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Abstract Background For decades, KRAS has always been a huge challenge to the field of drug discovery for its significance in cancer progression as well as its difficulties in being targeted as an “undruggable” protein. KRAS regulates downstream signaling pathways through protein–protein interactions, whereas many interaction partners of KRAS remain unknown. Results We developed a workflow to computationally predict and experimentally validate the potential KRAS-interacting proteins based on the interaction mode of KRAS and its known binding partners. We extracted 17 KRAS-interacting motifs from all experimentally determined KRAS-containing protein complexes as queries to identify proteins containing fragments structurally similar to the queries in the human protein structure database using our in-house protein–protein interaction prediction method, PPI-Miner. Finally, out of the 78 predicted potential interacting proteins of KRAS, 10 were selected for experimental validation, including BRAF, a previously reported interacting protein, which served as the positive control in our validation experiments. Additionally, a known peptide that binds to KRAS, KRpep-2d, was also used as a positive control. The predicted interacting motifs of these 10 proteins were synthesized to perform biolayer interferometry assays, with 4 out of 10 exhibiting binding affinities to KRAS, and the strongest, GRB10, was selected for further validation. Additionally, the interaction between GRB10 (RA-PH domain) and KRAS was confirmed via immunofluorescence and co-immunoprecipitation. Conclusions These results demonstrate the effectiveness of our workflow in predicting potential interacting proteins for KRAS and deepen the understanding of KRAS-driven tumor mechanisms and the development of therapeutic strategies.

The total organic carbon (TOC) content is an important index for source rock evaluation. However, due to the scarcity of rock samples, the vertical continuous TOC change curve cannot be obtained, and the test cost is high, which bring great limitations to the evaluation of source rocks. Predecessors have also studied some TOC prediction models, such as the Δlg R method, support vector regression (SVR), and back-propagation neural network, but the TOC predicted by the Δlg R method and SVR has low accuracy and is difficult to calculate. However, back-propagation neural networks always have problems such as local optimal solution and slow convergence speed. In this article, a convolution neural network prediction scheme is proposed. Taking the source rocks of Pinghu Formation in the Xihu Sag as the research object, the advantages of this method are proved by comparing the prediction results of the Δlg R method, SVR, and BP neural network method. The results show that the prediction accuracy of this method is more than 90%, meeting the prediction requirements of TOC. By predicting the TOC curve of Well A, the TOC variation characteristics of Pinghu Formation are finally obtained.

UAVs are often limited by limited resources when performing flight tasks, especially the contradiction between storage resources and computing resources when the huge YOLOv3 model is deployed on the edge UAVs. In this paper, we tend to compress YOLOv3 model in different aspects to achieve load availability at the edge. In this paper, deep separable convolution is introduced to reduce the computation of the model. Then, PR regularization term is used as the regularization term of sparse training to better distinguish scaling factors, and then the hybrid pruning combining channel pruning and layer pruning is carried out on the model according to scaling factors, in order to reduce the number of model parameters and the amount of calculation. Finally, since the training data is a 32-bit floating point number, DoReFa-Net quantization method is used to quantify the model, so as to compress the storage capacity of the model. The experimental results show that the compression scheme proposed in this paper can effectively reduce the number of parameters by 97.5 % and the calculation amount by 82.3 %, and can maintain the original detection efficiency of UAVs.

This paper explores a periodic watermark-based replay attack detection method for Unmanned Marine Vehicles modeled in the framework of the Takagi–Sugeno fuzzy system. The precise detection of replay attacks is crucial for ensuring the security of Unmanned Marine Vehicles; however, traditional timestamp-based or encoded measurement-dependent detection approaches often sacrifice system performance to achieve higher detection rates. To reduce the potential performance degradation, a periodic watermark-based detection scheme is developed, in which a compensation signal together with a periodic Gaussian watermark signal is integrated into the actuator. By compensation calculations conducted with all compensatory signals in each period, the position corresponding to a minimum value of the detection function can be derived. Then, the time that the attacks occurred can be ensured with the aid of the comparison between this position with the watermark signal in the same period. An application on a UMV is shown to demonstrate the effectiveness of the presented scheme in detecting replay attacks while minimizing control costs.

Transition-metal-catalyzed directed C–H functionalization with various carbene precursors has been widely employed for constructing a wide range of complex and diverse active molecules through metal carbene migratory insertion processes. Among various carbene precursors, iodonium ylides serve as a novel and emerging carbene precursor with features including easy accessibility, thermal stability and high activity, which have attracted great attention from organic chemists and have achieved tremendous success in organic transformation. In this review, recent progress on the application of iodonium ylides with multifunctional coupling characteristics in C–H bond activation reactions is summarized, and the potential of iodonium ylides is discussed.

Seasonal delta is a sandy fan-shaped sedimentary system formed by seasonal river flooding in continental basins. Modern sedimentary finds that seasonal deltas mainly develop in an arid-semi-arid climate environment with gentle terrain. However, the study of seasonal deltas in ancient rock records is still insufficient, leading to misinterpretations as other sedimentary systems, which poses challenges to the correct understanding of the origin and spatial distribution of sand body deposits. In this study, the red bed deposits of the Cretaceous Baxigai Formation in the west of the Tabei Uplift were selected as the research object. Based on core observation and description, a comprehensive analysis of the sedimentary environment, sedimentary characteristics and sedimentary model of the Baxigai Formation was carried out using data such as well-logging and analytical testing, combined with modern depositional characteristics. Research shows that the sedimentary sequence of the Baxigai Formation red bed developed a seasonal delta sedimentary system in the arid climate. The seasonal delta deposits provide an opportunity to understand the influencing of river flow and total sedimentary loading vary greatly on the sedimentary model, sedimentary mechanism, and distribution of sand bodies in arid climates. The sedimentary process of the seasonal delta in the Baxigai Formation was controlled by the dual-stage sedimentation process in an arid paleoclimate. During the flood period, the supercritical flow sedimentary structures are widely developed in the major distributary channels, and the distributary channels migrate and bifurcate frequently. On the delta plain, crevasse channels are formed through the avulsion of major distributary channels. During the dry period, due to the consumption of water seepage and evaporation transfer, terminal distributary channels are abandoned, crevasse channels are not developed, and the sediment is exposed. The density of the distributary channels is reduced, and the total sedimentary loading is low. The distributary channels sand body is the skeleton sand body of the seasonal delta. This study is of great significance for accurately explaining the formation process of seasonal deltas and provides a useful reference for further exploration and deployment of oil and gas reservoirs and paleogeographic reconstruction in the study area.

This paper surveys the recent advances in dynamic positioning (DP) control for marine crafts. DP of marine crafts means that a craft can maintain a fixed position and heading, or move along a predetermined trajectory slowly without the anchoring system, using only its own thruster system to counteract ocean disturbances. The survey is by no means exhaustive but provides a survey of some of the major technological advancements in DP controller design over the years of research and development. Firstly, the model of marine crafts and some difficult problems in DP control are introduced including the impact of multiple source disturbance, unavailable velocity measurement information, resource conservation and performance optimization, destabilizing impact of faults and network security and compound multi-constraint restrictions. Then, the DP control schemes in recent years are summarized and classified in detail. Finally, some theoretical and technical problems are proposed, including online data-driven model-free control, man–machine combination intelligent control and composite hierarchical anti-disturbance control to guide future investigations.