EV-COMM: A database of interspecies and intercellular interactions mediated by extracellular vesicles.

Intra- and inter-organismal interactions play a crucial role in the maintenance and function of individuals, as well as communities. Extracellular vesicles (EVs) have been identified as effective mediators for the communication both within and between species. They can carry and transport molecular cargoes to transmit biological messages. Several databases (ExoBCD, ExoCarta, EVpedia, EV-TRACK, Vesiclepedia) complied the cargoes information including DNA, RNA, protein, lipid and metabolite associated with EVs. Databases that refer to the complete records on both donor and recipient information are warranted to facilitate the understanding of the interaction across cells and species. In this study, we developed a database that compiled the records equipped with a structured process of EV-mediated interaction. The sources of donor and recipient were classified by cell type, tissues/organs and species, thus providing an extended knowledge of cell-cell, species-species interaction. The isolation and identification methods were presented for assessing the quality of EVs. Information on functional cargoes was included, where microRNA was linked to a prediction server to broaden its potential effects. Physiological and pathological context was marked to show the environment where EVs functioned. At present, a total of 1481 data records in our database, including 971 cell-cell interactions belonging to more than 40 different tissues/organs, and 510 cross-species records. The database provides a web interface to browse, search, visualize and download the interaction records. Users can search for interactions by selecting the context of interest or specific cells/species types, as well as functional cargoes. To the best of our knowledge, the database is the first comprehensive database focusing on interactions between donor and recipient cells or species mediated by EVs, serving as a convenient tool to explore and validate interactions. The Database, shorten as EV-COMM (EV mediated communication) is freely available at http://sdc.iue.ac.cn/evs/list/ and will be continuously updated.

Predicting Climate Change Effects on the Potential Distribution of Two Invasive Cryptic Species of the Bemisia tabaci Species Complex in China.

Middle East-Asia Minor 1 (MEAM1) and Mediterranean (MED) are two invasive cryptic species of the Bemisia tabaci species complex (Hemiptera: Aleyrodidae) that cause serious damage to agricultural and horticultural crops worldwide. To explore the possible impact of climate change on their distribution, the maximum entropy (MaxEnt) model was used to predict the potential distribution ranges of MEAM1 and MED in China under current and four future climate scenarios, using shared socioeconomic pathways (SSPs), namely SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, over four time periods (2021-2040, 2041-2060, 2061-2080, and 2081-2100). The distribution ranges of MEAM1 and MED were extensive and similar in China under current climatic conditions, while their moderately and highly suitable habitat ranges differed. Under future climate scenarios, the areas of suitable habitat of different levels for MEAM1 and MED were predicted to increase to different degrees. However, the predicted expansion of suitable habitats varied between them, suggesting that these invasive cryptic species respond differently to climate change. Our results illustrate the difference in the effects of climate change on the geographical distribution of different cryptic species of B. tabaci and provide insightful information for further forecasting and managing the two invasive cryptic species in China.

The impact of air pollution on COVID-19 pandemic varied within different cities in South America using different models.

There is a rising concern that air pollution plays an important role in the COVID-19 pandemic. However, the results were not consistent on the association between air pollution and the spread of COVID-19. In the study, air pollution data and the confirmed cases of COVID-19 were both gathered from five severe cities across three countries in South America. Daily real-time population regeneration (Rt) was calculated to assess the spread of COVID-19. Two frequently used models, generalized additive models (GAM) and multiple linear regression, were both used to explore the impact of environmental pollutants on the epidemic. Wide ranges of all six air pollutants were detected across the five cities. Spearman's correlation analysis confirmed the positive correlation within six pollutants. Rt value showed a gradual decline in all the five cities. Further analysis showed that the association between air pollution and COVID-19 varied across five cities. According to our research results, even for the same region, varied models gave inconsistent results. For example, in Sao Paulo, both models show SO2 and O3 are significant independent variables, however, the GAM model shows that PM10 has a nonlinear negative correlation with Rt, while PM10 has no significant correlation in the multiple linear model. Moreover, in the case of multiple regions, currently used models should be selected according to local conditions. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which will help states, health practitioners, and policy makers in combating the COVID-19 pandemic in South America.

Dataset-based assessment of heavy metal contamination in freshwater fishes and their health risks.

The ecological risks and health hazards of heavy metals pollution in Taihu Lake have received widespread concern. This study has developed a species-pollution dataset which includes a large amount of data on heavy metal pollution in Taihu fish. The heavy metal contamination poses a significant threat to human consumption, but no studies have been conducted to assess the risk of exposure to consumption of these fish and to make recommendations for their consumption. In this study, we systematically integrated the relevant data in the dataset, analyzed its contamination level using PI (single pollution index) and MPI (metal pollution index) models, and assessed health hazards of fish consumption using THQ (target hazard quotient) and ILCR (incremental lifetime cancer risk) models. Results showed that the contamination levels of heavy metals in fish varied in a feeding habit and living habit dependent manner. The risk of non-cancer health is the highest from consuming omnivorous fish, then from carnivorous and herbivorous fish. The ILCR model predicted that the long-term Taihu consumption of omnivorous fish may pose a potential carcinogenic risk, especially for children. In all, our study provided a comprehensive understanding on the risk of heavy metals in Taihu. Accordingly, it is recommended that children should try to choose herbivorous fish when consuming fish from Taihu Lake while avoiding long-term consumption of omnivorous fish.

Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks.

