zilongwang/ReadingBank huggingface dataset:
Papaya Papaya 100 Papaya juice Papaya 50.0 Papaya and milk Papaya 33.3 Wheat flour 76.8 Pasta and beef Wheat flour 61.4 Pasta and fish Wheat flour 61.4 Pasta macarronada Wheat flour 69.1 Pasta precooked Wheat flour 76.8 Pastel Wheat flour 25.6 Pavê Wheat flour 10.4 Peach Peach 100 Peach juice Peach 50.0 Peach, sweet Peach 57.3 Peach, canned Peach 50.0 Pineapple Pineapple 100 Pineapple juice Pineapple 40.0 Pirão Cassava flour 4.0 Pizza flour 33.2 Potato Potato 100 Potato pure Potato 61.0 Potato, fried Potato 91.4 Potato, Potato 91.4 Potato with beef Potato 19.0 Potato with chicken Potato 19.0 Puba de fuba Corn flour 21.5 Wheat flour 6.5 Quebra quebra Wheat flour 39.5 Queijadinha Wheat flour 17.0 Rabanada Wheat flour 12.3 Raisin Grapes 150 Regional Brazilian 37.3 Rice 24.0 Regional Middle West Beans 40.6 Rice 26.1 Regional North Beans 47.1 Rice 30.3 Regional Northeast Beans 40.6 Rice 26.1 Regional South Beans 37.3 Rice 24.0 Regional Southeast Beans 34.1 Rice 22.0
Supplemental Figure. Average (GW) ratio based on survival and good outcome. Supplemental 1. Grey white cutoff values for determining mortality and poor neurologic outcome. Best cutoff determined by Youden Index. FPR- false positive rate. For mortality For poor outcome Cutoff Sensitivity Specificity Cutoff Sensitivity Specificity Best cutoff 1.247 62.9 (55.2-70.2) 65.71 (53.4-76.7) 1.258 63.8 (57.0-70.1) 77.3 (54.6-92.2) FPR 10 1.187 33.5 (26.5-41.2) 90 (80.5-96) 1.222 44.5 (37.8-51.4) 90.9 (70.8-98.9) FPR 5 1.157 26.5 (20-33.8) 95.7 (88-99.1) 1.172 25.7 (20-32) 95.5 (77.2-99.9) FPR 3 1.094 20 (14.3-26.8) 97.1 (90.1-99.7) FRP 1 1.077 19.4 (13.8-26.2) 98.6 (92.3-100) FPR 0 1.066 16.47 (11.2-22.9) 100 (94.9-100) 1.077 15.6 (11-21.1) 100 (84.6-100) Supplemental Table 2. Combined GW ratio and EEG result for determining mortality and poor neurologic outcome. For mortality Sensitivity Specificity PPV NPV GW and malignant EEG 1.18 (0.14 – 4.19) 100 (94.87 – 100) 100 (15.81 – 100) 29.41 (23.70 – 35.64) GW ratio≤1.247 and malignant EEG 24.12 (17.9 – 31.26) 98.57 (92.3 – 99.96) 97.62 (87.43 – 99.94) 34.85 (28.23 – 41.93)
GW ratio≤1.1 and non-malignant EEG 18.82 (13.25 – 25.52) 95.71 (87.98 – 99.11) 91.43 (76.94 – 98.2) 32.68 (26.31 – 39.56) GW ratio≤1.247 and non-malignant EEG 38.82 (31.46 – 46.59) 67.14 (54.88 – 77.91) 74.16 (63.79 – 82.86) 31.13 (23.85 – 39.16) For poor outcome GW ratio≤1.1 and malignant EEG 0.92 (0.11 – 3.27) 100 (84.56 – 100) 100 (15.81 – 100) 9.24 (5.88 – 13.66) GW ratio≤1.258 and malignant EEG 22.02 (16.7 – 28.11) 100 (84.56 – 100) 100 (92.6 – 100) 11.46 (7.32 – 16.83) GW ratio≤1.1 and non-malignant EEG 15.6 (11.05 – 21.11) 95.45 (77.16 – 99.88) 97.14 (85.08 – 99.93) 10.24. (6.45 – 15.23) GW ratio≤1.258 and non-malignant EEG 39.91 (33.5 – 46.59) 77.27 (54.63 – 92.18) 94.79 (88.26 – 98.29) 11.04 (6.56 – 17.09)
Figure S2 Intracellular localization of LNPs <Methods> Cells (5 x 104) were seeded on glass bottom dishes (IWAKI, ASAHI GLASS CO., LTD, Japan) in 2 ml of culture medium 1 day before transfection. For transfection, the cells were incubated with a 2 ml aliquot of LNPs that were dually labeled with rhodamine (pDNA) and NBD (lipid envelope) in DMEM containing 10% serum. LNPs were incubated with the cells for 3 h. The nuclei were stained with 5 µg/mL Hoechst33342 (DOJINDO Laboratories, Kumamoto, Japan). Cell surface-bound LNPs were removed by washing 2 times with 1 ml of PBS containing heparin (20 Units/ml). The culture medium was replaced with the buffer, and microscopic observations were then performed. Scale bars indicated 10 µm. <Result> In the case of LNPPalmM, pDNA (red) was dispersed throughout the cytoplasm, and nuclear accumulation was negligable. Also, the pDNA free from the co-localization with lipid envelope (green) was clearly observed. In contrast, pDNA delivered with LNPPalmA was dominantly co-localized with lipid envelope, and furthermore, was extensively accumulated to the nuclear periphery. This support our conclusion that LNPPalmA is associated with the nuclear transport machinery and that the particles remain intact. Figure S3 Effects of retinoic acid on the intracellular localization of LNPs <Methods> Cells (5 x 104) were seeded on glass bottom dishes (IWAKI, ASAHI GLASS CO., LTD, Japan) in 2 ml of culture medium 1 day before transfection. For transfection, the cells were incubated with a 2 ml aliquot of LNPs including 1.6 µg pDNA labeled with rhodamine in DMEM supplied with 10% serum. The LNPs were
Table S1 Vehicle characteristics and depreciation. Model Powertrain Market segment Fuel consumption (l/ 100km) Energy consumption (kWh/ 100km) Engine size (kW, electric) Engine size (cm3, ICE) CO2 emission (g/km) Max power (kW) Fuel type Residual value after 4 years Volkswagen e-up! BEV A 11.7 60 0 60 Electricity 22% Volkswagen up! ICE A 4.5 999 105 44 Petrol 34% Renault Zoe BEV B 14.6 65 0 65 Electricity 22% Renault Clio dCi 75 ICE B 3.6 1461 95 55 Diesel 35% BMW i3 BEV C 12.9 125 0 125 Electricity 42% BMW Series 1, 116d ICE C 4.1 1496 107 85 Diesel 38% Nissan Leaf BEV C 15 80 0 80 Electricity 27% Honda Civic 1.6 ICE C 3.6 1597 94 88 Diesel 36% Toyota Prius Hybrid PHEV C 2.1 5.2 60 1798 49 100 Petrol/electricity 42% Toyota Auris 1.4 D-4D ICE C 4.2 1364 105 66 Diesel 38% Volvo V60 Plug-in Hybrid PHEV D 1.8 13.3 50 2400 48 158 Diesel/electricity 35% Volvo V60 D2 ICE D 3.8 1969 101 88 Diesel 42% Tesla Model S BEV S 18.1 310 0 285 Electricity 49% Audi A7 3.0 TDI ultra ICE E 4.8 2967 124 160 Diesel 42% Mitsubishi Outlander Plug-in Hybrid PHEV J 1.9 13.4 60 1998 44 149 Petrol/electricity 35% Mitsubishi Outlander 2.2 DI-D 4 WD ICE J 5.3 2268 139 110 Diesel 45%
Tab. S3.4: Energy supply matrix of Namibia (Year 2013). Primary energy sources / Imports Gross Energy Requirement (GER) Supply of EC (GSEC) of EC (Hypercycle) Level n-5 n-6 Natural resources (biophysical units) Primary energy (thermal equivalent) Fuels (TJ-EC) Electricity (TJ-EC) Fuels (TJ-EC) Electricity (TJ-EC) supply Hydro 340 MW 20,600 TJ-GER n/a 4,600 negl. negl. Biomass (wood charcoal) 140 ktons 4,000 TJ-GER 4,000 n/a 270 negl. On-grid wind 0.2 MW 2.1 TJ-GER n/a negl. negl. negl. On-grid solar (PV + CSP) 12 MW 0.0 TJ-GER n/a 0.0 negl. negl. Off-grid (wind + PV) 6.0 MW 25 TJ-GER n/a 5.5 negl. negl. Sub-total n/a 24,600 TJ-GER 4,000 4,600 270 0 Imports as GER (i.e. as primary energy) Fossil fuels (for electricity generation) 7.4 ktons 950 TJ-GER n/a 210 2.5 negl. Imports as EC (i.e. as secondary energy) Fossil fuels 750 ktons 38,000 TJ-GER 38,000 n/a n/a n/a Electricity (from SAPP) n/a 46,700 TJ-GER n/a 10,400 n/a n/a Sub-total n/a 84,700 TJ-GER 38,000 10,400 n/a n/a Energy sector (n-4) n/a 110,200 TJ-GER 42,000 15,200 270 negl. Source: own elaboration.
