Data-driven pose estimation methods often assume equal distributions between training and test data. However, in reality, this assumption does not always hold true, leading to significant performance degradation due to distribution mismatches. In this study, our objective is to enhance the cross-domain robustness of multi-view, multi-person 3D pose estimation. We tackle the domain shift challenge through three key approaches: (1) A domain adaptation component is introduced to improve estimation accuracy for specific target domains. (2) By incorporating a dropout mechanism, we train a more reliable model tailored to the target domain. (3) Transferable Parameter Learning is employed to retain crucial parameters for learning domain-invariant data. The foundation for these approaches lies in the H-divergence theory and the lottery ticket hypothesis, which are realized through adversarial training by learning domain classifiers. Our proposed methodology is evaluated using three datasets: Panoptic, Shelf, and Campus, allowing us to assess its efficacy in addressing domain shifts in multi-view, multi-person pose estimation. Both qualitative and quantitative experiments demonstrate that our algorithm performs well in two different domain shift scenarios. Due to the numerous real-world applications of 3D multi-view multi-person human pose estimation, such as human-computer interaction , virtualEven if more training data from various domains could solve this problem, it may be impractical due to the complexity of real-world scenarios and the high cost of 3D annotation. As a result, methods for successfully transferring a 3D pose estimator trained on a labeled source domain to a new unlabeled target domain are in high demand.Even if gathering more training data from diverse domains could solve this issue, it may be impractical due to the complexity of real-world scenarios and the high expense of 3D annotation. For this reason, there is a great need for methods that can successfully transfer a 3D pose estimator trained on a labeled source domain to a new unlabeled target domain.Our research focuses on domain adaptation for multi-view, multi-person 3D pose estimation with covariate shift. We develop an end-to-end deep learning model called “Domain Adaptive VoxelPose” that is based on the cutting-edge VoxelPose model . An unsuWe add three elements to the VoxelPose model to minimize the domain divergence between two domains in order to correct the domain shift. First, we train a domain classifier  using adIn conclusion, the following are the principal contributions of our work: (1) We provide domain adaptation components to reduce domain disparity between the selected target domain and the respective source domain. (2) To train a more domain-invariant feature extractor, we propose a novel method that applies an ensemble of dynamic dropout domain discriminators. (3) We employ Transferable Parameter Learning (TransPar) to reduce the negative effects of domain-specific knowledge throughout the learning process as well as to enhance the retention of domain-independent knowledge. (4) The suggested components are incorporated into the VoxelPose model, and the resulting system is capable of end-to-end training. Our method was evaluated on three datasets, and the results suggested that it could enhance the accuracy of cross-domain multi-view multi-person 3D pose estimation.The paper is organized as follows. 3D pose estimation from monocular inputs ,20,21,22Nevertheless, those efforts focused on the traditional context without taking domain adaptation into account. In this work, we adopt VoxelPose  as our fDeep learning algorithms are impacted by the domain-shift problem ,33,34, wAdditionally, ,44 attemrepancy,  adopted repancy, ,47 use crepancy,  and sortrepancy,  suggestsrepancy,  separateOn par with the developments on domain adaptation for image recognition tasks, some recent works also aim to address the domain shift in regression tasks ,52,53,54However, despite extensive research on the multi-view, multi-person 3D pose estimation task ,57,58,59We present a brief overview of the VoxelPose model, which serves as the baseline for our research. VoxelPose projects 2D joint heatmaps from various viewpoints into a voxelized 3D space, enabling the direct detection and prediction of 3D human poses. The process begins with the estimation of 2D heatmaps for each view, encoding the per-pixel likelihood of all joints. Features from all camera views are aggregated in the 3D voxel space and processed by the Cuboid Proposal Network to localize individuals. This projection into a common 3D space results in a more comprehensive feature volume, allowing for an accurate estimation of 3D joint positions. Furthermore, the Pose Regression Network is used to estimate a full 3D pose for each proposal. All camera views’ noisy and incomplete information is warped to a common 3D space to create a feature volume that may be used for 3D estimation.The ℓ that adheres to the triangle inequality, the disparity between any two hypotheses h and The groundwork for understanding this theory lies in the theorem titled “Bound with Disparity” . AccordiSubsequently, the target risk Here, D that separates the source and target samples. To accomplish this, we employ a dropout discriminator, which not only prevents mode collapse but also enhances the robustness of our algorithm.Building upon this premise, the essence of the The lottery ticket hypothesis refers to a hypothesis in deep learning that suggests finding sparse subnetworks, known as “winning tickets”, within over-parameterized neural networks. These winning tickets can achieve comparable or even better performance than the original large network when trained in isolation under suitable conditions.In the context of domain adaptation, the lottery ticket hypothesis can be applied to transfer learning scenarios. By identifying winning tickets or subnetworks that generalize well to both the source and target domains, we can effectively adapt the model from the source domain to the target domain. This can help in mitigating the issue of distribution shift between the two domains and improving the performance of the model on the target domain with limited labeled data. This is also the theoretical basis for transferable parameter learning.As shown in Campus : It is aShelf : The SheCMU Panoptic : This dan labeled samples K the number of keypoints for each input. The samples randomly selected from distribution D are denoted as D, where L is a loss function we shall explain later. In unsupervised domain adaptation, there exists a labeled source domain In this section, we detail our proposed methodology for supervised multi-view multi-person 3D pose estimation. In supervised multi-view multi-person 3D pose estimation, we have In the VoxelPose model, the feature representation refers to the feature map outputs of the base convolutional layers  Aligning representations at the image level often reduces the shift caused by variations in the images, such as image style, human body scale, illumination, etc. (2) The batch size is typically quite small while training a pose estimation network due to the utilization of high-resolution input. This approach allows more data to be employed in training the domain classifier.i-th training image by i-th image after the base 2D convolutional layers is denoted by Let’s represent the domain label of the To align the domain distributions, we need to simultaneously optimize the parameters of the base 2D network to maximize the above domain classification loss and minimize the keypoint regression loss. In order to optimize the base network used to maximize the domain classifier, we adopt the gradient reverse layer , as oppod at the end of each batch. This indicates that the pose estimator considers only the loss of the remaining discriminators when updating its parameters.In order to force the pose estimator to learn from a dynamic ensemble of discriminators, we propose the integration of adversarial feedback dropout in adversarial networks. The feedback of each discriminator is randomly excluded from the ensemble with a specific probability L, as shown in Equation  across all tests. We use Mean Per Joint Position Error (MPJPE) on the training set.We conducted our experiments on a computer equipped with an NVIDIA Tesla V100 GPU and implemented our algorithm using PyTorch. For the task of multi-view image feature extraction, we employed a pose estimation model built upon ResNet-50 . The bacWith the rapid advancement of 3D human pose estimation, the motion capture (mo-cap) system is emerging as an effective means to augment datasets. However, the Mocap system, originally designed for laboratory use, poses challenges for implementation in natural settings. A discernible visual disparity exists between laboratory data and outdoor scenarios, often leading to a performance gap between models trained in these different environments. Our initial experiment seeks to ascertain the applicability of the proposed method in this context.Results: The results of the different methods are summarized in As illustrated in Despite considerable advancements in multi-person 3D human pose estimation, numerous challenging scenarios persist, including obscured keypoints, invisible keypoints, and crowded backgrounds that hinder accurate keypoint localization. For a generalized 3D human position estimation system, precise operation in diverse social interaction environments is vital. This subsection examines the efficacy of 3D pose estimation in the context of group interactions.Results: Our findings, as well as those of other baselines, are included in In The training process of the adversarial domain adaptation component is characterized as a zero-sum, non-cooperative contest between the base feature extractor and the domain discriminator. As the domain discriminator learns to distinguish between source and target domain features, the feature extractor simultaneously learns domain-invariant feature representation to confound the domain discriminator, thereby enhancing cross-domain adaptation capability.Due to the single-adversarial method’s limited distribution alignment ability, the improvement is small and erratic. Mode collapse, as a consequence of overfitting to the feedback of a single discriminator, shows up as difficulties with convergence.The multi-adversarial domain adaptation method has been empirically validated as an efficacious technique for improving domain adaptation capabilities ,64. Thisd can complicate training. By striving to enhance the generalizability of the feature representations created by the base feature extractor, this type of dropout can be seen as a form of regularization. We found that employing any dropout rate within the range (0 < d ≤ 1) consistently outperformed a static ensemble of adversaries (d = 0). Specifically, utilizing a moderate dropout rate often led to superior results, as previously noted in [noted in ,65.The central concept of the aforementioned components is to acquire transferable feature representations by confusing a domain discriminator in a two-player game, leading to state-of-the-art results in various visual tasks ,41,64. DVoxelpose utilizes a 3D-CNN to estimate the 3D locations of the body joints based on the feature volume. This method suffers from a large number of domain-specific parameters. We believe that only partial “transferable parameters” are essential for learning domain-invariant information and generalizing well in UDA; on the other hand, “untransferable parameters” tend to suit domain-specific information and rarely generalize.In order to lessen the negative impacts of domain-specific knowledge during the learning process, we introduce Transferable Parameter Learning (TransPar) into our network, providing unique update rules for these two categories of parameters.While In this research, we present the Domain Adaptive VoxelPose model, which is an efficient method for cross-domain multi-view multi-person 3D pose estimation. Without needing any extra labeled data, one can build a robust pose estimator for a new domain using our method. Our strategy is based on the state-of-the-art VoxelPose model. We introduced a domain adaptation component and the dropout mechanism for our network based on the theoretical analysis that we did for cross-domain pose estimation. A collection of dropout discriminators is used to learn a robust model for the domain. We also introduced transfer parameter learning into our network, which used distinct updating rules for two types of parameters. These components are meant to alleviate the performance drop that is caused by domain shifting. Our methodology is validated on several different domain shift scenarios, and the adaptive method relatively outperforms the baseline VoxelPose method. This demonstrates the approach’s efficiency for cross-domain, multi-view, multi-person 3D pose estimation. In summary, our approach offers industrial advantages by strengthening the robustness of multi-view, multi-person estimation models in real-world conditions, minimizing errors and false positives, increasing operational efficiency, enhancing safety, and alleviating the burden of manual data labeling. However, it’s important to address some inherent limitations in the methodology that could potentially affect its precision and scalability.Primarily, our model confronts the challenge of quantization errors during the transition from 2D to 3D representations. This issue is particularly crucial as it may lead to discernible inaccuracies, undermining the algorithm’s efficacy. Secondly, the computational expenses for training our algorithm escalate with an increasing number of views. This scalability issue limits its applicability in contexts requiring large-scale, high-throughput processing. Moreover, our model currently lacks the incorporation of spatial-temporal data, despite the inherent correlation of human body postures over time. This limitation is especially poignant when the model needs to discern closely intertwined joints or intricate inter-human interactions.The limitations of the current model naturally guide our future research. One promising direction is the development of an end-to-end framework aiming to mitigate the cumulative effects of quantization errors. Furthermore, we recognize the urgent need for the seamless integration of spatial-temporal data to improve the model’s precision in capturing human interactions and movements over time.


