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Abstract Background Immunotherapy-based combinations have emerged as standard therapies for patients with metastatic renal cell carcinoma (mRCC). Pembrolizumab, a PD-1 inhibitor, combined with epacadostat, an indoleamine 2,3-deoxygenase 1 selective inhibitor, demonstrated promising antitumor activity in a phase 1 study in advanced solid tumors, including mRCC. Methods KEYNOTE-679/ECHO-302 was a randomized, open-label, parallel-group, multicenter, phase 3 study (NCT03260894) that compared pembrolizumab plus epacadostat with sunitinib or pazopanib as first-line treatment for mRCC. Eligible patients had histologically confirmed locally advanced or metastatic clear cell RCC and had not received systemic therapy. Patients were randomly assigned 1:1 to pembrolizumab 200 mg IV every 3 weeks plus epacadostat 100 mg orally twice daily versus sunitinib 50 mg orally once daily (4 weeks on treatment followed by 2 weeks off treatment) or pazopanib 800 mg orally once daily. Original dual primary end points were progression-free survival and overall survival. Enrollment was stopped when a phase 3 study in melanoma of pembrolizumab plus epacadostat compared with pembrolizumab monotherapy did not meet its primary end point. This protocol was amended, and primary end point was changed to investigator-assessed objective response rate (ORR) per RECIST 1.1. Results One-hundred-twenty-nine patients were randomly assigned to receive pembrolizumab plus epacadostat (n = 64) or sunitinib/pazopanib (n = 65). Median (range) follow-up, defined as time from randomization to data cutoff, was 10.3 months (2.2–14.3) and 10.3 months (2.7–13.8) in the pembrolizumab plus epacadostat and sunitinib/pazopanib arms, respectively. ORRs were similar between pembrolizumab plus epacadostat (31.3% [95% CI 20.2–44.1] and sunitinib/pazopanib (29.2% [18.6–41.8]). Grade 3–5 treatment-related adverse events occurred in 34.4% and 42.9% of patients in the pembrolizumab plus epacadostat and sunitinib/pazopanib arms, respectively. One patient in the sunitinib/pazopanib arm died of septic shock (not treatment-related). Circulating kynurenine levels decreased in the pembrolizumab plus epacadostat arm, but not to levels observed in healthy subjects. Conclusions ORRs were similar between pembrolizumab plus epacadostat and sunitinib/pazopanib as first-line treatment in patients with mRCC. Safety and tolerability appeared similar between treatment arms; no new safety concerns were identified. Antitumor responses observed in patients with RCC receiving pembrolizumab plus epacadostat may be driven primarily by pembrolizumab. Clinical trial registration ClinicalTrials.gov; NCT03260894 .

The morphology and volume fraction of coarse aggregate particles significantly influence the mechanical characteristics of concrete. This paper presents the development of a numerical model for pebble concrete by combining experiments with the discrete element method. The mesoscopic parameters between mortars and the contact surface between mortars and aggregates in the model were calibrated by comparing simulated stress–strain relationships with experimental data from pebble concrete. Subsequently, the model and mesoscopic parameters were validated by comparing them with experimental results. The obtained mesoscopic parameters were then utilized in the numerical modeling of uniaxial compression for single-shaped coarse aggregate concrete. Results showed that the peak stress of the elliptical aggregate concrete was 2.8 MPa higher than that of the oblate spherical aggregate concrete, leading to more cracks and severe damage in the former. Thus, elliptical aggregates proved more suitable as coarse aggregates for concrete than oblate spherical aggregates. Furthermore, the study investigated the influence of ellipsoidal aggregate volume fraction on concrete strength. The compressive strength reached the highest when the volume fraction of ellipsoidal aggregate was 57%. Therefore, the ideal coarse aggregate volume fraction was determined to be 57%. These results offer valuable insights into designing concrete mix proportions.

The risk of slope failure is determined by the degree of damage caused by the slope slide. For the special-high slope of some high-risk water conservancy and hydropower projects, the standard should be appropriately raised. Thus, the safety standard for these slopes is explored on the basis of reliability analysis. The slopes with high risk of failure are divided into special class I and special class II slopes depending on the risk levels and acceptable risk standards. The concept of reliability theory-based relative ratio of the safety margin is utilized to establish the relationship between annual failure probability and safety factor, thereby obtaining the reasonable safety factors for different slopes. Results show that the values of safety factors for special class I and special class II are 1.40 and 1.35, respectively. These results can provide a reference for exploring the safety standards of dams with a height of more than 200 m.

In this study, discrete element PFC3D software was used to simulate the uniaxial compression of C30 concrete specimen models after freezing−thawing damage. Based on the thickness of the freezing−thawing damage layer of the concrete and the uniaxial compression test, the meso-parameters of cement mortar in concrete and the meso-parameters of weak interface between the cement mortar and coarse aggregate under different freezing−thawing cycles were calibrated. The axial compression deformation produced in the experiment was compared with the axial displacement of particles obtained by numerical simulation to verify the accuracy of the model and the selected meso-parameters. Based on the numerical model, the effects of freeze−thaw damage on the mechanical properties of concrete were studied from the perspectives of the contact force between particles, crack development, and concrete cracking, and the characteristics of meso-scale failure of concrete model after freezing−thawing damage were analyzed. The results show that the stress−strain curves obtained by uniaxial compression simulation in the PFC3D-based numerical model of freezing−thawing concrete are consistent with the test results. During the uniaxial compression process in the freezing−thawing concrete model, the contact force at different stage points between the particles, crack development, and the number of cracked concrete fragments quantitatively reflected the degree of meso-scale concrete failure. With increasing numbers of freeze−thaw cycles, the failure severity of the model increased with obvious penetrating cracks, which is consistent with the failure pattern obtained by physical experiments. Therefore, numerical experiments can be used to study the meso-scale failure modes of concrete.

