Does congenital deafness affect the structural and functional architecture of primary visual cortex?

Deafness results in greater reliance on the remaining senses. It is unknown whether the cortical architecture of the intact senses is optimized to compensate for lost input. Here we performed widefield population receptive field (pRF) mapping of primary visual cortex (V1) with functional magnetic resonance imaging (fMRI) in hearing and congenitally deaf participants, all of whom had learnt sign language after the age of 10 years. We found larger pRFs encoding the peripheral visual field of deaf compared to hearing participants. This was likely driven by larger facilitatory center zones of the pRF profile concentrated in the near and far periphery in the deaf group. pRF density was comparable between groups, indicating pRFs overlapped more in the deaf group. This could suggest that a coarse coding strategy underlies enhanced peripheral visual skills in deaf people. Cortical thickness was also decreased in V1 in the deaf group. These findings suggest deafness causes structural and functional plasticity at the earliest stages of visual cortex

Fungal Planet description sheets: 1383–1435

Novel species of fungi described in this study include those from various countries as follows: Australia, Agaricus albofoetidus, Agaricus aureoelephanti and Agaricus parviumbrus on soil, Fusarium ramsdenii from stem cankers of Araucaria cunninghamii, Keissleriella sporoboli from stem of Sporobolus natalensis, Leptosphaerulina queenslandica and Pestalotiopsis chiaroscuro from leaves of Sporobolus natalensis, Serendipita petricolae as endophyte from roots of Eriochilus petricola, Stagonospora tauntonensis from stem of Sporobolus natalensis, Teratosphaeria carnegiei from leaves of Eucalyptus grandis × E. camaldulensis and Wongia ficherai from roots of Eragrostis curvula. Canada, Lulworthia fundyensis from intertidal wood and Newbrunswickomyces abietophilus (incl. Newbrunswickomyces gen. nov.)on buds of Abies balsamea. Czech Republic, Geosmithia funiculosa from a bark beetle gallery on Ulmus minor and Neoherpotrichiella juglandicola (incl. Neoherpotrichiella gen. nov.)from wood of Juglans regia. France, Aspergillus rouenensis and Neoacrodontium gallica (incl. Neoacrodontium gen. nov.)from bore dust of Xestobium rufovillosum feeding on Quercus wood, Endoradiciella communis (incl. Endoradiciella gen. nov.)endophyticin roots of Microthlaspi perfoliatum and Entoloma simulans on soil. India, Amanita konajensis on soil and Keithomyces indicus from soil. Israel, Microascus rothbergiorum from Stylophora pistillata. Italy, Calonarius ligusticus on soil. Netherlands , Appendopyricularia juncicola (incl. Appendopyricularia gen. nov.), Eriospora juncicola and Tetraploa juncicola on dead culms of Juncus effusus, Gonatophragmium physciae on Physcia caesia and Paracosmospora physciae (incl. Paracosmospora gen. nov.)on Physcia tenella, Myrmecridium phragmitigenum on dead culm of Phragmites australis, Neochalara lolae on stems of Pteridium aquilinum, Niesslia nieuwwulvenica on dead culm of undetermined Poaceae, Nothodevriesia narthecii (incl. Nothodevriesia gen. nov.) on dead leaves of Narthecium ossifragum and Parastenospora pini (incl. Parastenospora gen. nov.)on dead twigs of Pinus sylvestris. Norway, Verticillium bjoernoeyanum from sand grains attached to a piece of driftwood on a sandy beach. Portugal, Collybiopsis cimrmanii on the base of living Quercus ilex and amongst dead leaves of Laurus and herbs. South Africa , Paraproliferophorum hyphaenes (incl. Paraproliferophorum gen. nov.) on living leaves of Hyphaene sp. and Saccothecium widdringtoniae on twigs of Widdringtonia wallichii. Spain, Cortinarius dryosalor on soil, Cyphellophora endoradicis endophytic in roots of Microthlaspi perfoliatum, Geoglossum laurisilvae on soil, Leptographium gemmatum from fluvial sediments, Physalacria auricularioides from a dead twig of Castanea sativa , Terfezia bertae and Tuber davidlopezii in soil. Sweden, Alpova larskersii, Inocybe alpestris and Inocybe boreogodeyi on soil. Thailand, Russula banwatchanensis, Russula purpureoviridis and Russula lilacina on soil. Ukraine, Nectriella adonidis on over wintered stems of Adonis vernalis. USA, Microcyclus jacquiniae from living leaves of Jacquinia keyensis and Penicillium neoherquei from a minute mushroom sporocarp. Morphological and culture characteristics are supported by DNA barcodes

One sixth of Amazonian tree diversity is dependent on river floodplains

Amazonia's floodplain system is the largest and most biodiverse on Earth. Although forests are crucial to the ecological integrity of floodplains, our understanding of their species composition and how this may differ from surrounding forest types is still far too limited, particularly as changing inundation regimes begin to reshape floodplain tree communities and the critical ecosystem functions they underpin. Here we address this gap by taking a spatially explicit look at Amazonia-wide patterns of tree-species turnover and ecological specialization of the region's floodplain forests. We show that the majority of Amazonian tree species can inhabit floodplains, and about a sixth of Amazonian tree diversity is ecologically specialized on floodplains. The degree of specialization in floodplain communities is driven by regional flood patterns, with the most compositionally differentiated floodplain forests located centrally within the fluvial network and contingent on the most extraordinary flood magnitudes regionally. Our results provide a spatially explicit view of ecological specialization of floodplain forest communities and expose the need for whole-basin hydrological integrity to protect the Amazon's tree diversity and its function.Naturali

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

A Cell Proliferation Signature Is a Marker of Extremely Poor Outcome in a Subpopulation of Breast Cancer Patients

Breast cancer comprises a group of distinct subtypes that despite having similar histologic appearances, have very different metastatic potentials. Being able to identify the biological driving force, even for a subset of patients, is crucially important given the large population of women diagnosed with breast cancer. Here, we show that within a subset of patients characterized by relatively high estrogen receptor expression for their age, the occurrence of metastases is strongly predicted by a homogeneous gene expression pattern almost entirely consisting of cell cycle genes (5-year odds ratio of metastasis, 24.0; 95% confidence interval, 6.0-95.5). Overexpression of this set of genes is clearly associated with an extremely poor outcome, with the 10-year metastasis-free probability being only 24% for the poor group, compared with 85% for the good group. In contrast, this gene expression pattern is much less correlated with the outcome in other patient subpopulations. The methods described here also illustrate the value of combining clinical variables, biological insight, and machine-learning to dissect biological complexity. Our work presented here may contribute a crucial step towards rational design of personalized treatment