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Cambridge Butterfly Collection. Loreto, Peru Part 1 EN: This upload contains photographs taken by Eva van der Heijden at the Butterfly Genetics Group at the University of Cambridge, from a butterfly wing collection from Loreto, Peru, in collaboration with Green Gold Forestry. Individual sample names can be found in the information sheet. Further Information on individual samples from the Butterfly Genetics Group Collection can be found on the public database Earthcape (click here for the database, and here for FAQ). Please contact Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk) or Gabriela Montejo-Kovacevich (gmontejokovacevich[at]gmail.com) for further information. ES: Este repositorio contiene fotografías tomadas por Eva van der Heijden en el Butterfly Genetics Group de la Universidad de Cambridge, de mariposas de Loreto (Peru), en colaboración con la compañía Green Gold Forestry. Puede encontrar información sobre muestras individuales de Butterfly Genetics Group Collection en la base de datos pública Earthcape (haga clic aquí para la base de datos, y aquí para preguntas frecuentes) Por favor, póngase en contacto con Chris Jiggins (c.jiggins [arroba] zoo.cam.ac.uk) o Gabriela Montejo-Kovacevich (gmontejokovacevich[at]gmail.com) con sus preguntas o peticiones.
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Abstract Planning for power systems with high penetrations of variable renewable energy requires higher spatial and temporal granularity. However, most publicly available test systems are of insufficient fidelity for developing methods and tools for high- resolution planning. This paper presents methods to construct open-access test systems of high spatial granularity to more accurately represent current infrastructure and high temporal granularity to represent variability of demand and renewable resources. To demonstrate, a high-resolution test system representing the United States is created using only publicly available data. This test system is validated by running it in a production cost model, with results validated against historical generation to ensure that they are representative. The resulting open source test system can support power system transition planning and aid in development of tools to answer questions around how best to reach decarbonization goals, using the most effective combinations of transmission expansion, renewable generation, and energy storage. Documentation of dataset development A paper describing the process of developing the dataset is available at https://arxiv.org/abs/2002.06155. Please cite as: Y. Xu, Nathan Myhrvold, Dhileep Sivam, Kaspar Mueller, Daniel J. Olsen, Bainan Xia, Daniel Livengood, Victoria Hunt, Benjamin Rouillé d'Orfeuil, Daniel Muldrew, Merrielle Ondreicka, Megan Bettilyon, "U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies," 2020 IEEE PES General Meeting, Montreal, Canada, 2020. Dataset version history 0.1, January 31, 2020: initial data upload. 0.2, March 10, 2020: addition of Tabular Data Package metadata, modifications to cost curves and transmission capacities aimed at more closely matching optimization results to historical data. 0.2.1, March 25, 2020: corrected a bug in the wind profile generation process which was pulling the wrong locations for wind farms outside the Western Interconnection.
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Abstract Planning for power systems with high penetrations of variable renewable energy requires higher spatial and temporal granularity. However, most publicly available test systems are of insufficient fidelity for developing methods and tools for high- resolution planning. This paper presents methods to construct open-access test systems of high spatial granularity to more accurately represent current infrastructure and high temporal granularity to represent variability of demand and renewable resources. To demonstrate, a high-resolution test system representing the United States is created using only publicly available data. This test system is validated by running it in a production cost model, with results validated against historical generation to ensure that they are representative. The resulting open source test system can support power system transition planning and aid in development of tools to answer questions around how best to reach decarbonization goals, using the most effective combinations of transmission expansion, renewable generation, and energy storage. Documentation of dataset development A paper describing the process of developing the dataset is available at https://arxiv.org/abs/2002.06155. Please cite as: Y. Xu, Nathan Myhrvold, Dhileep Sivam, Kaspar Mueller, Daniel J. Olsen, Bainan Xia, Daniel Livengood, Victoria Hunt, Benjamin Rouillé d'Orfeuil, Daniel Muldrew, Merrielle Ondreicka, Megan Bettilyon, "U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies," 2020 IEEE PES General Meeting, Montreal, Canada, 2020. Dataset version history 0.1, January 31, 2020: initial data upload. 0.2, March 10, 2020: addition of Tabular Data Package metadata, modifications to cost curves and transmission capacities aimed at more closely matching optimization results to historical data. 0.2.1, March 25, 2020: [erroneous upload] 0.2.2, March 26, 2020: [erroneous upload]
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Planning for power systems with high penetrations of variable renewable energy requires higher spatial and tempo- ral granularity. However, most publicly available test systems are of insufficient fidelity for developing methods and tools for high- resolution planning. This paper presents methods to construct open-access test systems of high spatial granularity to more accurately represent current infrastructure and high temporal granularity to represent variability of demand and renewable resources. To demonstrate, a high-resolution test system representing the United States is created using only publicly available data. This test system is validated by running it in a production cost model, with results validated against historical generation to ensure that they are representative. The resulting open source test system can support power system transition planning and aid in development of tools to answer questions around how best to reach decarbonization goals, using the most effective combinations of transmission expansion, renewable generation, and energy storage. A paper describing the process of developing the dataset is available at https://arxiv.org/abs/2002.06155. Version history 0.1, January 31, 2020: initial data upload. 0.2, March 10, 2020: addition of Tabular Data Package metadata, modifications to cost curves and transmission capacities aimed at more closely matching optimization results to historical data.