We further used a combination of molecular dynamics (MD) simulations, electrophysiology, and mutagenesis to investigate the molecular mechanism of EAG1 current inhibition by imipramine binding to the PAS domain. We found that Tyr71, located at the entrance to the PAS domain cavity, serves as a “gatekeeper” limiting access of imipramine to the cavity. MD simulations indicate that the hydrophobic electrostatic profile of the cavity facilitates imipramine binding and in silico mutations of hydrophobic cavity-lining residues to negatively charged glutamates decreased imipramine binding. Probing the PAS domain cavity-lining residues with site-directed mutagenesis, guided by MD simulations, identified D39 and R84 as residues essential for the EAG1 channel inhibition by imipramine binding to the PAS domain. Taken together, our study identified specific residues in the PAS domain that could increase or decrease EAG1 current inhibition by imipramine binding to the PAS domain. These findings should further the understanding of molecular mechanisms of EAG1 channel regulation by ligands and facilitate the development of therapeutic agents targeting these channels.Ether-a-go-go (EAG) channels are key regulators of neuronal excitability and tumorigenesis. EAG channels contain an N-terminal Per-Arnt-Sim (PAS) domain that can regulate currents from EAG channels by binding small molecules. The molecular mechanism of this regulation is not clear. Using surface plasmon resonance and electrophysiology we show that a small molecule ligand imipramine can bind to the PAS domain of EAG1 channels and inhibit EAG1 currents  Ether-a-go-go (EAG) channels, also known as Kv10.1 and KCNH1 channels, contribute to the regulation of neuronal excitability , 3 and gA). The centrally located pore of the channel is formed from the PDs of all four subunits, with the p-loops between the S5 and S6 segments lining the selectivity filter of the channel. Each subunit of EAG channels also contains an N-terminal Per-Arnt-Sim (PAS) domain and a C-terminal cyclic nucleotide-binding homology (CNBH) domain  and EAG-like K+ (ELK) subfamilies , 18. SimFig. 1) domain , 20, 21.) domain , 23.Figuvia the binding to the PAS domains, so far, the only evidence in support of this mechanism is that the deletion of the PAS domain decreases the inhibition of EAG currents by chlorpromazine  to show that a small molecule ligand imipramine directly binds to the PAS domain of EAG1 channels. We then used electrophysiology and site-directed mutagenesis, guided by molecular dynamics (MD) simulations, to further examine the molecular mechanism of EAG1 current inhibition by imipramine.via binding to the PAS domain. Docking of imipramine into the PAS domain cavity and accompanied MD simulations identified Tyr71, located at the entrance to the cavity, as a “gatekeeper” residue, limiting access of imipramine to the binding site inside the cavity. Consistent with this, substitution of Tyr71 with glycine and valine increased EAG1 current inhibition by imipramine, while substitution with phenylalanine had no effect on the current inhibition by imipramine. MD simulations indicated that the hydrophobic profile of the cavity facilitates the binding of imipramine and in silico mutations of four PAS domain lining hydrophobic residues to negatively charged glutamates decreased imipramine binding. Computational modeling-driven mutagenesis studies of residues lining the PAS domain cavity identified residues D39 and R84 as essential for the EAG1 current inhibition by imipramine binding to the PAS domain. Consistent with this, substitutions of D39 and R84 residues with glycine substantially decreased EAG current inhibition by imipramine.We found that deletion of the PAS domain substantially decreased EAG1 current inhibition by imipramine, suggesting that the effect of imipramine is, at least in part, mediated via an open-pore block . Because of non-specific binding of imipramine to the CM5 sensor chip at high concentrations, we were unable to obtain the complete concentration dependence of the binding necessary to determine the binding affinity. However, the concentration-dependent increase in the SPR response for the tested imipramine concentrations indicates that similar to chlorpromazine, imipramine directly binds to the PAS domain of EAG1 channels.To test if imipramine can directly bind to the PAS domain of EAG1 channels we immobilized purified isolated PAS domains of EAG1 channels on the CM5 sensor ship using amine coupling and determined the SPR response over the range of concentrations of imipramine injected over the immobilized PAS domains. The SPR response increased with the increase in the imipramine concentration B. Becaus50 of 58.1 ± 9.7 μM at +50 mV  observed at the highest examined ligand concentrations, suggesting that direct binding of imipramine to the PAS domain is contributing to the EAG1 current inhibition  technique in the absence and presence of imipramine. Imipramine inhibited EAG1 currents in a concentration-dependent manner with the ICd before .Figure 2D). To sample thermodynamically available conformations, we used replica exchange solute tempering (REST2) (D). Consistent with these results, replacing the tyrosine at position 71 with a smaller glycine and valine increased EAG1 current inhibition for imipramine . These results strongly suggested that Y71 functions as a “gate-keeper” residue, restricting access of imipramine to the PAS domain cavity.To determine the structural basis of imipramine binding to the PAS domain, we performed computational modeling. While KCNH are the only known ion channels to include PAS domains as part of their amino acid sequence, PAS domain fold is quite common in non-ion channel proteins. In the PAS domains of other proteins, ligands typically bind inside the PAS domain cavity formed by the antiparallel β-strands sandwiched between the α-helices , 29, 30. (REST2)  to eluci (REST2) D. ConsisTo further investigate if the charge at position 71 may have an effect on EAG1 current inhibition by imipramine, Y71 was substituted for either glutamate or arginine. Imipramine inhibition of EAG channels with Y71E or Y71R mutation was not statistically significantly different from each other, suggesting that the effect of Y71 on EAG1 current inhibition by imipramine does not involve electrostatic interactions.A). The hydrophobic profile of the cavity should facilitate the binding of the hydrophobic tricyclic ring structure of imipramine. To investigate the dynamics of imipramine binding, the initial poses for imipramine binding obtained with the REST2 simulations were subjected to 100 ns MD simulations. The MD simulations indicated that imipramine remained in the binding pocket with its three rings facing the inside of the PAS domain cavity (B).The analysis of the electrostatic profile of the PAS domain indicated that the PAS domain cavity is lined with predominantly hydrophobic residues A. The hyn cavity B.Figure in silico generated mutant PAS domains with the cavity lining hydrophobic residues V80, V83, F87 and F130 mutated to glutamates (4E mutant) or phenylalanines (4T mutant). The mutant PAS domain structures were generated using Amber force field with the initial structures taken from the REST2 simulations. Imipramine was docked into the mutant PAS domains and subjected to 100 ns MD simulations. For the 4E mutant PAS domain imipramine drifted away from the cavity after the first 10 ns (C), while for the 4T mutant PAS domain imipramine stayed inside the cavity for the duration of the simulation (D). We next calculated imipramine binding free energies for the wild-type (WT) and two mutant PAS domains using MM-PBSA . To investigate the contribution of these residues to the imipramine inhibition of EAG1 currents, they were substituted one by one for glycine, and currents from the mutant channels were recorded over the range of imipramine concentrations (B–H). Substituting glycine for R84 showed a statistically significant increase in the IC50 of EAG1 current inhibition and this increase was statistically the same as the one observed for the ΔPAS mutation . To further examine the contribution of D39 and R84 residues to the current inhibition, we generated a double mutant D39G/R84G EAG1 channels. The double mutant channels generated currents and showed an even larger decrease in the current inhibition at 300 μM imipramine concentration than the one observed for the individual mutations . Most of the mutations that affected IC50 of EAG1 channel inhibition by imipramine had no statistically significant effect on the half-maximal activation voltage (V1/2) of the EAG1 channel, except for Y71E and D39G that generated a mild <5 mV shift to more depolarized potentials in V1/2 and Y71V that generated <5 mV shift to more hyperpolarized potentials . Importantly, Y71G that increased EAG1 current inhibition by imipramine and D39G/R84G double mutation that decreased EAG1 current inhibition had no effect on V1/2 (O and P). These results indicate that the considered mutations specifically affected EAG1 channel inhibition by imipramine without causing changes in imipramine-independent channel gating mechanisms.It is possible that the considered mutations in EAG1 channels substantially alter channel gating obscuring the interpretation of the mechanisms of imipramine inhibition. Contrary to this possibility, the current profile in the absence of the inhibitors was very similar for all mutant channels tested, except