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Linoleic acid metabolic pathway allows for an efficient increase of intramuscular fat content in pigs

Journal of Animal Science and Biotechnology 2019年第3期10卷 759-767页

作者： Sofia Gol Rayner Gonzalez-Prendes Lluis Bosch Marc Tor Josep Reixach Ramona N.Pena Joan Estany department of Animal Science Universitat de Lleida-Agrotecnio Center191 Rovira Roure25198 LleidaCataloniaSpain department of chemical Agricultural and Food Technology EngineeringUniversitat de GironaCampus de Montilivi17071 GironaCataloniaSpain Seleccion Batalle S.A. Av.Segadors s/n17421 RiudarenesCataloniaSpain

Background: Intramuscular fat(IMF) content is a relevant trait for high-quality meat products such as dry-cured ham,but increasing IMF has the undesirable correlated effect of decreasing lean ***,there is a need to find selection criteria for IMF independent from lean *** pigs,the proportion of linoleic(C18:2) and arachidonic(C20:4) acids decline with fat deposition and therefore they can be considered as indicators of *** aim of this research was to estimate the genetic variation for C18:2 and C20:4 in IMF and their genetic correlations with IMF and lean growth traits,with the objective to assess their potential as specific biomarkers of *** analysis was conducted using a full-pedigreed Duroc resource line with 91,448 records of body weight and backfat thickness(BT) at 180 days of age and 1371 records of fatty acid composition in the muscle gluteus ***: The heritability estimates for C18:2 and C20:4 in IMF,whether expressed in absolute(mg/g of muscle) or in relative(mg/g of fatty acid) terms,as well as for their ratio(C20:4/C18:2),were high(> 0.40),revealing that the C18:2 to C20:4 pathway is subjected to substantial genetic *** effects were not negligible,with values ranging from8% to 15% of the phenotypic *** genetic correlations of C18:2 and C20:4 with IMF and BT were negative(-0.75 to-0.66,for IMF,and-0.64 to-0.36,for BT),if expressed in relative values,but almost null(-0.04 to 0.07),if expressed in absolute values,except for C18:2 with IMF,which was highly positive(0.88).The ratio of C20:4 to C18:2 also displayed a stronger genetic correlation with IMF(-0.59) than with BT(-0.10).Conclusions: The amount of C18:2 in muscle can be used as an IMF-specific *** for the absolute amount of C18:2 is expected to deliver a similar response outcome as selection for IMF at restrained *** genetic analysis of the C18:2 metabolic pathway may provide new insights into differential fat deposition

关键词： Arachidonic Genetic parameters Intramuscular fat Linoleic Meat quality Swine

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Isolation of protein from Chlorella sorokiniana CY1 using liquid biphasic flotation assisted with sonication through sugaring-out effect

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Journal of Oceanology and Limnology 2019年第3期37卷 898-908页

作者： CHIA Shir Reen CHEW Kit Wayne SHOW Pau Loke SIVAKUMAR Manickam LING Tau Chuan TAO Yang Bioseparation Research Group Department of Chemical and Environmental EngineeringFaculty of EngineeringUniversity of Nottingham Malaysia CampusSemenyih 43500Malaysia department of chemical and Environmental engineering Faculty of EngineeringUniversity of Nottingham Malaysia CampusSemenyih 43500Malaysia Institute of Biological Sciences Faculty of ScienceUniversity of MalayaKuala Lumpur 50603Malaysia College of food Science and technology Nanjing Agricultural UniversityNanjing 210095China

