zilongwang/ReadingBank huggingface dataset:
Mental Component Score 50.6 (10.8) 48.4 (11.5) 0.16* Exercise = Usual care Footnotes Short Form-36 (SF-36); QLMI=Quality of Life After Myocardial Infarction questionnaire; AP-QLQ=Angina Pectoris-Quality of Life questionnaire; NR=not reported; NS=not significant * Calculated by authors of this report on independent two group t test. ** Adjusted for baseline difference between groups. Exercise = Usual care: no statistically significant difference (P > 0.05) between exercise and usual care groups at follow up Exercise > care: statistically significant difference (P < 0.05) between exercise and usual care groups at follow up NS*: The authors of this review have inferred a P value of > 0.05 based either on the 95% CI, or from narrative in the paper, rather than from directly observing the P-value. eTable 4. Summary of costs of exercise-based rehabilitation and usual care Author/ year Briffa 2005 Hambrecht 2004 Kovoor 2006/Hall 2002 Maddison 2014 Marchionni 2003 Oldridge 1991/93 Yu 2004 Follow-up (months) 12 12 12 6 14 12 24 Year of (currency) 1998 ($Aus) NR 1999 ($AUD) NR (€; Euros) 2000 ($USD) 1991 ($USD) 2003 ($USD) Cost of rehabilitation Mean cost/patient $694 NR $394 €127 $5246 $670 NR Costs considered Details of costed elements not provided NR counseling, education, patient travel NR NR space, equipment, staff, literature resources, operating costs, parking, patients costs NR Total healthcare costs Rehabilitation mean cost/patient $4937 $3708 ± 156 NR NR $17 272 NR $15 292 Usual care mean cost/patient $4541 $6086 ± 370 NR NR $12 433 NR $15 707 Absolute difference in mean cost/patient* $395 -$2378 NR NR $4839 $480 -$415 P value for cost difference 0.74 P < 0.001 P > 0.05 (see below) NR NR NR P > 0.05
Additional healthcare considered tests, patient expenses, ambulance cycle ergometers, training facilities, and supervising staff phone calls (p=0.10); hospital admissions (p=0.11); gated heart pool scan (p=0.50); exercise stress test (p=0.72); other diagnostics (p=0.37); visits to general practitioner (p=0.61), specialist doctor (p=0.35), or health-care professional (p=0.31) NR NR Service utilisation, physician emergency costs, days, allied health, other visits private clinic visit; cardiac clinic visits; public noncardiac visits; casualty visits; drugs Cost-effectiveness Rehabilitation mean health care benefits Quality of life– Heart 0.026 (95% CI, 0.013 to 0.039) NR NR NR NR NR NR car mean health care benefit Utility 0.010 (95% CI, −0.001 to 0.022) NR NR NR NR NR NR Incremental mean health care benefit Utility 0.013 (95% CI, NR), P = 0.38; +0.009 QALYS NR NR NR NR 0.052 (95% CI, 0.007 to 0.1) 0.06 QALYs Incremental cost effectiveness +$42,535 per QALY. Extensive sensitivity analyses reported. NR NR +€15,247 per QALY NR +$9,200 per QALY -$650 per QALY
The SENTINEL Trial Site and Investigator Listing Cedars-Sinai Medical Center PI: Raj Makkar Sub-I: Hasanian Al-Jilaihawi Los Angeles, CA United States Cleveland Clinic PI: Kapadia Sub-I: Amar Krishnaswamy Sub-I: E. Murat Tuzcu Sub-I: Stephanie Mick Cleveland, OH United States Columbia University Medical Center PI: Susheel Kodali Sub-I: Tamin Nazif New York, NY United States Emory University Hospital Midtown PI: Vinod Thourani Sub-I: Vasilis Babaliaros Sub-I: Chandan Devireddy Sub-I: Kreton Mavromatis Atlanta, GA United States MedStar Washington Hospital Center PI: Ron Waksman Sub-I: Lowell Satler Sub-I: Augusto Pichard Washington, DC United States University of Pennsylvania PI: Wilson Szeto Sub-I: Saif Anwaruddin Sub-I: Prashanth Vallabhajosyula Sub-I: Jay Giri Sub-I: Howard Herrmann Philadelphia, PA United States Barnes - Jewish Hospital PI: Alan Zajarias Sub-I: John Lasala St. Louis, MO United States Henry Ford Health System PI: Adam Greenbaum Sub-I: William O’Neill Sub-I: Marvin Eng Detroit, MI United States Morton Plant Hospital PI: Joshua Rovin Sub-I: Lang Lin Sub-I: Douglas Spriggs Clearwater, FL United States Weill Cornell Medical Center PI: Shing-Chiu Wong Sub-I: Geoffrey Bergman Sub-I: Salemi New York, NY United States The University of Texas - Health Science Center PI: Richard Smalling Sub-I: Biswajit Kar Sub-I: Pranav Loyalka Houston, TX United States University of Virginia Health System PI: D. Lim Sub-I: Michael Ragosta Charlottesville, VA United States University of Washington PI: Mark Reisman Sub-I: James McCabe Sub-I: Creighton Don Seattle, WA United States Icahn School of Medicine at Mount Sinai PI: Samin Sharma Sub-I: Annapoorna Kini Sub-I: George Dangas New York, NY United States
Supplemental Table 6. 30-day median total new lesion volume (unadjusted analysis) Test Arm (n=78) Control Arm (n=80) Observed Treatment Difference (test - control) Hodges-Lehmann Estimate of Location Shift (95% CI) P-value1 Protected Territories ITT, mm3 0 (0, 52.7) 0 (0, 83.1) 0.0 0 (0, 0) 0.8255 All Territories ITT, mm3 0 (0, 73.8) 0 (0, 108.1) 0.0 0 (0, 0) 0.8507 Data are presented as median (IQR) 1Based on Wilcoxon Test
Supplemental Table 8. Least Squares Means and Test of Main Effects (adjusted by Interaction of Valve Type and Treatment Arm, and High/Low BL Lesion Volume) – ITT Population Effect Mean Estimate (95% CI) Least Squares Mean new lesion volume (mm3), Protected territories Control Arm 162.8 (107.9, 245.5) 0.0248 Device Arm 83.3 (55.0, 126.1) Least Squares Mean new lesion volume (mm3), all territories Control Arm 311.1 (212.2, 456.3) 0.0500 Device Arm 180.6 (122.7, 265.8) Least Squares Mean new lesion volume (mm3), Protected territories Evolut R (n=45) 172.6 (103.7, 287.2) 0.166 SAPIEN 3 (n=108) 113.6 (81.8, 157.8) SAPIEN XT (n=29) 80.5 (42.6, 152.0) Squares Mean new lesion volume (mm3), all territories Evolut R (n=45) 408.0 (253.9, 655.6) 0.021 SAPIEN 3 (n=108) 228.2 (168.0, 309.9) SAPIEN XT (n=29) 143.1 (79.1, 258.7) Note: In to get stable TAVR device estimates from the model, Medtronic CoreValve (which only contributed 7 patients with imaging follow-up) was excluded from the analysis.