ΔPAS mutant that had been shown to affect the voltage-dependence of EAG1 channels , 35 Fig, A–N. MoO–P, and . Three mtentials Q. Import on V1/2 , O and PIn this study, we showed that imipramine inhibits EAG1 channels through binding to their PAS domains and identified specific PAS domain residues that facilitate or limit EAG1 current inhibition by imipramine. MD simulations coupled with electrophysiology and site-directed mutagenesis indicated that the PAS domain residue Y71 functions as a “gate-keeper”, limiting the PAS domain cavity access and EAG1 current inhibition by imipramine. In contrast, the hydrophobic profile of the cavity, and residues D39 and R84 flanking the entrance to the cavity facilitated EAG1 current inhibition by imipramine. These results provide insight into the molecular mechanism of EAG1 channel inhibition by imipramine binding to the PAS domain.via small molecule binding to their PAS domains. This regulation can be exploited for therapeutic purposes and it is still possible that some naturally occurring signaling molecules could regulate the activity of these channels via binding to their PAS domains in the native cellular environment.While it has been well established that proteins containing the PAS domain structural fold serve as ligand-binding modules, coupling the protein function to changes in the ligand concentration during cellular signaling , 29, 36,Before this study, the functional link between the direct binding of small molecule ligands to the PAS domain and current inhibition was only supported by the decrease in current inhibition by chlorpromazine for EAG1 channels lacking the PAS domain . HoweverD). The MD simulations show that in order to accommodate imipramine, Y71 has to move away from the cavity. This suggests that Y71 functions as a gate-keeper residue limiting access of imipramine to the cavity. In agreement with this, the substitution of Y71 with structurally similar phenylalanine did not affect EAG1 current inhibition by imipramine, while the substitution of Y71 with a smaller glycine or valine increased EAG1 current inhibition by imipramine. Since chlorpromazine is structurally similar to imipramine, most likely its binding to the PAS domain inhibits EAG1 currents via a similar mechanism. Interestingly, a tyrosine residue of Aryl hydrocarbon Receptor (AHR) transcription factor PAS domains, located at the position equivalent to the one of Y71 in the PAS domain of EAG1 channels, was also proposed to function as a “gatekeeper” residue regulating access and affinity of the ligands  was higher than the IC50 determined for EAG1 currents recorded from HEK293 in the whole-cell configuration (∼2 μM)  . This is (∼2 μM) , 39. Mor (∼2 μM) , 42, 43.he brain , 45, 46,Gain-of-function mutations in EAG1 channels are associated with severe neurological disorders, including epilepsy, Zimmerman–Laband and Temple–Baraitser syndromes , 6, 7, 8Xenopus laevis oocytes were purchased from Ecocyte Bioscience or Xenopus1 and injected with the cRNA using a Nanoinject II oocyte injector (Drummond).The cDNA encoding wild-type (WT) mouse EAG1 channels in pGH19 vector was kindly provided by G. Robertson . The mutant ΔPAS EAG1 channel in pGH19 with the deletion of the PAS domain residues 2 to 130 was generated by Bio Basic Inc and verified by DNA sequencing (Genewiz). Mutant EAG1 channels with single amino acid substitutions were generated by Genewiz and verified by DNA sequencing. cRNA was transcribed using the T7 mMessage mMachine kit (Thermo Fisher Scientific). Defolliculated For EAG1 current recordings, oocytes were placed into a handmade chamber containing a bath solution for the current recording. The currents were recorded using a TEVC technique with OC-725C amplifier (Warner Instruments) and pClamp11 software (Molecular Devices). The signals were digitized using Digidata 1550 (Molecular Devices). Patch pipettes were pulled from borosilicate glass and had resistances of 0.7 to 1.5 MΩ. The recording (bath) solution contained 96 mM NaCl, 4 mM KCl, 0.1 mM CaCl2, 1.8 mM MgCl2, and 5 mM HEPES, pH 7.5. The pipette solution contained 3 M KCl. The WT and mutant EAG1 currents were elicited by applying a series of 0.1-s voltage pulses ranging from −100 to +50 mV in 10-mV increments from a holding potential of −80 mV, followed by a 0.15-s voltage pulse to −100 mV. The currents were not leak-subtracted. Capacitive transients were excluded from the figures for clarity.50, the concentration of imipramine at half-maximal current inhibition, the plots of the percent current inhibition versus the concentration of imipramine were fitted with a Hill equation:0 represents the minimum % inhibition, Y∞ the maximum, and n is the Hill coefficient.Imipramine was purchased from Sigma. Imipramine stocks were prepared in high-purity water and then diluted with the bath solution to obtain the range of concentrations used for the dose-response experiments. The bath solution was changed using a gravity-fed solution changer. To determine the ICss/(V −Vrev), where Iss is the steady-state current recorded at the end of the 0.1 s voltage pulses, V is the test voltage and Vrev is the membrane reversal potential for K+ selective channels. For our experiments, Vrev was −83.9 mV, calculated based on the bath concentration of K+ of 4 mM and the intracellular K+ concentration of 109.5 mM (max) in the absence of imipramine. The normalized conductance was then plotted against the test voltage, and the plots were fit with a Boltzmann equation:1/2 is the half-maximal activation voltage (mV), and s is the slope of the relation (mV).To analyze voltage-dependence of the steady-state currents recorded from WT and mutant EAG1 channels with TEVC, the conductance (G) was calculated as G = I109.5 mM . The conp values <0.05 were considered significant. The data analysis and fitting of the plots were performed in Clampfit (Molecular Devices) and Origin .For each mutant and imipramine concentration, more than five recordings were obtained, each from a different oocyte harvested from at least three different frogs. The ligand concentrations were changed from low to high over the course of the experiments. The absence of detectable currents for F87G/F130G, V80G/F87G, and F87G/F130G double mutant channels was confirmed by recordings from more than 30 different oocytes harvested from at least three different frogs. The error bars on the figures correspond to the Standard Deviation (SD). Statistical analysis was performed using Student’s t-tests. 4hoi) (+ and Cl− ions were added to the system at 150 mM concentration. Molecular dynamics (MD) simulations were performed using CHARMM36   was prep4hoi) . Na+ andCHARMM36  forcefieCHARMM36  and NAMDCHARMM36 . A forceCHARMM36  and longCHARMM36 . LangeviCHARMM36 . A timesCHARMM36 . We firs+ and Cl− were added to the system to a final concentration of 150 mM. Simulations were performed with a 2 fs timestep at 310K temperature and pressure of 1 bar. A velocity-rescaling thermostat was used to maintain the temperature at 310K. During equilibration, the pressure was maintained at 2 bar using Berendsen barostat. During the production run, the system was simulated under an NPT ensemble with Parinello-Rahman barostat to maintain pressure at 1 bar using a compressibility of 4.5 × 10−5 bar−1 and a coupling constant of 0.5 ps. The simulations lasted for 100 ns for each complex. Here we used 80 and 2 as the solute and solvent dielectric constants.To simulate the ligand-bound PAS domain, we selected a snapshot of a replica exchange simulation where the binding pocket in the PAS domain was readily open and the Y71 was no longer blocking the entrance to the PAS domain cavity. Next, we used Autodock Vina  to dock Escherichia coli cells as previously described (2+ affinity chromatography using HisTrap HP column (Cytiva) and eluted on a linear gradient to 500 mM imidazole. The protein was further purified on a Superdex 200 Increase 10/300 column (Cytiva) equilibrated with 150 mM KCl, 1 mM TCEP, 10% glycerol, and 30 mM HEPES, pH 7.5. The protein concentration was determined with Bradford Protein Assay Kit (Pierce).DNA encoding PAS (residues 7–136) of mouse EAG1 channels (GI # Q60603) was synthesized by BioBasic and subcloned into pETM11 bacterial expression vector containing an N-terminal 6-His affinity tag followed by a tobacco etch virus (TEV) protease cleavage site. The DNA sequences were verified by sequencing (Genewiz). The PAS domains were expressed in BL21 (DE3) escribed , 63. TheThe purified protein was stored at −80 °C in aliquots and thawed immediately before the experiments. The molecular weight of the PAS domains used in the study was verified on Coomassie Blue-stained gels and with mass spectrometry at the Proteomics and Metabolomics Core Facility at Georgetown University Medical Center.All SPR binding experiments were performed on a CM5 chip (Cytiva) at 25 °C using a Biacore T200 Instrument . To probe imipramine binding to the PAS domains, the purified PAS domains were immobilized on the CM5 chip using a standard amine coupling chemistry in the presence of 10 mM sodium acetate buffer at pH 5.5 as the immobilization buffer (buffer used to directly dissolve ligands), as we described before . HBS-P btib5@georgetown.edu or jbklauda@umd.edu.All data are contained in the article or available on request by contacting the corresponding authors: This article contains The authors declare that they have no conflicts of interest with the contents of this article.


