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Naming and outline of Dothideomycetes-2014 including proposals for the protection or suppression of generic names

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Fungal Diversity 2014年第6期69卷 1-55页

作者： Nalin N.Wijayawardene Pedro W.Crous Paul M.Kirk David L.Hawksworth Saranyaphat Boonmee Uwe Braun Dong-Qin Dai Melvina J.D’souza Paul Diederich Asha Dissanayake Mingkhuan Doilom Singang Hongsanan E.B.Gareth Jones Johannes Z.Groenewald Ruvishika Jayawardena James D.Lawrey Jian-Kui Liu Robert Lücking Hugo Madrid Dimuthu S.Manamgoda Lucia Muggia Matthew P.Nelsen Rungtiwa Phookamsak Satinee Suetrong Kazuaki Tanaka Kasun M.Thambugala Dhanushka N.Wanasinghe Saowanee Wikee Ying Zhang andre aptroot H.A.Ariyawansa Ali H.Bahkali D.Jayarama Bhat Cécile Gueidan Putarak Chomnunti G.Sybren De Hoog Kerry Knudsen Wen-Jing Li Eric H.C.McKenzie andrew N.Miller Alan J.L.Phillips Marcin Piatek Huzefa A.Raja Roger S.Shivas Bernad Slippers Joanne E.Taylor Qing Tian Yong Wang Joyce H.C.Woudenberg Lei Cai Walter M.Jaklitsch Kevin D.Hyde Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of ScienceKunming 650201YunnanPeople’s Republic of China World Agroforestry Centre East and Central AsiaKunming 650201YunnanPeople’s Republic of China Institute of Excellence in Fungal Research Chiang Rai 57100Thailand Forestry and Agricultural Biotechnology Institute(FABI) University of PretoriaPretoria 0002South Africa CBS-KNAW Fungal Biodiversity Centre Uppsalalaan 83584 CT UtrechtThe Netherlands State Key Laboratory of Mycology Institute of MicrobiologyChinese Academy of SciencesBeijing100101People_s Republicof China Mycology Section Royal Botanic GardensKewRichmondSurrey TW93DSUK Department of Life Sciences The Natural History MuseumCromwell RoadLondon SW75BDUK Departamento de Biología VegetalⅡ Facultad de FarmaciaUniversidad Complutense de MadridPlaza Ramón y CajalMadrid 28040Spain School of Science Mae Fah Luang UniversityChiang Rai 57100Thailand Institut Für Biologie Martin-Luther-UniversitatBereich Geobotanik Und Botanischer GartenHerbariumNeuwerk 2106099 Halle(Saale)Germany Musée National d'histoire Naturelle 25 Rue MunsterL-2160 LuxembourgLuxembourg Institute of Plant and Environment Protection Beijing Academy of Agriculture and Forestry SciencesBeijing 100097People’s Republic of China Department of Botany and Microbiology College of ScienceKing Saud UniversityP.O.Box 2455Riyadh 11451Kingdom of Saudi Arabia Department of Environmental Science and Policy George Mason University4400 University DriveFairfaxVA 22030-4444USA Science and Education Integrative Research(Botany)The Field Museum1400 South Lake Shore DriveChicagoIL 60605-2496USA Institute of Plant Sciences University of GrazHolteigasse 68010GrazAustria Department of Life Science University of TriesteVia L.Giorgieri 10TriesteItaly Committee on Evolutionary Biology University of Chicago1025 E.57th StreetChicagoIL 60637USA Fungal Biodiversity Laboratory(BFBD) National Center for Gen

Article 59.1,of the International Code of Nomenclature for Algae,Fungi,and Plants(ICN;Melbourne Code),which addresses the nomenclature of pleomorphic fungi,became effective from 30 July 2011.Since that date,each fungal species can have one nomenclaturally correct name in a particular classification.All other previously used names for this species will be considered as synonyms.The older generic epithet takes priority over the younger name.Any widely used younger names proposed for use,must comply with Art.57.2 and their usage should be approved by the Nomenclature Committee for Fungi(NCF).In this paper,we list all genera currently accepted by us in Dothideomycetes(belonging to 23 orders and 110 families),including pleomorphic and nonpleomorphic genera.In the case of pleomorphic genera,we follow the rulings of the current ICN and propose single generic names for future usage.The taxonomic placements of 1261 genera are listed as an outline.Protected names and suppressed names for 34 pleomorphic genera are listed separately.Notes and justifications are provided for possible proposed names after the list of genera.Notes are also provided on recent advances in our understanding of asexual and sexual morph linkages in Dothideomycetes.A phylogenetic tree based on four gene analyses supported 23 orders and 75 families,while 35 families still lack molecular data.

