Color Cell Image Segmentation Based on Chan-Vese Model for Vector-Valued Images

In this paper, we propose a color cell image segmentation method based on the modified Chan-Vese model for vectorvalued images. In this method, both the cell nuclei and cytoplasm can be served simultaneously from the color cervical cell image. Color image could be regarded as vector-valued images because there are three channels, red, green and blue in color image. In the proposed color cell image segmentation method, to segment the cell nuclei and cytoplasm precisely in color cell image, we should use the coarse-fine segmentation which combined the auto dual-threshold method to separate the single cell connection region from the original image, and the modified C-V model for vectorvalued images which use two independent level set functions to separate the cell nuclei and cytoplasm from the cell body. From the result we can see that by using the proposed method we can get the nuclei and cytoplasm region more accurately than traditional model.

A Separating Algorithm for Overlapping Cell Images

The cell overlapping and adhesion phenomenon often exists in cell image processing. Separating overlapped cell into single ones is of great important and difficult in cell image quantitative analysis and automatic recognition. In this paper, an algorithm based on concave region extraction and erosion limit has been proposed to judge and separate overlapping cell images. Experimental results show that the proposed algorithm has a good separation effects on analog cell images. Then the method is applying in actual cervical cell image and obtains good separation result.

Incorporating adaptive genomic variation into predictive models for invasion risk assessment

Global climate change is expected to accelerate biological invasions,necessitating accurate risk forecasting and management strategies.However,current invasion risk assessments often overlook adaptive genomic variation,which plays a significant role in the persistence and expansion of invasive populations.Here we used Molgula manhattensis,a highly invasive ascidian,as a model to assess its invasion risks along Chinese coasts under climate change.Through population genomics analyses,we identified two genetic clusters,the north and south clusters,based on geographic distributions.To predict invasion risks,we employed the gradient forest and species distribution models to calculate genomic offset and species habitat suitability,respectively.These approaches yielded distinct predictions:the gradient forest model suggested a greater genomic offset to future climatic conditions for the north cluster(i.e.,lower invasion risks),while the species distribution model indicated higher future habitat suitability for the same cluster(i.e,higher invasion risks).By integrating these models,we found that the south cluster exhibited minor genome-niche disruptions in the future,indicating higher invasion risks.Our study highlights the complementary roles of genomic offset and habitat suitability in assessing invasion risks under climate change.Moreover,incorporating adaptive genomic variation into predictive models can significantly enhance future invasion risk predictions and enable effective management strategies for biological invasions in the future.

Toxicity and mechanisms of action of titanium dioxide nanoparticles in living organisms

Titanium dioxide nanoparticles(TiO_2 NPs) are one of the most widely used nanomaterials in the consumer products, agriculture, and energy sectors. Their large demand and widespread applications will inevitably cause damage to organisms and ecosystems. A better understanding of TiO_2 NP toxicity in living organisms may promote risk assessment and safe use practices of these nanomaterials. This review summarizes the toxic effects of TiO_2 NPs on multiple taxa of microorganisms, algae, plants, invertebrates, and vertebrates. The mechanism of TiO_2 NP toxicity to organisms can be outlined in three aspects: The Reactive Oxygen Species(ROS)produced by TiO_2 NPs following the induction of electron–hole pairs; cell wall damage and lipid peroxidation of the cell membrane caused by NP-cell attachment by electrostatic force owing to the large surface area of TiO_2 NPs; and TiO_2 NP attachment to intracellular organelles and biological macromolecules following damage to the cell membranes.

基于环境DNA-宏条形码技术的水生生态系统入侵生物的早期监测与预警

外来生物入侵是继生境破坏后造成生物多样性丧失的第二大威胁因素,已对入侵地的生态安全、经济和社会发展及人类健康等造成严重负面影响,成为21世纪五大全球性环境问题之一。作为水产养殖、航运和水生宠物交易大国,我国水生生态系统的生物入侵问题尤为严重。研究表明,系统地构建并应用早期监测预警技术是防控水生生态系统生物入侵最有效的途径。和陆生生物相比,水生生物群落的物种繁多、群落结构复杂、生物形体微小且在入侵初期群体规模极小、隐匿于水下、可用于物种鉴定的外部形态缺乏,使得在水生生态系统中构建并应用早期监测和预警体系在技术层面更具挑战。随着高通量测序技术的快速发展,环境DNA-宏条形码技术成为构建水生生态系统入侵生物早期监测与预警技术的首选。本文主要综述了基于环境DNA-宏条形码技术的水生生态系统入侵生物的早期监测与预警技术方法;解析了环境DNA-宏条形码监测系统的应用现状、技术优势;着重探讨了影响监测结果准确性的I型和II型错误及其产生原因,并为避免两类错误提供了可行的优化/改进方案;最后对该方法在水生入侵生物监测中的应用前景进行了展望。