The results of applications of various methods for measuring the parameters of high-speed loading using a strain gauge, a fiber Bragg grating located on a metal measuring rod and an interferometer monitoring the movement of the free boundary of the end of the rod are presented. Numerical simulation confirmed the adequacy of the description of the shock-wave process according to experimental data and showed that, with the thickness of the adhesive layer fixing the fiber Bragg grating and the strain gauge on a dimensional rod up to 100 µm, the deformation parameters of the sensors correspond to the parameters of the stress–strain state of the rod. Experimentally, a good correspondence of the results of measuring the magnitude of the relative deformation at a pulse duration of 10–100 µs using sensors of various types is shown, and an estimate of the limit values of the measured values of the deformation wave parameters is given. The pulsed mechanical strength of materials significantly depends not only on such properties as the speed of sound, Young’s modulus, static tensile strength, temperature, etc., but also on loading modes. It is known that when testing a material in different loading schemes in shock-wave ,3,4,5,6 In particular, the efficiency of electrophysical and electric power equipment is determined by the design features and the set of strength characteristics of the materials used. At the same time, the mechanical destruction of a solid dielectric leads to a loss of electrical strength and a catastrophic change in the operating conditions of equipment elements. This situation fully applies to pulsed magnetic systems, the solid insulation in which is in an electrically and mechanically stressed state.To create strong multi-component structures operating under pulsed and dynamic loads, it is necessary to take into account the characteristics of the properties of materials under these conditions. Identifying the features of deformation and destruction of materials requires monitoring the parameters of the acting pulse and the reaction of the material.The measurement of the parameters of the fast-flowing processes of shock loading and deformation has its own specifics. So, in , the maiAn overview of some methods for high-rate deformation and shock studies is given in .One of the widely used directions in the mechanics of a deformable body is tensometry. With the help of strain gauges, the parameters of shock-wave action and the reactions of materials are recorded when studying the deformation dependencies of various materials at deformation rates at the level of 103 1/s .The use of interferometric measurement methods, as well as methods using strain gauges, despite the convenience and accuracy, is difficult in conditions of limited geometry of the deformable object and with a high level of electromagnetic interference. Other measurement methods, for example, those described in , also haThe main purpose of this work is to substantiate the applicability of fiber Bragg gratings for measuring the parameters of the high-speed deformation of materials based on the comparison of data obtained using various experimental methods and the results of numerical simulations.A large number of scientific papers have been devoted to the theory and practical use of FBG ,34,35,36In most cases, interrogators are used to determine the deformation parameters or temperature of FBG, which allows for the achievement of a high measurement accuracy but does not allow for the study of high-speed deformation. Systems for measuring the parameters of the high-speed deformation of objects using FBG are used quite rare and are not described in as much detail in the scientific literature as low-frequency systems. One of the directions is the shock-wave and detonation diagnostic systems described in ,46,47,48A simple alternative method for measuring the velocity stretching and compression of objects using FBG, but with a limited dynamic range of measurements, is described in . In 50]50], the Both strain gauges and sensors based on fiber Bragg gratings must have mechanical contact with the object under study. Usually, such sensors are attached to the surface of the object with glue (adhesive). It is known that the parameters of the adhesive layer affect the accuracy of measurements. This effect can be reduced by using more advanced sensor designs  for filmThe numerical simulation method allows us to investigate the effect of deformation waves on various types of sensors used, to evaluate the effect of the method of connecting the sensor to the object under study, in particular, the thickness and stiffness of the adhesive layer, on the wave parameters directly in the sensitive area of the sensor, which is difficult to perform experimentally. Therefore, the numerical simulation contains two parts, discussed below: modeling the deformation wave directly in the sensors and modeling the sensor signal under such an impact.Aluminum measuring rod: Young’s modulus of 71 GPa, Poisson’s ratio of 0.33, and density of 2770 kg/mPhenolic substrate of the strain gauge: Young’s modulus of 32 GPa, Poisson’s ratio of 0.3, and density of 1800 kg/mFused quartz parallelepiped: Young’s modulus pf 73 GPa, Poisson’s ratio of 0.2, and density of 2200 kg/mGlue: Young’s modulus varied in the range of 0.15–15 GPa, Poisson’s ratio of 0.3, and density of 1000 kg/mThe propagation of the compression wave along the rod , simulatThe external action was set at the left end of the rod  in the fAs a result of the calculation, the dependence of the displacement and the speed of movement of the ends of the rod are obtained , allowinhe ratio  ν=2ϵνs, It follows from the calculation results that the relative deformation is distributed unevenly over the elements simulating the surface of the strain gauge and fiber with Bragg grating, if assuming that the deformation in a parallelepiped is similar to the deformation in an optical fiber. The degree of unevenness depends on the elastic modulus and geometric dimensions of the strain gauge, the Bragg grating and the adhesive layer. Nevertheless, according to the calculation results, it can be seen that with the modulus of elasticity of the adhesive layer of 1.5 GPa in the middle of the strain gauge and the Bragg grating, the relative deformations coincide with the relative deformations on the surface of the rod both in amplitude and in the shape of the pulse .An example of the dependence of the ratio of the value of It follows from the results of numerical simulation that a decrease in the elastic modulus of the adhesive layer leads to an increase in the heterogeneity of the distribution of the relative deformations of the optical fiber and the strain gauge. For example, there is not only an increase in the inhomogeneity of the distribution but also a decrease in relative deformations in its middle by about 2 times in the case of an elastic modulus of 0.15 kPa in the Bragg grating of 10 mm length.It can also be noted that an increase in the length of the glued part of the fiber to 100 mm, while maintaining the length of the Bragg grating equal to 10 mm, leads to a uniform distribution of relative deformations in the Bragg grating region, numerically coinciding with the relative deformations on the surface of the rod.For the strain gauge with the above parameters, at the calculated values of the elastic modulus of the adhesive, the relative deformation of the sensor material did not differ much from the one in the rod.Numerical simulation of the propagation of the deformation wave at different thicknesses of the adhesive layer between the sensors and the surface of the rod was also performed. In particular, when the thickness of the adhesive layer increases to 300 µm, the value of According to the simulation results, the correct choice of the elastic modulus of the adhesive and the thickness of the adhesive layer allow us to obtain the correct values of the measured value of the relative deformation of the rod and, more generally, of other objects.t: Numerical simulation of the signal caused by the reflection of radiation from FBG was performed in the Gaussian approximation of the radiation spectrum of a semiconductor laser and the reflection spectrum of FBG, similar to the work . Let thek is the proportionality coefficient, Then, the output voltage  follows : (3)U(t)Gaussian .Let us choose the value t = 0 (at the moment of the maximum relative compression of FBG). The greater the At operating point 1, compression of the FBG leads to an increase in the output signal, due to an increase in ints 2–6 , when FBThe output signal is not inverted with respect to the strain pulse of FBG at the working point For weak mechanical action, as shown in , the maxThe shape of the output signal will change, but all the patterns discussed above will remain for any other dependence (2) of the relative elongation on time.The scheme of the experimental setup is shown in The installation consists of the aluminum rod 1 with a rectangular cross-section of 10 mm × 10 mm and a length of 1 m; strain gauge sensor 3 (BF350-3AA) and fiber Bragg grating 4 glued to the rod at the distance of 0.5 m from the input end; mirror 5 glued to the output end of the rod; and Michelson interferometer 6, the scheme of which is given in a simplified form. Optical radiation for operation in the FBG sensor was created by semiconductor laser diode 14 placed on the Peltier element. The radiation reflected from the FBG, going through circulator 15, was transformed by photo detector 16. The fiber-optic circulator can be replaced with a splitter if there is a reserve of radiation power. The radiation that passed through the FBG was transformed by photodetector 17 in order to control the integrity of the optical fiber with the Bragg grating.The measurements were performed using FBG with the following parameters: the resonant wavelength The shock action at end 2 launched a compression wave that propagated along the rod, also causing compression of the sensors used and, reaching the end of the rod with attached mirror 5, caused its displacement, which created an interferometer signal. The reflected wave, which is a stretching wave, propagated along the rod in the opposite direction. An example of a waveform obtained directly from the oscilloscope is shown in S of the mirror by a quarter of the wavelength   near at The results of the numerical simulation can be interpreted as follows. When exposed to the left end, a compression wave is formed in the rod. The compression wave reaches the right free end and is reflected by the stretching wave. That is, at the first perturbation, only a compression wave affects the left end, and a compression and stretching wave affect the right end, so the displacement and velocity of the right end is 2 times greater than the left. When a re-reflected wave from the right one comes to the left end, then the stretching wave will also act on the left end and then the compression wave and its velocity and displacement will also be twice as large as the primary displacement caused only by compression under external pressure  with a rectangular one  does not significantly affect the simulation results, at