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

Marble Algorithm: a solution to estimating ecological niches from presence-only records.

We describe an algorithm that helps to predict potential distributional areas for species using presence-only records. The Marble Algorithm is a density-based clustering program based on Hutchinson's concept of ecological niches as multidimensional hypervolumes in environmental space. The algorithm characterizes this niche space using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. When MA is provided with a set of occurrence points in environmental space, the algorithm determines two parameters that allow the points to be grouped into several clusters. These clusters are used as reference sets describing the ecological niche, which can then be mapped onto geographic space and used as the potential distribution of the species. We used both virtual species and ten empirical datasets to compare MA with other distribution-modeling tools, including Bioclimate Analysis and Prediction System, Environmental Niche Factor Analysis, the Genetic Algorithm for Rule-set Production, Maximum Entropy Modeling, Artificial Neural Networks, Climate Space Models, Classification Tree Analysis, Generalised Additive Models, Generalised Boosted Models, Generalised Linear Models, Multivariate Adaptive Regression Splines and Random Forests. Results indicate that MA predicts potential distributional areas with high accuracy, moderate robustness, and above-average transferability on all datasets, particularly when dealing with small numbers of occurrences.

Patterns of diversity, areas of endemism, and multiple glacial refuges for freshwater crabs of the genus Sinopotamon in China (Decapoda: Brachyura: Potamidae).

Previous research has shown that the geographical distribution patterns of freshwater fishes and amphibians have been influenced by past climatic oscillations in China resulting from Pleistocene glacial activity. However, it remains unknown how these past changes have impacted the present-day distribution of Chinese freshwater crabs. This work describes the diversity and endemism of freshwater crabs belonging to Sinopotamon, a highly speciose genus endemic to China, and evaluates its distribution in terms of topography and past climatic fluctuations. Species diversity within Sinopotamon was found to be concentrated in an area from the northeastern edge of the Yunnan-Guizhou Plateau to the Jiangnan Hills, and three areas of endemism were identified. Multiple regression analysis between current climatic variables and Sinopotamon diversity suggested that regional annual precipitation, minimum temperature in the coldest month, and annual temperature range significantly influenced species diversity and may explain the diversity patterns of Sinopotamon. A comparison of ecological niche models (ENMs) between current conditions and the last glacial maximum (LGM) showed that suitable habitat for Sinopotamon in China severely contracted during the LGM. The coincidence of ENMs and the areas of endemism indicated that southeast of the Daba Mountains, and central and southeastern China, are potential Pleistocene refuges for Sinopotamon. The presence of multiple Pleistocene refuges within the range of this genus could further promote inter- and intraspecific differentiations, and may have led to high Sinopotamon species diversity, a high endemism rate and widespread distribution.

mMWeb--an online platform for employing multiple ecological niche modeling algorithms.

BACKGROUND: Predicting the ecological niche and potential habitat distribution of a given organism is one of the central domains of ecological and biogeographical research. A wide variety of modeling techniques have been developed for this purpose. In order to implement these models, the users must prepare a specific runtime environment for each model, learn how to use multiple model platforms, and prepare data in a different format each time. Additionally, often model results are difficult to interpret, and a standardized method for comparing model results across platforms does not exist. We developed a free and open source online platform, the multi-models web-based (mMWeb) platform, to address each of these problems, providing a novel environment in which the user can implement and compare multiple ecological niche model (ENM) algorithms. METHODOLOGY: mMWeb combines 18 existing ENMs and their corresponding algorithms and provides a uniform procedure for modeling the potential habitat niche of a species via a common web browser. mMWeb uses Java Native Interface (JNI), Java R Interface to combine the different ENMs and executes multiple tasks in parallel on a super computer. The cross-platform, user-friendly interface of mMWeb simplifies the process of building ENMs, providing an accessible and efficient environment from which to explore and compare different model algorithms.

Profound climatic effects on two East Asian black-throated tits (Ave: Aegithalidae), revealed by ecological niche models and phylogeographic analysis.

Although a number of studies have assessed the effects of geological and climatic changes on species distributions in East Asian, we still have limited knowledge of how these changes have impacted avian species in south-western and southern China. Here, we aim to study paleo-climatic effects on an East Asian bird, two subspecies of black-throated tit (A. c. talifuensis-concinnus) with the combined analysis of phylogeography and Ecological Niche Models (ENMs). We sequenced three mitochondrial DNA markers from 32 populations (203 individuals) and used phylogenetic inferences to reconstruct the intra-specific relationships among haplotypes. Population genetic analyses were undertaken to gain insight into the demographic history of these populations. We used ENMs to predict the distribution of target species during three periods; last inter-glacial (LIG), last glacial maximum (LGM) and present. We found three highly supported, monophyletic MtDNA lineages and different historical demography among lineages in A. c. talifuensis-concinnus. These lineages formed a narrowly circumscribed intra-specific contact zone. The estimated times of lineage divergences were about 2.4 Ma and 0.32 Ma respectively. ENMs predictions were similar between present and LGM but substantially reduced during LIG. ENMs reconstructions and molecular dating suggest that Pleistocene climate changes had triggered and shaped the genetic structure of black-throated tit. Interestingly, in contrast to profound impacts of other glacial cycles, ENMs and phylogeographic analysis suggest that LGM had limited effect on these two subspecies. ENMs also suggest that Pleistocene climatic oscillations enabled the formation of the contact zone and thus support the refuge theory.