Intelligent structure prediction and visualization analysis of non-coding RNA in osteosarcoma research

BackgroundOsteosarcoma (OS) is the most common bone malignant tumor in children and adolescents. Recent research indicates that non-coding RNAs (ncRNAs) have been associated with OS occurrence and development, with significant progress made in this field. However, there is no intelligent structure prediction and literature visualization analysis in this research field. From the perspective of intelligent knowledge structure construction and bibliometrics, this study will comprehensively review the role of countries, institutions, journals, authors, literature citation relationships and subject keywords in the field of ncRNAs in OS. Based on this analysis, we will systematically analyze the characteristics of the knowledge structure of ncRNAs in OS disease research and identify the current research hotspots and trends.MethodsThe Web of Science Core Collection (WoSCC) database was searched for articles on ncRNAs in OS between 2001 and 2023. This bibliometric analysis was performed using VOSviewers, CiteSpace, and Pajek.ResultsThis study involved 15,631 authors from 2,631 institutions across 57 countries/regions, with a total of 3,642 papers published in 553 academic journals. China has the highest number of published papers in this research field. The main research institutions include Nanjing Medical University (n = 129, 3.54%), Shanghai Jiao Tong University (n = 128, 3.51%), Zhengzhou University (n = 110, 3.02%), and China Medical University (n = 109, 2.99%). Oncology Letters (n =139, 3.82%), European Review for Medical Pharmacological Sciences (120, 3.31%), and Molecular Medicine Reports (n = 95, 2.61%) are the most popular journals in this field, with Oncotarget being the most co-cited journal (Co-Citation = 4,268). Wei Wang, Wei Liu, and Zhenfeng Duan published the most papers, with Wang Y being the most co-cited author. “miRNA”, “lncRNA” and “circRNA” are the main focuses of ncRNAs in OS studies. Key themes include “migration and invasion”, “apoptosis and proliferation”, “prognosis”, “biomarkers” and “chemoresistance”. Since 2020, hotspots and trends in ncRNA research in OS include “tumor microenvironment”, “immune” and “exosome”.ConclusionThis study represents the first comprehensive bibliometric analysis of the knowledge structure and development of ncRNAs in OS. These findings highlight current research hotspots and frontier directions, offering valuable insights for future studies on the role of ncRNAs in O

OV-VG: A Benchmark for Open-Vocabulary Visual Grounding

Open-vocabulary learning has emerged as a cutting-edge research area, particularly in light of the widespread adoption of vision-based foundational models. Its primary objective is to comprehend novel concepts that are not encompassed within a predefined vocabulary. One key facet of this endeavor is Visual Grounding, which entails locating a specific region within an image based on a corresponding language description. While current foundational models excel at various visual language tasks, there's a noticeable absence of models specifically tailored for open-vocabulary visual grounding. This research endeavor introduces novel and challenging OV tasks, namely Open-Vocabulary Visual Grounding and Open-Vocabulary Phrase Localization. The overarching aim is to establish connections between language descriptions and the localization of novel objects. To facilitate this, we have curated a comprehensive annotated benchmark, encompassing 7,272 OV-VG images and 1,000 OV-PL images. In our pursuit of addressing these challenges, we delved into various baseline methodologies rooted in existing open-vocabulary object detection, VG, and phrase localization frameworks. Surprisingly, we discovered that state-of-the-art methods often falter in diverse scenarios. Consequently, we developed a novel framework that integrates two critical components: Text-Image Query Selection and Language-Guided Feature Attention. These modules are designed to bolster the recognition of novel categories and enhance the alignment between visual and linguistic information. Extensive experiments demonstrate the efficacy of our proposed framework, which consistently attains SOTA performance across the OV-VG task. Additionally, ablation studies provide further evidence of the effectiveness of our innovative models. Codes and datasets will be made publicly available at https://github.com/cv516Buaa/OV-VG

Efficacy of mFOLFOX6 plus bevacizumab regimen in advanced colorectal cancer after deep hyperthermia: a single-center retrospective study