least in cases where the mechanical stress in the glass rod is approximately equal to the stress in the measuring rod. So, for such a case, some changes in the parameters of the glue, the thickness of its layer and the size of the glass rod do not lead to a change in the design speed and the magnitude of deformation in the glass rod. If the shape of the cross-section of the light guide used in the simulation affects the calculation result, then, presumably, this can only be the case when the value of the maximum mechanical stress in the optical rod will significantly differ from the mechanical stress in the dimensional rod itself.The model used also assumes that the mechanical parameters of the optical fiber cladding do not differ from these parameters of the fiber core with Bragg grating made in it, as well as the parameters of the quartz glass set during modeling.It can also be assumed that the measurement technique is not significantly affected by either the measurement of the resonant wavelength of the FBG itself or the measuring rod and the adhesive layer when the temperature changes, although they all have different coefficients of thermal expansion, since the temperature change can be considered a slow process and it is assumed that the temperature effect is compensated by the movement of the working point, i.e., the wavelengths of a semiconductor laser.The mechanical pulse of a rarefaction or compression wave in a real experiment, as well as in modeling, is not a Gaussian function. However, the use of the Gaussian approximation discussed above allows us to obtain analytical expressions for the output signal, which facilitates the analysis of the data obtained and allows us to describe the main patterns. The use of a Gaussian time function other than (4) affects only the shape of the pulse but does not change either the maximum value of the signal at the same maximum value of the stretching (compression) of the FBG or the principle of choosing the wavelength of a semiconductor laser (working point) for measurements.The interferometer has the greatest sensitivity to longitudinal deformation. However, technically, it is difficult to place the reflecting mirror of the interferometer directly on the deforming surface, maintaining only sensitivity to longitudinal displacements and providing a weak influence of transverse displacements. It should also be noted that when moving the interferometer mirror at a distance significantly exceeding FBG-based strain sensors glued to the surface of the object under study allow you to measure the deformation parameters using the technique described above. The deformation dynamic measurement range of such a technique is limited, and the sensitivity is not adjustable, but the frequency range of thw measurements is limited only by the bandwidth of the photocurrent amplifier and can reach hundreds of megahertz. The dynamic range of measurements can be increased using a semiconductor laser, a super luminescent diode with a larger spectrum width or several FBGs with different resonant wavelengths.The use of numerical modeling allowed us to prove that the adhesive layer gluing the sensors to the rod, with a layer thickness of 100 µm or less and a Young’s modulus of 1.5 GPa or more, as well as the sensors themselves, does not practically affect the accuracy of measuring the magnitude of deformation and the shape of the signal. That is, such a method of attaching sensors can be used when measuring the parameters of the deformation wave in the rod and determining the parameters of materials using the Hopkinson method, as well as, presumably, when studying the deformation waves of more complex objects. The correspondence of the measurement results of the deformation wave parameters found through three different methods  with a relative deformation value of up to 50


Coxsackievirus B3 (CVB3) is an enterovirus that causes diseases such as pancreatitis and myocarditis in humans. Approximately 10% of the CVB3 RNA genome consists of a highly structured 5′ untranslated region (5′ UTR) that is organized into six domains and contains a type I internal ribosome entry site (IRES). These features are common to all enteroviruses. Each RNA domain plays a vital role in translation and replication during the viral multiplication cycle. We used SHAPE-MaP chemistry to generate secondary structures of the 5′ UTR from the avirulent strain CVB3/GA and the virulent strain CVB3/28. Our comparative models show how key nucleotide substitutions cause major restructuring of domains II and III of the 5′ UTR in CVB3/GA. Despite these structural shifts, the molecule maintains several well-characterized RNA elements, which allows persistence of the unique avirulent strain. The results shed light on the 5′ UTR regions serving as virulence determinants and those required for fundamental viral mechanisms. We used the SHAPE-MaP data to produce theoretical tertiary models using 3dRNA v2.0. These models suggest a compact conformation of the 5′ UTR from the virulent strain CVB3/28 that brings critical domains into close contact. In contrast, the model of the 5′ UTR from the avirulent strain CVB3/GA suggests a more extended conformation where the same critical domains are more separated. Our results suggest that the structure and orientation of RNA domains in the 5′ UTR are responsible for low-efficiency translation, low viral titers, and absence of virulence observed during infection by CVB3/GA.IMPORTANCE Human enteroviruses, which include five different species and over 100 serotypes, are responsible for diseases ranging from mild respiratory infections to serious infections of pancreas, heart, and neural tissue. All enteroviral RNA genomes have a long and highly structured 5′ untranslated region (5′ UTR) containing an internal ribosome entry site (IRES). Major virulence determinants are located in the 5′ UTR. We present RNA structure models that directly compare the 5′ UTR derived from virulent and avirulent strains of the enterovirus coxsackievirus B3 (CVB3). The secondary-structure models show rearrangement of RNA domains known to be virulence determinants and conservation of structure in RNA elements known to be vital for translation and replication in the avirulent strain CVB3/GA. The tertiary-structure models reveal reorientation of RNA domains in CVB3/GA. Identifying the details of structure in these critical RNA domains will help direct antiviral approaches to this major human pathogen. Enterovirus of the family Picornaviridae. Infection by CVB3 causes pancreatitis, meningitis, and myocarditis in humans (–Coxsackievirus B3 (CVB3) is a positive-strand RNA virus in the genus n humans , 2. In fn humans . The codn humans  and a 2,n humans , 7. Thesn humans –10 and a humans –.By orchestrating viral processes throughout the infection cycle, the 5′ UTR plays a pivotal role in determining multiplication efficiency and virulence. Unsurprisingly, multiple studies have shown that CVB3 virulence is modulated by RNA sequences and RNA elements in the 5′ UTR –16. Give–16–p-toluene sulfonate (CMCT), were used to probe specific nucleotides in CVB3 (N-methylisatoic anhydride (NMIA) and 1-methyl-7-nitroisatoic anhydride (1M7), allow for structural analysis at single nucleotide resolution with higher statistical accuracy using backbone-specific chemicals , 2-keto-3-ethoxybutyraldehyde , and 1-cyclohexyl-(2-morpholinoethyl) carbodiimide metho- in CVB3 , 20. Thehemicals . The senhemicals –26. SHAPemicals –, 27–29. cals –27–, 31.In this study, we used SHAPE-MaP, TurboFold II, and 3dRNA v2.0 to generate secondary and tertiary models for 5′ UTR RNA from CVB3/28 and CVB3/GA. These models enable direct structural comparison of 5′ UTRs derived from virulent and avirulent viruses. At the secondary-structure level, the models show significant rearrangement in domain II, also called stem-loop II (SLII), a domain that is well known for involvement in the virulence phenotype in CVB3 and other enteroviruses , 32, 33.The 5′ UTR extends from the 5′ end of the genome to the start codon for polyprotein translation, position 743 in CVB3/28 and position 744 in CVB3/GA. A sequence alignment showing the positions of the 5′ UTR that differ between the two strains from the 5′ end to the polyprotein start codon is shown in We determined secondary structures for the 5′ UTR of the virulent CVB3/28 strain and the avirulent CVB3/GA strain using SHAPE-MaP and TurboFold II . Three bThe secondary-structure model derived from SHAPE-MaP analysis for CVB3/GA shows that the general domain organization is conserved compared to that of CVB3/28. However, nucleotide substitutions in each domain lead to secondary-structure changes, some of which are minor and some of which result in substantial reorganizations.Domain I, commonly called the cloverleaf, has only six positions with nucleotide substitutions and shows a secondary structure in CVB3/GA that is nearly identical to that of CVB3/28. Two substitutions occur in base-paired positions that maintain the helix by swapping a GC pair with a GU pair. The remaining substitutions occur in single-stranded positions. The change of U to A at position 54 adds a Watson-Crick pair to stem-loop 1d and shortens a three-nucleotide symmetric internal loop to a two-nucleotide symmetric internal loop. Interestingly, the three-nucleotide symmetric internal loop of CVB3/28 has been shown by nuclear magnetic resonance (NMR) to form pyrimidine-pyrimidine noncanonical pairs  that adoDomains II and III show the greatest frequency of nucleotide substitutions and also display the greatest shift in secondary structure . In CVB335–In the merged superdomain, the closing helix sits below relatively long single-stranded regions on both the 5′ and 3′ sides. The 5′ single-stranded region includes nucleotide substitutions at positions 101 to 103, which interrupt the pairs that form long-range interactions 1 (LR-1) in CVB3/28, also making the closing helix of the merged superdomain possible in CVB3/GA. The 3′ single-strand region emerges from the closing helix of the merged domain III. This region includes positions that are part of the domain IV closing helix in CVB3/28, so they have an influence on the secondary structure of both domains III and IV.Interestingly, the five-base helix and six-base helix separated by a two-nucleotide bulge loop (positions 110 and 111) which closes domain II in CVB3/28 are also present above the merged domain III in CVB3/GA. In addition, the stem-loop that caps domain II in CVB3/28 is also present in CVB3/GA. The stem region of this apical stem-loop has 8 nucleotide differences between CVB3/28 and CVB3/GA, but all are compensatory. In the capping region of domain II in CVB3/GA, the apical stem-loop is joined by a second stem-loop closed by positions 129 and 139. This stem-loop in CVB3/GA, made possible by substitutions at positions 130 and 138, replaces a functionally important bulge loop from 134 to 138 in CVB3/28 that is highly conserved in enteroviruses  of CVB3/28 are replaced by the domain III closing helix and domain II closing helix. Overall, domains II and III in CVB3/GA show substantial rearrangements in secondary structure compared to those