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The troposphere plays an important role in a range of weather and various climate changes. With the development of the Global Navigation Satellite System (GNSS), the zenith tropospheric delay (ZTD) retrieval using GNSS technology has become a popular method. Research on ZTD accuracies of state-space representation (SSR) corrections from different analysis centers derived from real-time precise point positioning (RT-PPP) is important for Earth observation correction, meteorological disaster forecasting, and warning with the increasing abundance of state-space representation (SSR) products obtained by the International GNSS Service (IGS) analysis center. Therefore, accuracies and availability of real-time orbits and clock errors obtained by the Chinese Academy of Sciences (CAS), GMV Aerospace and Defense (GMV), Centre National d'Etudes Spatiales (CNE), and Wuhan University (WHU) are evaluated, and the RT positioning performance and ZTD accuracies are analyzed for Global Positioning System (GPS), Galileo (GAL), and BeiDou Navigation Satellite System-3 (BDS3) satellites. The results indicate that CAS has the higher satellite availability, providing SSR corrections for 82 GPS, Galileo, and BDS3 satellites. The accuracies of GPS, Galileo, and BDS3 orbits are best at WHU, CAS, and WHU with values of 5.57, 5.91, and 11.77 cm, respectively; the standard deviations (SDs) of clock error are all better than 0.22, 0.19, and 0.55 ns, and the root mean square errors (RMSEs) are better than 0.54, 0.32, and 1.46 ns. CAS has the best signal-in-space ranging errors (SISREs) followed by WHU, while CNE and GMV are worse. In the RT-PPP test, convergence times for CAS and WHU are 14.9 and 14.4 min, respectively, with 3D positioning accuracy for both of around 3.3 cm, which is better than for CNE and GMV. Among them, WHU SSR has the higher accuracy of RT-PPP-derived ZTD, with an RMSE of 6.06 mm and desirable availability with a completeness rate of 89 %.

Abstract The nucleus accumbens (NAc) regulates reward-motivated behavior, but the temporal dynamics of NAc neurons that enable “free-willed” animals to obtain rewards remain elusive. Here, we recorded Ca2+ activity from individual NAc neurons when mice performed self-paced lever-presses for sucrose. NAc neurons exhibited three temporally-sequenced clusters, defined by times at which they exhibited increased Ca2+ activity: approximately 0, −2.5 or −5 sec relative to the lever-pressing. Dopamine D1 receptor (D1)-expressing neurons and D2-neurons formed the majority of the −5-sec versus −2.5-sec clusters, respectively, while both neuronal subtypes were represented in the 0-sec cluster. We found that pre-press activity patterns of D1- or D2-neurons could predict subsequent lever-presses. Inhibiting D1-neurons at −5 sec or D2-neurons at −2.5 sec, but not at other timepoints, reduced sucrose-motivated lever-pressing. We propose that the time-specific activity of D1- and D2-neurons mediate key temporal features of the NAc through which reward motivation initiates reward-seeking behavior.

Abstract We report multi-differential measurements of strange hadron production ranging from mid- to target-rapidity in Au+Au collisions at a center-of-momentum energy per nucleon pair of s NN $$ \sqrt{s_{\textrm{NN}}} $$ = 3 GeV with the STAR experiment at RHIC. K S 0 $$ {K}_S^0 $$ meson and Λ hyperon yields are measured via their weak decay channels. Collision centrality and rapidity dependences of the transverse momentum spectra and particle ratios are presented. Particle mass and centrality dependence of the average transverse momenta of Λ and K S 0 $$ {K}_S^0 $$ are compared with other strange particles, providing evidence of the development of hadronic rescattering in such collisions. The 4π yields of each of these strange hadrons show a consistent centrality dependence. Discussions on radial flow, the strange hadron production mechanism, and properties of the medium created in such collisions are presented together with results from hadronic transport and thermal model calculations.

This study investigates the use of virtual and augmented reality (VAR) in higher education on students' self-efficacy in science and engineering, and academic performance. An innovative VAR classroom is set up as an immersive and interactive learning environment at Nanyang Technological University for curriculum-based learning. This classroom is equipped with 6 individual VR displays and headsets, where high-end computers are connected to each display providing the computational power required for VAR technology. 46 undergraduate engineering students experience the VAR lessons over the course of 5 weeks. The self-efficacy and academic performance of the students are measured for quantitative analysis with surveys and regular (pre-/post-) tests as they participated in the lessons. Post-lesson focus-group discussions and interviews are conducted for qualitative analysis. The data analysis shows a significant increase in students' self-efficacy levels and academic performance after 5 weeks of VAR lessons. It is found that the collaborative, interactive and immersive nature of the VAR learning environment enriches students' learning experiences and thus contributes to their learning effectiveness. The study provides insights into how the VAR learning environment affects undergraduate students' learning, and how the VAR system can be deployed for effective STEM education

Aiming at the complex structure of wheel hub bearing parts, which is difficult to form, an upsetting-pre-forgingfinal forging forming process is adopted after process analysis. A three-dimensional finite element model is established by Deform software to study the process of wheel hub bearing forging, and the distribution law of temperature field, equivalent stress field and other important fields in the forging process are systematically analyzed. The influence of forging process parameters on the forming quality of wheel hub bearings is studied. Based on the results of Deform simulation, the optimal forging process parameters are used in the actual forging production, and the forging parts obtained have clear hierarchical levels in each region and good forming quality. The experiment verifies that the forging is complete and meets the product requirements under the optimized process parameters, which provides theoretical and technological guidance for the hot die forging of wheel hub bearing parts.

Abstract Voltage-gated sodium channels (VGSCs) initiate action potentials in electrically excitable cells and tissues. Surprisingly, some VGSC genes are aberrantly expressed in a variety of cancers, derived from “non-excitable” tissues that do not generate classic action potentials, showing potential as a promising pharmacological target for cancer. Most of the previous review articles on this topic are limited in scope, and largely unable to provide researchers with a comprehensive understanding of the role of VGSC in cancers. Here, we review the expression patterns of all nine VGSC α-subunit genes (SCN1A-11A) and their four regulatory β-subunit genes (SCN1B-4B). We reviewed data from the Cancer Genome Atlas (TCGA) database, complemented by an extensive search of the published papers. We summarized and reviewed previous independent studies and analyzed the VGSC genes in the TCGA database regarding the potential impact of VGSC on cancers. A comparison between evidence gathered from independent studies and data review was performed to scrutinize potential biases in prior research and provide insights into future research directions. The review supports the view that VGSCs play an important role in diagnostics as well as therapeutics of some cancer types, such as breast, colon, prostate, and lung cancer. This paper provides an overview of the current knowledge on voltage-gated sodium channels in cancer, as well as potential avenues for further research. While further research is required to fully understand the role of VGSCs in cancer, the potential of VGSCs for clinical diagnosis and treatment is promising.