Supplementary Table S2. qPCR primers for transcription factor genes. Gene Symbol Sense Primer Anti-sense Primer Tm1 Product Size2 Intron size3 otx2 CAACCACCTTACACGGTCAACG GCGAATAAAGCCTCCAGCACAT 61/59 155 627 emx3 CCAGAGGGACACCTTGAACT TCAGCTCCCACCACGTAAT 59/58 210 7553 dlx2a ATCTCTGGGCCTCACGCAAA CAGTGTTCATTCTCTGGCTGTG 58/59 188 1066 six3b GCACAAGCCACTGGACTGACTC GCAGCCCGATTCTGACATGGAG 62/62 147 898 pax6a GCTGAGGATGCTGAACGGTCA CTCGCCATTGGAGCTTATTGAGTT 60/58 151 3845 gbx1 GCCCGCATGACGAACTCAAAGG AGGTGTTCCCGTCGGAGAAGCT 61/62 192 1279 gbx2 ATTCCGTCCTTCCACGATTC TTCTTTCTGACACATGGCGT 58/58 174 1003 gbx2 3'UTR4 TGGTCTCTGCTGAAGCACATGATA GTGCCCGTGATGTCTTAAATGGTT 59/59 196 none 18S rRNA5 CGAAAGTCGGAGGTTCGAAG AGCTTTGCAACCATACTCCC 56/58 156 none 1. Melting temperature. 2. Length of the PCR product. 3. Length of the intron between the primers in the genome. 4. PCR primers used for the 3'UTR sequence to quantitate the endogenous gbx2 expression in embryos overexpressing hsp-gbx2. 5. PCR primers used for the 18S rRNA sequence that were quantitated as endogenous control.
Supplementary Table S3. qPCR primers for secreted factor genes. Gene Symbol Sense Primer Anti-sense Primer Tm1 Product Size2 Intron Size3 bmp2b CCGAGGAGCACTATGGGAAA CGCCAGGAATGGAGGTAAGG 58/60 154 1855 wnt1 TGTGTCCTCCTGGTGTCCTCTC TCTCTGGCGGCGACTTAGCA 62/60 165 1687 wnt3 TCTCGGGTGCTTGGAGGCTAT TTGACTCCTTCCGCTACACTGG 61/61 173 30581 GCATCCAGGAGTGTCAGCATCA GCACGAACGCCGATTCTCTG 61/60 156 21153 shha AAGGATGAGGAGAACACGGGAG TCTTCCCTCGTAGTGGAGTGATT 58/57 174 1421 AGCATGTGAGTGAGCAAAGTAAGGT CGACTCCCAAATGTGTCTGTCTCTA 59/61 187 1618 1. Melting temperature. 2. Length of the PCR product. 3. Length of the intron between the primers in the genome.
compost toilet/installed 0.10 0.30 0.20 0 0 0 3.4 7.0 4.5 diversion toilet/installed 0.10 0.30 0.20 0 0 0 3.4 7.0 4.5 WWTP & gravity 0.040 0.12 0.079 0.24 0.72 0.48 2.9 6.8 4.2 digestion plant & pressure sewer 0 0 0 0 0 0 3.7 7.1 4.7 I/A septic /installed 0 0 0 0.73 2.2 1.5 1.5 6.4 3.3
2 LCI data for in-vessel composting plant presented per t of feedstock Parameter Flow Amount Unit Reference Energy consumption Electricity 40-90 kWh Righi et al. 2013; Martínez-Blanco et al. (2009); Levis and Barlaz (2013) Diesel 0.64 – 5.65 L Righi et al. 2013; Martínez-Blanco et al. (2009) Water consumption Rain water 85 L et al. 2013 Freshwater 35 L Righi et al. 2013 Materials use Wood residue 250 kg Duggan, 2015 - personal communication Wasted food and residue 750 kg Duggan, 2015 - personal communication Gaseous emissions PTS 0.11 kg Righi et al. 2013 CH4 0.042-0.05 kg et al. 2013; Martínez-Blanco et al. (2009) NH3 0.04 kg Righi et al. 2013 H2S 0.02 kg Righi et al. 2013 NMVOC 0.01 kg Righi et al. 2013 N2O 0.092 kg Martínez-Blanco et al. (2009) Waste production Waste (landfilled) 0.13 t Righi et al. 2013 Valuable materials Compost 0.16-0.24 t Martínez-Blanco et al. (2009); Blengini (2008); White (2012) 3 Anaerobic Digestion and CHP – per t of feedstock Parameter Flow Amount Units Reference
36-58 Kg tonne-1 ww A 1.9 3.3 5.2 Digestate (whole) Kg tonne-1 ww T 5.2 1.2 3.5 - Kg tonne-1 ww A 4.1 1.2 3.5 - Heslop and McCabe (2012) Figure 1 Commercial and domestic wasted food and food residue (t)(derived from EPA, 2013a; RPS 2008; RPS 2010). References Apolio, 2011. http://www.environdec.com/en/Detail/?Epd=8563 (accessed 10.08.15).
