zilongwang/ReadingBank huggingface dataset:
Malta [29,30] – specific specialists registered under the Health Care Professions Act from the following areas of expertise: neurology, oncology and radiotherapy, anesthetics and intensive care medicine, psychiatry or of any other specialists register under the said Act, can prescribe medicinal preparations of the plant cannabis. Nabiximols is technically approved, but is currently not sold anywhere in the country. Nabiximols can only be prescribed by one of the above-mentioned specialists and only after this prescription is approved by the Superintendent of Public Health. Herbal cannabis is not allowed. No other cannabinoid-based medicines are allowed. Netherlands [31,32] – The Office of Medicinal Cannabis (OMC) is a governmental agency under the Dutch Ministry of Health, Welfare and Sports that was founded on January 1, 2001. It is responsible for all cultivation of cannabis for medical and scientific purposes, and has a monopoly on all transactions regarding these products, including import and export. The only company authorized to grow and distribute medical grade cannabis in the Netherlands is "Bedrocan". All physicians can prescribe herbal cannabis for any indication. The cannabis is mainly prescribed as inflorescence, while the patient decides on the ways of the consumption: smoking, vaporizing or making extract. The cannabis can be purchased in many pharmacies throughout the country. In addition to that, there is one pharmacy that makes medicinal oil from the same flowers ("Transvaal Apotheek"). Cannabis is usually given when other therapies are ineffective or causing adverse effects. The OMC offers varieties of medical cannabis, with levels of THC ranging from less than 1% to approximately 22% and CBD ranging from less than 1% to approximately 9%. Nabiximols and Dronabinol are also approved for use. Norway [33] – Cannabis is not authorized for medical use. On November 2016, Norway introduced new instructions by which cannabis can be prescribed for patients via a special permit. Only doctors working in hospitals can apply with a request for medical cannabis and there is no limit to specific indications. Each request is sent to the Norwegian Medicines Agency for approval and it must state why the patient cannot be treated with other approved drugs, including approved cannabinoid-based medicines. After approval, cannabis is sent from the Dutch company "Bedrocan" to a Norwegian pharmacist, which supplies the cannabis to the patient.
placebo Placebo: 37.2% M 3 mg: 28.8% M 10 mg: 24.4% PGI agonist Iloprost 16/09/2003 RR A02997 17 placebo COMPOSITEd OR 3.97 (95% CI 1.47-10.75) Improvement in NYHA class 12 weeks Baseline N. (%) Improved III Placebo: 59 (57.8) I: 60 (59.4) Placebo 12.7% Iloprost: 24.8% IV Placebo: 43 (42.2) I: 41 (40.6) 10% improvement in 6MWD Baseline, Mean±SD Improved I: 332±93 m 37.6% Placebo: 315±96 m 25.5% Selexipag 12/05/2016 GRIPHON21 placebo COMPOSITEe HR:0.60 (99% CI 0.46-0.78) Complication related to PAH Mean placebo: 63.7 weeks Mean selexipag: 70.6 weeks Hospitalization for worsening of PAH Placebo: 18.7% S: 13.6% Disease progression Placebo: 17.2% S: 6.6% PDE5 inhibitor Sildenafil 28/10/2005 A148114022 placebo 6MWD b 20 mg: 38 m (99% CI: 12-64) 40 mg: 45 m (99% CI: 21-70) 80 mg: 42 m (99% CI: 9-75) Baseline, Mean±SD Mean change from baseline compared to placebo (99% CI) 12 weeks Placebo: 344±79 - S 20 mg: 347±90 38 m (12-64) S 40 mg: 345±77 45 m (21-70) S 80 mg: 339±79 42 m (9-75) Tadalafil 6MWD b Baseline, Mean±SD Δ Baseline, Mean±SD
Table A4: Results of for all FSC stages k (including the different AEoL) for the Milk and dairy product in the 15 impact categories. FSC stage Impact category Total (sum of k=1 k=2 k=3 k=4 k=5 EoL - k=6 - k=7 - k=8 AEoL - k=9 - Feed CC kg eq OD kg eq HT.nce HT.ce PM kg eq IR.hh eq POF kg eq A H+ eq TE N eq FE kg P eq ME kg N eq Fecotox C
Table S4. Fraction of organic waste for different cities of Bangladesh Waste composition (%) Other urban areas Total 3423 7725 Pyrolysable 1843 4159 Fuel for pyrolysis 5 5 3 527 1514 5089 12837 Pyrolysable 2740 6912 Fuel for pyrolysis 6 784 2561 N K C 2 3 0 0 1 4 1 1 e
Conjugates dilution T2–QDs SAM 1/20 1/75 1/50 1/45 1/80 DAM FLISA – 1/50 1/40 – – GBA 1/15 1/50 1/25 1/30 1/60 Table SI-2. Analytical characteristics for mycotoxins’ determination by ELISA and multiplex FLISA in buffer solutions (n = 5). LOD, ng mL-1 IC50, ng mL-1 Linear range, ng mL-1 ELISA FLISA ELISA ELISA FLISA DON 1.4 0.16 27±2 11±1.4 6.5-80 2-29 ZEN 0.2 0.03 1.5±0.3 0.13±0.03 AfB1 0.12 0.01 1.34±0.05 0.11±0.02 T2 5 0.5 62±5 9±1.1 16-160 2-24 FB1 0.2 0.02 2.4±0.07 0.3±0.06 0.6-5.9 Table SI–3. Analytical performance of the column gel–based immunoassay. Parameter Value, % DON ZEN AfB1 T2 FB1 rate, % (Nfalse positive/N−) ×100) 2.1 1.9 1.4 2.6 2.4 False–negative rate, % (Nfalse negative/N+) ×100) 1.5 2.2 1.2 2.8 2.0 Specificity rate, % 97.6 96.0 98.5 97.4 96.8
Functionalized polyacrylamide as an acetylcholinesterase-inspired biomimetic device for electrochemical sensing of organophosphorus pesticides F. Sgobbi* and Sergio A. S. Machado São Carlos Institute of Chemistry, University of São Paulo, P.O. Box 780, Avenida Trabalhador São-carlense, 400, 13560-970 São Carlos, São Paulo, Brazil. *Corresponding author: livia.fsgobbi@gmail.com Table S1. Analytical characteristics of AChE-based biosensors for ATCh detection Electrodes LOD (mol L-1) Concentration Range (mmol L-1) Sensitivity (A mol-1L) Working Potential (V) vs (Ag/AgCl) Real Sample Analysis Storage and Stability* Reference 5.0×10-6 0.02-0.70 0.0724 0.600 Not applied - () AChE/CMC/GCE 1.0×10-7 0.002-0.40 - 0.660 Not applied 4 ºC 70% (30 days) () AChE-MWCNTs- Au-CHIT/GC 1.0×10-7 0.002-0.02 0.02-0.40 - 0.590 Not applied 4 ºC 90% (30 days) () - 0.03-0.0165 0.0915-0.2115 0.016 0.056 0.685 Not applied 4 ºC 90% (30 days) () 3.0x10-5 0.05-4.0 0.17 0.050 Not applied 4 ºC 95% (1 week) () 1.0x10-7 2x10-4-0.03 0.03-0.50 - 0.470 Apple, Cabbage, Lake water 4 ºC 90% (30 days) () - 0.054-0.254 0.0051 0.631 Vegetables 4 ºC 90.2% (30 days) () 3.0x10-6 0.01-1.0 0.00223 0.200 Not applied 4 ºC () AChE
Du, D., et al., 2007. Anal. Bioanal. Chem. 387(3), 1059-1065. Du, D., et al., 2010. Sensors and Actuators B-Chemical 143(2), 524-529. Li, X.-H., et al., 2006. Electroanalysis 18(22), 2163-2167. Liu, G., Lin, Y., 2006. Anal. Chem. 78(3), 835-843. Liu, T., et al., 2011. Sensors Actuat. B: Chem. 160(1), 1255-1261. Yang, L., et al., 2013. Anal. Biochem. 437(2), 144-149. Zamfir, L.-G., et al., 2011. Biosens. Bioelectron. 26(8), 3692-3695. Zhai, C., et al., 2013. Biosens. Bioelectron. 42, 124-130. Zhang, H., et al., 2014. Anal. Chim. Acta 827, 86-94. Zhou, Q., et al., 2013. Biosens. Bioelectron. 49, 25-31.
