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With the rapid development of the civil aviation industry, flight delays caused by convective weather are becoming increasingly severe. In terminal airspace with complex traffic environments, these delays can propagate to subsequent arrivals and departures, thereby affecting the operational efficiency of the terminal airspace and potentially disrupting the entire air traffic system. Accurate short-term forecasting of convective weather can help reduce flight delays, improve airspace utilization, and decrease the workload of air traffic controllers. This study, grounded in supervised deep learning, addresses civil aviation operational requirements by developing a short-term forecasting model for convective weather based on a Convolutional Neural Network-Transformer (CNN-Transformer). The model leverages eight types of weather products from civil aviation weather radar and ERA5, including basic reflectance, echo top, vertically integrated liquid, relative humidity, temperature, U-component of wind, V-component of wind, and vertical velocity. The training was conducted using 241 datasets, totaling 23,881 samples. To evaluate the model’s validity, ablation studies were performed on each parameter, and its performance was compared with the Centroid Method, Optical Flow, CNN-CNN, and CNN-Long Short-Term Memory (CNN-LSTM). According to six evaluation indicators, traditional radar echo extrapolation showed better forecast accuracy within 1 hour, while the CNN-Transformer-based short-term forecasting model for convective weather demonstrated superior performance for 2-6 hour forecasts.

With the rapid growth of flight volume, the impact of convective weather on flight operations in the terminal area has become more and more serious. In this paper, the typical flight paths (TFPs) are used to replace flight procedures as the routine flight paths in the terminal area, and the TFP of each flight is predicted by Random Forest (RF), Boosting Tree (BT) and K-Nearest Neighbour (KNN) algorithms based on the weather and flight plan characteristics. A multi-flight rerouting optimisation model by bi-level programming is established, which contains a flight flow optimisation model in the upper layer and a single flight path optimisation model in the lower layer. The simulated annealing algorithm and the bidirectional A* algorithm are used to solve the upper and lower models. This paper uses the terminal area of Guangzhou Baiyun Airport (ZGGG) and Wuhan Tianhe Airport (ZHHH) for case analysis. The RF algorithm has better performance in predicting TFPs compared with the BT and KNN algorithms. Compared to the historical radar trajectory, the flight path optimisation results show that for the Guangzhou terminal area, while meeting the Terminal Airspace Availability (TAA) as constraint, the flight flow increases and the flight distance reduces, effectively improving the operational efficiency within the terminal.

Semblis atrata is one of three Semblis species distributed in clean brooks and streams in northern Eurasia. Genomic DNA of an S. atrata sample was extracted and sequenced for assembly and annotation of its complete mitogenome. The complete mitochondrial genome of S. atrata was 14,909 bp in length and consisted of 13 protein-coding genes, 2 rRNA genes, and 22 tRNA genes. The S. atrata COX1 gene features a CGA start codon, and COX1, COX2, ND1, and ND5 exhibit incomplete stop codons that are presumed to be completed by the addition of 3’ A residues to the mRNA. The nucleotide composition was highly AT biased, accounting for 77.71% of the whole mitogenome. Phylogenetic analysis placed Semblis as sister to Eubasilissa. The complete mitochondrial genome will be helpful for further studies on the population genetics of this species and phylogenetic analyses of Trichoptera.

Terminal airspace is the convergence area of air traffic flow, which is the bottleneck of air traffic management. With the rapid growth of air traffic volume, the impact of convective weather on flight operations is becoming more and more serious. To change the conditions and improve the utilization of terminal area airspace, a convective weather terminal area capacity (CWTAC) model is developed to quantify the effect of convective weather on the capacity of the terminal area in this paper. The airspace of the terminal area is divided into major airspace, minor airspace and no-impact airspace according to the distribution of the air traffic flow. Under convective weather, their permeabilities are calculated and used as input features, and the actual availability rate is set to label. Three machine learning algorithms, support vector regression (SVR), random forest (RF), and artificial neural network (ANN), are used to predict the availability rate. Then, the terminal airspace capacity under convective weather can be calculated. The historical operation data of the Guangzhou terminal area and the Wuhan terminal area are taken to test machine learning algorithms and verify the CWTAC model. It shows that all three machine learning algorithms are practical, and ANN is the best one based on mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The predicted capacity of the CWTAC model accords well with the actual flight number in the terminal airspace under convective weather. The reasons why they are not entirely consistent are also analyzed.

