An Assessment of Multimodel Simulations for the Variability of Western North Pacific Tropical Cyclones and Its Association with ENSO

An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation. Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño—namely, eastern Pacific (EP) and central Pacific (CP) El Niño—and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño

Combination of Decitabine and a Modified Regimen of Cisplatin, Cytarabine and Dexamethasone: A Potential Salvage Regimen for Relapsed or Refractory Diffuse Large B-Cell Lymphoma After Second-Line Treatment Failure

ObjectiveThe prognosis for patients with relapsed or refractory diffuse large B-cell lymphoma (R/R-DLBCL) after second-line treatment failure is extremely poor. This study prospectively observed the efficacy and safety of decitabine with a modified cisplatin, cytarabine, and dexamethasone (DHAP) regimen in R/R-DLBCL patients who failed second-line treatment.MethodsTwenty-one R/R-DLBCL patients were enrolled and treated with decitabine and a modified DHAP regimen. The primary endpoints were overall response rate (ORR) and safety. The secondary endpoints were progression-free survival (PFS) and overall survival (OS).ResultsORR reached 50% (complete response rate, 35%), five patients (25%) had stable disease (SD) with disease control rate (DCR) of 75%. Subgroup analysis revealed patients over fifty years old had a higher complete response rate compared to younger patients (P = 0.005), and relapsed patients had a better complete response rate than refractory patients (P = 0.031). Median PFS was 7 months (95% confidence interval, 5.1-8.9 months). Median OS was not achieved. One-year OS was 59.0% (95% CI, 35.5%-82.5%), and two-year OS was 51.6% (95% confidence interval, 26.9%-76.3%). The main adverse events (AEs) were grade 3/4 hematologic toxicities such as neutropenia (90%), anemia (50%), and thrombocytopenia (70%). Other main non-hematologic AEs were grade 1/2 nausea/vomiting (40%) and infection (50%). No renal toxicity or treatment-related death occurred.ConclusionDecitabine with a modified DHAP regimen can improve the treatment response and prognosis of R/R-DLBCL patients with good tolerance to AEs, suggesting this regimen has potential as a possible new treatment option for R/R-DLBCL patients after second-line treatment failure.Clinical Trial RegistrationClinicalTrials.gov, identifier: NCT03579082

Crystal structure and biochemical analyses reveal Beclin 1 as a novel membrane binding protein

The Beclin 1 gene is a haplo-insufficient tumor suppressor and plays an essential role in autophagy. However, the molecular mechanism by which Beclin 1 functions remains largely unknown. Here we report the crystal structure of the evolutionarily conserved domain (ECD) of Beclin 1 at 1.6 Å resolution. Beclin 1 ECD exhibits a previously unreported fold, with three structural repeats arranged symmetrically around a central axis. Beclin 1 ECD defines a novel class of membrane-binding domain, with a strong preference for lipid membrane enriched with cardiolipin. The tip of a surface loop in Beclin 1 ECD, comprising three aromatic amino acids, acts as a hydrophobic finger to associate with lipid membrane, consequently resulting in the deformation of membrane and liposomes. Mutation of these aromatic residues rendered Beclin 1 unable to stably associate with lipid membrane in vitro and unable to fully rescue autophagy in Beclin 1-knockdown cells in vivo. These observations form an important framework for deciphering the biological functions of Beclin 1

Obstacle avoidance planning of autonomous vehicles using deep reinforcement learning

