What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?

With the growing success of multi-modal learning, research on the robustness of multi-modal models, especially when facing situations with missing modalities, is receiving increased attention. Nevertheless, previous studies in this domain exhibit certain limitations, as they often lack theoretical insights or their methodologies are tied to specific network architectures or modalities. We model the scenarios of multi-modal models encountering missing modalities from an information-theoretic perspective and illustrate that the performance ceiling in such scenarios can be approached by efficiently utilizing the information inherent in non-missing modalities. In practice, there are two key aspects: (1) The encoder should be able to extract sufficiently good features from the non-missing modality; (2) The extracted features should be robust enough not to be influenced by noise during the fusion process across modalities. To this end, we introduce Uni-Modal Ensemble with Missing Modality Adaptation (UME-MMA). UME-MMA employs uni-modal pre-trained weights for the multi-modal model to enhance feature extraction and utilizes missing modality data augmentation techniques to better adapt to situations with missing modalities. Apart from that, UME-MMA, built on a late-fusion learning framework, allows for the plug-and-play use of various encoders, making it suitable for a wide range of modalities and enabling seamless integration of large-scale pre-trained encoders to further enhance performance. And we demonstrate UME-MMA's effectiveness in audio-visual datasets~(e.g., AV-MNIST, Kinetics-Sound, AVE) and vision-language datasets~(e.g., MM-IMDB, UPMC Food101)

Spatial and temporal effects on the value of ecosystem services in arid and semi-arid mountain areas—A case study from Helan Mountain in Ningxia, China

In the context of global warming and the continued increase in greenhouse gas emissions, the expansion of the global arid and semi-arid zones will accelerate, and there will be a risk of further land degradation. Changes in land use are one of the human activities that cause the increase in CO2 concentration in the global atmosphere. This article takes Helan Mountain as the research object which is located in the arid and semi-arid regions. It analyzes the spatial and temporal effects of land use changes, ecosystem service value, and ecological risk of land use in Helan Mountain by using GIS technology and five periods of land use data in 2000, 2005, 2010, 2015, and 2020. The study results show the following: First, due to land use changes in the arid and semi-arid zones, grasslands lost the largest area (in total 50,407.11 ha) during the study period. Construction land is the highest dynamic land type that increased by 22,364.73 ha. Land use generally shows a trend of increasing before decreasing, with an inevitable increase in the degree of utilization. Second, the total ecosystem service value decreased by 0.1148 million yuan from 5,674.5490 to 5,674.6638 million yuan, which shows a slight change in the overall ecosystem service value. Third, the ecological risk of land use could be divided into five classes, with a transformation characteristic from high and low ecological risk to comparatively high, medium, and comparatively low ecological risk. Fourth, the western side of Helan Mountain is the principal supply area for ecosystem services in the entire study area, which is an area with high-grade ecological risk at the same time. The vulnerability and importance of this area need to be given high priority. This study will provide spatial guidelines for the protection and restoration of ecological security issues such as environmental damage and land degradation in the arid and semi-arid regions of the mountains

A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer.

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan-Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment

Optimal Codon Identities in Bacteria: Implications from the Conflicting Results of Two Different Methods

A correlation method was recently adopted to identify selection-favored ‘optimal’ codons from 675 bacterial genomes. Surprisingly, the identities of these optimal codons were found to track the bacterial GC content, leading to a conclusion that selection would generally shape the codon usages to the same direction as the overall mutation does. Raising several concerns, here we report a thorough comparative study on 203 well-selected bacterial species, which strongly suggest that the previous conclusion is likely an illusion. Firstly, the previous study did not preclude species that are suffering weak or no selection pressures on their codon usages. For these species, as showed in this study, the optimal codon identities are prone to be incorrect and follow GC content. Secondly, the previous study only adopted the correlation method, without considering another method to test the reliability of inferred optimal codons. Actually by definition, optimal codons can also be identified by simply comparing codon usages between high- and low-expression genes. After using both methods to identify optimal codons for the selected species, we obtained highly conflicting results, suggesting at least one method is misleading. Further we found a critical problem of correlation method at the step of calculating gene bias level. Due to a failure of accurately defining the background mutation, the problem would result in wrong optimal codon identities. In other words, partial mutational effects on codon choices were mistakenly regarded as selective influences, leading to incorrect and biased optimal codon identities. Finally, considering the translational dynamics, optimal codons identified by comparison method can be well-explained by tRNA compositions, whereas optimal codons identified by correlation method can not be. For all above reasons, we conclude that real optimal codons actually do not track the genomic GC content, and correlation method is misleading in identifying optimal codons and better be avoided

