dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Description of the meta-design. The relationship between the four CH 4 mitigation strategies and their effects on protozoa Relationship between methane and rumen protozoa concentration or CH 4 emission was assessed with three χ 2 tests. The effect of the following classes on CH 4 mitigation strategies was tested: (i) variation in protozoa v. no variation in protozoa, (ii) variation in CH 4 v. no variation in CH 4 and (iii) variation in protozoa and/or CH 4. In addition, the relationship between quantitative factors (see further) and rumen protozoa concentration (log 10 cells/ml) or CH 4 emission (g/kg DMI) was examined to gain a better understanding of the effect of these factors on the relationship between CH 4 and protozoa. This analysis was performed using all the experiments in the database except for those testing defaunation, as they presented a high leverage effect.
the difference between observed CH 4 emission and emission predicted by the response law). A factor influencing the slopes or residuals may explain differences in variations of CH 4 emission between experiments for a similar variation in protozoa concentration. A factor influencing the LSMeans may explain the differences in CH 4 emission between experiments for a same level of protozoa. Slopes and LSMeans of each experiment used in the determination of the response law were calculated and their correlation with quantitative factors was tested. Residuals (observed minus predicted CH 4 emission) were calculated for all the treatments in the database, except for experiments testing defaunation, in order to ensure a normal distribution of the residuals. The relationship between residuals and quantitative factors was tested using the GLM procedure with experiment as a fixed factor: Residuals ¼ α + α i + β factor + β i factor + e (3) where α, α i , β, β i and e were as defined in equation (1).
Description of the meta-design A summary of the main database parameters is given in Table  1. Information is presented separately for large and small ruminants, represented by 37 experiments with dairy and beef cattle and 39 experiments with sheep and goats. No statistical difference was observed between animal species for CH 4 emission (g/kg DMI, P = 0. 707; g/kg BW, P = 0. 207), intake level (g DMI/day per kg BW, P = 0. 492), gross energy of the diet (MJ/kg DM, P = 0. 452) or diet CP and OM content (g/kg DM, P = 0. 103 and 0. 645, respectively). In contrast, small ruminants had a more fibrous diet with a higher NDF content (g/kg DM, P < 0. 001) and a lower diet OM digestibility (%, P = 0. 001), inducing a higher proportion in the rumen of C2 and lower proportions of C3 and C4 (mol/100 mol, P < 0.
001) than in large ruminants. Rumen protozoa concentration (log 10 cells/ml) tended to be lower in small than in large ruminants (P = 0. 075). The CH 4 emission tended to be higher when expressed in g/kg digestible OM intake (P = 0. 074), and lower when expressed as a percentage of gross energy intake (P = 0. 097), in small compared with large ruminants. On these reduced data sets presenting measurements of OM digestibility or gross energy intake, CH 4 emission expressed in g/kg DMI did not differ between small and large ruminants (P = 0. 899 and 0. 481, respectively). To measure CH 4 emission, 31 experiments (N t = 88) used the SF 6 tracer technique and 45 experiments (N t = 131) used open or closed chambers. Protozoa concentration was determined from rumen samples taken before feeding (N exp = 19, N t = 64), after feeding (N exp = 34, N t = 89) and both before and after feeding (N exp = 17, N t = 47).
One experiment presenting a very high slope value had to be excluded to get a normal distribution of slopes (P = 0. 210) and LSMeans (P = 0. 141). The digestibility of OM and CP and the rumen proportion of C4 were positively correlated to LSMeans (P = 0. 013, P < 0. 001 and P = 0. 017, respectively) and slopes were correlated with intake (P = 0. 018) and CP digestibility (P = 0. 016). No other significant correlation was observed. Table  6  gives the within-experiment correlation between quantitative factors and residuals determined from equation (  2 ) for all the experiments in the database. The distribution of calculated residuals did not significantly differ from normality (P = 0. 054). They were positively correlated to rumen proportion of C2 (P = 0. 008) and the ratios C2/C3 and (C2 + C4)/C3 (P < 0.
Discussion The database was well balanced for animal species, with almost the same number of treatments between small and large ruminants. A confounding effect between diet composition and animal species was noteworthy, with large ruminants having a diet richer in energy than small ruminants. This led to differences in rumen fermentation profiles, with lower proportion of C2 and higher proportion of C3 in large ruminants. However, CH 4 emission (expressed in g/kg DMI or g/kg BW) and protozoa concentration (log 10 cells/ml) were homogeneously distributed between animal species. Consequently, it appears unlikely that any potential animal species effect would be revealed in further analyses. Influence of CH 4 mitigation strategy on CH 4 and protozoa Although the database was not built to evaluate mitigation strategies for their effect on CH 4 emission and rumen protozoa, the χ 2 tests highlighted that most experiments testing lipids or tannins reduced both protozoa concentration and CH 4 emission.