References Finklea, H.O., Avery, S., Lynch, M., Furtsch, T., 1987. Langmuir 3, 409–413. Hu, S.Q., Wu, Z.Y., Zhou, Y.M., Cao, Z.X., Shen, G.L., Yu, R.Q., 2002. Anal. Chim. Acta 458, 297–304. Kowalczyk, A., Nowicka, A.M., Jurczakowski, R., Fau, M., Krolikowska, A., Stojek, Z., 2011. Biosens. Bioelectron. 26, 2506–2512. Liu, Z., Huang, S., Jiang, D.C., Liu, B.H., Kong, J.L., 2004. Anal. Lett. 37, 2283–2301. Lasia, A.M., 2009. Modern Aspects of Electrochemistry. Modeling and Numerical Simulations, Schlesinger, M., (Ed.), Springer, New York.
Supplemental File Table 2 Regrouping of Data of Study Population in Singapore, 2012 to 2013 S/N Variable From To 1 Age (in years) 18-19 18-29 20-24 25-29 30-34 30-39 35-39 40-44 40-49 45-49 50-54 50-59 55-59 60-65 60-65 2 Housing Type1 Public (1 – 2 Room Flat) Public (1 – 2, 3, 4 or 5 Room Flat) Public (3 Room Flat) Public (4 Room Flat) Public (5 Room Flat) Public Executive/Studio Apartment Public Executive/Studio/Private Apartment/Landed Property Private Apartment Landed Property 3 No. of Children 0 0 1 1 2 2 3 ≥3 4 5 6 7 8 4 Employment Status Legislators, Senior Officials & Managers Employed Professionals Associate Professionals & Technicians Clerical & Support Service & Sales Craftsmen & Related Trade Plant & Machine Operators Cleaners, Labourers & Related Work Looking for Employment Not Employed Not Looking for Employment 5 Highest Educational Qualification None/Lower Primary Primary or Lower Primary Lower Secondary Secondary Post-Secondary Polytechnic Diploma Professional Qualification & Diploma Undergraduate Degree
Postgraduate Diploma/Degree 6 Monthly Income (MI) < S$500 MI < S$2,000 S$500 - S$999 S$1,000 - S$1,499 S$1,500 - $1,999 S$2,000 - $2,499 S$2,000 ≤ MI < S$4,000 S$2,500 - $2,999 S$3,000 - $3,499 S$3,500 – $3,999 S$4,000 – $4,499 S$4,000 ≤ MI < S$6,000 S$4,500 – $4,999 S$5,000 – $5,999 S$6,000 – $6,999 S$6,000 ≤ MI < S$8,000 S$7,000 – $7,999 S$8,000 – $8,999 S$8,000 ≤ MI < S$10,000 S$9,000 – $9,999 S$10,000 – $10,999 MI ≥ S$10,000 S$11,000 – $11,999 S$12,000 – $12,999 S$13,000 – $13,999 S$14,000 – $14,999 ≥S$15,000 Did not indicate Did not indicate 7 Dining Out Frequency Every day Every day 2-3 times a week 1-3 times a week Once a week 2-3 times a month 2-3 times a month or less frequent Once a month Once in 2-3 months Less than once in 2-3 Months In Singapore, housing can be broadly categorised into public and private housing. The former refers to government built apartments that generally have between 1 to 5 rooms (the living area and dining are also defined as rooms). On a limited basis, the government builds apartments with 6 rooms (known as “Public Executive” apartments). The retail cost of apartments in Singapore generally correlates positively with the number of rooms, although there may be other factors influencing the cost. The majority of Singaporeans (about 80%) live in public housing. Private housing is built by private estate developers and is generally considered the more expensive type of housing in Singapore. Table 3 Food Establishment Dining Frequencies of Study Population in Singapore, 2012 to 2013 Dining Frequencies Type of Food Establishments
IJOA 16-00229 Appendix Supplementary Figure 1: Heart rate (beats/min) ranges used to define ‘normality’ on obstetric early warning charts. Each vertical line represents a single vital signs chart, ordered on basis of the lowest heart rate value in the chart’s normal range. Beats/min Charts 1-120  Supplementary Figure 2: Respiratory rate (breaths/min) ranges used to define ‘normality’ on obstetric early warning charts. Each vertical line represents a single vital signs chart, ordered on basis of the lowest respiratory rate value in the chart’s normal range. Breaths/min Charts 1-120  Supplementary Figure 3: Systolic blood pressure (mmHg) ranges used to define ‘normality’ on obstetric early warning charts. Each vertical line represents a single vital signs chart, ordered on basis of the lowest systolic blood pressure value in the chart’s normal range. mmHg Charts 1-120  Supplementary Figure 4: Diastolic blood pressure (mmHg) ranges used to define ‘normality’ on obstetric early warning charts. Each vertical line represents a single vital signs chart, ordered on basis of the lowest diastolic blood pressure value in the chart’s normal range. mmHg
Question 13: If you indicated in your response to question 11 that your project contains components related to “management planning” or “both”, please select the specific types of activities that are in your GEF/LDCF/SCCF financed project in this category (Note: you may select as many responses as are relevant). If you did not select “management planning” or “both” as being a component of your GEF/LDCF/SCCF financed project, please proceed to question 14: Develop or increase access to markets Create or enhance public-private partnerships Activate a new drought plan Creation of adaptation targets Develop or revise a land use plan New health program Integrate climate into extension programs/services Household or livelihood diversification Develop adaptation investment plan Climate resilient coastal infrastructure management plan Conservation agriculture Revision of existing strategies / approach based on an evaluation Ecosystem-based adaptation adaptation Changes in natural resource management (NRM) Development of an adaptation or adaptive management plan "Mainstreaming" of climate adaptation into a plan Other: please describe ____________________ Question 14: If you indicated in your response to question 4 that your project contains components related to "information and communication tools or technology," please identify if these activities are related to "early warning system/global climate observing system", "any other information and tools to support decisions or management", or "both". If you did not select “information and communication technology” as being a component of your GEF/LDCF/SCCF financed project in question 4, please proceed to question 17. Early warning system / global climate observing system Any other information and tools to support decisions or management Both
