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Background The genus Zingiber of the Zingiberaceae is distributed in tropical, subtropical, and in Far East Asia. This genus contains about 100–150 species, with many species valued as important agricultural, medicinal and horticultural resources. However, genomic resources and suitable molecular markers for species identification are currently sparse. Results We conducted comparative genomics and phylogenetic analyses on Zingiber species. The Zingiber chloroplast genome (size range 162,507–163,711 bp) possess typical quadripartite structures that consist of a large single copy (LSC, 86,986–88,200 bp), a small single copy (SSC, 15,498–15,891 bp) and a pair of inverted repeats (IRs, 29,765–29,934 bp). The genomes contain 113 unique genes, including 79 protein coding genes, 30 tRNA and 4 rRNA genes. The genome structures, gene contents, amino acid frequencies, codon usage patterns, RNA editing sites, simple sequence repeats and long repeats are conservative in the genomes of Zingiber . The analysis of sequence divergence indicates that the following genes undergo positive selection ( ccsA, ndhA, ndhB, petD, psbA, psbB, psbC, rbcL, rpl12, rpl20, rpl23, rpl33, rpoC2, rps7, rps12 and ycf3 ). Eight highly variable regions are identified including seven intergenic regions ( petA-pabJ , rbcL-accD , rpl32-trnL-UAG , rps16-trnQ-UUG , trnC-GCA-psbM , psbC-trnS-UGA and ndhF-rpl32 ) and one genic regions ( ycf1 ). The phylogenetic analysis revealed that the sect. Zingiber was sister to sect. Cryptanthium rather than sect. Pleuranthesis . Conclusions This study reports 14 complete chloroplast genomes of Zingiber species. Overall, this study provided a solid backbone phylogeny of Zingiber . The polymorphisms we have uncovered in the sequencing of the genome offer a rare possibility (for Zingiber ) of the generation of DNA markers. These results provide a foundation for future studies that seek to understand the molecular evolutionary dynamics or individual population variation in the genus Zingiber .

Acute Type A Aortic Dissection (TAAD) is a life-threatening cardiovascular emergency, and early identification of high-risk patients for rupture is critical for optimizing surgical resource allocation. Existing studies predominantly focus on postoperative mortality prediction, lacking tools for in-hospital rupture risk assessment upon admission, with limitations including small sample sizes, feature redundancy, and single-algorithm bias. This study proposes an innovative framework integrating Boruta feature selection, Conditional Tabular Generative Adversarial Network (CTGAN) for data augmentation, and Blending ensemble strategy to enhance predictive performance. Utilizing 200 original TAAD cases, CTGAN synthesized 900 high-fidelity samples. Key features (e.g., CKMB, lactate) were selected via Boruta, and a Blending ensemble model combining eight base models (e.g., Random Forest, XGBoost) was developed. The model’s performance was evaluated using AUC, sensitivity, and F1-score. The Blending ensemble model achieved an AUC of 0.978, sensitivity of 0.920, and F1-score of 0.919, outperforming individual models. The study addresses small-sample constraints through CTGAN and leverages Blending to harness complementary strengths of diverse algorithms, providing a high-accuracy and interpretable tool for emergency triage. This framework fills the gap in TAAD rupture risk prediction and offers insights for clinical decision-making in resource-limited settings. The integration of Boruta feature selection, CTGAN data augmentation, and Blending ensemble strategy enhances model robustness and interpretability, making it a valuable tool for clinical applications.

Dextrocardia is a congenital abnormal position of the heart in which the main part of the heart is in the right chest, and the long axis of the heart points to the lower right. Cases of a combination of dextrocardia and sick sinus syndrome are rare. A 65-year-old female patient was admitted to hospital with palpitations and dizziness for 1 week. Mirror-image dextrocardia and sick sinus syndrome were diagnosed by an electrocardiogram, echocardiography, Holter monitoring, and X-rays. Finally, we successfully implanted a dual-chamber pacemaker into the patient. The patient had an uneventful recovery and was discharged when her symptoms had greatly improved 1 week later. When dextrocardia is present, using active fixation leads in the atrial and ventricular leads is easier for finding the pacing position with optimal sensing and pacing thresholds, and they reduce the incidence of falling off.

GRAS family proteins are one of the most abundant transcription factors in plants; they play crucial roles in plant development, metabolism, and biotic- and abiotic-stress responses. The GRAS family has been identified and functionally characterized in some plant species. However, this family in ginger (Zingiber officinale Roscoe), a medicinal crop and non-prescription drug, remains unknown to date. In the present study, 66 GRAS genes were identified by searching the complete genome sequence of ginger. The GRAS family is divided into nine subfamilies based on the phylogenetic analyses. The GRAS genes are distributed unevenly across 11 chromosomes. By analyzing the gene structure and motif distribution of GRAS members in ginger, we found that the GRAS genes have more than one cis-acting element. Chromosomal location and duplication analysis indicated that whole-genome duplication, tandem duplication, and segmental duplication may be responsible for the expansion of the GRAS family in ginger. The expression levels of GRAS family genes are different in ginger roots and stems, indicating that these genes may have an impact on ginger development. In addition, the GRAS genes in ginger showed extensive expression patterns under different abiotic stresses, suggesting that they may play important roles in the stress response. Our study provides a comprehensive analysis of GRAS members in ginger for the first time, which will help to better explore the function of GRAS genes in the regulation of tissue development and response to stress in ginger.