Riparian Western Riparian Woodland and Shrubland SRM 203: Riparian Woodland 1 Conifer Douglas-fir-Grand Fir-White Fir Forest and Woodland SAF 213: Grand Fir 0 Conifer Limber Pine Woodland SAF 219: Limber Pine 0 Conifer Lodgepole Pine Forest and Woodland SAF 218: Lodgepole Pine 0 Conifer Douglas-fir-Ponderosa Pine-Lodgepole Pine Forest and Woodland SAF 210: Interior Douglas-Fir 0 Conifer Douglas-fir-Grand Fir-White Fir Forest and Woodland SAF 211: White Fir 0 Conifer Ponderosa Pine Forest, Woodland and Savanna SAF 237: Interior Ponderosa Pine 0 Conifer Spruce-Fir Forest and Woodland SAF 206: Engelmann Fir 0 Conifer Spruce-Fir Forest and Woodland SAF 206: Engelmann Fir 0 Conifer Limber Pine Woodland SAF 209: Bristlecone Pine 0 Fig. S1 – (A) Flowchart showing the workflow of the riparian vegetation departure index, and (B) flowchart showing the workflow of the riparian vegetation conversion type classification. Fig. S2 –Example illustration of pixel-level conversion type classifications for an assessment site within the Weber River watershed, Utah. Insets 1 and 2 display two areas in which ‘conversion to agriculture’ and ‘negligible to minor vegetation conversion’ were the dominant conversion types.
Storm water management 0.125 0.1538 0.1288 Percieved wellbeing by visiting the area 0.375 0.1731 0.13496 Aesthetic 0.125 0.0769 0.122699 Total 1 1 1 Table S4. Perceived importance (average) of the investigated ES in comparison to other important services. The responses were achieved in the workshop with 8 civil servants, involving two steps: 1) determination of whether the ES or other aspect was most important, 2) then how much more important it was on a scale from 1 (very little) to 5 (a lot more). Increased access to cycle paths in the city Increased opportunity to drive in the city Increased public transportation in the city More sidewalks in the city More housing in the city Increased access to culture and entertainment in the city (cinema, theatre, etc.) Increased access café and restaurant in town Increased access to gyms and sports halls in the city Increased access to pharmacies, grocery stores, etc. in the city Increased access to shopping in the city More jobs in the city Average Normalized average Biodiversity 4.5 0.1 2.5 0.8 2.7 3.7 2.7 3.1 4.3 0.4 2.2 0.1193 Pollination 0.0 4.3 1.8 0.2 2.0 3.2 2.4 2.4 3.8 0.2 1.8 0.0968 Climate regulation 0.1 4.4 0.0 2.0 0.0 2.0 2.3 2.0 2.4 4.0 1.7 0.0900 Air quality 4.8 0.7 1.6 0.3 3.2 3.3 2.7 2.5 4.1 0.7 2.1 0.1115 0.5
pests and diseases Local climate regulation 3.8 ± 0.19 Regulation of waste and pollution Filtration, absorption, degradation and dilution of pollutants in air, and soil 4.6 ± 0.12 Noise reduction 3.7 ± 0.20 Flow regulation Flood Protection 4.2 ± 0.17 Cultural services Perceived wellbeing and health 4.4 ± 0.14 Aesthetic values 4.0 ± 0.18 Recreation and ecotourism 4.1 ± 0.15 Educational values 4.3 ± 0,16
2 2*. Correlation is significant at the 0.05 level (2-tailed), **. Correlation is significant at the 0.01 level (2-tailed), (B= Benzene), (T= Toluene), (EB= Ethylbenzene), (S= Styrene), (X= Xylene), (TALHC: Total aliphatic hydrocarbons), (TARHC: Total aromatic hydrocarbons), (THC: Total hydrocarbons), (CG: City gas), (Temp= Temperature), (Precip= Precipitation), (WS= Wind speed), (Hum= Humidity), and (SR= Solar radiation)
Table : Energy costs per study site Energy cost per unit a SITE A SITE B SITE C n Mean (SD) n Mean (SD) n Mean (SD) Charcoal (bag<5kg) (USD/kg) b 20 $0.24 ($0.03) 116 $0.27 ($0.10) 108 $0.22 ($0.07) Charcoal (bag>20kg) (USD/kg) b 22 $0.18 ($0.03) 6 $0.16 ($0.03) 14 $0.14 ($0.02) Electricity (USD/kWh) c $0.04 $0.02 $0.02 Methane (USD/m3) d $0.00 $0.00 $0.00 a Exchange rate: $1 USD = 7.25 ZMW (May 2015, oanda.com) b Charcoal cost calculated via local market and household survey (weight of full charcoal bags and cost recorded) c Electricity tariffs obtained via utility bills d Biogas provided free of charge at time of household surveys
Tap water production. underground water without treatment Similar to our water treatment. It represents average operation of tap water from underground water without further treatments. 1 kg Europe (- CH) Market for polyvinylchloride. bulk polymerised Lack of better knowledge 1 kg Global Gravel/sand quarry construction Written that it can be for construction 1 unit ROW Steel production, chromium steel 18/8. hot rolled Stainless But many different to choose from 1 kg Europe Electronics production. for control units Electronics. with different compartments 1 kg Europe Polyethylene pipe production, corrugated. DN 75 Pipe for ventilation. but I assume that it doesn't matter since I put it in kilo 1 m Europe Carbon dioxide production. liquid Used for cooling (only four diff. options. market and this (with two geographical specifications)) 1 kg Europe Hydrochloric acid production. from the reaction of hydrogen with chlorine Only one called hydrochloric acid production. (with three different geographical references) 1 kg Europe Transport, passenger car, medium size, petrol. EURO 5 Used for driving of personal. medium car with good engine 1 km Europe Transport, passenger car, medium size, diesel. EURO 5 Used for driving of personal. medium cars with good engine 1 km Europe
Supplemental Table 2. Patient Characteristics (n = 145) Characteristics Low grade NMIBC (%) n=23 High grade NMIBC (%) n=82 MIBC (%) n=40 Total (%) n=145 Age yrs (median, range) 67 (25,80) 70 (36,87) 64 (31, 86) 68 (25,87) 0.16 Gender 0.004 Male 13 (56) 67 (82) 37 (92) 117 (81) Female 10 (43) 15 (18) 3 (8) 28 (19) Smoking 0.99 Current 3 (13) 13 (16) 6 (15) 22 (15) Former 13 (57) 46 (56) 21 (53) 80 (55) 7 (30) 23 (28) 13 (32) 43 (30) Stage Ta 23 (100) 32 (39) 55 (52) Tis 12 (15) 12 (12) T1 38 (46) 38 (36) T2 21 (52) T3/T4 19 (48) MIBC = muscle invasive bladder cancer; NMIBC = non-muscle invasive bladder cancer
Empodisma minus Restionaceae Sedge/Rush ATe FAC E, F 11; 9; 7; 13 Entolasia spp. Poaceae Grass T - F 11; 13 Epacris microphylla Ericaceae Shrub ATw FAC F 11; 5; 13 Epacris obtusifolia Ericaceae Shrub ATw FAC F 11; 5; 13 Epacris paludosa Ericaceae Shrub ATw FACU E, F 11; 5; 7; 13 Epacris spp. Ericaceae Shrub T/ATw - F 13 Eriochilus cucullatus Orchidaceae Forb Tdr FACU F 11; 5; 13 Eucalyptus blaxlandii Myrtaceae Tree Tdr UPL F 11; 5; 13 Eucalyptus dalrympleana Myrtaceae Tree Tdr FACU F 11; 5; 13 Eucalyptus Myrtaceae Tree Tdr UPL F 11; 5; 13 Eucalyptus mannifera Myrtaceae Tree Tdr FACU F 11; 5; 13 Eucalyptus oreades Myrtaceae Tree Tdr UPL F 11; 5; 13 Eucalyptus pauciflora Myrtaceae Tree Tdr UPL F 11; 5; 13 Eucalyptus radiata Myrtaceae Tree Tdr UPL F 11; 5; 13 Eucalyptus sieberi Myrtaceae Tree Tdr UPL F 11; 5; 13 Eucalyptus sp. (seedling) Myrtaceae Tree Tdr UPL F 11; 13 Eucalyptus spp. Myrtaceae Tree Tdr - F 11; 13 Euchiton involucratus Asteraceae Forb Tda FACU E, F 11; 2; 5; 7 Euchiton spp. Asteraceae Forb T - F 11; 13 Gahnia filifolia Cyperaceae Sedge/Rush Tdr FACU F 11; 5; 13 Gahnia sieberiana Cyperaceae Sedge/Rush ATe FACU F 11; 9; 5; 13 Galium gaudichaudii Rubiaceae Forb Tdr UPL F 11; 5; 13 Galium spp. Rubiaceae Forb Tdr - F 11; 13 Geranium homeanum Geraniaceae Forb Tda FACU F 11; 5; 13
