Detección automática de la apnea del sueño infantil utilizando técnicas de deep learning y explainable artificial intelligence en señales de flujo aéreo

La alta prevalencia de la apnea obstructiva del sueño (AOS) pediátrica y las limitaciones de la prueba diagnóstica estándar han fomentado el estudio de estrategias alternativas que ayuden a su diagnóstico automático. Los métodos propuestos suelen basarse en técnicas de feature engineering, lo que implica una complejidad y subjetividad inherente. Otros utilizan técnicas de deep learning, que mejoran el rendimiento diagnóstico pero carecen de transparencia e interpretabilidad. En este trabajo proponemos evaluar un modelo explicable basado en redes neuronales convolucionales (CNN) para estimar la severidad de la AOS infantil utilizando la señal de flujo (FA). Para ello, se analizaron 1638 registros de FA, que fueron divididos en segmentos de 10 minutos. El modelo CNN estimó el número de eventos apneicos por segmento. Después, se aplicó el algoritmo Grad-CAM para identificar las regiones de FA en las que se fija la CNN al hacer su predicción. El modelo propuesto mostró una alta concordancia entre el índice de apnea-hipopnea estimado y el real (coeficiente de correlación intraclase = 0.87 en el grupo de test), así como un alto rendimiento diagnóstico (kappa de 4 clases = 0.38 y precisiones del 81.05%, 85.62% y 92.81% para 1, 5 y 10 eventos/h en el grupo de test). Grad-CAM reveló que la CNN se centra en el comienzo y el final del evento apneico, es decir, donde la señal FA cambia bruscamente de amplitud. Así, nuestra propuesta sería muy útil para identificar automáticamente los patrones respiratorios asociados con la AOS infantil y ayudar en su diagnóstico.Este estudio ha sido financiado por el Ministerio de Ciencia e Innovación - AEI y ERDF (PID2020-115468RB-I00 y PDC2021-120775-I00) y por el CIBER-BBN (CB19/01/00012). G.C. Gutiérrez Tobal ha recibido una ayuda postdoctoral de la Universidad de Valladolid. D. Álvarez es beneficiario de una ayuda Ramón y Cajal (RYC2019-028566-I) del Ministerio de Ciencia e Innovación - AEI, cofinanciada por el FSE. D. Gozal ha recibido financiación del NIH (AG061824 y HL166617)

Detección automática de la apnea del sueño infantil utilizando técnicas de deep learning y explainable artificial intelligence en señales de flujo aéreo

La alta prevalencia de la apnea obstructiva del sueño (AOS) pediátrica y las limitaciones de la prueba diagnóstica estándar han fomentado el estudio de estrategias alternativas que ayuden a su diagnóstico automático. Los métodos propuestos suelen basarse en técnicas de feature engineering, lo que implica una complejidad y subjetividad inherente. Otros utilizan técnicas de deep learning, que mejoran el rendimiento diagnóstico pero carecen de transparencia e interpretabilidad. En este trabajo proponemos evaluar un modelo explicable basado en redes neuronales convolucionales (CNN) para estimar la severidad de la AOS infantil utilizando la señal de flujo (FA). Para ello, se analizaron 1638 registros de FA, que fueron divididos en segmentos de 10 minutos. El modelo CNN estimó el número de eventos apneicos por segmento. Después, se aplicó el algoritmo Grad-CAM para identificar las regiones de FA en las que se fija la CNN al hacer su predicción. El modelo propuesto mostró una alta concordancia entre el índice de apnea-hipopnea estimado y el real (coeficiente de correlación intraclase = 0.87 en el grupo de test), así como un alto rendimiento diagnóstico (kappa de 4 clases = 0.38 y precisiones del 81.05%, 85.62% y 92.81% para 1, 5 y 10 eventos/h en el grupo de test). Grad-CAM reveló que la CNN se centra en el comienzo y el final del evento apneico, es decir, donde la señal FA cambia bruscamente de amplitud. Así, nuestra propuesta sería muy útil para identificar automáticamente los patrones respiratorios asociados con la AOS infantil y ayudar en su diagnóstico.Este estudio ha sido financiado por el Ministerio de Ciencia e Innovación - AEI y ERDF (PID2020-115468RB-I00 y PDC2021-120775-I00) y por el CIBER-BBN (CB19/01/00012). G.C. Gutiérrez Tobal ha recibido una ayuda postdoctoral de la Universidad de Valladolid. D. Álvarez es beneficiario de una ayuda Ramón y Cajal (RYC2019-028566-I) del Ministerio de Ciencia e Innovación - AEI, cofinanciada por el FSE. D. Gozal ha recibido financiación del NIH (AG061824 y HL166617)