关键词： Article 59.1 Ascomycota One name Phylogeny Pleomorphism

Re-evaluation of recent research on metabolic utilization of energy in poultry: Recommendations for a net energy system for broilers

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Animal Nutrition 2024年第1期16卷 62-72页

作者： Jean Noblet Aye-Cho Tay-Zar Shu-Biao Wu Pairat Srichana Pierre Cozannet Pierre-andre Geraert Mingan Choct INRAE Charoen Pokphand Foods Public Company Limited (CPF) School of Environmental and Rural Science University of New England Adisseo France S.A.S.

Different energy systems have been proposed for energy evaluation of feeds for domestic animals. The oldest and most commonly used systems take into account the fecal energy loss to obtain digestible energy(DE), and fecal, urinary and fermentation gases energy losses to calculate metabolizable energy(ME). In the case of ruminants and pigs, the net energy(NE) system, which takes into account the heat increment associated with the metabolic utilization of ME, has progressively replaced the DE and ME systems over the last 50 years. For poultry, apparent ME(AME) is used exclusively and NE is not yet used widely. The present paper considers some important methodological points for measuring NE in poultry feeds and summarizes the available knowledge on NE systems for poultry. NE prediction equations based on a common analysis of three recent studies representing a total of 50 complete and balanced diets fed to broilers are proposed; these equations including the AME content and easily available chemical indicators have been validated on another set of 30 diets. The equations are applicable to both ingredients and complete diets. They rely primarily on an accurate and reliable AME value which then represents the first limiting predictor of NE value. Our analysis indicates that NE would be a better predictor of broiler performance than AME and that the hierarchy between feeds is dependent on the energy system with a higher energy value for fat and a lower energy value for protein in an NE system. Practical considerations for implementing such an NE system from the commonly used AME or AMEn(AME adjusted for zero nitrogen balance) systems are presented. In conclusion, there is sufficient information to allow the implementation of the NE concept in order to improve the accuracy of feed formulation in poultry.

关键词： Poultry Feed evaluation Net energy Energy system

Uncovering the dominant contribution of intermediate volatility compounds in secondary organic aerosol formation from biomass-burning emissions

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National Science Review 2024年第3期11卷 150-158页

作者： Kun Li Jun Zhang David M.Bell Tiantian Wang Houssni Lamkaddam Tianqu Cui Lu Qi Mihnea Surdu Dongyu Wang Lin Du Imad El Haddad Jay G.Slowik andre S.H.Prevot Environment Research Institute Shandong University Laboratory of Atmospheric Chemistry Paul Scherrer Institute

Organic vapors from biomass burning are a major source of secondary organic aerosols(SOAs). Previous smog chamber studies found that the SOA contributors in biomass-burning emissions are mainly volatile organic compounds(VOCs). While intermediate volatility organic compounds(IVOCs) are efficient SOA precursors and contribute a considerable fraction of biomass-burning emissions, their contribution to SOA formation has not been directly observed. Here, by deploying a newly-developed oxidation flow reactor to study SOA formation from wood burning, we find that IVOCs can contribute ～70% of the formed SOA, i.e.>2 times more than VOCs. This previously missing SOA fraction is interpreted to be due to the high wall losses of semi-volatile oxidation products of IVOCs in smog chambers. The finding in this study reveals that SOA production from biomass burning is much higher than previously thought, and highlights the urgent need for more research on the IVOCs from biomass burning and potentially other emission sources.