subsp. indica 'Andabyeo' × O. sativa subsp. japonica 'SNU-SG1' 6, 7S, 3L) and 5 (wheat 1). Population specific Cha et al. 2002 Rice induced mutation. Glutinous japonica rice Hwacheong-wx: mutagenesis Leaves staying green after heading compared with wild type Single, recessive Rice chr 9L (part syntenic with wheat 1A) Gous et al. 2015 Barley: ND24260 × Flagship DH Visual greenness of spike, flag leaf and first leaf and leaf area under green and SPAD 3H, 4H, 5H, 6H and 7H Range 0.7% to 17.4% Glasshouse 20% field capacity, terminal stress and heat stress
A subdivision of the type of events according with the subgroup of hypertensive patients is provided in the supplemental table S3. Discussion The main pathological effects of aldosterone excess in the heart and kidney are vascular and perivascular inflammation, fibrosis and cellular hypertrophy (5-8). Thus left ventricular hypertrophy (9), inappropriate left ventricular mass (10,11), microalbuminuria and reduced renal function (12,13) are observed more frequently in patients with PA than in essential hypertensives. In addition, aldosterone excess has been shown to promote insulin resistance, thereby participating in the pathogenesis and progression of metabolic syndrome and diabetes mellitus (14,15). Consistently, in our study we observed a higher prevalence of left ventricular hypertrophy, microalbuminuria and metabolic syndrome in PA patients compared to non-PA hypertensives. Up to 30% of the hypertensive patients display the LREH (44), a clinical condition characterized by suppressed renin and “normal” aldosterone levels. The distinction between LREH and mild forms of PA can be challenging and relies on arbitrary cut-off values chosen to define failure of aldosterone suppression after confirmatory/exclusion testing. According to the Endocrine Society guidelines, no clear recommendations are provided on the optimal cut-off levels that determine positive confirmatory test results (4, 5). In our Hypertension Unit, the saline infusion test was regarded as positive when serum aldosterone levels were >5 ng/dL (or for a captopril challenge test when the ARR was >30 ng/dL/ng·mL-1·h-1). Accordingly, we allocated our low-renin patients into three categories: 1) PA, 2) LREH with a negative ARR test result and 3) LREH with a positive ARR test but with a negative confirmatory test result. Interestingly, patients with LREH and a positive screening test did not differ significantly in terms of clinical, metabolic and biochemical
Oral Anticoagulants 1 12 months 36 months 16 (1.8) 27 (3.2) 34 (5.7) 52 (6.0) 68 (8.2) 72 (11.7) <0.01 <0.01 <0.01 * Values are n (%).
Supplementary 4. Rates of Available Health Status Data in the Analytic Cohort at Each Time Point PCI (n = 892) CABG (n = 896) Time Point/Scale Dead Withdrawn Eligible Observed Dead Withdrawn Eligible Observed Baseline SAQ RDS PHQ-8 SF-12 EQ-5D 0 0 892 888 (100%) 872 (98%) 846 (95%) 846 (95%) 870 (98%) 0 0 896 879 (98%) 877 (98%) 845 (94%) 852 (95%) 862 (96%) 1 Month SAQ RDS PHQ-8 SF-12 EQ-5D 8 26 858 834 (97%) 821 (96%) 802 (94%) 795 (93%) 816 (95%) 9 38 849 802 (95%) 789 (93%) 770 (91%) 755 (89%) 787 (93%) 12 Months SAQ RDS PHQ-8 SF-12 EQ-5D 30 27 835 741 (89%) 730 (87%) 717 (86%) 716 (86%) 723 (87%) 31 38 827 706 (85%) 694 (84%) 678 (82%) 671 (81%) 701 (85%) 36 Months SAQ RDS PHQ-8 SF-12 EQ-5D 69 25 798 642 (81%) 632 (79%) 629 (79%) 608 (76%) 643 (81%) 50 41 805 643 (80%) 631 (78%) 627 (78%) 607 (75%) 645 (80%) Abbreviations: SAQ = Seattle Angina Questionnaire; RDS = Rose Dyspnea Scale; PHQ-8 = Patient Health Questionnaire-8; SF-12 = 12-Item Short Form Health Survey; EQ-5D = European Quality of Life-5 Dimensions.
of control, IgG or anti-MRP-14 antibody (B). Mean fluorescence quantification per field is shown adjacent to images, scale 20µm, nuclei is counterstained with DAPI (blue). n=3, *P < 0.05. Online Table 1 No Event P (n=34) (n=34) Age, years +/- SD 69.6 (11.2) 74.0 (10.6) 0.10 Female (%) 9 (26.5%) 15 (44.1%) 0.13 Race 0.28 White 23 (67.7%) 17 (50%) 7 (20.6%) 8 (23.5%) Asian 0 (0%) 3 (8.8%) Other 4 (11.8%) 6 (17.7%) Ethnicity Hispanic (%) 4 (12.1%) 6 (17.7%) 0.73 Smoker 0.14 Current 6 (17.7%) 4 (11.8%) Former 23 (67.7%) 18 (52.9%) Never 5 (14.7%) 12 (35.3%) Diabetes (%) 16 (47.1%) 20 (58.8%) 0.33 Hypertension (%) 32(94.12%) 29(85.3%) 0.43 Hyperlipidemia (%) 27(79.41%) 21 (61.8%) 0.11 Coronary artery disease (%) 18(52.94%) 19 (55.9%) 0.81 MRP 14, mean (SD) (ng/mL) 247 (62.4) 303.1 (113.6) 0.01
II DQ8 antigen (at a MFI of 2313) and still expressed antibodies against this antigen at 3, 6 and 12 months, albeit at a lower reactivity (MFIs: 760, 659 and 772, respectively). A third patient developed DSA against both HLA I B44, only detected after 6 months at a low MFI (607), and HLA II DQ2 at 3 months (MFI:1027) with a further increase at 6 months (3889). IFN-γ Elispot The low level of immunosuppression was confirmed by the anti-viral response assessed by the ex vivo IFNγ Elispot assay with immunodominant viral peptides. Three patients presented at baseline a positive T cell response against peptides derived from CMV or a pool of viral peptides (CEF) and this response did not decrease during follow-up while 2 other patients did not respond at baseline and this lack of T cell reactivity persisted during the follow-up. Detection of circulating donor-derived deoxyribonucleic acid (DNA) ESCORT patients’ plasma DNAs and exogenous ESC-derived cardiovascular progenitors were analyzed as described above. Polymorphisms used to detect exogenous ESC-derived cardiovascular progenitor DNA were those present in the cell line and absent in patients. MET p.Ile 377Ile c.1131C>T and SMAD4 p.Phe362Phe c.1086 T>C were cell-line specific and tested for all patients. For 2 cases, the EGFR p.Gln787Gln c.2366 G>A was added as both patients were homozygotes for the G allele. One sample was not informative due to low coverage depth. Our previous data showed that allelic ratios of > 0.005 (0.5%) could be considered as significant alterations and that sequencing data showing alterations at lower frequencies had to be considered as sequencing artefacts. In this study, no plasma showed a cell line-specific allele ratio of more than 0.0018 (0.18%, MET ESCORT 5.2). References
Triglycerides to total lipids ratio in very small VLDL (%) 1465 24 (4.8) 4.16 1.14 (1.03, 1.25) 1.09 (0.99, 1.19) 1.07 (0.98, 1.16) Tyrosine (mmol/l) 1462 0.053 (0.011) 8.61 1.02 (0.92, 1.12) 1.07 (0.97, 1.17) 1.06 (0.98, 1.15) Valine (mmol/l) 1464 0.17 (0.037) 5.82 1.03 (0.94, 1.14) 1.09 (1.00, 1.20) 0.92 (0.85, 1.01) 18:2 linoleic acid (mmol/l) 1462 2.3 (0.51) 5.34 1.17 (1.06, 1.29) 1.31 (1.18, 1.45) 1.04 (0.95, 1.14) 22:6 docosahexaenoic acid (mmol/l) 1462 0.15 (0.032) 8.14 1.05 (0.95, 1.15) 1.16 (1.06, 1.28) 0.94 (0.86, 1.04) -hydroxybutyrate (mmol/l) 1437 0.1 (0.033) 4.98 1.35 (1.18, 1.54) 1.21 (1.08, 1.36) 1.22 (1.08, 1.36) Legend Odds ratios (OR) adjusted for age, sex, fasting time, region, smoking status, educational attainment and triglycerides. Values represent effect estimate per one standard deviation higher metabolic marker. Coefficients of variation (CV) were calculated in 137 duplicate measures; SD: standard deviation.