Abstract Urban open spaces (UOS) are crucial for urban life, offering benefits across individual and societal levels. However, the understanding of the systematic dynamic of UOS scaling with city size and its potential non-linear performance remains a limited clarity area. This study bridges this gap by integrating urban scaling laws with remote sensing data from 1990 to 2020, creating a framework to analyze UOS trends in China. Our findings reveal that UOS growth is sub-linear scaling with city size, exhibiting economies of scale with scaling exponents between 0.55 and 0.65 and suggesting potential shortages. The distribution structure of UOS across cities is becoming increasingly balanced, as indicated by the rising Zipf’s slope from 0.66 to 0.88. Southeastern coastal cities outperform, highlighting spatial variations and path dependency in UOS development. Additionally, using metrics of Scale-adjusted metropolitan indicator (SAMI) and the ratio of open space consumption to population growth rates (OCRPGR), we observe a trend towards more coordinated development between UOS and population, with a declining proportion of uncoordinated cities. Our long-term, large sample coverage study of UOS in China may offer positive significance for urban ecological planning and management in similar rapidly urbanizing countries, contributing to critical insights for quantifying and monitoring urban sustainable development.

The urban traffic signal-controlled intersections are of great significance for solving the problem of urban road congestion. Previous research on congestion prediction mainly aggregated data at the level of road segments or traffic flow at a coarse regulated time interval. Fine-grained prediction of congestion events at the lane-level and cycle-level enables detailed a understanding of spatio-temporal dependencies, leading to congestion reduction, improved efficiency. This paper presents a Spatio-Temporal Neural Point Process (STNPP) model that combines Graph Neural Networks and Neural Temporal Point Process to predict congestion events at urban intersections. The proposed model allows for complete prediction of congestion events, including their occurrence, development, dissipation. In the process of spatial correlation modeling, graph neural networks are used to model the spatial relationships between both region and intersections. The current intersection and its upstream/downstream areas are modeled separately. To model the temporal correlations at individual intersections, we focus on a specific lane and capture the evolution of congestion events using the Neural Point Process Gated Recurrent Unit (NPPGRU), which captures the temporal granularity changes of signal-controlled cycles in congestion events. Using actual traffic speed and signal-controlled data from Hangzhou city, we validate that the proposed method achieves stable predictive performance.

The urban mobility structure is a summary of individual movement patterns and the interaction between persons and the urban environment, which is extremely important for urban management and public transportation route planning. The majority of current research on urban mobility structure discovery utilizes the urban environment as a static network to detect the relationship between people groups and urban areas, ignoring the vital problem of how individuals affect urban mobility structure dynamically. In this paper, we propose a spatio-temporal representational learning method based on reinforcement learning for discovering urban mobility structures, in which the model can effectively consider the interaction knowledge graph of individuals with stations while accounting for the spatio-temporal heterogeneity of individual travel. The experimental results demonstrate the advantages of individual travel-based urban mobility structure discovery research in describing the interaction between individuals and urban areas, which can account for the intrinsic influence more thoroughly.

Crowdsourced localization using geo-spatial big data has become an effective approach for constructing smart-city-based location services with the fast growing number of Internet of Things terminals. This paper presents a self-calibrated multi-floor indoor positioning framework using a combination of Wi-Fi ranging, crowdsourced fingerprinting and low-cost sensors (SM-WRFS). The localization parameters, such as heading and altitude biases, step-length scale factor, and Wi-Fi ranging bias are autonomously calibrated to provide a more accurate forward 3D localization performance. In addition, the backward smoothing algorithm and a novel deep-learning model are applied in order to construct an autonomous and efficient crowdsourced Wi-Fi fingerprinting database using the detected quick response (QR) code-based landmarks. Finally, the adaptive extended Kalman filter is adopted to combine the corresponding location sources using different integration models to provide a precise multi-source fusion based multi-floor indoor localization performance. The real-world experiments demonstrate that the presented SM-WRFS is proven to realize precise 3D indoor positioning under different environments, and the meter-level positioning accuracy can be acquired in Wi-Fi ranging supported indoor areas.

Background/purpose: The effect of aspirin on bone regeneration remains controversial. This study aimed to determine the effect of various concentrations of aspirin on cell viability, osteogenic differentiation, cell cycle, and apoptosis on ST2 cells to find an effective range of aspirin for bone regeneration induction. Materials and methods: Cell viability was measured with MTT assay after being stimulated with aspirin for 1 day, 2 days, 3 days, 5 days, and 7 days. Alkaline phosphatase (ALP) activity was measured after cells were treated for 1 day, 3 days, and 7 days. Expression of runt-related transcription factor 2 (Runx-2) was evaluated using Western-blot analysis at 3 days and 7 days. Flow cytometry was used for cell cycle and apoptosis measurement after cells were treated for 48 hours. Results: Lower concentrations of aspirin (1μΜ and 10μM) promoted cell growth and increased ALP levels and Runx-2 expression, while higher concentrations (100μΜ and 1000μΜ) inhibited cell growth (P