Prediction of Lime Tolerance in Rhododendron Based on Herbarium Specimen and Geochemical Data

Rhododendrons are typically known to be calcifuges that cannot grow well in lime soils. Data on lime tolerance of different taxa in Rhododendron are scarce. Habitats of naturally distributed specimens of genus Rhododendron were compiled as Chinese text-based locations from the Chinese Virtual Herbarium. The locations were then geocoded into latitude/longitude pairs and subsequently connected to soil characteristics including pH and CaCO3 from the Harmonized World Soil Database (HWSD). Using the upper quartile values of pH > 7.2 and CaCO3 > 2% weight in topsoil as threshold, we predicted the lime tolerant taxa. A dataset of 31,146 Rhododendron specimens including the information on taxonomy, GPS locations and soil parameters for both top- and subsoil was built. The majority of the specimens were distributed in soils with moderately acidic pH and without presence of CaCO3. 76 taxa with potential lime tolerance were predicted out of 525 taxa. The large scale data analysis based on combined data of geocoded herbarium specimens and HWSD allows identification of valuable Rhododendron species, subspecies or botanical varieties with potential tolerance to lime soils with higher pH. The predicted tolerant taxa are valuable resources for an in-depth evaluation of lime tolerance or for further use in horticulture and breeding

miR-96/HBP1/Wnt/β-catenin regulatory circuitry promotes glioma growth

AbstractWe found that miR-96 is overexpressed in glioma, and its level inversely correlates with the survival of patients. The reduction in miR-96 abundance suppresses the proliferation and colony formation of glioma cells. The tumorigenicity of U-87 MG cells is reduced by miR-96 silencing. miR-96 contributes to the activation of Wnt/β-catenin pathway in glioma cells. HMG-box transcription factor 1 (HBP-1), a Wnt/β-catenin pathway inhibitor, is suppressed by miR-96. The reactivation of Wnt/β-catenin signaling causes an increase in the proliferation of glioma cells, and a decrease in miR-96 expression. On the other hand, HBP1 silencing promotes miR-96 expression. Collectively, miR-96 contributes to the progression of glioma by enhancing the activation of the Wnt/β-catenin pathway, and the miR-96/HBP1/Wnt/β-catenin regulatory circuitry promotes the proliferation of glioma cells

Prediction of lime tolerance in Rhododendron based on herbarium specimen and geochemical data

Rhododendrons are typically known to be calcifuges that cannot grow well in lime soils. Data on lime tolerance of different taxa in Rhododendron are scarce. Habitats of naturally distributed specimens of genus Rhododendron were compiled as Chinese text-based locations from the Chinese Virtual Herbarium. The locations were then geocoded into latitude/longitude pairs and subsequently connected to soil characteristics including pH and CaCO3 from the Harmonized World Soil Database (HWSD). Using the upper quartile values of pH > 7.2 and CaCO3 > 2% weight in topsoil as threshold, we predicted the lime tolerant taxa. A dataset of 31,146 Rhododendron specimens including the information on taxonomy, GPS locations and soil parameters for both top-and subsoil was built. The majority of the specimens were distributed in soils with moderately acidic pH and without presence of CaCO3. 76 taxa with potential lime tolerance were predicted out of 525 taxa. The large scale data analysis based on combined data of geocoded herbarium specimens and HWSD allows identification of valuable Rhododendron species, subspecies or botanical varieties with potential tolerance to lime soils with higher pH. The predicted tolerant taxa are valuable resources for an in-depth evaluation of lime tolerance or for further use in horticulture and breeding

A New Related-Key Boomerang Distinguishing Attack of Reduced-Round Threefish-256

On Nov 2007, NIST announced the SHA-3 competition to select a new hash standard as a replacement of SHA-2. On Dec 2010, five submissions have been selected as the final round candidates, including Skein, which have components based on ARX. In this paper, a new related-key boomerang distinguishing attack is proposed on 31-round Threefish-256 with a time complexity of about $2^{234}$. Our improved attack is based on the efficient algorithms for calculating differentials of modular addition

Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities

We introduce the Qwen-VL series, a set of large-scale vision-language models designed to perceive and understand both text and images. Comprising Qwen-VL and Qwen-VL-Chat, these models exhibit remarkable performance in tasks like image captioning, question answering, visual localization, and flexible interaction. The evaluation covers a wide range of tasks including zero-shot captioning, visual or document visual question answering, and grounding. We demonstrate the Qwen-VL outperforms existing Large Vision Language Models (LVLMs). We present their architecture, training, capabilities, and performance, highlighting their contributions to advancing multimodal artificial intelligence. Code, demo and models are available at https://github.com/QwenLM/Qwen-VL.Comment: Code, demo and models are available at https://github.com/QwenLM/Qwen-V

TouchStone: Evaluating Vision-Language Models by Language Models

Large vision-language models (LVLMs) have recently witnessed rapid advancements, exhibiting a remarkable capacity for perceiving, understanding, and processing visual information by connecting visual receptor with large language models (LLMs). However, current assessments mainly focus on recognizing and reasoning abilities, lacking direct evaluation of conversational skills and neglecting visual storytelling abilities. In this paper, we propose an evaluation method that uses strong LLMs as judges to comprehensively evaluate the various abilities of LVLMs. Firstly, we construct a comprehensive visual dialogue dataset TouchStone, consisting of open-world images and questions, covering five major categories of abilities and 27 subtasks. This dataset not only covers fundamental recognition and comprehension but also extends to literary creation. Secondly, by integrating detailed image annotations we effectively transform the multimodal input content into a form understandable by LLMs. This enables us to employ advanced LLMs for directly evaluating the quality of the multimodal dialogue without requiring human intervention. Through validation, we demonstrate that powerful LVLMs, such as GPT-4, can effectively score dialogue quality by leveraging their textual capabilities alone, aligning with human preferences. We hope our work can serve as a touchstone for LVLMs' evaluation and pave the way for building stronger LVLMs. The evaluation code is available at https://github.com/OFA-Sys/TouchStone.Comment: https://github.com/OFA-Sys/TouchSton