the retrosigmoid approach is an important procedure used in the treatment of various pathologies affecting the posterior fossa, petroclival region, and jugular foramen. Mastering this technique requires a high level of manual skill, particularly in temporal bone drilling. The objective of this study was to describe an easily accessible and cost-effective model of the transmeatal extension of the retrosigmoid approach using cadaveric sheep heads. Five cadaveric sheep heads, fixed in alcohol and formalin with intravascular-colored silicone injection, were prepared for this study. Two heads (four sides) were designated for illustrative anatomical specimens, while three heads (six sides) were used for surgical simulation. Additionally, one head was used to prepare and dissect a dry skull. All critical steps of the transmeatal approach, including both supra- and inframeatal extensions, were successfully replicated on the model. A comparative anatomical analysis was conducted, focusing on the technical nuances of the model. The cadaveric sheep head serves as an effective model for the retrosigmoid approach with transmeatal extensions, primarily for training manual haptic skills. While the sheep model cannot precisely replicate human anatomy, it still offers valuable training opportunities for neurosurgeons, particularly when human cadaveric specimens are unavailable.

Spatially explicit analysis of production and consumption responsibility for the PM2.5-related health burden towards beautiful China

Promoting good health and ensuring responsible production and consumption are essential components of the Sustainable Development Goals (SDGs) established by of the United Nations, as well as the goals of beautiful China. While the health impacts of air pollution have garnered significant attention, there remains a paucity of studies comparing the disparities in responsibility arising from production versus consumption. This paper integrates the Weather Research and Forecasting - Comprehensive Air Quality Model with Extensions (WRF-CAMx) model, the multiregional input‒output (MRIO) model, and the global exposure mortality model (GEMM) to assess the extent of PM2.5-related premature deaths caused by production and consumption activities in 30 Chinese provinces. The findings reveal a spatial mismatch in health burdens between production and consumption. Considering pollutant emissions and their transfer only through the supply chain leads to the finding that the net outflow of emissions from producers is mainly located in most of the northern provinces of China. However, when atmospheric transport and health impacts are included, the producing provinces are mainly located in central China, while the consuming provinces are located in the southeastern coastal and remote western and northern regions. Additionally, the long-range impact of consumption provinces with respect to the health burden is more than twice as large as that of production provinces, and its potential impact on the health burden cannot be ignored. From a sectoral perspective, production emissions from the non-electricity industry and services sectors contribute to 60% of the health burden, while their consumption emissions contribute to over 80% of the health burden. Furthermore, consumption activities in the non-electricity industry and services sectors significantly influence production emissions in the transport, agriculture, and electricity sectors. The geographical separation of consumption and production regions facilitated by trade is a critical yet often overlooked aspect in current regional air quality planning in China. A more comprehensive analysis of life-cycle emissions driven by final consumption could yield greater reductions compared to direct production reductions.

Carbon nanotubes perpendicularly grown on graphene oxide nanosheets derived from metal-organic frameworks: Synergistic reinforcement of poly(l-lactic acid) scaffold

The construction of the nanohybrid composed of two carbonaceous nanomaterials is a promising strategy to inhibit their aggregation, and effectively exert the advantages of their excellent mechanical properties as reinforcement for polymers. Here, carbon nanotubes (CNTs) were in situ perpendicularly growing on the surface of graphene oxide (GO) through metal-organic frameworks (MOF)-derived strategy by chemical vapor deposition (CVD), aiming to facilitate their dispersion on poly(l-lactic acid) (PLLA) bone scaffold fabricated by selective laser sintering (SLS). Specifically, GO provided nucleation sites for the growth of MOF, and worked as the templates when CNTs grew, in which MOF provided catalysts and carbon sources simultaneously. The precursor was heated to 550 °C and kept for 2 h, then heated to 900 °C and kept for 2 h before cooling. The obtained nanohybrid (GO@CNT) exhibited a superior dispersion state than the pure GO in the scaffold at the same loading, especially when the loading was 0.75 wt%. Adding 0.75 wt% GO@CNT endowed the scaffold with a remarkable increment in tensile strength and compressive strength of 13.36 MPa and 24.72 MPa with enhancement of 55.35% and 18.85%, respectively, compared to the scaffold containing 0.75 wt% GO. A crack extension model combined with experiment results was proposed to better understand reinforcement mechanisms. Additionally, the scaffold containing GO@CNT exhibited benign cytocompatibility with a cell-spreading area of 86.31% and cell density of 564 cells/mm2 after culturing for 5 d according to the results of cell adhesion and immunofluorescence tests, making it a promising candidate for bone defect repair.

