dataesr/scientific-paragraphs-categorization huggingface dataset:
All rights reserved. This article is protected by copyright. All rights reserved. AtSco1 (PDB ID  6N5U ) atoms are depicted by brown spheres. This article is protected by copyright. All rights reserved. UV-Vis absorption spectrum of  Apo-Hcc1  (dotted black line), Cu  +2 -Hcc1  (solid orange line) and Cu  +1 -Hcc1  (solid grey line). All spectra were recorded in 50mM phosphate buffer, 200mM NaCl, at pH 7. This article is protected by copyright. All rights reserved. (A) Sequence of the full Hcc1 protein (Uniprot ID code:  Q8VYP0 ). The transmembrane domain is colored in cyan. The expressed sequence is colored in orange (residues 4 to 160, in the numbering of the desposited PDB structure). Crystal structure of Cu +1 -AtSco1: (B) Overall topology. The protein is depicted as orange ribbons. Metal ligands are highlighted in sticks.
Highlight: The copper binding site. Protein residues are shown in sticks colored as follows: C in orange, N in blue, O in red and S in yellow. The 2mFo gray mesh. The Cu +1 ion is shown as a brown sphere. Dashed lines represent bonding interactions. Comparison of Cu +1 -AtSco1 crystal structure (PDB ID  6N5U ) with that of Sco variants reported in other works: (C) Ni-bound human Sco1 (PDB ID  2GGT ), (D) Cu-bound S. cerevisiae Sco1 (PDB ID  2B7J ), ( E) Cu-bound B. japonicum Sco  ( PDB ID 4WBR ) and ( F) Apo B. subtilis Sco  ( PDB ID 1XZ0 ).
Ethical Aspect, Participants, and Methods The study was approved by the Regional Ethical Review Board in Lund with identification number 2015/207 and registered in clinicaltrials. gov as NCT02479334. The study was randomized cross-over with at least one week wash-out period between the treatments. The randomization was carried out with permuted block design with a block size of four. The inclusion criteria of the study covered healthy subjects aged 18-55 years with body mass index (BMI) 20-28 kg•m -2 and having normal range of hemoglobin (men: 134-170 g•L -1 , women: 117-153 g•L -1 ), creatinine (men: 60-105 µmol•L -1 , women: 45-90 µmol•L -1 ), fasting triacylglycerol (TG) (0. 4-1. 7 mmol•L -1 ), and fasting blood glucose (3. 5-5. 5 mmol•L -1 ). Persons with any of the following were excluded: smoking or chewing tobacco, vegetarian or vegan diet, stressed by venous blood sampling or previous experience of cannulation difficulties, taking dietary supplement or receiving any drug treatment including use of nonsteroidal anti-inflammatory drugs such as asthma and allergy medications, pregnancy or breastfeeding, diagnosed with liver disease or diabetes mellitus, having food allergies and/or gastrointestinal disorder.
Parameter Estimation Using Approximate Bayesian Computation Non-observable or non-independent epidemiological parameters were estimated using the number of surviving oysters during 12 dpe observed in the 12 replicate trials conducted on small populations of 10 oysters in tanks. Given that the likelihood evaluation of the model was not straightforward when only these data were employed, an Approximate Bayesian Computation (ABC) method was used. The ABC method consists of studying the similarity between observed and simulated data from intensive simulations, without the need for explicitly evaluating the likelihood function  (45). The general principle consists of generating multiple parameter sets from prior distributions. For a sampled parameter set, a model simulation is performed. Then, summary statistics of this simulation are compared with the observed data using a metric and a tolerance.
ACKNOWLEDGMENTS The oysters used in this work were produced at the IFREMER hatchery in Argenton and maintained under controlled conditions at the  IFREMER hatchery in La Tremblade  and at the IFREMER nursery in Bouin. We are very grateful to the hatchery and nursery teams for their assistance with oyster production. We wish to acknowledge  Bruno Petton  ( IFREMER, Lemar ) for oyster production and  Yoann Coray  and  Bhagat Lal Dutta (IFREMER, LGPMM)  for their help with coding.