BackgroundThis study aimed to explore the clinical efficacy and safety of a modified FOLFOX6 (oxaliplatin + leucovorin + 5-fluorouracil) plus bevacizumab regimen after deep hyperthermia in advanced colorectal cancer.MethodsA total of 80 colorectal cancer patients treated at our hospital were selected as research subjects. According to the random number table method, patients were divided into a control group (mFOLFOX6 plus bevacizumab) and a combination group (mFOLFOX6 plus bevacizumab after deep hyperthermia treatment), with 40 patients in each group. After six cycles of treatment, the objective response rate (ORR), disease control rate (DCR), levels of serum tumor markers carcinoembryonic antigen (CEA), vascular epidermal growth factor (VEGF), Karnofsky performance status (KPS) scores, and the occurrence of adverse events were compared between the two groups.ResultsAfter six cycles of treatment, the ORR in the combination group was higher than that in the control group, but the difference was not statistically significant (P>0.05). The DCR in the combination group was significantly higher than that in the control group (P<0.05). The serum CEA levels in the control and combination groups after treatment were significantly lower than those before treatment, and the serum CEA and VEGF levels in the combination group were significantly lower than those in the control group (all P<0.001). The KPS scores in both groups after treatment were higher than those before treatment, and the KPS scores in the combination group after treatment were significantly higher than those in the control group (all P<0.001). The incidence of fatigue and pain in the combination group was significantly lower than that in the control group (P<0.05).ConclusionmFOLFOX6 plus bevacizumab after deep hyperthermia is effective in advanced colorectal cancer patients, which can effectively improve their quality of life, and the adverse events are controllable and tolerable. A randomized or prospective trial will be required to further prove these data and explore its potentiality, especially if compared to conventional treatment

The impact of China’s new Environmental Protection Law on corporate environmental investments

As an important lever for China’s green development strategy, whether the new Environmental Protection Law can effectively form investment incentives for enterprises has attracted much attention and is also an important topic that theoretical research urgently needs to explore. This paper utilizes corporate data from non-financial listed companies in Shanghai and Shenzhen A-shares from 2007 to 2018. By adopting a double-difference model, it explores the incentive role and internal mechanism of the new Environmental Protection Law (EPL), implemented in 2015, as an environmental regulation on the environmental protection investment of enterprises, taking the new EPL’s enactment as a quasi-natural experiment. The study revealed a noteworthy and positive impact on motivation, which remained consistent even after various robustness tests. Additionally, the impact of incentives varied depending on the level of competition within the industry, financial constraints, and ownership type of the enterprises. Investigating the mechanism, it has been discovered that the incentive effect advances the environmental investment of firms through diminishing agency costs, enriching the quality of environmental information disclosure, and facilitating government subsidies to enterprises. This study not only verifies, from the factual empirical level, that environmental regulation policies can promote corporate environmental investment but also provides important evidence to support to a certain extent that the implementation of the new EPL can promote enterprises’ environmental governance behaviors. This article reveals the microeconomic effects of the new Environmental Protection Law from the perspective of corporate behavior strategies, and the research conclusions have important reference significance for the construction of national legal systems and the deepening of green development strategies

MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation

Ovarian cancer is one of the most harmful gynecological diseases. Detecting ovarian tumors in early stage with computer-aided techniques can efficiently decrease the mortality rate. With the improvement of medical treatment standard, ultrasound images are widely applied in clinical treatment. However, recent notable methods mainly focus on single-modality ultrasound ovarian tumor segmentation or recognition, which means there still lacks researches on exploring the representation capability of multi-modality ultrasound ovarian tumor images. To solve this problem, we propose a Multi-Modality Ovarian Tumor Ultrasound (MMOTU) image dataset containing 1469 2d ultrasound images and 170 contrast enhanced ultrasonography (CEUS) images with pixel-wise and global-wise annotations. Based on MMOTU, we mainly focus on unsupervised cross-domain semantic segmentation task. To solve the domain shift problem, we propose a feature alignment based architecture named Dual-Scheme Domain-Selected Network (DS2Net). Specifically, we first design source-encoder and target-encoder to extract two-style features of source and target images. Then, we propose Domain-Distinct Selected Module (DDSM) and Domain-Universal Selected Module (DUSM) to extract the distinct and universal features in two styles (source-style or target-style). Finally, we fuse these two kinds of features and feed them into the source-decoder and target-decoder to generate final predictions. Extensive comparison experiments and analysis on MMOTU image dataset show that DS2Net can boost the segmentation performance for bidirectional cross-domain adaptation of 2d ultrasound images and CEUS images. Our proposed dataset and code are all available at https://github.com/cv516Buaa/MMOTU_DS2Net.Comment: code: https://github.com/cv516Buaa/MMOTU_DS2Net paper:18 pages, 12 figures, 11 tables, 16 formula