Abstract We explored physiological effects of the sodium-glucose co-transporter-2 inhibitor empagliflozin on intact experimentally hypertrophic murine hearts following transverse aortic constriction (TAC). Postoperative drug (2–6 weeks) challenge resulted in reduced late Na+ currents, and increased phosphorylated (p-)CaMK-II and Nav1.5 but not total (t)-CaMK-II, and Na+/Ca2+ exchanger expression, confirming previous cardiomyocyte-level reports. It rescued TAC-induced reductions in echocardiographic ejection fraction and fractional shortening, and diastolic anterior and posterior wall thickening. Dual voltage- and Ca2+-optical mapping of Langendorff-perfused hearts demonstrated that empagliflozin rescued TAC-induced increases in action potential durations at 80% recovery (APD80), Ca2+ transient peak signals and durations at 80% recovery (CaTD80), times to peak Ca2+ (TTP100) and Ca2+ decay constants (Decay30–90) during regular 10-Hz stimulation, and Ca2+ transient alternans with shortening cycle length. Isoproterenol shortened APD80 in sham-operated and TAC-only hearts, shortening CaTD80 and Decay30–90 but sparing TTP100 and Ca2+ transient alternans in all groups. All groups showed similar APD80, and TAC-only hearts showed greater CaTD80, heterogeneities following isoproterenol challenge. Empagliflozin abolished or reduced ventricular tachycardia and premature ventricular contractions and associated re-entrant conduction patterns, in isoproterenol-challenged TAC-operated hearts following successive burst pacing episodes. Empagliflozin thus rescues TAC-induced ventricular hypertrophy and systolic functional, Ca2+ homeostatic, and pro-arrhythmogenic changes in intact hearts.

Background Overcoming immune suppression is a major barrier to eliciting potent CD8+ T cell responses against cancer. Treatment with anti-CD4 monoclonal antibody is an effective means for eliminating CD4+Foxp3+ regulatory (Treg) cells in preclinical models and has also demonstrated efficacy in early clinical trials. However, the underlying basis for treatment efficacy, more specifically the implications of codepleting other CD4-expressing cell compartments in tumor-bearing hosts, is not well understood.Methods Tumor-bearing mice were treated with anti-CD4 versus other therapies that preserve helper T cell function, and the priming, tissue distribution, and maintenance of tumor antigen-specific CD8 T cells were assessed. Antibody blockade and transgenic mouse models were used to determine the mechanisms of CD8 T cell priming. Single-cell RNA-sequencing (scRNAseq) was used to further characterize CD8 T cells that are primed by anti-CD4 therapy and to identify immunosuppressive CD4 T cell subsets in human melanoma following immune checkpoint blockade (ICB).Results Comparing anti-CD4 to dual ICB therapy, we show that anti-CD4 facilitates more robust priming of TCF-1+, IL-2-producing, tumor-specific CD8+ T cells that disseminate to tissues and form memory. By decoupling priming from homeostatic proliferation and associated cytokines, we find that anti-CD4 functions independently of creating homeostatic space for CD8+ T cells. We also show that depletion of CD4-expressing antigen-presenting cell subsets is not required for anti-CD4 efficacy. Instead, robust tumor-specific CD8+ T cell priming and memory generation required the removal of total antigen-specific CD4+ T cells, including both Tregs and CD4+ Foxp3-negative conventional (Tconv) cells. In particular, the elimination of CD4+ Tconv cells was necessary for the accumulation and maturation of conventional type-1 dendritic cells in tumor-draining LNs, which were required for CD8+ T cell priming. Accordingly, anti-CD4 treatment restored CD8+ T cell responses in mice cotreated with dual ICB. scRNAseq of melanoma tumors from patients who received ICB revealed the presence of Tr1 and Treg subsets, as well as CD4+ Tconv subsets that lacked clear transcriptional evidence of helper differentiation.Conclusions These findings underscore the underappreciated benefit of depleting CD4+ Tconv cells to promote systemic primary and memory CD8+ T cell responses against cancer.

Summary: Intervertebral disc (IVD) defects heal poorly and can cause back pain and disability. We identified that IVD herniation injury heals regeneratively in neonatal mice until postnatal day 14 (p14) and shifts to fibrotic healing by p28. This age coincides with the shift in expansive IVD growth from cell proliferation to matrix elaboration, implicating collagen crosslinking. β-aminopropionitrile treatment reduced IVD crosslinking and caused fibrotic healing without affecting cell proliferation. Bulk sequencing on naive IVDs was depleted for matrix structural organization from p14 to p28 to validate the importance of crosslinking in regenerative healing. We conclude that matrix changes are key drivers in the shift to fibrotic healing, and a stably crosslinked matrix is needed for IVD regeneration.

We report on the charged-particle multiplicity dependence of net-proton cumulant ratios up to sixth order from s=200 GeV p+p collisions at the Relativistic Heavy Ion Collider (RHIC). The measured ratios C4/C2, C5/C1, and C6/C2 decrease with increased charged-particle multiplicity and rapidity acceptance. Neither the Skellam baselines nor PYTHIA8 calculations account for the observed multiplicity dependence. In addition, the ratios C5/C1 and C6/C2 approach negative values in the highest-multiplicity events, which implies that thermalized QCD matter may be formed in p+p collisions.

Abstract The full data set of the Daya Bay reactor neutrino experiment is used to probe the effect of the charged current non-standard interactions (CC-NSI) on neutrino oscillation experiments. Two different approaches are applied and constraints on the corresponding CC-NSI parameters are obtained with the neutrino flux taken from the Huber-Mueller model with a 5% uncertainty. For the quantum mechanics-based approach (QM-NSI), the constraints on the CC-NSI parameters ϵ eα and ϵ eα s $$ {\epsilon}_{e\alpha}^s $$ are extracted with and without the assumption that the effects of the new physics are the same in the production and detection processes, respectively. The approach based on the weak effective field theory (WEFT-NSI) deals with four types of CC-NSI represented by the parameters [ε X ] eα . For both approaches, the results for the CC-NSI parameters are shown for cases with various fixed values of the CC-NSI and the Dirac CP-violating phases, and when they are allowed to vary freely. We find that constraints on the QM-NSI parameters ϵ eα and ϵ eα s $$ {\epsilon}_{e\alpha}^s $$ from the Daya Bay experiment alone can reach the order O $$ \mathcal{O} $$ (0.01) for the former and O $$ \mathcal{O} $$ (0.1) for the latter, while for WEFT-NSI parameters [ε X ] eα , we obtain O $$ \mathcal{O} $$ (0.1) for both cases.