Poranthera microphylla Phyllanthaceae Forb Tdr UPL F 11; 5; 13 Pratia surrepens Lobeliaceae Forb ATl FACW F 11; 5; 13 Prunella vulgaris Lamiaceae Forb Tda FACU E, F 11; 5; 7; 13 Pteridium esculentum Dennstaedtiaceae Fern T UPL F 11; 5; 13 Pterostylis spp. Orchidaceae Forb T - F 11; 13 Pultenaea capitellata Fabaceae (Faboideae) Shrub T FACU F 11; 5; 13 Pultenaea divaricata Fabaceae (Faboideae) ATw FACW E, F 11; 5; 7; 13 Pultenaea spp. Fabaceae (Faboideae) Shrub T/ATw - F 11; 13 Restionaceae indeterminate Restionaceae Sedge/Rush ATe - F 13 Schoenus apogon Cyperaceae Sedge/Rush ATe FAC F 6; 11; 9; 5; 13 Senecio diaschides Asteraceae Forb Tda FACU F 11; 5; 13 Senecio linearifolius Asteraceae Forb Tda UPL F 11; 5; 13 Senecio minimus Asteraceae Forb Tdr FACU F 11; 5; 13 Senecio spp. Asteraceae Forb T - F 11; 13 Sonchus Asteraceae Forb Tdr UPL F 11; 5; 2; 13 Sprengelia incarnata Ericaceae Shrub ATw FACW F 11; 5; 13 Stellaria pungens Caryophyllaceae Forb Tda FACU F 11; 5; 13 Stylidium graminifolium Stylidiaceae Forb T FACU F 11; 5; 13 Stylidium spp. Stylidiaceae Forb T - F 11; 13 Thelymitra spp. Orchidaceae Forb T - F 11; 13 Trachymene spp. Apiaceae Forb T - F 11; 13 Utricularia spp. Lentibulariaceae Forb ARp OBL E 8; 11; 5; 7; 13 Velleia montana Goodeniaceae Forb Tda FAC F 11; 5; 13 hederacea Violaceae Forb Tda FACU F 11; 5; 13
Supplementary Material: SUSPECT SCREENING AND QUANTIFICATION OF TRACE ORGANIC EXPLOSIVES IN WASTEWATER USING SOLID PHASE EXTRACTION AND LIQUID CHROMATOGRAPHY-HIGH RESOLUTION ACCURATE MASS SPECTROMETRY Helena Rapp-Wrightaǂ, Gillian McEneffaǂ, Bronagh Murphya, Sally Gambleb, Ruth Morganb, Matthew Beardahc and Leon Barrona a Analytical & Environmental Sciences Division, King’s College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK b UCL Department of Security and Crime Science, 35 Tavistock Square, London WC1H 9EZ, UK c Forensic Explosives Laboratory, Dstl Fort Halstead, TN14 7BP, UK ǂ Both contributed equally as first author. Table of contents: S1.0 Glassware treatment procedure S3 List of tables: Table S1. A list of all solid phase extraction (SPE) sorbents tested, their chemical and physical properties and their respective extraction method protocols as recommended by the manufacturer S4 Table S2. Optimised atmospheric pressure chemical ionisation (APCI) and mass spectrometric conditions for the selected explosive analytes S6 Table S3. Analyte recoveries (n=3) determined using solid-phase extraction (SPE) with HPLC-UV for 16 analytes and HPLC-HRMS for two peroxide compounds only S7 Table S4. Comparison of analyte recoveries with the selected sorbents using extraction methods with and without a post-elution evaporation step S9 S5. Recoveries for the three selected sorbents using the optimised method (n=3) S10 List of figures: Figure S1. Analyte elution profiles for (a) Oasis HLB, (b) Isolute ENV+ and (c) Bond Elut ENV methanol as the eluting solvent (n=2) S11 Figure S2. Analyte recoveries for (a) Oasis HLB, (b) Isolute ENV+ and (c) Bond Elut ENV methanol, acetonitrile and ethyl acetate as eluting solvents (n=3) S12 Figure S3. Comparison of analyte recoveries achieved for Milli-Q water samples
(Supporting Information for Biomaterials) In situ diselenide-crosslinked polymeric micelles for anticancer drug delivery V. G. Deepagan a,1, Seunglee Kwon a,1, Dong Gil You a,b, Van Quy Nguyen a, Wooram Um b,c, Hyewon Ko c, Hansang Lee a, Dong-Gyu Jo d, Young Mo Kang e, Jae Hyung Park a,c, a School of Chemical Engineering, College of Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea b Center for Theragnosis, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea c Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea d School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea e School of Medicine, Kyungpook National University, Daegu 41566, Republic of Korea *Corresponding author: Jae Hyung Park, Ph.D. Tel: +82-31-290-7288; fax: +82-31-299-6857; e-mail:
Model In this appendix we present a simple model of the choice between bank loans and bonds. This setting looks at a static choice between two perfectly substitutable sources of funding, subject only to a limit in the availability of the cheaper source. The firm output function is , with and . The firm has no internal funding and has to raise K externally. A firm is borrowing L from a bank and B from the bond market: (1) The bank has an upper limit D on the loan it may extend. This is a fixed amount of financing the bank has, one can think of it as a deposit base which cannot be easily increased: (2) The cost of deposits for the bank is R, and loans are competitively priced so that the bank charges R to the borrower. The bond market supplies bonds in the amount B at cost with and . The firm’s problem is The first order condition with respect to borrowing is: (3) To ensure that the firm utilizes both forms of funding in its optimum, we simply assume that (4) Under assumption (4), constraint (2) is binding, and the firm maximizes the loan it gets, so that . The first order condition with respect to B is:
Using L=D this becomes (5) We can differentiate (5) with respect to bank-loan supply D, allowing B to vary with D: (6) Finally, we can solve for BD: (7) The sign follows from the assumptions made about the functions f and y. Equation (7) shows that the quantity of bonds issued goes down when loan supply D increases, and up when D decreases. In other words, firms substitute between bonds and loans. When banks lend less, bond issuance goes up in response. What about price of bonds? We take the total derivative of y(B) with respect to D: (8) Bond yields go up when the loan supply falls; as firms are getting more funding out of the bond market, the price of bond funding is pushed up. The predictions of the model, as derived in equations (7) and (8), are consistent with the empirical findings in our paper. They key assumption is that the bank lending market is not clearing through price. This may reflect various frictions, including rationing as in Stiglitz and Weiss (1981). However, this is a particularly simple setting and it is reasonable to ask if these predictions are specific to our simple set-up. In fact, they are not. Holmström and Tirole (1997) derive an equilibrium with some similar properties, as can be seen from their equation (9):
Electronic Table 1 Search algorithm for substance abuse-related terms Term searched Related used less commonly‡ PubMed search algorithm# Number of hits Aberrant behaviors (aberrant) AND (behavior) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 14 Abuse Illicit use; Substance/drug use (abuse) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 2794 Addiction Dependence syndrome (addiction) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 448 Craving Urge (craving) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 55 Dependence (dependence) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 634 Diversion (diversion) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 35 Euphoria High (euphoria) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 8 Harmful use Hazardous use$ (harmful use OR hazardous use) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 2678 Intoxication (intoxication) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 91 Misuse Inappropriate use; Unsanctioned use; Nonadherence; Noncompliance (misuse) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 142 Nonmedical use Non-therapeutic use$ Extramedical use (nonmedical use OR non-medical use OR non-therapeutic use) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 35
Physical dependence (physical dependence) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 56 Psychological dependence Psychic dependence (psychologic* dependence) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 59 Tampering Altering; Manipulating (tamper*) AND (drug OR substance OR medication) AND (definition OR classification OR terminology) 19 ‡These related terms were not included in the search algorithms; they are for information only. #Searches were limited to English language articles published from 2000 to July 31, 2012. Child and pediatric population articles were excluded. $Terms pooled into a single search because used synonymously. *Indicates that the search is widened to the word root.