Data and methods The study draws on panel data from a two-wave survey supported by WFP. Each wave includes household socioeconomic and demographic information and anthropometric measurements of each child. The anthropometric measurements are matched to questions about whether households had received certain forms of WFP assistance during the previous year. Analyses then evaluate the relevant dynamic differences in nutrition status between baseline and end line using a difference in differences model. The following formula is thus estimated: Nutrition ihvt ¼ β 1 After þ β 2 Treat ihvt þ β 3 Treat Ã Treat ihv þ β 4 X ihvt þ 2 ihvt (1) where Nutrition captures the nutrition status of child i, in household h, in village v, at time t; After is a dummy variable taking the value of 1 for all end line observations and 0 for baseline observations; Treat is a variable that defines the assistance group into which a given child falls; After Ã Treat is the interaction of these two variables and isolates the programme impact; X is a vector of control variables; and 2 is the regression error term.
Although this partly fits in with a wider literature, the modality under investigation here is rather different. It implies that providing a small amount of nutrition-sensitive assistance may induce additional positive investments in nutrition in recipient households. 9. The data include all children ages 0-59 months at the time of baseline data collection and all children who were newly born in the households in the baseline sample. 10. Because the study only observed the assistance received by households at end line after the collection of the survey, it could not observe the treatment groups to which attritor households belonged. Thus, the analysis cannot directly show that attrition is not a function of the treatment received. Other results produced through the empirical specifications suggest, however, that attrition should be unrelated to treatment status.
Acknowledgements The research team gratefully acknowledges funding from the  International Initiative for Impact Evaluation  ( 3ie ), received through the  UK Department for International Development (DFID)  and the  World Food Programme (WFP). The authors are specifically thankful to the  WFP Niger Country Office  for its support throughout the project. They thank  M. Hambali Moussa  for leading a qualitative follow-up of the project,  Myroslava Purska  for excellent organisational support, and  Sveva Vitellozzi  for research assistance. They are grateful for the comments of participants of the Household in Conflict Network (HiCN) Annual Workshop in 2017, a project meeting in Kampala,  Uganda , and a series of presentations in Niger. They express their appreciation to reviewers at 3ie, DFID, and WFP for feedback; to  Amber Peterman  and  Ugo Gentilini  for additional comments; and to two anonymous reviewers, whose input has significantly strengthened the work.
Then, a subset of infants born from the RECIPAL mothers was followed during the first 3 months of age (SEPSIS). Preconception period. At enrolment, demographic, socioeconomic, anthropometric (weight, height, mid-upper-arm circumference) data and household characteristics were collected. Women were screened for urinary schistosomiasis and malaria. All women who were positive for schistosomiasis and malaria were immediately treated. Hemoglobin (Hb), c-reactive protein (CRP) and alpha-1-acid glycoprotein (AGP) levels were determined. Each month, women were visited at home, the first day of the last menstrual period was recorded and a urinary pregnancy test was performed. Gestational follow up. Once pregnant, women were followed up monthly at the maternity clinic. At each antenatal care visit, anthropometric data, clinical data (temperature, blood pressure, intake of intermittent preventive treatment for malaria, use of insecticide treated net) and obstetrical data were collected.
Women were screened monthly for malaria as well as for proteinuria and glycosuria. Maternal Hb, CRP and AGP levels were determined in the first and the third trimester of pregnancy. Urinary schistosomiasis was determined at delivery, and all women who were positive were treated after delivery. Malaria was also screened (using thick blood smear) at delivery in placental blood. In addition to the scheduled visits, women were invited to attend the maternity clinic anytime in case of symptoms. Infant follow-up. A subset of infants (born from April 2016) was followed monthly from birth to 3 months of age. At birth and at each scheduled visit, anthropometric data (weight, length, head circumference and mid-upper-arm circumference), breastfeeding and dietary practices, vaccine coverage and clinical data (temperature, heart and respiratory rate and symptoms) were collected.
Funding:  Fondation pour la Recherche Medicale (FRM , grant number  ECO20160736054 ) for PhD scholarship. The  French Agence Nationale de la Recherche  [ ANR-13-JSV1-0004 , grant 2013] and the  Fondation Simone Beer  under the auspices of the  Fondation de France  [ 00074147 , grant  2017 ] funded  RECIPAL  project. bioMerieux funded (aOR = 4.
