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We present our first results for a microscopic simulation of symmetric boost fission in terms of the antisymmetrized molecular dynamics (AMD) model. In AMD model, ground states of fissioning nuclei were prepared by a frictional cooling method and symmetrical boost momenta were given to nucleons inside to split the ground-state into fission fragments. After the simulation, we calculated the mass numbers and total kinetic energy (TKE) of the fission fragments. We also calculated orbital angular momenta of each fragment and identified them as spins, their mutual orientation and their orientation with respect to the linear momenta which defined the fission axis. Moreover, we found spin distribution of fission fragments was similar to the one given by the Fermi-gas model if spin cut-off parameter was adjusted. Finally, several ternary fission events were observed, emitting Tritium or 4He from the neck region, and average energy and angles of these ternary particles with respect to the fission axis were found to be in accord with experimental data.

This paper proposes a method of beforehand prediction of electric equipment faults based on chain-linked recurrent neural network algorithm, which takes the operating parameters of power equipment and other relevant environmental factors as inputs, and takes the fault characteristics as output judgment marks, and constructs a machine learning training model to realize the prediction of power equipment faults. The neural network algorithm adopted in this paper adopts a tree structure. Each sub-node can transfer information with its multiple superior nodes, so that the correlation between the data of the front and back nodes can be obtained, which meets the needs of the equipment fault prediction model. Considering that the occurrence of power transformer faults is sudden and greatly affected by changes in the surrounding environment, the input of prediction algorithms should consider more environmental factors. This method takes the historical data of various parameters including meteorological phenomena, geography data, and temperature of adjacent equipment and facilities as the training sample set, improves the learning model, gives the trend curve of each index, and gives a prompt at its threshold to ensure the prediction accuracy and give the index prediction. [ABSTRACT FROM AUTHOR]

Patients with metastatic colorectal cancer (mCRC) have poor long‐term survival. Rechallenge with anti‐epidermal growth factor receptor (anti‐EGFR) based therapy has shown certain activity as late‐line therapy. To further improve clinical outcomes, we evaluated the antitumor efficacy and safety of cetuximab in combination with camrelizumab and liposomal irinotecan in patients with RASwt mCRC pretreated with anti‐EGFR‐based therapy. Patients with RASwt mCRC who had received at least two prior systemic therapies, including anti‐EGFR‐based treatment in the metastatic or unresectable disease setting, were enrolled in cohort B. Patients were treated with cetuximab (500 mg/m2) and camrelizumab (200 mg) plus liposomal irinotecan (HR070803, 60 mg/m2) intravenously once every 2 weeks. The primary endpoint was the objective response rate (ORR) by RECIST v1.1. The secondary endpoints included disease control rate (DCR), progression‐free survival (PFS), overall survival (OS) and safety. At the data cutoff (23 November 2022), 19 patients were enrolled in the two stages, and 16 were evaluable for efficacy analyses. The ORR was 25% (95% confidence interval [CI]: 10.2%‐49.5%), and DCR was 75% (95% CI: 50.5%‐89.8%). The median PFS and OS were 6.9 (95% CI: 2.6‐11.2) and 15.1 (95% CI: 6.1‐24.0) months, respectively. Grade 3 treatment‐related adverse events (TRAEs) occurred in 15.8% (3/19) of patients. No grade ≥4 TRAEs were found in the safety population. Our study suggests that anti‐EGFR retreatment therapy with cetuximab plus camrelizumab and liposomal irinotecan (HR070803) is a promising late‐line treatment option with good antitumor activity and well‐tolerated toxicity in RASwt mCRC patients. [ABSTRACT FROM AUTHOR]

Background: YTHDF2 is one of important readers of N6-methyladenosine (m6A) modification on RNA. Growing evidence implicates that YTHDF2 takes an indispensable part in the regulation of tumorigenesis and metastasis in different cancers, but its biological functions and underlying mechanisms remain elusive in gastric cancer (GC). Aim: To investigate the clinical relevance and biological function of YTHDF2 in GC. Results: Compared with matched normal stomach tissues, YTHDF2 expression was markedly decreased in gastric cancer tissues. The expression level of YTHDF2 was inversely associated with gastric cancer patients' tumor size, AJCC classification and prognosis. Functionally, YTHDF2 reduction facilitated gastric cancer cell growth and migration in vitro and in vivo, whereas YTHDF2 overexpression exhibited opposite phenotypes. Mechanistically, YTHDF2 enhanced expression of PPP2CA, the catalytic subunit of PP2A (Protein phosphatase 2A), in an m6A-independent manner, and silencing of PPP2CA antagonized the anti-tumor effects caused by overexpression of YTHDF2 in GC cells. Conclusion: These findings demonstrate that YTHDF2 is down-regulated in GC and its down-regulation promotes GC progression via a possible mechanism involving PPP2CA expression, suggesting that YTHDF2 may be a hopeful biomarker for diagnosis and an unrevealed treatment target for GC. [ABSTRACT FROM AUTHOR]