The stability of front-end odometry output is the cornerstone of real-time mapping and high-precision localization. However, in autonomous driving, factors such as sensor types, driving speed, and signal occlusion can impact system performance, particularly causing trajectory drift, reduced accuracy, and decreased reliability during high-speed travel. This study employs a point-by-point update approach for odometry, achieving high-frequency odometry output and mapping updates nearly matching the point sampling rate, effectively eliminating point cloud distortions and providing solid support for tracking high-speed motion and navigation localization. This method emphasizes maintaining registration accuracy and robustness under high-frequency output conditions. Moreover, by utilizing backward propagation to optimize the estimation of posture and position, and ignoring inter-frame processing, this research implements an iterative extended Kalman filter, achieving tight coupling with IMU data. Additionally, the use of the iVox data structure accelerates point cloud searching, enhancing system processing efficiency. Testing in various scenarios has demonstrated that our method exhibits superior performance and robustness across different scene data, showcasing its potential application in the field of autonomous driving.

In the current era of urban construction, smart city management, and digital twinning, three-dimensional reconstruction of urban buildings is particularly important. Traditional methods have limitations in reconstructing complex geometric scenes, while new methods such as Nerf focus on using implicit MLP to represent the geometric space of the model, but suffer from slow training and rendering speeds. To address this issue, this paper proposes the use of 3D Gaussian scatter points for three-dimensional reconstruction of urban buildings, improving training speed and reconstruction quality through optimized and accelerated rendering algorithms. This method demonstrates high efficiency and editability, providing a new solution for urban building reconstruction.

With the increase of remote sensing image acquisition methods and the number of remote sensing image data, the traditional manual annotation and recognition methods can no longer meet the needs of the present production life. This study explores the use of deep learning techniques to improve the efficiency and accuracy of target detection in remote sensing satellite images, especially for small targets. Traditional target detection methods often face challenges in recognition accuracy and processing speed due to the specificity and complexity of satellite images, such as large size, variable lighting conditions and complex background. Therefore, this paper adopts the YOLOv5 model and introduces the CBAM (Convolutional Block Attention Module) attention mechanism, which significantly improves the detection of small and dense targets. Experimental validation, using the improved YOLOv5 model on the VisDrone2021 dataset, demonstrates that the model improves the mean average percision (mAP) by 1.9% while maintaining realtime performance. This paper provides new ideas for remote sensing image processing, especially for applications in the fields of urban planning and automatic driving, etc. Despite the progress made in this study, the detection of small targets in remote sensing images, the limited classification accuracy and the detection of dynamic targets still need further research.

This paper introduces an enhanced approach to loop closure detection in Simultaneous Localization and Mapping (SLAM) by integrating the Faster-LIO framework with Stable Triangle Descriptors (STD). SLAM, essential in autonomous driving and augmented reality, often encounters cumulative errors affecting mapping accuracy. Traditional detection methods based on sensor data like camera images and LiDAR point clouds struggle with environmental changes that alter scene appearance and geometry. Our approach utilizes LiDAR sensors and STD, exploiting the geometric stability of triangles to maintain robustness against rotational and translational changes. The process involves storing triangle descriptors from key frames in a hash table within the Faster-LIO framework, a voxel-based LiDAR-Inertial Odometry optimized for efficiency and speed. These descriptors are then matched across frames using a voting mechanism to ensure reliable loop closure detection. Validation on the KITTI dataset and a proprietary subterranean parking garage dataset demonstrates that this integration not only enhances loop closure detection but also simplifies computational demands by avoiding complex tree structures. This method shows promise for broader applications in robotics and autonomous systems, with future research focusing on refining the descriptor and expanding its applicability to other sensor modalities.

Urban three-dimensional modeling involves digitizing geographical elements such as urban ground and architecture, profoundly impacting urban management, planning, and development. In recent years, such models have demonstrated various advantages, but traditional challenges in acquiring comprehensive geographical information persist. The emergence of unmanned aerial vehicle (UAV) oblique photography offers a viable solution, enabling cost-effective acquisition of geographical information and facilitating the construction of three-dimensional urban models. However, UAV-based 3D reconstruction encounters certain issues.Conventional 3D reconstruction typically begins with data collection, involving two-dimensional images or point cloud data acquisition using imagery or LiDAR technology. Subsequent steps include data preprocessing, feature extraction and matching, surface model construction, texture mapping, rendering, and generating three-dimensional models. However, traditional methods exhibit limitations and drawbacks, such as inadequate adaptation to complex scenes, increased computational demands for large-scale scenes, and difficulties with dynamic scenes.Deep learning-based 3D reconstruction methods, like MVSNet, employ deep neural networks to infer scene depth information from multiple-view images, yielding high-quality 3D reconstruction results. However, they rely heavily on prior datasets and auxiliary information, limiting generalization.Neural Radiance Fields (NeRF) combine neural networks with volumetric rendering techniques, excelling in reconstructing objects with high precision and detail, handling dynamic scenes, and addressing areas with sparse viewpoints. However, NeRF's input requires sparse point clouds, commonly obtained using COLMAP, which has limitations. To address these limitations, using NeRF with global Structure-from-Motion (SfM) has emerged as a promising solution. This simultaneous processing of the entire dataset estimates camera trajectories and scene structure from shared features and information among multiple images, resulting in more accurate sparse point clouds and significantly improving image quality. Experimental validation using open-source and self-generated datasets demonstrates that this algorithm markedly enhances surface texture quality compared to traditional 3D reconstruction and NeRF with incremental SfM. In summary, this algorithm enhances 3D reconstruction efficacy and exhibits superior robustness, scalability, and accuracy compared to conventional methods.

Panoramic images, prized for their capacity to capture a comprehensive 360-degree field of view, find extensive applications across various domains. However, the challenge arises when attempting to seamlessly stitch together images captured by multiple cameras onto a spherical surface, particularly in cases where the cameras lack concentric alignment. To mitigate this misalignment issue, our paper introduces a novel method that integrates depth information into panoramic image stitching, aiming to enhance registration accuracy and seamline detection. The proposed method encompasses two pivotal steps. Firstly, depth information is employed to rectify the image’s placement on the panoramic sphere, facilitating a two-dimensional registration process. This initial step targets the elimination of misalignment problems between images while preserving image clarity. Subsequently, depth information is seamlessly integrated into the smoothing term of the Markov random field energy function to guide seamline detection. Leveraging depth information aids in circumventing foreground obstacles and directs the search through spatially smooth areas, thereby reducing the likelihood of misalignment issues. Experimental results substantiate the effectiveness of employing depth information for image correction on the spherical surface, especially in scenarios where cameras approximate concentric alignment. Furthermore, the integration of depth information into the stitching network construction markedly diminishes misalignment, leading to a notable improvement in panoramic image quality. This signifies a crucial advancement in the domain of panoramic image stitching from an academic perspective.

