zilongwang/ReadingBank huggingface dataset:
Electronic Supplementary Material – Appendix A Species used to classify hedgerows as ‘important’ (1997 Hedgerow Regulations; http://www.legislation.gov.uk/uksi/1997/1160/schedule/1/made, accessed 31 January 2013) Woodland species Barren strawberry Bluebell Broad buckler fern Broad-leaved helleborine Bugle Common cow-wheat Common dog violet Common polypody Dog’s mercury Early dog violet Early purple orchid Enchanter’s nightshade Giant fescue Goldilocks buttercup Great bell-flower Greater wood-rush Hairy brome Hairy woodrush Hard fern Hard shield fern Hart’s tongue Heath bedstraw Herb paris Herb-robert Lady fern Male fern Moschatel Narrow buckler-fern Nettle-leaved bell-flower Oxlip Potentilla sterilis Hyacinthoides non-scriptus Dryopteris dilatata Epipactis helleborine Ajuga reptans Melampyrum pratense Viola riviniana Polypodium vulgare Mercurialis perennis reichenbachiana Orchis mascula Circaea lutetiana Festuca gigantea Ranunculus auricomus Campanula latifolia Luzula sylvatica Bromus ramosus Luzula pilosa Blechnum spicant Polystichum aculeatum Asplenium scolopendrium Galium saxatile Paris quadrifolia Geranium robertianum Athyrium filix-femina Arum maculatum Dryopteris filix-mas Adoxa moschatellina Dryopteris carthusiana Campanula trachelium Primula elatior
Pignut Primrose Ramsons Sanicle Scaly male-fern Small cow-wheat Soft shield fern Sweet violet Toothwort Tormentil Wild strawberry Wood anemone Wood avens/Herb bennet Wood false-brome Wood horsetail Wood meadow-grass Wood melick Wood millet Wood sage Wood sedge Wood sorrel Wood speedwell Wood spurge Woodruff Yellow archangel Yellow pimpernel Conopodium majus Primula vulgaris Allium ursinum Sanicula europaea Dryopteris affinis Melampyrum sylvaticum Polystichum setiferum Viola odorata Lathraea squamaria Potentilla erecta Fragaria vesca Anemone nemorosa Geum urbanum Brachypodium sylvaticum Equisetum sylvaticum Poa nemoralis Melica uniflora Millium effusum Teucrium scorodonia Carex sylvatica Oxalis acetosella Veronica montana Euphorbia amygdaloides Galium odoratum Lamiastrum galeobdolon Lysimachia nemorum Woody species: Black-poplar tree Large-leaved lime Small-leaved lime Wild service-tree Populus nigra ssp betulifolia Tilia platyphyllos Tilia cordata Sorbus torminalis
Appendix A1: Connectivity calculation Polygons of coverage of CG in and around Hampshire were used to produce a 100×100 m raster, with the value of each cell corresponding to the % cover of CG within it. Connectivity to CG was calculated to the same resolution, with the connectivity of each cell being a function of the distances to and % coverage of all other cells in the landscape. Assuming a negative exponential dispersal kernel (Hanski 1994), connectivity C of each cell i was calculated such that (1) where A is the % coverage of CG habitat in cell j and dij is the Euclidian distance (km) between the centres of cells i and j. For the purposes of this study, the multiplier of α = 2 was selected to represent a mean distance of 1km. We extracted the connectivity to CG of each trap location from the raster using the R package maptools (Bivand & Lewin-Koh 2014). Mark-release-recapture (MRR) studies on common farmland moth species have found mean distances covered between first capture and first recapture to be up to 0.65 km (Merckx et al. 2009, 2010). However, the maximum distance between any two of the capture sites in both of these studies was only 1.44 km, therefore lifetime dispersal of the species studied is likely to be greater (Schneider 2003). A larger-scale MRR of Slade et al. (2013) on forest fragments around Wytham Woods, UK, had a maximum pairwise trap distance of 4.13km. In this study there were some highly mobile species which had modelled mean distances moved within one week that were greater than 1km, but these were woodland specialists that were rarely caught in our study. Macro-moth species in Slade et al. (2013) were more generally expected to have a net displacement of between 250 and 500m per week; given the scale of their study, we believe that 1km is a reasonable approximation of what the mean lifetime displacement might be for individuals in our study, hence we use a mean distance of 1km in our dispersal kernel.
We identified six species of micro-moth that were not were not specified to occur on “many/most/wide variety” of habitats in Sterling & Parsons (2012) despite strong evidence to the contrary. These species, Chrysoteuchia culmella, Crambus perlella, Agriphila straminella, Agriphila tristella, Agriphila inquinatella and Agriphila geniculea (Agassiz et al. 2013), all belonged to the sub-family Crambinae. They accounted for 2,614 of the individuals recorded in our study, were present in substantial numbers on arable fields and are nationwide. As such, we sought a second opinion from an author of Sterling & Parsons (2012) on these species, which confirmed that they are “generally widespread and occur in most grassland habitats, or habitats with grassy situations” (Mark Parsons, pers. comm. 11th August 2016). We took this extra information into account so that these species were not incorrectly classified as CG moths.