Vascular endpoint 1 (age, sex, smoking, systolic blood pressure, body mass index, type 2 diabetes and total Model 2 (Model 1 + 18 principal components explaining 95% variance of 225 metabolic markers) P-value for difference between Models 2 and 1 Myocardial infarction 0.800 (0.782, 0.819) 0.813 (0.795, 0.832) 7.30x10-5 Ischemic stroke 0.767 (0.748, 0.786) 0.775 (0.757, 0.794) 9.09x10-3 Intracerebral hemorrhage 0.797 (0.778, 0.815) 0.809 (0.791, 0.827) 2.98x10-4
Online Table 5. Index medications for the SGLT-2i groups, by country and overall Index therapy, n (%) South Korea Singapore Israel Canada Australia Total SGLT-2 168,322 33,890 1363 9736 8032 13,721 235,064 Dapagliflozin 135,803 (80.7) 8408 (24.8) 1013 (74.3) 3503 (36.0) 4121 (51.3) 10,845 (79.0) 163,693 (69.6) Empagliflozin 19,527 (11.6) 6633 (19.6) 240 (17.6) 6233 (64.0) 877 (10.9) 0 (0.0) 33,510 (14.3) Canagliflozin 0 (0.0) 3826 (11.3) 110 (8.1) 0 (0.0) 3034 (37.8) 2876 (21.0) 9846 (4.2) Ipragliflozin 12,992 (7.7) 8488 (25.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 21,480 (9.1) Luseogliflozin 0 (0.0) 2273 (6.7) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 2273 (1.0) Tofogliflozin 0 (0.0) 4262 (12.6) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 4262 (1.8) SGLT-2i, sodium-glucose co-transporter-2 inhibitors.
Primary care-based 10 (1/10) (19) 149 0 25 -25* Intervention Format One-on-one 100 (10/10) (19;32-34;36;38;39;41;42) 1,061 30 0 n/a Group 60 (6/10) (32;33;39-43) 792 33 25 8 Mode of delivery b Face-to-face 100 (10/10) (19;32-34;36;38-43) 1,061 30 n/a n/a Written Literature 20 (2/10) (33;41) 294 50 25 25* Telephone 40 (4/10) (19;33;41;42) 653 0 50 -50* Education Strategies Didactic 60 (6/10) (19;32;34;36;38;39;43) 340 50 0 50 Goal-Setting Dictated 60 (6/10) (19;32;36;39;40;42) 676 50 0 50 Goal-Setting Negotiated 80 (8/10) (19;33;34;38-42) 967 12 100 88 Situational Problem-Solving 70 (7/10) (32;33;38-43) 833 28 33 -5 Peer-led Discussion 10 (1/10) (41) 239 0 33 -33* Interactive Discussion Groups 30 (3/10) (32;33;40) 337 33 28 5* Feedback 50 (5/10) (19;32;36;39;40) 476 60 0 60 Diaries and Reports 40 (4/11) (32;36;39;40) 327 75 0 75* Duration Short Duration 50 (5/10) (34;36;38;39;41) 375 40 20 20 Long Duration 50 (5/10) (19;32;33;40;42) 686 20 40 -20
Duration Short Duration 45 (5/11) (35;36;39;41;44) 375 20 33 -13 Long Duration 55 (6/11) (19;32;33;37;40;42) 838 33 20 13 Intensity Low Intensity 45 (5/11) (19;33;35;36;41) 565 20 33 -13 High Intensity 55 (6/11) (32;37;39;40;42;44) 648 33 20 13 Interventionist b Nurse 36 (4/11) (19;33;35;41) 526 25 17 8* Dietitians 64 (7/11) (32;33;37;39-42) 944 29 0 29 Community Peer Worker 18 (2/11) (19;42) 349 0 25 -25* Multidisciplinary Team 64 (7/11) (19;32;33;39-42) 941 29 0 29 Content/Specific Components of Intervention Psychosocial 18 (2/11) (40;41) 456 0 33 -33* Diet 91 (10/11) (19;32;33;35-37;39;40;42;44) 974 30 0 30 Exercise 73 (8/11) (19;32;36;37;39;40;42;44) 836 25 33 -8 Self-Management of Blood Glucose 36 (4/11) (19;36;37;39) 356 0 43 -43* Medication Adherence 18 (2/11) (19;36) 188 0 33 -33* Recognition of Complications 27 (3/11) (35;36;41) 351 0 38 -38* Foot care 27 (3/11) (19;35;40) 439 0 38 -38* Supervised Exercise 9 (1/11) (32) 55 100 20 80*
Percent success rate Feature Feature prevalence (%) Number of participants studied With feature (%) Without feature (%) Rate difference (%) Intervention Setting Community-based 33.33 (2/6) (35;42) 273 0 50 -50* Home-based 50 (3/6) (19;33;38) 287 66.67 0 66.67* Hospital-based 66.67 (4/6) (32;33;35;42) 425 25 50 -25* Primary care-based 16.67 (1/6) (19) 149 0 40 -40* Intervention Format One-on-one 83.33 (5/6) (19;32;33;38;42) 542 40 0 40 Group 66.67 (4/6) (32;33;35;42) 425 25 50 -25* Mode of delivery Face-to-face 100 (6/6) (19;32;33;35;38;42) 615 33.33 n/a n/a Written Literature 16.67 (1/6) (32) 55 0 40 -40* Telephone 50 (3/6) (19;33;42) 446 33.33 33.33 0* Audio Visual 16.67 (1/6) (35) 73 0 40 -40* Teaching Method a Didactic 66.67 (4/6) (19;32;35;38) 318 25 50 -25* Goal-Setting Dictated 66.67 (4/6) (19;32;35;42) 477 0 100 -100* Goal-Setting Negotiated 66.67 (4/6) (19;33;38;42) 487 50 0 50* Situational Problem-Solving 66.67 (4/6) (32;33;38;42) 393 50 0 50* Interactive Discussion Groups 50 (3/6) (32;33;35) 225 33.33 33.33 0*
COREQ Outcome for studies: Question number Young et al, 2008 [18] Leach et al, 2010 [14] Van De Weyer et al, 2010 [13] Holliday et al, 2007 [16] Brown et al, 2014 [15] Lloyd et al, 2014 [45] Scobie et al, 2013 [12] Levack et al, 2011 [19] 1 Two of the authors conducted the interviews 1 The research team 1 1st focus group - an independent facilitator. 2nd focus group - one of the authors 1 First author 1 author 1 First author 1 2nd author 1 First author 1 2 Physician in training and final year medical student 1 Not stated 0 Not stated 0 Not stated 0 Not stated 0 Neurological physiotherapist undertaking masters degree 1 Not stated 0 Not stated 0 3 Not stated 0 Yes. Preliminary discussions 1 Not stated 0 Not stated 0 Not stated 0 Not stated 0 No - interviewer unknown 0 Not stated 0
4 Participants were informed that the interviewers had no involvement in goal setting and no previous experience. 1 All participants knew the objectives of the 1 Aim of research 1 Aim of research 1 Not stated 0 Unclear 0 Not stated 0 Not stated 0 5 The interviewers had no involvement in goal setting at the Rehab Unit or previous experience of goal setting in healthcare. 1 Not stated 0 One of the focus groups was led by a research psychologist, unrelated to the research. However the 2nd research was led by one of the authors 1 Not stated 0 Not stated 0 Bias reported and influence of researcher 1 Response bias removed. Interviewer worked but did not treat patients 1 Not stated 0 6 Not stated 0 Not stated 0 Not stated 0 Not stated 0 Not stated 0 Stated - grounded theory 1 Not stated 0 Constructivist grounded theory 1