Here we address these three challenges and report a model to quantify the regioisomeric composition of TGs in the context of lipidomics. To achieve these aims, we hypothesized that there are fragmentation patterns of available TGs can be used to predict the fragmentation of non-available TGs. Consequently, 1) we applied the multivariate constrained calibration to the quantification of the regioisomers of TGs; 2) we studied the influence of the fragmentation energy on the fragmentation patterns of TGs; 3) we analyzed qualitatively the fragmentation patterns of the regioisomers of TGs in published datasets; 4) we modeled the identified fragmentation patterns in terms of the position of the fatty acids, the structure of the fatty acids, and the interaction among the fatty acids; and 5) we quantified the regioisomers of TGs in vegetable oils with a limited number of standards.
Datasets to characterize, model, and quantify the regioisomers of TGs To build fragmentation models, only the TGs containing common fatty acids of vegetable or fish oils were selected: lauric acid (La), myristic acid (M), palmitic acid (P),  After optimization (see Section 3. 2), the fragmentations were carried out in the trap at 13 V of energy of fragmentation. The signals of the fragments were corrected by the isotopic distribution of the diacylglycerol-like ions  [35].
Software and statistics Data analysis and model development were performed in R 3. 3. 1 run under RStudio 0. 99. 902. The optimization of the fragmentation parameters was carried out with function optim from package stats (Nelder-Mead method, minimization of the sum of squares of the prediction of the measured structural vectors). The multivariate constrained regression to quantify the regioisomers was carried out by the function pcls from package mgcv  [36]. In all cases log stands for natural logarithm. The deviation of the quantification of the regioisomers was expressed by the bias. As the sum of the abundance of the possible regioisomers is necessarily 100%, the values are linearly dependent and the differences between the quantification and the reference values is always zero. To avoid this effect, the bias was defined as the absolute value of the difference between the measured regioisomeric composition and the prepared regioisomeric composition (in-house reference material).
Interpolation among pure standards To avoid senseless calibrations of the regioisomeric composition we used which were acquired at the same conditions as the Boston dataset. The matrices of regression were fitted by minimum of squares, which yielded the average of the biases below 5% (minimum sum of squares column in Table  1 ). While previous publications had quantified TGs with two different fatty acids, our methodology was also suitable for species with three different fatty acids. In comparison with previous studies, our methodology also avoided the quantification of the relative abundance of the regioisomers with values below 0%, above 100%, and with sum of the possible regioisomers different from 100%. On the other hand, by knowing the extremes of the calibration -i. e. the structural vectors of the pure regioisomers-we could estimate the matrix of regression (an example of the calculations can be found in SM-B5).
Influence of the energy of fragmentation on the fragmentation patterns represented by the structural vectors. To study the specific impact of the fragmentation energy on the structural vectors, we modified this parameter during the fragmentation of the TGs constituting the Nantes dataset. In this case, the fragmentation was performed on a Synapt G2S Q-ToF by CID of the [M+NH 4 ] + adducts (parent ion). All standards showed three types of fragments: (tertiary fragments)  [26, 38]. As the relative abundance of the diacylglycerol-like ions (secondary fragments) contains the structural information that we aim to model to quantify the regioisomers, we center the discussion about the factors that govern the generation of these ions. The signal of these secondary fragments presented a wide maximum in relation to the energy of fragmentation (e.
However, the reactions of consumption of the secondary fragments were not directly related to the structure of the parent ion, but to the structure of the secondary fragments  [26]. Consequently, the presence of tertiary fragments hampered the possibility of modeling the fragmentation patterns of TGs from their structure. These tertiary fragments must be minimized to relate the patterns of fragmentation into the primary ions to the structure of the parent ion (and, hence, the regioisomeric composition). Considering this factors, we chose an energy of fragmentation of 13 V in the Nantes dataset. At this energy of fragmentation, the production of the secondary fragments dominated their signal and there were no tertiary fragments. We will discuss in detail the mechanism of fragmentation in different instruments and adducts in Section 3.