, 2006). Defaunation studies did not necessarily observe a reduction of CH 4 emission. Difference in diets may explain this variable effect as removal of protozoa has a more marked effect on methanogenesis with high concentrate diets  (Hegarty, 1999). However, this effect was not clearly seen in our database, as two out of four experiments reporting no variation in CH 4 emission after defaunation used a diet with 83% of concentrate. Conversely, in the two experiments showing a reduction of CH 4 emission after defaunation, animals were fed a diet with more than 60% of concentrate. Chemicals, essential oils and organic acids were identified as methanogenesis reducers without affecting protozoa. Two different mechanisms can be pointed out for these additives. On the one hand, some essential oils are known to directly inhibit growth and activity of methanogens inducing a direct   reduction of CH 4 emission  (Calsamiglia et al.
, 2007). On the other hand, some chemicals and organic acids divert H 2 from methanogenesis to other pathways. For example, nitrate and sulphate are reduced to ammonia and hydrogen sulphide, respectively, with the consumption of 4 mol of H 2  (Ungerfeld and Kohn, 2006). Enhancing C3 synthesis with malate or fumarate, which are precursors of C3, is another way to divert H 2 from methanogenesis (Ungerfeld et al. , 2007). However, experiments testing organic acids in our database were inconclusive, as already reported in a previous review  (Hook et al. , 2010). A part of added fumarate may be used for C2 production, balancing the effect on C3 production  (Ungerfeld et al. , 2007). Finally, in our data set, forage modification, addition of probiotics, prebiotics or exogenous microbial products had a weak influence on protozoa concentration, whereas their effect on CH 4 emission was variable.
m. s. e. of the overall equations were only slightly improved. However, the change in the mean slope (or its s. e. ) associated with protozoa demonstrated confounding effects between quantitative interfering factors and experiment effect. A positive relationship between the C2/C3 ratio and CH 4 emission has already been quantified by a meta-analysis approach (Sauvant et al. , 2011). With the present database, a similar relationship was observed (P < 0. 05; data not shown), and the residuals of this relationship were evidenced to be positively correlated to rumen protozoa (P < 0. 001; data not shown). When substituting for the experiment effect, intake, OM and CP digestibility, and rumen proportion of C4 significantly influenced the response law relating CH 4 to protozoa, but strongly modified the slope associated with protozoa, and markedly increased the r.
m. s. e. This result shows that taking into account experimental effects provides the most precise estimate of the influence of protozoa on CH 4 production. When simultaneously adding intake, OM and CP digestibility and rumen proportion of C4 in equation (  2 ), C4 proportion was the main interfering quantitative factor, with a strong contribution to the explained variability. It is known that protozoa preferentially ferment OM to C4 rather than to C2 or C3  (Williams and Coleman, 1992; Brossard et al. , 2004). Surprisingly, in our database, we did not find any significant relationship between protozoa and C4, showing that C4 concentration cannot be considered as a direct indicator of rumen protozoa activity. Other microbial populations may be responsible for C4 production, such as Butyrivibrio fibrisolvens  (Stewart et al.
, 1997). Unfortunately, our database contained limited information about quantity or diversity of other rumen microbes, precluding further analyses. The response law relating CH 4 to protozoa is independent of qualitative factors such as method of CH 4 measurement, animal species or CH 4 mitigation strategy. No effect of mitigation strategies was observed on the relationship between protozoa and CH 4 , as only experiments showing a relevant within-experiment variation of protozoa concentration were included in the analysis, which strongly oriented the selection towards experiments testing lipids (nearly half of the eligible experiments).
Conclusion By building an exhaustive database from experiments with data for CH 4 emission and rumen protozoa concentration on the same groups of animals, we showed that a reduction of protozoa concentration was in most cases indicative of a reduction of CH 4 emission. We also quantitatively assessed the effect of a variation in protozoa concentration on CH 4 emission. We showed that this relationship was positively influenced by the proportion of butyrate in the rumen.