As urbanization accelerates, traffic congestion and frequent accidents have become prominent issues, prompting the development of intelligent transportation systems. This paper focuses on the research of vehicle and pedestrian detection algorithms to improve detection accuracy in complex traffic environments. Considering the limitations of traditional object detection algorithms in complex situations, this study adopts the deep learning-based YOLOv8 algorithm and introduces the Coordinate Attention (CA) module to enhance the model's feature extraction and localization capabilities. Experimental results show that the improved YOLOv8 network achieves a 1.1% increase in detection accuracy while maintaining its original speed. Furthermore, this paper constructs a vehicle and pedestrian dataset suitable for Chinese traffic scenes, providing an effective solution for autonomous driving assistance systems. Overall, this study holds significant reference value for vehicle and pedestrian detection in the field of intelligent transportation.

Abstract Cell-surface receptors form the front line of plant immunity. The leucine-rich repeat (LRR)-receptor-like kinases SOBIR1 and BAK1 are required for the functionality of the tomato LRR-receptor-like protein Cf-4, which detects the secreted effector Avr4 of the pathogenic fungus Fulvia fulva. Here, we show that the kinase domains of SOBIR1 and BAK1 directly phosphorylate each other and that residues Thr522 and Tyr469 of the kinase domain of Nicotiana benthamiana SOBIR1 are required for its kinase activity and for interacting with signalling partners, respectively. By knocking out multiple genes belonging to different receptor-like cytoplasmic kinase (RLCK)-VII subfamilies in N. benthamiana:Cf-4, we show that members of RLCK-VII-6, −7, and −8 differentially regulate the Avr4/Cf-4-triggered biphasic burst of reactive oxygen species. In addition, members of RLCK-VII-7 play an essential role in resistance against the oomycete pathogen Phytophthora palmivora. Our study provides molecular evidence for the specific roles of RLCKs downstream of SOBIR1/BAK1-containing immune complexes.

Abstract Background Adolescents and young adult (AYA) patients with soft tissue tumours including sarcomas are an underserved group with disparities in treatment outcomes. Methods To define the molecular features between AYA and older adult (OA) patients, we analysed the proteomic profiles of a large cohort of soft tissue tumours across 10 histological subtypes (AYA n = 66, OA n = 243), and also analysed publicly available functional genomic data from soft tissue tumour cell lines (AYA n = 5, OA n = 8). Results Biological hallmarks analysis demonstrates that OA tumours are significantly enriched in MYC targets compared to AYA tumours. By comparing the patient-level proteomic data with functional genomic profiles from sarcoma cell lines, we show that the mRNA splicing pathway is an intrinsic vulnerability in cell lines from OA patients and that components of the spliceosome complex are independent prognostic factors for metastasis free survival in AYA patients. Conclusions Our study highlights the importance of performing age-specific molecular profiling studies to identify risk stratification tools and targeted agents tailored for the clinical management of AYA patients.

The technology for 3D reconstruction of tree models based on point clouds has been extensively researched, necessitating effective datasets for the study of branch and leaf separation, skeleton point extraction, and tree parameter extraction methods. However, existing datasets for 3D tree models face several challenges, including insufficient data volume for deep learning network training, low accuracy of model ground truth impeding effective method precision evaluation, and a lack of dataset richness to satisfy the needs of multi-type method assessments. In response to these challenges, This paper introduces, for the first time, a fully automated method for generating structured three-dimensional synthetic tree models, and constructs a large-scale 3D synthetic tree dataset enriched with comprehensive structural information. This method facilitates automated computation across several processes, including the mass generation of simulated trees, separation of branches and leaves, noise generation, extraction of skeleton points, and volume calculation. To validate the usability of this dataset across various applications, this paper employs state-of-the-art (SoTA) algorithms to verify the accuracy of methods in 3D tree model reconstruction and carbon stock calculation, thereby thoroughly demonstrating the dataset’s effectiveness.

Mapping target crops before the harvest season for regions lacking crop-specific ground truth is critical for global food security. Utilizing multispectral remote sensing and domain adaptation methods, prior studies strive to produce precise crop maps in these regions (target domain) with the help of the crop-specific labelled remote sensing data from the source regions (source domain). However, existing approaches assume identical label spaces across those domains, a challenge often unmet in reality, necessitating a more adaptable solution. This paper introduces the Multiple Crop Mapping Generative Adversarial Neural Network (MultiCropGAN) model, comprising a generator, discriminator, and classifier. The generator transforms target domain data into the source domain, employing identity losses to retain the characteristics of the target data. The discriminator aims to distinguish them and shares the structure and weights with the classifier, which locates crops in the target domain using the generator’s output. This model’s novel capability lies in locating target crops within the target domain, overcoming differences in crop type label spaces between the target and source domains. In experiments, MultiCropGAN is benchmarked against various baseline methods. Notably, when facing differing label spaces, MultiCropGAN significantly outperforms other baseline methods. The Overall Accuracy is improved by about 10%.

Abstract Indirect Drive Inertial Confinement Fusion Experiments on the National Ignition Facility (NIF) have achieved a burning plasma state with neutron yields exceeding 170 kJ, roughly 3 times the prior record and a necessary stage for igniting plasmas. The results are achieved despite multiple sources of degradations that lead to high variability in performance. Results shown here, for the first time, include an empirical correction factor for mode-2 asymmetry in the burning plasma regime in addition to previously determined corrections for radiative mix and mode-1. Analysis shows that including these three corrections alone accounts for the measured fusion performance variability in the two highest performing experimental campaigns on the NIF to within error. Here we quantify the performance sensitivity to mode-2 symmetry in the burning plasma regime and apply the results, in the form of an empirical correction to a 1D performance model. Furthermore, we find the sensitivity to mode-2 determined through a series of integrated 2D radiation hydrodynamic simulations to be consistent with the experimentally determined sensitivity only when including alpha-heating.

In Pressurized Water Reactors, most of the failed fuel rods are often observed at the periphery of the fuel assembly, especially near the core baffle. The rod vibration-induced fretting wear is a significant failure mechanism strongly correlated with the coolant and support conditions. This paper presents a coupled vibration model of double-rod to predict the grid-to-rod fretting (GTRF) wear. A motion-dependent fluid force model is used to simulate the coolant cross flow, the gap constraints with asymmetric stiffness between spring and dimple on the vibration form, and the fretting wear are discussed. The results show the effect of the coupled vibration on the deterioration of wear, providing a sound theoretical explanation of some failure phenomena observed in the previous experiment. Exploratively, we analyze the impact of the baffle jet on the GTRF wear, which indicates that the high-velocity cross-flow will significantly affect the vibration forms while sharply changing the wear behavior.

