Plant diversity accurately predicts insect diversity in two tropical landscapes.

Plant diversity surely determines arthropod diversity, but only moderate correlations between arthropod and plant species richness had been observed until Basset et al. (Science, 338, 2012 and 1481) finally undertook an unprecedentedly comprehensive sampling of a tropical forest and demonstrated that plant species richness could indeed accurately predict arthropod species richness. We now require a high-throughput pipeline to operationalize this result so that we can (i) test competing explanations for tropical arthropod megadiversity, (ii) improve estimates of global eukaryotic species diversity, and (iii) use plant and arthropod communities as efficient proxies for each other, thus improving the efficiency of conservation planning and of detecting forest degradation and recovery. We therefore applied metabarcoding to Malaise-trap samples across two tropical landscapes in China. We demonstrate that plant species richness can accurately predict arthropod (mostly insect) species richness and that plant and insect community compositions are highly correlated, even in landscapes that are large, heterogeneous and anthropogenically modified. Finally, we review how metabarcoding makes feasible highly replicated tests of the major competing explanations for tropical megadiversity.

Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping.

BACKGROUND: Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. RESULTS: On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. CONCLUSION: The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

Fabrication of Fe-doped birnessite with tunable electron spin magnetic moments for the degradation of tetracycline under microwave irradiation.

Manganese oxides exhibit an excellent microwave absorption performance that could increase the degradation efficiency of organic pollutants in contaminated water. Incorporation of various transition metals into manganese oxides could bring about changes in their crystal structure and improve their physicochemical performance. In this work, a better microwave absorption material was obtained by adjusting and controlling the electron spin magnetic moments of Fe-doped birnessite. The powder X-ray diffraction, inductive coupled plasma emission spectrometer, X-ray photoelectron spectroscopy, and network analyses were performed to characterize the crystal structure, chemical composition, valence and content of the elements, and the microwave absorption performance of the obtained samples. Doping Fe into birnessite resulted in little changes to their crystal structure. The narrow energy spectrum of Fe (2p) revealed that the doped Fe was in the form of Fe (III) in birnessite structure. As the content of Fe (III) increased, the content of Mn (III) decreased accordingly. Substitution of Mn (III) by Fe (III) in the birnessite crystal lattice, confirmed by combining the characterization analyses with structure refinements for each doped sample, increased the overall numbers of unpaired electrons in birnessite structure, resulting in a higher electron spin magnetic moment and better microwave response. Compared with the non-doped sample, Fe-doped birnessite improved the efficiency of tetracycline degradation, which proved that Fe-doped birnessite indeed had better response towards the microwave, and thus, could be utilized for better removal of organic pollutants under microwave irradiation.

Higher fungal diversity is correlated with lower CO2 emissions from dead wood in a natural forest.

Wood decomposition releases almost as much CO2 to the atmosphere as does fossil-fuel combustion, so the factors regulating wood decomposition can affect global carbon cycling. We used metabarcoding to estimate the fungal species diversities of naturally colonized decomposing wood in subtropical China and, for the first time, compared them to concurrent measures of CO2 emissions. Wood hosting more diverse fungal communities emitted less CO2, with Shannon diversity explaining 26 to 44% of emissions variation. Community analysis supports a 'pure diversity' effect of fungi on decomposition rates and thus suggests that interference competition is an underlying mechanism. Our findings extend the results of published experiments using low-diversity, laboratory-inoculated wood to a high-diversity, natural system. We hypothesize that high levels of saprotrophic fungal biodiversity could be providing globally important ecosystem services by maintaining dead-wood habitats and by slowing the atmospheric contribution of CO2 from the world's stock of decomposing wood. However, large-scale surveys and controlled experimental tests in natural settings will be needed to test this hypothesis.

High-throughput monitoring of wild bee diversity and abundance via mitogenomics.

Bee populations and other pollinators face multiple, synergistically acting threats, which have led to population declines, loss of local species richness and pollination services, and extinctions. However, our understanding of the degree, distribution and causes of declines is patchy, in part due to inadequate monitoring systems, with the challenge of taxonomic identification posing a major logistical barrier. Pollinator conservation would benefit from a high-throughput identification pipeline.We show that the metagenomic mining and resequencing of mitochondrial genomes (mitogenomics) can be applied successfully to bulk samples of wild bees. We assembled the mitogenomes of 48 UK bee species and then shotgun-sequenced total DNA extracted from 204 whole bees that had been collected in 10 pan-trap samples from farms in England and been identified morphologically to 33 species. Each sample data set was mapped against the 48 reference mitogenomes.The morphological and mitogenomic data sets were highly congruent. Out of 63 total species detections in the morphological data set, the mitogenomic data set made 59 correct detections (93·7% detection rate) and detected six more species (putative false positives). Direct inspection and an analysis with species-specific primers suggested that these putative false positives were most likely due to incorrect morphological IDs. Read frequency significantly predicted species biomass frequency (R2 = 24·9%). Species lists, biomass frequencies, extrapolated species richness and community structure were recovered with less error than in a metabarcoding pipeline.Mitogenomics automates the onerous task of taxonomic identification, even for cryptic species, allowing the tracking of changes in species richness and distributions. A mitogenomic pipeline should thus be able to contain costs, maintain consistently high-quality data over long time series, incorporate retrospective taxonomic revisions and provide an auditable evidence trail. Mitogenomic data sets also provide estimates of species counts within samples and thus have potential for tracking population trajectories.

A mathematical model of cancer treatment by radiotherapy.

A periodic mathematical model of cancer treatment by radiotherapy is presented and studied in this paper. Conditions on the coexistence of the healthy and cancer cells are obtained. Furthermore, sufficient conditions on the existence and globally asymptotic stability of the positive periodic solution, the cancer eradication periodic solution, and the cancer win periodic solution are established. Some numerical examples are shown to verify the validity of the results. A discussion is presented for further study.

Testing three pipelines for 18S rDNA-based metabarcoding of soil faunal diversity.

A number of basic and applied questions in ecology and environmental management require the characterization of soil and leaf litter faunal diversity. Recent advances in high-throughput sequencing of barcode-gene amplicons ('metabarcoding') have made it possible to survey biodiversity in a robust and efficient way. However, one obstacle to the widespread adoption of this technique is the need to choose amongst many candidates for bioinformatic processing of the raw sequencing data. We compare three candidate pipelines for the processing of 18S small subunit rDNA metabarcode data from solid substrates: (i) USEARCH/CROP, (ii) Denoiser/UCLUST, and (iii) OCTUPUS. The three pipelines produced reassuringly similar and highly correlated assessments of community composition that are dominated by taxa known to characterize the sampled environments. However, OCTUPUS appears to inflate phylogenetic diversity, because of higher sequence noise. We therefore recommend either the USEARCH/CROP or Denoiser/UCLUST pipelines, both of which can be run within the QIIME (Quantitative Insights Into Microbial Ecology) environment.