Microalgae,a sustainable source of multi beneficial components has been discovered and could be utilised in pharmaceutical,bioenergy and food *** study aims to investigate the sugaring-out effect on the recovery of protein from wet green microalga,Chlorella sorokiniana CY1 which was assisted with sonication.A comparison of monosaccharides and disaccharides as one of the phaseforming constituents shows that the monosaccharides,glucose was the most suitable sugar in forming the phases with acetonitrile to enhance the production of protein(52% of protein).The protein productivity of microalgae was found to be significantly influenced by the volume ratio of both phases,as the yield of protein increased to 77%.The interval time between the sonication as well as the sonication modes were influencing the protein productivity as *** optimum protein productivity was obtained with 10s of resting time in between *** mode of sonication was suitable to break down the cell wall of microalgae compared to continuous mode as a lower protein yield was obtained with the application of continuous *** optimum condition for protein extraction were found as followed:200g/L glucose as bottom phase with volume ratio of 1:1.25,10s of resting time for ultrasonication,5s of ultrasonication in pulse mode and 0.25g of biomass *** high yield of protein about 81% could be obtained from microalgae which demonstrates the potential of this source and expected to play an important role in the future.

关键词： sugaring-out Chlorella sorokiniana CY1 liquid biphasic flotation sonication microalgae extraction

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Accuracy of two optical chlorophyll meters in predicting chemical composition and in vitro ruminal organic matter degradability of Brachiaria hybrid,Megathyrsus maximus, and Paspalum atratum

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Animal Nutrition 2017年第1期3卷 67-76页

作者： Martin P.Hughes Victor Mlambo Cicero H.O.Lallo Nasreldin A.D.Basha Ignatius V.Nsahlai Paul G.A.Jennings department of food Production Faculty of Food and AgricultureUniversity of the West Indies department of Animal Science School of Agricultural SciencesFaculty of AgricultureScience and TechnologyNorth-West University Open Tropical Forage-Animal Production Laboratory Department of Food ProductionFaculty of Food and AgricultureUniversity of the West Indies department of Animal & Poultry Science School of AgriculturalEarth and Environmental SciencesCollege of AgricultureEngineering and ScienceUniversity of KwaZulu-Natal department of Animal Nutrition Faculty of Animal Production Shampat CampusUniversity of Khartoum MARJEN Consulting Group

The objective of this study was to determine the accuracy and reliability of 2 optical chlorophyll meters:FieldScout CM 1,000 NDVI and Yara N-Tester, in predicting neutral detergent fibre(NDF), acid detergent fibre(ADF), acid detergent lignin(ADL), acid detergent insoluble nitrogen(ADIN) and in vitro ruminal organic matter degradability(IVOMD) of 3 tropical grasses. Optical chlorophyll measurements were taken at 3 stages(4, 8 and 12 weeks) of regrowth in Brachiaria hybrid, and Megathyrsus maximus and at 6 and 12 weeks of regrowth in Paspalum atratum(cv. Ubon). Optical chlorophyll measurements showed the highest correlation(r = 0.57 to 0.85) with NDF concentration. The FieldScout CM 1,000 NDVI was better than the Yara N-Tester in predicting NDF(R^2 = 0.70) and ADF(R^2 = 0.79) concentrations in Brachiaria hybrid and NDF(R^2 = 0.79) in M. maximus. Similarly, FieldScout CM 1,000 NDVI produced better estimates of 24 h IVOMD(IVOMD_(24 h)) in Brachiaria hybrid(R^2 = 0.81) and IVOMD_(48 h) in Brachiaria hybrid(R^2 = 0.65) and M. maximus(R^2 = 0.75). However, these prediction models had relatively low concordance correlation coefficients, i.e., CCC >0.90, but random errors were the main source of bias. It was,therefore, concluded that both optical chlorophyll meters were poor and unreliable predictors of ADIN and ADL concentrations. Overall, the FieldScout CM 1,000 NDVI shows potential to produce useful estimates of IVOMD_(24h) and ADF in Brachiaria hybrid and IVOMD_(48 h) and NDF concentrations in M. maximus.