Selectivity analysis of diaminopyrimidine-based inhibitors of MTHFD1, MTHFD2 and MTHFD2L

The mitochondrial enzyme methylenetetrahydrofolate dehydrogenase (MTHFD2) is involved in purine and thymidine synthesis via 1C metabolism. MTHFD2 is exclusively overexpressed in cancer cells but absent in most healthy adult human tissues. However, the two close homologs of MTHFD2 known as MTHFD1 and MTHFD2L are expressed in healthy adult human tissues and share a great structural resemblance to MTHFD2 with 54% and 89% sequence similarity, respectively. It is therefore notably challenging to find selective inhibitors of MTHFD2 due to the structural similarity, in particular protein binding site similarity with MTHFD1 and MTHFD2L. Tricyclic coumarin-based compounds (substrate site binders) and xanthine derivatives (allosteric site binders) are the only selective inhibitors of MTHFD2 reported till date. Nanomolar potent diaminopyrimidine-based inhibitors of MTHFD2 have been reported recently, however, they also demonstrate significant inhibitory activities against MTHFD1 and MTHFD2L. In this study, we have employed extensive computational modeling involving molecular docking and molecular dynamics simulations in order to investigate the binding modes and key interactions of diaminopyrimidine-based inhibitors at the substrate binding sites of MTHFD1, MTHFD2 and MTHFD2L, and compare with the tricyclic coumarin-based selective MTHFD2 inhibitor. The outcomes of our study provide significant insights into desirable and undesirable structural elements for rational structure-based design of new and selective inhibitors of MTHFD2 against cancer.

Mavacamten for Obstructive Hypertrophic Cardiomyopathy: Rationale for Clinically Guided Dose Titration to Optimize Individual Response.

Mavacamten is the first and only cardiac myosin inhibitor approved in 5 continents for the treatment of adults with symptomatic New York Heart Association class II and III obstructive hypertrophic cardiomyopathy. An evidence-based rationale was used to develop individualized mavacamten dosing, guided by commonly used clinical parameters. Echocardiography is recommended as part of routine clinical assessment of patients with hypertrophic cardiomyopathy, and left ventricular (LV) outflow tract gradient and LV ejection fraction are parameters that can be readily assessed and monitored by echocardiography. Therefore, an echocardiography-based, clinically guided dose-titration strategy was developed to optimize patient benefit from mavacamten for the treatment of symptomatic obstructive hypertrophic cardiomyopathy while minimizing the risk of LV ejection fraction reduction. Results from clinical trials paired with extensive modeling and simulation analyses support a dose-titration and monitoring strategy based on serial echocardiographic measures of Valsalva LV outflow tract gradient and LV ejection fraction. This dosing approach allows for the identification of the lowest individualized mavacamten dose and exposure required to provide improvements in LV outflow tract obstruction, functional capacity, and symptoms. Mavacamten is primarily metabolized by CYP2C19 (cytochrome P450 2C19), and CYP2C19 metabolizer phenotype has an effect on mavacamten exposure. Therefore, this approach has also been demonstrated to provide a favorable safety profile irrespective of patients' CYP2C19 metabolizer status. The dose-titration strategy includes additional considerations for the potential onset of systolic dysfunction in the context of intercurrent illness, and for the potential of drug-drug interactions with inhibitors and substrates of cytochrome P450 enzymes. This posology is reflected in the mavacamten US prescribing information.

Analytical model of friction at low shear rates for soft materials in 3D printing.