关键词： atmospheric chemistry biomass burning secondary organic aerosol intermediate volatility organic compounds

Remote estimates of suspended particulate matter in global lakes using machine learning models

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International Soil and Water Conservation Research 2024年第1期12卷 200-216页

作者： Zhidan Wen Qiang Wang Yue Ma Pierre andre Jacinthe Ge Liu Sijia Li Yingxin Shang Hui Tao Chong Fang Lili Lyu Baohua Zhang Kaishan Song Northeast Institute of Geography and Agroecology CASChangchunJilin130102China Department of Geology Indiana University∼Purdue UniversityIndianapolisIN44602USA University of Chinese Academy of Sciences Beijing100100China College of Geography and Urban Planning Liaocheng UniversityLiaochengChina

Suspended particulate matter(SPM)in lakes exerts strong impact on light propagation,aquatic ecosystem productivity,which co-varies with nutrients,heavy metal and micro-pollutant in waters.In lakes,SPM exerts strong absorption and backscattering,ultimately affects water leaving signals that can be detected by satellite sensors.Simple regression models based on specific band or hand ratios have been widely used for SPM estimate in the past with moderate accuracy.There are still rooms for model accuracy improvements,and machine learning models may solve the non-linear relationships between spectral variable and SPM in waters.We assembled more than 16,400 in situ measured SPM in lakes from six continents(excluding the Antarctica continent),of which 9640 samples were matched with Landsat overpasses within±7 days.Seven machine learning algorithms and two simple regression methods(linear and partial least squares models)were used to estimate SPM in lakes and the performance were compared.To overcome the problem of imbalance datasets in regression,a Synthetic Minority Over-Sampling technique for regression with Gaussian Noise(SMOGN)was adopted in this study.Through comparison,we found that gradient boosting decision tree(GBDT),random forest(RF),and extreme gradient boosting(XGBoost)models demonstrated good spatiotemporal transferability with SMOGN processed dataset,and has potential to map SPM at different year with good quality of Landsat land surface reflectance images.In all the tested modeling approaches,the GBDT model has accurate calibration(n=6428,R^(2)=0.95,MAPE=29.8%)from SPM collected in 2235 lakes across the world,and the validation(n=3214,R^(2)=0.84,MAPE=38.8%)also exhibited stable performance.Further,the good performances were also exhibited by RF model with calibration(R^(2)=0.93)and validation(R^(2)=0.86,MAPE=24.2%)datasets.We applied GBDT and RF models to map SPM of typical lakes,and satisfactory result was obtained.In addition,the GBDT model was evaluated by historical SPM measurements coincident with di

关键词： Landsat imagery Remote estimation SPM Transferability

Possible Uses for Silymarin in Human Health: Systematic Review

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American Journal of Plant Sciences 2024年第2期15卷 95-109页

作者： Gisele Mara Silva Gonçalves andre Bernardo de Camargo Eisingher Postgraduate Program in Health Sciences Pontifical Catholic University of Campinas (PUC-Campinas) Campinas Brazil

Silymarin, from the fruit of Silybum marianum, is known for its hepatoprotective action. The aim of this study was to review the mechanisms of action of the silymarin phytocomplex to expand the possibilities for its application in human health. The search for published articles was carried out on the CAPES Journals Portal platform, which covers worldwide scientific databases. Publications from 2010 to 2022 were included. Of the 311 articles retrieved, 21 were included. The articles discuss the diversity of silymarin’s applications and the possibility of optimizing its bioavailability using drug delivery systems. Silymarin shows promise in numerous diseases, such as liver, kidney, cardiovascular, respiratory and others. Its antiviral action has been demonstrated in studies and silymarin has the potential to be used as a complementary therapy in the treatment of many diseases, with the expectation that, in the future, it will be used in therapeutic protocols for exclusive use.

关键词： Silymarin Flavonoids Phytotherapeutic Drugs

Fungal diversity notes 1036-1150:taxonomic and phylogenetic contributions on genera and species of fungal taxa

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Fungal Diversity 2019年第3期96卷 1-242页