Agonist Receptor Conc. % Inhibition in Gefitinib activity ± SEM Methoxamine α1-adrenergic receptor agonist 0.001μM 0.01μM 0.1μM 1μM 10μM -3.82 ± 0.31 0.82 ± 0.19 -5.6 ± 0.60 -5.34 ± 0.31 -5.13 ± 0.44 Clonidine α2 adrenergic receptor agonist 0.01μM 0.1μM 1μM 10μM -3.97 ± 0.16 -9.24 ± 0.07 -2.86 ± 0.06 -5.03 ± 0.47 -2.27 ± 0.14 Carbachol Muscarinic acetylcholine receptor agonist 0.00001μM 0.0001μM 0.001μM 0.01μM 0.1μM 1μM 10μM -0.75 ± 0.32 -11.12 ± 0.10 -4.19 ± 0.65 -4.69 ± 0.74 -7.48 ± 0.18 16.56 ± 2.89 26.44 ± 0.61 Serotonin 5 0.001μM 0.01μM 0.1μM 1μM 10μM 0.36 ± 0.27 -11.59 ± 0.96 -9.59 ± 0.52 -6.63 ± 0.36 -7.15 ± 0.48 I-TAC CXCR3 -0.67 ± 0.19 1.01 ± 0.27 -4.79 ± 0.20 SDF-1 CXCR4 -6.49 ± 0.89 -16.74 ± 0.80 -9.66 ± 0.28 IL8 CCR, CXCR -1.96 ± 0.02 -1.53 ± 0.09 -2.84 ± 0.04 -3.27 ± 0.02 -4.38 ± 0.00 -9.63 ± 0.11
Supplementary Data Suppl. Table 1: cpDNA regions Primers Sequences Restriction enzymes matK AF-MR matK-AF 5'-CTATATCCACTTATCTTTCAGGAGT-3' AluI, AseI, BglII, Bsp1286I, DpnI, EcoRI, EcoRV, HaeIII, HhaI, HindIII, HinfI, MboII, MspI, NcoI, PstI, PvuII, RsaI, Sau3AI, Sau96I, ScrFI, StyI, TaqI, XbaI, XhoI matK-MR 5'-CCAGGAAGAGGTTAATCGTAAATGA-3' rpo C1-C2 rpoC1 5'-AAGCGGAATTTGTGTGCTTGTG-3' rpoC2 5'-TAGACATCGGTACTCCAGTGC-3' rbcL-ORF106 rbcL 5'-ATGTCACCACAAACAGAAACTAAAGCAAGT-3' ORF-106 5'-ACTACAGATCTCATACTACCCC-3'
Fig. S11 Hysteresis loops of MGF@Fe3O4 sample measured at 298 K after acid treated for diffirent time. Table S1: EMW absorption performance of materials reported in open literatures Samples in matrices wt % Max |RL| [dB] Thickness [mm] (RL ≤ -10 dB) Testing frequency range [GHz] Frequency [GHz] (RL ≤ -10 dB) Refs 3D graphene foam@Fe3O4 in wax 27 -64.4 2.4 1-18 12-18 This work nanosheet 25 -51.5 1.8 2-18 12.4-17.5 [1] graphene/SiO2 composites 7 -42 2.1 X-band 8.4-12 [2] Carbon Hollow Microspheres 20 -84 3.9 2-18 6.7-10 [3] Fe/C 30 -43 3.0 2-18 ----- [4] rGO/a-Fe2O3 composite 8 -33.5 3.0 1-18 10.8-17.2 [5] Fe3O4/GCs Composites 30 -32 3.5 2-18 6.9-11.9 [6] Thermally reduced graphene 1 -43.5 3.5 2-18 9.4-16.8 [7] Porous graphene 30 -44.2 3.0 0.5-18 4.1-6.8 16.0-17.8 [8] RGO–Fe3O4 40 -15.4 2.0 0.5-18 10.4-13.2 [9]
(pH 5.5 24h) 552.7 ± 3.3 0.23 ± 0.03 -38.9 ± 2.2 BZL-pLNPs (pH 7.4 after 4h) 303.5 ± 2.0 0.19 ± 0.03 -45.2 ± 1.6 BZL-pLNPs (pH 7.4 after 24h) 372.2 ± 10.8 0.29 ± 0.03 -44.7 ± 1.2 SFN-pLNPs (pH 5.5 after 4h) 330.5 ± 2.8 0.16 ± 0.03 -48.2 ± 4.2 SFN-pLNPs (pH 5.5 after 24h) 674.7 ± 37.1 0.35 ± 0.04 -41.5 ± 1.0 SFN-pLNPs (pH 7.4 after 4h) 315.3 ± 4.3 0.17 ± 0.01 -49.8 ± 5.0 SFN-pLNPs (pH 7.4 after 24h) 398.9 ± 6.4 0.32 ± 0.02 -47.0 ± 2.6 Table S6. IC50 values of BZL determined for the different cell lines. Cell line IC50 (µM) MDA-MB-231 8.3 MCF-7 9.7 PC3-MM2 12.4 Caco-2 0.6 EA.hy926 4.6 Video S1. Fe3O4-LNPs attracted towards the point where the magnetic field is higher and translocated to another place of the well after moving the magnet. (See the provided Video File for details).