Low rare earth content and high intrinsic magnetic properties make SmFe12-based permanent magnets a potential candidate for NdFeB-based magnets in medium to high temperature environments. However, the easy formation of multiple ferromagnetic intergranular phases limits the preparation of high coercivity magnets. Composition optimization is an indispensable measure for the realization of high-performance magnets. In this work, off-stoichiometric composition design is employed to prepare SmFe12-based alloys. Ti is first introduced to inhibit the precipitation of α-Fe phase, obtaining a high coercivity of 484 kA/m, while the ribbon undergoes a reduction of remanence significantly. After regulating Fe–Co content to 11.5, the remanence recoveries from 0.51 to 0.65 T with a slight increase in coercivity (484–493 kA/m). Finally, Fe content is optimized to further improve magnetic properties, with both coercivity and remanence are improved simultaneously, reaching 509 kA/m and 0.70 T, respectively. Phase evolution, microstructure and their relationship with magnetic properties are investigated in detail. Micromagnetic simulation shows that the increased domain wall pinning effect induced by the refinement of grain size is the main reason for high coercivity. This work provides a feasible compositional design method for the preparation of high performance SmFe12-based alloys.

We report the first measurements of cumulants, up to 4th order, of deuteron number distributions and proton-deuteron correlations in Au+Au collisions recorded by the STAR experiment in phase-I of Beam Energy Scan (BES) program at the Relativistic Heavy Ion Collider. Deuteron cumulants, their ratios, and proton-deuteron mixed cumulants are presented for different collision centralities covering a range of center-of-mass energy per nucleon pair sNN=7.7 to 200 GeV. It is found that the cumulant ratios at lower collision energies favor a canonical ensemble over a grand canonical ensemble in thermal models. An anti-correlation between proton and deuteron multiplicity is observed across all collision energies and centralities, consistent with the expectation from global baryon number conservation. The UrQMD model coupled with a phase-space coalescence mechanism qualitatively reproduces the collision-energy dependence of cumulant ratios and proton-deuteron correlations.

Abstract Background High‐risk soft tissue sarcomas of the extremities and trunk wall (eSTS), as defined by the Sarculator nomogram, are more likely to benefit from (neo)adjuvant anthracycline‐based therapy compared to low/intermediate‐risk patients. The biology underpinning these differential treatment outcomes remain unknown. Methods We analysed proteomic profiles and clinical outcomes of 123 eSTS patients. A Cox model for overall survival including the Sarculator was fitted to individual data to define four risk groups. A DNA replication protein signature‐Sarcoma Proteomic Module 6 (SPM6) was evaluated for association with clinicopathological factors and risk groups. SPM6 was added as a covariate together with Sarculator in a multivariable Cox model to assess improvement in prognostic risk stratification. Results DNA replication and cell cycle proteins were upregulated in high‐risk versus very low‐risk patients. Evaluation of the functional effects of CRISPR‐Cas9 gene knockdown of proteins enriched in high‐risk patients using the cancer cell line encyclopaedia database identified candidate drug targets. SPM6 was significantly associated with tumour malignancy grade (p = 1.6e‐06), histology (p = 1.4e‐05) and risk groups (p = 2.6e‐06). Cox model analysis showed that SPM6 substantially contributed to a better calibration of the Sarculator nomogram (Index of Prediction Accuracy = 0.109 for Sarculator alone versus 0.165 for Sarculator + SPM6). Conclusions Risk stratification of patient with STS is defined by distinct biological pathways across a range of cancer hallmarks. Incorporation of SPM6 protein signature improves prognostic risk stratification of the Sarculator nomogram. This study highlights the utility of integrating protein signatures for the development of next‐generation nomograms.

The chiral magnetic effect (CME) is a phenomenon that arises from the QCD anomaly in the presence of an external magnetic field. The experimental search for its evidence has been one of the key goals of the physics program of the Relativistic Heavy-Ion Collider. The STAR Collaboration has previously presented the results of a blind analysis of isobar collisions (_{44}^{96}Ru+_{44}^{96}Ru, _{40}^{96}Zr+_{40}^{96}Zr) in the search for the CME. The isobar ratio (Y) of CME-sensitive observable, charge separation scaled by elliptic anisotropy, is close to but systematically larger than the inverse multiplicity ratio, the naive background baseline. This indicates the potential existence of a CME signal and the presence of remaining nonflow background due to two- and three-particle correlations, which are different between the isobars. In this postblind analysis, we estimate the contributions from those nonflow correlations as a background baseline to Y, utilizing the isobar data as well as Heavy Ion Jet Interaction Generator simulations. This baseline is found consistent with the isobar ratio measurement, and an upper limit of 10% at 95% confidence level is extracted for the CME fraction in the charge separation measurement in isobar collisions at sqrt[s_{NN}]=200 GeV.

The differential cross section for Z0 production, measured as a function of the boson's transverse momentum (pT), provides important constraints on the evolution of the transverse momentum dependent parton distribution functions (TMDs). The transverse single spin asymmetry (TSSA) of the Z0 is sensitive to one of the polarized TMDs, the Sivers function, which is predicted to have the opposite sign in p+p →W/Z+X from that which enters in semi-inclusive deep inelastic scattering. In this Letter, the STAR Collaboration reports the first measurement of the Z0/γ⁎ differential cross section as a function of its pT in p+p collisions at a center-of-mass energy of 510 GeV, together with the Z0/γ⁎ total cross section. We also report the measurement of Z0/γ⁎ TSSA in transversely polarized p+p collisions at 510 GeV.

Purpose: Ultra High Dose-Rate (UHDR) radiation has been reported to spare normal tissue, compared with Conventional Dose-Rate (CDR) radiation. However, important work remains to be done to improve the reproducibility of the FLASH effect. A better understanding of the biologic factors that modulate the FLASH effect may shed light on the mechanism of FLASH sparing. Here, we evaluated whether sex and/or the use of 100% oxygen as a carrier gas during irradiation contribute to the variability of the FLASH effect. Methods and Materials: C57BL/6 mice (24 male, 24 female) were anesthetized using isoflurane mixed with either room air or 100% oxygen. Subsequently, the mice received 27 Gy of either 9 MeV electron UHDR or CDR to a 1.6 cm2 diameter area of the right leg skin using the Mobetron linear accelerator. The primary postradiation endpoint was time to full thickness skin ulceration. In a separate cohort of mice (4 male, 4 female), skin oxygenation was measured using PdG4 Oxyphor under identical anesthesia conditions. Results: Neither supplemental oxygen nor sex affected time to ulceration in CDR irradiated mice. In the UHDR group, skin damage occured earlier in male and female mice that received 100% oxygen compared room air and female mice ulcerated sooner than male mice. However, there was no significant difference in time to ulceration between male and female UHDR mice that received room air. Oxygen measurements showed that tissue oxygenation was significantly higher when using 100% oxygen as the anesthesia carrier gas than when using room air, and female mice showed higher levels of tissue oxygenation than male mice under 100% oxygen. Conclusions: The skin FLASH sparing effect is significantly reduced when using oxygen during anesthesia rather than room air. FLASH sparing was also reduced in female mice compared to male mice. Both tissue oxygenation and sex are likely sources of variability in UHDR studies. These results suggest an oxygen-based mechanism for FLASH, as well as a key role for sex in the FLASH skin sparing effect.

