Optimizing S-box Implementations Using SAT Solvers: Revisited

We propose a new method to encode the problems of optimizing S-box implementations into SAT problems. By considering the inputs and outputs of gates as Boolean functions, the fundamental idea of our method is representing the relationships between these inputs and outputs according to their algebraic normal forms. Based on this method, we present several encoding schemes for optimizing S-box implementations according to various criteria, such as multiplicative complexity, bitslice gate complexity, gate complexity, and circuit depth complexity. The experimental results of these optimization problems show that, compared to the encoding method proposed in FSE 2016, which represents these relationships between Boolean functions by their truth tables, our new encoding method can significantly reduce accelerate the subsequent solving process by 2-100 times for the majority of instances. To further improve the solving efficiency, we propose several strategies to eliminate the redundancy of the derived equation system and break the symmetry of the solution space. We apply our method in the optimizations of the S-boxes used in Ascon, ICEPOLE, PRIMATEs, Keccak/Ketje/Keyak, Joltik/Piccolo, LAC, Minalpher, Prøst, and RECTANGLE. We achieve some new improved implementations and narrow the range of the optimal values for different optimization criteria of these S-boxes

Downregulation of Long Non-coding RNA FALEC Inhibits Gastric Cancer Cell Migration and Invasion Through Impairing ECM1 Expression by Exerting Its Enhancer-Like Function

Long non-coding RNAs (lncRNAs) have been shown to play important roles in many human diseases. However, their functions and mechanisms in tumorigenesis and development remain largely unknown. Here, we demonstrated that focally amplified lncRNA in epithelial cancer (FALEC) was upregulated and significantly correlated with lymph node metastasis, TNM stage in gastric cancer (GC). Further experiments revealed that FALEC knockdown significantly inhibited GC cells migration and invasion in vitro. Mechanistic investigations demonstrated that small interfering RNA-induced silencing of FALEC decreased expression of the nearby gene extracellular matrix protein 1 (ECM1) in cis. Additionally, ECM1 and FALEC expression were positively correlated, and high levels of ECM1 predicted shorter survival time in GC patients. Our results suggest that the downregulation of FALEC significantly inhibited the migration and invasion of GC cells through impairing ECM1 expression by exerting an enhancer-like function. Our work provides valuable information and a novel promising target for developing new therapeutic strategies in GC

A protocol of Chinese expert consensuses for the management of health risk in the general public

IntroductionNon-communicable diseases (NCDs) represent the leading cause of mortality and disability worldwide. Robust evidence has demonstrated that modifiable lifestyle factors such as unhealthy diet, smoking, alcohol consumption and physical inactivity are the primary causes of NCDs. Although a series of guidelines for the management of NCDs have been published in China, these guidelines mainly focus on clinical practice targeting clinicians rather than the general population, and the evidence for NCD prevention based on modifiable lifestyle factors has been disorganized. Therefore, comprehensive and evidence-based guidance for the risk management of major NCDs for the general Chinese population is urgently needed. To achieve this overarching aim, we plan to develop a series of expert consensuses covering 15 major NCDs on health risk management for the general Chinese population. The objectives of these consensuses are (1) to identify and recommend suitable risk assessment methods for the Chinese population; and (2) to make recommendations for the prevention of major NCDs by integrating the current best evidence and experts’ opinions.Methods and analysisFor each expert consensus, we will establish a consensus working group comprising 40–50 members. Consensus questions will be formulated by integrating literature reviews, expert opinions, and an online survey. Systematic reviews will be considered as the primary evidence sources. We will conduct new systematic reviews if there are no eligible systematic reviews, the methodological quality is low, or the existing systematic reviews have been published for more than 3 years. We will evaluate the quality of evidence and make recommendations according to the GRADE approach. The consensuses will be reported according to the Reporting Items for Practice Guidelines in Healthcare (RIGHT)

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning

MicroRNA-125a-5p regulates the effect of Tregs on Th1 and Th17 through targeting ETS-1/STAT3 in psoriasis