4, after the identification and modeling of the fragmentation patterns in the datasets. In conclusion, to model the fragmentation patterns of the regioisomers in different instruments, the neutral losses of the fatty acids of TGs should be the main mechanism of fragmentation (i. e. the secondary fragments should not decompose into tertiary fragments). This behavior is expected to take place at low energy of fragmentation. As the energy transfer and the type of adduct are instrument-specific, the fragmentation conditions should be optimized on every instrument as described before. than zero. This behavior indicates a higher trend of saturated fatty acids to be lost in the external position, which agrees with previous results  [29].
Consequently, we modeled the fragmentation tendencies of the fatty acids by the fragmentation parameters γ according to the trend groups identified in Section 3. Consequently, despite the difference among adducts and instruments (Table  2 ), the fragmentation model showed good repeatability for the same instrument. To test the capacity of quantification of the regioisomers of TGs by the model, we compared the quantification bias of the regioisomers of TG(S/O/O), TG(S/S/O), and TG(P/S/O) in the Boston dataset with the quantification by multivariate constrained regression (Section 3. 1). First, we predicted the structural vectors by the γ parameters in Table  2  for the Boston dataset as in SM-C. Subsequently we used these vectors as the columns of the regression matrix to apply the multivariate constrained regression as in SM-B.
The average of the quantification bias by using the model was below 10% in all cases (right column in Table  1 ). In comparison with the quantifications without non-modeled structural vectors, the bias was higher for the regioisomers of TG(S/O/O) and TG(S/S/O), but lower for the regioisomers of TG(P/S/O) (Table  1 , last column). In conclusion, despite a moderate increase of the error, the bias below 10% was also acceptable to use the model to quantify the regioisomers of TGs. The complexity of the fragmentation of PUFAs affected the quality of the prediction, specifically in species with highly unsaturated fatty acids (AA, EPA, and DHA) when another PUFA was present in the TG. For example, SP O/AA in TG(rac-O/AA/L) in the Boston dataset was -0. 80 while the model predicted 0. 49 (SM-A). We address this lack of predictability to a simplification in modeling the interaction between PUFAs into only one parameter.
Most of the species in the Boston dataset with more than one highly unsaturated fatty acids were of the form TG(rac-A/B/A) whereas the interaction via London dispersion forces between the highly unsaturated fatty acids could be different in the regioisomer TG(rac-A/A/B). Consequently, we speculate that the interaction between highly unsaturated fatty acids depended on the structure of the fatty acids and their relative position in the glycerol backbone. Considering that these species corresponded to the adduct [M+NH 4 ] + , we also speculate that the hydrogens of the ammonia and the folded structure of the highly unsaturated fatty acids interact via hydrogen bonds to produce complex fragmentation patterns. This factor would be absent in adducts without this effect, such as [M+Li] + or [M+Na] +.
In summary, this complex behavior warrants further research to understand and model the fragmentation patterns of different adducts of TGs with highly unsaturated fatty acids  [39]. Specifically, this research is necessary to allow the quantification of the regioisomers of TGs in fish oils or derived products, such as oils enriched in DHA and EPA (accounting for more than 50% of the fatty acids in these oils). Comparing adducts and instruments in the datasets under study, the fragmentation parameters (γ ext , γ MU , γ L , γ ALA , γ GLA , and γ IN ) showed the same sign but different absolute value. This behavior of the fragmentation parameters was the mathematical expression of the observation of common qualitative trends of fragmentation for the adducts and modes of ionization (Section 3.
3). However, this behavior cannot be extrapolated to other adducts, as discussed later. Considering the same adduct in different instruments, we can first compare the behavior of the [M+NH 4 ] + adducts in the Nantes and Boston datasets. On the one hand, the Nantes dataset was acquired by CID in a Q-ToF at low energy of fragmentation to prevent the presence of tertiary fragments (see Section 3. 2). On the other hand, the Boston dataset was acquired on an ion-trap analyzer with CID energy of fragmentation of 30 (unitless scale) without the presence of tertiary fragments. CID fragmentations in both Q-ToF and ion-trap are classified as low energy activation and the energy transferred is vibrational  [40]. However, we observed tertiary fragments in the Nantes dataset at high energy of fragmentation, but not in the Boston dataset.