Table S2. RGB values extracted from ‘textures’ or images of metal pellets using image editor (Photoshop CS6). Supplementary Figures Figure S1. Schematic of experimental set-up: 1-induction furnace, 2-crucible, 3-melt, 4-hood, 5-ductline, 6-cooler, 7-bag filter, 8-fan (the arrow indicates the flow towards the chimney) Figures S2 – S7 are provided as separate high-resolution files.
prednisone equivalents Hormone replacement therapy as clinically indicated Withhold immune checkpoint therapy prednisone equivalents Hormone replacement therapy as clinically indicated Withhold or discontinue immune checkpoint therapy prednisone equivalents Hormone replacement therapy as clinically indicated Permanently discontinue immune checkpoint therapy Thyroid Hypothyroidism and hyperthyroidism Hormone replacement therapy for hypothyroidism Initiate immediate medical management for hyperthyroidism as appropriate No recommended dose adjustments for immune checkpoint therapy Hormone replacement therapy for hypothyroidism Initiate immediate medical management for hyperthyroidism Withhold or discontinue immune checkpoint therapy Hormone replacement therapy for hypothyroidism Initiate immediate medical management for hyperthyroidism Permanently discontinue immune checkpoint therapy Adrenal Adrenal insufficiency Withhold immune checkpoint therapy Corticosteroids at mg/kg/day prednisone equivalents Permanently discontinue immune checkpoint therapy Corticosteroids at mg/kg/day prednisone equivalents Permanently discontinue immune checkpoint therapy Pancreas Type 1 diabetes mellitus Insulin for type 1 diabetes Insulin for type 1 diabetes Withhold immune checkpoint therapy and administer for hyperglycemia Insulin for type 1 diabetes Permanently discontinue immune checkpoint therapy for hyperglycemia aBased on available management guidelines and prescribing information [35,37,38,40,41].
Supplementary Table 1: FDA approval of checkpoint inhibitors for advanced NSCLC Generic name Alternate Names FDA approval Second-Line Nivolumab (q2 weekly) ONO-4538, BMS-936558, MDX1106 PD-1 inhibitor BMS PD-L1 unselected squamous (March 4, 2015) non-squamous (October 9, 2015) Pembrolizumab (q3 weekly) Lambrolizumab, MK-3475 PD-1 Inhibitor Merck PD-L1 selected regardless of histology ≥50% PD-L1+ (October 2, 2015) ≥1% PD-L (October 24, 2016) Atezolizumab (q3 weekly) MPDL3280A PD-L1 inhibitor Roche PD-L1 unselected regardless of histology (October 18, 2016) First Line Pembrolizumab (q3 weekly) Lambrolizumab, MK-3475 PD-1 Inhibitor PD-L1 selected regardless of histology ≥50% PD-L1+ (October 24, 2016) NSCLC, non-small cell lung cancer; PD-1, programmed cell death protein 1; Programmed death-ligand 1(-positive)
porosity 0.375 [42] tortuosity 3 [42] tortuosity 3 [42] Boltzman constant; Lennard-Jones potential of species 59.7 [K] [41] Boltzman constant; Lennard-Jones potential of species 809.1 [K] [41] Boltzman constant; Lennard-Jones potential of species 189 [K] [41] ] CO Boltzman constant; Lennard-Jones potential of CO species 91.7 [K] [41] CH4 Boltzman constant; Lennard-Jones potential of CH4 species 481.8 [K] [41] collision diameter of 2.827 [41] collision diameter of the species 2.641 [41] collision diameter of the species 4.486 [41] CO collision diameter of the species 3.69 [41]
Supplemental Information Sustainable silicon photovoltaics manufacturing in a global market: a techno–economic, tariff and transportation framework Sergio Castellanos1,2,3,4*, José E. Santibañez-Aguilar5**, Benjamin B. Shapiro6, Douglas M. Powell7, Ian M. Peters8, Tonio Buonassisi8, Daniel M. Kammen2,3,6, Antonio Flores-Tlacuahuac5 1 Berkeley Energy & Climate Institute, University of California, Berkeley, CA 94720, USA 2 Renewable and Appropriate energy Laboratory, University of California, Berkeley, CA 94720, USA 3 Energy and Resources Group, University of California, Berkeley, CA 94720, USA 4 Escuela de Gobierno y Transformación Pública, Tecnológico de Monterrey, Campus Ciudad de México, CDMX, México 5 Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501, Monterrey 64849, Mexico 6 Goldman School of Public Policy, University of California, Berkeley, CA, 94720, USA 7 Boston Consulting Group, Detroit, MI, USA 8 Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA * sergioc@berkeley.edu ** santibanez.ezequiel@itesm.mx Supplementary Material 1.- Mathematical model associated to the considered supply chain Nomenclature Indexes , , Subscripts or superscripts to indicate a variable or activity associated with nodes
Supplementary Material Table S1. Description of cattle herds in Västra Götaland county and Sweden. Västra Götaland Sweden Number of herds 882 4 973 Average herd size 66 70 Milk delivery per farm, kg ECM 536 000 537 000 Milk delivery per cow, kg ECM 8 300 8 400 Beef: Number of suckler cow herds 2 022 11 375 Average suckler cow herd size 15 17 Number of herds with finishing cattle 6 391 35 183 Average finishing cattle herd size 26 27 References: Statistics Sweden, 2014. Agricultural statistics 2013. Retrieved 15 11 16 from Swedish Dairy Association. 2012. Cattle statistics. 37 p. Table S2. Description of assumed farms with dairy cows, cow-calf production and finishing cattle in the Reference and three alternative scenarios: Ecosystem, Nutrients and Climate. Reference Ecosystem, Nutrients and Climate Dairy cow Cow-calf Finishing cattle cow Cow-calf Finishing cattle Location 20% in plains district, 80% in semi-forested district 100% in district 100% in district 20% in plains district and 80% in district 100% in district 100% in district Head, no.a 180 35 150 bulls, 75 heifers or 75 steers 180 35 150 bulls, 75 heifers or 75 steers Housing system Loose housing in insulated barn with cubicles and automatic milking system Loose housing in uninsulated barn with deep straw bedding housing in a mix of barns with deep straw bedding, slatted floor or cubicles Loose housing in insulated barn with cubicles and automatic milking system Loose housing in uninsulated barn with deep bedding of reed canary grass Loose housing with cubicles aLiving animals for cows, number of slaughtered animals per year for finishing cattle. Reference: Bostad, E., Swensson, C., Pinzke, S. 2011. Labour input in specialist beef bull production in Sweden. Agricultural Engineering International: CIGR Journal 18, 1-12. Figure S1a. Flow of cattle in the Reference and in the alternative scenario Ecosystem, ♀ is heifers, ♂ is bulls, and ♂ is steers, widths of arrows indicate the number of slaughtered animals/calving replacement heifers per year, areas of circles indicate cow population sizes, whereas yellow and green imply indoor and grazing periods, respectively.
(1), 4.9 for government responsiveness (column (2), and 3.5 for effectiveness in corruption control (column (3). The coefficient on the dummy non-government, however, is negative and statistically significant. Moreover, the magnitude of this coefficient is rather large for government responsiveness (-0607) and effectiveness of corruption control (-0.448). Table A3: Regression results for scores on selected governance issues Explanatory variable Dependent variable Citizens, in general, satisfy with city government City government is responsive to citizens City government is effective in corruption control (1) (2) (3) Non-government -0.301** -0.607*** -0.448 (0.128) (0.125) (0.155) Constant 4.779 4.921 3.518 (0.087) (0.084) (0.113) N 492 492 492 Adj-R2 0.05 0.13 0.14 Note: the regressions are on model (1) presented in Section 3, using Stata areg command.