The biological properties of silicone elastomers such as polydimethylsiloxane (PDMS) have widespread use in biomedicine for soft tissue implants, contact lenses, soft robots, and many other small medical devices, due to its exceptional biocompatibility. Additive manufacturing of soft materials still has significant challenges even with major advancements that have occurred in development of these technologies for customized medical devices and tissue engineering. The aim of this study was to develop a mathematical model of tangential stress in relation to shear stress, shear rate, 3D printing pressure and velocity, for non-Newtonian gels and fluids that are used as materials for 3D printing. This study used FENE (finitely extensible nonlinear elastic model) model, for non-Newtonian gels and fluids to define the dependences between tangential stress, velocity, and pressure, considering viscosity, shear stress and shear rates as governing factors in soft materials friction and adhesion. Experimental samples were fabricated as showcases, by SLA and FDM 3D printing technologies: elastic polymer samples with properties resembling elastic properties of PDMS and thermoplastic polyurethane (TPU) samples. Experimental 3D printing parameters were used in the developed analytical solution to analyse the relationships between governing influential factors (tangential stress, printing pressure, printing speed, shear rate and friction coefficient). Maple software was used for numerical modelling. Analytical model applied on a printed elastic polymer, at low shear rates, exhibited numerical values of tangential stress of 0.208-0.216 N m - 2 at printing velocities of 0.9 to 1.2 mm s - 1, while the coefficient of friction was as low as 0.09-0.16. These values were in accordance with experimental data in literature. Printing pressure did not significantly influence tangential stress, whereas it was slightly influenced by shear rate changes. Friction coefficient linearly increased with tangential stress. Simple analytical model of friction for elastic polymer in SLA 3D printing showed good correspondence with experimental literature data for low shear rates, thus indicating possibility to use it for prediction of printing parameters towards desired dimensional accuracy of printed objects. Further development of this analytical model should enable other shear rate regimes, as well as additional soft materials and printing parameters.

LSR-Det: A Lightweight Detector for Ship Detection in SAR Images Based on Oriented Bounding Box

Convolutional neural networks (CNNs) have significantly advanced in recent years in detecting arbitrary-oriented ships in synthetic aperture radar (SAR) images. However, challenges remain with multi-scale target detection and deployment on satellite-based platforms due to the extensive model parameters and high computational complexity. To address these issues, we propose a lightweight method for arbitrary-oriented ship detection in SAR images, named LSR-Det. Specifically, we introduce a lightweight backbone network based on contour guidance, which reduces the number of parameters while maintaining excellent feature extraction capability. Additionally, a lightweight adaptive feature pyramid network is designed to enhance the fusion capability of the ship features across different layers with a low computational cost by incorporating adaptive ship feature fusion modules between the feature layers. To efficiently utilize the fused features, a lightweight rotating detection head is designed, incorporating the idea of sharing the convolutional parameters, thereby improving the network’s ability to detect multi-scale ship targets. The experiments conducted on the SAR ship detection dataset (SSDD) and the rotating ship detection dataset (RSDD-SAR) demonstrate that LSR-Det achieves an average precision (AP50) of 98.5% and 97.2% with 3.21 G floating point operations (FLOPs) and 0.98 M parameters, respectively, outperforming the current popular SAR arbitrary-direction ship target detection methods.

Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

Abstract The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.

An improved viscoelastic k- ε -v2-f turbulence model for intermediate and high drag reduction regimes

In this work, an improved anisotropic k-ε-v2-f model based on the finite extensible nonlinear elastic model with the Peterlin approximation for viscoelastic channel flows is proposed. This model is tested using direct numerical simulation (DNS) data for friction Reynolds numbers (Reτ) in the range of 120–1000, friction Wiesenberg numbers (Wiτ) in the range of 25–116, viscosity ratios (β) in the range of 0.6–0.9, and maximum polymer extensibility values (L2) in the range of 900–14 400. The flow characteristics of viscoelastic fluids with various parameters obtained from the new model agree well with existing DNS results. By adding closures for the flow, shear, and transverse components, the incomplete prediction of nonlinear terms from interactions between the fluctuating components of the conformation and velocity gradient tensors is improved. Compared with DNS results, these closures can fully obtain each component and significantly improve the accuracy of the flow direction component in the intermediate and high drag reduction regimes. Furthermore, the model in this paper retains the advantages of an anisotropic model, does not require a damping function, is simple to construct, and is easily extended to a variety of bounded flows.

Intelligent resource allocation in hybrid RF/LiFi networks via deep deterministic policy gradient based DRL mechanism

A hybrid radio frequency (RF) and light fidelity (LiFi) network combines the strengths of RF and LiFi technologies. RF offers broad coverage, while LiFi provides high data rates. As these technologies operate on non-interfering spectra, they can co-exist without interfering with each other. This setup not only boosts data rate but also makes the network more reliable, especially when physical obstacles might block signals. However, resource management in hybrid RF/LiFi networks is challenging because of the dynamic environment and the different characteristics of the two technologies. Efficient resource allocation maximizes the data rate in these networks. In this paper, we introduce a model-free deep reinforcement learning (DRL) approach to solve the resource allocation problem in hybrid RF/LiFi networks. Our DRL model is designed to handle real-world conditions, considering factors like blockages and user mobility. Unlike traditional methods that need extensive modeling and assumptions, our approach learns directly from interacting with the environment, making it highly adaptable and robust. Through simulations, it is observed that our method improves resource utilization and overall network performance, achieving a 62.8% increase in sum rate and a 42.8% improvement in optimal transmit power compared to conventional methods.