BackgroundZimberelimab (GLS-010) is a novel fully human monoclonal immunoglobulin G4 (IgG4) against the programmed cell death-1 (PD-1) receptor.AimTo evaluate the affinity, competitive blocking capability, T cell activation effect, cytotoxic effector functions by Fc, preliminary anti-tumor activity, and pharmacokinetics of GLS-010.MethodsThe affinity of GLS-010 to PD-1 and the ability of GLS-010 to block the PD-L1/2 to PD-1 interaction on the cell surface were measured. An allogeneic mixed lymphocyte reaction was conducted to evaluate the inhibitory effect of GLS-010 on Tregs and stimulatory effect on T cell proliferation and activation. Pharmacodynamics and pharmacokinetics were evaluated in tumor-bearing mice and cynomolgus monkeys, respectively.ResultsThe equilibrium dissociation constant (KD) for the association between GLS-010 and PD-1 was 1.75×10-10 M. GLS-010 could effectively block the binding of PD-L1/2 to PD-1. GLS-010 showed statistically significant anti-tumor effects in the MC38 model in human PD-1 knock-in mice. The RO rate on in the low-, moderate-, and high-dose groups were 64.50%-48.53% in CD3+T, 58.87%-40.12% in CD8+T, and 66.26%-49.07% in CD4+T, respectively. With the increasing dose from 2 mg/kg to 18 mg/kg, the systemic exposure level of GLS-010 (AUC0-last) and C0 increased proportionally, while the proportion of AUC0-last was higher than the proportion of the increase in the dose.ConclusionsAs a fully human anti-PD-1 monoclonal antibody, GLS-010 has a high affinity to PD-1 and shows potent anti-tumor effects in vivo and in vitro. The results support that GLS-010 could be investigated in clinical trials in tumor patients.

Few studies have explored the role of Cenozoic tectonic evolution in shaping the patterns and processes of extant animal distributions in and around East Asia. In this study, we selected South Chinese brown frogs as a model to examine the phylogenetic and biogeographical consequences of Miocene tectonic events within South China and its margins. We used mitochondrial and nuclear molecular data to reconstruct phylogenetic interrelationships among Chinese brown frogs using Bayesian and maximum likelihood analyses. The phylogeny results show that there are four main clades of Chinese brown frogs. Excepting the three commonly known Chinese brown frog species groups, R. maoershanensis forms an independent clade nearest to the R. japonica group. Phylogeny and P-distance analyses confirmed R. maoershanensis as a valid species. Among South Chinese brown frogs, there are four subclades associated with four geographical areas: (I) R. maoershanensis; (II) R. japonica; (III) R. chaochiaoensis; and (IV) other species of the R. longicrus species group. Divergence times, estimated using mitochondrial sequences, place the vicariance events among the four subclades in the middle to late Miocene epoch. Our results suggest that (1) South Chinese brown frogs originated due to a vicariance event separating them from the R. chensinensis species group at the time of the Geological movement (~18 million years ago, Ma) in southern Tibet and the Himalayan region; (2) the separation and speciation of R. maoershanensis from the R. japonica group occurred due to the dry climate at approximately 16 Ma; (3) South Chinese brown frogs migrated from South China to Japan at the time (~10.8 Ma) that the global sea-level fell and the East China Sea Shelf Basin was swamp facies, when a land gallery may have formed across the sea to connect the two areas; and (4) R. chaochiaoensis separated from other species of the R. longicrus species group during the uplift of the Tibetan Plateau at approximately 9.5 Ma.