Double) (Brazilian codfish, hakefish) - southeastern and southern (250 to 500 m depth) "Gerival" (shrimps) - bays and estuaries in the Paraná coast GER S 433 433 Handline (old permission) HL BEEZ 1 3 28 3 1 8 4 11 1 7 67 Surface handline (mackrel and albacore) and surface gillnet (pelagic fish) - northern and northeastern HL N/NE 1 29 1 11 4 46 Bottom handline (diverse fish) - northeastern HL NE 1 10 177 41 11 2 18 1 261 Bottom handline (diverse fish) - southeastern and southern HL SE/S 148 120 15 19 302 Jigger (squid) - southeastern and southern JIG SE/S 4 12 27 6 49
Manual (algae) - southeastern and southern MAN SE/S 1 1 Manual (algae) - southeastern and southern MAN SE/S 2 2 Gillnet (old permission, revised) MIX BEEZ 59 173 1 3 7 2 40 54 82 197 258 13 889 Gillnet + trawl (old permission) MIX BEEZ 1 17 11 6 35 Gillnet + purse-seine (old permission) MIX BEEZ 2 22 7 31 Gillnet + longline (old permission) MIX BEEZ 9 1 2 43 321 46 58 24 504 Coastal gillnet - diverse MIX BEEZ 1 69 70 Diverse - northern MIX N 2 3 5 Pots (lobsters) and surface gillnet (mackerel) - northeasterna and northern MIX N/NE 1 1 42 1 2 53 12 112 Coastal surface gillnet (mackerel) and surface handline MIX N/NE 3 2 4 494 1 22 25 113 664
(pelagic fish) - northern and northeastern Coastal surface gillnet (mullets) and surface handline (tunas and correlated) - northern and northeastern MIX N/NE 1 1 3 5 10 Vertical longline/pots (porgy) and horizontal surface longline (pelagic fish) - northern and northeastern MIX N/NE 1 2 54 19 1 77 Pots (lobster) and vertical longline (demersal fish) - northeastern and northern MIX N/NE 25 38 56 352 208 100 7 153 74 1 1 1015 Pots (lobster) and handline (demersal fish) - northeastern and northern MIX N/NE 2 6 241 1 19 71 2 342 Pots (lobster) + handline (mackrel) - MIX N/NE 3 25 13 1188 15 60 21 160 4 1 1490
- southeastern and southern Pots/creels (ornamental fish) - Brazilian POT BEEZ 5 5 10 Pots/creels (octopus) - northeastern and northern POT N/NE 2 2 1 5 Pots (red-crab) - southern POT S 1 1 Pots/creels (octopus) - southern POT S 16 3 19 Pots/creels (octopus) - southeastern POT SE 5 5 Pots - southeastern and southern POT SE/S 4 8 12 (skipjack tuna) - southeastern and southern PS SE/S 1 1 Purse-seine (sardine) - southeastern and southern PS SE/S 1 2 312 8 211 534 Purse-seine (sardine and anchovy) - southeastern and southern PS SE/S 66 14 47 1 128
Revisiting the LCA+DEA method in fishing fleets. How should we be measuring efficiency? SUPPORTING MATERIAL Contents: Section S1 – Characteristics of the species captured by the Cantabrian purse seining fleet. ………. p.S3 Table S1. Edible meat contain and protein content of the species captured by the Cantabrian purse seining fleet. ………. p.S3 Table S2. Trophic level (TL) of the species captured by the Cantabrian purse seining fleet. ………. p.S3 Section S2 – ReCIPE impact categories. ………. p.S4 Table S3. Overview of the ReCIPE midpoint categories. ………. p.S4 Section S3 – Inputs and outputs of the Data Envelopment Analysis (DEA) matrices. ………. p.S5 Table S4. Input/output data envelopment analysis (DEA) matrices using the Egalitarian endpoint perspective. ………. p.S5 Table S5. Input/output data envelopment analysis (DEA) matrices using the Hierarchist endpoint perspective. ………. p.S7 Table S6. Input/output data envelopment analysis (DEA) matrices using the Individualist endpoint perspective. ………. p.S9 Section S4 – Efficiency scores () and and target reduction (%) scores of the DEA matrices CRS model. ………. p.S11 S7. Efficiency scores () and target reduction scores (%) for the Egalitarian endpoint perspective. ………. p.S11 Table S8. Efficiency scores () and target reduction scores (%) for the Hierarchist endpoint perspective. ………. p.S13
S9. Efficiency scores () and target reduction scores (%) for the Individualist endpoint perspective. ………. p.S15 Figure S1. Relation between the Global Warming Potential (GWP) and the efficiency (Ф) of the 32 DMUs using the Individualist (I) perspective. ………. p.S17 Figure S2. Relation between the Global Warming Potential (GWP) and the efficiency (Ф) of the 32 DMUs using the Hierarchist (H) perspective. ………. p.S17 Section S5 – Efficiency scores () and and target reduction (%) scores of the DEA matrices using VRS model. ………. p.S18 S10. Efficiency scores () and target reduction scores (%) for the Egalitarian endpoint perspective. ………. p.S18 S11. Efficiency scores () and target reduction scores (%) for the Hierarchist endpoint perspective. ………. p.S20 Table S12. Efficiency scores () and target reduction scores (%) for the Individualist endpoint perspective. ………. p.S20
Section S1 – Characteristics of the species captured by the Cantabrian purse seining fleet. S1. Edible meat contain and protein content of the species captured by the Cantabrian purse seining fleet. Specie Edible meat contain (%) Protein content (%) Engraulis encrasicolus 62 21 Sardina pilchardus 62 20 Scomber scombrus 61 20 Trachurus trachurus 52 20 Thunnus alalunga 64 22 S2. Trophic level (TL) of the species captured by the Cantabrian purse seining fleet. Specie Trophic level (TL) SD Engraulis encrasicolus 3.1 0.45 Sardina pilchardus 3.1 0.10 Scomber scombrus 3.6 0.20 Trachurus trachurus 3.7 0.00 Thunnus alalunga 4.3 0.20
Table 1. Geographical origin and the place of purchase. Geographical Origin Spain (n=30) Czech Republic (n=12) Turkey (n=2) PDO La Mancha 6 Aragón 2 Krokos Kozani 2 Labelled Spanish Saffron 18 8 Turkey 1 2 India 1 Unknown 2 2
Appendix A. Kinetic models of lutein dissolution profile from the tablets B and D using the dissolution medium water with P80 2% (w/v) at 37°C in the dissolution apparatus II from USP. Each model has its representative equation and the correlation coefficient (R2). Kinetic models Products Equation R2 a b Zero order B 1.376 –10.45 0.91 D 7.048 –29.73 0.98 order B 0.0597 –0.2736 0.99 D 0.1489 –0.1866 0.84 Higuchi B 10.34 –27.93 0.82 D 43.32 –93.11 0.99 Korsmeyer Peppas B 1.888 –1.432 0.91 D 3.250 –1.812 0.94
SUPPLEMENTARY MATERIAL September, 2016 FOODCHEM-D-16-02404R1 Figure 1. Geographical location of the Jaguaribe area in the state of Ceará, Brazil. A: The region of milk production in Ceará (a, b, c, d, e, f and g). In B, an enlarged image shows the more specific location of the Jaguaribe area and cheese factories (F1 at F10). FIGURE CAPTIONS - Figure 2. Production flowchart of Jaguaribe Coalho cheese. Fig. 3. Reversed-phase chromatographic separation of water soluble peptides extracted from Jaguaribe Coalho cheese. Numbering refers to peaks sequenced in tables 2 and 3. Figure 4. Reversed-phase high performance liquid chromatography of Coalho cheese produced in the Jaguaribe region. C1 to C10: Samples 1 to 10 of the cheeses provided by the different cheese factories.