作者： Kevin D.Hyde Danushka S.Tennakoon Rajesh Jeewon DJayarama Bhat Sajeewa S.N.Maharachchikumbura Walter Rossi Marco Leonardi Hyang Burm Lee Hye Yeon Mun Jos Houbraken Thuong T.T.Nguyen Sun Jeong Jeon Jens Christian Frisvad Dhanushka N.Wanasinghe Robert Lucking andre aptroot Marcela E.S.Caceres Samantha C.Karunarathna Sinang Hongsanan Rungtiwa Phookamsak Nimali Ide Silva Kasun M.Thambugala Ruvishika S.Jayawardena Indunil C.Senanayake Saranyaphat Boonmee Jie Chen Zong-Long Luo Chayanard Phukhamsakda Olinto L.Pereira Vanessa P.Abreu andre Wilson Campos Rosado Buyck Bart Emile Randrianjohany Vale rie Hofstetter Tatiana B.Gibertoni Adriene Mayrada Silva Soares Helio Longoni Plautz Jr Helen Maria Pontes Sotao William Kalhy Silva Xavier Jadson Diogo Pereira Bezerra Thays Gabrielle Linsde Oliveira Cristina Mariade Souza-Motta Oliane Maria Correia Magalhaes Digvijayini Bundhun Dulanjalee Harishchandra Ishara S.Manawasinghe Wei Dong Sheng-Nan Zhang Dan-Feng Bao Milan C.Samarakoon Dhandevi Pem Anuruddha Karunarathna Chuan-Gen Lin Jing Yang Rekhani H.Perera Vinit Kumar Shi-Ke Huang Monika C.Dayarathne Anusha H.Ekanayaka Subashini C.Jayasiri Yuanpin Xiao Sirinapa Konta Tuula Niskanen Kare Liimatainen Yu-Cheng Dai Xiao-Hong Ji Xue-Mei Tian Armin Mesic Sanjay K.Singh Kunthida Phutthacharoen Lei Cai Touny Sorvongxay Vinodhini Thiyagaraja Chada Norphanphoun Napalai Chaiwan Yong-Zhong Lu Hong-Bo Jiang Jin-Feng Zhang Pranami D.Abeywickrama Janith V.S.Aluthmuhandiram Rashika S.Brahmanage Ming Zeng Thilini Chethana Deping Wei Martina Reblova Jacques Fournier Jana Nekvindova Renan do Nascimento Barbosa Jose Ewerton Felintodos Santos Neiva Tintide Oliveira Guo-Jie Li Damien Ertz Qiu-Ju Shang Alan J.L.Phillips Chang-Hsin Kuo Erio Camporesi Timur S.Bulgakov Saisamorn Lumyong E.B.Gareth Jones Putarak Chomnunti Eleni Gentekaki Frank Bungartz Xiang-Yu Zeng Sally Fryar Zdenko Tkalcec Junmin Liang Guangshuo Li Ting-Chi Wen Paras Nath Singh Yusufjon Gafforov Itthayakorn Promputtha Erandi Yasanthika Ishani D.Goonasekara Rui-Lin Zhao Qi Zhao Paul M Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of ScienceKunming 650201YunnanPeople’s Republic of China School of Science Mae Fah Luang UniversityChiang Rai 57100Thailand Center of Excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand East and Central Asia Regional Office World Agroforestry Centre(ICRAF)Kunming 650201YunnanPeople’s Republic of China Department of Biology Faculty of ScienceChiang Mai UniversityChiang Mai 50200Thailand Mushroom Research Foundation 128 M.3 Ban Pa Deng T.Pa PaeA.Mae TaengChiang Mai 50150Thailand Department of Plant Medicine National Chiayi University300 Syuefu RoadChiayi City 60004TaiwanPeople’s Republic of China Department of Health Sciences Faculty of ScienceUniversity of MauritiusReduitMauritius Formerly Department of Botany Goa UniversityGoaIndia No.128/1-J Azad Housing SocietyCurcaGoa Velha 403108India Department of Crop Sciences College of Agricultural and Marine SciencesSultan Qaboos UniversityPO Box 8123 Al KhoudOman Sect.Environmental Sciences Dept.MeSVAUniversity of L’Aquila67100 CoppitoAQItaly Division of Food Technology Biotechnology&AgrochemistryCollege of Agriculture&Life SciencesChonnam National UniversityGwangju 61186Korea Fungal Resources Research Division NNIBRSangju-si 37242Korea Westerdijk Fungal Biodiversity Institute Uppsalalaan 83584 CT UtrechtThe Netherlands Department of Biotechnology and Biomedicine DTUBioengineeringTechnical University of Denmark2800 Kongens LyngbyDenmark Botanischer Garten und Botanisches Museum Berlin Freie Universitat BerlinKonigin-Luise-StraßBe 6-814195 BerlinGermany Laboratorio de Botanica/Liquenologia Instituto de BiocienciasUniversidade Federal de Mato Grosso do SulAvenida Costa e Silva s/nBairro UniversitarioCampo GrandeMatto Grosso do Sul CEP 79070-900Brazil Departamento de Biociencias Universidade Federal de SergipeItabaianaSergipe CEP:49500-000Brazil Shenzhen Key Laboratory of Microbial