7 No patients or carers declined to participate however 2 physiotherapists refused due to work pressures. 1 No drop outs 1 No drop outs 1 No drop outs 1 No drop outs 1 No drop outs 1 7 chose not to participate - no explanation 0 No drop outs 1 8 No guide mentioned. 0 No guide. No validated tool. 0 Prompt questions used. No mention of pilot study or validity 0 Questions used. Was pilot tested with 6 patients. 1 Interview schedule used but does not state if validated or piloted. 0 Interview guide used and 2 pilot studies conducted 1 Interview used but does not state if validated or piloted. 0 Interview questions came as data was collected 0 9 Not stated 0 N/A 0 Not stated 0 The interviewer made notes immediately after the focus group 1 Not stated 0 Not stated 0 Not stated 0 Yes 1 10 Yes data saturation occurred around 6-9 interviews 1 Yes 1 No 0 No 0 No 0 Discussed - partial saturation 1 No 0 Yes discussed and achieved 1
11 Yes 1 Yes 1 Yes 1 Yes 1 Yes 1 Audit trail kept 1 Yes 1 Yes 1 12 Derived from data1 Derived from the data 1 Derived from data 1 Derived from data 1 Derived from data 1 Derived from data 1 Derived from data 1 Derived from data 1 13 In a table - refers to a theme from one participant 1 No 0 No 0 No 0 No 0 No 0 Yes discussed how they dealt with these 1 No 0
Supplementary Material: EfECT Observation Tool TE Name: ______________ Observer’s name: _________________ Date: _______ Observable Teaching Skills and Behaviours Evidence* 1 2 3 4 Reflective Practice TE identifies areas of strengths, areas for improvement and ways to improve Reflection Questioning TE asks questions to engage learners in the learning content (to check for prior knowledge, open ended questions, responding to learner contributions with follow-up questions) LP/Obs TE asks questions to gauge learning (to check and recall) LP/Obs Interactive Classroom Management and Feedback TE sets up activities with clear instructions Obs TE monitors and interacts with individual/groups of learners where appropriate to provide feedback Obs TE evaluates and comments on learner contributions in feedback Obs TE uses paired or group work to encourage active participation of learners LP/Obs TE calls on learners to answer questions individually Obs Resources TE makes effective use of the board and learning aids LP/Obs TE adapts coursebook in a motivating way LP/Obs Planning Lesson plans contain clear, well-written learning outcomes LP Lesson is staged logically and coherently LP Assessment of learning Learning outcomes are referred to and assessed throughout the lesson to consolidate learning LP/Obs
That the 95% confidence interval for task motivation is below and does not include 0 but the 95% confidence interval for task performance does include 0 supported Hypothesis 3b, that the mindfulness meditation condition would have a stronger negative effect on task motivation than on task performance. In sum, mindfulness meditation impaired task motivation but not performance. Reference: Harris R.J., Bradburn M.J., Deeks J.J., Harbord R., Altman D.G. and Sterne J.A., metan: Fixed-and random-effects meta-analysis, Stata Journal 8 (1), 2008, 3–28.
Table A. CLASS definitions of Positive Climate and specific examples for sleep-time. Positive Climate Low (1,2) Mid (3. 4, 5) High (6, 7) CLASS Definition Specific Examples to Sleep-time CLASS Definition Specific Examples to Sleep-time Definition Specific Examples to Sleep-time Disconnection, flat affect and limited enthusiasm from teachers and children. Limited to no affection shown and they rarely display respect. Children tell the teacher personal things and the teacher may ignore them or end the conversation with perfunctory comments such us “OK, now go to sleep.” interactions do not appear pleasurable e.g. a child gets upset and says “I don’t want to nap” and the other children/ teachers do not show concern. Teacher sits away from children, ignoring them whilst doing work or cleaning. General positive regard but constrained at Teacher displays positive affect with some children and not others or mismatched affect. Sometimes communicates respect and affection, other times this is not evident. Sometimes close proximity to children and asks more than one question “why can’t you sleep?” “Are you too hot?” but not genuinely interested and may go back to managerial tasks quickly. Some shared smiles between children on mats/bedding other times children are agitated and less positive towards one another. Sometimes sincere with words like “please” and “thank you” but not consistently and may sound insincere Strong emotional connectedness, close proximity and generally a warm and enthusiastic environment. Frequent use of affection and demonstrations of respect. Teacher sits to children with positive physical contact of patting children on back to soothe child whilst smiling and making positive comments with children, “Wow you are really resting well today.” Communicates respect by using names, eye contact and with language like “Could you please help your friend with that?” Children show concern for their peers.