Unique post-translational oxime formation in the biosynthesis of the azolemycin complex of novel ribosomal peptides from Streptomyces sp. FXJ1.264

Streptomycetes are a rich source of bioactive specialized metabolites, including several examples of the rapidly growing class of ribosomally-biosynthesized and post-translationally-modified peptide (RiPP) natural products. Here we report the discovery from Streptomyces sp. FXJ1.264 of azolemycins A–D, a complex of novel linear azole-containing peptides incorporating a unique oxime functional group. Bioinformatics analysis of the Streptomyces sp. FXJ1.264 draft genome sequence identified a cluster of genes that was hypothesized to be responsible for elaboration of the azolemycins from a ribosomally-biosynthesized precursor. Inactivation of genes within this cluster abolished azolemycin production, consistent with this hypothesis. Moreover, mutants lacking the azmE and azmF genes accumulated azolemycin derivatives lacking the O-methyl groups and an amino group in place of the N-terminal oxime (as well as proteolysed derivatives), respectively. Thus AzmE, a putative S-adenosyl methionine-dependent methyl transferase, is responsible for late-stage O-methylation reactions in azolemycin biosynthesis and AzmF, a putative flavin-dependent monooxygenase, catalyzes oxidation of the N-terminal amino group in an azolemycin precursor to the corresponding oxime. To the best of our knowledge, oxime formation is a hitherto unknown posttranslational modification in RiPP biosynthesis

1. スポロトリコーシス5例(第443回千葉医学会例会 第16回千葉皮膚科臨床談話会)

A potential stable stem-loop structure in the 5’-terminal sequence (left) and a triple stem-loop structure in 3’-terminal sequences (right) were predicted with a RNA structure software. (PDF 60 kb

Decision Boundary Optimization for Few-shot Class-Incremental Learning

Few-shot class-incremental learning (FSCIL) is gaining prominence in real-world machine learning applications, including image classification and face recognition. Existing methods often employ parameter freezing for the backbone and classify based on metric learning. However, these methods suffer from two significant problems. Firstly, training the backbone solely on base classes limits its performance on novel classes due to information loss. Secondly, conventional metric-based strategies for prototype generation tend to introduce confusion in decision boundaries during few-shot tasks. To address these challenges, we propose a novel approach called Decision Boundary Optimization Network (DBONet) for few-shot class-incremental learning. To tackle the first issue, DBONet incorporates an augmentation feature extractor along with a corresponding loss function. This augmentation feature extractor combines samples from different categories to capture richer features. For the second issue, we leverage limited sample representativeness information by introducing the Prototype Generation Module (PGM) into DBONet, enabling the generation of more representative prototypes. The prototypes produced by PGM significantly contribute to the accurate delineation of decision boundaries. Furthermore, we exploit intra-class information to enhance classification precision. Extensive experiments on CIFAR100, miniImageNet, and CUB200 datasets demonstrate that our proposed approach achieves new state-of-the-art results

Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction

Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining accurate folding landscape using co-evolutionary information is fundamental to the success of modern protein structure prediction methods. As the state of the art, AlphaFold2 has dramatically raised the accuracy without performing explicit co-evolutionary analysis. Nevertheless, its performance still shows strong dependence on available sequence homologs. Based on the interrogation on the cause of such dependence, we presented EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in low-data regime and even achieve encouraging performance with single-sequence predictions. Being able to make accurate predictions with few-shot MSA not only generalizes AlphaFold2 better for orphan sequences, but also democratizes its use for high-throughput applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic structure generation method which could explore alternative conformations of protein sequences, and the task-aware differentiable algorithm for sequence generation will benefit other related tasks including protein design.Comment: version 2.0; 28 pages, 6 figure