关键词： Optical chlorophyll measurements chemical composition Prediction model Tropical grass

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Advances of macroalgae biomass for the third generation of bioethanol production

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Chinese Journal of chemical engineering 2020年第2期28卷 502-517页

作者： Inn Shi Tan Man Kee Lam Henry Chee Yew Foo Steven Lim Keat Teong Lee department of chemical engineering Curtin UniversitySarawak Campus CDT 25098009 MiriSarawakMalaysia chemical engineering department Universiti Teknologi PETRONAS32610 Seri IskandarPerakMalaysia Centre for Biofuel and Biochemical Research Institute of Self-Sustainable BuildingUniversiti Teknologi PETRONAS32610 Seri IskandarPerakMalaysia department of chemical engineering Lee Kong Chian Faculty of Engineering and ScienceUniversiti Tunku Abdul Rahman43000SelangorMalaysia School of chemical engineering Universiti Sains MalaysiaEngineering CampusSeri Ampangan14300 Nibong TebalPulau PinangMalaysia

In recent years, utilization of renewable sources for biofuel production is gaining popularity due to growing greenhouse gas(GHG) emissions which causes global warming. There has been a great effort in exploring alternative feedstock for bioethanol production. In this context, the production of third-generation bioethanol from macroalgae has emerged as an alternative feedstock to food crop-based starch and lignocellulosic *** is mainly due to the fast growth rate of macroalgae, no competition with agricultural land, high carbohydrate content and relatively simple processing steps compared to lignocellulosic biomass. This review paper provides an insight of recent innovative approaches for macroalgae bioethanol production, including conventional and advanced hydrolysis process to produce fermentable sugar, various fermentation technologies, economic analysis and life cycle assessment. With the current technology maturity, efficient utilization of macroalgae as sustainable source for bioethanol and other value-added chemicals production could be achieved in the near future.

关键词： Macroalage Hydrolysis Biomass Fermentation Bioethanol

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Linoleic acid metabolic pathway allows for an efficient increase of intramuscular fat content in pigs.

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畜牧与生物技术杂志(英文版) 2019年第3期

作者： Sofia Gol Rayner González-Prendes Lluís Bosch Marc Tor Josep Reixach Ramona N.Pena Joan Estany department of Animal Science，universitat de Lleida - Agrotecnio Center department of chemical，agricultural and food technology engineering，universitat de girona，campus de montilivi Selección Batallé S.A.，Av.Segadors s/n

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detect and attribute the extreme maize yield losses based on spatio-temporal deep learning

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Fundamental Research 2023年第6期3卷 951-959页

作者： Renhai Zhong Yue Zhu Xuhui Wang Haifeng Li Bin Wang Fengqi You Luis F.Rodríguez Jingfeng Huang K.C.Ting Yibin Ying Tao Lin College of Biosystems engineering and food Science Zhejiang UniversityHangzhouZhejiang 310058China International campus Zhejiang UniversityHainingZhejiang 314400China Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking UniversityBeijing 100871China School of Geosciences and Info-Physics Central South UniversitySouth Lushan RoadChangsha 410000China NSW department of Primary Industries Wagga Wagga Agricultural InstitutePine Gully Road Wagga WaggaNSW 2650Australia Robert Frederick Smith School of chemical and Biomolecular engineering Cornell UniversityIthacaNY 14853USA department of agricultural and Biological engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Institute of Applied Remote Sensing and Information technology Zhejiang UniversityHangzhouZhejiang 310058China Key Laboratory of On Site Processing Equipment for agricultural Products Ministry of Agriculture and Rural AffairsHangzhouZhejiang 310058China Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province HangzhouZhejiang 310058China

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food *** the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains *** this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in *** found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in *** results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.

关键词： Crop yield estimation deep Learning Long short-term memory Multi-task learning Extreme yield loss Attribution analysis

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Feedstock choice,pyrolysis temperature and type influence biochar characteristics:a comprehensive meta-data analysis review