Abstract Background Psoriasis is an inflammatory disease mediated by helper T (Th)17 and Th1 cells. MicroRNA-125a (miR-125a) is reduced in the lesional skin of psoriatic patients. However, the mechanism by which miR-125a participates in psoriasis remains unclear. Methods The levels of miR-125a-5p and its downstream targets (ETS-1, IFN-γ, and STAT3) were detected in CD4+ T cells of healthy controls and psoriatic patients by quantitative real-time PCR (qRT-PCR). In vitro, transfection of miR-125a-5p mimics was used to analyze the effect of miR-125a-5p on the differentiation of Th17 cells by flow cytometry. Imiquimod (IMQ)-induced mouse model was used to evaluate the role of upregulating miR-125a-5p by intradermal injection of agomir-125a-5p in vivo. Results miR-125a-5p was downregulated in peripheral blood CD4+ T cells of psoriatic patients, which was positively associated with the proportion of regulatory T cells (Tregs) and negatively correlated with the Psoriasis Area and Severity Index (PASI) score. Moreover, the miR-125a-5p mimics promoted the differentiation of Tregs and downregulated the messenger RNA (mRNA) levels of ETS-1, IFN-γ, and STAT3 in murine CD4+ T cells. Furthermore, agomir-125a-5p alleviated psoriasis-like inflammation in an IMQ-induced mouse model by downregulating the proportion of Th17 cells. Conclusions miR-125a-5p may have therapeutic potential in psoriasis by restoring the suppressive function of Tregs on Th17 cells through targeting STAT3, and on Th1 cells indirectly through targeting ETS-1 and IFN-γ

Establishment and validation of an RNA binding protein-associated prognostic model for ovarian cancer

Abstract Background Ovarian cancer (OC) is one of the most common gynecological malignant tumors worldwide, with high mortality and a poor prognosis. As the early symptoms of malignant ovarian tumors are not obvious, the cause of the disease is still unclear, and the patients’ postoperative quality of life of decreases. Therefore, early diagnosis is a problem requiring an urgent solution. Methods We obtained the gene expression profiles of ovarian cancer and normal samples from TCGA and GTEx databases for differential expression analysis. From existing literature reports, we acquired the RNA-binding protein (RBP) list for the human species. Utilizing the online tool Starbase, we analyzed the interaction relationship between RBPs and their target genes and selected the modules of RBP target genes through Cytoscape. Finally, univariate and multivariate Cox regression analyses were used to determine the prognostic RBP signature. Results We obtained 527 differentially expressed RBPs, which were involved in many important cellular events, such as RNA splicing, the cell cycle, and so on. We predicted several target genes of RBPs, constructed the interaction network of RBPs and their target genes, and obtained many modules from the Cytoscape analysis. Functional enrichment of RBP target genes also includes these important biological processes. Through Cox regression analysis, OC prognostic RBPs were identified and a 10-RBP model constructed. Further analysis showed that the model has high accuracy and sensitivity in predicting the 3/5-year survival rate. Conclusions Our study identified differentially expressed RBPs and their target genes in OC, and the results promote our understanding of the molecular mechanism of ovarian cancer. The current study could develop novel biomarkers for the diagnosis, treatment, and prognosis of OC and provide new ideas and prospects for future clinical research

A Novel Voltage-Abnormal Cell Detection Method for Lithium-Ion Battery Mass Production Based on Data-Driven Model with Multi-Source Time Series Data

Before leaving the factory, lithium-ion battery (LIB) cells are screened to exclude voltage-abnormal cells, which can increase the fault rate, troubleshooting difficulty, and degrade pack performance. However, the time interval to obtain the detection results through the existing voltage-abnormal cell method is too long, which can seriously affect production efficiency and delay shipment, especially in the mass production of LIBs when facing a large number of time-critical orders. In this paper, we propose a data-driven voltage-abnormal cell detection method, using a fast model with simple architecture, which can detect voltage-abnormal cells based on the multi-source time series data of the LIB without a time interval. Firstly, our method transforms the different source data of a cell into a multi-source time series data representation and utilizes a recurrent-based data embedding to model the relation within it. Then, a simplified MobileNet is used to extract hidden feature from the embedded data. Finally, we detect the voltage-abnormal cells according to the hidden feature with a cell classification head. The experiment results show that the accuracy and average running time of our model on the voltage-abnormal cell detection task is 95.42% and 0.0509 ms per sample, which is a considerable improvement over existing methods