Online energy consumption forecast for battery electric buses using a learning-free algebraic method.

Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data.

Identification of USP2 as a novel target to induce degradation of KRAS in myeloma cells.

Inducing the degradation of KRAS represents a novel strategy to combat cancers with KRAS mutation. In this study, we identify ubiquitin-specific protease 2 (USP2) as a novel deubiquitinating enzyme of KRAS in multiple myeloma (MM). Specifically, we demonstrate that gambogic acid (GA) forms a covalent bond with the cysteine 284 residue of USP2 through an allosteric pocket, inhibiting its deubiquitinating activity.

Socioeconomic status moderate the relationship between mental health literacy, social participation, and active aging among Chinese older adults: evidence from a moderated network analysis.

Objective: The aging population represents a formidable global challenge, with China experiencing an accelerated demographic shift. While previous research has established a directional link between mental health literacy, social participation, and active aging, the moderating effect of socioeconomic status (SES) on these associations remains underexplored. This study sought to address this gap by employing moderated network analysis, in contrast to the total score approaches commonly used in prior literature.

Development and validation of a machine learning-based model to predict survival in patients with cirrhosis after transjugular intrahepatic portosystemic shunt.

Background: Although numerous prognostic scores have been developed for patients with cirrhosis after Transjugular intrahepatic portosystemic shunt (TIPS) placement over years, an accurate machine learning (ML)-based model remains unavailable. The aim of this study was to develop and validate a ML-based prognostic model to predict survival in patients with cirrhosis after TIPS placement.

Methods: In this retrospective study in China, patients diagnosed with cirrhosis after TIPS placement from 2014 to 2020 in our cohort were included to develop a ML-based model.