Content posttest p .052 .000 .013 Transfer posttest r .664 .537 .271 1 p .000 .000 .013 Correlations for Experiment 3 (N=108) Language pretest Language posttest Content posttest Transfer posttest Language pretest r 1 .428 .471 .537 p .000 .000 .000 Language posttest r .428 1 .527 .838 p .000 .000 .000 Content posttest r .471 .527 1 .515 p .000 .000 .000 Transfer posttest r .537 .838 .515 1 p .000 .000 .000
Ceramic at regional storage/CH U 4.00E-3 kg Lime, hydrated, packed, at U 2.00E-3 kg Transport, lorry >16 t, fleet average/RER U 1.90E-2 tkm Transport, freight, rail/RER U 4.30E-2 tkm Resource use Water, cooling, unspecified nature origin/m3 2.50E-2 m3 Water, river 2.20E-2 m3 Emission to air Heat, waste 3.32 MJ Emission to water Hydrocarbons, unspecified 2.00E-4 kg Acetic acid 5.00E-4 kg BOD 5, Biological Oxygen Demand 1.00E-3 kg COD, Chemical Oxygen Demand 1.00E-3 kg DOC, Dissolved Organic Carbon 4.00E-4 kg TOC, Total Organic Carbon 4.00E-4 kg Nitrogen 1.00E-4 kg 2. ALD chemical reaction inventory (For 1g Al2O3) ALD ( A 30 nm thick Al2O3 film; with 300-cycle ALD processes at 200℃) Input Output TMA 2.80g CH4 0.94g H2O 26.03g 1.00g N2 682.00g N2 682.00g ALD system Cambridge NanoTech Savannah 200 49.6% TMA emissions 1.39g Reactor 4 inch standard wafer,150mm inner diameter and 5 mm inner depth 97.97% Waste water 25.48g Total energy 1.71GJ 3. Infrastructure, equipment and tools (Economic inventory) (For 1g Al2O3) The economic inventory with Cambridge NanoTech Company data and RS Means 2013 Items Description quantities Total cost ALD system Savannah S100: Standard 4” ALD- reactor; S100 Dome Lid w/ cassette: Dome lid with cassette for 10 wafers 0.0282 Piece $3,130 Roughing pump Alcatel 2005I – Fomblin prepped 0.0282 Piece $83 (Price) Precursor line Hot precursor lines 0.0282 Pieces $479 (Price) Laboratory Area is around 6m2 0.00376 Piece $48 (Cost) Computer Assume 3 years’ service life (Williams 2004) 0.094 $94 (Price) 4. Detailed process-based LCA modeling in Gabi 6.0
Manufacture of measuring instruments and machinery for cultural activities and office work Other industries Production and distribution of electrical power, heat power, gas, and water C (Construction) Construction Construction T (Transportation, storage, and postal services) Transportation, storage, and postal services Transportation, storage, and postal services O (Other services) Other services Wholesale and retail industry Other services Supplemental Table S2: The standard coal-equivalent (kgce) coefficients for each energy source. Kind of energy Standard coefficient Kind of energy Standard coefficient Raw coal 0.7143 tce/t Diesel oil 1.4571 tce/t Cleaned coal 0.9000 tce/t Fuel oil 1.4286 tce/t Other washed coal 0.2857 tce/t Liquefied petroleum gas (LPG) 1.7143 tce/t Briquettes 0.6000 tce/t Refinery gas 1.5714 tce/t Coke 0.9714 tce/t Natural gas 13.300 tce/104 m3 Coke oven gas 6.1430 tce/104 m3 Other petroleum products 1.2000 tce/t Other gas 3.5700 tce/104 m3 coking products 1.3000 tce/t oil 1.4286 tce/t Heat 0.0341 tce/106 J Gasoline 1.4714 tce/t Electricity 0.1229 tce/104 kW•h Kerosene 1.4714 tce/t Source: NBSC (2010). China Energy Statistical Yearbook. National Bureau of Statistics of the
EF1 0.01 kg N2O-N kg-1 N input IPCC (2006) EF1FR 0.003 kg N2O-N kg-1N EF2 0.01 kg N2O-N (kg + kg NOx-N volatilized)-1 EF3 0.0075 kg N2O-N kg leaching per runoff FracGASF 0.10 kg NH3-N + NOx-N kg-1 N applied FracLEACH − (H) 0.30 kg N kg-1 N additions Burning crop residue CH4 2.7 g kg-1 dry matter burnt IPCC (2006) N2O 0.07 g kg-1 dry matter burnt CO2 1185 g kg-1 dry matter Rice residue to crop ratio 0.75 Fraction area 0.5 Dry matter fraction 1.0 Fraction burn 0.29 Fraction oxidized 0.90 Rw: rainy season rain-fed, Lw: rainy season irrigated, and Id: dry season irrigated GHG emissions from raw materials production 1) Fuel production Diesel fuel GHG emissions from diesel fuel production = Total amount of diesel fuel emission factor of diesel fuel production (S1) Gasoline fuel GHG emissions from gasoline fuel production = Total amount of gasoline fuel emission factor of gasoline fuel production (S2) 2) Chemical fertilizer production GHG emissions from chemical fertilizer production = Total amount of N in rice fields emission factor of N production + Total amount of P in rice fields emission factor of P production + Total amount of K in rice fields emission factor of K production (S3) GHG emissions from utilization 1) Fuel utilization Diesel fuel GHG emissions from diesel fuel utilization = Total amount of diesel fuel emission factor of diesel fuel combustion (S4) Gasoline fuel GHG emissions from gasoline fuel utilization = Total amount of gasoline fuel emission factor of gasoline fuel combustion (S5) 2) Insecticide and herbicide utilization
Supplementary Information: Human operational factors and the strategy of score attribution according to survey results Human operational factor Measured human operational factor elements Attributed scores 1 (less optimal) 2 3 4 5 (most optimal) External factor Consumers’ perception Permanent complaints Frequent complaints Complaints sometimes Complaints only during interventions Does not complain at all Experience Experience < 2 years 2-5 years 5-10 years years > 20 years Trainings, courses Never got Once More than one, less than once a year Once a year More than once a year Certification None Class 1 Class 2 Class 3 4 Autonomy Autonomy Evaluated by operator Autonomy in emergency Evaluated by operator Work conditions Complexity of work Evaluated by operator Workload Evaluated by operator Difficulty Evaluated by operator Motivation Importance of work as an operator Evaluated by operator Personal implication Evaluated by operator