Consenso colombiano de atención, diagnóstico y manejo de la infección por SARS-COV-2/COVID-19 en establecimientos de atención de la salud Recomendaciones basadas en consenso de expertos e informadas en la evidencia

The “Asociación Colombiana de Infectología” (ACIN) and the “Instituto de Evaluación de Nuevas Tecnologías de la Salud” (IETS) created a task force to develop recommendations for Covid 19 health care diagnosis, management and treatment informed, and based, on evidence. Theses reccomendations are addressed to the health personnel on the Colombian context of health services. © 2020 Asociacion Colombiana de Infectologia. All rights reserved

Fungal Planet description sheets: 1042–1111

Novel species of fungi described in this study include those from various countries as follows: Antarctica, Cladosporium arenosum from marine sediment sand. Argentina, Kosmimatamyces alatophylus (incl. Kosmimatamyces gen. nov.) from soil. Australia, Aspergillus banksianus, Aspergillus kumbius, Aspergillus luteorubrus, Aspergillus malvicolor and Aspergillus nanangensis from soil, Erysiphe medicaginis from leaves of Medicago polymorpha, Hymenotorrendiella communis on leaf litter of Eucalyptus bicostata, Lactifluus albopicri and Lactifluus austropiperatus on soil, Macalpinomyces collinsiae on Eriachne benthamii, Marasmius vagus on soil, Microdochium dawsoniorum from leaves of Sporobolus natalensis, Neopestalotiopsis nebuloides from leaves of Sporobolus elongatus, Pestalotiopsis etonensis from leaves of Sporobolus jacquemontii, Phytophthora personensis from soil associated with dying Grevillea mccutcheonii. Brazil, Aspergillus oxumiae from soil, Calvatia baixaverdensis on soil, Geastrum calycicoriaceum on leaf litter, Greeneria kielmeyerae on leaf spots of Kielmeyera coriacea. Chile, Phytophthora aysenensis on collar rot and stem of Aristotelia chilensis. Croatia, Mollisia gibbospora on fallen branch of Fagus sylvatica. Czech Republic, Neosetophoma hnaniceana from Buxus sempervirens. Ecuador, Exophiala frigidotolerans from soil. Estonia, Elaphomyces bucholtzii in soil. France, Venturia paralias from leaves of Euphorbia paralias. India, Cortinarius balteatoindicus and Cortinarius ulkhagarhiensis on leaf litter. Indonesia, Hymenotorrendiella indonesiana on Eucalyptus urophylla leaf litter. Italy, Penicillium taurinense from indoor chestnut mill. Malaysia, Hemileucoglossum kelabitense on soil, Satchmopsis pini on dead needles of Pinus tecunumanii. Poland, Lecanicillium praecognitum on insects' frass. Portugal, Neodevriesia aestuarina from saline water. Republic of Korea, Gongronella namwonensis from freshwater. Russia, Candida pellucida from Exomias pellucidus, Heterocephalacria septentrionalis as endophyte from Cladonia rangiferina, Vishniacozyma phoenicis from dates fruit, Volvariella paludosa from swamp. Slovenia, Mallocybe crassivelata on soil. South Africa, Beltraniella podocarpi, Hamatocanthoscypha podocarpi, Coleophoma podocarpi and Nothoseiridium podocarpi (incl. Nothoseiridium gen. nov.)from leaves of Podocarpus latifolius, Gyrothrix encephalarti from leaves of Encephalartos sp., Paraphyton cutaneum from skin of human patient, Phacidiella alsophilae from leaves of Alsophila capensis, and Satchmopsis metrosideri on leaf litter of Metrosideros excelsa. Spain, Cladophialophora cabanerensis from soil, Cortinarius paezii on soil, Cylindrium magnoliae from leaves of Magnolia grandiflora, Trichophoma cylindrospora (incl. Trichophoma gen. nov.) from plant debris, Tuber alcaracense in calcareus soil, Tuber buendiae in calcareus soil. Thailand, Annulohypoxylon spougei on corticated wood, Poaceascoma filiforme from leaves of unknown Poaceae. UK, Dendrostoma luteum on branch lesions of Castanea sativa, Ypsilina buttingtonensis from heartwood of Quercus sp. Ukraine, Myrmecridium phragmiticola from leaves of Phragmites australis. USA, Absidia pararepens from air, Juncomyces californiensis (incl. Juncomyces gen. nov.) from leaves of Juncus effusus, Montagnula cylindrospora from a human skin sample, Muriphila oklahomaensis (incl. Muriphila gen. nov.)on outside wall of alcohol distillery, Neofabraea eucalyptorum from leaves of Eucalyptus macrandra, Diabolocovidia claustri (incl. Diabolocovidia gen. nov.)from leaves of Serenoa repens, Paecilomyces penicilliformis from air, Pseudopezicula betulae from leaves of leaf spots of Populus tremuloides. Vietnam, Diaporthe durionigena on branches of Durio zibethinus and Roridomyces pseudoirritans on rotten wood. Morphological and culture characteristics are supported by DNA barcodes