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Biochar 2020年第4期2卷 421-438页

作者： James A.Ippolito Liqiang Cui Claudia Kammann Nicole Wrage‑Mönnig Jose MEstavillo Teresa Fuertes‑Mendizabal Maria Luz Cayuela Gilbert Sigua Jeff Novak Kurt Spokas Nils Borchard department of Soil and Crop Sciences Colorado State UniversityFort Collins 80523USA School of Environmental Science and engineering Yancheng Institute of Technology9 Yingbin AvenueYancheng 224051China department of Applied Ecology Geisenheim UniversityVon‑Lade‑Straße 165366 GeisenheimGermany Faculty of agricultural and Environmental Sciences Grassland and Fodder SciencesUniversity of RostockJustus‑von‑Liebig‑Weg 618059 RostockGermany department of Plant Biology and Ecology University of the Basque Country(UPV/EHU)Apdo.64448080 BilbaoSpain department of Soil and Water Conservation and Waste Management CEBAS-CSICCampus Universitario de Espinardo30100 MurciaSpain United States department of Agriculture Agriculture Research ServiceCoastal Plains Research Center2611 West Lucas StreetFlorenceSC 29501USA United States department of Agriculture Agriculture Research ServiceSoil and Water Management Research UnitUniversity of Minnesota439 Borlaug Hall1991 Buford CircleSt.PaulMN 29501USA Natural Resources Institute Finland(Luke) Latokartanonkaari 900790 HelsinkiFinland

Various studies have established that feedstock choice,pyrolysis temperature,and pyrolysis type influence final biochar physicochemical ***,overarching analyses of pre-biochar creation choices and correlations to biochar characteristics are severely ***,the objective of this work was to help researchers,biochar-stakeholders,and practitioners make more well-informed choices in terms of how these three major parameters influence the final biochar *** approximately 5400 peer-reviewed journal articles and over 50,800 individual data points,herein we elucidate the selections that influence final biochar physical and chemical properties,total nutrient content,and perhaps more importantly tools one can use to predict biochar’s nutrient *** on the large dataset collected,it appears that pyrolysis type(fast or slow)plays a minor role in biochar physico-(inorganic)chemical characteristics;few differences were evident between production *** temperature,however,affects biochar’s longevity,with pyrolysis temperatures>500℃ generally leading to longer-term(i.e.,>1000 years)*** pyrolysis temperatures also led to biochars containing greater overall C and specific surface area(SSA),which could promote soil physico-chemical ***,based on the collected data,it appears that feedstock selection has the largest influence on biochar *** surface area is greatest in wood-based biochars,which in combination with pyrolysis temperature could likely promote greater changes in soil physical characteristics over other feedstock-based ***-and other grass-based biochars appear to have cation exchange capacities greater than other biochars,which in combination with pyrolysis temperature could potentially lead to longer-term changes in soil nutrient *** collected data also suggest that one can reasonably predict the availability of various biochar nutrients(e.g.,N,P

关键词： Biochar Total elemental analysis Plant-available elemental analysis Physico-chemical characteristics·Meta-analysis

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Selecting the Packaging System for Liquid foods Using an ANP Model

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Journal of agricultural Science and technology 2010年第2期4卷 56-67页

作者： E. Bottani R. Rizzo G. Vignali department of lndustrial engineering University of Parma viale G.P. Usberti 181/A-campus universitario Parma 43124 Italy

This paper provides a model to identify the most appropriate packaging system （i.e. the industrial process by which the product is hygienically coupled with the container） for different liquid foods. The model developed is grounded on the Analytic Network Process （ANP） methodology, a powerful and flexible tool used in decision making. To select the appropriate packaging system, the model takes into account several factors, including packaging material, cost, safety, performance （e.g., speed and accuracy）, plant reliability and flexibility, which may directly or indirectly affect the suitability of the process. Moreover, the chemical and physical properties of commercial liquid foods, and the packaging technology are included in the model as further variables to be considered for the final choice. We try to answer the following research questions： is a filling system appropriate for a given liquid food？ Is it compatible with a defined packaging technology？ When several filling systems are available for a given liquid food, how can the most appropriate one be selected？ The ease of application of the model and the robustness of the results provided have been tested on a sample of 59 commercial liquid foods, for which the model allowed to identify the most suitable packaging system, providing useful practical guidelines. The model was developed in close collaboration with a panel of experts, operating in the beverage bottling industry, and validated by the same experts; thus, the resulting tool is suitable for use as a decision support system by food and bottling industries.

