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Objective: To explore whether the upper and/or middle mediastinal nodes (UMMN) should be dissected in Siewert type II adenocarcinoma (AC) according to the incidence of lymph node metastasis. Additionally, to investigate the association between the length of esophageal involvement (LEI) and the UMMN metastases. Methods: A cohort with Siewert type II AC who were operated on by a surgical team that routinely treated esophagogastric junction (EGJ) tumors with esophagectomy and extended lymphadenectomy were assessed retrospectively. The primary endpoint of the research was the metastasis rate of UMMN. Results: A total of 94 patients with EGJ tumor from July 2018 to September 2022 were enrolled. Station 106recR (6.4%, 6/94) was the only station among upper mediastinal nodes (UMN) that presented positive nodes. Middle mediastinal nodes (MMN) metastases of station 107, 109 and station 108 were 2.1% (2/94) and 5.0% (4/80), respectively. Among the 11 patients with MMN or UMN metastases, 63.6% (7/11) had lesser than seven metastatic nodes, and 54.5% (6/11) had a pathological N stage ≤2. LEI >3 cm (p = 0.042) showed a higher risk for MMN metastases in univariable logistic analysis. However, no independent risk factor for mediastinal node metastases was detected. Conclusion: This study demonstrated that the incidence of positive MMN and UMN is relatively low in resectable Siewert type II AC, which indicated that it is not necessary to perform a routine dissection upon these stations. LEI >3 cm might be associated with higher risk for mediastinal node metastasis. Certain patients could benefit from extended lymphadenectomy since most of the patients with positive MMN or UMN have a limited number of metastatic nodes. [ABSTRACT FROM AUTHOR]

Deep Neural Network (DNN)-based point cloud semantic segmentation has presented significant breakthrough using large-scale labeled aerial laser point cloud datasets. However, annotating such large-scaled point clouds is time-consuming. Self-Supervised Learning (SSL) is a promising approach to this problem by pre-training a DNN model utilizing unlabeled samples followed by a fine-tuned downstream task involving very limited labels. The traditional contrastive learning for point clouds selects the hardest negative samples by solely relying on the distance between the embedded features derived from the learning process, potentially evolving some negative samples from the same classes to reduce the contrastive learning effectiveness. This work proposes a hard-negative sample-aware self-supervised contrastive learning algorithm to pre-train the model for semantic segmentation. We designed a k-means clustering-based Absolute Positive And Negative samples (AbsPAN) strategy to filter the possible false-negative samples. Experiments on two typical ALS benchmark datasets demonstrate that the proposed method is more appealing than supervised training schemes without pre-training. Especially when the labels are severely inadequate (10% of the ISPRS training set), the results obtained by the proposed HAVANA method still exceed 94% of the supervised paradigm performance with full training set. [ABSTRACT FROM AUTHOR]

This study introduces a one‐dimensional phenomenological constitutive model designed to describe two‐way shape memory effect (TWSME) and its associated cyclic degradation. The model utilizes the logistic function to formulate the phase transformation equation, incorporates the expansion of key parameters into their respective fatigue functions to characterize the fatigue phenomenon, and integrates a phase transformation rate regulation function into the differential form of the phase transformation equation. This integration facilitates the control over the entire phase transformation process and the simulation of incomplete transformations. The model is distinguished by its comprehensive functionality, simple form, ease of calculation, and the clear and direct influence of key parameters. Furthermore, it offers a degree of flexibility because each function within the framework is replaceable. The simulation of the TWSME strain and recovery stress hysteresis loop deformation has been successfully conducted, enabling the description of internal hysteresis loops caused by incomplete transformation. The validity of the model is corroborated by comparing it with existing experimental results. [ABSTRACT FROM AUTHOR]

Dual base editors (DBEs) enable simultaneous A-to-G and C-to-T conversions, expanding mutation types. However, low editing efficiency and narrow targeting range limit the widespread use of DBEs in plants. The single-strand DNA binding domain of RAD51 DBD can be fused to base editors to improve their editing efficiency. However, it remains unclear how the DBD affects dual base editing performance in plants. In this study, we generated a series of novel plant DBE-SpGn tools consisting of nine constructs using the high-activity cytidine deaminase evoFERNY, adenosine deaminase TadA8e and DBD in various fusion modes with the PAM-flexible Streptococcus pyogenes Cas9 (SpCas9) nickase variant SpGn (with NG-PAM). By analysing their editing performance on 48 targets in rice, we found that DBE-SpGn constructs containing a single DBD and deaminases located at the N-terminus of SpGn exhibited the highest editing efficiencies. Meanwhile, constructs with deaminases located at the C-terminus and/or multiple DBDs failed to function normally and exhibited inhibited editing activity. We identified three particularly high-efficiency dual base editors (C-A-SpGn, C-A-D-SpGn and A-C-D-SpGn), named PhieDBEs (Plant high-efficiency dual base editors), capable of producing efficient dual base conversions within a narrow editing window (M 5  ~ M 9 , M = A/C). The editing efficiency of C-A-D-SpGn was as high as 95.2% at certain target sites, with frequencies of simultaneous C-to-T and A-to-G conversions as high as 81.0%. In summary, PhieDBEs (especially C-A-D-SpGn) can produce diverse mutants and may prove useful in a wide variety of applications, including plant functional genomics, precise mutagenesis, directed evolution and crop genetic improvement, among others., (© 2024 The Author(s). Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)