Ethical considerations The study protocol was approved by the local independent ethics committee (Comité de Protection des Personnes, Est I) on 21 February 2013 under the number 2013/14 and EudraCT number 2013-000414-37, and by the Agence National de Sécurité des Médicaments et des Produits de Santé (ANSM, French national agency for the safety of medical products and devices, approval number 2013-000414-37). Specific insurance for the study has been contracted by the sponsor from the Hospital Mutual Insurance Company (policy number: 129234). Each patient will provide written informed consent concerning the study design and outcomes. If the information about the study cannot be provided to the patient, his or her relatives will be duly informed about the study.
Systematic samples will be performed at the puncture site in case of any change in skin colour (for example, redness) and/or the presence of pus or oozing suggestive of infection. All samples will be analyzed by the bacteriology laboratory. If infection is confirmed, antibiotic therapy will be initiated. A clinical event committee comprising two physicians (Dr Aho, from the Department of Epidemiology and Hospital Hygiene, University Hospital of Dijon, and Professor Piroth, from the Department of Infectiology, University Hospital of Dijon, France) blinded to the treatment allocation will independently analyze data and adjudicate as to the presence or not of catheter infection. Bleeding complications are defined as a drop in hemoglobin levels requiring the transfusion of at least two units of packed red blood cells.
Sample size calculation The sample size was calculated using PASS software (version 11, Kayesville, Utah, United States), based on several assumptions. The study by Hermite et al. [41]  compared the event-free survival of catheters with citrate locks versus saline solution in ICU patients with an indication for extracorporeal RRT. In this study, the median duration of catheter survival with citrate locking solution was 12 days (interquartile range (IQR): 8 to 17], versus six days  [3] [4] [5] [6] [7] [8] [9] [10]  for catheters locked with saline solution (hazard ratio (HR): 2. 12, 95% confidence interval (CI): 1. 32 to 3. 4), P = 0. 0019). The study by Weijmer et al. [15]  reported an RR of 0. 69 (95%CI: 0. 44 to 1. 08), P = 0. 11) for premature withdrawal of non-tunneled, uncuffed catheters with citrate versus heparin.
Acknowledgements 1. Center of Clinical Investigation-Clinical Epidemiology/Clinical Trials, University Hospital of Dijon. 2. Prof Binquet , Epidemiologist,  Emilie Galizzi ,  Logistic Coordination  and  Cassandra Porebski ,  Technician of Clinical Research. 3. Medical Intensive Care Unit, University Hospital of Dijon  ( Thérèse Devaux -Study Nurse ). 4. Dr Fagnoni  and  Dr Boulin , Pharmacists,  Pauline Mondoloni  and  Pauline Gueneau -Clinical Trials, Department of Pharmacy, University Hospital of Dijon. 5. Bernas Medical, Paris, France (Pascal Doeble -CitraLoCK ). 6. Direction of  Clinical Research, University Hospital of Dijon (Catherine Renaud, Promoter  and  Eléonore Mapanas , Monitor). 7. Fiona Ecarnot  ( EA3920 ,  University Hospital Besancon, France ), for translation and editorial assistance.
Study design The THERAD study is a monocentric, randomised, single-blind, controlled trial and has been registered on clinicaltrials. gov since  19 February 2013 [ClinicalTrials. gov: NCT01796314]. The AD patient/caregiver dyads in the intervention group receive a therapeutic educational programme and the dyads in the control group benefit from usual care. The THERAD study will enrol 170 dyads, 85 in the intervention group and 85 in the control group. The entry and follow-up procedures are illustrated in Figure  1. The study protocol was approved by the French Ministry of Health. Informed consent was obtained from all enrolled participants. The Toulouse University Hospital has delivered ethical approval. Blinding of raters is performed in a single-blind experimental design: participants know the full facts (group session), but not the experimenters.
The randomisation was done with STATA® Version 11. 0. We performed a blocked randomisation. We used a block of four, of fixed size, without stratification. The allocation procedure was conducted by the Department of Epidemiology of the Toulouse University Hospital in Toulouse, France. The randomisation list is printed and kept in this department, protected by a password. At enrolment, a fax is sent by the THERAD study group to this department, together with the participant code and information regarding assignment. The investigators are unaware of the randomisation list. The assignment is concealed for the raters at follow-up visits.
Secondary outcome measure The secondary endpoints are: the frequency and severity of the patient's behavioural and psychological symptoms of dementia assessed by the NeuroPsychiatric Inventory  [31] ; the patient's functional autonomy assessed by the Activities of Daily Living scale  [32]  and the Instrumental Activities of Daily Living scale  [33] ; the patient's QoL assessed by the Logsdon QoL-AD scale, self-reported; the primary caregiver's burden assessed by the Zarit Burden Inventory  [34] ; the primary caregiver's QoL assessed by the Nottingham Health Profile  [35] ; and the primary caregiver's mood assessed by the mini-Geriatric Depression Scale  [36]. We are collecting these data at 2 months, 6 months and 12 months. Lastly, we are collecting information on factors that can influence QoL, as demonstrated in the literature, such as acute disease for both the patient and his/her primary caregiver, change in home care support, hospitalisation or institutionalisation  [37].