Role of Agricultural Extension in Addressing Food Security

Agricultural extension services play a pivotal role in global efforts to achieve food security, acting as a critical link between research, farmers, and policymakers. This comprehensive review examines the multifaceted contributions of agricultural extension to food security goals, drawing on theoretical frameworks, empirical evidence, and case studies from diverse global contexts. This paper explores how extension services address the four dimensions of food security: availability, access, utilization, and stability. It analyses the evolution of extension models, from traditional top-down approaches to participatory and market-led systems, assessing their relative effectiveness in enhancing agricultural productivity and food security. This review paper highlights innovative practices, including the integration of information and communication technologies (ICT), community-based approaches, and public-private partnerships, which have shown promise in overcoming persistent challenges. The case studies from both developed and developing countries provide insights into successful strategies and their impacts on crop yields, farmer incomes, and nutritional outcomes. This paper also addresses critical challenges facing extension services, including financial constraints, technological barriers, and socio-cultural issues, proposing potential solutions and policy recommendations. By synthesizing current knowledge and identifying future research directions, this review underscores the indispensable role of agricultural extension in achieving global food security goals and emphasizes the need for continued innovation and adaptation in extension practices to meet evolving challenges in the agricultural sector.

Delineating the stepwise millisecond allosteric activation mechanism of the class C GPCR dimer mGlu5

Two-thirds of signaling hormones and one-third of approved drugs exert their effects by binding and modulating the G protein-coupled receptors (GPCRs) activation. While the activation mechanism for monomeric GPCRs has been well-established, little is known about GPCRs in dimeric form. Here, by combining transition pathway generation, extensive atomistic simulation-based Markov state models, and experimental signaling assays, we reveal an asymmetric, stepwise millisecond allosteric activation mechanism for the metabotropic glutamate receptor subtype 5 receptor (mGlu5), an obligate dimeric class C GPCR. The dynamic picture is presented that agonist binding induces dimeric ectodomains compaction, amplified by the precise association of the cysteine-rich domains, ultimately loosely bringing the intracellular 7-transmembrane (7TM) domains into proximity and establishing an asymmetric TM6-TM6 interface. The active inter-domain interface enhances their intra-domain flexibility, triggering the activation of micro-switches crucial for downstream signal transduction. Furthermore, we show that the positive allosteric modulator stabilizes both the active inter-domain 7TM interface and an open, extended intra-domain ICL2 conformation. This stabilization leads to the formation of a pseudo-cavity composed of the ICL2, ICL3, TM3, and C-terminus, which facilitates G protein coordination. Our strategy may be generalizable for characterizing millisecond events in other allosteric systems.

Study on Coaches’ and Athletes’ Attitude Towards the Consumption of Doping Substances

Doping continues to attract the attention of specialists in the field of Sports Science due to its frequency of occurrence and the need for scientific substantiation of educational interventions. In all the conditional factors of doping behaviour, coaches and athletes are the key factors. Coaches are often identified as a potential enabler in athletes' doping, but precisely for this reason, they continue to be identified with their status as important potential agents in doping prevention. In turn, the athlete is the product of his own development and social-educational-cultural factors. The behavior of rejection or acceptance of the use of prohibited substances is determined by internal, subjective factors and external, social-environmental factors. An extensive model of risk factors, at international level, shows that interventions are needed at different levels for the educational effect to increase. In order to identify the attitude of coaches and athletes working in the Romanian sports system, between February and April 2022, online, with the help of Google forms, two questionnaires about anti-doping knowledge and attitudes were applied, one for athletes and one for coaches. The analysis of the results found that experienced coaches are better informed and have objective views on the impact of doping in sport. On the other hand, athletes resort to doping under peer pressure, lack of time required for training and as a result of competition pressure. Knowledge of these attitudinal peculiarities allows the formulation of recommendations on educational activities in the field of anti-doping education. Keywords: coaches, athletes, anti-doping, doping substances