This article is the tenth series of the Fungal Diversity Notes,where 114 taxa distributed in three phyla,ten classes,30 orders and 53 families are described and illustrated.Taxa described in the present study include one new family(viz.Pseudoberkleasmiaceae in Dothideomycetes),five new genera(Caatingomyces,Cryptoschizotrema,Neoacladium,Paramassaria and Trochilispora)and 71 new species,(viz.Acrogenospora thailandica,Amniculicola aquatica,A.guttulata,Angustimassarina sylvatica,Blackwellomyces lateris,Boubovia gelatinosa,Buellia viridula,Caatingomyces brasiliensis,Calophoma humuli,Camarosporidiella mori,Canalisporium dehongense,Cantharellus brunneopallidus,C.griseotinctus,Castanediella meliponae,Coprinopsis psammophila,Cordyceps succavus,Cortinarius minusculus,C.subscotoides,Diaporthe italiana,D.rumicicola,Diatrypella delonicis,Dictyocheirospora aquadulcis,D.taiwanense,Digitodesmium chiangmaiense,Distoseptispora dehongensis,D.palmarum,Dothiorella styphnolobii,Ellisembia aurea,Falciformispora aquatic,Fomitiporia carpinea,F.lagerstroemiae,Grammothele aurantiaca,G.micropora,Hermatomyces bauhiniae,Jahnula queenslandica,Kamalomyces mangrovei,Lecidella yunnanensis,Micarea squamulosa,Muriphaeosphaeria angustifoliae,Neoacladium indicum,Neodidymelliopsis sambuci,Neosetophoma miscanthi,N.salicis,Nodulosphaeria aquilegiae,N.thalictri,Paramassaria samaneae,Penicillium circulare,P.geumsanense,P.mali-pumilae,P.psychrotrophicum,P.wandoense,Phaeoisaria siamensis,Phaeopoacea asparagicola,Phaeosphaeria penniseti,Plectocarpon galapagoense,Porina sorediata,Pseudoberkleasmium chiangmaiense,Pyrenochaetopsis sinensis,Rhizophydium koreanum,Russula prasina,Sporoschisma chiangraiense,Stigmatomyces chamaemyiae,S.cocksii,S.papei,S.tschirnhausii,S.vikhrevii,Thysanorea uniseptata,Torula breviconidiophora,T.polyseptata,Trochilispora schefflerae and Vaginatispora palmae).Further,twelve new combinations(viz.Cryptoschizotrema cryptotrema,Prolixandromyces australi,P.elongatus,P.falcatus,P.longispinae,P.microveliae,P.neoalardi,P.polhemorum,P.protubera

关键词： 71 new taxa Ascomycota Basidiomycota Dothideomycetes Eurotiomycetes Lecanoromycetes Leotiomycetes Pezizomycetes Phylogeny Taxonomy

Notes for genera:Ascomycota

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Fungal Diversity 2017年第5期86卷 1-594页

