LION : Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge

Multimodal Large Language Models (MLLMs) have endowed LLMs with the ability to perceive and understand multi-modal signals. However, most of the existing MLLMs mainly adopt vision encoders pretrained on coarsely aligned image-text pairs, leading to insufficient extraction and reasoning of visual knowledge. To address this issue, we devise a dual-Level vIsual knOwledge eNhanced Multimodal Large Language Model (LION), which empowers the MLLM by injecting visual knowledge in two levels. 1) Progressive incorporation of fine-grained spatial-aware visual knowledge. We design a vision aggregator cooperated with region-level vision-language (VL) tasks to incorporate fine-grained spatial-aware visual knowledge into the MLLM. To alleviate the conflict between image-level and region-level VL tasks during incorporation, we devise a dedicated stage-wise instruction-tuning strategy with mixture-of-adapters. This progressive incorporation scheme contributes to the mutual promotion between these two kinds of VL tasks. 2) Soft prompting of high-level semantic visual evidence. We facilitate the MLLM with high-level semantic visual evidence by leveraging diverse image tags. To mitigate the potential influence caused by imperfect predicted tags, we propose a soft prompting method by embedding a learnable token into the tailored text instruction. Comprehensive experiments on several multi-modal benchmarks demonstrate the superiority of our model (e.g., improvement of 5% accuracy on VSR and 3% CIDEr on TextCaps over InstructBLIP, 5% accuracy on RefCOCOg over Kosmos-2).Comment: Technical Report. Project page: https://rshaojimmy.github.io/Projects/JiuTian-LION Code: https://github.com/rshaojimmy/JiuTia

Diversity and aggregation patterns of plant species in a grass community

Abstract Both composition and aggregation patterns of species in a community are the outcome of community self-organizing. In this paper we conducted analysis on species diversity and aggregation patterns of plant species in a grass community, Zhuhai, China. According to the sampling survey, in total of 47 plant species, belonging to 16 families, were found. Compositae had 10 species (21.3%), seconded by Gramineae (9 species, 19.1%), Leguminosae (6 species, 12.8%), Cyperaceae (4 species, 8.5%), and Malvaceae (3 species, 6.4%). The results revealed that the means of aggregation indices I δ , I and m * /m were 21.71, 15.71 and 19.89 respectively and thus individuals of most of plant species strongly followed aggregative distribution. Iwao analysis indicated that both individuals of all species and clumps of all individuals of all species followed aggregative distribution. Taylor's power law indicated that individuals of all species followed aggregative distribution and aggregation intensity strengthened as the increase of mean density. We held that the strong aggregation intensity of a species has been resulted from the strong adaptation ability to the environment, the strong interspecific competition ability and the earlier establishment of the species. Fitting goodness of the mean, I, I δ , m * /m with probability distributions demonstrated that the mean (density), I, I δ , and m * /m over all species followed Weibull distribution rather than normal distribution. Lophatherum gracile, Paederia scandens (Lour.) Merr., Eleusine indica, and Alternanthera philoxeroides (Mart.) Griseb. were mostly aggregative, and Oxalis sp., Eleocharis plantagineiformis, Vernonia cinerea (L.) Less., and Sapium sebiferum (L.) Roxb, were mostly uniform in the spatial distribution. Importance values (IV) showed that Cynodon dactylon was the most important species, seconded by Desmodium triflorum (L.) DC., Cajanus scarabaeoides (L.) Benth., Paspalum scrobiculatum L., and Rhynchelytrum repens. Oxalis sp., Eleocharis plantagineiformis, and Vernonia cinerea (L.) Less. were the least important species in the community. Summed dominance ratio (SDR2) revealed that Cynodon dactylon and Desmodium triflorum (L.) DC. were the most dominant species in the community, followed by Rhynchelytrum repens, Paspalum scrobiculatum L., and Cajanus scarabaeoides (L.) Benth

Identification of Long Non-Coding RNA MIR4435-2HG as a Prognostic Biomarker in Bladder Cancer