S Table 2 Antibiotic Resistance Ontology (ARO) category Predicted gene Identity (%) e aminocoumarin resistance protein alaS 84 cysB 92 aminoglycoside resistance protein kdpE 78 antibiotic inactivation enzyme; aminoglycoside resistance protein AAC(6')-Ic 91 antibiotic inactivation enzyme; beta-lactam resistance protein SRT-2 94 antibiotic inactivation enzyme; beta-lactam resistance protein; gene modulating beta-lactam resistance NmcR 54 antibiotic resistant gene variant or mutant; resistance protein Neisseria meningititis PBP2 conferring resistance to 39 antibiotic resistant gene variant or mutant; resistance protein; protein modulating permeability to antibiotic Enterobacter aerogenes omp36 mutants 65 antibiotic resistant gene variant or mutant; isoniazid resistance protein Mycobacterium tuberculosis katG mutations conferring resistance to isoniazid 66 mupirocin resistance protein; antibiotic resistant gene variant or mutant Bifidobacteria intrinsic ileS conferring resistance to mupirocin 24 antibiotic target protection protein; fluoroquinolone resistance protein mfd 81 efflux pump conferring antibiotic resistance acrF 59 oqxB 87 mdtA 67
Table S1. Medication used during the study n AD monotherapy 2* AD combination therapy 3 AP monotherapy 1 AD and AP combination therapy 7 AD and MS combination therapy 3 AP and MS combination therapy 1 AD, AP, and MS combination therapy 2 *Both patients were treated with a tricyclic antidepressant. AD: Antidepressant, AP: Antipsychotic, MS: Mood stabilizer. Note: the dose of the medications was not altered during the study period
Appendix A: Search terms for databases Search terms for all databases consisted of two parts associated with the two most important selection criteria: a) procedural learning, and b) participants with dyslexia. Search terms are presented for each individual database below. Ovid (used to search PsycInfo, Medline, and ERIC) (“procedural learning” or “implicit learning” or “artificial grammar learning” or “statistical learning”), ti, ab. (dyslexia or “reading difficulties” or “reading disorder” or “reading disability” or “spelling difficulties” or “spelling disorder” or “spelling disability”), ti, ab. a and b. CINAHL: AB (“procedural learning” or “implicit learning” or “artificial grammar learning” or “statistical learning”) AND AB (dyslexia or “reading difficulties” or “reading disorder” or “reading disability” or “spelling difficulties” or “spelling disorder” or “spelling disability”). LLBA: ab(“procedural learning” or “implicit learning” or “artificial grammar learning” or “statistical learning”) AND ab(dyslexia or “reading difficulties” or “reading disorder” or “reading disability” or “spelling difficulties” or “spelling disorder” or “spelling disability”). Pubmed: ((“procedural OR “implicit learning”[Title/Abstract] OR “artificial grammar learning”[Title/Abstract] OR “statistical learning”[Title/Abstract]) AND (dyslexia[Title/Abstract] OR “reading difficulties”[Title/Abstract] OR “reading disorder”[Title/Abstract] OR “reading disability”[Title/Abstract] OR “spelling difficulties”[Title/Abstract] OR “spelling disorder”[Title/Abstract] OR “spelling disability”[Title/Abstract])). OATD: abstract:( (“procedural learning” OR “implicit learning” OR “artificial grammar learning” OR “statistical learning”) AND (dyslexia OR “reading difficulties” OR “reading disorder” OR “reading disability” OR “spelling difficulties” OR “spelling disorder” OR “spelling disability”). Appendix B: Supplementary materials Study Data abstracted for effect size calculation
SEM – images of commercial as-received (left) and reduced Co3O4 NPs Figure S8 TEM – images of commercial as-received (left) and reduced Co3O4 NPs Figure S9 Figure S10. CTH of p-CNB: activity (solid bars) and maximum p-CAN yield (hatched bars) obtained in different solvents with CoOx/ACF (Co loading 4.0 wt.%). Reaction conditions: 333 K; - 0.126 M; molar ratio N2H4/p-CNB = 3.
CO2 HYD RWGS α1 α2 β1 β2 Cu 0.01 0.01 0.01 0.01 0.01 0.06 0.01 0.01 0.01 0.03 0.01 0.01 0.01 0.03 0.01 0.01 0.02 0.04 0.01 0.02 Pd 0.02 0.09 0.01 0.01 Kissinger plots Equation used for Kissinger plot analysis is given in Eq. S3. Eq. S3 where β: heating rate, K min-1; TM: temperature at maximum rate, K; R: gas constant, kJ mol-1; Ea: apparent activation energy; [H2]m: hydrogen concentration at TM. 2 TPR analysis Fig. S1 TPR profile of CuO (Sigma-Aldrich). The data was collected by mass spectrometer. TPR conditions: 20 mL min-1 of 5 vol% H2/Ar flow with a heating rate of 10 K min-1.
Supplementary tables and figures Supplementary Table 1 Type of anti-psychotic medication just before entering the initial acute oral treatment phase ATC Term/Preferred Term Paliperidone palmitate (n=352) Oral antipsychotics (n=363) Total number of subjects (n=715) Number of subjects with at least one antipsychotic medication just before entering the initial acute oral treatment phase 217 (61.6) 221 (60.9) 438 (61.3) Benzamides 15 (4.3) 15 (4.1) 30 (4.2) Amisulpride 15 (4.3) 13 (3.6) 28 (3.9) Tiapride 0 2 (0.6) 2 (0.3) Butyrophenone derivatives 43 (12.2) 42 (11.6) 85 (11.9) Haloperidol 43 (12.2) 42 (11.6) 85 (11.9) Diazepines, oxazepines, thiazepines and oxepines 55 (15.6) 49 (13.5) 104 (14.5) Clozapine 0 1 (0.3) 1 (0.1) Loxapine 3 (0.9) 1 (0.3) 4 (0.6) Olanzapine 36 (10.2) 21 (5.8) 57 (8.0) Quetiapine 8 (2.3) 16 (4.4) 24 (3.4) Quetiapine fumarate 11 (3.1) 10 (2.8) 21 (2.9) Indole derivatives 4 (1.1) 8 (2.2) 12 (1.7)
Supplementary Data An Integrated Life Cycle Costing and Life Cycle Human Health Impact Analysis of Municipal Solid Waste Management Options in Hong Kong using Modified Eco-efficiency Indicator Kok Sin WOON a and Irene M. C. LO a* a Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China; *Corresponding author: Email: cemclo@ust.hk; Fax: Tel: 852-23587157. Fig. A.1. Impact Pathway Approach (EC ESCAPE, 2009) Table A.1. Summary of LCA data inventory (air pollution) for landfill extension (LFE) Sub-process Compound Quantity per FUa Source Waste Transport CO2 1.10 kg DEFRA, 2011; Woon and Lo, 2013 NOx 2.11×10-2 kg HKEPD, 2009 SO2 2.01×10-4 kg RSPb 2.30×10-4 kg Biological Reactions at Landfill Cells CO2 183 kg IPCC, 2006; Woon and Lo, 2013 Flare Systemc NOx 3.21×10-2 kg HKEPD, 2009 SO2 1.96×10-3 kg Leachate Collection NOx 3.91×10-2 kg
Table S3. The multi-regional input-output table structure for China Output Input Intermediate use Final use Er Total output Region 1 Region 30 Total Region 1 Region 30 Export Total Sector 1 Sector 30 Sector 1 Sector 30 Con CapF Con CapF Intermediate input Region 1 Sector 1 AX E F X’ Sector 30 Region 30 Sector 1 Sector 30 Import MI MF V Total input X COD discharge coefficient T Note: Con is consumption, CapF is Capital formation, Er is Error.