We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0\\% gain in overall reasoning performance and a 4.05\\(\\) inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks -- narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Virus-like particles (VLPs) are nanostructures assemble from viral proteins. Besides widely used for vaccine development, VLPs have also been explored as nanocarriers for cargo delivery as they combine the key advantages of viral and non-viral vectors. While it protects cargo molecules from degradation, the VLP has good cell penetrating property to mediate cargo passing the cell membrane and released into cells, making the VLP an ideal tool for intracellular delivery of biomolecules and drugs. Great progresses have been achieved and multiple challenges are still on the way for broad applications of VLP as delivery vectors. Here we summarize current advances and applications in VLP as a delivery vector. Progresses on delivery of different types of biomolecules as well as drugs by VLPs are introduced, and the strategies for cargo packaging are highlighted which is one of the key steps for VLP mediated intracellular delivery. Production and applications of VLPs are also briefly reviewed, with a discussion on future challenges in this rapidly developing field.

Background Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACS PR ) and disease-free survival (TACS DFS ). Methods Among 713 consecutive patients, with 275 in training cohort, 222 patients in internal validation cohort, and 216 patients in external validation cohort, we developed and validated a multitask machine learning model for simultaneously predicting peritoneal recurrence (TACS PR ) and disease-free survival (TACS DFS ). The accuracy of the model for prediction of peritoneal recurrence and prognosis as well as its association with adjuvant chemotherapy were evaluated. Results The TACS PR and TACS DFS were independently associated with peritoneal recurrence and disease-free survival in three cohorts, respectively (all P  < 0.001). The TACS PR demonstrated a favorable performance for peritoneal recurrence in all three cohorts. In addition, the TACS DFS also showed a satisfactory accuracy for disease-free survival among included patients. For stage II and III diseases, adjuvant chemotherapy improved the survival of patients with low TACS PR and low TACS DFS , or high TACS PR and low TACS DFS , or low TACS PR and high TACS DFS , but had no impact on patients with high TACS PR and high TACS DFS . Conclusions The multitask machine learning model allows accurate prediction of peritoneal recurrence and survival for GC and could distinguish patients who might benefit from adjuvant chemotherapy.

The interaction between green finance and other factors, such as ecological environment, has been a research hotspot nowadays. Especially, the reasonable guiding of capital into energy conservation and environmental protection industries would greatly affect those factors, so as to the relation between them. This paper aimed to analyze the relationships between green finance, technological progress, and ecological performance quantitatively. The entropy method was used to respectively construct the system of index for green finance and technological progress, and index for ecological performance was measured by the super-SBM model. The panel vector autoregressive (PVAR) model was selected to empirically analyze dynamic relationships based on datasets from 30 provinces in China during 2008–2019 period. The results told that (1) from 2008 to 2019, China’s overall level of green finance, technological progress and ecological performance increased to varying degrees. Spatially, the areas with high-developed green finance greatly coincided with those such as large cities or the eastern coast that had good financial development. The distribution of technological progress index were similar, except some underdeveloped areas with relatively advanced scientific research institutes. The ecological performance, however, was high in the South and low in the north. (2) In the lag for 3 years, the influence of green finance on ecological performance in different regions was all positive for that all the coefficient symbols that passed the significance test were above 0, while that on technological progress was negative first and then positive. And the effects of technological progress on ecological performance were positive in ecological regions and negative in low ecological regions (0.0893 and -0.1211 in the case of three-stage lag respectively). (3) The contribution of green finance to ecological performance was high according to the results of variance decomposition, maintained at about 30%, and that of technological progress increased year by year (from 0.000 to 0.039). Therefore, we proposed to strengthen the development of green finance in underdeveloped regions. The emphasis should be laid on the researches and applications of green technology, the formulation of financing policies in innovation compensation and the establishment of a dynamic monitoring system for the ecological environment.

We present a spectroscopic sample of 24 broad-line AGNs (BLAGNs) at 3 ≲ z ≲ 6 selected using F322W2+F444W NIRCam/WFSS grism spectroscopy of the central 100 arcmin2 area of the NEXUS survey. Among these BLAGNs, 15 are classified as Little Red Dots (LRDs) based on their rest-frame UV–optical spectral slopes and compact morphology. The number density of LRDs is ∼10−5 cMpc−3, with a hint of declining towards lower redshift. These BLAGNs span broad Hα luminosities of ∼1042.2–1043.5 erg s−1, black hole masses of ∼106.3–108.4 M⊙, and Eddington ratios of ∼0.1–1, though the estimates of black hole mass and Eddington ratio carry large systematic uncertainties. Half of the LRDs show strong Balmer absorption, suggesting high-density gas surrounding the line-emitting region. We detect extended (hundreds of parsecs) rest-frame UV–optical emission from the host galaxy in the majority of these LRDs, which contributes significantly or even dominantly to their total UV emission and largely accounts for the peculiar UV upturn of their spectral energy distribution. We also measure the small-scale (≲1 cMpc) clustering of these BLAGNs and LRDs by cross-correlating with a photometric galaxy sample. Extrapolating the power-law two-point correlation function model to large linear scales, we infer a linear bias of 3.69−2.21+2.78 and typical halo masses of a few ×1011h−1 M⊙ for BLAGNs at the sample median redshift of z ∼ 4.5. However, the inferred linear bias and halo masses of LRDs, while formally consistent with those for BLAGNs at ∼1.5σ, appear too large to be compatible with their space density, suggesting LRDs may have strong excess clustering on small scales.