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Abstract Motivation Antibodies are widely used experimental reagents to test expression of proteins. However, they might not always provide the intended tests because they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable and irreproducible research results. While many proposals have been developed to deal with the problem of antibody specificity, they may not scale well to deal with the millions of antibodies that have ever been designed and used in research. In this study, we investigate the feasibility of automatically extracting statements about antibody specificity reported in the literature by text mining, and generate reports to alert scientist users of problematic antibodies. Results We developed a deep neural network system called Antibody Watch and tested its performance on a corpus of more than two thousand articles that report uses of antibodies. We leveraged the Research Resource Identifiers (RRID) to precisely identify antibodies mentioned in an input article and the BERT language model to classify if the antibodies are reported as nonspecific, and thus problematic, as well as inferred the coreference to link statements of specificity to the antibodies that the statements referred to. Our evaluation shows that Antibody Watch can accurately perform both classification and linking with F-scores over 0.8, given only thousands of annotated training examples. The result suggests that with more training, Antibody Watch will provide useful reports about antibody specificity to scientists.
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Fig. 3: Maxillolabial complex of Opamyrma hungvuong worker, nontype (AKY05vii17-06, China, Guangxi). (A) Scanning electron microscope image of maxillolabial complex in ventral view, labrum removed; (B) right maxilla in outer view; (C) labium in lateral view; (D) labium in dorsal view. Abbreviations: Ams = anteromedian sclerite; Gcss = galeal crown's stout seta; Hyp = hypopharynx; Lbp = labial palp; Lcn = lacinia; Mxco = maxillary comb; Mxp = maxillary palp; Mxst = maxillary stipes; Prm = prementum; Sglb = subglossal brush.
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Fig. 8: Scanning electron microscope images of metasoma of Opamyrma hungvuong worker, nontype (AKY05vii17-06, China, Guangxi). (A) petiole in lateral view; (B) petiole in ventral view; (C) helcium in anterior view; (D) helcium in ventral view; (E) pretergite of abdominal segment IV in dorsal view; (F) gaster in lateral view; (G) gaster in ventral view. Abbreviations: Absg = abdominal segment; Prsn = presternite; Prtg = pretergite; Ptlt = petiolar laterotergite; Ptsn = petiolar sternite; Tss = tergosternal suture.
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Fig. 13: Male genitalia of Opamyrma hungvuong, nontype (Dai19iii2019-029, Son La, Vietnam). (A) genital capsule in dorsal view; (B) genital capsule in ventral view; (C) abdominal sternite IX in ventral view; (D) cupula in ventral view; (E) left paramere with basiventral part of right paramere and cupula, in unfolded outer view; (F) left volsella in lateral view; (G) left penisvalva in lateral view. Abbreviations: Bm = basimere; Cu = cupula; Cs = cuspis; Dg = digitus; Lp = lateral apodeme; Pv = penisvalva; Spl = spinescent lobe; Spc = spiculum; Tm = telomere; Va = valvura.
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Fig. 4: Tentorium of Opamyrma hungvuong worker, nontype (AKY05vii17-06, China, Guangxi). (A) anterior part of dorsal sclerite of cranium in dorsal view; (B) part of dorsal sclerite of cranium around right antennal socket with anterior part of tentorium, in inner ventral view; (C) part of ventral sclerite of cranium with posterior part of tentorium in inner dorsal view; (D) right half of tentorium in dorsal view (lacking posterior tentorial arm). Abbreviations: Ata = anterior tentorial arm; Atp = anterior tentorial pit; Ct = corpotendon; Dta = dorsal tentorial arm; Ep = external plate; Ip = internal plate; Lclp = lateral portion of clypeus; Mdb = mandible; Occ = occipital carina; Pgr = postgenal ridge; Pta = posterior tentorial arm; Ptg = peritorular groove; Ptp = posterior tentorial pit; Tb = tentorial bridge.
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Fig. 9: Sting apparatus of Opamyrma hungvuong worker, nontype (AKY05vii17-06, China, Guangxi). (A) spiracular plate in lateral view; (B) quadrate plate in lateral view; (C) anal arcs and anal plate in flattened dorsal view; (D) oblong plate and triangular plate in lateral view; (E) gonostylus in lateral view; (F) furcula in anterodorsal view; (G) furcula in posterior view; (H) sting in lateral view; (I) sting in dorsal view; (J) basal part of sting in lateral view; (K) Scanning electron microscope image of apical parts of sting and lancet in lateral view; (L) lancet and fulcral arm in lateral view. Abbreviations: Ana = anal arc; Anp = anal plate; Ap = anterior apodeme; Fa = fulcral arm; Gs = gonostylus; Lc = lancet; Ll = lateral lobe; Mc = medial connection; Pa = posterior arm; St = sting; Tp = triangular plate; Vc = valve chamber.
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