C31 H37 -0.284 0.116 0.611 C33 H41 -0.843 0.291 0.175 γ-decalactone L10 -0.841 0.291 0.304 δ-decalactone L11 0.973 0.105 0.054 (Z)-6-γ-dodecenolactone L12 -0.671 0.624 0.272 hydroxy lactone L13 0.897 -0.005 0.048 γ-dodecalactone L14 -0.611 0.783 0.089 δ-dodecalactone L15 0.988 0.135 0.015 γ-hexalactone L3 0.841 0.100 0.052 γ-octalactone L6 -0.661 0.470 0.381 6-pentyl-α-pyrone L9 -0.549 0.374 0.412 geranyl acetone T8 0.201 0.459 0.502
Supplementary data-1: Recovery of the analyte at different injected concentrations Spiked concentration (µg/L) Recovery (%) ± RSD (n=5) 50 98.24 ± 1.2 100 97.96 ± 2.3 500 98.08 ± 0.9 1000 100.53 ± 1.4 5000 100.89 ± 2.0 Supplementary data-2: Recovery of the analyte in presence metals
Table S1. Characteristics of Sudan dyes. Analytes Molecular structure Molar mass (g mol -1) pKa Log P Sudan I 248.3 11.65 4.24 II 276.3 11.65 5.16 Sudan III 352.4 11.44 5.74 IV 380.4 N.a 6.66 Sudan Red G 278.3 11.8 4.99 Sudan orange G 214.2 11.67 2.44 Table S2. Comparison between proposed method and other extraction methods for determination of Sudan dyes. Analytical method Analyte LOD (ng mL-1) LDR (ng mL−1) ER% PF Analysis time (min) SFE–capillary liquid chromatography Sudan I Sudan II Sudan III Sudan IV 28.8 23.2 32.3 42.0 50-1000 50-1000 50-1000 50-1000 - - - - - - - - 35 OS-AALLME/ HPLC-UV Sudan I Sudan II Sudan III Sudan IV Orange G 7.7 8.3 12.1 10.6 84.8 27-2900 29-2300 40-2600 36-2800 280-3100 - - - - 37 34 37 33 35 4.5 Ultrasound-assisted LLE/HPLC-DAD Sudan I Sudan II Sudan III Sudan IV 50 60 70 100 200-5000 200-5000 200-5000 500-5000 - - - - - - - - 20 DSSBME-HPLC-UV Sudan I Sudan II Sudan III Sudan IV 0.19 0.39 0.56 0.75 0.5-1000 1.0-1000 5.0-1000 5.0-1000 - - - - 147 61 35 38 ≥20 U-HF–LPME– HPLC–UV I Sudan II III Sudan IV 0.09 0.31 0.63 0.95 1.0-1000 1.0-1000 5.0-1000 10.0-1000 - - - - 186 52 38 37 ≥40 ILE–HPLC/UV Sudan I Sudan II Sudan III Sudan IV 12.8 12.0 11.2 13.2 20-20000 20-20000 20-20000 20-20000 - - - - - - - - 32
This work I Sudan II Sudan III Sudan IV Sudan orange G Sudan red G 0.7 1.6 1.0 2.0 8.0 1.5 2.5-1000 5.0-1200 3.0-1000 7.0-1000 25-1000 5.0-1000 65 72 79 67 58 73 100 110 121 103 90 113 7.0
For medicinal purposes 22.6% Other 3.2% I don't use 3.2% 7 Preferred type No clusters - Liquid 59.7% Crystallized honey 22.6% Basic honey 43.5% Organic 22.6% Monofloral/varietal honey 17.7% Multifloral 24.2% Honeydew 9.7% It doesn't matter 12.9% I don't use honey 3.2% a Models created using Two-step clustering in SPSS. b Means on a scale from 1 to 7 (1 = “Several times in a week”; 2 = ” Once a week”, 3 = ” A couple of times a month”, 4 = ” Once a month”; 5 = ”Few times a year”, 6 = ” Once a year”, 7 = “More seldom than once a year or never”).
Supplementary Table 1: Nutritional composition of control bread and breads enriched with 3%, 6% and 9% of artichoke stem powder (ASP). Values are expressed for 100 g of fresh product. Control bread 3% ASP bread 6% ASP bread 9% ASP bread Proteins (g) 8.9 8.9 8.9 9.0 Lipids (g) 0.6 0.6 0.6 0.6 Carbohydrates (g) 62.6 60.7 58.3 56.9 Dietary Fiber (g) 1.8 3.2 4.5 5.9 Energy (kcal) 295 290 283.2 280.8
GRADEpro Grading of Outcomes Patient or population: Patients with Suspected Tumours Settings: Clinical Intervention: Primary Outcomes Outcomes Illustrative comparative (95% CI) Relative effect (95% CI) No of Participants (studies) Quality of the evidence (GRADE) Comments Assumed risk Corresponding risk Cryotherapy RAPN Recurrence Rates population RR 23.39 (5.7 to 96.05) 767 (4 studies) ⊕⊕⊕⊝ moderate1,2,3 115 per 1000 1000 per 1000 (654 to 1000) Moderate 0 per 1000 0 per 1000 (0 to 0) Overall Complication Study population RR 0.84 (0.64 to 1.11) 1101 (4 studies) ⊕⊕⊝⊝ low1,4 140 per 1000 117 per 1000 (89 to 155) Moderate 164 per 1000 138 per 1000 (105 to 182) Clavien 3 or Higher Complications Study population RR 0.71 (0.36 to 1.41) 24 per 1000 17 per 1000 (9 to 34)
Peanut Arachis hypogaea Profilin Ara h 5 AAD55587.1 Peanut Arachis hypogaea δ-conglutin Ara h 6 AAD56337.1 Peanut Arachis hypogaea δ-conglutin Ara h 7 AAD56719.1 Peanut Arachis hypogaea PR-10 Ara h 8 AAQ91847.1 Peanut Arachis hypogaea nsLTP Ara h 9 ABX75045.1 Peanut Arachis hypogaea Oleosin Ara h 10 AAU21499.2 Peanut Arachis hypogaea Oleosin Ara h 11 AAZ20276.1 Peanut Arachis hypogaea Oleosin Ara h 14 AAK13450.1 Soya Glycine max Profilin Gly m 3 CAA11756.1 Soya Glycine max PR-10 Gly m 4 CAA42646.1 Soya Glycine max β-conglutin Gly m 5 AAB23463.1 Soya Glycine max α-conglutin Gly m 6 BAA74953.1 Soya Glycine max δ-conglutin Gly m 7 ACS49840.1 Soya Glycine max δ-conglutin Gly m 8 AAB71140.1 Mung bean Vigna radiata PR-10 Vig r 1 AAX19889.1 Mung Bean radiata β-conglutin Vig r 2 ABW23574.1 Mung bean Vigna radiata δ-conglutin Vig r 4 CAA50008.1 Mung bean Vigna radiata Bet v 1 family member Vig r 6 BAA74451.1 Green bean Phaseolus vulgaris nsLTP Pha v 3 ADC80502.1