Analysis Data are collected in the Access database and SAS software (developped by the SAS institute based in Cary, North Carolina, USA) will be used to perform the statistical analysis. Intention-to-treat analysis using a linear mixed model will be performed, adjusting for patient (disease severity) and caregiver (burden) characteristics and occurrence of life events that may interfere with the patient's QoL. Attrition of dyads has been taken into account. At the moment, at the 12-month visit, there is less than 5% attrition. Moreover, the total sample size required for the study was calculated anticipating a risk of 20% attrition to take into account this phenomenon, which is particularly important in our population  [45].
During the follow-up visits, we check the influence of the intervention on the carer's perception of QoL by collecting, at each visit, data concerning his/her burden and depression. Indeed, burden and depression have been pointed out to influence the caregiver's rating of the QoL  [22, 49, 50, 52, 53]. We also collect data regarding patient's cognition, which can also influence the caregiver's rating of the QoL. The intervention in itself is designed to improve caregivers' knowledge and to modify their attitude or behaviour, but not to specifically decrease their burden or improve their mood. Furthermore, psychosocial interventions have shown mild to modest efficacy in mitigating caregiver burden in high-quality metaanalyses. Many studies showed improvements in caregiver burden-associated symptoms (for example, mood, coping, self-efficacy) even when caregiver burden itself was minimally improved  [57].
Acknowledgments The authors acknowledge the cooperation of the participating teams and their staff; the  JACIE Accreditation Office in Barcelona , the  JACIE Board and Executive Committee , the  JACIE Medical Directors , the  JACIE Accreditation Committee , all JACIE inspectors and EBMT registered transplant programs that worked hard to prepare and achieve JACIE accreditation; the EBMT presidents and the working party chairs; the EBMT statistical office in Leiden; the EBMT co-ordination offices in Barcelona, Paris and London , the  Austrian Registry (ASCTR) , the  Czech BMT Registry , the  French Registry (SFGM-TC) , the  German Registry (DRST) , the  Italian Registry (GITMO) , the  Dutch Registry (HOVON) , the  Spanish BMT Registry (GETH) , the  Swiss Registry (SBST) , the  Turkish BMT Registry  and the  British Registry (BSBMT).
Funding This study was funded by the  European Group for Blood and Marrow Transplantation (EBMT)  and the  European Leukemia Net (ELN). EBMT is supported by grants from the corporate members:  Amgen Europe ,  ViroPharma Europe ,  Celegene International SARL, Genzyme Europe B. V. , Gilead Sciences Europe Ltd. , Miltenyl Biotec GmbH, Schering-Plough International Inc. , Bristol Myers Squibb, CaridiaBCT Europe NV, Cephalon Europe, F. Hoffmann-La Roche Ltd, Fresenius Biotech GmbH, Therakos Inc. , Alexion Europe, Chugai Sanofi -Aventis ,  Merck Sharp and Dohme ,  Novartis ,  Pfizer ,  Pierre Fabre Médicament. AG was in part supported by a grant from the  Bangerter Foundation  on outcome research.