Atmospheric aerosols have important impacts on air quality and the Earth–atmospheric energy balance. However, as computing power is limited, Earth system models generally use coarse spatial grids and parameterize finer-scale atmospheric processes. These parameterizations and the simulation of atmospheric aerosols are often sensitive to model horizontal resolutions. Understanding the sensitivities is necessary for the development of Earth system models at higher resolutions with the deployment of more powerful supercomputers. Using the Energy Exascale Earth System Model (E3SM) version 1, this study investigates the impact of horizontal grid spacing on the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols (ERFaer) over the contiguous United States. We examine the resolution sensitivity by comparing the nudged simulation results for 2016 from the low-resolution model (LR) and the regional refinement model (RRM). As expected, the simulated emissions of natural dust, sea salt, and marine organic matter are substantially higher in the RRM than in the LR. In addition, RRM simulates stronger aqueous-phase production of sulfate through the enhanced oxidation of sulfur dioxide by hydrogen peroxide due to increased cloud liquid water content. In contrast, the gas-phase chemical production of sulfate is slightly suppressed. The RRM resolves more large-scale precipitation and produces less convective precipitation than the LR, leading to increased (decreased) aerosol wet scavenging by large-scale (convective) precipitation. Regarding aerosol effects on clouds, RRM produces larger temporal variabilities in the large-scale liquid cloud fractions than LR, resulting in increased microphysical cloud processing of aerosols (more interstitial aerosols are converted to cloud-borne aerosols via aerosol activation) in RRM. Water vapor condensation is also enhanced in RRM compared to LR. Consequently, the RRM simulation produces more cloud droplets, a larger cloud droplet radius, a higher liquid water path, and a larger cloud optical depth than the LR simulation. A comparison of the present-day and pre-industrial simulations indicates that, for this contiguous United States domain, the higher-resolution increases ERFaer at the top of the model by about 12 %, which is mainly attributed to the strengthened indirect effect associated with aerosol–cloud interactions.

Abstract Near infrared energy remains untapped toward the maneuvering of entire solar spectrum harvesting for fulfilling the nuts and bolts of solar hydrogen production. We report the use of Au@Cu7S4 yolk@shell nanocrystals as dual-plasmonic photocatalysts to achieve remarkable hydrogen production under visible and near infrared illumination. Ultrafast spectroscopic data reveal the prevalence of long-lived charge separation states for Au@Cu7S4 under both visible and near infrared excitation. Combined with the advantageous features of yolk@shell nanostructures, Au@Cu7S4 achieves a peak quantum yield of 9.4% at 500 nm and a record-breaking quantum yield of 7.3% at 2200 nm for hydrogen production in the absence of additional co-catalysts. The design of a sustainable visible- and near infrared-responsive photocatalytic system is expected to inspire further widespread applications in solar fuel generation. In this work, the feasibility of exploiting the localized surface plasmon resonance property of self-doped, nonstoichiometric semiconductor nanocrystals for the realization of wide-spectrum-driven photocatalysis is highlighted.

The application of the INS/GNSS integrated system is widespread in vehicle navigation. However, in certain complex urban areas, achieving accurate positioning for autonomous vehicles at level 3 and beyond presents a challenge. Moreover, ensuring the continuous availability of the navigation system is crucial for maintaining vehicle safety under all circumstances. Consequently, the specifications of the Inertial Measurement Unit (IMU), which have a direct correlation with cost, dominate the performance of the navigation system in environments where GNSS signals are denied or challenging. To implement level 3 autonomous vehicles, a strategy based on redundant IMUs (RIMU) is employed to not only enhance accuracy and availability but reduce the overall cost of the navigation system. This research mainly focuses on the development of a robust autonomous vehicular navigation system by designing an integrated algorithm for the low-cost RIMU and GNSS receiver within the INS/GNSS integration scheme. The positioning performance is analyzed by using a reference system and a benchmark system in the open sky, GNSS challenging, and GNSS outage scenarios.

Traffic sign detection is an important part of autonomous driving technology, and it is also important to have a large-scale dataset applicable to Chinese traffic scenarios. The article proposes a text-based self-labelled traffic sign dataset which consists of 3153 images, of which 2903 images are used for training and 250 images are used for validation. And an improved YOLOv7 algorithm is provided that incorporates the BiFormer attention mechanism into the YOLOv7 network to enhance its ability to detect small objects. This approach has the advantage of improved accuracy but may increase runtime. To mitigate this problem, the improved YOLOv7 network undergoes model pruning to compress the model size and increase its speed. Experimental results show that the improved YOLOv7 network in this paper improves the average accuracy by 2.9% while maintaining almost the same speed as the original network. After testing, the model has a real-time effect and practical significance. In conclusion, the text-based self-annotated dataset and the improved YOLOv7 network proposed in this paper have important reference values for text-based traffic sign recognition in automatic driving assistance systems.

In autonomous driving systems, a positioning method that can be used in scenarios with no satellite signals or long signal interruptions is a must. In this paper, we address the problems in map construction methods and map matching methods in scenes without satellite signals or long signal interruptions, and construct a semantic map matching-based localization method to meet the localization requirements by means of monocular vision sensors on the basis of weighing the accuracy and cost of map construction and localization. In this paper, firstly, the method of map construction is studied and a static semantic map construction method based on monocular camera is constructed. Then the map matching localization method is studied, and a semantic map matching based localization method is constructed to align the local map built during localization with the pre-built global semantic map to obtain the current location information. Finally, this paper constructs a method to fuse the visual odometry and map matching localization results, so as to obtain more accurate localization results.

Simultaneous Localization and Mapping (SLAM), as one of the core technologies in autonomous driving, provides environment perception, decision-making, and path planning to ensure safe vehicle operation. This paper proposes a tightly-coupled fusion mapping method based on LiDAR and Inertial Measurement Unit (IMU). By introducing IMU, performing extrinsic calibration, and time synchronization with the LiDAR, the issue of mapping drift caused by inconsistent coordinate systems is addressed, leading to improved mapping accuracy. Furthermore, to compensate for IMU zero-bias errors, IMU preintegration is employed to calibrate the point cloud and optimize the initial values of the LiDAR odometry. A novel iVox voxel-based point cloud registration method is used to enhance registration efficiency without compromising mapping accuracy. Lastly, the Iterative Extended Kalman Filter (IEKF) is incorporated into the back-end state estimation to propagate and correct the estimated state using IMU data, enabling precise state estimation and map updates. This research is of significant importance in addressing the challenges of low mapping accuracy and real-time performance in single-sensor SLAM, providing an effective solution for environment perception and path planning in autonomous driving systems.

