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Background To assess the efficacy and safety of tucidinostat plus exemestane as a neoadjuvant strategy in early-stage breast cancer. Methods This prospective, open-label, single-arm phase II trial enrolled patients with stage II-III breast cancer with hormone receptor-positive and human epidermal growth factor receptor 2 (HER2)-negative. Eligible patients received tucidinostat plus exemestane, and then breast-conserving surgery (BCS) or modified radical mastectomy. Results Among 20 enrolled patients, 3 of them achieved preoperative endocrine prognostic index (PEPI) score of 0. Additionally, complete cell cycle arrest was observed in 7, radiologic objective response rate in 10, and disease control rate in 20 patients, pathological complete response in 1 patient, and 5 patients performed BCS. Ki67 suppression from baseline to surgery was observed in 17 of patients, with the Ki67 change ratio of −73.5%. Treatment-emergent adverse event included neutropenia, leukopenia, thrombocytopenia, lymphopenia, hypoalbuminemia, aspartate aminotransferase elevation, glutamyl transpeptidase elevation, anemia, and alanine aminotransferase elevation. Conclusions Despite the rate of PEPI score 0 was not high, tucidinostat plus exemestane as a neoadjuvant therapy might be well tolerated and showed promising clinical responses in patients with early hormone receptor-positive, HER2-negative breast cancer. To clarify the safety and efficacy of this strategy, further investigation is warranted. Clinical Trial Registration ChiCTR2100046678. [ABSTRACT FROM AUTHOR]

The impact of supercooled water droplets is the cause of aircraft icing, and the acquisition of impingement characteristics is the key to icing prediction and the design of ice protection systems. The introduction of water droplet collection efficiency is required to obtain the characteristics for the Lagrangian method. In this work, a traditional flow tube method, a local flow tube method, and a statistical method are established to calculate the local collection efficiency, based on Lagrangian droplet trajectories. Through the numerical simulations of the air–droplet flow field around an NACA 0012 airfoil, the accuracies of the three methods in regard to collection efficiency are verified. Then, these three methods are applied to obtain the results for water droplet trajectories and the collection efficiency of an S-shaped duct, a 2D engine cone section and an icing wind tunnel. The results show that the distributions of water droplet collection efficiency obtained by the three methods are consistent and the three methods are all feasible when the water droplets do not overlap or cross before hitting the aircraft surfaces. When the water droplets are shadowed by upstream surfaces or blown by air injection, the droplet trajectories might overlap or even cross, and the local collection efficiencies obtained by the traditional flow tube method, local flow tube method, and statistical method might differ. The statistical method is relatively accurate. However, not all the droplet impingement characteristics obtained by the three methods are different due to these effects, and the non-crossing of the droplet trajectories is not a necessary condition for the use of the flow tube method. The effects of trajectory crossings are analyzed and discussed in detail in different situations for the three methods. This work is helpful for understanding and accurately calculating the droplet impingement characteristics and is of great significance for simulations of the aircraft icing process and anti/de-icing range. [ABSTRACT FROM AUTHOR]

Background The lifespan of patients diagnosed with de novo metastatic breast cancer (dnMBC) has been prolonged. Nonetheless, there remains substantial debate regarding immediate breast reconstruction (IBR) for this particular subgroup of patients. The aim of this study was to construct a nomogram predicting the breast cancer-specific survival (BCSS) of dnMBC patients who underwent IBR. Methods A total of 682 patients initially diagnosed with metastatic breast cancer (MBC) between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. All patients were randomly allocated into training and validation groups at a ratio of 7:3. Univariate Cox hazard regression, least absolute shrinkage and selection operator (LASSO), and best subset regression (BSR) were used for initial variable selection, followed by a backward stepwise multivariate Cox regression to identify prognostic factors and construct a nomogram. Following the validation of the nomogram with concordance indexes (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCAs), risk stratifications were established. Results Age, marital status, T stage, N stage, breast subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, radiotherapy, and chemotherapy were independent prognostic factors for BCSS. The C-indexes were 0.707 [95% confidence interval (CI), 0.666–0.748] in the training group and 0.702 (95% CI, 0.639–0.765) in the validation group. In the training group, the AUCs for BCSS were 0.857 (95% CI, 0.770–0.943), 0.747 (95% CI, 0.689–0.804), and 0.700 (95% CI, 0.643–0.757) at 1 year, 3 years, and 5 years, respectively, while in the validation group, the AUCs were 0.840 (95% CI, 0.733–0.947), 0.763 (95% CI, 0.677–0.849), and 0.709 (95% CI, 0.623–0.795) for the same time points. The calibration curves for BCSS probability prediction demonstrated excellent consistency. The DCA curves exhibited strong discrimination power and yielded substantial net benefits. Conclusions The nomogram, constructed based on prognostic risk factors, has the ability to provide personalized predictions for BCSS in dnMBC patients undergoing IBR and serve as a valuable reference for clinical decision making. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is an emerging privacy-preserving technology for machine learning, which enables end devices to cooperatively train a global model without uploading their local sensitive data. Because of limited network bandwidth and considerable communication overhead, communication efficiency has become an essential bottleneck for FL. Existing solutions attempt to improve this situation by reducing communication rounds while usually come with more computation resource consumption or model accuracy deterioration. In this paper, we propose a parameter Prediction-Based DL (PBFL). In which an extended Kalman filter-based prediction algorithm, a practical prediction error threshold setting mechanism and an effective global model updating strategy are included. Instead of collecting all updates from participants, PBFL takes advantage of predicting values to aggregate the model, which substantially reduces required communication rounds while guaranteeing model accuracy. Inspired by the idea of prediction, each participant checks whether its prediction value is out of the tolerance threshold limits and only uploads local updates that have an inaccurate prediction value. In this way, no additional local computational resources are required. Experimental results on both multilayer perceptrons and convolutional neural networks show that PBFL outperforms the state-of-the-art methods and improves the communication efficiency by >66% with 1% higher model accuracy. [ABSTRACT FROM AUTHOR]

SMART (Single-cycle Multi-hop Asynchronous Repeated Traversal) Network-on-Chip (NoC), a recently proposed dynamically reconfigurable NoC, enables single-cycle long-distance communication by building single-bypass paths directly between distant communication pairs. However, such a single-cycle single-bypass path will be readily broken when contention occurs. Thus, packets will be buffered at intermediate routers with blocking latency from other contending packets, and extra router-stage latency to rebuild the remaining path when available, reducing the bypassing benefits that SMART NoC offers. In this article, we for the first time propose an effective contention-minimized routing algorithm to achieve maximal bypassing in SMART NoCs. Specifically, we identify two potential routes for packets: direct route, with which packets can reach the destination in a single bypass; and indirect route, with which packets can reach the destination in multiple bypasses via a (multiple) intermediate router(s). The novel feature of the proposed routing strategy is that, contrary to an intuitive approach, not the routes with minimal distance but the indirect routes via the arbitrary intermediate routers (even if they may be non-minimal) that avoid contentions yield the minimized end-to-end latency. Our new routing strategy can greatly enrich the path diversity, effectively minimize the conflicts between communication pairs, greatly balance the workloads and fully utilize bypass paths. Evaluation on realistic benchmarks demonstrates the effectiveness of the proposed routing strategy, which achieves average performance improvement by 35.48 percent in communication latency, 28.31 percent in application schedule length, and 37.59 percent in network throughput, compared with the current routing in SMART NoCs. [ABSTRACT FROM AUTHOR]