8%)* 2000 5,646 (47%) 6,395 (53%) 12,041 (11. 2%)* 2001 5,848 (47%) 6,596 (53%) 12,444 (11. 5%)* 2002 5,923 (45%) 7,302 (55%) 13,225 (12. 3%)* 2003 6,140 (46%) 7,128 (54%) 13,268 (12. 3%)* 2004 6,488 (46%) 7,727 (54%) 14,215 (13. 2%)* 2005 6,917 (45%) 8,613 (55%) 15,530 (14. 4%)* 2006 6,986 (45%) 8,564 (55%) 15,550 (14. 4%)* Center size (over centers: allogeneic) 1 st quartile (# of trpl) 637 (30%) 1,562 (70%) 2,199 (5%)* 2 nd quartile (# of trpl) 2,344 (40%) 3,419 (60%) 5,763 (14%)* 3 rd quartile (# of trpl) 4,276 (41%) 6,040 (59%) 10,316 (25%)* 4 th quartile (# of trpl) 14,751 (63%) 8,594 (37%) 23,345 (56%)* Center size (over centers: autologous) 1 st quartile (# of trpl) 658 (17%) 3,280 (83%) 3,938 (6%)* 2 nd quartile (# of trpl) 2,682 (26%) 7,323 (74%) 10,005 (15%)* 3 rd quartile (# of trpl) 7,386 (40%) 10,900 (60%) 18,286 (28%)* 4 th quartile (# of trpl) 16,725 (49%) 17,327 (51%) 34,052 (51%)* GNI/cap (over countries) 1 st quartile (# of trpl) 5,041 (20%) 20,348 (80%) 25,389 (23%)* 2 nd quartile (# of trpl) 12,968 (39%) 20,325 (61%) 33,293 (31%)* 3 rd quartile (# of trpl) 10,891 (48%) 11,812 (52%) 22,703 (21%)* 4 th quartile (# of trpl) 20,559 (77%) 5,960 (23%) 26,519 (25%)* Age (4 classes) 0 to 20 years 5,024 (44%) 6,462 (56%) 11,486 (11%)* 20+ to 40 years 11,182 (43%) 14,649 (57%) 25,831 (24%)* 40+ to 60 years 23,861 (47%) 26,953 (53%) 50,814 (47%)* Over 60 years 9,392 (47%) 10,381 (53%) 19,773 (18%)* Conditioning (allogeneic) Standard 13,948 (53%) 12,540 (47%) 26,488 (64%)* Reduced 6,479 (57%) 4,965 (43%) 11,444 (27%)* Not known in database 1,581 (43%) 2,110 (57%) 3,691 (9%)* Conditioning (autologous) Standard 14,803 (47%) 16,399 (53%) 31,202 (47%)* Reduced 164 (35%) 304 (65%) 468 (1%)* Not known in database 12,484 (36%) 22,127 (64%) 34,611 (52%)* Percentages reflect row percentages (*column percentages) (Numbers do not always add up, due to some missing values); trpl: transplanted.
Methods In order to determine a personalized CoM position, two SESCs were identified for each subject: one using high-end sensors, the other with low-cost portable ones. In addition, a literature-based estimate was performed using the anthropometric data provided by Winter  [10]  and kinematic measurements from the Vicon and the Kinect. Thus, we evaluated the performance of the following estimates: (i) Vicon-SESC, identified from the Vicon-AMTI6 data; (ii) Vicon-Winter using Vicon's body posture measurements and Winter's anthropometric data; (iii) Kinect-SESC, identified from data obtained with the Kinect-WBB; and (iv) Kinect-Winter using Kinect's postural measurements and Winter's anthropometric data. Due to the limited information available from the Kinect, we assume a skeletal model composed of nine rigid bodies (see Figure  1 ).
During this experiment, 40 postures were collected from each subject. This was done to make sure that the orientation of each segment was changed; in this way, the data was considered varied enough so that the parameters corresponding to each segment could be identified. Please refer to the Supplementary Material for more information regarding the calibration postures. To study the performance of a subject-specific SESC, we simultaneously measured: (a) the orientation of the subject's body segments using an eight-camera Vicon system and a Kinect; and (b) the CoM ground projection with the AMTI6 force platform and the WBB.
Data Acquisition and Processing Following the standard recommendations, the Kinect was placed facing the subject. The distance to the subject ranged between 2. 5 and 3 m. The WBB was placed on top of the AMTI6 force platform to obtain measurement from both devices at the same time, as was previously done by Huurnink et al. [23]. Like they did, we found a good agreement between measured CoP trajectories; suggesting that the placement had a minimal effect on the measurement. The Vicon and AMTI6 data were recorded using Vicon's Nexus software (Oxford Metrics Group, Oxford, UK), thus ensuring synchronization; they were sampled at 100 Hz and 1000 Hz respectively. A custom program recorded the Kinect's skeleton measurements using OpenNI (PrimeSense Ltd. , Tel Aviv, Israel) and the WBB ones thanks to the wiiuse-project  [34].
Discussion In this work, we proposed a new method for estimating the whole body center of mass that can be used outside of the laboratory by utilizing the statically-equivalent serial chain and a Kinect. We evaluated the differences between the SESC's CoM estimate obtained from Vicon-AMTI6 data to one created using the Kinect-WBB. For this purpose, the SESC parameters were identified twice over the same static postures, using low-cost and high-end equipment. For completeness, the CoM was also estimated using anthropometric table data  [10]  to compare these results to the SESC estimate. Figure  5  summarizes this process. Over all, we observed a good performance of the CoM estimations, especially for the Vicon-SESC case.