domains CVB3/28, but several individual local secondary-structure elements remain unchanged.When the secondary structures of domain IV in CVB3/28 and CVB3/GA are compared, the conserved structural elements far outnumber the differences . As mentOur SHAPE-MaP results show that despite having 21 nucleotide substitutions, the very long and complex domain V is nearly identical in CVB3/28 and CVB3/GA . The cloSecondary-structure interactions are necessarily defined by base-paired regions. The comparative models described above focus on changes that alter base pairing in each domain. However, single-stranded regions also adopt local orientations that depend upon base identity and each domain has sequence changes in positions that are single-stranded in both CVB3/28 and CVB3/GA. Most of the differences are single base changes, but a few are more extensive. Among the single base changes, a C-to-U alteration in stem-loop 1d of domain I converts a UACG tetraloop to a CACG tetraloop, and base changes at positions 568, 577, 581, and 582 are clustered in the IDRR of domain V. The longest string of base changes in a single-stranded region occurs from position 118 to position 127 in domain II, where CVB3/GA has UCAUGAG and CVB3/28 has AACCA-C. Again, domain II displays a significant alteration.We used the 3dRNA v2.0 Web server and associated Web modules to analyze theoretical tertiary structures for the 5′ UTR of the virulent CVB3/28 strain and the avirulent CVB3/GA strain.The 3dRNA v2.0 Web server includes a recommended step that uses direct coupling analysis data to generate energy probabilities of direct interactions. During the modeling process, which incorporates SHAPE-MAP data, five structures are generated and ranked by RNAScore. The RNAScore process is a novel scoring system that ranks the structures based on all nonhydrogen atoms, allowing for closer computational prediction of near native structures . A lowerThe most obvious distinction between the tertiary models of the 5′ UTR in the two strains is the general compactness of CVB3/28 compared to the more dispersed conformation of CVB3/GA . The modIn CVB3/GA, domain II displays the two stem-loop pattern on the periphery of the molecule, but overall, superdomain II-III is more compact and globular than domain II of CVB3/28 . As showThe long basal stem of domain IV in both CVB3/28 and CVB3/GA extends outward from the more globular center and displays the cruciform junction loop on the end of the extension with similar overall orientation . HoweverDomain V of CVB3/GA is in close contact with domain III and in the center of the most globular region of the molecule. The IDRR of the domain adopts a distorted helical conformation . The begPicornaviridae genome  is a defining feature of the e genome . By circe genome . Domain e genome  and has e genome .Type I IRES elements require the assembly of canonical translation initiation factors (eIFs) and an array of host noncanonical proteins known as IRES transacting factors (ITAFs) to initiate translation , 42, theCritical RNA elements in the cloverleaf and IRES control efficient replication and translation, which in turn influence multiplication and virulence. For example, we showed previously that secondary-structure changes in the IRES accompany changes in CVB3 virulence . In the Given the fundamental hierarchical role of RNA secondary structure in the folding pathway and final tertiary structure of the molecule , 45, theSeveral studies have examined sequence differences in the 5′ UTR between CVB3/GA and other pathogenic strains of CVB3 , 22. WhiThe major changes in secondary structure in domains II and III are joined by more limited changes that impact the basal helix of domain IV. By increasing the length of the domain IV closing helix and reducing the single-stranded regions, there may be reduced flexibility that leads to the extended orientation observed in the CVB3/GA tertiary model. Outside domains II, III, and IV, most secondary structures remain largely unchanged. Furthermore, many of the local RNA structural elements in domains II, III, and IV also remain unchanged. These results underscore the hierarchical folding of RNA elements within domains as well as domains themselves and also supports independent formation of RNA domains in the 5′ UTR .Chimeric studies have mapped the cardiovirulence phenotype of CVB3 to the 5′ UTR  and specStudies of the avirulent CVB3/GA show that the virus multiplies less efficiently and to lower titers in mouse hearts  and in mWe present theoretical tertiary-structure models of the 5′ UTR from CVB3/28 and CVB3/GA that were generated by the computational methods of 3dRNA v2.0 informed by our SHAPE-MaP results. Despite steady progress in both experimental methods and computational methods, determining tertiary structure in large RNA molecules remains a challenge , but effAt the level of overall conformation, our tertiary models suggest that the CVB3/28 5′ UTR is much more compact than the CVB3/GA 5′ UTR. Even without dissecting the orientation of individual domains, a more compact organization is likely to bring functional elements of the 5′ UTR into closer proximity. Little direct experimental evidence is available to describe spatial relationships and orientation of 5′ UTR domains. Our theoretical tertiary-structure models serve as a starting point for exploring these three-dimensional relationships. By analyzing locations in the 5′ UTR known to bind ITAFs and canonical translation factors, there are strong arguments supporting orientations that bring some domains together.Stem-loops b and d in domain I bind the ITAF poly(rC)-binding protein 2 (PCBP2) and viral protein 3CD, respectively , 49, 50.53–Biophysical methods such as NMR, small-angle X-ray scattering (SAXS), and cryo-electron microscopy (cryo-EM) have explored the tertiary structure of enteroviral 5′ UTR stem-loop D in domain I , 56–58 a56–The viral IRES presents an attractive therapeutic target . For entTAATACGACTCACTATAGG) along with the ribozyme sequence (ATGAGGCCGAAAGGCCGAAAACCCGGTATCCCGGGTTC) inserted between the T7 promoter and the 5′ end of the 5′ UTR sequence. This construction enabled production of a 5′ UTR with the authentic 5′-end nucleotide.Plasmids containing copies of full-length CVB3/GA and full-length CVB3/28 cDNA genomes were kindly provided by Nora Chapman and Steve Tracy of the Enterovirus Research Laboratory, University of Nebraska Medical Center. Plasmids were modified to include an upstream T7 promoter (E. coli Sure cells. Plasmid DNA from both strains was isolated using a QIAprep Spin miniprep kit (Qiagen) according to the manufacturer’s protocol. CVB3/GA DNA was linearized with the restriction enzyme BssHII. Efficient transcription by T7 RNA polymerase requires a template with no overhang; therefore, DNA polymerase Klenow fragment (1 U) and deoxynucleoside triphosphates (dNTPs) (32 μM) were added to blunt the end of the linearized DNA caused by cleavage with BssHI. CVB3/28 plasmid DNA was linearized with the restriction enzyme Ecl136II, which generates a blunt end. Digested DNA was purified through phenol and phenol-chloroform extraction followed by ethanol precipitation.Plasmids were maintained in The 5′ UTR RNA for CVB3/GA and CVB3/28 was produced with MEGAScript T7 transcription kit (Invitrogen), following the manufacturer’s instructions. Reactions were performed identically for both strains apart from the input of 10 μg of CVB3/GA template DNA and 4 μg of CVB3/28 template DNA due to low RNA production from CVB3/GA. The transcription reaction mixtures were incubated at 37°C for 4 h. Turbo DNase I (2 U) (Thermo Fisher) was added, and reaction mixtures were incubated at 37°C for an additional 30 min to remove the DNA template. Upon completion, RNA was extracted with equal volumes phenol-chloroform and chloroform, precipitated with isopropanol, and pelleted by centrifugation, and the pellet was washed with 70% ethanol. The final RNA product was purified using the MEGAclear transcription clean-up kit (Invitrogen), following the manufacturer’s protocol. RNA yield was quantified with a Qubit fluorometer (Invitrogen).2) was added. RNA was folded during incubation at 37°C for 20 min. RNA modification was performed in a duplicate series of three reactions: a modification reaction, a no-reagent control (unmodified), and a denatured control. The modified and denatured control reaction mixtures were prepared with a final concentration of 1 mM 1M7 dissolved in DMSO, and the unmodified reaction mixture was prepared with neat DMSO. To prepare the modified and unmodified reaction mixtures, 9 μL of folded RNA was added to the respective tubes and incubated for 85 s at 37°C. The denatured control reaction mixture was prepared with 10 pmol of purified RNA in 3 μL of RNase-free H2O, 50% formamide, and 1× DC reaction buffer  before incubation at 95°C for 10 min. The denatured RNA was then added to denatured control reaction tubes containing a final concentration of 1 mM 1M7 in DMSO and incubated at 95°C for 1 min. The duplicate RNA samples from all three reactions were pooled and purified using RNeasy mini spin columns (Qiagen) according to the manufacturer’s protocol and quantified using the Qubit fluorometer (Invitrogen).The SHAPE-MaP protocol of Smola et al.  was used2) was added to each tube and incubated at 25°C for 2 min. SuperScript II reverse transcriptase (1 U) was added and incubated at 25°C for 10 min and then at 42°C for 3 h. SuperScript II was inactivated by incubation at 70°C for 15 min before the samples were rapidly cooled on ice. Second-strand synthesis was performed with the NEBNext module following the manufacturer’s instructions. The subsequent double-stranded DNA (dsDNA) was purified from the synthesis reaction using PureLink PCR micro-spin columns (Invitrogen) according to the manufacturer’s instructions and eluted in 10 μL of RNase-free water. The dsDNA from each reaction was quantified using a Qubit fluorometer. The dsDNA samples were submitted to the Genomics Core Facility at the University of Nebraska Medical Center (UNMC) for sequencing using the randomer work-flow protocol from the developers of SHAPE-MaP  and incubated at 65°C for 5 min before being placed on ice. While on ice, 8 μL 1× MaP buffer . A FASTA file of the reference sequence 5′ UTR of each strain along with secondary-structure data (from SHAPE-MaP), was subjected a direct coupling analysis (DCA) by the Xiao Lab DCA webserver (http://biophy.hust.edu.cn/DCA).The TurboFold II CT file output containing the predicted nucleotide interactions was converted to dot-bracket notation (DBN file) with the Mathews lab Web server (http://biophy.hust.edu.cn/3dRNA). Distance geometry and energy minimization were selected. The resulting theoretical structures were evaluated with 3dRNAscore, which measures the energy distribution between nucleotides from the predicted structure. The lower the score, the higher the probability of accurate base pairing and tertiary interactions. The 3dRNA v2.0-generated .pdb file was loaded in PyMOL version 2.5  for visualization and false coloring  file. Next, the reference FASTA file, DBN file, and DI file were uploaded to the 3dRNA v2.0 Web server (coloring .