S4 As shown in quation (e), where t represents coefficient in 2010 and t' represents in 2007. Both of t and t’ are calculated at the 2010 price. Given the short time interval the value of t can be assumed to equal t'. Discharge coefficients of crop cultivation and forestry were zero but those of livestock and livestock products and fishery were very high. (e) Table S4 The discharge coefficients of agricultural and service sectors in 2010 Sector Sector Discharge coefficients (kg/million yuan) Agricultural sectors Crop cultivation 0 Forestry 0 Livestock and livestock products 7865.18 Fishery and aquaculture 1252.36 Service sectors Transportation and warehousing 2.10 Wholesale and retail trade 76.89 Accommodation and catering industry 48.42 Leasing and business services 47.99 Research and experimental development 255.26 Other service 84.75 S5 Table S5 Industrial sectors Code Sector Code S1 Agriculture S2 Coal mining and processing S3 petroleum and natural gas S4 Metal ore mining S5 Non-metallic minerals and other mining S6 Food processing production Beverages and Tobacco products S7 Textiles S8 Wearing apparel leather, furs, down and related products S9 Sawmills, fibreboard, and products of wood, bamboo, cane, palm, straw, Furniture etc. S10 Paper and paper products; Printing and record medium reproduction; Cultural products S11 Petroleum refining coking and nuclear fuel S12 Chemical industry S13 Non-metallic mineral products S14 Metal smelting and processing S15 Manufacture of products S16 Manufacture of general and special purpose machinery S17 Manufacture of transport equipment S18 Manufacture of electrical machinery and equipment
Appendix – A IS 15464 (2004) : Requirements of anhydrous ethanol for use in automotive fuel S.No Properties Requirements 1 Relative Density at 15.6°C Max 0.796 2 Ethanol content, % by volume at 15.6°C Min 99.50 3 Alkalinity - Nil 4 Acidity (as CH3 COOH) mg/l Max 30 5 Residue on evaporation, percent by mass Max 0.005 6 Aldehyde content (as CH3CHO) mg/l Max 60 7 Methyl alcohol, mg/liter, ppm Max 300 8 Copper, mg/kg Max 0.1 9 Appearance - Clear & bright Appendix – B IS 2796 (2014): BS IV Motor Gasoline – Specification – Requirements for 10% ethanol blended gasoline (E10) S.No Properties Requirements 1 Density @ 15°C, kg/m3 - 720-775 2 Distillation Recovery upto 70°C (% vol) Recovery upto 100°C (% vol) Recovery upto 150°C (% vol) Min Max 10-58 40-70 7 5 210
(5) where is predicted per-acre sale price for field x; 2.47105 is the coefficient converting acres to hectares; 0.91 represents the deflator used to convert 2014 to 2008 dollars; is field x’s area in hectares. The equivalent annualized per-hectare net return was calculated as: (6) where 0.05 refers to the 5% discount rate as mentioned above. SI Tables S1. Watershed level P abatement cost Scenarios Costs for 46% TP Reduction ($ Million) Costs for 61% DRP Reduction ($ Million) for 78% DRP Reduction ($ Million) CPs only 1.98 3.13 NA Mixing CPs and land uses 1.17 2.32 5.82 Optimizing based on mixed strategy and reduced alfalfa & timber prices (-20%) 1.89 3.17 8.35
Table A.7 Descriptive statistics of excess returns and performance ratios of fossil-free FTSE 100 portfolios (1996-2016). Source Portfolio label N Mean Median StDev DR Sharpe Sortino ΔVar ΔSharpe FTSE 100 Market portfolio 251 0.59% 0.96% 3.73% 3.77% 0.1589 0.1572 - Fossil-free portfolios
(0.0052) (0.0025) (0.0024) MktRf 0.5857*** 0.0627 0.1214 (0.1401) (0.0878) (0.0896) SMB 0.3057 -0.1622 -0.1384 (0.2361) (0.1235) (0.1216) HML 0.6166*** 0.3818*** 0.4162*** (0.1903) (0.1107) (0.1108) WML 0.1496 0.0461 0.0587 (0.1541) (0.0671) (0.0707)
Supplementary Fig. S1. Expected heterozygosity (He) at flanking loci 0.8, 4.3 and 7.7 kb grouped by single nucleotide polymorphism (SNP) haplotype. The microsatellite (MS) diversity from all three countries found in the single mutant SGKAA (n = 25), double mutant SGEAA (n = 87) and triple mutant SGEGA (n = 15) Plasmodium falciparum dihydropteroate synthetase (Pfdhps) haplotypes are presented. He was calculated for SNP haplotypes with observation n ≥ 10.
Appendix D. 2008 and 2014 French guidelines concerning targeted antibiotic prescriptions in urinary tract infections (UTIs). Clinical situation Alphabetical list of recommended antibiotics (choice to be adapted to antibiotic susceptibility data) 2014 guidelines [13] 2008 guidelines [13] First-line treatment Second-line treatment First-line treatment Acute complicateda cystitisb Amoxicillin In decreasing order of recommendation: 1. Pivmecillinam 2. Nitrofurantoin 3. In alphabetical order: Cefixime or co-amoxiclav or cotrimoxazole or fluoroquinolonesd (ciprofloxacin or ofloxacin) 4. Oral fosfomycin (after expert advice) In alphabetical order: Amoxicillin Cefixime Co-amoxiclav Cotrimoxazole Nitrofurantoin Pivmecillinamc Quinolones:d ciprofloxacin, enoxacin, lomefloxacin, norfloxacin or ofloxacin Acute pyelonephritise Amoxicillin In alphabetical order: Cefixime Co-amoxiclav Cotrimoxazole Fluoroquinolones:d ciprofloxacin, levofloxacin or ofloxacin In alphabetical order: Amoxicillin Cefixime Co-amoxiclav Cotrimoxazole Fluoroquinolones:d ciprofloxacin, levofloxacin or ofloxacin UTI in a male patiente Fluoroquinolones:d ciprofloxacin, levofloxacin or ofloxacin Cotrimoxazole In alphabetical order: Cotrimoxazole Fluoroquinolone:d ciprofloxacin, levofloxacin or ofloxacin a Only non-pregnant women with no known urological abnormalities or comorbidities are considered to have uncomplicated UTIs.
Figure S-20 13C spectra of 3b Figure S-21 HRMS spectra of 3b Figure S-22 1H NMR spectra of 3c Figure S-23 13C spectra of 3c Figure S-24 HRMS spectra of 3c Figure S-25 HRMS spectra of DFT-Mg2+complex Figure S-26 HRMS spectra of [DFT-FL]-Mg2+complex Figure S-27 Job’s plot (stoichiometry determination of the DFT-Mg2+ adduct) in DMSO: water 7: 3, v/v, 0.1 M HEPES buffer, pH 7.4 (λex = 420 nm). Figure S-28 Job’s plot (stoichiometry determination of the DFT-FL-Mg2+ adduct) in ethanol: water, 3: 7, v/v, 0.1 M HEPES buffer, pH 7.4 (λex = 420 nm). Figure S-29 Determination of the binding constant of DFT with Mg2+using the fluorescence technique.