关键词： Packaging technology packaging system liquid foods Analytic Network Process decision support system.

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Fungal diversity notes 253-366:taxonomic and phylogenetic contributions to fungal taxa

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Fungal Diversity 2016年第3期78卷 1-237页

作者： Guo Jie Li Kevin D.Hyde Rui Lin Zhao Sinang Hongsanan Faten Awad Abdel-Aziz Mohamed A.Abdel-Wahab Pablo Alvarado Genivaldo Alves-Silva Joseph F.Ammirati Hiran A.Ariyawansa Abhishek Baghela Ali Hassan Bahkali Michael Beug D.Jayarama Bhat Dimitar Bojantchev Thitiya Boonpratuang Timur S.Bulgakov Erio Camporesi Marcela CBoro Oldriska Ceska Dyutiparna Chakraborty Jia Jia Chen K.W.Thilini Chethana Putarak Chomnunti Giovanni Consiglio Bao Kai Cui Dong Qin Dai Yu Cheng Dai Dinushani A.Daranagama Kanad Das Monika C.Dayarathne Eske de Crop Rafael J.V.de Oliveira Carlos Alberto Fragoso de Souza JoséIde Souza Bryn T.M.dentinger Asha J.Dissanayake Mingkwan Doilom E.Ricardo Drechsler-Santos Masoomeh Ghobad-Nejhad Sean P.Gilmore Aristóteles Góes-Neto MichałGorczak Charles H.Haitjema Kalani Kanchana Hapuarachchi Akira Hashimoto Mao Qiang He John K.Henske Kazuyuki Hirayama Maria J.Iribarren Subashini C.Jayasiri Ruvishika S.Jayawardena Sun Jeong Jeon Gustavo H.Jerônimo Ana L.Jesus E.B.Gareth Jones Ji Chuan Kang Samantha C.Karunarathna Paul M.Kirk Sirinapa Konta Eric Kuhnert Ewald Langer Haeng Sub Lee Hyang Burm Lee Wen Jing Li Xing Hong Li Kare Liimatainen Diogo Xavier Lima Chuan Gen Lin Jian Kui Liu Xings Zhong Liu Zuo Yi Liu J.Jennifer Luangsa-ard Robert Lücking H.Thorsten Lumbsch Saisamorn Lumyong Eduardo M.Leaño Agostina V.Marano Misato Matsumura Eric H.C.McKenzie Suchada Mongkolsamrit Peter E.Mortimer Thi Thuong Thuong Nguyen Tuula Niskanen Chada Norphanphoun Michelle A.O’Malley Sittiporn Parnmen Julia Pawłowska Rekhani H.Perera Rungtiwa Phookamsak Chayanard Phukhamsakda Carmen L.A.Pires-Zottarelli Olivier Raspé Mateus A.Reck Sarah C.O.Rocha AndréL.C.M.Ade Santiago Indunil C.Senanayake Ledo Setti Qiu Ju Shang Sanjay K.Singh Esteban B.Sir Kevin V.Solomon Jie Song Prasert Srikitikulchai Marc Stadler Satinee Suetrong Hayato Takahashi Takumasa Takahashi Kazuaki Tanaka Li Ping Tang Kasun M.Thambugala Donnaya Thanakitpipattana Michael K.Theodorou Benjarong Thongbai Tuksaporn Thummarukcharoen Qing Tian Saowaluck Tibpromma Annemieke State Key Laboratory of Mycology Institute of MicrobiologyChinese Academy of SciencesNo 31st West Beichen RoadChaoyang DistrictBeijing 100101People’s Republic of China Centre of Excellence in Fungal Research and School of Science Mae Fah Luang UniversityChiang Rai 57100Thailand Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of ScienceKunming 650201YunnanPeople’s Republic of China department of Botany and Microbiology College of ScienceKing Saud UniversityP.O.Box:2455Riyadh 1145Saudi Arabia department of Botany and Microbiology Faculty of ScienceSohag UniversitySohag 82524Egypt ALVALAB C/La Rochela nº47E-39012 SantanderSpain Micolab Programa de Pós-Graduação em Biologia de FungosAlgas e PlantasDepartamento de BotânicaUniversidade Federal de Santa CatarinaCampus Universitário TrindadeCEP:88040-900 FlorianópolisSanta CatarinaBrazil department of Biology University of WashingtonBox 351800SeattleWA 98195-1800USA Guizhou Key Laboratory of agricultural Biotechnology Guizhou Academy of Agricultural SciencesGuiyang 550006GuizhouPeople’s Republic of China National Fungal Culture Collection of India(NFCCI) Biodiversity and Palaeobiology GroupMACS’Agharkar Research InstituteG.G.Agarkar RoadPune 411004India PO Box 116 HusumWA 98623-0116USA Formerly Department of BotanyGoa UniversityGoaIndia No.128/1-J Azad Housing SocietyCurcaP.O.Goa Velha 403108India *** 345 Shipwatch LaneHerculesCA 94547USA Microbe Interaction Laboratory(BMIT) BIOTECNational Science and Technology Development Agency(NSTDA)113 Thailand Science ParkThanon PhahonyothinTombon Khlong NuengAmphoeKhlong LuangPathum Thani 12120Thailand Academy of Biology and Biotechnology Southern Federal UniversityRostov-on-Don 344090Rostov regionRussia A.M.B.Gruppo Micologico Forlivese BAntonio“Cicognani” Via Roma Forlì Italy 18 A.M.B.Circolo Micologico“Giovanni Carini”C.P.314BresciaItaly Societàper gli Studi Naturalistici della Romagna C.P.144Bagnacavallo