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records

Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving. We propose EHRAgent, an LLM agent empowered with a code interface, to autonomously generate and execute code for multi-tabular reasoning within electronic health records (EHRs). First, we formulate an EHR question-answering task into a tool-use planning process, efficiently decomposing a complicated task into a sequence of manageable actions. By integrating interactive coding and execution feedback, EHRAgent learns from error messages and improves the originally generated code through iterations. Furthermore, we enhance the LLM agent by incorporating long-term memory, which allows EHRAgent to effectively select and build upon the most relevant successful cases from past experiences. Experiments on three real-world multi-tabular EHR datasets show that EHRAgent outperforms the strongest baseline by up to 29.6% in success rate. EHRAgent leverages the emerging few-shot learning capabilities of LLMs, enabling autonomous code generation and execution to tackle complex clinical tasks with minimal demonstrations.Comment: Work in Progres

Advances in holliday junction recognition protein (HJURP): Structure, molecular functions, and roles in cancer

Oncogenes are increasingly recognized as important factors in the development and progression of cancer. Holliday Junction Recognition Protein (HJURP) is a highly specialized mitogenic protein that is a chaperone protein of histone H3. The HJURP gene is located on chromosome 2q37.1 and is involved in nucleosome composition in the mitotic region, forming a three-dimensional crystal structure with Centromere Protein A (CENP-A) and the histone 4 complex. HJURP is involved in the recruitment and assembly of centromere and kinetochore and plays a key role in stabilizing the chromosome structure of tumor cells, and its dysfunction may contribute to tumorigenesis. In the available studies HJURP is upregulated in a variety of cancer tissues and cancer cell lines and is involved in tumor proliferation, invasion, metastasis and immune response. In an in vivo model, overexpression of HJURP in most cancer cell lines promotes cell proliferation and invasiveness, reduces susceptibility to apoptosis, and promotes tumor growth. In addition, upregulation of HJURP was associated with poorer prognosis in a variety of cancers. These properties suggest that HJURP may be a possible target for the treatment of certain cancers. Various studies targeting HJURP as a prognostic and therapeutic target for cancer are gradually attracting interest and attention. This paper reviews the functional and molecular mechanisms of HJURP in a variety of tumor types with the aim of providing new targets for future cancer therapy

Impact and potential value of immunosenescence on solid gastrointestinal tumors

Solid gastrointestinal tumors often respond poorly to immunotherapy for the complex tumor microenvironment (TME), which is exacerbated by immune system alterations. Immunosenescence is the process of increased diversification of immune genes due to aging and other factors, leading to a decrease in the recognition function of the immune system. This process involves immune organs, immune cells, and the senescence-associated secretory phenotype (SASP). The most fundamental change is DNA damage, resulting in TME remodeling. The main manifestations are worsening inflammation, increased immunosuppressive SASP production, decreased immune cell antitumor activity, and the accumulation of tumor-associated fibroblasts and myeloid-derived suppressor cells, making antitumor therapy less effective. Senotherapy strategies to remove senescent cells and block key senescence processes can have synergistic effects with other treatments. This review focuses on immunoenescence and its impact on the solid TME. We characterize the immunosenescent TME and discuss future directions for antitumor therapies targeting senescence

LSDP5 Enhances Triglyceride Storage in Hepatocytes by Influencing Lipolysis and Fatty Acid β-Oxidation of Lipid Droplets

Lipid storage droplet protein 5 (LSDP5) is a lipid droplet-associated protein of the PAT (perilipin, adipophilin, and TIP47) family that is expressed in the liver in a peroxisome proliferator-activated receptor alpha (PPARα)-dependent manner; however, its exact function has not been elucidated. We noticed that LSDP5 was localized to the surface of lipid droplets in hepatocytes. Overexpression of LSDP5 enhanced lipid accumulation in the hepatic cell line AML12 and in primary hepatocytes. Knock-down of LSDP5 significantly decreased the triglyceride content of lipid droplets, stimulated lipolysis, and modestly increased the mitochondrial content and level of fatty-acid β-oxidation in the mitochondria. The expression of PPARα was increased in LSDP5-deficient cells and required for the increase in the level of fatty acid β-oxidation in LSDP5-deficient cells. Using serial deletions of LSDP5, we determined that the lipid droplet-targeting domain and the domain directing lipid droplet clustering overlapped and were localized to the 188 amino acid residues at the N-terminus of LSDP5. Our findings suggest that LSDP5, a novel lipid droplet protein, may contribute to triglyceride accumulation by negatively regulating lipolysis and fatty acid oxidation in hepatocytes