Doublecortin-like kinase 1 (DCLK1) is a functional serine/threonine (S/T)-kinase and a member of the doublecortin family of proteins which are characterized by their ability to bind to microtubules (MTs). DCLK1 is a proposed cancer driver gene, and its upregulation is associated with poor overall survival in several solid cancer types. However, how DCLK1 associates with MTs and how its kinase function contributes to pro-tumorigenic processes is poorly understood. This review builds on structural models to propose not only the specific functions of the domains but also attempts to predict the impact of individual somatic missense mutations on DCLK1 functions. Somatic missense mutations in DCLK1 are most frequently located within the N-terminal MT binding region and likely impact on the ability of DCLK1 to bind to αβ-tubulin and to polymerize and stabilize MTs. Moreover, the MT binding affinity of DCLK1 is negatively regulated by its auto-phosphorylation, and therefore mutations that affect kinase activity are predicted to indirectly alter MT dynamics. The emerging picture portrays DCLK1 as an MT-associated protein whose interactions with tubulin heterodimers and MTs are tightly controlled processes which, when disrupted, may confer pro-tumorigenic properties. DCX) a DCLK1 homologue, were identified as the cause of cerebral cortical malformations, such as subcortical band heterotopia (double cortex) and lissencephaly .,82.73,82While phosphorylation can be involved in forming recognition sites for interactors, phosphorylation of the DC domains, N-terminal region, and DC1-DC2 linker also negatively impact tubulin binding . Two in Although the AlphaFold2 algorithm predicts alpha-helical arrangements for the N-terminal region adjacent to DC1 in both DCX and DCLK1 ,64, in tFrom the αβ-tubulin interacting residues identified in DCX-DC1 and DCX-DC2, there are several phosphorylation sites located within the equivalent tubulin binding faces in DCLK1: S77, S83, T92, T94, S96, T107 in DC1; and T218 and S228 in DC2  20,49,5,53,83. TLike the disordered PEST domain (discussed below), the N-terminal and DC-linker regions are predicted to be hot-spots for protein–protein interactions . InteracThe first structure of DCLK1 was obtained in 2003 by Kim et al., who successfully crystallized the bacterially expressed DCLK1-DC1 domain  that mediates the conformational change between an “open” and “closed” state . Most stThe DC2 domains of DCLK1 and DCX have been more challenging to study by X-ray crystallography and NMR. Multiple studies have demonstrated the thermodynamic unstable nature of the isolated DC2 domains of DCLK1 and DCX, resulting in partially unfolded proteins and protein aggregates ,75,80. NSeveral residues in the N-terminal region preceding the DC domains of DCX are confirmed substrates of multiple kinases and phosphatases; DCX-S47 is phosphorylated by MT affinity-regulating kinase 1 (MARK1) and protein kinase A (PKA) ,74, whilDCX-S28 is a substrate of cyclin-dependent kinase 5 (CDK5) ,89 and aThe function of the PEST linker region (residues 283–381) in DCLK1 is poorly understood and it is predicted to be disordered according to AlphaFold2 B. PEST sDepending on their specific motifs, PEST domains can be recognized by various proteolytic regulators ,97,98,99The PEST linker region of the zebrafish ortholog DCLK2, which shares a 60% sequence similarity with the human DCLK1 PEST domain, has been proposed to drive localization to MTs ,103. SimSeveral threonine and serine residues within the PEST domains of DCX and DCLK1 are predicted substrates for a number of protein kinases including CDK5, CDK1, glycogen synthase kinase 3 (GSK3), mitogen-activated protein kinase 3 (MAPK3/ERK1) and c-Jun N-terminal kinase (JNK)  66]. Ho. Ho66]. In DCX, phosphorylation of S297 shifted the distribution of DCX away from MT bundles, and a phospho-mimetic S297D substitution led to impaired MT polymerization in vitro . Phospho2+/calmodulin-dependent protein kinase 1 (CAMK) domain with 46% sequence identity . A . A 53]. te cleft B,C 51]..10 helixactivity ,64.D511N mutant has higher tubulin polymerization activities than DCLK1WT . Fe. Fe83]. LK1-IN-1 , whereasLK1-IN-1 . The latLK1-IN-1 . CDK11 aLK1-IN-1 . Lastly,LK1-IN-1 ,113,114.However, the lack of overlap between the potential substrates within these studies supports the existence of context and isoform dependent DCLK1 substrates. This not only highlights that the catalytic activity of DCLK1 may regulate additional tumor-promoting cellular processes, but also argues for a need to develop (isoform) selective DCLK1 inhibitors to dissect the contribution of DCLK1 kinase activity to tumorigenesis .DCLK1 gene is mutated in 394 out of 13,582 cases across 31 projects which include over 20 different cancer types and primary sites [According to the National Cancer Institutes’ genomic data commons portal, the ry sites . DCLK1 iWithin the N-terminal region, the substitution of the hydrophobic and neutral alanine at position 18 with a slightly bulkier hydrophobic and neutral valine is observed four times  but due Substitutions in DCLK1 that are most likely to impact on MT binding are T49 to methionine (M), R80Q/W, and K138N located within DC domains, as these residues configure isomeric binding to αβ-tubulin residues described above A,D. EspeDue to their spatial proximity to W150, the V59A and R60C substitutions are likely to impact on the open-close DC conformation A–C as thMultiple (auto-)phosphorylation sites within the DC linker and PEST region have been identified for both DCLK1 and DCX and have been functionally linked to altered localization and MT polymerization activity ,106,107.Amino-acid substitutions in the PEST region could also affect proteolytic cleavage. The frequent R326H/C substitution  is prediMost mutations within the kinase domain have been classified as either altering functional or structural integrity and therefore having a negative impact on the catalytic activity . SubstitDCLK1 is a MT-associated, multidomain protein with a functional S/T-kinase domain whose auto-phosphorylation activity is required to overcome autoinhibitory interactions with its C-terminal tail A. The DCAssuming that a majority of somatic missense mutations in DCLK1 have oncogenic potential, it is somewhat puzzling that mutations in the N-terminal half of the protein seem to reduce association with MTs while mutations in the C-terminal half seem to have the opposite effect. Although at first glance this may appear paradoxical in light of the tightly regulated DCLK1/MT interactions, it could suggest that affecting the ratio of DCLK1 bound to MTs interferes with proper MT dynamics, which underpins mitosis, cell signaling, trafficking migration and other cancer-relevant activities. Indeed, such a bimodal mechanism on MT dynamics has been demonstrated for MT depolymerase mitotic centromere-associated kinesin (MCAK) . HoweverMany studies have shown that the overexpression of DCLK1 in cancer cells results in induction of EMT, stemness and increased cell migration and invasion, which can be reversed with small molecules targeting the kinase domain of DCLK1 ,47,126.


Underground coal mining can cause the deformation, failure, and collapse of the overlying rock mass of a coal seam. If the mining design, monitoring, early warning, or emergency disposal are improper, in that case, it can often lead to mining disasters such as roof falls, water inrush, surface collapse, and ground fissures, seriously threatening the safety of mine engineering and the geological environment protection in mining areas. To ensure the intrinsic security of the entire coal mining process, aspace–time continuous sensing system of overburden deformation and failure was developed, which breaks through the limitations of traditional monitoring methods that characterize the evolution process of overlying rock deformation and ground subsidence. This paper summarizes the classification of typical overburden deformation and failure modes. It researches the space–time continuous sensing of rock–soil mass above the coal seam based on Distributed Fiber Optic Sensing (DFOS). A multi-range strain optical fiber sensing neural series from micron to meter was developed to achieve synchronous sensing of overburden separation, internal micro–cracks, and large rock mass deformation. The sensing cable–rock mass coupling test verified the reliability of the optical fiber monitoring data. The sensing neural network of overburden deformation was constructed using integrated optical fiber layout technology on the ground and underground. Different sensing nerves’ performance and application effects in overburden deformation and failure monitoring were compared and analyzed with field monitoring examples. A physical model was used to carry out the experimental study on the overburden subsidence prediction during coal mining. The results showed that the optical fiber monitoring data were reliable and could be used to predict overburden subsidence. The reliability of the calculation model for overlying rock subsidence based on space–time continuous optical fiber sensing data was verified in the application of mining subsidence evaluation. A systematic review of the shortcomings of current overburden deformation observation technology during coal mining was conducted, and a space–time continuous sensing system for overburden deformation and failure was proposed. This system integrated sensing, transmission, processing, early warning, decision-making, and emergency response. Based on the fusion of multi-parameter sensing, multi-method transmission, multi-algorithm processing, and multi-threshold early warning, the system realized the real-time acquisition of space–time continuous information for the overburden above coal seams. This system utilizes long-term historical monitoring data from the research area for data mining and modeling, realizing the prediction and evaluation of the evolution process of overburden deformation as well as the potential for mining subsidence. This work provides a theoretical reference for the prevention and control of mining disasters and the environmental carrying capacity evaluation of coal development. Coal resources are one type of energy mineral resource . They arIn the past thirty years, the global economy’s rapid growth has resulted in an increase sharply in demand for energy consumption; among them, coal has become an important source of energy due to its rich reserves . As showUnderground mining in coal mines often causes the deformation, separation, fracture, and collapse of overlying strata. Mine pressure is gradually created due to the movement of rock strata. In order to scientifically reveal the essence of mine pressure during mining, scholars at home and abroad have proposed a lot of hypotheses and theories of stope . In 1916However, due to the differences in the occurrence, development, and evolution of overlying rock deformation under different geological conditions, meanwhile, the deformation, movement, failure, and collapse of overlying strata is a continuous development process; the deformation both in vertical and horizontal of overlying strata and coal seams will change dynamically. Therefore, the deformation coordination and interaction between rock (soil) layers above the coal seam have a strong space–time effect. Most current studies have not considered this space–time continuous process, so they cannot fully reveal the core mechanism of overburden deformation during mining. Consequently, breaking through the technical bottleneck of coal mining monitoring is urgent. Thus, the space–time continuous cognition from overburden deformation and failure to surface-subsidence occurrence and development can be realized. The time-effect mechanism of overburden under coal mining was revealed. On this basis, a monitoring and guarantee system integrating sensing, transmission, processing, early warning, decision-making, and emergency was established based on an optical fiber neural sensing network, which is of great significance to the real-time intelligently sensing of overburden deformation, movement, failure, and collapse during coal mining.i [1]max is the maximum principal stress of rock layer i, and [σ] is the tensile strength of rock layer i. Generally, the three-hinged arch structure is formed in the roof rock layer after failure. If the effective span L of the rock layer is greater than the limit span iL of the three-hinged arch structure, the three-hinged arch will collapse. Among them:i, Global scholars have conducted many laboratory experiments and on-site tests about overburden deformation mechanism during coal mining. Systematic research has utilized similar material physical model tests, field in situ tests, numerical simulations, and theoretical calculations ,19,20. Ai ,23 are:[i is:τ] is the shear strength of rock layer i, C is the cohesive of rock layer i, σα is the normal stress on sliding surface of rock layer i, and φ is the internal friction angle of rock layer i. Then, the