Fungal Planet description sheets: 1042–1111

Novel species of fungi described in this study include those from various countries as follows: Antarctica, Cladosporium arenosum from marine sediment sand. Argentina, Kosmimatamyces alatophylus (incl. Kosmimatamyces gen. nov.) from soil. Australia, Aspergillus banksianus, Aspergillus kumbius, Aspergillus luteorubrus, Aspergillus malvicolor and Aspergillus nanangensis from soil, Erysiphe medicaginis from leaves of Medicago polymorpha, Hymenotorrendiella communis on leaf litter of Eucalyptus bicostata, Lactifluus albopicri and Lactifluus austropiperatus on soil, Macalpinomyces collinsiae on Eriachne benthamii, Marasmius vagus on soil, Microdochium dawsoniorum from leaves of Sporobolus natalensis, Neopestalotiopsis nebuloides from leaves of Sporobolus elongatus, Pestalotiopsis etonensis from leaves of Sporobolus jacquemontii, Phytophthora personensis from soil associated with dying Grevillea mccutcheonii. Brazil, Aspergillus oxumiae from soil, Calvatia baixaverdensis on soil, Geastrum calycicoriaceum on leaf litter, Greeneria kielmeyerae on leaf spots of Kielmeyera coriacea. Chile, Phytophthora aysenensis on collar rot and stem of Aristotelia chilensis. Croatia, Mollisia gibbospora on fallen branch of Fagus sylvatica. Czech Republic, Neosetophoma hnaniceana from Buxus sempervirens. Ecuador, Exophiala frigidotolerans from soil. Estonia, Elaphomyces bucholtzii in soil. France, Venturia paralias from leaves of Euphorbia paralias. India, Cortinarius balteatoindicus and Cortinarius ulkhagarhiensis on leaf litter. Indonesia, Hymenotorrendiella indonesiana on Eucalyptus urophylla leaf litter. Italy, Penicillium taurinense from indoor chestnut mill. Malaysia, Hemileucoglossum kelabitense on soil, Satchmopsis pini on dead needles of Pinus tecunumanii. Poland, Lecanicillium praecognitum on insects' frass. Portugal, Neodevriesia aestuarina from saline water. Republic of Korea, Gongronella namwonensis from freshwater. Russia, Candida pellucida from Exomias pellucidus, Heterocephalacria septentrionalis as endophyte from Cladonia rangiferina, Vishniacozyma phoenicis from dates fruit, Volvariella paludosa from swamp. Slovenia, Mallocybe crassivelata on soil. South Africa, Beltraniella podocarpi, Hamatocanthoscypha podocarpi, Coleophoma podocarpi and Nothoseiridium podocarpi (incl. Nothoseiridium gen. nov.)from leaves of Podocarpus latifolius, Gyrothrix encephalarti from leaves of Encephalartos sp., Paraphyton cutaneum from skin of human patient, Phacidiella alsophilae from leaves of Alsophila capensis, and Satchmopsis metrosideri on leaf litter of Metrosideros excelsa. Spain, Cladophialophora cabanerensis from soil, Cortinarius paezii on soil, Cylindrium magnoliae from leaves of Magnolia grandiflora, Trichophoma cylindrospora (incl. Trichophoma gen. nov.) from plant