作者： Nalin N.Wijayawardene Kevin D.Hyde Kunhiraman C.Rajeshkumar David L.Hawksworth Hugo Madrid Paul M.Kirk Uwe Braun Rajshree V.Singh Pedro W.Crous Martin Kukwa Robert Lücking Cletus P.Kurtzman andrey Yurkov Danny Haelewaters andre´aptroot H.Thorsten Lumbsch Einar Timdal Damien Ertz Javier Etayo Alan J.L.Phillips Johannes Z.Groenewald Moslem Papizadeh Laura Selbmann Monika C.Dayarathne Gothamie Weerakoon E.B.Gareth Jones Satinee Suetrong Qing Tian Rafael F.Castanda-Ruiz Ali H.Bahkali Ka-Lai Pang Kazuaki Tanaka Dong Qin Dai Jariya Sakayaroj Martina Hujslová Lorenzo Lombard Belle D.Shenoy Ave Suija Sajeewa S.N.Maharachchikumbura Kasun M.Thambugala Dhanushka N.Wanasinghe Bharati O.Sharma Subhash Gaikwad Gargee Pandit Laura Zucconi Silvano Onofri Eleonora Egidi Huzefa A.Raja Rampai Kodsueb Marcela E.S.Caceres Sergio Perez-Ortega Patrícia O.Fiuza Josiane Santana Monteiro Larissa N.Vasilyeva Roger G.Shivas Maria Prieto Mats Wedin Ibai Olariaga Adebola Azeez Lateef Yamini Agrawal Seyed Abolhassan Shahzadeh Fazeli Mohammad Ali Amoozegar Guo Zhu Zhao Walter P.Pfliegler Gunjan Sharma Magdalena Oset Mohamed A.Abdel-Wahab Susumu Takamatsu Konstanze Bensch Nimali Indeewari de Silva AndréDe Kesel Anuruddha Karunarathna Saranyaphat Boonmee Donald H.Pfister Yong-Zhong Lu Zong-Long Luo Nattawut Boonyuen Dinushani A.Daranagama Indunil C.Senanayake Subashini C.Jayasiri Milan C.Samarakoon Xiang-Yu Zeng Mingkwan Doilom Luis Quijada Sillma Rampadarath Gabriela Heredia Asha J.Dissanayake Ruvishika S.Jayawardana Rekhani H.Perera Li Zhou Tang Chayanard Phukhamsakda Margarita Hernańdez-Restrepo Xiaoya Ma Saowaluck Tibpromma Luis F.P.Gusmao Darshani Weerahewa Samantha C.Karunarathna Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of ScienceKunming 650201YunnanPeople’s Republic of China Center of Excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand World Agroforestry Centre East and Central AsiaKunming 650201YunnanPeople’s Republic of China Mushroom Research Foundation 128 M.3 Ban Pa Deng T.Pa PaeA.Mae TaengChiang Mai 50150Thailand National Fungal Culture Collection of India(NFCCI) Biodiversity and Palaeobiology(Fungi)GroupAgharkar Research InstitutePuneMaharashtra 411004India Royal Botanic Gardens KewRichmondSurrey TW93DSUK Department of Life Sciences The Natural History MuseumCromwell RoadLondon SW75BDUK Jilin Agricultural University Changchun 130118JilinPeople’s Republic of China Centro de Geno´mica y Bioinforma´tica Facultad de CienciasUniversidad Mayor de ChileCamino La Pira´mide 5750HuechurabaSantiagoChile Martin-Luther-Universita¨t Institut fu¨r BiologieBereich Geobotanik und Botanischer GartenHerbariumNeuwerk 2106099 Halle(Saale)Germany Westerdijk Fungal Biodiversity Institute Uppsalalaan 83584 CT UtrechtThe Netherlands Department of Plant Taxonomy and Nature Conservation Faculty of BiologyUniversity of Gdan´skWita Stwosza 5980-308 Gdan´skPoland Botanic Garden and Botanical Museum Königin-Luise-Str.6-814195 BerlinGermany Mycotoxin Prevention and Applied Microbiology Research Unit National Center for Agricultural Utilization ResearchAgricultural Research ServiceU.S.Department of AgriculturePeoriaIL 61604USA Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures Inhoffenstraße 7B38124 BrunswickGermany Farlow Reference Library and Herbarium of Cryptogamic Botany Harvard University22 Divinity AvenueCambridgeMA 02138USA Department of Organismic and Evolutionary Biology Harvard University22 Divinity AvenueCambridgeMA 02138USA ABL Herbarium G.v.d.Veenstraat 1073762 XK SoestThe Netherlands Science&Education The Field Museum1400

Knowledge of the relationships and thus the classification of fungi,has developed rapidly with increasingly widespread use of molecular techniques,over the past 10–15 years,and continues to accelerate.Several genera have been found to be polyphyletic,and their generic concepts have subsequently been emended.New names have thus been introduced for species which are phylogenetically distinct from the type species of particular genera.The ending of the separate naming of morphs of the same species in 2011,has also caused changes in fungal generic names.In order to facilitate access to all important changes,it was desirable to compile these in a single document.The present article provides a list of generic names of Ascomycota(approximately 6500 accepted names published to the end of 2016),including those which are lichen-forming.Notes and summaries of the changes since the last edition of‘Ainsworth&Bisby’s Dictionary of the Fungi’in 2008 are provided.The notes include the number of accepted species,classification,type species(with location of the type material),culture availability,life-styles,distribution,and selected publications that have appeared since 2008.This work is intended to provide the foundation for updating the ascomycete component of the"Without prejudice list of generic names of Fungi"published in 2013,which will be developed into a list of protected generic names.This will be subjected to the XIXth International Botanical Congress in Shenzhen in July 2017 agreeing to a modification in the rules relating to protected lists,and scrutiny by procedures determined by the Nomenclature Committee for Fungi(NCF).The previously invalidly published generic names Barriopsis,Collophora(as Collophorina),Cryomyces,Dematiopleospora,Heterospora(as Heterosporicola),Lithophila,Palmomyces(as Palmaria)and Saxomyces are validated,as are two previously invalid family names,Bartaliniaceae and Wiesneriomycetaceae.Four species of Lalaria,which were invalidly published are transferred to Taphrina and validated as new combinat