Supplementary Table 1. Comparisons of baseline and clinical characteristics between included and excluded patients. Categorical values are displayed as count (percentage). Continuous variables are displayed as median (25th percentile, 75th percentile). Characteristics Included patients (n=302) Excluded patients (n=88) P-value Age (years) Parity Primipara Multipara Type of delivery Vaginal delivery Caesarean section Initial mental status Alert Verbal Painful Unresponsive Initial vital signs Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Heart rate (beats/min) Body temperature (℃) index 32.0 (30.0, 35.0) 183 (60.6) 119 (39.4) 203 (67.2) 99 (32.8) 286 (94.7) 10 (3.3) 2 (0.7) 4 (1.3) 113.5 (97.0, 125.3) 68.0 (59.0, 78.0) 98.0 (85.0, 118.0) 37.0 (36.4, 37.7) 0.9 (0.7, 1.1) 31.0 (29.0, 33.8) 47 (53.4) 41 (46.6) 64 (72.7) 24 (27.3) 83 (94.3) 4 (4.5) 0 (0.0) 1 (1.1) 113.0 (103.5, 124.5) 68.0 (60.5, 77.5) 96.0 (81.5, 109.5) 37.1 (36.3, 37.9) 0.8 (0.7, 1.0) 0.05 0.23 0.33 0.83 0.73 0.86 0.18 0.58 0.20
Supplementary Table 2. Comparisons of initial laboratory findings, amount of blood transfusion, and clinical outcome between included and excluded patients. Categorical values are displayed as count (percentage). Continuous variables are displayed as median (25th percentile, 75th percentile). INR, international normalized ratio. Variables Included patients (n=302) Excluded patients (n=88) P-value Initial laboratory findings Hemoglobin (g/dL) Hematocrit (%) Platelets (ⅹ103/µL) Prothrombin time (INR) Amount of blood transfusion (Units) Packed red cells Fresh frozen plasma Platelet concentrate Clinical outcome Massive transfusion Embolization Hysterectomy Length of hospital stays Intensive care unit admission In-hospital death 9.6 (8.0, 11.0) 29.1 (24.5, 33.0) 144.0 (111.0, 183.0) 1.1 (1.0, 1.4) 6.0 (3.0, 12.0) 3.0 (1.0, 7.0) 0.0 (0.0, 10.0) 101 (33.4) 155 (51.3) 3 (1.0) 3.0 (2.0, 4.0) 28 (9.3) 2 (0.7) 9.9 (8.2, 11.0) 29.4 (25.7, 32.9) 156.0 (127.3, 194.5) 1.1 (1.0, 1.2) 4.0 (2.0, 8.0) 2.0 (0.0, 5.0) 0.0 (0.0, 0.0) 16 (18.2) 35 (39.8) 1 (1.1) 2.0 (2.0, 4.0) 11 (12.5) 1 (1.1) 0.57 0.43 0.16 0.37 <0.01 <0.01 0.01 <0.01 0.06 1.00 0.85 0.37 0.54
(17) S7, S8 tert-Butyl (7) S9, S10 tert-Butyl (8) S11, S12 (20) S13, S14 (21) S15, S16 acid (22) S17, S18
(29) tert-Butyl (30) S31, S32 TFA salt (ent-2.TFA) S33, S34 TFA salt (ent-3.TFA) S35, S36 triacetate (31) S37, S38 triacetate (32) S39, S40 tert-Butyl (E-33) S41, S42 tert-Butyl (E-34) S43, S44
13C NMR (100 MHz, CD3OD) Crystallographic analysis Table 2. Crystal data and structure refinement parameters for compound 22 22 Empirical formula C19H37NO7Si Formula weight 419.59 Temperature, T (K) 95(2) Wavelength, λ (Å) 1.54184 Crystal system Orthorhombic Space group P212121 Unit cell dimensions a (Å) 6.50720(10) b (Å) 11.73290(10) c (Å) 31.2873(5) V (Å3) 2388.73(6) Formula per unit cell, Z 4 Dcalcd (g/cm3) 1.164 Absorption coefficient, μ (mm-1) 1.173 F(0 0 0) 912.0 Crystal size (mm) 0.305 × 0.176 × 0.062 Range for data collection (o) 2.825-76.671 Index ranges –8 ≤ h ≤ 8 –14 ≤ k ≤ 14 –39 ≤ l ≤ 36 Independent reflections (Rint) 4968 (0.0981)
Suppl. Table 5 Comparison of diffusion and cellular effects on PP changes. Quality control material of the level 2 (QC2) is chosen because of its ctHb which comparable to patients´ blood. Gas PCO2 [mmHg/30 min] PO2 [mmHg/30 min] Diffusion QC2 high -2.01 -3.51 low -2.11 6.45 Diffusion plus Cells Patients high 1.32 -24.6 low -0.35 3.47
550 0.7 0.9 600 8.5 9.8 650 55.4 61.0 Simplification of the explosive eigenvalue for stage-1B The expression for the explosive eigenvalue for a general fuel (for γ = 1) is given by: (S 61) (S 62) The expression can be further approximated as (S 63) (S 64) For high pressures and low temperature and high pressures (T < 600 K, P > 10 bar) , which yields (S 65) For α = 1 and β = 1 we get (S 66) Note this approximation has significant error in the absolute value of λ1B and not recommended to be used above the limited temperature and pressure range. Prediction for first-stage ignition delay for propane under varying equivalence ratios and pressure
Table 6: Comparison of important parameters used in predicting first-stage ignition delay for propane under varying T0, P0 and equivalence ratio. T0 = 600 K T0 = 650 K φ = 1 P0 =10 bar P0 =50 bar P0 =10 bar P0 =50 bar α(T0) 0.5 0.5 0.5 0.5 β(T0) 0.94 0.95 0.85 0.9 θ(T0) 0.75 0.93 0.46 0.8 λ1A(T0) (s-1) 8.5 13.1 55.4 102.9 λ1B(T0) (s-1) 3.0 3.2 25.3 37.5 τstage-1A (s) 1.2 0.7 0.2 0.1 τOQ´OOHpeak (s) 3.2 1.8 0.4 0.2 Tstage-1,max (K) 690 760 690 760 T0 = 600 K T0 = 650 K φ = 0.5 P0 =10 bar P0 =50 bar P0 =10 bar P0 =50 bar α(T0) 0.5 0.5 0.5 0.5 β(T0) 0.94 0.95 0.85 0.9 θ(T0) 0.75 0.93 0.46 0.8 λ1A(T0) (s-1) 7.0 11.4 46.1 87.3 λ1B(T0) (s-1) 3.0 3.2 25.5 37.6 τstage-1A (s) 1.5 0.8 0.24 0.11 τOQ´OOHpeak (s) 4.0 2.1 0.51 0.25 Tstage-1,max (K) 690 760 690 760
BASE OIL 20 Figure S9 – TPD-SSIMS profiles for Cu, Cl and CN-complexes on copper treated with 100 ppm of Irgamet®39 in Base oil 20 at 70 °C for 24 hours. Figure S10 – TPD-SSIMS profiles for representative ions for TTA–complexes and PO3– on copper treated with 100 ppm of Irgamet®39 in Base oil 20 at 70 °C for 24 hours. Inset showing trends excluding P contamination. Figure S11 – TPD-SSIMS profiles for TTA molecular ion (m/z = –132) and TA ion (m/z = –68) on copper treated with 100 ppm of Irgamet®39 in Base oil 20 at 70 °C for 24 hours. GEMINI X Figure S12 – profiles for Cu, Cl and CN-complexes on copper treated with 100 ppm of Irgamet®39 in Gemini X at 70 °C for 24 hours. Figure S13 – TPD-SSIMS profiles for representative ions for TTA–complexes and PO3 on copper treated with 100 ppm of Irgamet®39 in Gemini X at 70 °C for 24 hours. Figure S14 – TPD-SSIMS profiles for TTA molecular ion (m/z = –132) and TA ion (m/z = –68) on copper treated with 100 ppm of Irgamet®39 in Gemini X at 70 °C for 24 hours. HYVOLT III Figure S15 – TPD-SSIMS profiles for Cu, Cl and CN-complexes on copper treated with 100 ppm of Irgamet®39 in HyVolt III at 70 °C for 24 hours. Figure S16 – profiles for representative ions for TTA–complexes and PO3 on copper treated with 100 ppm of Irgamet®39 in HyVolt III at 70 °C for 24 hours.
Appendix A. Chinese and English positive, negative and neutral words. L1 L2 Positive Negative Neutral Positive Negative Neutral 生日 生气 地址 birthday angry address 新娘 破产 机场 bride bankrupt airport 庆祝 血腥 背景 celebrate bloody background 聪明 炸弹 细胞 clever bomb cell 勇气 负担 覆盖 courage burden cover 可爱 困惑 风俗 cute confused custom 美味 犯罪 细节 delicious detail 优秀 危险 信封 excellent dangerous envelope 著名 阴暗 手指 famous dark finger 友好 肮脏 频率 friendly frequency
温柔 疾病 姿势 gentle disease gesture 诚实 失败 习惯 honest failure habit 希望 愚蠢 模仿 hope foolish imitate 珠宝 有害 逻辑 jewelry harmful logic 善良 恐怖 杂志 kindness horrible magazine 渊博 饥饿 观察 knowledgeable hungry observe 幸运 苦难 段落 lucky misery paragraph 乐观 凶手 操场 optimistic murderer playground 父母 痛苦 提醒 parent painful remind 愉快 贫穷 居民 pleasant poverty resident 珍贵 监狱 剪刀 precious prison scissors 公主 惩罚 座位 princess punishment seat 纯洁 拒绝 形状 pure refuse shape 浪漫 自私 衬衫 romantic selfish shirt 满意 奴隶 信号 satisfied slave signal 真诚 可怕 帐篷 sincere terrible tent 甜蜜 疲倦 蔬菜 sweet tired vegetable 信任 悲剧 车辆 tragedy vehicle 财富 丑陋 村庄 wealth ugly village 智慧 浪费 手表 wisdom waste watch 成就 滥用 平衡 achievement abuse balance 先进 事故 气球 advanced accident balloon 勇敢 害怕 浴室 afraid bathroom 明亮 烦恼 黑板 bright annoyed blackboard 冠军 癌症 蜡烛 champion cancer candle 舒适 疯狂 椅子 comfortable crazy chair 自信 危机 圆圈 confident crisis circle 兴奋 残酷 时钟 excited clock 公正 损害 窗帘 fair damage curtain 鲜花 死亡 词典 flower death dictionary 友谊 绝望 抽屉 friendship desperate drawer 花园 困难 编辑 garden difficult editor
礼物 失望 电梯 gift disappointed elevator 英俊 灾难 家具 handsome disaster furniture 天堂 厌恶 假设 heaven disgusted hypothesis 假日 离婚 钥匙 holiday divorce key 荣誉 洪水 行李 flood luggage 幽默 禁止 镜子 humor forbidden mirror 有趣 仇恨 嘴巴 interesting hate mouth 笑话 地狱 蘑菇 joke hell mushroom 忠诚 侮辱 附近 loyal insult nearby 繁荣 懒惰 飞机 prosperous lazy plane 彩虹 孤单 平台 rainbow lonely platform 放松 噪声 肩膀 relaxed noise shoulder 尊敬 囚犯 皮肤 respect prisoner skin 奖赏 争吵 调查 reward quarrel survey 安全 后悔 茶壶 safe regret teapot 微笑 压力 电报 smile stress telegraph 温暖 自杀 趋势 warm suicide trend 婚礼 弱点 卡车 wedding weakness truck
Fig. S2. SAED pattern of the Zn2GeO4-CNT composite microspheres. Fig. S3. Morphologies and elemental mapping images of the ZnO-CNT composite microspheres: a,b) FE-SEM images, c,d) TEM images, e) HR-TEM image, and f) elemental mapping images of zinc, oxygen, and carbon components.
Figure S14. CVs of M1 at various scan rates in MeCN solution with 0.1mol dm-3 TEAPF6. Note: the current is normalized to the square of scan rate. References 1. S. Conte, G. G. Rodriguez-Calero, S. E. Burkhardt, M. A. Lowe, H. D. Abruna, RSC Adv., 3 (2013) 1957-1964. 2. S. M. Zhang, J. K. Xu, B. Y. Lu, L. Q. Qin, L. Zhang, S. J. Zhen, D. Z. Mo, J. Polym. Sci. Pol. Chem., 52 (2014) 1989-1999.
Figure S1 SEM images of the product under other conditions: a,b: Hydrothermal products after sulfuric acid (10 mol·L-1) assisted reaction; c,d: The product of a,b after KOH-activation process (CNSA-10); e,f: The product after direct heat treatment (900°C without KOH) of the hydrothermal product with the assistance of sulfuric acid (6 mol·L-1) Figure S2 FTIR spectra of the hydrothermal product, CNSA-0, CNSA-2 and CNSA-6. The IR spectra of the products are carried out to further reveal their surface organic groups. Results show the hydrothermal product contains many surface organic groups like ether linkage (C-O-C,1100~1150 cm-1), hydrocarbyl in saturated (C-H, 2900~3000 cm-1) and carbanyl group (C=O,1620~1640 cm-1) hydrocarbons. When the samples are further treated by KOH at high temperature, only the chemical groups of C-O-C and C=O are found in the final products. By comparing the peaks of these two groups in each product, higher intensity of the peak for C=O group are shown in CNSA-6 than CNSA-0 and CNSA-2. This result suggests more oxidized structure may form during the KOH-activation process in CNSA-6. Figure S3 Pore size distribution of CNSA-0, CNSA-2 and CNSA-6. Figure S4 Cycling performance of the sample CNSA-10 as LIB anode. Figure S5 Charge-discharge curves of CNSA-6 for the first three cycles as LIB anode (Current density: 100 mA·g-1). Figure S6 Charge-discharge curves of CNSA-6 for the first three cycles as SIB anode (Current density: 100 mA·g-1). Figure S7 Cyclic voltammogram of CNSA-6 as SIB anode tested between 0.005 and 3.0 V (Scanning rate: 0.2 mV·s-1)
Supplementary materials Males Females LOD N > LOD Mean ± SE Median Range N > LOD Mean ± SE Median Range PFSAsa PFBSb PFHxSc PFCAsd PFBAe PFHxAg PFHpAh PFOAi PFTeAj 18.5 10.7 918 36.2 6.2 91.2 26.5 235 0 10 0 0 0 0 3 15 nd 93.057 ± 18.878 nd nd nd nd * 1475 ± 148 nd 78.316 nd nd nd nd * 1422 [<10.7 ; 216.248] [<26.5 ; 93.853] [<235 ; 2712] 0 12 0 0 0 0 4 17 nd 151.394 ± 27.344 nd nd nd nd * 1187 ± 172 nd 144.086 nd nd nd nd * 948 [<10.7 ; 380.644] [<26.5 ; 208.434] [<235 ; 3258] PFASs (pg/ mL ww) mean concentrations ± standard errors, ranges and LODs (limits of detection) of female and male chick-rearing adult kittiwakes Rissa tridactyla from Kongsfjorden, Svalbard. PFASs have been measured in plasma. * Sample size too small for mean and median calculation a PFSAs: Perfluoroalkyl sulfonic acids b PFBS: Perfluorobutanesulfonate c PFHxS: Perfluorohexanesulfonate d PFCAs: Perfluoroalkyl carboxylic acids e PFBA: Perfluorobutanoate f PFPA: Perfluoropentanoate g Perfluorohexanoate h PFHpA: Perfluoroheptanoate i PFOA: Perfluorooctanoate j PFTeA: Perfluorotetradecanoate. PFTeA was detected in less than 70% of the samples in the BMR data set.
Table S3. Associations of household income with 300m and 1000m surrounding green adjusted for age, gender, marital status and country of origin. Results are given as mean difference β (95% CI) relative to the reference category (mean difference = 0.0 min/week). Statistically significant mean differences β (P < 0.05) are given in bold. Income (quintiles) NDVI TOP10NL 300m 1000m 300m Mean difference (95% CI) β % of IQR Mean difference (95% CI) β % of IQR Mean difference (95% CI) β % of IQR Mean difference (95% CI) β % of IQR Q1 (< €15,200) -0.03 (-0.03, 20 -0.02 (-0.02, -0.02) 14 -0.03 (-0.03, -0.02) 11 -0.02 (-0.03, -0.02) 7 Q2 (€15,200 - 19,399) -0.03 (-0.03, 21 -0.01 (-0.01, -0.01) 10 -0.04 (-0.04, -0.04) 16 -0.02 (-0.02, -0.01) 5 Q3 (€19,400 - 24,199) -0.02 (-0.02, 15 -0.01 (-0.01, -0.01) 7 -0.03 (-0.03, -0.03) 13 -0.01 (-0.02, -0.01) 4 Q4 (€24,200 - 30,999) -0.01 (-0.01, 10 -0.01 (-0.01, 0.00) 4 -0.02 (-0.02, -0.02) 9 -0.01 (-0.01, -0.01) 3 Q5 (≥ €31,000) ref . ref . ref . ref .
Appendix A. Supplementary tables Table S1. Results of ROC curve analysis for P. vivax and P. falciparum using environmental data and elevation P. vivax Data partition AUC P. value Data partition AUC P. value 0.792 <0.001 P.falciparum-1 0.744 <0.001 P.vivax-2 0.794 <0.001 P. falciparum -2 0.741 <0.001 P.vivax-3 0.793 <0.001 P. falciparum -3 0.742 <0.001 P.vivax-4 0.795 <0.001 P. falciparum -4 0.733 <0.001 P.vivax-5 0.789 <0.001 P. falciparum -5 0.737 <0.001 P.vivax-6 0.792 <0.001 P. falciparum -6 0.736 <0.001 P.vivax-7 0.786 <0.001 P. falciparum -7 0.739 <0.001 P.vivax-8 0.792 <0.001 P. falciparum -8 0.739 <0.001 P.vivax-9 0.791 <0.001 P. falciparum -9 0.735 <0.001 P.vivax-10 0.795 <0.001 P. falciparum -10 0.730 <0.001 Average 0.775 Average 0.721 The AUC of ROC for each partition of presence data from the MaxEnt, and P value for individual partitions, under the null hypothesis that the prediction is random (AUC=0.5).
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Supplementary Table 2. Top 10 Genes Downregulated in RJ423-200ba429 Cells vs RJ423EV Cells Ensembl ID Gene Name Deseq Adj pval logFC ENSMUSG00000005397 Nid1 1.1 x 10-268 11.9 ENSMUSG00000019929 Dcn 3.1 x 10-228 10.5 ENSMUSG00000030220 Arhgdib 1.5 x 10-203 9.8 ENSMUSG00000051579 Tceal8 4.3 x 10-190 11.5 ENSMUSG00000045362 Tnfrsf26 3.2 x 10-186 10.2 ENSMUSG00000032911 Cspg4 2.0 x 10-184 14.5 ENSMUSG00000023046 Igfbp6 2.8 x 10-174 8.3 ENSMUSG00000030657 Xylt1 1.3 x 10-164 11.6 ENSMUSG00000021464 Ror2 2.3 x 10-163 14.5 ENSMUSG00000069601 Ank3 7.2 x 10-154 9.9
ONLINE APPENDIX (Not for publication) Table A1. Co-authors per Paper (Numbers of Papers) in English-language Economic History Journals, 1963-2011 YEAR EEH EHR JEH Top 3 BH SEHR AEHR CLIO EREH BHR EHDR Others 1963 1.11 1.00 1.05 1.11 1.00 1.00 1.11 1.07 (18) (23) (9) (8) (7) (18) 1973 1.14 1.00 1.13 1.09 1.00 1.00 1.00 1.05 1.02 (21) (24) (30) (9) (7) (9) (19) 1983 1.52 1.19 1.14 1.25 1.31 1.00 1.50 1.22 1.13 1.26 (23) (21) (44) (16) (8) (16) (9) (16) 1993 1.33 1.22 1.53 1.37 1.38 1.15 1.13 1.00 1.11 1.06 1.17 (21) (27) (30) (24) (13) (16) (9) (9) (16) 2003 1.85 1.41 1.43 1.55 1.43 1.23 1.25 1.33 1.69 1.08 1.31 1.33 (20) (17) (28) (21) (13) (24) (15) (13) (13) (16) 2011 1.92 1.76 1.84 1.83 1.86 1.75 1.53 1.38 1.54 1.78 1.31 1.40 1.70 (38) (50) (32) (37) (12) (15) (13) (13) (18) (16) (20) Key: TOP3 (EEH = Explorations in Economic History; EHR = Economic History Review; JEH = Journal of Economic History). Others (BH = Business History; SEHR = Scandinavian Economic History Review; REVHE = Revista de Historia Economica; AEHR = Australian Economic History Review; CLIO = Cliometrica; EREH = European Review of Economic History; BHR = Business History Review; EHDR = Economic History of Developing Regions).
Supernumerary embryos cryopreserved Continuous 230 1.03 0.99 1.07 0.179 0 112 Reference 1 27 1.23 0.89 1.71 0.218 2-5 81 1.30 1.04 1.63 0.021 ≥6 10 1.18 0.76 1.82 0.458 0 112 Reference ≥1 118 1.12 0.47 2.63 0.803 a Reference groups: male factor=0; uterine factor=0; DOR=0; Other=0. Note: Modified Poisson regression model, incorporating GEE, was used. BMI, body mass index; CI, confidence interval; DOR, diminished ovarian reserve; E2, estradiol; FSH, follicle stimulating hormone; hCG, human chorionic gonadotropin; LCL, lower confidence limit; RR, relative risk; UCL, upper confidence limit.
Supplemental Table 3. Multivariable analysis for clinical factors & live birth following fresh embryo transfer in donor oocyte IVF cycles of the first cycle from each donor All cycles Number of cycle 95% CI Predictors Categories RR LCL UCL P-value Number of oocytes retrieved Continuous 204 1.03 1.01 1.05 0.0118 ≤10 62 Reference 10< N <20 95 1.77 1.21 2.57 0.003 ≥20 47 1.79 1.15 2.80 0.010 ≤10 62 Reference >10 142 1.77 1.23 2.56 0.002 Number of mature oocytes Continuous 204 1.03 1.01 1.06 0.007 ≤10 84 Reference 10< N <20 97 1.50 1.09 2.07 0.013 ≥20 23 1.61 0.97 2.66 0.067 ≤10 84 Reference >10 120 1.51 1.10 2.08 0.011 Number of oocytes fertilized Continuous 204 1.04 1.01 1.07 0.004 ≤10 134 Reference 10< N <20 63 1.38 1.05 1.82 0.022 ≥20 7 1.08 0.48 2.42 0.861 ≤10 134 Reference >10 70 1.35 1.02 1.79 0.037 Number of cleaved embryos Continuous 204 1.04 1.01 1.08 0.003 ≤10 137 Reference 10< N <20 61 1.38 1.04 1.84 0.027 ≥20 6 1.26 0.59 2.70 0.555 ≤10 137 Reference
Table S26. Experimental versus calculated mass content of carbon, hydrogen and aromatics Sample name Calculated through component matrix ASTM C/ wt% H/ wt% aromatics/ wt% aromatics/ wt% 1 DSZ-HM 85.48 14.52 3.10 3.10 2 RP3-BH 85.99 14.01 10.54 10.34 4 M190-2 85.52 14.48 0.00 0.02 5 MY-1 85.89 14.11 13.22 13.03 6 MY-2 86.14 13.86 3.74 3.77 7 TD-06 85.59 14.41 4.35 4.30
8 MT1112 86.21 13.79 22.54 22.23 9 RP3 86.37 13.63 21.01 20.85 HWY 85.44 14.56 0.96 1.03 8220 86.41 13.59 9.29 9.43 FT-iso 84.81 15.19 0.10 0.05 D40 85.35 14.65 1.04 1.00 M180 85.69 14.31 0.13 0.09 851-2 86.62 13.38 7.77 7.80
a1nm b1.5nm c2nm d3nm e4nm Figure S13 The snapshot of configurations of methane molecules in slit-like chlorite pore at 333K and 20MPa under different pore sizes a1nm b1.5nm c2nm d3nm e4nm Figure S14 The snapshot of configurations of methane molecules in slit-like C4 pore at 333K and 20MPa under different pore sizes a1nm b1.5nm c2nm d3nm e4nm Figure S15 The snapshot of configurations of methane molecules in slit-like C10 pore at 333K and under different pore sizes Figure S16 The potential energy distribution curves of quartz and methane under different pressure (pore size of 4nm) (a) and different pore sizes (pressure of 20MPa) (b) Figure S17 The potential energy distribution curves of montmorillonite and methane under different pressure (pore size of 4nm) (a) and different pore sizes (pressure of 20MPa) (b) Figure S18 The potential energy distribution curves of illite and methane under different pressure (pore size of 4nm) (a) and different pore sizes (pressure of 20MPa) (b) Figure S19 The potential energy distribution curves of kaolinite and methane under different pressure (pore size of 4nm) (a) and different pore sizes (pressure of 20MPa) (b) Figure S20 The potential energy distribution curves of chlorite and methane under different pressure (pore size of 4nm) (a) and different pore sizes (pressure of 20MPa) (b) Figure S21 The potential energy distribution curves of C4 and methane under different pressure (pore size of 4nm) (a) and different pore sizes (pressure of 20MPa) (b)
transfer facilities can work as material processing facility where sorting, recovery, or recycling of specific material like plastic, metals, and fiber can occur. We assume that the transfer facility is where WTE conversion occurs. According to [4] refuse trucks traveled 25,690 miles annually which is equivalent to 98.8 miles per day. Considering the fuel economy of 4.8 mile per gallon (MPG) and carried payload of 9.8 wet tons of NRP, diesel consumption for treating 1 ton of NRP from collection to WTE facility is estimated as 2.1 gallons. Using the GREET model, we estimated GHG emission due transportation of NRP to WTE facility of 6,400 gCO2e per a t NRP. TABLE S10 Transportation of NRP to WTE facility Value Unit miles-traveled 98.8 Miles Fuel economy 4.8 MPG Carried Payload (wet ton) 9.8 tons Fuel type Diesel Method of transportation Refuse truck References [1] (ACC) American Chemistry Council. 2014. Resin Review: The Annual Statistical Report of the North American Plastics Industry. Washington, DC: American Chemistry Council. [2] Ocean Recovery Alliance. 2015. Plastic-to-Fuel Project Developer’s Guide. Hong Kong: Ocean Recovery Alliance & American Chemistry Council. [3] Themelis, N., and Mussche, C. 2014. Energy and Economic Value of Municipal Solid Waste (MSW), Including Non-recycled Plastic (NRP), Currently Landfilled in the Fifty States. New York: Columbia University Earth Engineering Center. [4] GREET (Greenhouse gases, Regulated Emissions and Energy use in Transportation). 2015. Greenhouse gases, Regulated Emissions, and Energy use in Transportation. Retrieved from Argonne National Laboratory: . [5] EIA (U.S. Energy Information Administration). 2016. Petroleum and Liquids – Refiner Petroleum Product Prices by Sales Type. Retrieved from U.S. Energy Information Administration: . [6] OPIS (Oil Price Information Service). 2016. OPIS International Feedstock Intelligence Report. Retrieved from Oil Price Information Service: . [7] Energy Petroleum Coke. 2015. Energy Argus Petroleum Coke. Retrieved from Argusmedia.com: .
Department of Health & Family Welfare Services Puducherry Puducherry "acute coronary" OR "heart attack" OR infarction site:http://health.puducherry.gov.in/ Department of Health and Family Welfare, Government of Punjab Punjab "acute coronary" OR "heart attack" OR infarction site:http://pbhealth.gov.in/ Department of Medical Health and Family Welfare Rajasthan Rajasthan "acute coronary" OR "heart attack" OR infarction site:http://www.rajswasthya.nic.in/ Health Care, Services & Family Welfare Department Sikkim Sikkim "acute coronary" OR "heart attack" OR infarction site:http://sikkimhealth.gov.in/ Health & Family Welfare Department Tamil Nadu Nadu "acute coronary" OR "heart attack" OR infarction site:http://www.tnhealth.org/ Telengana State Portal Health, Medical & Family Welfare Telangana "acute coronary" OR "heart attack" OR infarction site:http://www.telangana.gov.in/ Department of Health & Family Welfare Tripura Tripura "acute coronary" OR "heart attack" OR infarction site:http://tripuranrhm.gov.in/ Department of Medical Health & Family Welfare Uttar Pradesh Uttar Pradesh "acute coronary" OR "heart attack" OR infarction site:http://uphealth.up.nic.in/ Uttarakhand Health & Family Welfare Society Uttarakhand "acute coronary" OR "heart attack" OR infarction site:http://www.ukhfws.org/ Department of Medical Health and Family Welfare Uttarakhand Uttarakhand "acute coronary" OR "heart attack" OR infarction site:http://health.uk.gov.in/ Health & Family Welfare Department West Bengal "acute coronary" OR "heart attack" OR infarction site:http://www.wbhealth.gov.in/
4. I often receive conflicting instructions from the clinical teacher. 148(31.2) 165(34.8) 161(34.0) 5. Sometimes, the work the clinical teacher assigns is too difficult or complicated. 93(19.6) 197(41.6) 184(38.8) Role overload 1. I feel I have no time to take a vacation. 211(44.5) 151(31.9) 112(23.6) 2. I have too much work to complete in a day. 116(24.5) 206(43.5) 152(32.1) 3. My work seems to get harder and harder. 88(18.6) 167(35.2) 219(46.2) 4. I am responsible for a lot of work or tasks, and they are too much to deal with. 62(13.1) 213(44.9) 199(42.0) Role ambiguity 1. I know how to use my time properly. 33(7.0) 191(40.3) 250(52.7) 2. I know what I have to do every day. 30(6.3) 130(27.4) 314(66.2) 3. I know the clinical teacher’s expectations for my work. 72(15.2) 191(40.3) 211(44.5) Appendix 3 The absolute frequency of ratings per item of the Professional Identity Questionnaire for Nursing Students. Sub-dimension Item Ratings(n(%)) Conformation Neutral Unconformity Professional self-image 1. I would like to be a nurse. 213(44.9) 175(36.9) 86(18.1) 2. I will not change my current career direction. 247(52.1) 137(28.9) 90(19.0) 3. I like my major and intend to actively develop in this direction. 219(46.2) 162(34.2) 93(19.6) 4. I am proud to work in the field of nursing. 189(39.9) 210(44.3) 75(15.8)
- Immediately after nurse-to-nurse shift report, I am able to communicate with physicians regarding patient care: Mean=3 pre, Mean=1 post () Australia interviews (n=10) BSHO implemented 3 years prior to study 4 themes: Acknowledging patients as partners Amending inaccuracies Passive engagement Handover as interaction (, ) USA Surveys (n=844), measures of patient falls, medication errors and nurse overtime (n=9 nurses per unit for 10 shifts in the month) Blended handover: recorded and BSHO used BSHO (including use of SBAR) in for 13 months Statistically significantly improvements for patients for: Made sure I knew who my nurse was: Mean= 4.56 pre, Mean= 4.76 post Encourage me to be involved in care: Mean= 4.36 pre, Mean= 4.59 post Communicated important information shift to shift: Mean= 4.40 pre, Mean= 4.60 post Patient narrative responses at 3 and 13 months: globally positive comments indicating good and professional care (n=106-93; 42-48%), BSHO not used or used inconsistently (n=24-34; 10-18%), felt informed, good explanations (n=20-19; 8-10%) Statistically significant improvements for nurses for: Report is an effective means of communication: Mean= 4.04 pre, Mean= 3.98 post Report helps assure accountability: Mean= 3.43 pre, Mean= 3.85 post Report is relatively stress free: Mean= 3.63 pre, Mean= 3.48 post Report helps prevent patient safety problems: Mean= 3.41 pre, Mean= 3.93 post Report promotes patient involvement: Mean= 2.64 pre, Mean= 3.81 post Nurse perceptions of bedside handover: checks of intravenous medication and safety going well, BSHO is too time consuming, recorded and BSHO needs to stop to improve handover Falls and medication errors decreased. No change in overtime data with the change to BSHO () Australia Survey (259) and audit BSHO in place for 5 months at time of audit (including use of SBAR) Nurses received one or two handovers at shift commencement. Handover (significantly shorter for those receiving one handover)
); ), USA (Sample not provided), 125 units (64 medical/surgical units, 61 speciality units including ICU, health and paediatrics), at 14 Kaiser Permanente medical centres (initial roll out at 37 hospitals) Patient satisfaction surveys, patient surveys and audit 14/16 ), USA (Sample not provided), 19 inpatient units, at 1 magnet-designated hospital Patient satisfaction surveys 14/16 ), USA (Sample not provided), 1 emergency unit, at 1 urban hospital Patient satisfaction surveys and informal nurse feedback 11/16 ), USA (Sample not provided), 1 behavioural health unit, at 1 community medical centre Nurse surveys 10/16 ), USA Patients (n=44), 1 medical-surgical intermediate care unit, at 1 tertiary care hospital Patient satisfaction surveys, patient surveys and nurse interviews 12/16 ), USA (Sample not provided), system-wide implementation, at 1 Catholic hospital Patient satisfaction surveys and informal patient and nurse feedback 10/16 ), USA Patients (n= not provided) and nurses (n=17), 1 unit, at 1 cancer centre Patient and nurse surveys and measures of falls and medication errors 8/16 ), USA (Sample not provided, more than 10 000 nurses work for health care system), 6 units and 1 eating disorder unit, at 7 hospitals part of second largest (in nation), secular health system Patient satisfaction surveys and nurse satisfaction surveys 12.5/16 ), USA Patients (satisfaction survey, n=8-20/month; structured interview, n=42) and nurses (n=18 Structured interview with patients, patient satisfaction surveys and nurse surveys 14/16
3. How often do you adapt your day to problems caused by inflammatory bowel disease? Never Occasionally Often Always Not applicable I adapt my diet I various activities I avoid sex I alcohol I plan my day so that I am always near a toilet I plan my day in view of the fact that I have inflammatory disease 4. How do you administer self-care to prevent or relieve symptoms of inflammatory bowel disease? Occasionally Often Always Not applicable Self-care to make sure I sleep well Self-care to manage stress I find out more about inflammatory bowel disease I look for new approaches to living with inflammatory bowel disease I use natural remedies Other kinds of self-care _________________________________________________________________________________ MEDICATION AND CONTACTING HEALTHCARE PROVIDERS 5. Do you take medication for inflammatory bowel disease as prescribed by a doctor? Never Occasionally Often Always No prescription medication 6. Do you know whom to contact if you experience symptoms of inflammatory disease? No Yes 7. How familiar are you with the symptoms for which you should contact a healthcare provider? Totally unfamiliar Relatively unfamiliar Relatively familiar Totally familiar 8. Tobacco use Do you smoke? Do you use snuff? Yes, every day. Yes, every day Yes, occasionally Yes, occasionally No, I quit in__________(year) No, I quit in __________(year)
SUPPLEMENTARY DATA ‘Would raising the total cholesterol diagnostic cut-off from 7.5mmol/L to 9.3mmol/L improve detection rate of patients with monogenic Familial Hypercholesterolaemia?’ Futema M1, Kumari M2, Boustred C3, Kivimaki M4, and Humphries SE1 Table S1. Baseline characteristics of the Whitehall II cohort (n=4896). TG and HDL-C measurements were available for 845 subjects. %age Male AGE (years) TC (mmol/L) TG (mmol/L) HDL-C (mmol/L) BMI Mean (SD) 75 44 (±6) 5.9 (±1.1) 2.1 (±1.4) 1.47 (±0.4) 24 (±3.3) SD = standard deviation
Figure 4 - XRD patterns of fresh, pretreated and spend Na2WO4/Mn/SiO2 catalysts Experimental plan An experimental plan of all experiments is listed in 2. 2- Experimental plan of all temperature ramp and dynamic experiments Experiment Catalyst pretreatment Catalyst Amount (g) Temp. ramp (K/min) Max. Temp. (K) Reduction Feed composition time of reduction Feed (min) TPR 1 Purge He, no heating 0.06 2 1093 N2:H2 (9:1, 60 sccm/min) 400 TPR 2 Oxidation 10 K/min – 1023 K for 1 h, Purge He 0.06 2 1093 N2:H2 (9:1, 60 sccm/min) 400 O2 TPD Purge He, no heating 1 5 1093 He (99.999 %, 20 sccm/min) 160 O2 TPD Oxidation 10 K/min – 1023 K for 1 h, Purge He 1 5 1093 He (99.999 %, 20 sccm/min) 160 TPSR – CH4 Oxidation 10 K/min – 1023 K for 1 h, Purge He 1 1 1073 30 sccm/min methane (99.99 %) 800 TPSR – CH4 Oxidation 10 K/min – 1023 K for 1 h, Purge He 1 2 1073 30 sccm/min methane (99.99 %) 400 TPSR – CH4 Oxidation 10 K/min – 1023 K for 1 h, Purge He 1 3 1073 30 sccm/min methane (99.99 %) 265 TPSR – CH4 Oxidation 10 K/min – 1023 K for 1 h, Purge He 1 4 1073 30 sccm/min methane (99.99 %) 200 TPSR – CH4 Oxidation 10 K/min – 1023 K for 1 h, Purge He 1 5 1073 30 sccm/min methane (99.99 %) 160
S1 Detailed information on the materials used in the work. Chemicals Molar mass /g·mol−1 Source Initial mass fraction purity Final mass fraction purity Purification method Analytical method 4-Bromopyrazole 146.97 Beijing Ouhe Chemical Technology Co. Ltd. 0.987 0.994 Recrystallization HPLCa Methanol 32.04 Sinopharm Chemical Reagent Co., Ltd.,China 0.995 0.995 --- GCb Ethanol 46.07 0.995 0.995 --- GC 18.02 Our lab Conductivity < 2 μScm-1 Distillation Conductivity meter a High-performance liquid chromatograph. b Gas chromatograph.
Supplementary data Table S1 The reference asymmetry factors (Equation 1) and reference recovery values (Equation 2) obtained with the column packed with glass beads applying the dynamic flow-through system (Figure 2). Further details are described in section 2.4.1 and 2.4.2. Compound name Reference asymmetry factor n=2 Reference recovery (%) n=2 Atenolol 3.18 2.56 99.7 94.8 Bendroflumethiazide 1.37 1.34 99.4 95.0 Carbamazepine 1.36 1.38 97.2 104.8 Diclofenac 1.43 1.42 100.2 100.9 Furosemide 1.44 1.43 101.1 100.1 Oxazepam 1.42 1.40 99.9 103.6
Table SI 2. LC-MS/MS parameters LC: Nexcera (Shimadzu Co. Ltd., Mobile Phase Pump A Deionized water Pump B Methanol Pump C 0.1% NH4OH in Pre-clean Methanol Pump D Deionized water Flow rate Ternary Gradient (A/B/C Pump) 0.25 mL/min Sample Loading (D Pump) 1 mL/min Column Scrubber column (Pump A) HLB 2.1 x 20 mm Scrubber column (Pump B) OASIS WAX 2.1 x 20 mm Scrubber column (Pump C) Ghost Trap DS 7.6 x 30 mm Scrubber column (Pump D) OASIS HLB 2.1 x 20 mm Pre-concentration column WAX 2.1 x 20 mm Analytical column Inertsustain C18 2.1 x 50 mm Auto sampler Injection Volume 3 mL MS: LCMS-8080 (Shimadzu Co. Ltd., Interface Parameter Interface HV - 4.5 kV Probe Temperature 300 °C HSID Temperature 200 °C Nebulizer Gas (Air) 2 L/min Heating Gas (Air) 12 L/min Curtain Gas (N2) 3 L/min Exhaust Gas ON
Table SI 3. Quality control and acceptance criteria Prior to analysis Criteria Initial precision and recovery (IPR) LFB-NIES (n=7) <= +/- 30% a Procedure blank Procedure (n=7) <= LCMRL For all batches Initial Calibration Calibration Point >= 5 r2 >= 0.99 Deviation at each point <= +/- 30% Calibration Verification Cal 5 <= +/- 20% Blank Analysis for Instrument LRB-NIES <= Cal 0 <= LCMRL Blank Analysis for Method LRB-EPA <= LCMRL Frequency of Blank analysis in a sequence After every tenth sample <=LCMRL Ongoing Precision and recovery standard (OPR) EPA LFB-EPA Recovery <= +/- 30% a Quality Control Sample LFM-EPA <= +/- 30% a a Except for PFPeA, PFHxA and PFHpA for these <= +/- 40%; Z
PFHxS SI 7. Surrogate recoveries (%) in all the 25 drinking water samples Sample ID Sample 1 70 78 69 110 95 102 92 107 Sample 2 99 95 46 92 85 64 41 43 Sample 3 76 102 46 79 109 102 80 69 Sample 4 89 94 - 82 82 75 60 54 Sample 5 97 90 53 90 101 108 101 81 Sample 6 75 87 47 69 86 96 84 70 Sample 7 82 84 70 100 110 118 98 102 Sample 8 75 86 48 92 100 91 83 76 Sample 9 73 96 50 81 91 114 93 102 Sample 10 87 98 114 118 168 154 111 115 Sample 11 102 95 49 92 84 91 83 81 Sample 12 117 106 50 90 100 111 109 85 Sample 13 85 89 56 82 93 89 76 65 Sample 14 82 88 58 82 90 92 71 51 Sample 15 90 89 64 108 120 99 60 34 Sample 16 75 76 59 84 91 74 57 47 Sample 17 92 92 37 91 102 84 69 57 Sample 18 87 97 55 87 90 82 57 48 Sample 19 92 85 51 94 102 86 73 60 Sample 20 96 104 48 79 93 72 54 44 Sample 21 94 71 45 91 89 71 49 43 Sample 22 84 104 49 94 113 106 78 57 Sample 23 121 90 53 90 68 19 51 57 Sample 24 98 105 57 82 97 92 79 99 Sample 25 45 37 28 53 47 39 34 36
their poor desorption from the Carbograph 2TD/1TD tubes, the data for acenaphthene, fluorene, and anthracene was omitted from the plots. The 6/15/16 plots show the tightest distributions among the PAHs with the fewest outlier measurements, indicating good reproducibility in the measurements. The calibration levels (1-8) correspond to the following PAH amounts: 0, 0.02, 0.05, 0.1, 0.2, 0.5, 1, and 2 ng. Figure S5a: Figure S5b:
Supplementary 1. Different types of deep natural eutectic solvents (NADES) used for the extraction of Ginkgo biloba leaves and Panx ginseng stems and leaves. Abbreviation Components Molar ratio Class of NADES Component 1 Component 2 Component 3 N1 Malic acid Choline chloride 1:1 Acid: base N2 Malic acid Glucose 1:1 Acid: neutral N3 Choline chloride Glucose 5:2 Base: neutral N4 Proline Malic acid 1:1 Amino acid: acid N5 Glucose Fructose Sucrose 1:1:1 Neutral: neutral: neutral N6 Glycerol Proline Sucrose 9:4:1 Neutral: amino acid: neutral
Supplementary Table 2. HPTLC parameters for the determination of four groups of plant metabolites in Gingko biloba leaves and in Panax ginseng leaves and stems. Groups of compounds Developing system Derivatization reagent Detection Ginkgolides1 Toluene-EtOAc-Acetone: MeOH (20:10:10:1.2, v/v/v/v) Acetic anhydride UV 366 nm Ginkgolic acids (8:2:0.2, v/v/v) - UV 366 nm Ginkgo phenolics EtOAc-AcOH-HCOOH: H2O (100:11:11:27, v/v/v/v) Natural Products/PEG reagent UV 254 nm UV 366 nm Ginsenosides CHCl3-EtOAc-MeOH: H2O (15: 40: 22: 10, v/v/v/v) Anisaldehyde White light 1Sodium acetate impregnated plates were used for the analysis.
0.50-250 0.50 1.64 0.3 0.5 7.0 9.4 0.50-250 0.50 0.56 0.3 0.5 7.5 7.6 1.00-250 1.00 0.56 0.7 1.0 7.3 8.1 0.15-250 0.50 0.91 0.3 0.5 6.2 8.5 0.50-250 0.50 0.88 0.8 1.1 7.1 8.0 0.25-250 0.25 0.84 0.2 0.4 2.1 6.4 0.83-250 0.83 0.85 0.2 1.1 8.2 10.0 1.00-250 1.00 2.54 0.9 1.0 5.6 7.1 1.00-100 1.00 8.24 0.2 0.4 6.4 7.5 1.00-250 1.00 1.01 0.3 0.5 3.9 6.7 0.01-250 0.010 0.52 0.2 0.4 3.1 5.4
Figure S17: 1H NMR spectrum of compound 6(400 MHz, CDCl3). Figure S18. 13C NMR spectrum of compound 6 (100 MHz, CDCl3). Figure S19: 1H NMR spectrum of compound 7(400 MHz, CDCl3). Figure S20. 13C NMR spectrum of compound 7 (100 MHz, CDCl3). Figure S21: 1H NMR spectrum of compound 8(400 MHz, CDCl3). Figure S22. 13C NMR spectrum of compound 8 (100 MHz, CDCl3). Figure S23: 1H NMR spectrum of compound 9 (400 MHz, CDCl3).
Supplementary Table 1. Antibodies and their source, pre-treatment and visualization methods used in immunohistochemistrya Antigen Antibody source, clone, and dilution Epitope retrieval methodb Detection system Platform CD3 Dako – rabbit polyclonal 1:400 pH 8 alkaline phosphatase-labeled polymer (Ventana) Ventana Benchmark XT MUM-1 Dako – mouse monoclonal, MUM1p, 1:50 pH 8 alkaline phosphatase-labeled polymer, with proprietary amplifier (Ventana) Ventana Benchmark XT CD18 (canine) Peter Moore, UC Davis, mouse monoclonal CA1.4E9, 1:80 pH 6 Goat/anti-mouse horseradish peroxidase-labeled polymer (Dako) Dako Autostainer CD79a Dako - mouse monoclonal HM57, 1:200 pH 9 Goat/anti-mouse horseradish peroxidase-labeled polymer (Dako) Dako Autostainer LLC Dako – rabbit polyclonal,1:12000 pH 6 alkaline phosphatase-labeled polymer (Ventana) Ventana Benchmark XT a Processed 4 μm sections from paraffin-embedded tissues were mounted on silane-treated glass slides, deparaffinized and rehydrated prior to immunochemistry. b Heat-induced.
Table S1 Correlations, means and standard deviations of group evaluations, shooter bias (left, N = 102), and avoidance bias (right, N = 107) Shooter Bias Avoidance Bias Measure M SD 1 2 3 4 5 M SD 1 2 3 4 5 1. RT Bias 8.11 34.62 20.34 41.03 2. Response bias (c) -0.03 0.30 0.21* -0.05 0.30 0.13 3. bias 0.01 0.11 0.01 0.13 4. Sensitivity (d’) -0.02 0.52 0.00 0.05 -0.28 0.68 0.08 0.13 5. German - Muslim 11.86 23.33 -0.04 -0.09 0.12 9.82 24.23 -0.18 0.16 0.00 6. German - Turkish 8.83 20.58 -0.07 0.00 0.00 0.77*** 10.92 22.78 -0.10 0.07 0.82*** Note. * p < .05, ** p < .01, *** p < .001 For correlational analyses, we computed difference scores of reaction times (RT), errors, d’ and c, as indices of the shooter bias according to the following formulas: RTbias = (RTobject/Arab target – RTobject/White target) +
Appendices Appendix A. Screening model for assessing the leaching potential of volatile solutes The governing equations for transport of volatile chemicals in the dual-porosity model can be expressed as (Hantush et al., 2002): (A.1) for mobile region, for immobile region, (A.2) where C, R, and k are the solute concentration [M/L3], retardation factor ( and ) [‒], and decay constant (km=ln(2)/T1/2, m and kim=ln(2)/ T1/2, im) [T-1], respectively. The subscripts indicate mobile (m) and immobile (im) regions. Independent variables are the time t [T] and the vertical coordinate z [L]. Here, ρb, Kd, and Kh represent the soil bulk density [M/L3], distribution coefficient [L3/M], and dimensionless Henry’s constant [‒], respectively. And f, κ*, θ*, and T1/2 signify the fraction of soil [‒], volumetric air content [‒], volumetric water content [‒], and chemical half-lives [T] in two regions, respectively. The u and µ indicate the mean pore water velocity (u=q/θm, where q is the water flux through a unit area perpendicular to the direction of water flow) [L/T] and the uptake by plant roots (µ=ku/(kmθmRm), where ku is the first-order uptake rate constant) [T-1], respectively. The α represents the mass transfer rate (, where and b
Cg and C* are the solute concentration in the gas phase at the soil surface [M/L3] and that of the atmosphere [M/L3] which is above a (stagnant) boundary layer of thickness l [L], respectively. Also, Dl, M0, and δ(t) indicate the dispersion coefficient in the mobile phase (corresponding to the sum of the last two terms in Eq. (A.3)), initial solute mass applied in unit area, and Dirac-delta function, respectively. Taking the Laplace transform of the governing equations, initial condition, and boundary conditions results in: for a total mass balance, (A.9) , (A.10) , (A.11) , (A.12) , (A.13) , (A.14) . (A.15)
Here, P and Tr represent the Peclet number (P=hu/Dm) [‒] and apparent residence time (Tr =hRm/u) [T], respectively. Leached, volatilized, and degraded fractions of chemicals in soil (equivalent to a ratio of the residual mass Mr in an arbitrary soil depth z within the root zone depth h to the initial mass M0 applied in the surface) are indicated by Ilea, Ivol, and Ideg, respectively. When P approaches infinity (or is extremely high), Eq. (A.10) in the soil depth d finally reduces to: . (A.16) Using Eq. (A.16), Hantush et al. (2002) assessed the leaching of various agrochemicals under varying test conditions, different soil textures and recharge rates. However, since we will not consider both crop uptake and physical nonequilibrium transport (i.e., dual-porosity type formulation) in our new leaching tool (that is, by neglecting µ and ϕ), Eq. (A.16) is further reduced to: . (A.17) Either assuming the root zone h is 1 m or dividing the denominator and numerator in the exponential term by h, we get:
Figure S9. Homologies of the identified Msi2 binding motif in mouse Shh 3’-UTR with Shh mRNA from other species. (a) Distance tree view of the identified Msi2 binding motif in Shh 3’-UTR across species. Unknown indicates Msi2 binding motif sequence of mouse Shh. (b) List of homologies of the Msi2 binding motif in Shh 3’-UTR by BLASTS pairwise alignment. Figure S10. Msi2 represses the Hh signaling. (a) In situ hybridization for Shh and IHC for Msi2 in control and DTG HFs 36 and 3 days post-depilation. (b) In -situ hybridization for Shh and IHC for Msi2 in control and cKO HFs 36 hours and 3 days post-depilation. (c) IHC for Gli1 in control and DTG, or in control and cKO HFs at P24 3 days post-depilation. Control and DTG mice were treated with Dox at P18 and depilated at P21. Control and cKO mice were depilated at P21. Scale bar: 50 m. (d) Western blotting for Gli1 in control and DTG skin 36 hours post-depilation at P21. Control and DTG mice were treated with Dox from P18 and depilated at P21. Control mice were topically treated with a single 25 L application of vehicle (95% acetone/5% DMSO), while DTG mice were given a single 25 L topical application of SAG (0.06 g per L in vehicle, 120 mM). Second application was done 24 hours later. Ve: vehicle. SUPPLEMENTARY MATERIALS AND METHODS Mouse strains TRE-MSI2 and Msi2flox/flox mice were previously described (; ). K14-rtTA and mice were obtained from Jackson Laboratory (stock numbers 007678 and 004782).