Design of the sparrow search algorithm (SSA) for airborne radioactive hotspot detection.

In the context of using aircraft as a pivotal tool for detecting radioactive hotspots, the acquisition of radioactivity data was conducted through a CeBr3 scintillation crystal detector mounted on a helicopter. However, challenges arose, including managing extensive data volumes, computationally demanding tasks, and susceptibility to local optima issues. To address these challenges and leverage the benefits of the Sparrow Search Algorithm (SSA) in global optimization and convergence speed, an improved SSA was devised. This improved version integrated SSA principles with the intricacies of searching for radioactive hotspots. The algorithm employed a matrix segmentation method to process data matrices derived from measured data, aiming to enhance efficiency and accuracy. An empirical analysis was conducted, performing 100 iterations on an experimental matrix to scrutinize the impact of matrix segmentation. Computation times and results were compared across different segmentation levels, confirming the favorable algorithmic outcomes of the method. The practical viability and convergence stability of the algorithm were further assessed using genuine measured data, with segmented matrices generated for evaluation. Remarkably, a comparison between computational outcomes and manually identified data reaffirmed the algorithm's reliability in effectively detecting radioactive hotspots.

An over-the-horizon potential safety threat vehicle identification method based on ETC big data.

Smart cars rely on sensors like LIDAR and high-precision map-based perception for driving environment sensing. However, they can't detect low-speed vehicles beyond visual range, affecting safety and comfort. Manual vehicles face similar challenges. Low-speed driving contributes to expressway accidents due to limited visibility, road design, and equipment performance. To enhance safety, an over-the-horizon potential safety threat vehicle identification method using ETC big data is proposed. It consists of three layers. The first layer is the vehicle section travel speed sensing layer based on the wlp-XGBoost algorithm. The second layer is the in-transit vehicle position estimation layer based on the DR-HMM algorithm. The third layer is the Multi-information fusion of potential safety threat vehicle identification layer. Dynamic real-time detection and identification of potential safety threats in expressway sections were achieved, and simulations were conducted using real-time ETC data from Quanxia section on an ETC platform. Results show accurate prediction of vehicle speed and position in different road sections and traffic situations, with over 95% accuracy and recall in identifying potential safety threat vehicles. It perceives changes in the traffic conditions of road sections in real-time based on the changing trend of potential safety threat vehicle numbers, providing a vital reference for speed planning and risk avoidance.

Chloroquine enhances the efficacy of chemotherapy drugs against acute myeloid leukemia by inactivating the autophagy pathway.

Current therapy for acute myeloid leukemia (AML) is largely hindered by the development of drug resistance of commonly used chemotherapy drugs, including cytarabine, daunorubicin, and idarubicin. In this study, we investigated the molecular mechanisms underlying the chemotherapy drug resistance and potential strategy to improve the efficacy of these drugs against AML. By analyzing data from ex vivo drug-response and multi-omics profiling public data for AML, we identified autophagy activation as a potential target in chemotherapy-resistant patients. In THP-1 and MV-4-11 cell lines, knockdown of autophagy-regulated genes ATG5 or MAP1LC3B significantly enhanced AML cell sensitivity to the chemotherapy drugs cytarabine, daunorubicin, and idarubicin. In silico screening, we found that chloroquine phosphate mimicked autophagy inactivation. We showed that chloroquine phosphate dose-dependently down-regulated the autophagy pathway in MV-4-11 cells. Furthermore, chloroquine phosphate exerted a synergistic antitumor effect with the chemotherapy drugs in vitro and in vivo. These results highlight autophagy activation as a drug resistance mechanism and the combination therapy of chloroquine phosphate and chemotherapy drugs can enhance anti-AML efficacy.

Targeting C/EBPα overcomes primary resistance and improves the efficacy of FLT3 inhibitors in acute myeloid leukaemia.

The outcomes of FLT3-ITD acute myeloid leukaemia (AML) have been improved since the approval of FLT3 inhibitors (FLT3i). However, approximately 30-50% of patients exhibit primary resistance (PR) to FLT3i with poorly defined mechanisms, posing a pressing clinical unmet need. Here, we identify C/EBPα activation as a top PR feature by analyzing data from primary AML patient samples in Vizome. C/EBPα activation limit FLT3i efficacy, while its inactivation synergistically enhances FLT3i action in cellular and female animal models. We then perform an in silico screen and identify that guanfacine, an antihypertensive medication, mimics C/EBPα inactivation. Furthermore, guanfacine exerts a synergistic effect with FLT3i in vitro and in vivo. Finally, we ascertain the role of C/EBPα activation in PR in an independent cohort of FLT3-ITD patients. These findings highlight C/EBPα activation as a targetable PR mechanism and support clinical studies aimed at testing the combination of guanfacine with FLT3i in overcoming PR and enhancing the efficacy of FLT3i therapy.

Discovery of a Potent and Selective Degrader for USP7.

The tumor suppressor p53 is the most frequently mutated gene in human cancer and more than half of cancers contain p53 mutations. The development of novel and effective therapeutic strategies for p53 mutant cancer therapy is a big challenge and highly desirable. Ubiquitin-specific protease 7 (USP7), also known as HAUSP, is a deubiquitinating enzyme and proposed to stabilize the oncogenic E3 ubiquitin ligase MDM2 that promotes the proteosomal degradation of p53. Herein, we report the design and characterization of U7D-1 as the first selective USP7-degrading Proteolysis Targeting Chimera (PROTAC). U7D-1 showed selective and effective USP7 degradation, and maintained potent cell growth inhibition in p53 mutant cancer cells, with USP7 inhibitor showing no activity. These data clearly demonstrated the practicality and importance of PROTAC as a preliminary chemical tool for investigating USP7 protein functions and a promising method for potential p53 mutant cancer therapy.

Accucopy: accurate and fast inference of allele-specific copy number alterations from low-coverage low-purity tumor sequencing data.

BACKGROUND: Copy number alterations (CNAs), due to their large impact on the genome, have been an important contributing factor to oncogenesis and metastasis. Detecting genomic alterations from the shallow-sequencing data of a low-purity tumor sample remains a challenging task. RESULTS: We introduce Accucopy, a method to infer total copy numbers (TCNs) and allele-specific copy numbers (ASCNs) from challenging low-purity and low-coverage tumor samples. Accucopy adopts many robust statistical techniques such as kernel smoothing of coverage differentiation information to discern signals from noise and combines ideas from time-series analysis and the signal-processing field to derive a range of estimates for the period in a histogram of coverage differentiation information. Statistical learning models such as the tiered Gaussian mixture model, the expectation-maximization algorithm, and sparse Bayesian learning were customized and built into the model. Accucopy is implemented in C++ /Rust, packaged in a docker image, and supports non-human samples, more at http://www.yfish.org/software/ . CONCLUSIONS: We describe Accucopy, a method that can predict both TCNs and ASCNs from low-coverage low-purity tumor sequencing data. Through comparative analyses in both simulated and real-sequencing samples, we demonstrate that Accucopy is more accurate than Sclust, ABSOLUTE, and Sequenza.