Notes on 113 fungal taxa are compiled in this paper,including 11 new genera,89 new species,one new subspecies,three new combinations and seven reference *** geographic and taxonomic range of fungal taxa are *** the Ascomycota the new genera Angustospora(Testudinaceae),Camporesia(Xylariaceae),Clematidis,Crassiparies(Pleosporales genera incertae sedis),Farasanispora,Longiostiolum(Pleosporales genera incertae sedis),Multilocularia(Parabambusicolaceae),Neophaeocryptopus(Dothideaceae),Parameliola(Pleosporales genera incertae sedis),and Towyspora(Lentitheciaceae)are *** introduced species are Angustospora nilensis,Aniptodera aquibella,Annulohypoxylon albidiscum,Astrocystis thailandica,Camporesia sambuci,Clematidis italica,Colletotrichum menispermi,***,Comoclathris pimpinellae,Crassiparies quadrisporus,Cytospora salicicola,Diatrype thailandica,Dothiorella rhamni,Durotheca macrostroma,Farasanispora avicenniae,Halorosellinia rhizophorae,Humicola koreana,Hypoxylon lilloi,Kirschsteiniothelia tectonae,Lindgomyces okinawaensis,Longiostiolum tectonae,Lophiostoma pseudoarmatisporum,Moelleriella phukhiaoensis,***,Mucoharknessia anthoxanthi,Multilocularia bambusae,Multiseptospora thysanolaenae,Neophaeocryptopus cytisi,Ocellularia arachchigei,***,Ochronectria thailandica,Ophiocordyceps karstii,Parameliola acaciae,***,Parastagonospora cumpignensis,Pseudodidymosphaeria phlei,Polyplosphaeria thailandica,Pseudolachnella brevifusiformis,Psiloglonium macrosporum,Rhabdodiscus albodenticulatus,Rosellinia chiangmaiensis,Saccothecium rubi,Seimatosporium pseudocornii,***,Sigarispora ononidis and Towyspora *** combinations are provided for Eutiarosporella dactylidis(sexual morph described and illus trated)and Pseudocamarosporium ***,illustrations and/or reference specimens are designated for Aposphaeria corallinolutea,Cryptovalsa ampelina,Dothiorella vidmadera,Ophiocordyceps formosana,P

关键词： Ascomycota Basidiomycota Neocallimastigomycota Oomycota.Zygomycota Phylogeny Taxonomy New genus New species

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