S3 Scheme of inputs and outputs for oil palm and rubber plantation Oil palm plantationa Rubber plantationb Seedling and cultivation Seedling Cultivation Felling of rubber tree Input Input Input Input Fertilizer usage Rubber seeds Diesel fuel Fuel (chainsaw) Nitrogen fertilizer Plastic bags Gasoline Gasoline Phosphorus fertilizer Soil Chemical usage 2-stroke oil Potassium fertilizer Plastic Paraquat Lubricant Plant growing media Diesel fuel Grammoxone Diesel fuel Agriculture bag Chemical usage Glyphosate-48 Output Soil Mancozeb Freshwater for mixing chemical timber Coconut dust Fosetyl aluminium Fertilizer Hevea branches Chemical Freshwater for mixing chemical Nitrogen fertilizer Consumption for watering Phosphorus fertilizer Captan Freshwater Potassium fertilizer Triram Diesel fuel Output Glyphosat Gasoline Fresh latex Paraquat Electricity Grammoxone Fertilizer Carbendazim Nitrogen fertilizer Mancozeb Phosphorus fertilizer Water consumption Potassium fertilizer Freshwater Output Transportation Rubber seedlings Diesel fuel Gasoline Electricity consumption Electricity output Fresh fruit bunches(FFB) aThe scheme of input and output of oil palm plantation was obtained from MTEC, (2014); Suttayakul et al.,( 2016), bThe scheme of input and output of rubber plantation was obtained from MTEC (2014) and TRF (2015)
Transport to USA Heavy fuel oil, ship 1.01 Administrative Operations Operations at crushing Electricity from diesel 0.03 Diesel vehicles 0.01 Operations at mine Electricity from diesel 1.75X10-03 Diesel vehicles 0.03 Gasoline vehicles 4.23E-05 Water Treatment Operations at mine Aluminum sulfate 2.49X10-03 Sodium hydroxide 1.75X10-03 Operations at crushing Aluminum sulfate 1.16 38.6
(98.7% with TAG, 1.3 DAG) 2) (70% with 3.7% MAG, 2.8% Na2O Schulz et al. (2011) FAME of linseed oil H2SO4, CaO,NaOH 3:1,4:1,5:1,6:1 (molar ratio) 0.5,1,5 130 0.5-15 h Pure Felizardo et al. (2011) 20-50% FFA in acidulated soap-stocks Zn, AcZn 1:1.04~1:1.65 ratio) 0.1,0.2,0.3 wt% (mass of Zn) 180,210,220,230 90,180 min Crude and neutralized by H2SO4 Gole and Gogate (2014) Nagchampa oil Zinc acetate 2:1 ratio) 0.1% of oil 200 240 min 25 min (microwave) Pure Costa et al. (2015) Sludge from WWTP NA 1:2 (mass ratio) NA 200 120 min Pure Kombe et al. (2013) Jatropha NA 2.24:1 (mass ratio) NA 65 73 min Pure Kombe (2015) Castor NA 2.34:1 (mass NA 56 85 min Pure Echeverri, D. A., Perez, W. A., and Rios, L. A. (2013a). Synthesis of maleated-castor oil glycerides from biodiesel-derived crude glycerol. Industrial Crops and Products, 49, 299-303. Echeverri, D. A., Cardeño, F., and Rios, L. A. Glycerolysis of crude methyl esters with crude glycerol from biodiesel production. Journal of the American Oil Chemists' Society, 90(7),
SO2 0.00 t/year EUR 783/t USD - iii) Similar application Puma (2011) NOx 0.00 t/year EUR 664/t USD - iii) Similar application Puma (2011) Total VOCs 41.54 t/year EUR 425/t USD 57,023.84 iii) Similar application Puma (2011) Storage Tanks Total VOCs 32.49 t/year EUR 425/t USD 44,604.60 iii) Similar application Puma (2011) Flares Particulates 1.21 t/year EUR 1,285/t USD 5,013.89 iii) Similar application Puma (2011) SO2 0.00 t/year EUR 783/t USD - iii) Similar application Puma (2011) NOx 0.08 t/year EUR 664/t USD 167.15 iii) Similar application Puma (2011) Total VOCs 0.16 t/year EUR 425/t USD 213.97 iii) Similar application Puma (2011) Soil/Water Pollution Treatment of Effluents DBO 4,706.41 kg/year USD 0.15/kg DBO USD 705.96 i) Market Ana (2015) Waste Generation by Plant Co-processing (Dangerous) 86,071.49 kg/year USD 53.00/t USD 12,134.36 iii) Similar application Trucost (2013) Soil Use Occupied by Plant Occupied Area 14.53 ha/year USD 11.29/ha/year USD 436.46 ii) Reliable scientific studies Van der Ploeg e De Groot (2010)
Table 2 - Refinements in the method proposed Perspective Application Evaluation Structural Evaluation Performance Evaluation Method Structure - No improvement was noted or considered relevant. - Definition of Evaluation Frontiers: inclusion of a step dedicated to a preliminary data collection for the establishment of spatial frontiers of evaluation; - Identification of Environmental Externalities: inclusion of a step to preliminarily identify the effects of externalities on nature and consequences for stakeholders and businesses. - No improvement was or considered relevant. Sequence of Steps - No improvement was noted or considered relevant. - No improvement was noted or considered relevant. - No improvement was noted or considered relevant. Description of Steps - Materiality Evaluation: inclusion of an embryonic systemic structure as a suggestion to accelerate the dynamics of design of systemic structures; - Externality Valuation: change in the priority order to obtain monetary values, indicating calculation procedures supported by Environmental Economy techniques as a priority. - Definition of Evaluation Frontiers: improvement in the definition of the terms spatial evaluation frontier and level of evaluation; - Identification of Environmental Externalities: elimination of the term holistically from the graphical representation of the method; - SEER Evidencing Stage: replacement of the term strategic options for process technologies in the graphical representation of method. - Externality Valuation: inclusion of a suggestion to analyze historical events of the sector to which the evaluated company belongs in order to identify and, if pertinent, assess high risk scenarios; - Data Collection: inclusion of guidelines on the need for standardized data collection to ensure comparability. Techniques and Tools - Identification of Environmental Externalities: adequacy of the procedure to identify positive externalities when using the Product-Input Diagram. - No improvement was noted or considered relevant. - No improvement was noted or considered relevant.
Table A4. Emission factors of air pollutants from industrial and utility boilers. Combustion device Fuel VOC NOX PM2.5 SO2 Industrial boiler (g kJ -1) Oil a 0.006 0.13 0.02 0.094 Coal a 0.002 0.24 0.04 0.38 Gas a 0.005 0.054 0.004 0.013 Utility boiler (g kWh-1) Residual oil 0.02 b 3.5 c 0.42 c 3.7 c Coal a 0.010 2.5 0.04 1.8 Gas a 0.002 0.2 0.001 0.003 Refused 0.13 b 0.9 b 0.61 b 0.6 b Note: a Data from Ke et al. [33]. Gas-fired utility boiler applies the (nature gas combine cycle) NGCC technology. b Data from default values in the GREET model. c Data from the CEM annual report [32]. d Refuse incineration is used in Macao local power plants.