condition of shear failure at point  is τ = τα − [τ], if Δτ takes the maximum value [Δτ]max at point , then point  is the most susceptible to shear failure in the rock layers.Assuming ΔFiber optic sensing technology integrates the advantages of both optical sensing and data transmission. By using the change in the scattered light characteristic quantity in the optical fiber caused by external factors, the related physical amount of the measured object along the optical fiber can be continuously monitored for a long distance  Figure . In the According to the characteristics of mine engineering monitoring, glass fiber-reinforced plastic (GFRP), metal-based, and fixed-point cables are commonly used. The GFRP cable adopts high-strength polyurethane as the reinforcement, which not only ensures a high strain transfer performance, but also makes it resistant to tension, compression, and impact during the test. Therefore, it is widely used in deformation, internal force, and damage monitoring in mine engineering. The metal-based cable adopts a particular technique of steel wire twisting and packaging, which has strong wear resistance and ensures its survival rate under high-pressure grouting and pouring backfilling. It is suitable for the deformation monitoring of materials with a high elastic modulus . The fixed-point cable calculates the deformation of the measured target by using the strain value between its two fixed points, which is suitable for discontinuous and large deformation monitoring of rock–soil mass. The overburden separation and internal microcracks can be accurately identified based on the high-sensitivity cable between two contiguous fixed points. Different cables’ parameters are shown in The coupling performance between the sensing cable and the rock–soil mass affects the quality of measured data, significantly impacting the reliability of the monitoring results . TherefoIn recent years, more and more monitoring methods have been used to investigate the failure law of overlying rock and the evolution process of mining subsidence. Traditional sensing elements such as resistance strain gauges, displacement gauges, osmometers, temperature gauges, and electrodes are primarily used in the laboratory and on-site tests. Although these monitoring technologies can obtain the strain, displacement, seepage pressure, temperature, resistivity, and other physical parameters of the local position of the overlying rock–soil mass on the coal seam, it is generally found that the measurement accuracy of the sensing element is low, the layout is cumbersome, the durability is poor, and the data are discrete. It is impossible to comprehensively track the cumulative deformation of the overlying strata induced by continuous mining . ObtainiMany research results show that the layout process and quality of the monitoring system directly influence the monitoring results. Therefore, it is essential to select the corresponding layout technique according to different test environments and objects in the safety mining monitoring of mine engineering. Currently, most test systems combine ground with underground techniques in the practical application of mine engineering. The surface layout method is mainly the ground monitoring system layout. Firstly, implement a monitoring borehole on the ground. Then, according to the different monitoring conditions and geological environment, it is essential to select the appropriate sensing cable and device for the borehole layout. There are four stages: (a) making a counterweight guiding hammer; (b) implanting a sensing cable; (c) borehole backfilling coupled with steady; (d) sensing cable protection .The underground layout method mainly uses high-strength lightweight pipe fittings as the sensing cable (sensor) attachment carrier, which is buried in the rock mass above the coal seam. Firstly, the underground upwards borehole is designed according to the stope conditions. Secondly, Implement a monitoring borehole above the working face with a certain angle. Meanwhile, cable layout and grouting operations are carried out. Finally, the initial value is collected after the borehole reaches the coupling strength. Regularly collect monitoring data based on the actual mining progress of the working face. The response characteristics of overburden deformation and failure in the borehole control height under mining action are analyzed based on the initial value of the sensing cable and the regular data. Zhang Pingsong et al. carried out a dynamic monitoring test for roof overburden combining optical and electrical technology, designed a set of monitoring sections, implemented two roof boreholes at depths of 150 m and 135 m, controlled the advance distance of 96 m, controlled the vertical height of 70 m, and obtained a caving zone height of 18.5 m and water–flowing fractured zone height of 54.5 m . The resBased on the mode distribution characteristics of overlying rock deformation and failure, the space–time continuous strain data of overlying rock can be obtained using fiber optic sensing technology, which can accurately grasp the mining progress and conditions. The stress concentration and electric field (resistivity) mutation areas are captured, combined with the variation law of the geothermal gradient, pore water pressure, and overburden resistivity, which can infer the evolution stage and potential failure mode of overburden stability. At the same time, the early warning analysis of periodic anomalies, multi-parameter common anomalies, and the statistical analysis of historically accumulated data should be strengthened. The early warning model based on critical indicators such as deformation, temperature, seepage pressure, and resistivity should be researched to improve the early warning accuracy of overlying rock deformation during coal mining.To study the evolution mechanism of deformation and failure for overburden, providing a theoretical basis for disaster prevention and control of mining subsidence, Liu Yu  et al. rPiao Chunde  predicteFor a long time, global scholars have researched the occurrence, development, and failure characteristics of overburden deformation during mining through empirical formulas, mechanical analysis, numerical calculation, model tests, and field detection. Karmis M (1983) and Palchik (2002) found that there were three different moving zones  above the coal seam under the condition of longwall mining through field measurement ,43. BaseIn particular, clustering, regression analysis, Bayesian inference, genetic algorithms, and gray theory can be introduced into the integrated overburden deformation sensing system for data mining and modeling with the rapid development and broad application of machine learning, chatGPT, and other technologies. Mining out the disaster characteristic information can reflect the overlying rock deformation and instability during coal mining. The temporal information, spatial information, multi-modulus, and other attributes of monitoring data were used to construct the hypergraph structure of big data for overburden deformation. Hypergraph clustering and segmentation technology were used to complete big data mining and prediction of overburden deformation. Bayesian rules were used to optimize feature information mining and prediction results. According to the strain evolution characteristics of overburden, combined with the variation laws of geothermal gradient, pore water pressure and overburden resistivity, the stress concentration area and electric field (resistivity) mutation area were captured, and the evolution stage and potential failure mode of overburden stability were inferred accordingly. The primary processes of this method are as follows: Through a data cleaning method based on the topic model and integrating the effectiveness analysis mechanism of the IoT sensing data source, intelligent processing of massive data in the whole process of overburden deformation and failure sensing is carried out. These strengthened the machine learning research of key layer data missing from error correction, association analysis, and data mining. The hyperparameters were optimized by combining optimization algorithms such as moving average, random forest, Kalman filter, and multi-variate Bayesian. The trend term displacement of overburden was predicted based on the Kalman filter. The Bayesian optimized random forest model was used to predict the periodic term displacement of overburden, and the predictive value of accumulated displacement of overburden was formed. The model’s accuracy was verified and combined with the measured data of displacement to improve the prediction accuracy of overburden deformation .The current methods are difficult to continuously obtain the impact of overlying rock–soil mass deformation during coal mining on local deformation of underground tunnels and ground subsidence. Therefore, it is vital to overcome the technical barriers of sensing massive space–time continuous information in overburden during coal mining and carry out space–time continuous sensing research on the evolution from overlying rock deformation and failure to surface subsidence. Based on this, an integrated safety guarantee system for coal mining that covers sensing–transmission–processing–early warning–decision is established : (a) A mThis paper summarized the typical types of overburden deformation and failure modes. It compared and analyzed the parameter performance and applicable conditions of sensing cables (sensors) suitable for overburden deformation monitoring in mine engineering. Based on the achievements of the author’s research team in the coupling test for sensing cable–rock mass, a high-precision, large-capacity, and full-coverage distributed sensing network for mining was constructed, combined with the ground–underground integrated cable layout techniques. A space–time continuous sensing system for the overburden deformation and failure integrating sensing–transmission–processing–warning–decision–emergency was proposed. The main conclusions are as follows:(1) There are three typical modes of tensile failure, overall shear, and shear slip in the overburden deformation and failure induced by mining. Their occurrence, development, and evolution have a cumulative space–time effect. To clarify the evolution process and disaster mechanism of overburden deformation and failure, research on the space–time continuous state sensing for overlying strata should be strengthened, the space–time continuous sensing data of overburden deformation and failure should be integrated, and the accurate characterization of overburden deformation and failure should be realized. It is necessary to focus on the abrupt abnormal data, consider the gradual change data of overburden deformation regularity simultaneously, and realize the scientific evaluation for the stability of overlying strata during mining by combining the distribution of stratum profile and the field measured data.(2) According to the characteristics of mine engineering monitoring, combined with the performance parameters and application conditions of the sensing cables (sensors) used for overburden deformation monitoring, a multi-range strain optical fiber sensing nerve series from micrometer to meter is formed to realize the simultaneous sensing of overburden separation layer and its internal microcracks as well as large deformation of the overlying rock. The testing method, process, and criteria for the cable–rock coupling test to verify the reliability of optical fiber monitoring data were introduced. By utilizing the integrated method of laying cables on the ground and underground, the installation process of cables has been improved, and a distributed fiber optic neural sensing network has been set up to achieve space–time continuous and precise sensing for the overburden deformation during the whole cycle of coal production. In particular, for the large deformation monitoring of mines, it was proposed to reduce the diameter of the fixed-point cable’s outer sheath, and adapt the shape of the fixed point to be an olive shape, which can not only avoid scratching the sensing cable during cable layout but can also improve the coupling performance between the cable and the rock–soil mass.(3) Based on DFOS technology, a space–time continuous sensing system for the deformation of rock–soil mass above the coal seam during mining was constructed, integrating multi-parameter sensing, multi-method transmission, multi-algorithm processing, and multi-threshold warning. The real-time sensing of space–time continuous information under mining was realized. The disaster risk of coal mining and the potential of ground subsidence was accurately predicted by combining computer intelligence analysis algorithms such as clustering, regression analysis, Bayesian inference, genetic algorithm, and gray theory to realize the whole process cognition from the deformation, failure, and collapse of overburden to the occurrence, development, and evolution of ground subsidence.