Obstacle avoidance path planning in a dynamic circumstance is one of the fundamental problems of autonomous vehicles, counting optional maneuvers: emergency braking and active steering. This paper proposes emergency obstacle avoidance planning based on deep reinforcement learning (DRL), considering safety and comfort. Firstly, the vehicle emergency braking and lane change processes are analyzed in detail. A graded hazard index is defined to indicate the degree of the potential risk of the current vehicle movement. The longitudinal distance and lateral waypoint models are established, including the comfort deceleration and stability coefficient considerations. Simultaneously, a fuzzy PID controller is installed to track to satisfy the stability and feasibility of the path. Then, this paper proposes a DRL process to determine the obstacle avoidance plan. Mainly, multi-reward functions are designed for different collisions, corresponding penalties for longitudinal rear-end collisions, and lane-changing side collisions based on the safety distance, comfort reward, and safety reward. Apply a special DRL method-DQN to release the planning program. The difference is that the long and short-term memory (LSTM) layer is utilized to solve incomplete observations and improve the efficiency and stability of the algorithm in a dynamic environment. Once the policy is practiced, the vehicle can automatically perform the best obstacle avoidance maneuver in an emergency, improving driving safety. Finally, this paper builds a simulated environment in CARLA and is trained to evaluate the effectiveness of the proposed algorithm. The collision rate, safety distance difference, and total reward index indicate that the collision avoidance path is generated safely, and the lateral acceleration and longitudinal velocity satisfy the comfort requirements. Besides, the method proposed in this paper is compared with traditional DRL, which proves the beneficial performance in safety and efficiency

Scheme for Polarization Detection and Suppression of TRAD

This paper focuses on the countermeasures of airborne towed radar active decoy. Moreover, a detection and suppression scheme of Towed Radar Active Decoy (TRAD) using polarization information is proposed. A full-polarization radar echo model with the presence of jamming is established. Furthermore, based on a monopulse radar angle measurement system, the difference between the characteristics of TRAD and radar targets in different polarization channels is analyzed. A detection scheme is proposed using the differences of target Polarization Scattering Matrix (PSM) divergence with the presence and absence of TRAD. Subsequently, the corresponding jamming suppression algorithm is developed. The efficiency and advantages of the proposed algorithm are validated via theoretical analyses and simulation studies. This paper provides an effective method to detect TRAD and can provide a significant reference for improving the precision strike of terminal guidance radar under jamming

Corrosion and Residual Strength Analysis of High Pressure Die Casting AM Series Mg Alloys

Higher pressure die casting (HPDC) AM series (Mg-Al-Mn) Mg alloys have wide application potential in the automobile industry. To promote its application, systematic investigation on the corrosion performance and corrosion residual strength of HPDC AM50+1Ce and AM60 was carried out. The corrosion of HPDC AM50+1Ce was more uniform, while the pitting corrosion of AM60 was more severe, and the mechanical properties of HPDC AM60 was more sensitive to corrosion. The residual strength of AM50+1Ce and AM60 after corrosion of 648 h was 199 MPa and 183 MPa, respectively. The findings can contribute to a better understanding of the corrosion and residual strength of HPDC AM series Mg alloys

An embedded vertical‐federated feature selection algorithm based on particle swarm optimisation

Abstract In real life, a large amount of data describing the same learning task may be stored in different institutions (called participants), and these data cannot be shared among participants due to privacy protection. The case that different attributes/features of the same instance are stored in different institutions is called vertically distributed data. The purpose of vertical‐federated feature selection (FS) is to reduce the feature dimension of vertical distributed data jointly without sharing local original data so that the feature subset obtained has the same or better performance as the original feature set. To solve this problem, in the paper, an embedded vertical‐federated FS algorithm based on particle swarm optimisation (PSO‐EVFFS) is proposed by incorporating evolutionary FS into the SecureBoost framework for the first time. By optimising both hyper‐parameters of the XGBoost model and feature subsets, PSO‐EVFFS can obtain a feature subset, which makes the XGBoost model more accurate. At the same time, since different participants only share insensitive parameters such as model loss function, PSO‐EVFFS can effectively ensure the privacy of participants' data. Moreover, an ensemble ranking strategy of feature importance based on the XGBoost tree model is developed to effectively remove irrelevant features on each participant. Finally, the proposed algorithm is applied to 10 test datasets and compared with three typical vertical‐federated learning frameworks and two variants of the proposed algorithm with different initialisation strategies. Experimental results show that the proposed algorithm can significantly improve the classification performance of selected feature subsets while fully protecting the data privacy of all participants