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Six new species of the genus Rhaphidophora Serville, 1838 are described from China: R. hexagoniproctalis Wang, Di & He sp. nov., R. heterodentis Shen, Wang & He sp. nov., R. imbricofurca Shen, Wang & He sp. nov., R. glenoides Qin, Wang & He sp. nov., R. impressa Wang, Qin & He sp. nov., R. stenoterminata Zhang, Wang & He sp. nov. A key with previously described species from China is provided.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
New fossil records of Xyelidae (Hymenoptera) from the Middle Jurassic of Inner Mongolia, China
(2021)
A new genus and two new species of Xyelidae Newman, 1834 (Hymenoptera Linnaeus, 1758), Platyxyela tenuis sp. nov. and Scleroxyela daohugouensis gen. et sp. nov. are described and illustrated from the Middle Jurassic Daohugou Beds of Ningcheng, Inner Mongolia, China. Platyxyela tenuis sp. nov. (Xyelinae Newman, 1834, Liadoxyelini Rasnitsyn, 1966) can be distinguished from its congeners by short forewing length and ovipositor sheath strongly narrowed toward acute apex. Scleroxyela daohugouensis gen. et sp. nov. is placed within Macroxyelinae Ashmead, 1898, Xyeleciini Benson, 1945 based on pterositigma completely sclerotised and costal space sclerotised before ptrostigma, 1-Rs half as long as 1-M, and 1m-cu short. It is the first recorded species of Xyeleciini in Daohugou Beds. Furthermore, an updated key of identification of subfamilies and tribes of Xyelidae is provided. Our findings of new morphological data provide insights into the early evolution of Hymenoptera, as well as effectively enrich our understanding of the diversity of Xyelidae in the Mesozoic.
The present corrigendum corrects errors that occurred in: Zheng Y., Hu H., Chen D., Chen J., Zhang H. & Rasnitsyn A.P. 2021. New fossil records of Xyelidae (Hymenoptera) from the Middle Jurassic of Inner Mongolia, China. European Journal of Taxonomy 733: 146–159. https://doi.org/10.5852/ejt.2021.733.1229
Eight species of the genus Notosemus Förster, 1869 (Hymenoptera: Ichneumonidae) are reported, three of which are new to science: N. albimaculatus Sheng & Sun, sp. nov. and N. planus Sheng & Sun, sp. nov., both collected from Xizang Autonomous Region, SW China, and N. wugongicus Sheng & Sun, sp. nov., collected from Jiangxi Province, S China. One new record for China, N. bohemani (Wesmael, 1855), was reared from Zeiraphera griseana (Hübner, 1789) (Lepidoptera, Tortricidae), a leaf pest of Larix principis-rupprechtii Mayr. (Pinaceae) in Ningxia Hui Autonomous Region, NW China. A key to the species of the genus Notosemus is provided.
A wide variety of enzymatic pathways that produce specialized metabolites in bacteria, fungi and plants are known to be encoded in biosynthetic gene clusters. Information about these clusters, pathways and metabolites is currently dispersed throughout the literature, making it difficult to exploit. To facilitate consistent and systematic deposition and retrieval of data on biosynthetic gene clusters, we propose the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard.
In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with different properties, including areas of production, vintage years, fermentation processes, and varietals. Four popular machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and backpropagation neural network (BPNN)—were used to build identification models for different classification tasks. Experimental results show that BPNN achieved the best performance, with accuracies of 94% and 92.5% in identifying production areas and varietals, respectively; and SVM achieved the best performance in identifying vintages and fermentation processes, with accuracies of 67.3% and 60.5%, respectively. Results demonstrate the effectiveness of the developed E-nose, which could be used to distinguish different wines based on their properties following selection of an optimal algorithm.
Atmospheric aerosols and their effect on clouds are thought to be important for anthropogenic radiative forcing of the climate, yet remain poorly understood1. Globally, around half of cloud condensation nuclei originate from nucleation of atmospheric vapours2. It is thought that sulfuric acid is essential to initiate most particle formation in the atmosphere3,4, and that ions have a relatively minor role5. Some laboratory studies, however, have reported organic particle formation without the intentional addition of sulfuric acid, although contamination could not be excluded6,7. Here we present evidence for the formation of aerosol particles from highly oxidized biogenic vapours in the absence of sulfuric acid in a large chamber under atmospheric conditions. The highly oxygenated molecules (HOMs) are produced by ozonolysis of α-pinene. We find that ions from Galactic cosmic rays increase the nucleation rate by one to two orders of magnitude compared with neutral nucleation. Our experimental findings are supported by quantum chemical calculations of the cluster binding energies of representative HOMs. Ion-induced nucleation of pure organic particles constitutes a potentially widespread source of aerosol particles in terrestrial environments with low sulfuric acid pollution.
This paper reports on Monte Carlo simulation results for future measurements of the moduli of time-like proton electromagnetic form factors, |GE | and |GM|, using the ¯pp → μ+μ− reaction at PANDA (FAIR). The electromagnetic form factors are fundamental quantities parameterizing the electric and magnetic structure of hadrons. This work estimates the statistical and total accuracy with which the form factors can be measured at PANDA, using an analysis of simulated data within the PandaRoot software framework. The most crucial background channel is ¯pp → π+π−,due to the very similar behavior of muons and pions in the detector. The suppression factors are evaluated for this and all other relevant background channels at different values of antiproton beam momentum. The signal/background separation is based on a multivariate analysis, using the Boosted Decision Trees method. An expected background subtraction is included in this study, based on realistic angular distribuations of the background contribution. Systematic uncertainties are considered and the relative total uncertainties of the form factor measurements are presented.