Figure S-47 Effect of pH on the emission intensity of DFT-Nap(10 µM) and 7:3 DMSO -HEPES buffer (10mM, λex = 420 nm, pH 7.4). Figure S-48 Plot of emission intensities of DFT-Nap(10 µM) alone (1) and in presence of different cations(10 µM): Hg2+ (2), Zn2+ (3), Pb2+ (4), Mn2+ (5), Ag+ (6), Au+ (7), Co2+ (8), Fe3+ (9), Ni2+ (10), Ca2+ (11), Cu2+ (12), Cd2+ (13), Cr3+ (14) and Al3+ (15) (black bar) and along with Mg2+ (red bar) respectively. Figure S-49 Fluorescence spectral changes of DFT-cya (10 µM) due to addition of different cation (λex =420 nm, in ethanol: water, 3: 7, v/v, 0.1 M HEPES buffer, pH 7.4 (λex = 420 nm). Figure S-50 Effect of pH on the emission intensity of DFT-cya(10 µM) and 7:3 DMSO buffer (10mM, λex = 420 nm, pH 7.4). Figure S-51 Plot of emission intensities of DFT-cya(10 µM) alone (1) and in presence of different cations(10 µM): Hg2+ (2), Zn2+ (3), Pb2+ (4), Mn2+ (5), Ag+ (6), Au+ (7), Co2+ (8), Fe3+ (9), Ni2+ (10), Ca2+ (11), Cu2+ (12), Cd2+ (13), Cr3+ (14) and Al3+ (15) (black bar) and along with Mg2+ (red bar) respectively.
Figure S3 DSC curve of the Na[FSA]–[C2C1im][FSA] system at x(Na[FSA]) = 0.2. Figure S4 DSC curve of the Na[FSA]–[C2C1im][FSA] system at x(Na[FSA]) = 0.3. Figure S5 DSC curve of the Na[FSA]–[C2C1im][FSA] system at x(Na[FSA]) = 0.4. Figure S6 DSC curve of the Na[FSA]–[C2C1im][FSA] system at x(Na[FSA]) = 0.5. Figure S7 DSC curve of the Na[FSA]–[C2C1im][FSA] system at x(Na[FSA]) = 0.6. Figure S8 DSC curve of the Na[FSA]–[C2C1im][FSA] system at x(Na[FSA]) = 0.7. Figure S9 DSC curve of the Na[FSA]–[C2C1im][FSA] system at x(Na[FSA]) = 0.8. Figure S10 DSC curve of the Na[FSA]–[C2C1im][FSA] system at x(Na[FSA]) = 0.9. Figure S11 DSC of the Na[FSA]–[C2C1im][FSA] system at x(Na[FSA]) = 1.0.
Table S1. Sample Found(uM) Recovery(%) RSD(%, n=3) 3 3 ±0.2 106% 2.5 Serum 1 4.5 4.5±0.1 97.8% 1.8 3.5 3.5±0.2 105.7% 2.3 3 3±0.1 103% 1.8 Serum 2 3.5 3.5±0.1 102.8% 1.7 5 5±0.2 104% 1.9
Fig. S13. Li’s equation plot (intensity at 500 nm) of 1-Zn2+, assuming 1:1 stoichiometry for association between 1-Zn2+ and S2-. ‘α’ presents [(Ax-Amax)/(A0-Amax)]. Fig. S14. Determination of the detection limit based on change in the ratio of 1-Zn2+ (5 μM).
DMSO) and Zn2+ (20 μM) at various concentrations of S2- (10, 20, 30, and 40 μM). Error bars represent the standard error from three independent experiments (P < 0.05). Fig. S19. Quantification of mean fluorescence intensities of a2, b2, c2, d2, and e2 in Fig 10 (a). D.L. means the detection limit based on the 3/slop.
Table S4: Mean (+/-SE) cover or biomass of wrack at intertidal elevations (low, mid, high) of armoured shorelines, natural (unarmoured) shorelines (sandy beaches, rockpools and rocky shores), eco-engineered shorelines (man-made rock pools and planted saltmarshes). See Tables S1-3 for further detail. (%) Low intertidal Seawalls: 0.63% (0.17) Sandy beaches: 3.75% (1.14) Rocky shores: 0.36% (0.07) Rockpools: 2.06% (0.99) Seawalls: 0.63% (0.17) rockpool: 12.97% (2.36) Natural rockpools: 2.06% (0.99) rockpool: 12.97% (2.36) Mid intertidal beaches: 1.10% (0.23) Rocky shores: 0.61% (0.10) Rockpools: 1.65% (0.27) rockpool: 1.00% (0.27) Saltmarsh: 13.90% (2.25) Natural rockpools: 1.65% (0.27) rockpool: 1.00% (0.37) High intertidal Sandy beaches: 18.58% (2.41) Rocky shores: 1.42% (0.18) Rockpools: 3.11% (0.68) Marine biomass (g) Low intertidal Seawalls: 0.01g (0.01) rockpool: 0.93 g (0.18) Natural rockpools: 0.02 g (0.01) rockpool: 0.93 g (0.18)
Mid intertidal rockpool: 0.02 g (0.01) Saltmarsh: 0.11g (0.04) Natural rockpools: 0.13 g (0.03) rockpool: 0.02 g (0.01) Terrestrial biomass (g) Low intertidal Seawalls: 0.33g (0.10) rockpool: 13.00 (4.03) Natural rockpools: 0.27 (0.09) rockpool: 13.00 (4.03) Mid intertidal rockpool: 1.21g (0.42) Saltmarsh: 10.06g (0.04) Natural rockpools: 1.48g (0.44) rockpool: 1.21g (0.42)
0.022 0.019 0.06 0.027 0.046 0.045 0.052 0.031 0.034 0.035 0.029 0.022 0.014 0.014 0.022 0.034 0.028 0.036 0.048 0.013 0.032 0.035 0.036 0.043 0.034 0.017 0.025 0 0.065 0.031 0.01 0.013 0.016 0.03 0.061 0 0.009 0.04 0.013 0 0.091 0.019 0.01 0.04 0.088 0.06 0.082 0.092 0.06 0.109 0.043 0.13 0.048 0.01 0.032 0.037 0.02 0.013 0.01 0.021 0.02 0.032 0.021 0.054 0.014 0.034 0.031 0.013 0.043 0.091 0.012 0 0.016 0 0 0.088 0.012 0.02 0.009 0.014 0 0.019 0 0.014 0.011 0.019 0 0.12 0.09 0.03 0.03
Table S1. Photoelectrochemical performance and durability of hematite reported in literature. Morphology (Method) Technique Photocurrent density (mA cm-2)a Stability Reference Mesoporous (Anodic electrodeposition) Ti and Sn co-doping 1.4 - [] Nanorods (hydrothermal) Hematite core-shell 1.2 - [] Nanorods (hydrothermal) Ti surface treatment 2.0 ~2 h [] Nanorods (hydrothermal) Sn surface treatment 2.25 - [] Colloidal nanoparticles (Electrophoretic) Sn (or Ti) surface treatment and Co-Pi catalyst deposition 1.1 - [] Nanorods (hydrothermal) Ti doping and Al surface treatment 1.3 - [] Nanorods (hydrothermal) TiO2 shell by PECVD 0.9b ~10 h [] Mesoporous (Cathodic electrodeposition) Zn and Ti co-doping 1.5 - [] Cauliflower-like structure (APCVD) Al2O3 (or TiO2) overlayer by ALD 2.3 ~1 h [] Nanowire nanostructures (Doctor blading) Ti surface treatment and cobalt ion treatment 0.7 - [] Mesoporous (Cathodic electrodeposition) Sn doping and Nb surface treatment, cobalt ion treatment 2.0 ~24 h this study aAll photocurrents were measured at 1.23 V vs. RHE under AM 1.5 condition (100 mW bObtained at E = 1.6 V vs. RHE Figure S1. (a) XRD patterns of Sn-H/Nb samples without and with Co2+-treatment and (b) deconvolution of XPS Co 2p bands.