Table A.3 Emergy accounting of systems experiment Item 2005 FDD-MT 1 Sunlight 1.68E+12 2 Rain 1.19E+13 3 Wind 2.04E+12 4 cycle 1.58E+13 5 N deposition 5.72E+11 R 2.82E+13 1 3 1 3 1 3 1 3 1 3 1 3 15 1 3 17 40
Transformity 1.02E+05 Indicator EYR 1.66 ELR 2.23 ESI 0.74 FDD-NT Sunlight 1.68E+12 Rain 1.19E+13 Wind 2.04E+12 4 Earth cycle 1.58E+13 15 30 16
5 N deposition 5.72E+11 Total R 2.82E+13 6 △SOM 1.32E+13 Total N 1.32E+13 7 P fertilizer 7.35E+12 8 N fertilizer 3.42E+13 2 1 2 1 2 1 2 1 2 1 2
Total FR 5.03E+12 Total Input 9.66E+13 Output (Y) Yield g 6.10E+04 Transformity 9.67E+04 Indicator EYR 1.75 ELR 1.91 ESI 0.92 FCD-MT Sunlight 1.68E+12 90
Indicator EYR 1.45 ELR 1.70 ESI 0.86 FCD-NT Sunlight 1.68E+12 Rain 1.19E+13 Wind 2.04E+12 4 Earth cycle 1.58E+13 5 N deposition 5.72E+11
△Q 1.05E+14 Indicator EYR 1.37 ELR 1.91 ESI 0.72 EBR 2.74 ESIQ 1.43 D-O/p-p Sunlight 1.68E+12 Rain 1.19E+13 3 Wind 2.04E+12
14 Total FR 8.95E+12 Total Input 9.06E+13 Output (Y) g 6.30E+04 Transformity 8.55E+04 SOM increase g 1.03E+04 Transformity 3.89E+05
Table A5 Energy coefficients used in the present study Item Values Unit References Phosphate fertilizer (P2O5) 12.3 MJ kg-1 (Zentner et al., 1998) Nitrogen fertilizer (N) 68.4 MJ kg-1 (Zentner et al., 1998) Chemicals 222 MJ kg-1 (Zentner et al., 1998) Diesel Machinery 121.3 MJ kg-1 (Zentner et al., 1998) Labor Grain Estimated by average protein, fat, carbohydrates contents of different crops (NNDSRR), and energy content for each compound, i.e., MJ kg-1, MJ kg-1, and MJ kg-1 for protein, fat, and carbohydrates. Wheat 16.4 MJ kg-1 Canola 26.3 MJ kg-1 Flax 25.7 MJ kg-1 Sunflower 28.5 MJ kg-1 Pea 17.0 MJ kg-1 Chickpea 18.0 MJ kg-1 Lentil 16.9 MJ kg-1 SOM 22.6 MJ kg-1 Estimated as showed in Table A2
Parameters: unwl(t), ufwl(t), and ucwl(t) are the carbon inputs of non-woody litter, fine woody litter and coarse woody litter to the carbon pool of litter and soil at time t (unit: tC/ha); xnwl(t), xfwl(t), and xcwl(t) are the weight of carbon in non-woody litter, fine woody litter and coarse woody litter at time t; anwl, afwl, acwl are the invasion rate of non-woody litter, fine woody litter and coarse woody litter, and they are 1.47, 0.80, and 0.11, respectively (unit: year-1); xext(t), xcel(t), xlig(t), xhum1(t), xhum2(t) are the weight of organic carbon in each decomposition compartment of extractives, celluloses, lignin-like compounds, simple humus or complicated humus at time t cij, the concentration of compound group j in litter type i, cnwl-ext, cnwl-cel, cnwl-lig, cfwl-cel, cfwl-lig, ccwl-ext, ccwl-cel, ccwl-lig are 0.27, 0.51, 0.22, 0.03, 0.65, 0.32, 0.03, 0.69, and 0.28, respectively ; kext, kcel, klig, khum1, and khum2 are the decomposition rates of compartment extractives, celluloses, lignin-like compounds, simple humus or complicated humus, and they are 1.21, 0.44, 0.32, 0.015 and 0.0015, respectively (unit: year-1); pi, the proportion of mass decomposed in compartment i transferred to a subsequent compartment, and pi is 0.2 Fls, the carbon budget of litter and soil Ej, the carbon emission from litter and soil Figure SI-1. Comparison of the model results and experimental results for DBH and Height of the Larix principis-rupprechti Figure SI-2.The carbon cycle of forest litter and soil in the Yasso Model
K=soil erosion 0.32945 Web Soil Survey 119 16.9 Low 0.37 0.14 to 1.42 1 0.19929 SL=Slope+Length 0.57062 Agricultural handbook 703, Table 4.2 119B 4.4 Low 0.37 3.5 0.18160 C=cropping management 0.1752 Table S2 120B 40.3 Low 0.32 0.14 to 1.42 3.5 P=Practice 0.9275 Table S2 133 4 Low 0.32 0.14 to 1.42 1 933B 12.6 Low 0.32 0.14 to 1.42 2.5 0.37146 total Overall weight ave. k slope % weight average 84.8 Low 0.3294 2.2 Average Soil erosion (A) 5.419112814 Table S. Erosion Calculation and Soil Characteristics for Harrison location 1, Nebraska (Case C1) Soil Erosion Using RUSLE Latitude area (acre) Longitude A=R*K*LS*C*P Location 42 41' 5"N 209.6 103-53'-25"W 5.177516056 D. Harrison, NE Dominant Soil Characteristics determinant of Runoff Potential Parameter Value Reference Soil code soil type % K %Org Clay % of Soils Slope % mid-point slope % weight R=Rainfall erosivity 65.29 EPA R calculator 5122 0.329 0.37 1.48 8.8 32.9 1.5 0.4935 K=soil erosion 0.37 Web Soil Survey 5125 0.427 0.37 1.48 8.8 42.7 4.5 1.9215 1.284524 Agricultural handbook 703, Table 4.2 5137 0.038 0.37 2.5 9 10.5 3 0.315 C=cropping management 0.181 S2 5143 0.105 0.37 1.48 8.8 6.5 18 1.537451 P=Practice 0.928 S2 5126 0.065 0.37 2.5 9 3.8 7.5 0.374507 5976 0.036 0.32 1.38 10.6 3.6 1.5 0.070959 total weighted average slope % weight average 1 0.3682 8.8854 100 4.6
reported electricity generation to estimate water consumed for each power plant. Key assumptions about what cooling technologies were used for each individual power plant were compiled from the Union of Concerned Scientists’ [12] power generation database. The water intensity (gallons per MWh) of different types of power generation and cooling technologies were compiled from Mielke et al. [9] and updated with operational values from Macknick et al. [26]. For each power plant, the average water intensity was multiplied by the monthly power generation to obtain water consumption estimates (see Supporting Information for more the known ranges of water intensity). Coal and natural gas used in Pennsylvania production It is impossible to track the path of every molecule of natural gas or every tonne of coal from extraction to consumption in power production. As a result, the end product results were calculated under the assumption that the proportion of fuel consumed in the power sector applies uniformly across watersheds. The amount of fuel extracted for use in power generation was estimated using EIA datasets (Tables S3 and S4). Table S3. Coal extraction and coal used for power generation in Pennsylvania [27, 28]. Year Coal consumed for power generation (Thousand Short tons) Coal extracted (Thousand short tons) Proportion coal produced consumed in power 2012 41,602 55,506 0.75 2011 47,403 59,899 0.79 2010 50,888 58,964 0.86 2009 48,853 59,143 0.83 Table S4. Shale gas extraction and use in power generation [29, 30]. Year Pennsylvania Natural Gas Deliveries to Electric Power Consumers Million Cubic Feet Pennsylvania Natural Gas Gross Withdrawals from Shale Gas Million Cubic Feet Pennsylvania market gas production (MMcf) Proportion of gas delivered to power consumers 2012 393775 2042632 2256696 0.17 2011 306266 1068288 1310592 0.23