关键词： Article 59 Classification One name Pleomorphic fungi Taxonomy

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你的奖励由谁买单:信用卡市场的再分配

新金融 2024年第1期 59-66页

作者： Sumit Agarwal andrea Presbitero andre F.Silva Carlo Wix 新加坡国立大学国际货币基金组织经济政策研究中心美国联邦储备委员会

将信用卡奖励作为一个理想的实验进行研究,以量化零售金融市场中消费者之间的再分配。通过对有奖励和无奖励的信用卡进行比较,可以发现:无论收入如何,成熟的消费者都能从有奖励的信用卡中获利,而有的消费者则要付出代价。为了探究其背... 详细信息

将信用卡奖励作为一个理想的实验进行研究,以量化零售金融市场中消费者之间的再分配。通过对有奖励和无奖励的信用卡进行比较,可以发现:无论收入如何,成熟的消费者都能从有奖励的信用卡中获利,而有的消费者则要付出代价。为了探究其背后的机制,本文利用银行在信用卡层面主动提高账户限额的方法进行研究。结果表明:奖励卡会诱发更多的消费,使消费者有更高的未付余额;消费者在偿还信用卡时还会遵循次优平衡匹配启发模式,从而产生更高的成本;银行通过提供比无奖励的同类信用卡更低的利率来激励消费者使用奖励卡;从教育程度较低的地区到较高的地区,从较贫穷的地区到较富裕的地区,信用卡奖励以重新分配的方式扩大了现有的差距。本刊编译此篇美联储的工作论文,以期就信用卡相关问题为读者带来启发。

关键词：消费金融信用卡金融复杂性用卡奖励

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Machine Learning and Statistical Analysis in Groundwater Monitoring for Total Dissolved Solids Assessment in Winkler County, Texas

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Journal of Geoscience and Environment Protection 2024年第6期12卷 1-29页

作者： Azuka I. Udeh Osayamen J. Imarhiagbe Erepamo J. Omietimi Abdulqudus O. Mohammed Oluwatomilola andre-Obayanju Department of Geoscience University of Texas Permian Basin Odessa USA Department of Geology University of Pretoria Pretoria South Africa Department of Geology University of Benin Benin Nigeria

This research aims to develop reliable models using machine learning algorithms to precisely predict Total Dissolved Solids (TDS) in wells of the Permian basin, Winkler County, Texas. The data for this contribution was obtained from the Texas Water Development Board website (TWDB). Five hundred and ninety-three samples were obtained from two hundred and ninety-eight wells in the study area. The wells were drilled at different county locations into five aquifers, including Pecos Valley, Dockum, Capitan Reef, Edward Trinity, and Rustler aquifers. A total of fourteen different water quality parameters were used, and they include Potential hydrogen (pH), Sodium, Chloride, Magnesium, Fluoride, TDS, Specific Conductance, Nitrate, Total Hardness, Calcium, Temperature, Well Depth, Sulphate, and Bicarbonates. Four machine learning regression algorithms were developed to get a good model to help predict TDS in this area: Decision Tree regression, Linear regression, Support Vector Regression, and K-nearest neighbor. The study showed that the Decision Tree produced the best model with attributes like the coefficient of determination R2 = 1.00 and 0.96 for the training and testing, respectively. It also produced the lowest score of mean absolute error MAE = 0.00 and 0.04 for training and testing, respectively. This study will reduce the cost of obtaining different water quality parameters in TDS determination by leveraging machine learning to use only the parameters contributing to TDS, thereby helping researchers obtain only the parameters necessary for TDS prediction. It will also help the authorities enact policies that will improve the water quality in areas where drinking water availability is a challenge by providing important information for monitoring and assessing groundwater quality.

关键词： Machine Learning Regression Aquifers Winkler County Sinkholes