Fungal Planet description sheets: 1436–1477

Novel species of fungi described in this study include those from various countries as follows: Argentina, Colletotrichum araujiae on leaves, stems and fruits of Araujia hortorum. Australia, Agaricus pateritonsus on soil, Curvularia fraserae on dying leaf of Bothriochloa insculpta, Curvularia millisiae from yellowing leaf tips of Cyperus aromaticus, Marasmius brunneolorobustus on well-rotted wood, Nigrospora cooperae from necrotic leaf of Heteropogon contortus, Penicillium tealii from the body of a dead spider, Pseudocercospora robertsiorum from leaf spots of Senna tora, Talaromyces atkinsoniae from gills of Marasmius crinis-equi and Zasmidium pearceae from leaf spots of Smilax glyciphylla. Brazil, Preussia bezerrensis from air. Chile, Paraconiothyrium kelleni from the rhizosphere of Fragaria chiloensis subsp. chiloensis f. chiloensis. Finland, Inocybe udicola on soil in mixed forest with Betula pendula, Populus tremula, Picea abies and Alnus incana. France, Myrmecridium normannianum on dead culm of unidentified Poaceae. Germany, Vexillomyces fraxinicola from symptomless stem wood of Fraxinus excelsior. India, Diaporthe limoniae on infected fruit of Limonia acidissima, Didymella naikii on leaves of Cajanus cajan, and Fulvifomes mangroviensis on basal trunk of Aegiceras corniculatum. Indonesia, Penicillium ezekielii from Zea mays kernels. Namibia, Neocamarosporium calicoremae and Neocladosporium calicoremae on stems of Calicorema capitata, and Pleiochaeta adenolobi on symptomatic leaves of Adenolobus pechuelii. Netherlands, Chalara pteridii on stems of Pteridium aquilinum, Neomackenziella juncicola (incl. Neomackenziella gen. nov.) and Sporidesmiella junci from dead culms of Juncus effusus. Pakistan, Inocybe longistipitata on soil in a Quercus forest. Poland, Phytophthora viadrina from rhizosphere soil of Quercus robur, and Septoria krystynae on leaf spots of Viscum album. Portugal (Azores), Acrogenospora stellata on dead wood or bark. South Africa, Phyllactinia greyiae on leaves of Greyia sutherlandii and Punctelia anae on bark of Vachellia karroo. Spain, Anteaglonium lusitanicum on decaying wood of Prunus lusitanica subsp. lusitanica, Hawksworthiomyces riparius from fluvial sediments, Lophiostoma carabassense endophytic in roots of Limbarda crithmoides, and Tuber mohedanoi from calcareus soils. Spain (Canary Islands), Mycena laurisilvae on stumps and woody debris. Sweden, Elaphomyces geminus from soil under Quercus robur. Thailand, Lactifluus chiangraiensis on soil under Pinus merkusii, Lactifluus nakhonphanomensis and Xerocomus sisongkhramensis on soil under Dipterocarpus trees. Ukraine, Valsonectria robiniae on dead twigs of Robinia hispida. USA, Spiralomyces americanus (incl. Spiralomyces gen. nov.) from office air. Morphological and culture characteristics are supported by DNA barcodes

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Search for a standard model Higgs boson produced in association with a top-quark pair and decaying to bottom quarks using a matrix element method

A search for a standard model Higgs boson produced in association with a top-quark pair and decaying to bottom quarks is presented. Events with hadronic jets and one or two oppositely charged leptons are selected from a data sample corresponding to an integrated luminosity of 19.5fb-1 collected by the CMS experiment at the LHC in pp collisions at a centre-of-mass energy of 8TeV. In order to separate the signal from the larger tt¯  + jets background, this analysis uses a matrix element method that assigns a probability density value to each reconstructed event under signal or background hypotheses. The ratio between the two values is used in a maximum likelihood fit to extract the signal yield. The results are presented in terms of the measured signal strength modifier, µ, relative to the standard model prediction for a Higgs boson mass of 125GeV. The observed (expected) exclusion limit at a 95 % confidence level is µ < 4.2 (3.3), corresponding to a best fit value (Formula presented.)