Table S2. Comparison of various parameters with other isotherm studies Particle size Surface area (m2 As(V) conc. (mg time (h) pH As(V) mg site density (mg Synthetic goethite powder 103 5̶ 25 24 5 4.8 0.05 [] Schwertmannite powder 206.1 10-35 24 5 104.2 0.51 [] Zeolite powder 450 100 100 6.5 76 - [] Magnetic wheat straw with Fe3O4 powder 4.73 (wheat straw) 10 - - 30.24 - [] Amorphous hydrous ferric oxide powder 290 10 4 4 66.7 0.23 [] Honeycomb briquette cinders with Fe3O4 and MnO2 granule, 0.5mm - 100 20 7 1.45 - [] Iron hydroxide-coated alumina granule, 0.5-1.0mm 250 0.3 48 7.2 15.9 - [] Synthetic siderite granule, 0.075 mm 182 0-25 182 - 10.7 0.06 [] Granular ferric hydroxide granule, 0.009-0.125 mm 236 0-0.1 432 7 20 0.08 [] Irregular shaped granular schwertmannite granule, 1-2 mm 199.4 0- 5 24 7 31.7 0.16 [] Coal mine drainage sludge (CMDS) powder 135 5-25 24 6.9 40.8 0.3 [] PUCMDS granule, 0.105-0.15 mm 23 5-25 24 7.2 28.1 1.22 This study Table S3. Results of Thomas modeling for arsenic removal in rapid small-scale column tests Parameter (unit) SC1 SC2 SC3 SC4 Breakthroughs (BV) 8190 13540 10160 14330
Thomas rate constant, kt 0.0013 0.00071 0.0011 0.00015 Maximum sorption capacity (mg g-1) 13.1 20.8 12.9 18.1 R2 0.963 0.799 0.931 0.716 Table S4. Kinetic data of the adsorption of As(III) and As(V) onto CMDSPU Adsorbent pH As(III) As(V) References First order (min-1) Second order (g order (min-1) Second order (g Ferrihydrite 4.6 9.2 0.002 0.000 1.9×10-2 1.8×10-2 0.582 0.888 0.582 0.415 [] Magnetic oxide 7 - 5.8×10-2 - 1.6×10-2 [] Goethite Hematite Magnetite 6.5 7.3 7.5 - 4×10-4 1.1×10-3 7.4×10-4 - 1.8×10-4 1×10-3 4.2×10-4 [] Treated laterite 7.4 0.06 0.29 0.03 0.08 [] CMDS 6.9 - - 4.84×10-3 5.35×10-4 [] PUCMDS 3 5 7 9 11 - 3.0×10-2 4.2×10-2 4.0×10-2 7.6×10-2 3.7×10-2 - 8.83×10-2 8.47×10-2 0.41×10-2 1.1×10-2 0.63×10-2 This study Table S5. Arsenic and heavy metal recovered from the column studies after regeneration and backwashing (Q: 29.17 mL min-1, 1% NaOH solution) Adsorbent species Column regeneration cycles Total heavy metal Heavy metal removed Heavy metal recovered Heavy metal recovery
Steel (plates, jointing, profiles) 4,71 Hot-rolled plates EPD Norwegian Steel Association NEPD 00255E 0,34 0,25 chromium steel 18/8 {RER}| steel production, converter, chromium steel 18/8 | Alloc Def, S Swedish el mix Steel (reinforcement) 2,38 EPD Celsa The International EPD® System EPD nr. S-P-00305 0,06 0,14 Reinforcing steel, production, RER (Ecoinvent 3.1) Sawn timber 0,11 Norwegian Wood industry federation EPD NEPD-307-179-EN 0,05 0,11 Sawm dried timber, RER (Ecoinvent 3.1, adapted to Swedish electricity mix) Fiber cement 0,88 Narley Eternit EPD 0,05 0,29 Fibre cement corrugated slab {GLO}| market for | Alloc Def, U (Ecoinvent 3 - allocation, default - unit) Paper board 1,28 1 kg Solid unbleached board {GLO}| market for | Alloc Def, U (Ecoinvent 3 - allocation, default - unit) 0,02 0,51 1 kg Solid unbleached board {GLO}| market for | Alloc Def, U (Ecoinvent 3 - allocation, default - unit) Particle board 0,50 Forestia EPD NEPD00274E 0,36 0,23 1 m3 Particle board, for indoor use {RER}| production | Alloc Def, U (Ecoinvent 3 - allocation, default - unit) Density 700 kg/m3 LVL 0,48 Metsä wood EPD Obtained from 0,06 0,27 1 m3Laminated timber element, transversally prestressed, for outdoor use {RER}| laminated timber element production, for outdoor use | Alloc Def, U (Ecoinvent 3 - allocation, default - unit) Density 430 kg/m3
2012 2012 2012 2014 2014 2014 Annual report average disclosure score CSR average disclosure score Overall combined score Annual report average disclosure score CSR average disclosure score Overall combined score ARC 0.73 1.5 2.23 1.22 2.54 3.76 Agnico Eagle 1.44 2.7 4.14 1.46 3.26 4.72 Barrick 1.41 1.63 3.04 2.15 3.15 5.30 Cameco 1.15 2.54 3.69 1.63 3.35 4.98 Canadian Natural R. 1.12 1.46 2.58 1.09 2.20 3.28 Cenovus 1.44 1.59 3.03 1.57 2.93 4.50 Eldorado 0.69 1.28 1.97 1.17 1.74 2.91 Enbridge 1.48 3.39 4.87 1.85 4.11 5.96 Encana 0.77 2.16 2.93 1.09 2.83 3.91 Enerplus 0.93 1.15 2.08 1.78 3.02 4.80 Quantum 0.74 1.78 2.52 1.24 1.72 2.96 Goldcorp 1.46 2.78 4.24 1.89 3.74 5.63 Husky 0.86 1.42 2.28 1.05 2.33 3.37 IAMGOLD 0.98 2.04 3.02 1.15 3.28 4.43 Imperial Oil 0.63 2.09 2.72 1.15 2.39 3.54 Suncor 1.04 3.15 4.19 1.61 3.61 5.22 Teck Resources 1.61 3.34 4.95 1.83 4.22 6.04 TransCanada 1.52 2.07 3.59 2.09 3.67 5.76 Yamana 1.02 2.58 3.6 1.30 3.54 4.84 Figure 3: Average integrated reporting word count for sample Canadian companies measured for fiscal years 2012 and 2014 Table 6: Average disclosure word count (quantity) for individual companies measured for fiscal years 2012 and 2014 2012 2012 2012 2014 2014 2014
South and central Europe Boat (transoceanic ship) Semi trailer (lorry ˃32 metric ton EURO4) 0.10 Central Africa Boat (transoceanic ship) Semi trailer (lorry ˃32 metric ton EURO4) 0.15 Latin America Boat (transoceanic ship) Semi trailer (lorry ˃32 metric ton EURO4) 0.20 Oceania Boat (transoceanic ship) Semi trailer (lorry ˃32 metric ton EURO4) 0.30 Asia Boat (transoceanic ship) Semi trailer (lorry ˃32 metric ton EURO4) 0.27 North America Boat (transoceanic ship) Semi trailer (lorry ˃32 metric ton EURO4) 0.14 Latin America Air freight 13.7 Asia Air freight 20 Own estimates on data from Ecoinvent.