Under the current trend of intelligence and automation, simultaneous positioning and mapping technology has become one of the research hotspots. The main problems of SLAM technology research are to improve the robustness of mapping and positioning, establish an efficient back-end optimization system, and improve the generalization of SLAM technology. This paper proposes to fuse the intensity information of the point cloud and the geometric information of the environment scene to construct a globally consistent environment feature descriptor and use the non-iterative two-step method to perform the nearest neighbour search on the point cloud in the point cloud registration stage. Then use the globally consistent descriptor that has been constructed to extract the laser point cloud descriptor by using the ring partition method, combine the method based on domain search to search for the closest point cloud frame, and finally use Intensity-ICP to complete the loopback frame. Fine registration, outputs the optimal pose transformation, to complete the loop detection. We use our self-built mobile platform to verify the robustness and generalization of the improved laser SLAM algorithm in public datasets and campus datasets. Experimental results show that the improved algorithm reduces the trajectory drift of the mobile platform and improves the efficiency of point cloud registration and loop closure detection.

As a representative of new energy, photovoltaic power stations have developed rapidly in China, with annual installed capacity increasing. At present, research on photovoltaic power plants mainly concentrates on carbon neutrality based on the whole life cycle, and there are few studies on the thermal environment problems that may be caused by photovoltaic power plants. This paper takes the area where the photovoltaic power station is located in Quyang County, Baoding City, Hebei Province, and the surrounding mountains as the research area. It uses Gaofen-1 and Landsat 8 remote sensing images to study the changes in land cover and surface temperature before and after the construction of mountain photovoltaic power stations over a period of 8 years from 2013 to 2020.Given the lack of quantitative research on the impact of mountain photovoltaic power stations on the surrounding environment using remote sensing technology, this paper focuses on the influence of the distribution and proportion of mountain photovoltaic power stations on the spatial pattern of the thermal environment in the region. Through experimental verification and result analysis, it can be seen that under the geographical location and time conditions of the study area, the proportion of photovoltaic power stations shows a strong correlation with the average surface temperature, which is one of the main factors causing the temperature rise in mountainous areas. It is a land cover type with heat island effects in the traditional sense.

As autonomous driving technology advances, ensuring the system's safety in rain and snow has emerged as a pivotal research topic. In rainy and snowy weather, rain and snow can generate noise points within the point cloud captured by the Light Detection and Ranging (LiDAR), significantly impeding the LiDAR's sensing capability. To address this problem, we first manually label the point cloud data gathered in rain and snow, categorizing all points into noise points and non-noise points. Subsequently, we analyze the intensity and spatial distribution characteristics of the rain and snow noise points and employ the gamma distribution curve to illustrate the spatial distribution characteristics of these noise points. Finally, we propose a Low-Intensity Dynamic Statistical Outlier Removal (LIDSOR) filter, an enhancement of the existing Dynamic Statistical Outlier Removal (DSOR) filter. Experimental results suggest that the LIDSOR filter can effectively eliminate rain and snow noise points while preserving more environmental feature points. Additionally, it consumes fewer computational resources. The filter we propose in this paper significantly contributes to the safe operation of the autonomous driving system in diverse complex environments.

We report results on an elastic cross section measurement in proton–proton collisions at a center-of-mass energy s=510 GeV, obtained with the Roman Pot setup of the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The elastic differential cross section is measured in the four-momentum transfer squared range 0.23≤−t≤0.67 GeV2. This is the only measurement of the proton-proton elastic cross section in this t range for collision energies above the Intersecting Storage Rings (ISR) and below the Large Hadron Collider (LHC) colliders. We find that a constant slope B does not fit the data in the aforementioned t range, and we obtain a much better fit using a second-order polynomial for B(t). This is the first measurement below the LHC energies for which the non-constant behavior B(t) is observed. The t dependence of B is also determined using six subintervals of t in the STAR measured t range, and is in good agreement with the phenomenological models. The measured elastic differential cross section dσ/dt agrees well with the results obtained at s=540 GeV for proton–antiproton collisions by the UA4 experiment. We also determine that the integrated elastic cross section within the STAR t-range is σelfid=462.1±0.9(stat.)±1.1(syst.)±11.6(scale) μb.

IntroductionThe loose-patch clamp technique was first developed and used in native amphibian skeletal muscle (SkM), offering useful features complementing conventional sharp micro-electrode, gap, or conventional patch voltage clamping. It demonstrated the feedback effects of pharmacological modification of ryanodine receptor (RyR)-mediated Ca2+ release on the Na+ channel (Nav1.4) currents, initiating excitation–contraction coupling in native murine SkM. The effects of the further RyR and Ca2+-ATPase (SERCA) antagonists, dantrolene and cyclopiazonic acid (CPA), additionally implicated background tubular-sarcoplasmic Ca2+ domains in these actions.Materials and methodsWe extend the loose-patch clamp approach to ion current measurements in murine hippocampal brain slice cornu ammonis-1 (CA1) pyramidal neurons. We explored the effects on Na+ currents of pharmacologically manipulating RyR and SERCA-mediated intracellular store Ca2+ release and reuptake. We adopted protocols previously applied to native skeletal muscle. These demonstrated Ca2+-mediated feedback effects on the Na+ channel function.ResultsExperiments applying depolarizing 15 ms duration loose-patch clamp steps to test voltages ranging from −40 to 120 mV positive to the resting membrane potential demonstrated that 0.5 mM caffeine decreased inward current amplitudes, agreeing with the previous SkM findings. It also decreased transient but not prolonged outward current amplitudes. However, 2 mM caffeine affected neither inward nor transient outward but increased prolonged outward currents, in contrast to its increasing inward currents in SkM. Furthermore, similarly and in contrast to previous SkM findings, both dantrolene (10 μM) and CPA (1 μM) pre-administration left both inward and outward currents unchanged. Nevertheless, dantrolene pretreatment still abrogated the effects of subsequent 0.5- and 2-mM caffeine challenges on both inward and outward currents. Finally, CPA abrogated the effects of 0.5 mM caffeine on both inward and outward currents, but with 2 mM caffeine, inward and transient outward currents were unchanged, but sustained outward currents increased.ConclusionWe, thus, extend loose-patch clamping to establish pharmacological properties of murine CA1 pyramidal neurons and their similarities and contrasts with SkM. Here, evoked though not background Ca2+-store release influenced Nav and Kv excitation, consistent with smaller contributions of background store Ca2+ release to resting [Ca2+]. This potential non-canonical mechanism could modulate neuronal membrane excitability or cellular firing rates.