*E-mail addresses: jinwoo03@postech.ac.kr (J. Lee), ysung@snu.ac.kr (Y.-E. Sung) 1These authors contributed equally to this work. Fig. S1. molecular model of Fe2+(phen)3 complex generated by ChemDraw 3D software and estimation of the complex size. Fig. S2. Schematic description of the observing direction of the top view and the side view referred in the caption of Fig. 3a-c. Fig. S3 FT-EXAFS spectra of reference Fe-foil. Fig. S4 X-ray diffraction patterns for m-FePhen-C and m-FePhen-C-AF.
Table S3. A summary of the performance of various NPMCs for ORR under AEMFC. Catalyst Catalyst loading (mg cm-2) Ionomer (Company) Membrane (Company) Power density @ 0.65 V (mW·cm-2) Max. Power density (NPMC cathode)a (mW·cm-2) Max. Power density (Pt/C cathode)b (mW·cm-2) Temp. Oxidant Pressure (kPa) Ref. m-FePhen-C 1 AS-4 (Tokuyama) A901 (Tokuyama) 188 272 206 50 O2 180 This work Fe-N/C 2 I2 (Acta S.P.A) In-house membrane 260 379 536 80 O2 atm S1 CNT/HDC 2 I2 (Acta S.P.A) In-house membrane 178 221 525 50 O2 atm S2 HNCS 4 AS-4 (Tokuyama) A201 (Tokuyama) 50 68 80 60 O2 atm S3 N,S,Fe doped graphene 4 aQAPS-S8 aQAPS-S8 (In-house) 113 164 Not presented 60 O2 100 S4 Fe/Co-NpGr 2.5 Fumion (Fumatech) FAA (FumaTech) 30 35 60 50 O2 atm S5
Table S1: BET surface area, CO2 physisorption and CH4 productivities of Ta3N5 samples. Samples aSurface area/m2 g-1 bCO2 physisorption/mg cCH4 productivity/μmol h-1 dNormalized CH4 productivity/ μmol h-1 m-2 Cuboid 5.4 1.68 0.141 0.026 Nanorod 6.6 1.93 0.051 0.008 Particle 11.1 1.17 0.022 0.002 a,bThe surface area and CO2 physisorption were investigated by the (BET) method. cThe CH4 yield was determined Ta3N5 samples (0.1 g) within irradiation of 7 hours. dCalculated from dividing the apparent CH4 productivity by BET surface area. Table S2: The N/Ta and O/Ta ratios on the surface and at the 15 nm depth estimated by XPS Samples N/Ta surface O/Ta surface O/Ta 15nm Cuboid 1.54 0.27 0.04 Nanorod 1.55 0.31 0.05 Particle 1.47 0.34 0.19 Fig. S1. XPS spectra of as-synthesized Ta3N5 samples. (a) Ta 4f, (b) N 1s and Ta 4p,
Fig. S12. Optical band gaps of the (a) Ta3N5 samples determined by UV/Vis diffuse reflectance spectroscopy (Fig. S4). (b) Valence-band spectra of the Ta3N5 samples obtained by XPS. (c) Diagram of the band energy of the Ta3N5 samples with different structures and the electrochemical potentials for CO2 reduction. Fig. S13. STEM image and EDS mapping profiles for Ta3N5 cuboids with simultaneous photo-deposition of Pt and MnOx as cocatalysts.
Table S3: Prediction of the refractive indices for the virtual library of monomers contain S atom Name Structure of Monomers Index (n) Name Structure of Monomers Index (n) m1.mol 1.658441 m2.mol 1.691609 m3.mol 1.691609 m4.mol m5.mol 1.658441 m6.mol 1.658441 m7.mol 1.658441 m8.mol 1.665712 m9.mol 1.665712 m10.mol 1.665712 m11.mol 1.665712 m12.mol 1.665712 m14.mol 1.665712 m15.mol 1.672245 m16.mol 1.672245 m17.mol 1.672245 m18.mol 1.672245 m19.mol 1.672245 m20.mol 1.672245 m21.mol 1.672245 m22.mol 1.672245 m23.mol 1.672245 m24.mol 1.672245 m25.mol 1.672245 m26.mol 1.677645 m27.mol 1.677645 m28.mol 1.677645 m29.mol 1.677645 m30.mol 1.677645 m31.mol 1.682674 m32.mol 1.686165 m33.mol 1.688885 m34.mol 1.690052 m35.mol 1.667247 m37.mol 1.688885 m38.mol 1.688885 m39.mol 1.69629 m40.mol 1.70255 m41.mol 1.706475 m42.mol 1.699809
Table S2. Results of two-way ANOVA (amendment and mulch addition) for concentrations of PLFAs, fatty acid ratios and diversity indices of bacterial and fungal communities. PLFAs Diversity indices of microbial community Source of variation Total PLFAs (F) Bacteria (B) Actinobacteria Gram+ Gram¯ F/B Gram+/ Gram¯ Bacteria Fungi Richness Simpson Richness Simpson Amendment (A) *** *** *** ** *** ** *** *** *** *** *** *** *** *** Mulch (M) n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. *** *** *** *** *** ** A x M n.s. n.s. n.s. * n.s. n.s. n.s. ** * ** ** *** *** *** ***, **, * significant at P < 0.001, P < 0.01 and P < 0.05, respectively; n.s.: not significant.
Supplemental section: Supplemental table 1: 4°c 37°c Titer Average St dev Titer Average St dev T0 6.45 6.39 0.21447611 6.45 6.39 0.21447611 6.3 6.3 6.45 6.45 6.4 6.4 6.45 6.45 5.95 5.95 6.75 6.75 6.6 6.6 6.25 6.25 6.3 6.3 T1 6.35 6.32 0.05700877 4.9 5.2 0.2236068 6.3 5.5 6.4 5.3 6.3 5.2 6.25 5.1 T2 6.3 6.33 0.20796634 4.8 4.82 0.083666 6.05 4.8 6.45 4.9 6.6 4.9 6.25 4.7 T3 5.95 6.15 0.21794495 4.4 4.47 0.22248595 6.1 4.2 6 4.8 6.2 4.55 6.5 4.4