CH3Cl COS SO2 Fig. S5. The mass spectra of nitrogen-/sulfur-/chlorine-containing contaminants produced during alkali metal-added sludge pyrolysis. m/z 10% 20% 30% 40% 50% 67
17 NH3 18 H2O 27 HCN 30 C2H6 34 H2S 42 C3H6 44 CO2 46 NO2 48 CH3SH 50 CH3Cl 56 C4H8 60 COS 64 SO2 67 C4H5N
study 1 = 1/7 study 2 = 4/7 study 3 = 3/7 study 4 = 2/7 study 5 = 1/7 study 6 = 3/7 study 7 = 4/7 study 8 = 2/7 9 = 1/7 1 5 4 3 1 4 5 3 1
study 1 = 6/15 study 2 = 6/15 3 = 7/15 study 4 = 7/15 study 5 = 6/15 study 6 = 5/15 study 7 = 4/15 study 8 = 8/15 study 9 = 6/15 4 4 4 4 4 3 2 5 4
Conjunctivitis 26 (2.1) 17 (1.4) 9 (0.7) 0 0 0 Eczema 25 (2.1) 24 (2.0) 1 (<0.1) 0 0 0 Depression 25 (2.1) 11 (0.9) 13 (1.1) 1 (<0.1) 0 0 Gastro-oesophageal reflux disease 25 (2.1) 17 (1.4) 7 (0.6) 1 (<0.1) 0 0 Data are n (%) based on the total N = 1215. TEAEs were defined as occurring between the first administration of study drug and 30 days after the last administration of study drug, inclusive. Patients were counted only once with each preferred term for the most severe case of that preferred term. TEAE = treatment-emergent adverse event. Supplementary Table S2. Most common TEAEs occurring in ≥20% of patients by Gorlin syndrome status. TEAE Number of patients with TEAEs, n (%) With Gorlin n = 219 Without Gorlin n = 990 Total N = 1215 Muscle spasm 171 (78.1) 635 (64.1) 807 (66.4) Alopecia 171 (78.1) 572 (57.8) 747 (61.5) Dysgeusia 127 (58.0) 533 (53.8) 663 (54.6) Weight decreased 98 (44.7) 393 (39.7) 493 (40.6) Decreased appetite 43 (19.6) 260 (26.3) 303 (24.9)
[1] Logan BE, Hamelers B, Rozendal RA, Schrorder U, Keller J, Freguia S, Aelterman P, Verstraete W and Rabaey K. Microbial fuel cells: Methodology and technology. Environ. Sci. Technol. 2006;40:5181-5192 [2] Ge Z, Li J, Xiao L, Tong YR and He Z. Recovery of Electrical Energy in Microbial Fuel Cells. Environ. Sci. Technol. Lett. 2014;1:137-141
release rates. Fig. S4 NH4+ accumulations at different pH. (A) NH4+ production, and (B) the mole ratio between released PO43- and NH4+. Fig. S5 SCFAs accumulation production and composition at different pH. (A) SCFAs production, and (B) SCFAs composition. Fig. S1 Fig. S2 Fig. S3 Fig. S4 Fig. S5
Table S1. RDP classification of the DGGE bands sequenced and corresponding matches (BLASTN) using the NCBI database with an indication of the similarity percentages and sources of origin. The presence/absence of each band in each sample tested together with its intensity are also shown. Taxonomic placement (50% confidence level) Band n° Co-current flow flow Closest relatives in Blast (Name and accession number) Similarity (%) Source of origin Phylum Cyanobacteria/Chloroplast 1 xx x Uncultured cyanobacterium (FJ902235) 96 Sample from 53m deep water column 1 xx x Plastid Picochlorum sp.(KM218890) 95 Commercial evaporated sea salts Class Cyanobacteria Order Chloroplast Family Chloroplast Genus Chlorophyta 2 xx Plastid uncultured Trebouxiophyceae (JX869268) 97 Freshwater 3 xxx xxx Plastid Chlorella sp. (KM218900) 97 Sediment 3 xxx xxx Plastid Chlorella sorokiniana (KJ742376) 96 Algal-bacterial biomass from a photobioreactor 4 x Plastid Oocystis solitaria (KC358234) 96 Culture collection Phylum Proteobacteria Class Deltaproteobacteria Order Family
Genus 5 xx Vampirovibrio chlorellavorus (NR_104911) 95 Ukranian reservoir water 5 xx Uncultured Vampirovibrio sp. (KP710184) 94 Arid raceway Class Alphaproteobacteria 6 x xx Uncultured Sphingomonas sp. (EU154462) 98 Draining 6 x xx Sphingomonas sp. (KJ191089) 97 Sediment of high-arsenic groundwater Order Rhodospirillales Family Rhodospirillaceae 7 xx Uncultured bacterium (KF836196) 97 Rock aquifer 7 xx Uncultured Rhodospirillaceae (KM355206) 95 Sediment 8 x Uncultured bacterium (KC358102) 99 Large-discharge carbonate springs Order Rhodobacterales 9 xx x Maricaulis sp. (KC018456) 99 Sediment 9 xx x Uncultured Caulabacter (JQ048251) 99 Soil from agricultural land Phylum Chloroflexi 10 x x Uncultured Anaerolineaceae bacterium (JN038290) 96 Petroleum-contaminated soil
10 x x Uncultured Chloroflexi bacterium (EU374065) 95 Mediterranean see sediment Class Anaerolineae Order Anaerolineales Family Anaerolineaceae 11 x Uncultured bacteria (KP641120) 99 High-rate denitrifying reactor treated with synthetic wastewater Phylum Bacteroidetes Class Sphingobacteriia Order Sphingobacteriales 12 xxx x Bacteroidetes bacterium 94 Cyanobacterial consortium from hot lake
S1 Summary of sugar recovery based on raw biomass form different liquid streams during pretreatment and enzymatic hydrolysis*. Type of Biomass Sugars in Raw Biomass (g) Dilute alkali pretreatment Dilute acid pretreatment Sugars from enzymatic hydrolysis (g) Sugars from pretreatment liquid (g) Recovery (%) Sugars from enzymatic hydrolysis (g) Sugars from pretreatment liquid (g) Recovery (%) Switchgrass 34.3 (22.9) 27.4 (11.4) 1.2 (7.8) 83.4 (83.8) 29.5 (1.4) 1.4 (12.4) 90.1 (60.1) Kenaf 40.8 (15.6) 31.7 (9.2) 1.3 (4.8) 80.9 (89.4) 39.7 (1.1) 1.5 (10.3) 101.0 (73.4) Hemp 36.5 (17) 35.2 (8.8) 1.0 (4.7) 99.2 (79.5) 36.0 (1.2) 1.0 (6.8) 101.4 (47.3) Sorghum 35.2 (22.1) 33.1 (12.3) 2.3 (5.6) 100.6 (81.1) 32.1 (1.5) 1.8 (13.5) 96.3 (68.0) *Sugar values reported are all based on the glucan and xylan (in parenthesis) content in raw biomass; Determining total theoretical ethanol yield from glucan and xylan (gallons/ dry ton)
Ketoprofen (C16H14O3) (22071-15-4) 254.28 2.91 ± 0.33 0.19 4.23 ± 0.10 20 Diclofenac (15307-86-5) 296.15 4.55 ± 0.57 1.77 4.18 ± 0.10 -2.26 ± 0.50 5 Primidone (C12H14N2O2) (125-33-7) 218.25 0.83 ± 0.50 0.83 12.26 ± 0.40 -1.07 ± 0.40 10 Carbamazepine (C15H12N2O) (298-46-4) 236.27 1.89 ± 0.59 1.89 13.94 ± 0.20 -0.49 ± 0.20 10 Salicylic acid (C7H6O3) (69-72-7) 138.12 2.01 ± 0.25 -1.13 3.01 ± 0.10 1
Metronidazole (C6H9N3O3) (443-48-1) 171.15 -0.14 ± 0.30 -0.14 14.44 ± 0.10 2.58 ± 0.34 20 Gemfibrozil (C15H22O3) (25812-30-0) 250.33 4.30 ± 0.32 2.07 4.75 1 Amitriptyline C20 H23 N (50-48-6) 277.40 2.28 9.18±0.28 1 Personal care products Triclosan (C12H7Cl3O2) (3380-34-5) 289.54 5.34 ± 0.79 5.28 7.80 ± 0.35 1 Benzophenone C13 H10O (119-61-9) 182.22 3.21 ± 0.29 3.21 - 5
Propoxur (C11H15NO3) (114-26-1) 209.24 1.54 1 Ametryn (C9H17N5S) (843-12-8) 227.33 2.967± 0.12 2.97 3.71±0.41 10 Clofibric acid (C10H11ClO3) (882-09-7) 214.65 -1.06 3.18 ±0.10 1 DEET (C12H17NO) (134-62-3) 191.27 2.42 ± 0.23 2.42 -1.37 ± 0.7 1
6 Chlorobi 1.13% 2.00% 0.57% 5.11% 4.15% 1.06% 1.51% 1.85% 2.11% 7 Saccharibacteria 0.20% 0.27% 0.95% 1.20% 1.02% 4.12% 4.33% 1.57% 1.61% 8 Actinobacteria 0.60% 1.17% 1.41% 0.77% 0.59% 2.26% 0.84% 0.80% 0.80% 9 Spirochaetae 1.57% 0.17% 3.32% 0.37% 0.37% 0.59% 0.38% 0.38% 0.36% 10 other phyla 13.35% 8.68% 17.93% 4.89% 5.44% 12.26% 10.18% 11.41% 14.27%
Actinobacteria 7 sewage sludge thermophilic Proteobacteria - Pseudomonas Awasthi et al., 2017 Firmicutes Acinetobacter 8 spent mushroom and pig mature maturation Chloroflexi Anaerolinaceae - Meng et al., 2018 -, not available.
2. Bright SA; Greene LM; Greene TF; Campiani G; Butini S; Brindisi M; Lawler M; Meegan MJ; Williams DC; Zisterer DM. Biochem Pharmacol. 2009, 77, 310.
13C-NMR (13C-APT) 4.6. NMR spectra of (3β) 3,N-Dihydroxy-urs-12-en-28-amide (19) 1H-NMR 13C-NMR (13C-APT) 4.7. NMR spectra of 3-Oxo-N-methyl-N-methoxy-urs-12-en-28-amide (24) 1H-NMR 13C-NMR (13C-APT) 5. References 1. Siewert, B.; Pianowski, E.; Obernauer, A.; Csuk, R. Bioorgan. Med. Chem. 2014, 22, 594. 2. Beseda, I.; Czollner, L.; Shah, P.S.; Khunt, R.; Gaware, R.; Kosma, P.; Stanetty, C.; del Ruiz-Ruiz, M.C.; Amer, H.; Mereiter, K.; Da Cunha, T.; Odermatt, A.; D.; Jordis, U. Bioorgan. Med. Chem. 2010, 18, 433. 3. Siewert, B.; Csuk, R. Eur. J. Med. Chem. 2014, 74, 1. 4. Siewert, B.; Wiemann, J.; Köwitsch, A.; Csuk, R. Eur. J. Med. Chem. 2014, 72, 84. 5. Takemoto, T.; Kometani, K. Justus Liebigs Ann. Chem. 1965, 685, 237. 6. Wen, X.; Sun, H.; Liu, J.; Cheng, K.; Zhang, P.; Zhang, L.; Hao, J.; Zhang, L.; Ni, P.; Zographos, S.E.; Leonidas, D.D.; Alexacou, K.-M.; Gimisis, T.; Hayes, J.M.; Oikonomakos, N.G. J. Med. Chem. 2008, 51, 3540.
7. Kashiwada, Y.; Wang, H.-K.; Nagao, T.; Kitanaka, S.; Yasuda, I.; Fujioka, T.; Yamagishi, T.; Cosentino, L.M.; Kozuka, M.; Okabe, H.; Ikeshiro, Y.; Hu, C.-Q.; Yeh, E.; Lee, K.-H. J. Nat. Prod. 1998, 61, 1090. 8. Miyazawa, M.; Y.; Imanishi, K. J. Agric. Food Chem. 2005, 53, 2312-2315. 9. Chiang, Y.-M.; J.-Y.; Kuo, C.-C.; Chang, C.-Y.; Kuo, Y.-H. Phytochemistry 2005, 66, 495.
Table S1 Fitting results of the Nyquist plots of PB@PANI, PB@PANI–NP and PB after two cycles at 100 mA g−1 using the equivalent circuit. Samples RS (Ω cm−2) CPE1 (S s1/2 cm−2) Rf (Ω cm−2) CPE2 (S s1/2 cm−2) Rct (Ω cm−2) W (S s1/2 cm−2) PB@PANI 1.5 1.9×10−5 7.3 1.2×10−5 187.4 0.38 PB@PANI–NP 1.7 4.3×10−6 7.9 1.3×10−5 331.1 0.42 PB 2.7 6.9×10−6 9.3 8.9×10−6 431.8 0.38 Fig. S7. Charge−discharge profiles of (a) PB and (b) PB@PANI–NP at different rates.
Supporting Information A anion hydroborate/carba-hydroborate as a room temperature Na-ion solid electrolyte Matteo Brighi, Fabrizio Murgia, Zbigniew Łodziana, Pascal Schouwink, Anna Wolczyk and Radovan Černý* Figure S1 - SEM images of the Na2-x(CB11H12)x(B12H12)1-x for x=1/3, but representative of all the three compositions. Figure S2 - in situ S-XPD of sample a) Na5/3(CB11H12)1/3(B12H12)2/3 b) Na4/3(CB11H12)2/3(B12H12)1/3 and c) Na3/2(CB11H12)1/2(B12H12)1/2. In Figure a) as the temperature increases, the structure transforms from I-cell to P-cell (as shown by the appearance of reflections (210) and (320)). About 466 K I-cell recovers.
Location of care within the hospital D Strong High Triage depends upon SMA Type, clinical status, goals of care, and institutional specific resources. All providers should be aware of SMA management. The NMD team should be consulted. Acute Respiratory Care Protocols C Strong High and aggressive respiratory protocols should be implemented. This should include proactive use of bi-level NIV and augmented secretion clearance prior to empiric oxygen supplementation. Augmented secretion clearance D Strong High Secretion mobilization and coughing device techniques should be the priority during acute respiratory illness. Family members as partners in management D Strong High Parents and homecare providers should be engaged to aid in the assessment and management by acute care providers. Role of the NMD team D Strong High care providers should contact NMD providers to assist with acute care protocols. Endotracheal intubation C# Strong High Threshold for endotracheal intubation should be established at admission. Extubation criteria D Strong High If pulmonary consolidation was demonstrated on radiograph, reexpansion should be established prior to extubation, and oxygen supplementation should be weaned to minimum or off. NIV should be implemented as transitional support following extubation. Nutrition / Aerodigestive Issues D Strong High Oral feeds for infants and those with possible oropharyngeal dysfunction should be held during acute illness due to risk of aspiration. Enteral tube or parenteral nutrition should be considered early. See Nutrition Section. Fluid management D Lack of consensus Mid The role of early and aggressive fluid provision/repletion is uncertain with the exception general sepsis management and fluid status assessment. Acute infectious disease management D Lack of consensus Mid The of expectorated sputum and viral studies to direct antimicrobial therapy is uncertain. Screening for viral pathogens may be considered based upon clinical presentation. Empiric antimicrobial coverage for resistant organisms may be considered. Discharge considerations D Strong Mid Discharge medical status will depend on the comfort and skill of family and outpatient medical care team, and consideration of available outpatient services, follow up care and indications for re-hospitalization.