The abnormal expression of long non-coding RNAs(lncRNAs) is closely related to the prognosis of patients. This finding may indicate a new target for the treatment of malignant tumors. Non-muscle invasive bladder cancer (NMIBC) is the most common subtype of bladder cancer, and BCG intravesical therapy is the first-line treatment for NMIBC, but about half of NMIBC patients relapse within two years after BCG treatment. Therefore, it is necessary to screen out lncRNAs related to the prognosis and treatment of BGC-resistant patients. Here, we performed differential expression analysis of lncRNAs in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and screened MIR4435-2HG as the only BCG-response-related lncRNA. Then, the prognosis value of MIR4435-2HG was validated in several publicly available cohorts, and confirmed its prognostic value in bladder cancer of different stages. In addition, we also analyzed its genetic alterations, clinical relevance, function enrichment, and association with other biomarkers. Further validation of the expression of MIR4435-2HG might be helpful to instruct NMIBC prognosis and treatment

Characterization of Reduced Graphene Oxide (rGO)-Loaded SnO2 Nanocomposite and Applications in C2H2 Gas Detection

Acetylene (C2H2) gas sensors were developed by synthesizing a reduced graphene oxide (rGO)-loaded SnO2 hybrid nanocomposite via a facile two-step hydrothermal method. Morphological characterizations showed the formation of well-dispersed SnO2 nanoparticles loaded on the rGO sheets with excellent transparency and obvious fold boundary. Structural analysis revealed good agreement with the standard crystalline phases of SnO2 and rGO. Gas sensing characteristics of the synthesized materials were carried out in a temperature range of 100–300 °C with various concentrations of C2H2 gas. At 180 °C, the SnO2–rGO hybrid showed preferable detection of C2H2 with high sensor response (12.4 toward 50 ppm), fast response-recovery time (54 s and 23 s), limit of detection (LOD) of 1.3 ppm and good linearity, with good selectivity and long-term stability. Furthermore, the possible gas sensing mechanism of the SnO2–rGO nanocomposites for C2H2 gas were summarized and discussed in detail. Our work indicates that the addition of rGO would be effective in enhancing the sensing properties of metal oxide-based gas sensors for C2H2 and may make a contribution to the development of an excellent ppm-level gas sensor for on-line monitoring of dissolved C2H2 gas in transformer oil

Circular RNAs and Drug Resistance in Genitourinary Cancers: A Literature Review

In recent years, systematic treatment has made great progress in genitourinary tumors. However, some patients develop resistance to the treatments, resulting in an increase in mortality. Circular RNAs (circRNAs) form a class of non-coding RNAs with high stability and significant clinical relevance. Accumulating evidence indicates that circRNAs play a vital role in cancer development and tumor chemotherapy resistance. This review summarizes the molecular and cellular mechanisms of drug resistance mediated by circRNAs to common drugs used in the treatment of genitourinary tumors. Several circRNAs were identified to regulate the responsiveness to systemic treatments in genitourinary tumors, including chemotherapies such as cisplatin and targeted therapies such as enzalutamide. Canonically, cicrRNAs participate in the competing endogenous RNA (ceRNA) network, or in some cases directly interact with proteins, regulate downstream pathways, and even some circRNAs have the potential to produce proteins or polypeptides. Several cellular mechanisms were involved in circRNA-dependent drug resistance, including autophagy, cancer stem cells, epithelial-mesenchymal transition, and exosomes. The potential clinical prospect of circRNAs in regulating tumor drug resistance was also discussed

The chromosome-level quality genome provides insights into the evolution of the biosynthesis genes for aroma compounds of Osmanthus fragrans

Genome sequences: Discovering a sweet secret A high-quality genome sequence for sweet olive, Osmanthus fragrans, has revealed which genes make the ornamental tree’s blossoms so fragrant. Lianggui Wang at Nanjing Forestry University in China and coworkers used cutting-edge approaches to produce a high-resolution picture of sweet olive’s genome. Analysis of the full sequence revealed that sweet olive has a large number of genes, more than 45,000. Further investigation showed that the entire genome had been duplicated, or accidentally copied, several million years ago. Thanks to the duplication, sweet olive has extra copies of the genes that produce its scent compound, resulting in its very strong scent. These results are only the beginning. Having this first sequence makes sequencing other varieties of sweet olive much easier, and future comparisons between genomes will help to pinpoint genes that control many other traits