Cardiac arrhythmias cause significant morbidity and mortality and pose a major public health problem. They arise from disruptions in the normally orderly propagation of cardiac electrophysiological activation and recovery through successive cardiomyocytes in the heart. They reflect abnormalities in automaticity, initiation, conduction, or recovery in cardiomyocyte excitation. The latter properties are dependent on surface membrane electrophysiological mechanisms underlying the cardiac action potential. Their disruption results from spatial or temporal instabilities and heterogeneities in the generation and propagation of cellular excitation. These arise from abnormal function in their underlying surface membrane, ion channels, and transporters, as well as the interactions between them. The latter, in turn, form common regulatory targets for the hierarchical network of diverse signaling mechanisms reviewed here. In addition to direct molecular-level pharmacological or physiological actions on these surface membrane biomolecules, accessory, adhesion, signal transduction, and cytoskeletal anchoring proteins modify both their properties and localization. At the cellular level of excitation–contraction coupling processes, Ca2+ homeostatic and phosphorylation processes affect channel activity and membrane excitability directly or through intermediate signaling. Systems-level autonomic cellular signaling exerts both acute channel and longer-term actions on channel expression. Further upstream intermediaries from metabolic changes modulate the channels both themselves and through modifying Ca2+ homeostasis. Finally, longer-term organ-level inflammatory and structural changes, such as fibrotic and hypertrophic remodeling, similarly can influence all these physiological processes with potential pro-arrhythmic consequences. These normal physiological processes may target either individual or groups of ionic channel species and alter with particular pathological conditions. They are also potentially alterable by direct pharmacological action, or effects on longer-term targets modifying protein or cofactor structure, expression, or localization. Their participating specific biomolecules, often clarified in experimental genetically modified models, thus constitute potential therapeutic targets. The insights clarified by the physiological and pharmacological framework outlined here provide a basis for a recent modernized drug classification. Together, they offer a translational framework for current drug understanding. This would facilitate future mechanistically directed therapeutic advances, for which a number of examples are considered here. The latter are potentially useful for treating cardiac, in particular arrhythmic, disease.

Abstract Mesothelioma is an aggressive cancer of the mesothelial layer associated with an extensive fibrotic response. The latter is in large part mediated by cancer-associated fibroblasts which mediate tumour progression and poor prognosis. However, understanding of the crosstalk between cancer cells and fibroblasts in this disease is mostly lacking. Here, using co-cultures of patient-derived mesothelioma cell lines and lung fibroblasts, we demonstrate that fibroblast activation is a self-propagated process producing a fibrotic extracellular matrix (ECM) and triggering drug resistance in mesothelioma cells. Following characterisation of mesothelioma cells/fibroblasts signalling crosstalk, we identify several FDA-approved targeted therapies as far more potent than standard-of-care Cisplatin/Pemetrexed in ECM-embedded co-culture spheroid models. In particular, the SRC family kinase inhibitor, Saracatinib, extends overall survival well beyond standard-of-care in a mesothelioma genetically-engineered mouse model. In short, we lay the foundation for the rational design of novel therapeutic strategies targeting mesothelioma/fibroblast communication for the treatment of mesothelioma patients.

Abstract PACS1 syndrome is a neurodevelopmental disorder (NDD) caused by a recurrent de novo missense mutation in PACS1 (p.Arg203Trp (PACS1R203W)). The mechanism by which PACS1R203W causes PACS1 syndrome is unknown, and no curative treatment is available. Here, we use patient cells and PACS1 syndrome mice to show that PACS1 (or PACS-1) is an HDAC6 effector and that the R203W substitution increases the PACS1/HDAC6 interaction, aberrantly potentiating deacetylase activity. Consequently, PACS1R203W reduces acetylation of α-tubulin and cortactin, causing the Golgi ribbon in hippocampal neurons and patient-derived neural progenitor cells (NPCs) to fragment and overpopulate dendrites, increasing their arborization. The dendrites, however, are beset with varicosities, diminished spine density, and fewer functional synapses, characteristic of NDDs. Treatment of PACS1 syndrome mice or patient NPCs with PACS1- or HDAC6-targeting antisense oligonucleotides, or HDAC6 inhibitors, restores neuronal structure and synaptic transmission in prefrontal cortex, suggesting that targeting PACS1R203W/HDAC6 may be an effective therapy for PACS1 syndrome.

Abstract The effective mass at the Fermi level is measured in the strongly interacting two-dimensional (2D) electron system in ultra-clean SiGe/Si/SiGe quantum wells in the low-temperature limit in tilted magnetic fields. At low electron densities, the effective mass is found to be strongly enhanced and independent of the degree of spin polarization, which indicates that the mass enhancement is not related to the electrons’ spins. The observed effect turns out to be universal for silicon-based 2D electron systems, regardless of random potential, and cannot be explained by existing theories.

Aptamers, short single DNA or RNA oligonucleotides, have shown immense application potential as molecular probes for the early diagnosis and therapy of cancer. However, conventional cell-SELEX technologies for aptamer discovery are time-consuming and laborious. Here we discovered a new aptamer BC-3 by using an improved rapid X-Aptamer selection process for human bladder carcinoma, for which there is no specific molecular probe yet. We show that BC-3 exhibited excellent affinity in bladder cancer cells but not normal cells. We demonstrate that BC-3 displayed high selectivity for tumor cells over their normal counterparts in vitro, in mice, and in patient tumor tissue specimens. Further endocytosis pathway analysis revealed that BC-3 internalized into bladder cancer cells via clathrin-mediated endocytosis. Importantly, we identified ribosomal protein S7 (RPS7) as the binding target of BC-3 via an integrated methodology (mass spectrometry, colocalization assay, and immunoblotting). Together, we report that a novel aptamer BC-3 is discovered for bladder cancer and its properties in the disease are unearthed. Our findings will facilitate the discovery of novel diagnostic and therapeutic strategies for bladder cancer.

RHIC nine-magnet spin flipper has been operated successfully during RHIC polarized proton Run 17, with 97% spin flip efficiency achieved. The results show the importance of mirror resonance removal, small spin tune spread, and proper spin flipper driving tune sweep speed. Detailed spin tracking simulations, based on a Lorentz force and Thomas-BMT differential equation numerical solver code for accuracy, have been carried out to understand the experimental results. Agreement within measurement accuracy is obtained at injection energy, 23.8 GeV. It is not as tight at 255 GeV, reasons for that are exposed. These measurements and numerical studies allow to determine the sensitivity of spin-flip efficiency to the dispersion slopes at the two Siberian snakes and to the ac dipole frequency sweep speed. They also provide guidance for future developments at BNL’s electron-ion collider.