debris, Tuber alcaracense in calcareus soil, Tuber buendiae in calcareus soil. Thailand, Annulohypoxylon spougei on corticated wood, Poaceascoma filiforme from leaves of unknown Poaceae. UK, Dendrostoma luteum on branch lesions of Castanea sativa, Ypsilina buttingtonensis from heartwood of Quercus sp. Ukraine, Myrmecridium phragmiticola from leaves of Phragmites australis. USA, Absidia pararepens from air, Juncomyces californiensis (incl. Juncomyces gen. nov.) from leaves of Juncus effusus, Montagnula cylindrospora from a human skin sample, Muriphila oklahomaensis (incl. Muriphila gen. nov.)on outside wall of alcohol distillery, Neofabraea eucalyptorum from leaves of Eucalyptus macrandra, Diabolocovidia claustri (incl. Diabolocovidia gen. nov.)from leaves of Serenoa repens, Paecilomyces penicilliformis from air, Pseudopezicula betulae from leaves of leaf spots of Populus tremuloides. Vietnam, Diaporthe durionigena on branches of Durio zibethinus and Roridomyces pseudoirritans on rotten wood. Morphological and culture characteristics are supported by DNA barcodes

Phylogenomic analysis of a 55.1 kb 19-gene dataset resolves a monophyletic Fusarium that includes the Fusarium solani Species Complex

Scientific communication is facilitated by a data-driven, scientifically sound taxonomy that considers the end-user¿s needs and established successful practice. In 2013, the Fusarium community voiced near unanimous support for a concept of Fusarium that represented a clade comprising all agriculturally and clinically important Fusarium species, including the F. solani species complex (FSSC). Subsequently, this concept was challenged in 2015 by one research group who proposed dividing the genus Fusarium into seven genera, including the FSSC described as members of the genus Neocosmospora, with subsequent justification in 2018 based on claims that the 2013 concept of Fusarium is polyphyletic. Here, we test this claim and provide a phylogeny based on exonic nucleotide sequences of 19 orthologous protein-coding genes that strongly support the monophyly of Fusarium including the FSSC. We reassert the practical and scientific argument in support of a genus Fusarium that includes the FSSC and several other basal lineages, consistent with the longstanding use of this name among plant pathologists, medical mycologists, quarantine officials, regulatory agencies, students, and researchers with a stake in its taxonomy. In recognition of this monophyly, 40 species described as genus Neocosmospora were recombined in genus Fusarium, and nine others were renamed Fusarium. Here the global Fusarium community voices strong support for the inclusion of the FSSC in Fusarium, as it remains the best scientific, nomenclatural, and practical taxonomic option availabl

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives