Sample ID	Organ	Related microorganism	GenBank accession no.	Similarity %

12Ue0622	Placenta	Not interpretable	–	–

12Ue1119	Placenta	Chlamydia abortus	Z49871	100

12Ue1503	Placenta	Uncultured Chlamydiales bacteriumclone HE210023biof	JX083111	99.3

12Ue1510	Placenta	Chlamydia abortus	NR_036834	100

13Ue0490	Placenta	Chlamydia abortus	Z49871	94.7

13Ue0499	Placenta	Not interpretable	 –	–

13Ue0815	Placenta	Chlamydiales bacterium cvE71	JF706724	96

13Ue0857	Placenta	Chlamydia abortus	Z49871	100

13Ue1009	Placenta	Chlamydia abortus	Z49871	99.3

13Ue1293	Placenta	Chlamydia abortus	Z49871	100

13Ue1359	Placenta	Chlamydia abortus	Z49871	100

14A0078	Placenta	Chlamydia abortus	Z49871	98.5

15A0068	Placenta	Chlamydia abortus	Z49871	90.8

15A0076	Placenta	Not interpretable	–	–

15A0078	Placenta	Chlamydia abortus	Z49871	99.5

15A0079	Placenta	Chlamydia abortus	Z49871	99.8

15A0080	Placenta	Chlamydia abortus	Z49871	99.5

15A0082	Placenta	Chlamydia abortus	Z49871	96.2

15A0087	Placenta	Uncultured Chlamydiales bacteriumclone P-9	AF364575	97

15A0091	Placenta	Chlamydia abortus	Z49871	92

15A0092	Placenta	Chlamydia abortus	Z49871	100

15A0093	Placenta	Chlamydia abortus	Z49871	90.8

15A0096	Placenta	Chlamydia abortus	Z49871	99.5

15A0096	Abomasal content	Chlamydia abortus	Z49871	99.5

15A0099	Placenta	Not interpretable	–	–

15A0104	Placenta	Chlamydia abortus	Z49871	100

15A0107	Placenta	Not interpretable	–	–

15A0111	Placenta	Chlamydia abortus	Z49871	99.5

15A0113	Placenta	Chlamydia abortus	Z49871	100

15A0114	Placenta	Chlamydia abortus	Z49871	99.5

15A0117	Placenta	Not interpretable	–	–

15A0118	Placenta	Chlamydia abortus	Z49871	96.8

15A0121	Placenta	Chlamydia abortus	Z49871	98.1

15A0122	Placenta	Chlamydia abortus	Z49871	97.6

15A0126	Abomasal content	Not interpretable	–	–

15A0129	Placenta	Not interpretable	–	–

15A0137	Placenta	Not interpretable	–	–

15A0148	Placenta	Not interpretable	–	–

15A0155	Placenta	Parachlamydia acanthamoebae strain Bn9	NR_026357	90.3

15A0160	Placenta	Not interpretable	–	–

15A0172	Placenta	Not interpretable	–	–

Sample ID	Organ	Related microorganism	GenBank accession no.

12Ue0622	Placenta	Not interpretable	–

12Ue1119	Placenta	Chlamydia abortus	Z49871

12Ue1503	Placenta	Uncultured Chlamydiales bacteriumclone HE210023biof	JX083111

12Ue1510	Placenta	Chlamydia abortus	NR_036834

13Ue0490	Placenta	Chlamydia abortus	Z49871

13Ue0499	Placenta	Not interpretable	–

13Ue0815	Placenta	Chlamydiales bacterium cvE71	JF706724

13Ue0857	Placenta	Chlamydia abortus	Z49871

13Ue1009	Placenta	Chlamydia abortus	Z49871

13Ue1293	Placenta	Chlamydia abortus	Z49871

13Ue1359	Placenta	Chlamydia abortus	Z49871

14A0078	Placenta	Chlamydia abortus	Z49871

15A0068	Placenta	Chlamydia abortus	Z49871

15A0076	Placenta	Not interpretable	–

15A0078	Placenta	Chlamydia abortus	Z49871

15A0079	Placenta	Chlamydia abortus	Z49871

15A0080	Placenta	Chlamydia abortus	Z49871

15A0082	Placenta	Chlamydia abortus	Z49871

15A0087	Placenta	Uncultured Chlamydiales bacteriumclone P-9	AF364575

15A0091	Placenta	Chlamydia abortus	Z49871

15A0092	Placenta	Chlamydia abortus	Z49871

15A0093	Placenta	Chlamydia abortus	Z49871

15A0096	Placenta	Chlamydia abortus	Z49871

15A0096	Abomasal content	Chlamydia abortus	Z49871

15A0099	Placenta	Not interpretable	–

15A0104	Placenta	Chlamydia abortus	Z49871

15A0107	Placenta	Not interpretable	–

15A0111	Placenta	Chlamydia abortus	Z49871

15A0113	Placenta	Chlamydia abortus	Z49871

15A0114	Placenta	Chlamydia abortus	Z49871

15A0117	Placenta	Not interpretable	–

15A0118	Placenta	Chlamydia abortus	Z49871

15A0121	Placenta	Chlamydia abortus	Z49871

15A0122	Placenta	Chlamydia abortus	Z49871

15A0126	Abomasal content	Not interpretable	–

15A0129	Placenta	Not interpretable	–

15A0137	Placenta	Not interpretable	–

15A0148	Placenta	Not interpretable	–

15A0155	Placenta	Parachlamydia acanthamoebae strain Bn9	NR_026357

15A0160	Placenta	Not interpretable	–

15A0172	Placenta	Not interpretable	–


λ	Combined t-test	Linear regression	Combined Wilcoxon	Combined BWS

0		0	0.0	0


	Out-of-hours GP services	EDs

Usual care	UCC	Usual care	UCC

All employees	12	12	3	3

Physicians	19	20	7	12

Support staff member	33	32	4	4


Vision	Number of patients	Percent

Total	50	100


 	Controls	Welders	P value

 	(n = 21)	(n = 21)

Mean age (years ± SD)	58.6 ± 10.8	58.5 ± 10.7	ns

Gender

Female	6 (28.6%)	1 (4.8%)	0.038

Male	15 (71.4%)	20 (95.2%)	ns

Tobacco smoking

Smoking status	 	 	ns

Current smokers	9 (43.0%)	6 (28.6%)

Former smokers	9 (43.0%)	12 (57.1%)

Never smokers	3 (14.0%)	3 (14.3%)

Cumulative consumption (P-Y ± SD)	24.6 ± 29.0	22.2 ± 15.2	ns

Age at onset (years ± SD)	16.8 ± 3.4	18.3 ± 4.2	ns

Age at cessation (years ± SD)	51.6 ± 14.0	48.8 ± 11.2	ns

Duration (years ± SD)	32.6 ± 16.7	33.7 ± 10.3	ns

Duration since cessation (years ± SD)	5.7 ± 9.3	8.2 ± 10.4	ns

Welding fumes exposure	 	 	-

Positive occupational questionnaire	0	21

Age at onset (years ± SD)	-	19.9 ± 7.6

Age at cessation (years ± SD)	-	48.2 ± 9.9

Duration (years ± SD)	-	27.0 ± 9.2

Duration since cessation (years ± SD)	-	9.2 ± 10.4


Age	Quebec, Geometric Mean (95th Percentile)	Ontario, Geometric Mean (95th Percentile)

4	0.03 (0.13)	0.06 (0.17)

8	0.02 (0.05)	0.05 (0.12)


	SASIn = 123–124	Non-SASIn = 5514–5515	Totaln = 5635–5639

Mean	SD	Mean	SD	Mean	SD	p  value


	First Contact (Admission)	Second contact (Discharge)	Third Contact (Community- based phase)	Defaulted & Loss to follow-up

Children’s Sex (%)

 Male	46	46.2	47	44

 Female	54	53.8	53	56

Children’s Age, months (%)

 6–11	38.7	33.9	25	32

 12–23	45.3	49.2	55	46

 24–35	12	14.4	14	16

 36+	4	2.5	6	6

Mother’s religion(%)

 Hindu	29.3	30.5	30	28

 Muslim	6	5.1	6	4

 Sarna	54	56.8	55	60

 Others	10.7	7.6	9	8

Mother’s caste(%)

 SC	9.3	11	10	10

 ST	68	70.3	71	72

 OBC	15.3	14.4	14	12

 General	7.4	4.2	5	6

Mother’s age (%)

 18–25	46.7	47.5	47	42

 26–30	24	22.9	24	26

 31+	29.3	29.7	29	32

Mother’s education (%)

 No schooling	67.3	66.1	66	70

 1–8	26.1	29	29	28

 9+	6.6	5.2	5	2

Total children and mothers	150	116	100	50


1st author and year	N	ALD	HBV or HCV	Cryptogenic	ALD + HCV	Miscellaneous

Krowka 1999	14	1	1	3	3	1 HCC + HCV, 1 sarcoidosis,2 PBC, 1 Budd Chiari, 1 AIH

Hoeper 2005	11	7	1	0	2	1 biliary atresia

Reichenberger 2006	14	7	3	 	 	3 PBC, 1 Budd Chiari

Sussman 2006	8	4	1	2	1

Fix 2007	35	7	9	3	10	1 HIV, 2 HCC, 2 PBC, 1 AIH

Hoeper 2007 (bosentan)	18	11	3 	1	0	1 PVT, 2 AIH

Hoeper 2007 (iloprost)	13	6	2 	0	0	1 biliary duct atresia, 4 AIH

Gough and White 2009	11	4	2	0	4	1 ALD + NASH

Hemnes 2009	13	1	8	0	0	1 PBC

Melgosa 2010	13	2	7	0	0	1 hemochromatosis, 2 PBC, 1 portal vein thrombosis

Halank 2011	13	8	1	3	0	1 PBC

Hollatz 2012	11	5	1	0	3	1 ALD + NASH + PBC, 1 biliary atresia

Savale 2013	34	20	4	 	3	6 PVT, 1 AIH

Total	 	83 (40%)	43 (20%)	12 (5%)	26 (12%)	41 (20%)

1st author and year	N	ALD	HBV or HCV	Cryptogenic	ALD + HCV	Miscellaneous

Hoeper 2007 (bosentan)	18	11	3	1	0	1 PVT, 2 AIH

Hoeper 2007 (iloprost)	13	6	2	0	0	1 biliary duct atresia, 4 AIH

Gough and White 2009	11	4	2	0	4	1 ALD + NASH

Hemnes 2009	13	1	8	0	0	1 PBC

Melgosa 2010	13	2	7	0	0	1 hemochromatosis, 2 PBC, 1 portal vein thrombosis

Halank 2011	13	8	1	3	0	1 PBC

Hollatz 2012	11	5	1	0	3	1 ALD + NASH + PBC, 1 biliary atresia

Savale 2013	34	20	4		3	6 PVT, 1 AIH

Total		83 (40%)	43 (20%)	12 (5%)	26 (12%)	41 (20%)


Food Item	Copacabana	Bertioga	Itacimirim	Pântano do Sul	Vitória sland	TOTAL

Algae		1				1

Anything	3	3	3	7	1	17

Bonito	1	4	1	1	1	8

Crabs	1	2	1	2		6

Crustacean	2					2

Deteriorated fish/meat	1	5		7		13

Fish	2		15	8	2	27

Meat			1			1

Mouse/snake				1		1

Octopus		2			1	3

Sardine	3	6	2	2	1	14

Shrimp	1		2			3

Small mollusks	4	1				5

Squid		3		3		6

Interviews	10	11	15	11	2	49

Food Item	Copacabana	Bertioga	Itacimirim

Algae		1

Anything	3	3	3

Bonito	1	4	1

Crabs	1	2	1

Crustacean	2

Deteriorated fish/meat	1	5

Fish	2		15

Meat			1

Mouse/snake

Octopus		2

Sardine	3	6	2

Shrimp	1		2

Small mollusks	4	1

Squid		3

Interviews	10	11	15


Process	Tool	Parameters	Subject database

Screening of rRNAs	BLASTALL	-p blastn –e 1e-3 –f T – b 1 –v 1 –M BLOSUM62

Identification of tRNAs	tRNA scan SE	-b G

ORF calling	MetaGene Annotator	-m

Known protein identification	BLASTALL	-p blastp –e 1e-1 –F T – b 50 –v 50 –M BLOSUM 62	UNIREF 100

Environmental Protein Identification	BLASTALL	-p blastp –e 1e-1 –F T –b 50 –v 50 –M BLOSUM62	MGOL

Process

Screening of rRNAs

Identification of tRNAs

ORF calling

Known protein identification

Environmental Protein Identification

Tool	Parameters	Subject database

BLASTALL	-p blastn –e 1e-3 –f T – b 1 –v 1 –M BLOSUM62

tRNA scan SE	-b G

MetaGene Annotator	-m

BLASTALL	-p blastp –e 1e-1 –F T – b 50 –v 50 –M BLOSUM 62	UNIREF 100

BLASTALL	-p blastp –e 1e-1 –F T –b 50 –v 50 –M BLOSUM62	MGOL


S/P value		Mean

Group 2	Positive	1.46

Negative	0.13

Group 3		0.01

Group 4		0.06

Group 5	Negative	0.07

Positive n = 2	-


Sociodemographic	Total N = 70

N	(%)

Gender of the respondent

 Female	54	77

Type of the respondent

 Compound members	55	79

 Compound manager	15	21

Age of the respondent

 19–35 year	38	54

 Above 36 year	32	46

Education of the respondent

 No education	25	36

 Primary	21	30

 Secondary	17	24

 Above secondary	7	10

Occupation of the respondent

 Housewife	54	77

 School teacher	1	1

 Business	12	17

 Service	3	4


	Simplex Assay	Multiplex Assay

CHIKV	DENV	Leptospira	CHIKV	DENV	Leptospira

Slope	−3.13	−3.19	−3.25	−3.41	−3.15	−3.37

Efficiency	2.09	2.06	2.03	1.96	2.08	1.98

	 Assay	ex Assay

CHIKV	DENV	Leptospira	CHIKV	DENV	Leptospira

Slope	−3.13	−3.19	−3.25	−3.41	−3.15	−3.37

Efficiency	2.09	2.06	2.03	1.96	2.08	1.98


Databases	Contents

National Eye Institute: NEI Bank Project	Transcriptome profiling for different tissues of the eye for a variety of different species including mouse and human

Mouse Retina Serial Analysis of Gene Expression (SAGE)	Gene expression data from the embryonic and postnatal mouse retina.

Scripps Friedlander Gene Expression	Expression of retinal transcripts for the mouse retina during eight different postnatal stages.

Retinal Information Network	Mapped loci and cloned genes associated with inherited retinal disease.

Retinal International Website	Series of databases listing: retinal diseases, mutations affecting the eye, current animal models, and known proteins involved in retinal disease.

GENSTAT: Retina Project	Cell specific labeling in the retina using BAC transgenic mice.

Retina Central	Integrated platform for gene related information regarding the adult mammalian retina

Databases

National Eye Institute: NEI Bank Project

Mouse Retina Serial Analysis of Gene Expression (SAGE)

Scripps Friedlander Gene Expression

Retinal Information Network

Retinal International Website

GENSTAT: Retina Project

Retina Central

Contents

Transcriptome profiling for different tissues of the eye for a variety of different species including mouse and human

Gene expression data from the embryonic and postnatal mouse retina.

Expression of retinal transcripts for the mouse retina during eight different postnatal stages.

Mapped loci and cloned genes associated with inherited retinal disease.

Series of databases listing: retinal diseases, mutations affecting the eye, current animal models, and known proteins involved in retinal disease.

Cell specific labeling in the retina using BAC transgenic mice.

Integrated platform for gene related information regarding the adult mammalian retina


Immune cells	Enriched genes	References

All T cells	Human: CD3G, CD3D, TRA, CD6, CD5, NPDC1, CD28, CAMK4, GFI1, GATA3, SH2D1A, TRB, TNFRSF25, NK4, TACTILE, BCL11B, CD3E, INPP4B, MAL, NPDC1, ITM2A, ITK, LCK, NFATC3, RORA, MGC19764, TCF7, ZAP70, LEF1, SPOCK2, PRKCQ, SATB1, RASGRP1, LRIG1, DPP4, CD3Z, PDE4D, FYN, WWP1, LAT, DUSP16, KIAA0748, CDR2, STAT4, FLT3LG, IL6ST	78–79

CD8+T cells	Human: FCGBP, C1ORF21, PHEMX, KLRG1, ZNF145, ADRB2, DUSP2, IL2RB, CCL5, GZMC, TBX21, CCL4L, GZMH, PRF1, GNLY, CST7, GPR56, KLRC1, S100B, D12S2489E, CD8B1, CD8A	79, 81, 82

T regulatory cells	Human: AKAP2, ANXA2, C8FW, CALM2, CDH13, ENTPD1, EPSTI1, F5, GBP2, GBP5, HS3ST3B1, HSPCA, ICA1, LAIR2, LGALS3, LOC51191, MKP-7, NINJ2, P53DINP1, PMAIP1, PTPLA, GPR2, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DRB4, HLA-DRB5, HPGD, PTT1, SAT, SHMT2, TMOD3, TNFRSF6, VIL2, LRRC32, CHAC1, EGR1, PYCR1, IL13, C3AR1, ZBED2, IL10, GAB3, NR4A1, IL1R2, ULBP1, IL17F, EIF4EBP1, RGS16, BCAT1, VEGFA, GIMAP1, GPR44, GPT2, PSPH, NLN, MS4A3, UCK2, TNIP3, TNFRSF9, IL7R, NR4A3, CSGALNA, CT1, SLC43A1, ZBTB32, SCFD2, HK2, C17ORF96, C1ORF186, LYZ, SFXN4, CYP1B1, SLC7A5, C17ORF58, SLC4A5, CD52, KLRB1, S100A4, POLA1, SLC7A1, ATPBD4, UBXN8, CASP8, FAIM3, IL12RB2, MPP7, ZNF831, BNIP3L, LRP8, UTRN, SOCS3, LY9, EVI2B, SFXN3, TNFRSF4, ING4, BLM, TNFSF11, SLFN5, CCR8, CD244, MYO1F, HOMER1, ATF3, AGPAT4, CTH, JAKMIP1, TFRC, RPL26L1, MICAL1, GPRASP1, TMX4, FYB, ZNF792, WDR4, HUWE1, CD83, TRIB2, LARP1B, SLC38A5, AUH, CTPS, CYTH1, APOL3, CHD6, IL1R1, LPCAT4, CITED2, DUSP4, SSBP2, LAYN, ZFAND2B, PIK3R1, GCNT4, PPP3CA, TIMP1, SIRPG, CCR3, PTP4A3, SERINC5, MAGOHB, GIMAP4, KLHL24, CCNG2, WDR70, MYBL1, CNPY4, TIMM8A, CCDC28A, MNAT1, MS4A2, GZMB, E2F5, HIBCH, C9ORF91, NHP2, CDC25B, VDR, CSF1, GPATCH4, NFKBID, TMCO7, EHBP1, FAM173B, FARS2, FLVCR2, NPM3, PNPO, P2RY8, TMEM63A, MXI1, MRTO4, SESN2, C1ORF38, STK38, SLC6A6, ALG14, LXN, TNFRSF18, ACSS1, UBA7, MTSS1, PSMA6, FAM8A1, RAB31, TNRC6B, SESN1, CEP68, LAPTM5, PLAGL2, LRRC33, EIF2B3, URB2, DDX21, ITGA4, RGS2, RORC, IFNAR2, CSDA, SORL1, LRRC37B, IL10RA, PRKACB, CBWD1, SMAD3, C1ORF96, FCER1A, HSP90AB1, EMG1	83–87

B cells	Human: PSEN2, FZD5, PLEKHF2, SIDT2, MTPN, TUBB6, FLJ21127, SMC6L1, KIAA1026, NUP88, GNA12, RNTRE, GSTZ1, RFX5, XYLT1, GL012, EPB41L2, LOC51760, SCRN1, PTD008, CYB561D2, SCAP2, MMP11, ERDJ5, TTC7A, PMAIP1, GARNL4, TMEPAI, SH3BP5, EDD, UCP4, EVI5L, PIK3C2B, SMAD3, LHFPL2, LOC57228, GM2A, HRK, ZNF207, LOC92497, F5, DMXL1, CLIC4, PRCP, DKFZP434C0328, STRBP, PHF16, JUP, TEAD2, PPP3CA, CNR2, ATP5B, ABCA1, BMF, ZCCHC7, KIAA0274, SLC22A3, SPI1, AMFR, ANXA4, EBF, ITPR1, HIST1H2BK, COPS3, COL14A1, SAV1, APOBEC3B, CCNG2, STAG3, LOC348938, POLD4, TBC1D1, MK2S4, APG4A, DAPP1, TNFRSF18, HERPUD1, POU2F2, ADK, CKIP-1, ACTA2, PARP14, MTSS1, DTX1, DDR1, RRAS2, RNF141, SYPL, SSPN, CORO1C, NFKBIE, CHD7, SP140, WDR34, BTNL9, ATP6V0A1, UROS, HLA-DOA, C22ORF13, IFI27, JMJD2B, WEE1, ODC1, KIAA0746, WDR11, SYNGR2, FOXP1, BLR1, MYO1E, MYBL2, GYLTL1B, SETBP1, KLF1, INPPL1, LOC54103, ZNF154, MHC2TA, CD24, ARHGEF3, BIRC3, TRIM56, HLA-DPB1, UVRAG, CD38, PEA15, FLJ10697, TLR10, ARHGAP10, HLA-DRA, SHMT2, LGALS9, FLJ10853, CEBPB, PRICKLE1, LCP21, CR1, KLHL14, TLR7, C20ORF72, HLA-DQB2, CORO1A, PRKCEHECW2, FLJ25604, TRIM26, FBXO41, GCNT1, LRMP, UBE2J1, IGKV3D, PCCA, GLDC, CYSLTR1, BTLA, NET5, HLA-DPA1, BLNK, CD79B, TM4SF8, TFEB, CD11ORF24,ADRBK2, CDKN2A, RIPK2, STX7, NCF4, SRGAP2, SLC2A5, CTSZ, AIM2, TCF4, TPD52, MAP3K8, MOBKL2B, HLA-DOB, IFIT3, MGC24039, BRD4, RALGPS2, SEMA4B, BSG,IGKC, HLA-DRB6, FUBP1, UNC84B, IFNGR2, HSPA5, HLA-DRB5, MARCKS, KYNU, PACAP, RHOBTB2, FLJ12363, TMED8, FGD2, FBXO10, IL4R, CD1C, MGC50844,MRPL49, CTSH, LYN, WASPIP, C3ORF6, EGR1, IGKV15, SPAP1, CHERP, IGHG3, ADAM28, HLA-DMA, HLA-DMB, PALM2-AKAP2, CD86, LAF4, LOC283663, FREB,DKFZP586A0522, UREB1, IGLL1, HLA-DQA2, KIAA1219, PLCG2, MARCH-1, BCNP1, PNOC, CD20, PSCD1, BCL11A, VPS28, SWAP70, SYK, BCL7A, NAP1L, TEM6, BTK,RAB30, FCGR2B, STAT6, BRDG1, LOC201895, ITGB1, CD74, CORO2B, VPREB3, HSPC182, MGC15619, RHOH, IGHA, CYBASC3, KIAA0125, RAM2, CD83, FCRH1, FLII,SNX10, IGHG1, EPHX1, FLJ10979, GTPAP, IGLL3, E2F5, TNFRSF17, BLK, FLJ00332, SNX2, CD200, HLA-DQB1, TRIO, CYBB, PIK3AP1, IRF4, CD22, BACE2, IGJ, PAX5,RGS13, TCL1A, MEF2C, C13ORF18, POU2AF1, HHEX, IRF8, BANK1, OSBPL10, SLC2A1, NCF1, IGL@, IGLC2, SPAP1, IGHG3, HLA-DMA, HLA-DMB, CD79A, HSPA6, IGHD,KIAA0476, SLC7A7, NKG7, CD19, SAMD9, LY86, SPIB, NAPSB, RNASE6, IGHM, LY64, CD72, IRTA2, CD1D, HLA-DQA1, SAS, CTSH, LYN, WASPIP, C3ORF6, EGR1, FREB,DKFZP586A0522	88, 79, 89

Resting NK cells	Human: IL18RAP, PRF1, GZMH, TKTL1	90–92

Macrophages	aMurine: Pecr, Tmem195, Ptplad2, 1810011H11Rik, Fert2, Tlr4, Pon3, Mr1, Arsg, Fcgr1, Camk1, Pld3, Tpp1, Ctsd, Pla2g15, Lamp2, Pla2g4a, MerTK, Tlr7, Cd14, Tbxas1, Fcgr3, Fgd4, Sqrdl, Csf3r, Plod1, Tom1, Myo7a, A930030A15Rik, Sepp1, Glul, Cd164, Tcn2, Dok3, Ctsl, Tspan14	93–95

M1 macrophages	aMurine: Cd38, Cfb, Slfn4, H2-Q6, Fpr1, Slfn1, Gpr18, Ccrl2, Fpr2, Cxcl10, Mpa2IOasl1, Tlr2, Ms4a4c, LOC100503664, Irak3, Hp, Itgal, Herc6, Cd300lf, Isf20, Pstpip2, Cp, Isg15, Herc6, Probe, 1452408_at, Ifi44, E030037K03, Rik, Saa3, Ifit1, Marco, F11r, Rsad2, Ddx60, Pilr1, Cpd, Gngt2, Mx1, Pyhin1, Epb4.1l3, Slfn8, Arhgap24, Nfkbiz, Gbp6, Stat1, Zpb1, D14Erd668e, Ddx58, Tuba4a, H2-T10, Ebi3, Fam176b, Xaf1, Stat2, Sepx1, Ifit2	96

M2 macrophages	aMurine: Ptgs1, Egr2, Olfm1, Flrt2, P2ry1, Vwf, Amz1, Tmem158, Tiam1, Rhoj, Mmp9, Mrc1, Bcar3, Il6st, Tanc2, Mmp12, Tcfec, Clec71, Matk, Myc, Clec10a, Atp6v0a1, Lmna, Chst7, Atp6v0d2, Emp2, Socs6, Atp6v0a1, Plk2, Ptpla	96

Classical monocytes	Human: S100A12, ALOX5AP, PAD14, NRG1, MCEMP1, THBS1, CRISPLD2, F13A1, MOSC1, CYP27A1, CD163, QPCT, ADAM19, ASGR2, RNASE4, ALDH1A1, EDG3, PROK2, CLEC4D, S100A9, OSM, CSPG2, GALS2, ANG, VNN2, EBI2, UTS2, RPPH1, MGST1, IL8, CCR2, SELL, DYSF, MT1F, CD14, RANSE6, CSF3R, STAB1, CYP1B1, ADHFE1, PLA2G7, MMP25, ZNF395, SLC2A3, EGR1, CMTM2, CD9	97, 98

Intermediate monocytes	Human: GFRA2, NKG7, PLVAP, PLAC8, MARCKSL1, E2F2, G8P4, CLEC10A, SCD, COTL1, SLC29A1, DDIT4, TGM2, LILRA3, ATF5, GPA33, C1QC, EVA1, MERTK, MGLL, DDEFL1, MARCO, NR1H3, FBP1, ACP2, GBP1, GPBAR1, SASH1, OLFM1, TIMP1, HLA-DOA, CAMK1, POUFUT1, EPB4IL3, H19, ZNF703, SNX5, CLEC10, AK-ALPHA-1, DPB1, DHRS9, MTHFD2, RGL1, PRDM1, FADS1, SLC2A8, CSK, ISOC2, CD300C, FGD2	97, 98

Nonclassical monocytes	Human: C1QA, HES4, CDKN1C, C1QB, RHOC, CLEC4F, ADA, TAGLN, TCF7L2, CD798, CTSL, SFTPD, ABI3, SETBP1, FGFRL1, CD798, SPRED1, SNFT, EVL, MTSS1, SH2D1B, CYFIP2, NELF, STS-1, SIGLEC10, CKS1B, PTP4A3, LYPD2, SIDT2, INSIG1, LTB, PAPSS2, ABCC3, CASP5, VOM1, HSPB1, GNGT2, HMOX1, RRAS, RNF122, CDH23, FMNL2, RGS12, SGGB3A1, FER1L3, CALML4, IFITM3, CKB, CEACAM3, IFITM1	97, 98

Mature DC’s	Human: ACCL5, CXCL10, CCR7, IL15, IF127, IF144L, IFIH1, IFIT1, MX1, ISG15, ISG20, IRF7, GBP4, DUSP5, NFKB1A, ATF3, TNFSF10, IL6, IL8, IL7R, CCL4, TNFA1P6, IFIT3, OASL, GBP1, HES4, CYP27B1, RIPK2, TNFRSF9, SOD2, CD38, CD44, CD80, CD83, CD86, INDO MT2A, TRAF1, GADD45B, MT1M, MTIP2, BIRC3, USP18, TUBB2A, CCL8, EBI3, IFITM1, MT1B, MT1E, MT1G, MT1H, GADD45A, CD200, LAMP3, RGS1, SAT1	99, 100

Monocyte-derived DC’s	Human: CD1A, CD1B, CD1C, CD23A, MRC1, CD36, HLA-DQA, HLA-DP light chain, HLA-DG protein 41, RIL, RAP1GAP, CCND2, DUSP5, PPIC, STAC, PRKACB, TRIB2, SHB	99–101

Mature neutrophils	Human: ALPL, IL8RB, FCGR3B, SEMA3C, HM74, SOD2, FCGR3A, IL-8, STHM, IL8RA, FCGR2A, CSF3R, NCF2, AOAH	102, 103

Immature neutrophils	Human: AZU1, ELA2, BPI, LCN2, MPO, CTSG, MMP8, DEFA4, DEFA3, CAMP, X-CGD	103–105


Gene	Primer name	Primer sequence

C8J_0133	C8J0133-F	TATTGCTGGGCATAGGAAAGG

	C8J0133-R	TCTAGCAGCTTCTCTTGGAGTA

C8J_0398	C8J0398-F	GCAACATCTACCGTGATGCTAA

	C8J0398-R	ACATATCTACAATCCACCAAATCCA

C8J_0648	C8J0648-F	GTATCAGCAGACATAAGACAAGG

	C8J0648-R	TGCTTTCTTCTAGGTACTCTTTATC

C8J_1333	C8J1333-F	TGAGCTTGCACAAGATGATACC

	C8J1333-R	GCACCAGAATACAAACCCTTCT

C8J_1342	C8J1342-F	GTTGATTTAGTGGCAGTTGGTG

	C8J1342-R	CTCTTTCTACTGCTCCTTGAATACT

C8J_1423	C8J1423-F	AAATTTATGCGCGTGCTTT

	C8J1423-R	AACTATGCCACCAAGCAAA

C8J_1619	C8J1619-F	CCAAAGTGGATAGTATTGCAAGAATTAG

	C8J1619-R	GACGACTTAAAGAACTTGAAACTGG

frdA	qfrdA-F	GTGTGCCTTGGACTAGAGTTAC

	qfrdA-R	CTGCGATATAGCAAGTTCTCCA

ccpA-2	qccpA-2-F	GTGGTATCATTTCTTGTAATACCTGTC

	qccpA-2-R	TGATGAGGATTTGCTGTCCAT

racR	qracR-F	ACGGATACAGCGTTTCAAGAG

	qracR-R	ACTCTTAAGCGACCGATGATAAC

flhB	qflhB-F	GGAAGGAGATCCTCAGGTTAAAG

	qflhB-R	GCATAATGCGTTGGGTTTGT

kpsM	qkpsM-F	TGTGGAACCTTTAAGAACTTTGC

	qkpsM-R	AAGCAAAGGACGAGGAGTTAG

cmeB	qcmeB-F	GCCATAGGGATCGTTGTAGATG

	qcmeB-R	CTATCCAAGCGATGCAAGAAGT

16S rRNA	16S-qF	AGAGATGCATTAGTGCCTTCGGGA

	16S-qR	ACTAAGGATAAGGGTTGCGCTCGT

Gene	Primer name	Primer sequence

C8J_0133	C8J0133-F	TATTGCTGGGCATAGGAAAGG

	C8J0133-R	TCTAGCAGCTTCTCTTGGAGTA

C8J_0398	C8J0398-F	GCAACATCTACCGTGATGCTAA

	C8J0398-R	ACATATCTACAATCCACCAAATCCA

C8J_0648	C8J0648-F	GTATCAGCAGACATAAGACAAGG

	C8J0648-R	TGCTTTCTTCTAGGTACTCTTTATC

C8J_1333	C8J1333-F	TGAGCTTGCACAAGATGATACC

	C8J1333-R	GCACCAGAATACAAACCCTTCT

C8J_1342	C8J1342-F	GTTGATTTAGTGGCAGTTGGTG

	C8J1342-R	CTCTTTCTACTGCTCCTTGAATACT

C8J_1423	C8J1423-F	AAATTTATGCGCGTGCTTT

	C8J1423-R	AACTATGCCACCAAGCAAA

C8J_1619	C8J1619-F	CCAAAGTGGATAGTATTGCAAGAATTAG

	C8J1619-R	GACGACTTAAAGAACTTGAAACTGG

frdA	qfrdA-F	GTGTGCCTTGGACTAGAGTTAC

	qfrdA-R	CTGCGATATAGCAAGTTCTCCA

ccpA-2	qccpA-2-F	GTGGTATCATTTCTTGTAATACCTGTC

	qccpA-2-R	TGATGAGGATTTGCTGTCCAT

ra	qracR	ACGG

cR	-F	ATACAGCGTTTCAAGAG

	qracR-R	ACTCTTAAGCGACCGATGATAAC

flhB	qflhB-F	GGAAGGAGATCCTCAGGTTAAAG

	qflhB-R	GCATAATGCGTTGGGTTTGT

kpsM	qkpsM-F	TGTGGAACCTTTAAGAACTTTGC

	qkpsM-R	AAGCAAAGGACGAGGAGTTAG

cmeB	qcmeB-F	GCCATAGGGATCGTTGTAGATG

	qcmeB-R	CTATCCAAGCGATGCAAGAAGT

16S rRNA	16S-qF	AGAGATGCATTAGTGCCTTCGGGA

	16S-qR	ACTAAGGATAAGGGTTGCGCTCGT


City(project)	Activities described in final report

Birmingham(Walking Revolution)	Led walks to the park for key stage 2 pupils at one city primary schoolPledge cards for individualsThemed walksCommunity Street AuditsSmall grants fundWalking ChampionsSocial media workStrategic work to share learning

Cambridge(Walk Local)	Promotion of Walk4Life and WfH campaigns (various media), maps and community walks. 7 walks with 108 participantsWalk to work week activities with 17 employersHealth walks in 3 medical centres. 27 participantsBeat the Street piloted in 4 primary schools. 890 participants (3 week intervention)Weekly buggy walks / wild play with young parents and children under 5 years of age. 230 attendances

Leeds and Bradford(Best foot forward)	Themed walksCommunity Street AuditsSmall grants fundTendering delivery to local community organisationsSocial media workStrategic work to share learning3 million steps social reward scheme involved388 people who walked over 15 Million steps in Spring 2015.

Manchester(Get Active)	Active Oldham Outdoors Project:26 Level 1 health walks with 143 participants14 new volunteer walk leadersCapital funding from Public health to improve walking infrastructure in 9 parksActive Trafford Greenspace Project:20 led walks with 278 participants23 new volunteer walk leaders3 GP Surgeries piloting ‘walk prescribing’ or other methods of promoting walking to patients.3 Workplace Walk led walks events held5 new Workplace Walks mapped and promotedBridgewater Canal Walks Project:22 recreational themed walks with 74 participantsSelf-led walking trailPartnership working with a community Leisure TrustFour week Walking FestivalEast Manchester Moving:23 led walks with 163 participants10 led cycle rides with 96 participants8 volunteer action days24 volunteers trained as volunteer walk leaders	Salford Ranger team project:1 new self-guided walking route11 additional recreational walks with 107 participants12 volunteers trained as walk leaders12 led cycle rides with 108 participantsStockport Walkaday Walks Project:13 additional walks with 217 participants4 new volunteer walk leadersTameside Active Outdoors Project:4 new health walks with 30 participants18 new volunteer walk leaders1 new patient from new exercise referral programme5 new self-guided walking routes in production – Proposed launch in 2016The Green Corridor Project:9 led health walks with 91 new participants.5 volunteers trained as walk leaders

Norwich(Walk to)	Group walking champions led walks including 1 km group walk for people with a learning disabilityPledge cards for individualsCommunity Street Audit (1)Small grants fundBeat the Streets project for school children: 1890 participatedSocial media workStrategic work to share learningHealth walks: 185 walks, 12 volunteers and 154 new participantsBeat the Street: 1890 participatedTravel plans: numbers not reported


Bacteria	Oral n/N (%)	Vaginal n/N (%)	P value


 	Reference	Nr studies included	Nr subjects included	Pooled effect size (95% CI)

Metabolic dysregulation

Metabolic syndrome	Pan et al. 20121[22]	29	155,333	OR = 1.42 (1.28 to 1.57)

Abdominal obesity	Xu et al. 20111[57]	15	34,832	OR = 1.38 (1.22 to 1.57)

Immuno-inflammatory dysregulation

C-Reactive Protein	Howren et al. 20091[58]	49	51,234	d = 0.15 (0.10 to 0.21)

Interleukin-6	 	61	24,837	d = 0.25 (0.18 to 0.31)

Interleukin-1β	 	14	756	d = 0.35 (0.03 to 0.67)

Interleukin-1RA	 	9	1,214	d = 0.25 (0.04 to 0.46)

Interleukin-6	Dowlati et al. 20102[59]	16	892	MD = 1.8 pg/mL (1.2 to 2.3)

TNF-α	 	13	788	MD = 4.0 pg/mL (2.2 to 5.7)

Interleukin-1β	 	9	533	MD = -1.6 pg/mL (-3.6 to 0.4)

Soluble Interleukin-2R	Liu et al. 20122[60]	8	596	MD = 0.56 pg/mL (0.28 to 0.84)

Interleukin-6	 	18	923	MD = 0.68 pg/mL (0.44 to 0.92)

TNF-α	 	15	995	MD = 0.55 pg/mL (0.13 to 0.99)

Interleukin-1β	 	10	580	MD = -0.53 pg/mL (-1.36 to 0.31)

Autonomic dysregulation

Heart rate variability	Rottenberg, 20072[61]	13	686	d = 0.33 (0.18 to 0.49)

Heart rate variability	Kemp et al. 20102[62]	14	726	Hedges’ g = -0.21 (-0.40 to -0.02)4

HPA-axis dysregulation

Higher cortisol 3	Stetler and Miller 20111[63]	354	18,374	d = 0.60 (0.54 to 0.66)

Higher ACTH	 	96	3,812	d = 0.28 (0.16 to 0.41)

Higher CRH	 	16	888	d = -0.53 (-1.71 to 0.65)

Saliva morning cortisol	Knorr et al. 20101[64]	20	2,318	MD = 2.6 nmol/l (1.0 to 4.2)

Saliva evening cortisol	 	10	1,617	MD = 0.3 nmol/l (0.03 to 0.5)

	Reference	Nr studies included	Nr subjects included	Pooled effect size (95% CI)

Saliva evening cortisol		10	1,617	MD = 0.3 nmol/l (0.03 to 0.5)


Gene	BMPR1A	SMAD4	PTEN	STK11

Name	Bone morphogenetic protein receptor, type Ia	Mothers against decapentaplegic, drosophila, homolog of, 4	Phosphatase and tensin homologue	Serine/threonine protein kinase 11

Function of the protein	receptor	signalling protein	phosphatase	kinase

MIM	*601299	*600993	*601728	*602216

Chromosome	10q22.3	18q21	10q23.31	19q13.3

Size	168.5 kbp	38.5 kbp	108 kbp	23 kbp

Exons	13	11	9	10

Amino acids	532	552	403	433

Transcript (bp)	3613	8365	9007	3276

Mass of protein	60 kDa	60 kDa	47 kDa	48.6 kDa

Gene	BMPR1A	SMAD4	PTEN

Name	Bone morphogenetic protein receptor, type Ia	Mothers against decapentaplegic, drosophila, homolog of, 4	Phosphatase and tensin homologue

Function of the protein	receptor	signalling protein	phosphatase

MIM	*601299	*600993	*601728

Chromosome	10q22.3	18q21	10q23.31

Size	168.5 kbp	38.5 kbp	108 kbp

Exons	13	11	9

Amino acids	532	552	403

Transcript (bp)	3613	8365	9007

Mass of protein	60 kDa	60 kDa	47 kDa

STK11

Serine/threonine protein kinase 11

kinase

*602216

19q13.3

23 kbp

10

433

3276

48.6 kDa


Variable	Cases (n=110)	Controls (n=110)

Average age at blood collection, years (SD)	44.2 (7.3)	44.2 (7.2)

Average age at cancer diagnosis, years (SD)	55.9 (8.6)	n/a

Average time from blood collection to diagnosis, years (SD)	12.6 (5.1)	n/a

Date of blood collection, median, calendar years (range)	1990 (1986-1991)	1990 (1986-1991)

Date of birth, median, calendar years (range)	1948 (1923-1955)	1948 (1923-1955)

Male sex, number (%)	82 (74.5%)	82 (74.5%)

Average sample storage time in freezer, years (SD)	24.7 (1.5)	24.7 (1.5)

Blood collection to glioblastoma diagnosis, number/time period

 0–5 years	8	n/a

 5–10 years	26	n/a

 10–15 years	35	n/a

 15–20 years	37	n/a

 >20 years	4	n/a

Variable	Cases (n=110)	Controls (n=110)

Average age at blood collection, years (SD)	44.2 (7.3)	44.2 (7.2)

Average age at cancer diagnosis, years (SD)	55.9 (8.6)	n/a

Average time from blood collection to diagnosis, years (SD)	12.6 (5.1)	n/a

Date of blood collection, median, calendar years (range)	1990 (1986-1991)	1990 (1986-1991)

Date of birth, median, calendar years (range)	1948 (1923-1955)	1948 (1923-1955)

Male sex, number (%)	82 (74.5%)	82 (74.5%)

Average sample storage time in freezer, years (SD)	24.7 (1.5)	24.7 (1.5)

Blood collection to glioblastoma diagnosis, number/time period

0–5 years	8	n/a

5–10 years	26	n/a

10–15 years	35	n/a

15–20 years	37	n/a

>20 		n

Variable	Cases (n=110)	Controls (n=110)

years	4	/a


Subject area	Frequency	% N = 8619 a

Medicine	5406	62.7

Immunology and Microbiology	3075	35.7

Biochemistry, Genetics and Molecular Biology	1681	19.5

Pharmacology, Toxicology and Pharmaceutics	533	6.2

Agricultural and Biological Sciences	407	4.7

Veterinary	283	3.3

Multidisciplinary	234	2.7

Social Sciences	179	2.1

Chemistry	170	2.0

Environmental Science	133	1.5

Nursing	123	1.4

Subject area	Frequency

Medicine	5406

Immunology and Microbiology	3075

Biochemistry, Genetics and Molecular Biology	1681

Pharmacology, Toxicology and Pharmaceutics	533

Agricultural and Biological Sciences	407

Veterinary	283

Multidisciplinary	234

Social Sciences	179

Chemistry	170

Environmental Science	133

Nursing	123


Variability of U6 and miR-99b as reference assays

	hsa-miR-99b	U6

	P-value	C of V	P-value	C of V

Nucleus accumbens	0.69	0.40	0.024*	0.71

EDL	0.64	0.22	0.13	0.86

Soleus	0.23	0.23	0.09	0.21


Variables

Males/females

Energy (Cal)

Carbohydrate (%)

Total fat (%)

Saturated fat (%)

Protein (%)

Cholesterol (g)

Fiber (g)


Name	Species	Activity against	Cytotoxic activity	Anticancer activity	Reference

Sesquin 	Vigna sesquipedalis 	MCF-7 and M1	Not tested	Yes	[44]

Limenin	Phaseolus limensis 	L1210 and M1	Not tested	Yes	[41]

Lunatusin	Phaseolus lunatus 	MCF-7rabbit reticulocyte	Yes	Yes	[45]

Purple pole defensin	Phaseolus vulgaris cv. “Extra-long Purple Pole bean”	HepG2, MCF7, HT-29, and SiHa	No	Yes	[46]

Coccinin	Phaseolus coccineus cv. “Major”	HL60 and L1210	No	Yes	[47]

Phaseococcin	Phaseolus coccineus 	L1210 and HL60	No	Yes	[48]

γ-Thionin	Capsicum chinense 	HeLa	No	Yes	[49]

NaD1	Nicotiana alata 	U937	Not tested	Yes	[67]

Mitogenic defensin	Phaseolus vulgaris 	MCF-7, murine splenocytes	Yes	Yes	[68]

Vulgarinin	Phaseolus vulgaris 	MCF-7, L1210, and M1	Not tested	Yes	[69]

Cloud bean defensin	Phaseolus vulgaris cv. cloud bean	L1210 and MBL2	Not tested	Yes	[70]

Nepalese	Phaseolus angularis 	L1210, MBL2	Not tested	Yes	[71]

Gymnin	Gymnocladus chinensis Baill	M1, HepG2, and L1210	Not tested	Yes	[72]


 	Odds ratio	95% CI	p-value

Aortic valve replacement	4.36	2.68–7.07	< 0.001

Chronic heart failure	1.81	1.10–2.99	< 0.001

EuroSCORE (per point)	1.10	1.03–1.17	< 0.001

Age (per year)	1.05	1.03–1.07	< 0.001

	Odds ratio	95% CI

Aortic valve replacement	4.36	2.68–7.07

Chronic heart failure	1.81	1.10–2.99

EuroSCORE (per point)	1.10	1.03–1.17

Age (per year)	1.05	1.03–1.07


Pulse feature	Frequency	Proportion of the total (%)

Rate

Normal	33	76.7

Slow	1	2.3

Fast	6	14.0

Uneven	2	4.6

Left pulse

Slippery	15	34.9

Thin	15	34.9

Soft/weak	23	53.5

Wiry	22	51.2

Right pulse

Slippery	18	41.9

Thin	19	44.2

Soft/Weak	25	58.1

Wiry	15	34.9

Pulse feature

Rate

Normal

Slow

Fast

Uneven

Left pulse

Slippery

Thin

Soft/weak

Wiry

Right pulse

Slippery

Thin

Soft/Weak

Wiry


	Cases (number, %)

Gender

Female	72 (14.9)

Male	411 (85.1)

Age (years)

≤60	273 (56.5)

>60	210 (43.5)

Tumor length

≤3	138 (28.6)

>3	345 (71.4)

Tumor location

Upper	27 (5.6)

Middle	247 (51.1)

Lower	209 (43.3)

Vessel involvement

Negative	407 (84.3)

Positive	76 (15.7)

Perineural invasion

Negative	390 (80.7)

Positive	93 (19.3)

Differentiation

Well	71 (14.7)

Moderate	323 (66.9)

Poor	89 (18.4)

Depth of invasion

T1	87 (18.0)

T2	80 (16.6)

T3	265 (54.9)

T4	51 (10.5)

Nodal metastasis

Negative	274 (56.7)

Positive	209 (43.3)


Case 1	Case 2	Case 3	Case 4	Case 5	Case 6	Case 7	Our patient


Clinical / laboratory characteristic	Number of patients (of n = 99)

Altered level of consciousness	99

Fever	93

Headache	54

Seizures	58

Prodromal symptoms	77

Focal neurological signs	44

 Cerebellar signs	12

 Extrapyramidal signs	6

Neuropsychiatric symptoms	21

Cerebrospinal fluid

 Protein >40 mg/dl	22

 CSF:plasma glucose ratio < 30%	39

 Pleocytosis	26

 Gram stain positivity	0

 Bacterial culture positivity	0

cal / laboratory characteristic	ts (of n = 99)

Altered level of consciousness	99

Fever	93

Headache	54

Seizures	58

Prodromal symptoms	77

Focal neurological signs	44

Cerebellar signs	12

Extrapyramidal signs	6

Neuropsychiatric symptoms	21

Cerebrospinal fluid

Protein >40 mg/dl	22

CSF:plasma glucose ratio < 30%	39

Pleocytosis	26

Gram stain positivity	0

Bacterial culture positivity	0


Implementation unit	No. of pools	Average pool size	No. of An. gambiae processed	No. of An. gambiae collected	% of An. gambiae processed	Positive	95% CI

Cinkasse	166	15.9	2646	2782	95.1	0	0–0.073

Kpendjal	172	14.5	2495	2807	88.9	0	0–0.077

Tone	291	13.9	4050	5283	76.7	0	0–0.047

Total	629	14.6	9191	10,872	84.5	0	0–0.021

Implementation uni	No. of pools		No. of An. 	No. o		Po


	Subjects (n = 35)

Age (yrs)	22.6 ± 3.5

Height (cm)	177.1 ± 7.1

Weight (kg)	77.0 ± 11.0


 	Time period	Findings

1	Any time-point	Brain death.

2	<24 h from ROSC	Early myoclonus status# and bilateral absence of N20 peak on SSEP after rewarming.

3	72 h after rewarming	GCS-M 1–2 and bilateral absence of N20 peak on SSEP performed 48–72 hours after rewarming, or later.

4	72 h after rewarming	A treatment refractory status epilepticus* and GCS-M 1–2.

	Time period

1	Any time-point

2	<24 h from ROSC

3	72 h after rewarming

4	72 h after rewarming

Findings

Brain death.

Early myoclonus status# and bilateral absence of N20 peak on SSEP after rewarming.

GCS-M 1–2 and bilateral absence of N20 peak on SSEP performed 48–72 hours after rewarming, or later.

A treatment refractory status epilepticus* and GCS-M 1–2.


	Response rate	p value	Local control rate	p value	Disease-free survival	p value	Overall survival	p value

Algorithm		0.008		0.879		0.408		0.311

RAT group	77.3%		94.1%		43.8%		38.9%

MC group	100.0%		90.9%		21.5%		67.1%

	Response rate	p value	Local control rate	p value	Disease-free survival	p value	Overall survival	p value

Tumor origin		0.309		0.367		<0.001		0.088

Lung cancer	93.8%		88.5%		38.2%		62.2%

Metastasis	80.0%		100.0%		10.0%		50.0%

RT dose		0.571		0.132

48–50 Gy	100.0%		83.3%		n/a		n/a

60 Gy	87.2%		94.1%		n/a		n/a

Tumor size		0.143		0.629

≤3 cm	83.3%		95.7%		n/a		n/a

>3 cm	100.0%		83.3%		n/a		n/a


Percentage of biobanks which get specimens from...	n	%

Direct from individuals donating them	343	75

Residual specimens acquired from clinical care in hospitals, clinical laboratories, or pathology departments	261	57

Research	60	13

Residual specimens from public health departments or programs	19	4

Vendors	8	2

Organ/body donation organization	7	2

Other repositories	7	2

Other	6	1

Orphaned collections	4	1

Percentage of biobanks which get specimens from...	n	%

Vendors	8	2

Organ/body donation organization	7	2

Other repositories	7	2

Other	6	1

Orphaned collections	4	1


Strategy	Intervention	Comments

Formulary restriction	Only certain agents available for use	Effective, minimal effort to maintain, usurps prescriber autonomy, ‘squeezing the balloon’

Pre-authorization	Certain agents available if specific criteria are met	Effective, allows education, requires designated call person, creates tension between prescriber and stewardship program, clinician may circumvent the system

Audit and feedback	Review of ordered antimicrobials or culture results	Customizable, educational, maintains prescriber autonomy, recommendations specific to clinical situation, optional, time intensive, requires reviewer with broad knowledge

Guidelines/clinical pathways	Development of institution specific recommendations for management of specific disease states	Ability to standardize practice and meet quality measures; customized to institutional needs, resources and formulary, compliance usually not mandatory; perception of ‘cookbook medicine’

Education	Education to clinicians regarding appropriate antimicrobial use	Alters behavioral patterns, improves compliance with guidelines, decreased misuse, poor efficacy of passive education, diminishing effect over time, rotation of personal means repeated education needed

Biomarkers	Use of laboratory information (procalcitonin, etc.) to assist in therapy decisions	Objective marker, well validated in certain situations, must be used with overall patient evaluation, false positive and negative results can occur, clinicians must be able to interpret to use correctly

Antibiotic cycling	Scheduled removal and substitution of antimicrobial or class	May decrease resistance rates, compliance difficult due to patient allergies, national guidelines, and adverse events

Combination therapy	Use of two or more agents to prevent resistance and improve clinical outcomes	Evidence for improvement in clinical outcomes is variable, minimal data to support decrease in resistance, increased risk of allergy and toxicity, increased cost

Streamlining/de-escalation	Narrowing of broad spectrum therapy when no pathogen isolated or targeting of pathogen isolated	Decreases antimicrobial use and costs, allows narrowest effective therapy if culture data available, possible under-treatment if no culture data acquired, treatment of colonizers

Dose optimization	Use of pharmacokinetic-pharmacodynamic (PK-PD) parameters to optimize dosing	Improves likelihood of achieving PK-PD targets, may allow use of agents against resistant pathogens, may decrease selection of resistant strains, some data improves clinical outcomes, requires expertise and time, may be limited by microbiologic data available (MIC), limited data on clinical outcomes

Computerized decision support	Use of information technology to assist clinicians in antimicrobial decision making	Can be integrated into EHR, integrates local data for customized recommendations, requires significant technology and personnel input to set up, alert fatigue, requires constant updating

IV to oral conversion	Agents with excellent oral bioavailability are switched from IV to oral	Decreases cost and need for IV access, may decrease length of stay, only certain agents have excellent oral bioavailability

Strategy	Intervention	Comments

Formulary restriction	Only certain agents available for use	Effective, minimal effort to maintain, usurps prescriber autonomy, ‘squeezing the balloon’

Pre-authorization	Certain agents available if specific criteria are met	Effective, allows education, requires designated call person, creates tension between prescriber and stewardship program, clinician may circumvent the system

Audit and feedback	Review of ordered antimicrobials or culture results	Customizable, educational, maintains prescriber autonomy, recommendations specific to clinical situation, optional, time intensive, requires reviewer with broad knowledge

Guidelines/clinical pathways	Development of institution specific recommendations for management of specific disease states	Ability to standardize practice and meet quality measures; customized to institutional needs, resources and formulary, compliance usually not mandatory; perception of ‘cookbook medicine’

Education	Education to clinicians regarding appropriate antimicrobial use	Alters behavioral patterns, improves compliance with guidelines, decreased misuse, poor efficacy of passive education, diminishing effect over time, rotation of personal means repeated education needed

Biomarkers	Use of laboratory information (procalcitonin, etc.) to assist in therapy decisions	Objective marker, well validated in certain situations, must be used with overall patient evaluation, false positive and negative results can occur, clinicians must be able to interpret to use correctly

Antibiotic cycling	Scheduled removal and substitution of antimicrobial or class	May decrease resistance rates, compliance difficult due to patient allergies, national guidelines, and adverse events

Comb	Use of two or more agents to prevent resistance and improve clinical out	Evidence for improvement in clinical outcomes is variable, minimal data to support decrease in resist

Strategy	Intervention	Comments

ination therapy	comes	ance, increased risk of allergy and toxicity, increased cost

Streamlining/de-escalation	Narrowing of broad spectrum therapy when no pathogen isolated or targeting of pathogen isolated	Decreases antimicrobial use and costs, allows narrowest effective therapy if culture data available, possible under-treatment if no culture data acquired, treatment of colonizers

Dose optimization	Use of pharmacokinetic-pharmacodynamic (PK-PD) parameters to optimize dosing	Improves likelihood of achieving PK-PD targets, may allow use of agents against resistant pathogens, may decrease selection of resistant strains, some data improves clinical outcomes, requires expertise and time, may be limited by microbiologic data available (MIC), limited data on clinical outcomes

Computerized decision support	Use of information technology to assist clinicians in antimicrobial decision making	Can be integrated into EHR, integrates local data for customized recommendations, requires significant technology and personnel input to set up, alert fatigue, requires constant updating

IV to oral conversion	Agents with excellent oral bioavailability are switched from IV to oral	Decreases cost and need for IV access, may decrease length of stay, only certain agents have excellent oral bioavailability


Days post CD instillation	Reactors

PC (GC mL−1)	TDC (GC mL−1)

0.25	7.4	7.3

1	5.7	6.2

2	4.4	5.1

3	ND	ND

4	ND	ND

5	ND	ND

6	ND	ND

7	ND	ND

8	ND	ND

9	ND	6.0

10	ND	6.0

11	ND	6.4

12	ND	6.9

13	ND	7.1

14	ND	7.5

15	ND	7.8

16	ND	7.8


Variable	Pre-study	Post-study	Treatment interaction p-value

Saliva IgA concentration (μg . mL-1)			0.461

 Low	333 ± 181	314 ± 152

 Medium	292 ± 143	302 ± 151

 High	315 ± 159	289 ± 158

Saliva protein IgA concentration (μg . mg-1)			0.675

 Low	471 ± 162	484 ± 156

 Medium	444 ± 170	419 ± 155

 High	535 ± 224	499 ± 187

Saliva IgA secretion rate (μg . min-1)			0.476

 Low	173 ± 135	220 ± 163

 Medium	185 ± 118	233 ± 129

 High	207 ± 118	212 ± 74


Variable	Univariate analysis	Multivariate analysis

		OR	95% CI	p-value	Adjusted OR	95% CI	p-value


			Gene set = G	Centroid set = C

Data set	S	D	j	Description	cj0	cj1	vjP

Colon cancer	Swirl	1	1772	myosin heavy chain	44	125	61

			249	desmin	1958	467	793

			1582	p cadherin	53	174	83

			1423	myosin reg light chain 2	763	196	213

			745	ORF, xq terminal portion	188	226	179

Leukemia 1	Swirl	1	3532	glutathione S-transferase	37	1460	569

Medulloblastoma	Swirl	1	977	zinc finger protein HZfq	38	-102	87

Prostate cancer	Swirl	1	8850	cDNA DKFZp564A072	24	215	110

Leukemia 2	Swirl	1	8828	HLA class II alpha	785	41345	7280

			Gene set = G	Centroid set = C

Data set	S	D	j	Description	cj0	cj1	vjP

			745	ORF, xq terminal portion	188	226	179

Leukemia 1	Swirl	1	3532	glutathione S-transferase	37	1460	569

Medulloblastoma	Swirl	1	977	zinc finger protein HZfq	38	-102	87

Prostate cancer	Swirl	1	8850	cDNA DKFZp564A072	24	215	110

Leukemia 2	Swirl	1	8828	HLA class II alpha	785	41345	7280


Features analysed	P. aeruginosa pangenome

Genomes	181

Total genes	1,117,803

Average genome size	6175 genes

Pangenome size (non-redundant genes)	16,820

Core genome	2503 genes

Accessory genome	9108 genes

Unique genes	5209

Average unique genes/strain	16

Gene families under positive selection	233

Features analysed

Genomes

Total genes

Average genome size

Pangenome size (non-redundant genes)

Core genome

Accessory genome

Unique genes

Average unique genes/strain

Gene families under positive selection


	Whole sample	18- 30 years old sample

Support	PCOS (%)	Control (%)	Χ2 (p)	PCOS (%)	Control (%)	Χ2 (pcp


Parameters	Group	Before	After	Before versus after	Group comparison

Median	25–75 percentile	Median	25–75 percentile	p value∗	p value∗∗

Stool Hp antigen	L. gasseri group	1.45	0.38–2.38	1.17	0.48–2.13	0.588	0.958

Placebo group	1.22	0.38–2.83	1.24	0.44–2.50	0.584

UBT	L. gasseri group	19.4	10.4–35.0	21.9	11.1–31.2	0.395	0.5

Place	1	10.3–36.	1	10.


	N (%)

Median age at diagnosis (range)	63 (20–92)

Histological subtype

 High-grade serous	90 (92.7)

 High-grade endometrioid	5 (5.2)

 Clear cell	2 (2.1)

FIGO stage at diagnosis

 I	7 (7.2)

 II	4 (4.1)

 III	73 (75.3)

 IV	13 (13.4)

ER-status

 Positive	50 (51.5)

 Negative	1 (1)

 Unknown	46 (47.4)

PR-status

 Positive	20 (20.1)

 Negative	11 (11.3)

 Unknown	66 (68)

Primary Surgery status

 Complete debulking	37 (38.1)

 Suboptimal debulking (>1 cm residual disease)	44 (45.3)

 No surgery	16 (16.5)

Platinum-sensitivity

 Platinum-sensitive	39 (40)

 Platinum-resistant	58 (60)

Number of lines of prior chemotherapy

 1	18 (18.6)

 2	15 (15.5)

 3	21 (21.6)

 4	17 (17.5)

 5	10 (10.3)

 ≥ 6	16 (16.5)


Variable	Breakpoint-Specific PCR	Concordance, %

CN1	CN2+	Fail	Total

Sequence read depth: Thailand

 CN1	57 (22a)	13 (13)	1	71	...

 CN2+	0	17 (7)	0	17	...

 Total	57	30	1	88	85

qPCR: Thailand

 CN1	33 (11b)	3 (2)	1	37	...

 CN2+	1 (0)	9 (4)	1	11	...

 Total	34	12	2	48	91

qPCR: Indonesia

 CN1	25	0	2	27	...

 CN2+	0	0	0	0	...

 Total	25	0	2	27	100

Variable	Breakpoint-Specific PCR	Concordance, %

CN1	CN2+	Fail	Total

Total	25	0	2	27	100


							Yield (%)

Entry	Catalyst	Additive (equiv.)	Solvent	Temp.(K)	O2 (MPa)	Time (h)	FDCA	DFF	HFCA	FFCA	Comments	Reference

1	Ru(OH)x/CeO2	—	Water	413	0.25	6	38	12	50	—	Reuse (3 times)	[173]

2b	Ru(OH)x/MgAl2O4	—	Water	413	0.5	18	56	4	30	—	Reuse (4 times), leaching (Mg: 0.9%, Ru: 0.02%)	[174]

3c	Ru(OH)x/La2O3	—	[EMIm] [OAc]	373	3	5	48	—	12	—	Leaching (9.9%)	[175]

4	Au/CeO2	NaOH (4)	Water	338	Flow (1)	8	>99	—	—	—	Au/CeO2: reuse (3 times), leaching (no)	[176]

	Au/TiO2	NaOH (4)	Water	338	Flow (1)	8	>99	—	—	—		[176]

5	Pt/C	NaOH (2)	Water	295	0.68	6	79	—	21	—	Pt/C: reuse (3 times),	[177]

	Pd/C	NaOH (2)	Water	295	0.68	6	71	—	29	—	Au/C: leaching (no)	[177]

	Au/C	NaOH (2)	Water	295	0.68	6	8	—	92	—		[177]

6	Au/hydrotalcite	—	Water	368	Flow (0.1)	7	>99	—	—	—	Reuse (3 times), leaching (no)	[178]

7d	Au/TiO2	HTFA (18–21)	AcOH	403	1	3	—	—	—	78	—	[179]

8	Au/HY zeolite	NaOH (4)	Water	333	0.3	6	>99	—	—	—	Reuse (4 times)	[180]

9	Ru/C	—	Water	383	2	—	—	29	—	<1	Reuse (5 times, decrease in activity), leaching (no)	[181]

10e	Ag/OMS-2	—	IPA	438	1.5 (air)	6	—	99	—	—	Reuse (6 times)	[182]

11	Au–Cu/TiO2	NaOH (4)	Water	368	1	4.5	95	—	5	—	Reuse (5 times), leaching (≤2% after 5th reuse)	[183]

12	Pt–Bi/C	NaHCO3 (4)	Water	373	4 (air)	6	98	—	1	—	Reuse (5 times), leaching (<0.5%)	[184]

13f	Au8Pd2/AC	NaOH (2)	Water	333	0.3	2	99	—	1	—	Reuse (5 times), leaching (no)	[185]

14	Au–Pd/CNT	—	Water	373	0.5	12	94	—	—	2	Reuse (6 times)	[186]

Entry	Catalyst	Additive (equiv.)	Solvent	Temp.(K)	O2 (MPa)

1	Ru(OH)x/CeO2	—	Water	413	0.25

2b	Ru(OH)x/MgAl2O4	—	Water	413	0.5

3c	Ru(OH)x/La2O3	—	[EMIm] [OAc]	373	3

4	Au/CeO2	NaOH (4)	Water	338	Flow (1)

	Au/TiO2	NaOH (4)	Water	338	Flow (1)

5	Pt/C	NaOH (2)	Water	295	0.68

	Pd/C	NaOH (2)	Water	295	0.68

	Au/C	NaOH (2)	Water	295	0.68

6	Au/hydrotalcite	—	Water	368	Flow (0.1)

7d	Au/TiO2	HTFA (18–21)	AcOH	403	1

8	Au/HY zeolite	NaOH (4)	Water	333	0.3

9	Ru/C	—	Water	383	2

10e	Ag/OMS-2	—	IPA	438	1.5 (air)

11	Au–Cu/TiO2	NaOH (4)	Water	368	1

12	Pt–Bi/C	NaHCO3 (4)	Water	373	4 (air)

13f	Au8Pd2/AC	NaOH (2)	Water	333	0.3

14	Au–Pd/CNT	—	Water	373	0.5

	Yield (%)

Time (h)	FDCA	DFF	HFCA	FFCA	Comments	Reference

6	38	12	50	—	Reuse (3 times)	[173]

18	56	4	30	—	Reuse (4 times), leaching (Mg: 0.9%, Ru: 0.02%)	[174]

5	48	—	12	—	Leaching (9.9%)	[175]

8	>99	—	—	—	Au/CeO2: reuse (3 times), leaching (no)	[176]

8	>99	—	—	—		[176]

6	79	—	21	—	Pt/C: reuse (3 times),	[177]

6	71	—	29	—	Au/C: leaching (no)	[177]

6	8	—	92	—		[177]

7	>99	—	—	—	Reuse (3 times), leaching (no)	[178]

3	—	—	—	78	—	[179]

6	>99	—	—	—	Reuse (4 times)	[180]

—	—	29	—	<1	Reuse (5 times, decrease in activity), leaching (no)	[181]

6	—	99	—	—	Reuse (6 times)	[182]

4.5	95	—	5	—	Reuse (5 times), leaching (≤2% after 5th reuse)	[183]

6	98	—	1	—	Reuse (5 times), leaching (<0.5%)	[184]

2	99	—	1	—	Reuse (5 times), leaching (no)	[185]

12	94	—	—	2	Reuse (6 times)	[186]


Antimicrobial	MIC (μg/ml)

Nisin A	12.5

Nisin V	6.25

M21A	6.25

Citric Acid	2340

99% cinnamaldehyde	625

Lactoferrin	>2500

Sodium acetate	250,000

Sodium benzoate	25,000

Carvacrol	310

Potassium sorbate	37,500

Nisaplin®	393


Variable	Presence of hypertension	P-value*

Yes (n = 398)	No (n = 460)

Total cholesterol (mmol/L)†	4.6 (0.8)	4.6 (0.9)	0.972


Current density	44 mA g−1 (mAh g−1)	110 mA g−1 (mAh g−1)	220 mA g−1 (mAh g−1)	660 mA g−1 (mAh g−1)	1320 mA g−1 (mAh g−1)	Retention rate (%)


Number of region	Color	Maximum number of correctly sequenced syllables	Minimum number of correctly sequenced syllables

1	dark red	90	75

2	red	74	61

3	light red	60	45

4	light blue	44	31

5	blue	30	15

6	dark blue	14	0


EZ Metric Endpoint	Weighting factor

24 hpf mortality	1.0

120 hpf mortality	0.95

Heart malformation	0.12

Brain malformation	0.12

Yolk sac edema	0.1

Notochord malformation	0.08

Curved axis	0.08

Trunk malformation	0.06

Delayed developmental progression	0.06

Occluded circulation	0.04

Eye malformation	0.04

Jaw malformation	0.04

Lack of spontaneous movement	0.04

Somite malformation	0.02

Motility	0.02

Lack of touch response	0.02

Snout malformation	0.02

Otic malformation	0.02

Caudal/pectoral fin malformation	0.02

Atypical pigmentation	0.02

Atypical swim bladder inflation	0.02


Parameter	Symbol	Nominal value

Maximal firing rate	Qmax	250 s−1

Average spike-threshold	θ	15 mV

Spike-threshold deviation	σ	3.3 mV

Synaptic decay rate	α	50 s−1

Synaptic rise rate	β	200 s−1

Synaptic strength from e to e neurons	νee	1 mV s

Synaptic strength from i to e neurons	νei	−1.8 mV s

Synaptic strength from s to e neurons	νes	3.2 mV s

Synaptic strength from i to i neurons	νii	−1.8 mV s

Synaptic strength from e to i neurons	νie	1 mV s

Synaptic strength from s to i neurons	νis	3.2 mV s

Synaptic strength from r to s neurons	νsr	−0.8 mV s

Synaptic strength from e to s neurons	νse	2.2 mV s

Synaptic strength from s to r neurons	νrs	0.6 mV s

Synaptic strength from e to r neurons	νre	1.6 mV s

Average noise level	νsnφn	2.0 mV

Noise standard deviation	σn	0.2 mV

Cortico-thalamic delay	τ	0.04 s

Cortical damping rate	γ	100 s−1


	All homes	Box-shaped, Metal-roofed	Round-shaped, Thatch-roofed

ACH	Mean (SD)	Mean (SD)	Mean (SD)	p-value

Closed	3 (2.9)	3 (2.7)	3 (3.3)	0.50

Windows Open	9 (7.1)	13 (8.1)	5 (2.9)	0.01

Windows & Door	20 (13.1)	27 (9.7)	13 (12.8)	0.01

Open

*TB Risk (10h exposure)

	All homes	Box-shaped, Metal-roofed	Round-shaped, Thatch-roofed

ACH	Mean (SD)	Mean (SD)	Mean (SD)	p-value

Closed	55.4 (27.8)	58.3 (24.7)	52.5 (31.5)	0.62

Windows Open	21.5 (14.1)	24.7 (18.1)	18 (8.4)	0.32

Windows & Door	9.6 (4.7)	8.9 (3.6)	10 (5.6)	0.44

Open


Gene set/pathway	NTk q value	NEk q value

Regulation of cell growth (GO:0001558)	0.0	0.0

Nuclear pore complex (GO:0046930)	0.0	0.03

Negative regulation of cellular metabolism (GO:0031324)	0.0	0.04

Chromatin remodeling (GO:0016568)	0.0	0.055

PI3K-AKT signaling pathway (humanpaths)	0.0	0.1

Histone acetyltransferase binding (GO:0035035)	0.0	0.2

cAMP/Ca2+ signaling (humanpaths)	0.0	0.3

Spliceosome complex (GO:0005681)	0.0	0.9

Gene set/pathway

Regulation of cell growth (GO:0001558)

Nuclear pore complex (GO:0046930)

Negative regulation of cellular metabolism (GO:0031324)

Chromatin remodeling (GO:0016568)

PI3K-AKT signaling pathway (humanpaths)

Histone acetyltransferase binding (GO:0035035)

cAMP/Ca2+ signaling (humanpaths)

Spliceosome complex (GO:0005681)


	Rainy season 2009	Dry season 2010

Sensitivity%	56.4	20.7

Specificity%	72.4	97.3

PPV%	67.5	43.3

NPV%	62	92.5

P-values	< 0.001	< 0.001


			No. of individuals with genotype (%)

Population and disease	No.	VariantAllele Frequency (%)	Ser/Ser	Ser/Leu	Leu/Leu	P valuea

Ghana, delivering women

 P. falciparum positive	541	0.1	540 (99.8)	1 (0.2)	0 (0)	0.49

 P. falciparum negative	554	0	554 (100)	0 (0)	0 (0)

Germany

 Sepsis patients	223	18.1	170 (76.2)	43 (19.3)	10 (4.5)	0.19

 Controls	188	19.2	138 (74.4)	48 (25.5)	2 (1.1)b

Bangladesh

 Leprosy patients	263	16.3	186 (70.7)	68 (25.9)	9 (3.4)	0.22

 Controls	280	17.2	188 (67.1)	88 (31.5)	4 (1.4)

Turkey						0.11

 Leprosy patients	60	10.8	47 (78)	13 (21)	0 (0)

 Controls	100	6.5	88 (88)	11 (11)	1 (1)


	Healthy	Milder fibrosis	Significant fibrosis	Early Cirrhosis

Number	42	24	32	12

Age (Mean ± SD)	32.9 ± 9.2	37.4 ± 12.4	34.4 ± 11.7	38.5 ± 14.4

Gender (Male/Female)	42/0	22/2	28/4	10/2

	Healthy	Milder fibrosis

Number	42	24

Age (Mean ± SD)	32.9 ± 9.2	37.4 ± 12.4

Gender (Male/Female)	42/0	22/2

Significant fibrosis	Early Cirrhosis

32	12

34.4 ± 11.7	38.5 ± 14.4

28/4	10/2


14–16 weeks	22–24 weeksc	30–32 weeks	36–38 weeksc	5 year. follow-up


GEO ID	Description

GSE43559 (gene expression)

GSM1065332	PGC E13 F3-Control biological rep1

GSM1065333	PGC E13 F3-Control biological rep2

GSM1065334	PGC E13 F3-Vinclozolin biological rep1

GSM1065335	PGC E13 F3-Vinclozolin biological rep2

GSM1065336	PGC E16 F3-Control biological rep1

GSM1065337	PGC E16 F3-Control biological rep2

GSM1065338	PGC E16 F3-Vinclozolin biological rep1

GSM1065339	PGC E16 F3-Vinclozolin biological rep2

GSE59510 (promoter methylation)

GSM1438556	E16-Vip2/Cip2

GSM1438557	E13-Vip2/Cip1

GSM1438558	E13-Vip1/Cip1

GSM1438559	E16-Vip1/Cip1

GSM1438560	E16-Vip2/Cip1

GSM1438561	E13-Vip2/Cip2

GEO ID	Description

GSM1438558	E13-Vip1/Cip1

GSM1438559	E16-Vip1/Cip1

GSM1438560	E16-Vip2/Cip1

GSM1438561	E13-Vip2/Cip2


Variables	Total cholesterol<150 mg/dL(n = 68)	Total cholesterol>150 mg/dL(n = 97)

Age in years	39.8 ± 7.9	41.1 ± 7.2

Men	72%	68%

Women	28%	32%

Black	65%	55%

Hispanic	33%	29%

White	0%	16%

Annual income

Less than $10,000	89%	88%

$11,000-$20,000	9%	10%

More than $20,000	2%	2%

Cigarette smoker	81%	80%

Hazardous alcohol use	64%	48%

Drug use	42%	39%

Albumin in mg/dl	4.0 ± 0.4	4.1 ± 0.5

Body mass index in kg/m2	26.2 ± 6.2	27.4 ± 6.7

Number of years diagnosed with HIV	9.7 ± 6.6	8.7 ± 5.6

Variables	Total cholesterol<150 mg/dL(n = 68)	Total cholesterol>150 mg/dL(n = 97)

Body mass index in kg/m2	26.2 ± 6.2	27.4 ± 6.7

Number of years diagnosed with HIV	9.7 ± 6.6	8.7 ± 5.6


	EG mean (SD)	CG mean (SD)

H1: General self-care managementa

 Baseline	46.19 (13.13)	46.31 (8.97)

 1 month	50.57 (4.89)	50.76 (6.73)

H2: Self-care specific to HFa

 Baseline	49.37 (20.37)	46.25 (14.50)

 1 month	75.23 (10.60)	69.48 (16.66)

H3: Perceived competence to manage HFa

 Baseline	20.13 (4.08)	19.06 (5.30)

 1 month	23.29 (3.27)	23.85 (4.02)

H4: Behavioral Questionnaire of the Elderly-Health activities

 H4a A-motivationb

 Baseline	7.88 (3.98)	5.87 (3.46)

 1 month	4.14 (2.18)	6.08 (3.64)

H4b: External extrinsic motivationb

 Baseline	13.00 (4.77)	15.13 (4.73)

 1 month	16.29 (6.40)	13.85 (5.32)

H4c: Internal extrinsic motivationa

 Baseline	16.65 (2.93)	16.38 (3.16)

 1 month	19.29 (1.77)	17.54 (3.23)

H4d: Intrinsic motivationa

 Baseline	11.13 (5.58)	9.63 (4.26)

 1 month	10.86 (7.55)	9.77 (5.48)

H5: Family Care Climate Questionnaire-Patient versiona

 Baseline	57.56 (7.97)	60.19 (5.24)

 1 month	61.86 (6.63)	62.46 (6.57)

H6: Family Care Climate Questionnaire-Family versiona

 Baseline	58.13 (4.94)	57.94 (6.69)

 1 month	60.21 (4.93)	57.85 (6.45)

	EG mean (SD)

H1: General self-care managementa

Baseline	46.19 (13.13)

1 month	50.57 (4.89)

H2: Self-care specific to HFa

Baseline	49.37 (20.37)

1 month	75.23 (10.60)

H3: Perceived competence to manage HFa

Baseline	20.13 (4.08)

1 month	23.29 (3.27)

H4: Behavioral Questionnaire of the Elderly-Health activities

H4a A-motivationb

Baseline	7.88 (3.98)

1 month	4.14 (2.18)

H4b: External extrinsic motivationb

Baseline	13.00 (4.77)

1 month	16.29 (6.40)

H4c: Internal extrinsic motivationa

Baseline	16.65 (2.93)

1 month	19.29 (1.77)

H4d: Intrinsic motivationa

Baseline	11.13 (5.58)

1 month	10.86 (7.55)

H5: Family Care Climate Questionnaire-Patient versiona

Baseline	57.56 (7.97)

1 month	61.86 (6.63)

H6: Family Care Climate Questionnaire-Family versiona

Baseline	58.13 (4.94)

1 month	60.21 (4.93)


Characteristic	Notifying hospitals	Cardiac arrests/eligible for donation	p value	OR (95 % CI)

Total, n (%)	27 (100)	141/465 (30.3)

Hospital beds, n (%)

< 150 beds	10 (37.0)	29/62 (46.7)	0.002	2.28 (1.32; 3.93)


Factors	Odds ratio	95% confidence interval

Nicardipine vs. labetalol	2.73	1.113-6.698

Female	3.311	1.36-8.064

No history of stroke	5.38	1.565-18.468

Factors	Odds ratio	95% confidence interval

No history of stroke	5.38	1.565-18.468


Strains	Clinical signs first evident	No. leisions/total	Mortalitye	Main clinical signs	Salient gross lesions

 	 	 	 	SPF chickens

Herts/33	2 dpi	12/12	8/8	severe depression, lethargy, ruffled feathers, eyelid edema, diarrhea, open-mouth breathing	Spleen, Atr; Intestene, Hem; Proventriculus, Hem; Pancreas, Nec; Thymus, Hem; Larynx and trachea, Hem;

ZJ1	2 dpi	12/12	8/8	severe depression, lethargy, ruffled feathers, eyelid edema, diarrhea, open-mouth breathing	Spleen, Nec; Intestene, Hem + Nec; Proventriculus, Hem; Pancreas, Nec; Thymus, Hem; Larynx and trachea, Hem;

JS-5-05-Go	2 dpi	12/12	8/8

XJ-2/97	2 dpi	12/12	8/8

JS-3-05-Ch	2 dpi	12/12	8/8

 	 	 	 	Geese

Herts/33	4 dpi	12/12	4/8	morderate depression, diarrhea, crouch, nasal discharge, eyelid edema	Spleen, Atr; Intestene, Hem, Proventriculus, Ed + Hem; Pancreas, Nec;Thymus, Hem; Larynx and trachea, Hem;

ZJ1	3 dpi	12/12	5/8	morderate to severe depression, diarrhea, crouch, nasal discharge, eyelid edema	Spleen, Nec; Intestene, Hem + Nec; Proventriculus, Ed + Hem; Pancreas, Nec; Thymus, Hem; Larynx and trachea, Hem;

JS-5-05-Go	3 dpi	12/12	6/8

XJ-2/97	3 dpi	12/12	5/8

JS-3-05-Ch	3 dpi	12/12	6/8

Strains	Clinical signs first evident	No. leisions/total	Mortalitye	Main clinical signs	Salient gross lesions

Herts/33	4 dpi	12/12	4/8	morderate depression, diarrhea, crouch, nasal discharge, eyelid edema	Spleen, Atr; Intestene, Hem, Proventriculus, Ed + Hem; Pancreas, Nec;Thymus, Hem; Larynx and trachea, Hem;

ZJ1	3 dpi	12/12	5/8	morderate to severe depression, diarrhea, crouch, nasal discharge, eyelid edema	Spleen, Nec; Intestene, Hem + Nec; Proventriculus, Ed + Hem; Pancreas, Nec; Thymus, Hem; Larynx and trachea, Hem;

JS-5-05-Go	3 dpi	12/12	6/8

XJ-2/97	3 dpi	12/12	5/8

JS-3-05-Ch	3 dpi	12/12	6/8


	PKS/NRPS	LuxRI	Bacteriocin	Siderophore	Terpene	Ectoine

Leisingera sp. ANG-Vp	1	1	0	1	0	1

Leisingera sp. ANG-M1	1	1	0	0	0	1

Leisingera sp. ANG1	1	1	0	1	0	0

Leisingera sp. ANG-DT	1	1	0	1	0	0

Leisingera sp. ANG-S	1	1	0	1	0	0

Leisingera sp. ANG-S3	1	1	0	1	0	0

Leisingera sp. ANG-M6	1	1	0	1	0	0

Leisingera sp. ANG-S5	1	1	0	1	0	0

Leisingera sp. ANG-M7	1	1	0	1	0	0

Ruegeria sp. ANG-R	1	2	3	0	0	1

Ruegeria sp. ANG-S4	2	2	3	0	0	0

Tateyamaria sp. ANG-S1	0	1	2	1	1	0

	PKS/NRPS

Leisingera sp. ANG-Vp	1

Leisingera sp. ANG-M1	1

Leisingera sp. ANG1	1

Leisingera sp. ANG-DT	1

Leisingera sp. ANG-S	1

Leisingera sp. ANG-S3	1

Leisingera sp. ANG-M6	1

Leisingera sp. ANG-S5	1

Leisingera sp. ANG-M7	1

Ruegeria sp. ANG-R	1

Ruegeria sp. ANG-S4	2

Tateyamaria sp. ANG-S1	0


	Fixed effect	Random effect	% variances

	M	D	Y	G	G×M	G×D	G×Y	G	G×M	G×D	G×Y	Res	h2b

TCSA_DI	-	-	x	x	-	-	-	35	-	-	-	65	0.52

NbFr_DI	-	-	x	x	-	-	x	13	-	-	1	86	0.38

BmFr_DI	-	-	x	x	-	-	-	17	-	-	-	83	0.29


 	Veterinary rating of pet owner compliance in endemic regions

 	Number clinics responding (% responding)

 	Excellent	Good	Reasonable	Poor	Non-compliant	Responders

France	10 (21)	7 (15)	19 (40)	4 (9)	7 (15)	47

Spain	0 (0)	21 (51)	17 (41)	3 (7)	0 (0)	41

Italy	26 (24)	76 (71)	2 (2)	3 (3)	0 (0)	107

Total	36 (18)	104 (53)	38 (19)	10 (5)	7 (4)	195


	Gene features	Clinical covariates	Samples

UPP	44,928	21	251

MAINZ	22,283	21	200

TransBig	22,283	21	198

VDX	22,283	21	344

TCGA	19,420	5	3096

	Gene features	Clinical covariates	Samples

VDX	22,283	21	344

TCGA	19,420	5	3096


	Patients with NTG (n = 24)	Controls (n = 24)

	66.7 ± 1	67.2 ± 1


Outcome	Model A	Model B	Model C

BMI z-score increment

LMW metabolites	0.07 (0.02, 0.13)*	0.03 (–0.03, 0.09)	0.03 (–0.03, 0.09)

HMW metabolites	–0.003 (–0.05, 0.05)	–0.01 (–0.06, 0.04)	–0.01 (–0.06, 0.04)

DEHP metabolites	–0.01 (–0.06, 0.04)	–0.02 (–0.07, 0.03)	–0.01 (–0.06, 0.03)

Overweight OR

LMW metabolites	1.08 (0.97, 1.19)	1.01 (0.90, 1.14)	1.01 (0.90, 1.13)

HMW metabolites	0.97 (0.86, 1.10)	0.96 (0.85, 1.08)	0.96 (0.86, 1.09)

DEHP metabolites	0.95 (0.84, 1.08)	0.95 (0.84, 1.06)	0.95 (0.85, 1.07)

Obesity OR

LMW metabolites	1.12 (0.99, 1.25)	1.02 (0.89, 1.16)	1.02 (0.90, 1.17)

HMW metabolites	1.02 (0.89, 1.17)	1.03 (0.88, 1.20)	1.04 (0.89, 1.22)

DEHP metabolites	1.01 (0.88, 1.16)	1.02 (0.88, 1.19)	1.04 (0.89, 1.20)

Model A adjusts only for urinary creatinine; model B alsoincludes sex, poverty–income ratio, parental education, serum cotinine,age, and race/ethnicity category; model C includes model B covariatesplus caloric intake and television watching. All associations are withthe natural log of the phthalate metabolite concentration. Resultsrepresent increase in odds of BMI z-score per unit log increase inphthalate metabolite. *p < 0.05.


Features for Event Detection	Results	Ref

Spatio-temporal of Markov Random Field (MRF) for motion vectors and object tracking	Overall success rate is 94.6% for horizontal and vertical traffic	[110]

Discrete Cosine Transform (DCT) coefficient feature vector with traffic detections using Gaussian mixture HMM	HMM classifier correctly detected 91% to 94% under different illumination situations	[107]

Spatio-temporal motion trajectories using MRF	Able to detect accidents at a very high recall rate, which is more than 90%	[114]

Blob tracker using Kalman filter	Results reported that the percentage of falsely classified vehicle modes was about 3%	[100]

Extracted features: shape, position, motion and track object using Kalman filter	Correctly classify five types of vehicle behavior and pedestrians with 86% to 96% accuracy	[105]

Motion trajectories and hierarchical clustering	Half total error rate (HTER) = 11% and area under the curve (AUC) > 0.8	[109]

Motion trajectories; velocities of motion vector	The accuracy increases up to 95%, tested on two video clips (277 frame sequences)	[116]

Target trajectories with context free grammar (CFG) representations	Results reported using (1) stochastic CFG, TP = 86%, TN = 14%; (2) HMM, TP = 55%, TN = 45%	[117]

Optical flow and K-means clustering	Only visual sample results; no empirical analysis	[102]

Local motion patterns to initialize the Gaussian mixture model (GMM)	Accuracy of detection measured = 83.23%, and the error rate is 16.77%	[113]

Motion trajectories and C-means clustering	Results reported that the false rejection rate (FRR) = 6%, and the false acceptance rate (FAR) = 8.3%	[111]

Track target trajectories and learn rules using grammar representations	The accuracy increased from 73% to 98.6% when tested on five traffic sub-events with different parsing parameters, θ and ω	[94]

Lane tracking using particle swarm optimization (PSO) particle filters (PF)	Results indicated that particle filter output is smoother than the PSO-PF algorithm	[103]

Features for Event Detection	Results	Ref

Spatio-temporal of Markov Random Field (MRF) for motion vectors and object tracking	Overall success rate is 94.6% for horizontal and vertical traffic	[110]

Discrete Cosine Transform (DCT) coefficient feature vector with traffic detections using Gaussian mixture HMM	HMM classifier correctly detected 91% to 94% under different illumination situations	[107]

Spatio-temporal motion trajectories using MRF	Able to detect accidents at a very high recall rate, which is more than 90%	[114]

Blob tracker using Kalman filter	Results reported that the percentage of falsely classified vehicle modes was about 3%	[100]

Extracted features: shape, position, motion and track object using Kalman filter	Correctly classify five types of vehicle behavior and pedestrians with 86% to 96% accuracy	[105]

Motion trajectories and hierarchical clustering	Half total error rate (HTER) = 11% and area under the curve (AUC) > 0.8	[109]

Mo	The acc	[116]

tion trajectories; velocities of motion vector	uracy increases up to 95%, tested on two video clips (277 frame sequences)

Target trajectories with context free grammar (CFG) representations	Results reported using (1) stochastic CFG, TP = 86%, TN = 14%; (2) HMM, TP = 55%, TN = 45%	[117]

Optical flow and K-means clustering	Only visual sample results; no empirical analysis	[102]

Local motion patterns to initialize the Gaussian mixture model (GMM)	Accuracy of detection measured = 83.23%, and the error rate is 16.77%	[113]

Motion trajectories and C-means clustering	Results reported that the false rejection rate (FRR) = 6%, and the false acceptance rate (FAR) = 8.3%	[111]

Track target trajectories and learn rules using grammar representations	The accuracy increased from 73% to 98.6% when tested on five traffic sub-events with different parsing parameters, θ and ω	[94]

Lane tracking using particle swarm optimization (PSO) particle filters (PF)	Results indicated that particle filter output is smoother than the PSO-PF algorithm	[103]


The pair of parameters	R Spearman	p

Number of metastases & WBC	0.795611	0.000000

Number of metastases & LYM	0.785315	0.000000

Number of metastases & MON	0.788080	0.000000

Number of metastases & GRA	0.755175	0.000001

Number of metastases & MCV	0.632756	0.000102

Number of metastases & MCH	0.515611	0.002525


Cell Category	All Features (k = 5)	P-Fuzzy (k = 4)	Q-Fuzzy (k = 4)

Precision	Recall	F1 Score	Precision	Recall	F1 Score	Precision	Recall	F1 Score


Characteristics	Private health facilities	Public health facilities	p value

Frequency (%)	Frequency (%)

Location

 Urban	194/241 (81)	25/52 (48)	<0.01*

 Rural	47/241 (19)	27/52 (52)	0.02*

Level of facility

 Health centre II		31/52 (59.6)

 Health centre III	–	16/52 (30.8)	–

 Health centre IV		3/52 (5.8)

 Hospital		2/52 (3.8)

Type of facility		Ownership

 Drug shop	170/241 (71)	Government 39/52 (75)

 Private clinic	59/241 (24)	Private for profit 6/52 (12)	–

 Pharmacy	12/241 (5)	Private not for profit 7/52 (13)

Drug shop/private clinic registered			–

 Yes	191/241 (79)	–

 No	50/241 (21)

Days when facility is open

 Monday to friday	241/241 (100)	53/53 (100)	–

 Saturdays	232/241 (96)	49/53 (92.4)

 Sundays	191/241 (79)	39/53 (74)

Number of Children with fever seen on atypical day	Median 4 (IQR 3)	Median 8 (IQR 6)

Children with cough seen on atypical day	Median 5 (IQR 7)	Median 10 (IQR 11)

Children with diarrhoea seen on atypical day	Median 2 (IQR 2)	Median 3 (IQR 3)

Patients’ register present

 Yes	93/241 (39)	53/53 (100)	<0.01*

 No	148/241 (61)	–

Characteristics	Private health facilities	Public health facilities	p value

Frequency (%)	Frequency (%)

Location

Urban	194/241 (81)	25/52 (48)	<0.01*

Rural	47/241 (19)	27/52 (52)	0.02*

Level of facility

Health centre II		31/52 (59.6)

Health centre III	–	16/52 (30.8)	–

Health centre IV		3/52 (5.8)

Hospital		2/52 (3.8)

Type of facility		Ownership

Dr	170/241 (71	Gov

ug shop	)	ernment 39/52 (75)

Private clinic	59/241 (24)	Private for profit 6/52 (12)	–

Pharmacy	12/241 (5)	Private not for profit 7/52 (13)

Drug shop/private clinic registered			–

Yes	191/241 (79)	–

No	50/241 (21)

Days when facility is open

Monday to friday	241/241 (100)	53/53 (100)	–

Saturdays	232/241 (96)	49/53 (92.4)

Sundays	191/241 (79)	39/53 (74)

Number of Children with fever seen on atypical day	Median 4 (IQR 3)	Median 8 (IQR 6)

Children with cough seen on atypical day	Median 5 (IQR 7)	Median 10 (IQR 11)

Children with diarrhoea seen on atypical day	Median 2 (IQR 2)	Median 3 (IQR 3)

Patients’ register present

Yes	93/241 (39)	53/53 (100)	<0.01*

No	148/241 (61)	–


Outcome	LMWH (D or E) vs. Control	SE	P-Value	Impact on Risk

<underline>VTE Risk in Placebo Trials</underline>	<underline>Relative Risk</underline>

Both drugs vs. placebo	0.50 (0.40 - 0.61)		<0.001	↓by 50%

RR difference between drugs (E vs. D)	0.23 (i.e. 12%)	0.25	0.36	NS

<underline>Region (vs. North American)</underline>

European trial	0.33	0.49	0.50	NS

Global trial	0.20	0.58	0.73	NS

LMWH dosing1	-0.09	0.53	0.87	NS

LMWH treatment duration2	-0.21	0.31	0.51	NS

Year of publication	0.04	0.04	0.34	NS

<underline>Risk of Major Bleeds</underline>

Both drugs vs. placebo	1.19(0.39 - 3.55)		0.76	NS

Risk difference between Drugs (E vs. D)	-0.94	1.25	0.45	NS

<underline>Risk of HIT</underline>

Both drugs vs. placebo	1.13 (0.36 - 3.53)		0.83	NS

RR difference between Drugs (E vs. D)	-0.85	2.0	0.48	NS

<underline>Risk of Death</underline>

Both drugs vs. placebo	0.72 (0.22-2.34)		0.59	NS

RR difference between Drugs (E vs. D)	0.22	1.28	0.86	NS

<underline>VTE Risk in UH Trials</underline>

Both drugs vs. UH	0.83 (0.66-1.05)		0.12	NS

RR difference between drugs (E vs. D)	-0.24 (i.e. - 22%)	0.61	0.66	NS

<underline>Risk of Major Bleeds</underline>

Both drugs vs. UH	0.70 (0.35-1.38)		0.30	NS

RR difference between Drugs (E vs. D)	NA

<underline>Risk of HIT</underline>

Both drugs vs. UH	0.61 (0.21-1.77)		0.36	NS

RR difference between Drugs (E vs. D)	0.56	1.53	0.71	NS

<underline>Risk of Death</underline>

Both drugs vs. UH	0.59 (0.12-3.1)		0.53	NS

RR difference between Drugs (E vs. D)	NA

Outcome	LMWH (D or E) vs. Control	SE	P-Value	Impact on Risk

VTE Risk in Placebo Trials	Relative Risk

Both drugs vs. placebo	0.50 (0.40 - 0.61)		<0.001	↓by 50%

RR difference between drugs (E vs. D)	0.23 (i.e. 12%)	0.25	0.36	NS

Region (vs. North American)

European trial	0.33	0.49	0.50	NS

Global trial	0.20	0.58	0.73	NS

LMWH dosing1	-0.09	0.53	0.87	NS

LMWH treatment duration2	-0.21	0.31	0.51	NS

Year of publication	0.04	0.04	0.34	NS

Risk of Major Bleeds

Both drugs vs. placebo	1.19(0.39 - 3.55)		0.76	NS

Risk difference between Drugs (E vs. D)	-0.94	1.25	0.45	NS

Risk of HIT

Both drugs vs. placebo	1.13 (0.36 - 3.53)		0.83	NS

RR difference between Drugs (E vs. D)	-0.85	2.0	0.48	NS

Risk of Death

Both drugs vs. placebo	0.72 (0.22-2.34)		0.59	NS

RR difference between Drugs (E vs. D)	0.22	1.28	0.86	NS

VTE Risk in UH Trials

Both drugs vs. UH	0.83 (0.66-1.05)		0.12	NS

RR difference between drugs (E vs. D)	-0.24 (i.e. - 22%)	0.61	0.66	NS

Risk of Major Bleeds

Both drugs vs. UH	0.70 (0.35-1.38)		0.30	NS

RR difference between Drugs (E vs. D)	NA

Risk of HIT

Both drugs vs. UH	0.61 (0.21-1.77)		0.36	NS

RR difference between Drugs (E vs. D)	0.56	1.53	0.71	NS

Risk of Death

Both drugs vs. UH	0.59 (0.12-3.1)		0.53	NS

RR difference between Drugs (E vs. D)	NA


Quality indicators of colonoscopy	Patients included in primary analysis1	Patients included in secondary analysis2	Recommended	Provincial endoscopist performance, 2015

Cecal intubation rate, %	98	97	≥ 95	97 - 99

PR, screening, men %	36	45	-	34 - 50

PR, screening, women %	34	34	-	34 - 50

ADR, screening, men, %	25	40	≥ 25	-

ADR, screening, women, %	19	30	≥ 15	-

Inadequate bowel preparation, %	7	6	≤ 10	3 - 7

Withdrawal time3 (SD)	6 (5)	6 (5)	6-7	-

Complications

 Bleeding rate, %	0.0	0.0	≤ 0.1	0.1 - 0.5

 Bowel perforation rate, %	0.0	0.0	≤ 0.1	0.1 - 0.8

 Other rate, %	0.0	0.0	-	-

Quality indicators of colonoscopy	Patients included in primary analysis1	Patients included in secondary analysis2	Recommended	Provincial endoscopist performance, 2015

PR, screening, men %	36	45	-	34 - 50

PR, screening, women %	34	34	-	34 - 50

ADR, screening, men, %	25	40	≥ 25	-

ADR, screening, women, %	19	30	≥ 15	-

Inadequate bowel preparation, %	7	6	≤ 10	3 - 7

Withdrawal time3 (SD)	6 (5)	6 (5)	6-7	-

Complications

Bleeding rate, %	0.0	0.0	≤ 0.1	0.1 - 0.5

Bowel perforation rate, %	0.0	0.0	≤ 0.1	0.1 - 0.8

Other rate, %	0.0	0.0	-	-


Respondent profile (%)	Child profile (%)

Relationship to child	Sex

Mother	67.3	Male	53.8

Father	15.7	Female	46.2

Grandmother	15.7	Age group (years)

Grandfather	1.2	1-2	32.7

Occupation	3-5	32.9

PMEB	28.7	6-10	34.4

Other white collar	19.7	Race

Blue collar	7.4	Chinese	64.4

Housewife	37.6	Malay	16.7

Unemployed	2.9	Indian/other	18.9

Other	3.7

Education

None	2.0

Primary	13.3

Secondary	37.3

Tertiary (polytechnic)	19.7

Tertiary (university)	27.8

Respondent profile (%)

Tertiary (university)	27.8


	ZOI (mm)a	MIC (μg/mL)	MBC (μg/mL)

Microorganisms	Eugenol	Isoeugenol	Eugenol	Isoeugenol	Eugenol	Isoeugenol

Gram-positive


	Item 1	Item 2	Item 3	Proprioceptive drift

	Block 1	Block 2	Block 1	Block 2	Block 1	Block 2	Block 1	Block 2


Promoter	Line name	Replica	Total plant #	Total GUS+	Total GUS−	%GUS+

pLeExt1	<underline>LIG29-2</underline>	3	18	18	0	100

LIG31A-19	2	11	11	0	100

LIG26-7	2	11	9	2	81.82

pAtCortex	<underline>CoG11-6</underline>	3	13	13	0	100

CoG29-1	2	10	10	0	100

CoG5b-23	2	10	0	10	0

pAtE29	<underline>29G4b-5</underline>	3	15	15	0	100

29G7-32	2	15	15	0	100

pAtS13	<underline>13G26b-28</underline>	3	14	14	0	100

13G3b-100	2	11	11	0	100

pAtS18	<underline>XG5A-5</underline>	3	19	19	0	100

XG2C-7	2	10	0	10	0

pCaMV35S	<underline>SG15A-2</underline>	2	11	11	0	100

SG19b-5	2	10	0	10	0

Control vector	NEG15B-5	3	18	0	18	0


Personal	Community–related	Service-related

• Age	• Presence of and links to NHCs	• Training

• Gender	• Distance between CHW and TBA families	• Experience

• Education	• Distances among , CHW and rural health center	• Supervision and support by relevant community and health system structures

• Socio-economic status	• Payment or in-kind compensation

• Language	• Motivation

• Tribal affiliation	• Availability of means of transport (e.g. bicycle)

• Religion	• Possession of a cell phone

 •Employment	• Availability of various supplies and drugs that the CHW and TBA might need to provide the defined services

• Membership in an association

Personal	Community–related	Service-related

• Age	• Presence of and links to NHCs	• Training

• Gender	• Distance between CHW and TBA families	• Experience

• Education	• 	• Supervision and support by relevant community and health system structures

• Socio-economic status	• Payment or in-kind compensation

• Language	• Motivation

• Tribal affiliation	• Availability of means of transport (e.g. bicycle)

• Religion	• Possession of a cell phone

	• Availability of various supplies and drugs that the CHW and TBA might need to provide the defined servic

•Employment	Distances among , CHW and rural health center	es

• Membership in an association


	Physiotherapist	Patient

Timing of data collection	Before training	At follow-up

Measurements	Questionnaire	Questionnaire	Observation video/audio	Assessing patient files	Reflection report	Questionnaire

1a. To what extent is the PSG performed as intended?

 Performance and recording of the PSG steps			X	X

 Patients’ involvement in the PSG						X

1b. How satisfied are patients about the PSG?

 Patients’ satisfaction with the PSG						X

1c. What are physiotherapists’ experiences with the PSG?

 Physiotherapists’ experiences with the PSG					X

2. Did the physiotherapists’ intention to use the PSG, the actual use of the PSG, and their client-centred competences improve after training?

 Intention to use the PSG	X	X

 Actual use of the PSG	X	X

 Client-centred competences	X	X


Food Group	Factors	B	β	P-value

Grain

	Activity	0.334	0.235	0.018

	Tobacco use	−0.866	−0.168	0.090

Vegetable

	Age	−0.038	−0.192	0.057

	Diet related chronic disease	−0.353	0.164	0.033

Fruit

	Education level	0.164	0.177	0.079

	Diet related chronic disease	−0.326	−0.173	0.085

Dairy

	Gender	0.410	0.262	0.006

	Rural/urban	−0.257	−0.180	0.059

Meat/Beans

	No statistically significant model

Food Group

Grain

Vegetable

Fruit

Dairy

Meat/Beans

Factors	B	β	P-value

Activity	0.334	0.235	0.018

Tobacco use	−0.866	−0.168	0.090

Age	−0.038	−0.192	0.057

Diet related chronic disease	−0.353	0.164	0.033

Education level	0.164	0.177	0.079

Diet related chronic disease	−0.326	−0.173	0.085

Gender	0.410	0.262	0.006

Rural/urban	−0.257	−0.180	0.059

No statistically significant model


LVEDV (ml)

LVESV (ml)

LVSV (ml)

LVCO (ml/min)

LVM (g)

Papillary muscle (g)

RVEDV (ml)

RVESV (ml)

RVSV (ml)

RVCO (ml/min)

RVM (g)


		95% Confidence Interval

	Odds Ratio	Low	High	P value

Race/ethnicity

 White	Reference

 African American	1.34	0.88	2.05	.17

 Hispanic	0.92	0.57	1.47	.73

 Other	0.82	0.53	1.25	.35

Graduated college	1.09	0.92	1.31	.32

Employed	0.88	0.73	1.07	.21

Prescription coverage	1.49	1.22	1.82	< .001

Income ($25,000 unit)	1.08	1.01	1.15	.04

Out-of-pocket spending on prescriptions as a proportion of income	2.42	1.21	4.84	.01

Cost cutting actions	0.95	0.77	1.16	.60

Ask for generic	1.06	0.90	1.24	.48

Children in home	0.92	0.81	1.05	.21

Marital status

 Married	reference	reference	reference

 Single	1.49	1.02	2.20	.04

 Divorced/separated	1.11	0.91	1.36	.31

Widowed	1.20	0.97	1.50	.10

		95% Confidence Interval

	Odds Ratio	Low	High

Race/ethnicity

White	Reference

African American	1.34	0.88	2.05

Hispanic	0.92	0.57	1.47

Other	0.82	0.53	1.25

Graduated college	1.09	0.92	1.31

Employed	0.88	0.73	1.07

Prescription coverage	1.49	1.22	1.82

Income ($25,000 unit)	1.08	1.01	1.15

Out-of-pocket spending on prescriptions as a proportion of income	2.42	1.21	4.84

Cost cutting actions	0.95	0.77	1.16

Ask for generic	1.06	0.90	1.24

Children in home	0.92	0.81	1.05

Marital status

Married	reference	reference	reference

Single	1.49	1.02	2.20

Divorced/separated	1.11	0.91	1.36

Widowed	1.20	0.97	1.50


Treatment regimens	Main results	References no.

1. Heatstroke rats received intravenous or intracerebroventricular injection of HUCBCs	MSCs attenuate cerebral ischemic damage by reducing overproduction of TNF‐α, IL‐1β, and IL‐6, but stimulating production of IL‐10.	Chen et al. (2005) Chen et al. (2006) Liu et al. (2009)

2. Heatstroke rats received intravenous injection of HICBC‐derived CD34+ cells	MSCs reduce cerebral ischemic damage by reducing overproduction of both TNF‐α and ICAM‐1, but stimulating production of IL‐10.	Hwang et al. (2008) Chen et al. (2007) Tseng et al. (2014)

3. Heatstroke rats received subcutaneous injection of granulocyte‐colony stimulating factor	The factor attenuates cerebral ischemic damage by reducing overproduction of both TNF‐α and ICAM‐1, but stimulating production of IL‐10, EPSs, GDNF, and VEGF.	Yung et al. (2011)

4. Heatstroke mice received intravenous injection of human dental pulp‐derived stem cells or HUCBCs	MSCs attenuate cerebral ischemic damage by reducing overproduction of TNF‐α, intercellular adhesion molecule 1, and oxidative damage markers, but promoting both hypothalamo–pituitary–adrenocortical axis activity and IL‐10 production.	Tseng et al. (2014) Tseng et al. (2015)


Polymer type	Applications	Reference

Bio-PE	Plastics bags, milk and water bottles, food packaging films, toys	Vona et al. 1965; Aamer et al. 2008

Bio-PE and blends	Agricultural mulch films	Kasirajan and Ngouajio 2012

Polymer type	Applications	Reference

Bio-	Plastics bags, milk and water bottles, food packaging films, 	Vona et al. 1965; Aamer e

PE	toys	t al. 2008

Bio-PE and blends	Agricultural mulch films	Kasirajan and Ngouajio 2012


EC name	definition	gene name	Microarray available	ORF

4.1.2.13	Fructose-bisphosphate aldolase	fbaI	x	Ot01g03020

		fbaI	x	Ot03g00610

		fbaII	x	Ot10g01490

3.1.3.11	Fructose-1,6-bisphosphatase		x	Ot03g00330

			x	Ot14g01140

5.3.1.1	Triosephosphate isomerase		x	Ot09g00080

5.3.1.9	Glucose-6-phosphate isomerase		x	Ot11g02980

5.4.2.2	phosphoglucomutase		x	Ot15g02630

2.7.7.27	ADP-glucose pyrophosphorylase	AGPLU1		Ot07g03280

		agpsu1	x	Ot07g02930

		agplu2		Ot20g00490

2.4.1.21	Soluble Starch synthase	SSIII-C		Ot06g03410

		SSIII-B	x	Ot13g01250

		SSII	x	Ot16g02790

		SSI	x	Ot13g01230

		SSIII-A	x	Ot16g01560

2.4.1.242	Granul-bound starch synthase	gbssI	x	Ot06g03200

2.4.1.18	Starch branching enzyme	SBEII(?)	x	Ot03g00840

		SBEI	x	Ot04g04110

2.4.1.1	starch phosphorylase			Ot11g00280

			x	Ot04g02110

		SPho1		Ot11g01020

2.4.1.25	disproportionating enzyme	DPE1	x	Ot02g05750

				Ot11g02290

				Ot11g02300

3.2.1.1	α-amylase	Aamy1	x	Ot16g00380

			x	Ot02g05490

		Aamy2	x	Ot10g00260

		Aamy3	x	Ot07g02010

3.2.1.2	β-amylase		x	Ot02g06980

		Bamy2	x	Ot03g03190

		bamy1		Ot03g03170

3.2.1.68	Isoamylase (debranching enzyme)	dbe1 (Isa1)	x	Ot14g02550

		Isa2		Ot02g07230

		dbeII (Isa3)	x	Ot12g00310

3.2.1.142	Pullulanase	spu	x	Ot01g03030

2.7.9.4	α-glucan, water dikinase (GWD)	SR1-A	x	Ot13g01510

		R1 C		Ot16g02370

				Ot04g04170

	Glucose transporter			Ot03g05590

	Glucose transporter			Ot14g01870

	Glucose transporter			Ot08g01260

	Maltose transporter	MEX1	x	Ot09g03160

EC name	definition	gene name	Microarray available	ORF

4.1.2.13	Fructose-bisphosphate aldolase	fbaI	x	Ot01g03020

		fbaI	x	Ot03g00610

		fbaII	x	Ot10g01490

3.1.3.11	Fructose-1,6-bisphosphatase		x	Ot03g00330

			x	Ot14g01140

5.3.1.1	Triosephosphate isomerase		x	Ot09g00080

5.3.1.9	Glucose-6-phosphate isomerase		x	Ot11g02980

5.4.2.2	phosphoglucomutase		x	Ot15g02630

2.7.7.27	ADP-glucose pyrophosphorylase	AGPLU1		Ot07g03280

		agps	x	Ot07g02930

EC name	definition	gene name	Microarray available	ORF

		u1

		agplu2		Ot20g00490

2.4.1.21	Soluble Starch synthase	SSIII-C		Ot06g03410

		SSIII-B	x	Ot13g01250

		SSII	x	Ot16g02790

		SSI	x	Ot13g01230

		SSIII-A	x	Ot16g01560

2.4.1.242	Granul-bound starch synthase	gbssI	x	Ot06g03200

2.4.1.18	Starch branching enzyme	SBEII(?)	x	Ot03g00840

		SBEI	x	Ot04g04110

2.4.1.1	starch phosphorylase			Ot11g00280

			x	Ot04g02110

		SPho1		Ot11g01020

2.4.1.25	disproportionating enzyme	DPE1	x	Ot02g05750

				Ot11g02290

				Ot11g02300

3.2.1.1	α-amylase	Aamy1	x	Ot16g00380

			x	Ot02g05490

		Aamy2	x	Ot10g00260

		Aamy3	x	Ot07g02010

3.2.1.2	β-amylase		x	Ot02g06980

		Bamy2	x	Ot03g03190

		bamy1		Ot03g03170

3.2.1.68	Isoamylase (debranching enzyme)	dbe1 (Isa1)	x	Ot14g02550

		Isa2		Ot02g07230

		dbeII (Isa3)	x	Ot12g00310

3.2.1.142	Pullulanase	spu	x	Ot01g03030

2.7.9.4	α-glucan, water dikinase (GWD)	SR1-A	x	Ot13g01510

		R1 C		Ot16g02370

				Ot04g04170

	Glucose transporter			Ot03g05590

	Glucose transporter			Ot14g01870

	Glucose transporter			Ot08g01260

	Maltose transporter	MEX1	x	Ot09g03160


	n	%

Disease stage

 Indeterminate	32	64

 Advanced (cardiomyopathy)	13	26

 No data	5	10

Treatment history*

 Received etiological treatment	40	80

Benznidazole	19	47.5

Nifurtimox	17	42.5

Benznidazole and nifurtimox	4	10

Discontinued treatment (side effects)	6	15

 Untreated	10	20

Due to advancement of the disease	5	50

Other (patient declined or unable)	5	50


Variable	Men N = 52	Women N = 87

 	Mean (SD)	Mean (SD)

Duration of arthritis* (years)	4.8 (2.9)	5.6 (2.8)

 	Median (IQR)	Median (IQR)

Number of swollen joints	2.0 (1.0, 8.0)	5.0 (2.0, 11.0)

Number of tender joints	5.0 (2.0, 8.5)	6.0 (2.0, 15.0)

Number of both swollen and tender joints	1.0 (0.0, 3.5)	2.0 (0.0, 5.0)

Larsen score1	27.5 (13.0,31.0)	11.0 (1.0, 21.0)

HAQ score (0–3)	0.5 (0.1, 1.1)	0.8 (0.4, 1.3)

 	%	%

Rheumatoid factor + ve	39.1	33.8

Erosions1	75.0	56.5

Satisfied ARA RA criteria (4/7 definition)	22.9	45.8

Satisfied ARA RA criteria (tree definition)	41.7	60.2

Steroid use for at least 3 months	5.8	17.2


	Small-voxel correct	Small-voxel not correct	p value

Malignant lymph nodes (n = 148)

 Standard-voxel correct	112	1	< 0.001

 Standard-voxel not correct	20	15

Benign lymph nodes (n = 61)

 Standard-voxel correct	42	12	0.04

 Standard-voxel not correct	3	4

All lymph nodes (n = 209)

 Standard-voxel correct	154	13	0.13

 Standard-voxel not correct	23	19


Health Outcome	Total (N = 460)	Urban Parañaque (N = 250)	Rural Tagum (N = 210)	p-value

Ultrasound findings (%)

 Normal scans	68.3	62.4	75.2	< 0.01

 Abnormal scans	31.7	37.6	24.8

Maternal deaths possibly averteda (%)

 Normal	93.7	91.2	96.6	0.02

 Abnormal placentation

Low lying	2.4	2.4	2.4

Placenta previa	1.3	2.0	0.5

 Fetal malpresentation

Transverse	2.6	4.4	0.5

Neonatal deaths possibly avertedb (%)

 Normal	74.6	71.2	78.6	0.02

 Fetal malpresentation

Breech	24.1	26.8	20.9

Oligohydramnios	0.2	0.0	0.5

Polyhydramnios	0.4	0.8	0.0

Twinsc	1.3	2.4	0.0

Health Outcome	Total (N = 460)	Urban Parañaque (N = 250)	Rural Tagum (N = 210)	p-value

Normal scans	68.3	62.4	75.2	< 0.01

Abnormal scans	31.7	37.6	24.8

Maternal deaths possibly averteda (%)

Normal	93.7	91.2	96.6	0.02

Abnormal placentation

Low lying	2.4	2.4	2.4

Placenta previa	1.3	2.0	0.5

Fetal malpresentation

Transverse	2.6	4.4	0.5

Neonatal deaths possibly avertedb (%)

Normal	74.6	71.2	78.6	0.02

Fetal malpresentation

Breech	24.1	26.8	20.9

Oligohydramnios	0.2	0.0	0.5

Polyhydramnios	0.4	0.8	0.0

Twinsc	1.3	2.4	0.0


	Cumulative no. with a newly detected HPV infection (cumulative%)

	6 months	12 months	24 months	36 months	P value for log-rank test

HIV-negative women

Total sample:

 Cryotherapy arm (n = 271)	15 (5.5)	21 (7.9)	25 (9.7)	28 (11.2)	0.0004

 Control arm (n = 269)	17 (6.3)	30 (11.5)	45 (18.0)	59 (24.8)

Sexual activity reported during 1 month prior to enrolment or at 1 or 6 months after enrollment:

 Cryotherapy arm (n = 238)a	13 (5.5)	18 (7.7)	22 (9.7)	25 (11.3)	0.002

 Control arm (n = 236)b	16 (6.8)	27 (11.8)	42 (19.4)	56 (27.2)

No sexual activity reported during 1 month prior to enrollment and at 1 and 6 months after enrollment:

 Cryotherapy arm (n = 31)a	2 (6.5)	3 (10.4)	3 (10.4)	3 (10.4)	0.96

 Control arm (n = 29)b	1 (3.5)	3 (10.3)	3 (10.3)	3 (10.3)

HIV-positive women

Total sample:

 Cryotherapy arm (n = 38)	9 (23.7)	16 (42.1)	19 (50.8)	20 (53.7)	0.8

 Control arm (n = 34)c	7 (20.6)	14 (42.8)	15 (46.6)	16 (50.5)

Sexual activity reported during 1 month prior to enrollment or at 1 or 6 months after enrollment:

 Cryotherapy arm (n = 33)	8 (24.2)	12 (36.4)	15 (46.4)	16 (49.8)	0.65

 Control arm (n = 29)c	7 (24.1)	14 (49.4)	15 (54.0)	15 (54.0)

No sexual activity reported during 1 month prior to enrollment and at 1 and 6 months after enrollment:

 Cryotherapy arm (n = 5)	1 (20.0)	4 (80.0)	4 (80.0)	4 (80.0)	0.08

 Control arm (n = 3)	0	0	0	0

	Cumulative no. with a newly detected HPV infection (cumulative%)

	6 months	12 months	24 months	36 months	P value for log-rank test

HIV-negative women

Total sample:

Cryotherapy arm (n = 271)	15 (5.5)	21 (7.9)	25 (9.7)	28 (11.2)	0.0004

Control arm (n = 269)	17 (6.3)	30 (11.5)	45 (18.0)	59 (24.8)

Sexual activity reported during 1 month prior to enrolment or at 1 or 6 months after enrollment:

Cryotherapy arm (n = 238)a	13 (5.5)	18 (7.7)	22 (9.7)	25 (11.3)	0.002

Control arm (n = 236)b	16 (6.8)	27 (11.8)	42 (19.4)	56 (27.2)

No sexual activity reported during 1 month prior to enrollment and at 1 and 6 months after enrollment:

Cryotherapy arm (n = 31)a	2 (6.5)	3 (10.4)	3 (10.4)	3 (10.4)	0.96

Control arm (n = 29)b	1 (3.5)	3 (10.3)	3 (10.3)	3 (10.3)

HIV-positive women

Total sample:

Cryotherapy arm (n = 38)	9 (23.7)	16 (42.1)	19 (50.8)	20 (53.7)	0.8

Control arm (n = 34)c	7 (20.6)	14 (42.8)	15 (46.6)	16 (50.5)

Sexual activity reported during 1 month prior to enrollment or at 1 or 6 months after enrollment:

Cryotherapy arm (	8 (	12	15 (46.4	16 (49.8	0.6

	Cumulative no. with a newly detected HPV infection (cumulative%)

	6 months	12 months	24 months	36 months	P value for log-rank test

n = 33)	24.2)	 (36.4)	)	)	5

Control arm (n = 29)c	7 (24.1)	14 (49.4)	15 (54.0)	15 (54.0)

No sexual activity reported during 1 month prior to enrollment and at 1 and 6 months after enrollment:

Cryotherapy arm (n = 5)	1 (20.0)	4 (80.0)	4 (80.0)	4 (80.0)	0.08

Control arm (n = 3)	0	0	0	0


Incentive	Study design (N)	Year(s) data collection	Relevant outcome measures	Healthcare quality

Western New York Physician Incentive Program (WNY-P4P) [16]	Pre-post testExperimental group: patients (n = 624) whose physicians (n = 21) participated in P4P schemeControl group: sample of diabetic patients from health plan	2002-2003	• Quality of care based on a composite score which was based on process and outcome measures.	• Average of physician's composite scores increased 48% (baseline to end of project).• 13 out of 21 physicians improved their average composite score enough to earn some level of financial reward.• Of the 8 physicians not receiving reward, 6 improved their composite score.

Integrated Healthcare Association Pay-for-performance Program (IHA-P4P) [30]	Cross-sectional analysis of linked 2006 clinical performance scores from IHA-P4P and survey data from the 2nd National Study of Physician Organizations among 108 California physician organizations.	2006	• Association between clinical performance and the use of chronic management processes• Association between clinical performance and electronic medical record capabilities• Association between clinical performance and participation in external quality improvement initiatives.	• Physician organizations investing more heavily in care management processes (e.g. patient registries, physician reminders and feedback, patient reminders and education) may achieve better performance scores.• Use of organized quality improvement efforts (e.g. participation in QI program) may be associated with increased delivery of recommended care processes, which in the context of the study translated into better performance on the clinical measures that were rewarded in the P4P scheme.

Practice Incentive Program Diabetes Incentive (PIP-DI) [38]	Retrospective study based on dataset from BEACH study (data from 100 consecutive encounters of 1000 GPs that are yearly randomly selected. Each encounter contains data on up to 4 problems treated, drugs prescribed, treatments conducted, referrals written and pathology).N = 12187: <underline>1. Treatment group 1</underline>: accredited practices that use IT for internet, prescribing and medical records; <underline>2. Treatment group 2</underline>: practices that are accredited and do not use IT for internet, prescribing and medical records; <underline>3. Control group</underline>: practices that are not accredited and do not use IT, for internet, prescribing and medical records.	April 2002-March 2007 from	• Percentage of patients that received a glycosylated haemoglobin blood test during GP consult	• PIP-DI increased probability of a HbA1c test being ordered by 20 percentage points.• For patients from Aboriginal and Torres Straight Islander background the increase was more than 35 percentage points.

Practice Incentive Program Diabetes Incentive (PIP-DI) [37]	Descriptive study based on semi structured face-to-face interviews (22 GP practices)	2003	• Implementation of components of diabetes cycle of care	• Financial incentives may promote better clinical management. GPs claiming incentives were more likely to comply with all requirements than GPs that did not claim incentives.

External incentives (including financial incentives). [39]	Cross-sectional study: telephone survey among 1104 physician organizations (PO) with 20 or more physicians	2000-2001	• Extent of use of organized CMPs on the basis of summary measure: PO care management index, external incentives (bonus from health plans, public recognition, better contracts with health plans) quality reporting to outside organization (HEDIS data, clinical outcome data, results of quality improvement projects, patient satisfaction data), IT use	• External incentives and clinical IT were most strongly associated with CMP use.• Use of the most strongly associated incentives (public recognition and better contracts for healthcare quality) was associated with use of 1.3 and 0.7 additional CMPs (significant).• Receiving a bonus for scoring well on quality of care measures was not significantly associated with CMP use.

Incentive	Study design (N)	Year(s) data collection	Relevant outcome measures	Healthcare quality

Western New York Physician Incentive Program (WNY-P4P) [16]	Pre-post testExperimental group: patients (n = 624) whose physicians (n = 21) participated in P4P schemeControl group: sample of diabetic patients from health plan	2002-2003	• Quality of care based on a composite score which was based on process and outcome measures.	• Average of physician's composite scores increased 48% (baseline to end of project).• 13 out of 21 physicians improved their average composite score enough to earn some level of financial reward.• Of the 8 physicians not receiving reward, 6 improved their composite score.

Integrated Healthcare Association Pay-for-performance Program (IHA-P4P) [30]	Cross-sectional analysis of linked 2006 clinical performance scores from IHA-P4P and survey data from the 2nd National Study of Physician Organizations among 108 California physician organizations.	2006	• Association between clinical performance and the use of chronic management processes• Association between clinical performance and electronic medical record capabilities• Association between clinical performance and participation in external quality improvement initiatives.	• Physician organizations investing more heavily in care management processes (e.g. patient registries, physician reminders and feedback, patient reminders and education) may achieve better performance scores.• Use of organized quality improvement efforts (e.g. participation in QI program) may be associated with increased delivery of recommended care processes, which in the context of the study translated into better performance on the clinical measures that were rewarded in the P4P scheme.

Practice Incentive Program Diabetes Incentive (PIP-DI) [3	Retrospective study based on dataset from BEACH study (data from 100 consecutive encounters of 1000 GPs that are yearly randomly selected. Each encounter contains data on up to 4 problems treated, drugs prescribed, treatments conducted, referrals written and pathology).N = 12187: 1. Treatment group 1: accredited practices that use IT for internet, prescribing and medical records; 2. Treatment 	Apr	• Percentag	• PIP-DI increased probability of a HbA1c test being ordered by 20 percentage points.• For patients

Incentive	Study design (N)	Year(s) data collection	Relevant outcome measures	Healthcare quality

8]	group 2: practices that are accredited and do not use IT for internet, prescribing and medical records; 3. Control group: practices that are not accredited and do not use IT, for internet, prescribing and medical records.	il 2002-March 2007 from	e of patients that received a glycosylated haemoglobin blood test during GP consult	 from Aboriginal and Torres Straight Islander background the increase was more than 35 percentage points.

Practice Incentive Program Diabetes Incentive (PIP-DI) [37]	Descriptive study based on semi structured face-to-face interviews (22 GP practices)	2003	• Implementation of components of diabetes cycle of care	• Financial incentives may promote better clinical management. GPs claiming incentives were more likely to comply with all requirements than GPs that did not claim incentives.

External incentives (including financial incentives). [39]	Cross-sectional study: telephone survey among 1104 physician organizations (PO) with 20 or more physicians	2000-2001	• Extent of use of organized CMPs on the basis of summary measure: PO care management index, external incentives (bonus from health plans, public recognition, better contracts with health plans) quality reporting to outside organization (HEDIS data, clinical outcome data, results of quality improvement projects, patient satisfaction data), IT use	• External incentives and clinical IT were most strongly associated with CMP use.• Use of the most strongly associated incentives (public recognition and better contracts for healthcare quality) was associated with use of 1.3 and 0.7 additional CMPs (significant).• Receiving a bonus for scoring well on quality of care measures was not significantly associated with CMP use.


 	Resource	Mean frequency per patient	Price per item (in euro)

 	10 Most frequent resource items

1	Tissue obtainment (microbiology and clinical chemistry)	18.1	13.73

2	Antibodies against any pathogen by using complement fixation test of haemagglutination inhibition essay	10.4	4.84

3	General ward nursing (one day)	9.4	375.00

4	Sodium	8.2	1.76

5	Potassium	8.2	1.76

6	Creatinine	6.6	1.76

7	Glucose	6.4	1.76

8	Leukocytes	6.1	1.76

9	C-reactive protein	6.0	4.84

10	Urea	5.9	1.76

 	10 Most expensive resource items

1	Intensive care unit nursing (one day)	0.6	1,730.00

2	Surgical treatment of thorax empyema	0.2	1,084.75

3	Laryngoscopy	0.0	999.00

4	Microbiological determination on isolated DNA/RNA	0.0	867.61

5	General ward nursing (one day)	9.4	375.00

6	Flexible bronchoscopy	0.2	209.25

7	DNA/RNA amplification (qualitative)	<0.1	208.60

8	Computer tomography of thorax	0.2	195.83

9	Immunopathologic research	<0.1	109.57

10	Computer tomography airways	<0.1	160.01


Model	Data	Description	Section with model details

M1	Aggregate data only	Aggregates the IPD and includes observational data through random effects on change from baseline 6MWD	2.1

M2	Aggregate and IPD	Extends M1 to combine aggregate data and IPD	2.2

M3	Aggregate and IPD	Extends M2 to include covariate adjustments on change from baseline 6MWD	2.3

M4	Aggregate and IPD	Extends M3 to include interactions between treatment effect and covariates	2.4

S1	Aggregate and IPD	Same as M4 but SE in observational studies are inflated by a factor of 10 to downweight their evidence	3.3

S2	Aggregate and IPD	Same as M4 but constructed a control arm for observational studies where all patients assumed to deteriorate by 25 m from baseline in 6MWD	3.4

Model	Data	Description	Section with model details

M1	Aggregate data only	Aggregates the IPD and includes observational data through random effects on change from baseline 6MWD	2.1

M2	Aggregate	Extends M1

	 and IPD	to combine aggregate data and IPD	2.2

M3	Aggregate and IPD	Extends M2 to include covariate adjustments on change from baseline 6MWD	2.3

M4	Aggregate and IPD	Extends M3 to include interactions between treatment effect and covariates	2.4

S1	Aggregate and IPD	Same as M4 but SE in observational studies are inflated by a factor of 10 to downweight their evidence	3.3

S2	Aggregate and IPD	Same as M4 but constructed a control arm for observational studies where all patients assumed to deteriorate by 25 m from baseline in 6MWD	3.4


Representation of compound	K	Prediction accuracy for each class (%)

		Networking triads (SN)	Non-networking triads (SP)

SMILES	3	92.67	92.03

	5	89.79	92.92

Graph	3	95.34	99.48

	5	94.18	99.48


Category	Example	Concept	Global theme

Locus of control	Improve mental health	Additive	Resilience

Removing negative friendships	Subtractive

Autonomy	Internal

Success of peers	External

Increase physical activity	Additive	Self-management

Eating out less	Subtractive

Goal setting	Internal

Access to gym	External

Locus of control	Access to gym	External	Self-management