This fact points to a higher transfer of energy on the Q-ToF system, at least when compared to the ion-trap. At low energy of fragmentation in the Q-ToF, the energy transferred would be sufficient to excite the vibrational states of the ester groups of the fatty acids of TGs to yield their neutral loss of the fatty acids (secondary fragments). However, it would not be enough to excite the vibrational states of the functional groups of these secondary fragments to yield the tertiary fragments. Consequently, at low energy of fragmentation, we would expect the same mechanism of fragmentation by CID on the ion-trap in the Boston dataset and on the Q-ToF in the Nantes dataset. Another potential difference in the fragmentation process during CID is the gas of collision. In CID, the collisions with a noble gas convert the translational energy of the ion into internal energy, including the vibrational states.
In this process, the heavier the collision gas, the higher the transfer of energy  [40]. In this context, we would not expect to change the mechanism of fragmentation by changing the gas of collision, only the degree of dissociation would change. In conclusion and despite the differences in the analyzers, we expected the same mechanism of fragmentation of the [M+NH 4 ] + adducts in the Nantes and Boston datasets. This justifies the presence of the same qualitative fragmentation patterns for the two datasets. We address the differences in the quantitative patterns among instruments to: 1) the differences in energy transfer to the analytes during both the ionization and the fragmentation, and 2) the different time-range between fragmentation and detection for Q-ToF and ion-trap instruments. We can also compare the fragmentation trends for the [M+H] + adducts on the five instruments in the Pardubice dataset.
In this case, the regioisomers of TGs fragmented spontaneously in APCI from the [M+H] + adduct and did not report the presence of tertiary fragments in any of the five analyzers  [35]. As TGs are thermolabile, we address the fragmentation to thermal decomposition during the ionization and/or the ion transfer in the mass spectrometers. Despite the differences in the analyzers, thermal energy is also transferred to the vibrational states of the molecule. Thus, we would expect the same mechanism of fragmentation among the five analyzers used in the Pardubice dataset. This common mechanism justifies the same qualitative fragmentation patterns for the [M+H] + adducts in APCI in these five analyzers. In a similar way, we address the differences in the quantitative patterns to the differences in energy transfer to the analytes during both the ionization (e.
Quantification of the regioisomers in vegetable oils After ensuring the underlying assumptions of the model, we predicted the regression matrices of the different regioisomers from the fragmentation parameters γ ext , γ MU , γ L , γ ALA , γ GLA , and γ IN in the Nantes dataset (Table  2 ). An illustrative example of the calculations can be found in SM-C6. As the samples were vegetable oils, we considered that all fatty acids with 18 carbons and 3 unsaturations were ALA, which cannot be distinguished from GLA by the mass of their neutral loss. The absences of GLA was verified by gas chromatography analysis of the fatty acids (data not shown). No species contained highly unsaturated fatty acids, which are not present in vegetable oils. We compared our quantification and the previously reported values for these oils (.
Conclusions In this study, we describe a framework to quantify the regioisomeric composition of TGs by modeling their mass spectrometry fragmentation. This methodology was applicable even for species of TGs that were not included in the training dataset. To achieve this aim, we enclosed the structural information of the TGs in the fragmentation parameters γ. These parameters predicted the tendency of a fatty acid to suffer a neutral loss, which were considered as reactions in competition. The fragmentation parameters γ presented the same sign for the different adducts and instruments in the studied datasets. This behavior suggests the existence of common fragmentation mechanism and the use of a common model predict the fragmentation patterns of TGs in different instruments. However, the fragmentation parameters γ presented different absolute values for the different adducts and instruments in the studied datasets.
This behavior suggests the necessity of fitting the common model for every instrument. After the study of the assumptions of the model, the quantification of the regioisomers of TGs in vegetable oils was in good agreement with previous results, even for regioisomers that were not present in the training dataset. As limitation, the fragmentation patterns of TGs with more than one highly unsaturated fatty acids were too complex to be modeled with the up-to-date data. However, after description of these patterns, this framework presents the capability of predicting the fragmentation patterns of these TGs, which are the main components of fish oils. As an interesting perspective to this work, an inter-laboratory ring study may establish a universal minimal set of standards of TGs to model their fragmentation patterns.