In Kazakhstan, plague outbreaks occur when its main host, the great gerbil, exceeds an abundance threshold. These live in family groups in burrows, which can be mapped using remote sensing. Occupancy (percentage of burrows occupied) is a good proxy for abundance and hence the possibility of an outbreak. Here we use time series of satellite images to estimate occupancy remotely.In April and September 2013, 872 burrows were identified in the field as either occupied or empty. For satellite images acquired between April and August, ‘burrow objects’ were identified and matched to the field burrows. The burrow objects were represented by 25 different polygon types, then classified (using a majority vote from 10 Random Forests) as occupied or empty, using Normalized Difference Vegetation Indices (NDVI) calculated for all images. Throughout the season NDVI values were higher for empty than for occupied burrows.Occupancy status of individual burrows that were continuously occupied or empty, was classified with producer’s and user’s accuracy values of 63 and 64% for the optimum polygon. Occupancy level was predicted very well and differed 2% from the observed occupancy. This establishes firmly the principle that occupancy can be estimated using satellite images with the potential to predict plague outbreaks over extensive areas with much greater ease and accuracy than previously. Great gerbils are burrowing, mainly folivorous rodents that live in semi-desert environments. They live in family groups usually consisting of one male, one or more females and their offspring, and they occupy one burrow per family . As there is a natural change in the NDVI curve of vegetation between spring and autumn, we hypothesize that this curve is different for occupied and empty burrows.1The first is to assess the NDVI trend of burrow occupancy classes from April to August2The second is to use the information obtained to develop an algorithm, using Random Forests, to classify a burrows’ occupancy status, allowing occupancy levels to be estimated from satellite imagery.Two objectives are addressed:22.1The study area is located in eastern Kazakhstan, south of Lake Balkhash. Data collection was carried out in 38 squares of 250 by 250 m and six squares of 200 by 200 m in April 2013 and repeated in September 2013. All squares were located in one so-called ‘sector’ , a monit2.2Five 1.8 m resolution ortho-rectified Worldview-2 images  with spe2.3oo) in both April and September; empty–empty (ee); empty in April-occupied in September (eo) and occupied in April-empty in September (oe).During the two field campaigns, 872 burrows were mapped and categorized in the field as either occupied or empty in April 2013 and in September 2013. This allowed four categories of change between the two months: occupied–occupied . Then, the mean NDVI values for all the burrow-circles were calculated for each month.2.4To identify the great-gerbil burrows (both occupied and empty) in the sector in the satellite images, the semi-automatic classification method as described in a previous study was used , with so2.5oo, ee, eo and oe) using the field data. The objects that had a constant occupancy status in both April and September (oo and ee) were used in the Random Forests as these can be expected to have the strongest signal. For each of these objects the NDVI was determined. Furthermore, they were buffered with several distances to determine the NDVI of the surroundings of the burrows, since gerbils forage both on and close to occupied burrows, leading to a different seasonal pattern of vegetation for occupied and empty burrows. Indeed, as the surface of (occupied) burrows is hardly vegetated, the vegetation is visible in the imagery only around the occupied burrows. To enhance the signal from the surrounding vegetation, both the burrow systems combined with their surroundings and the surroundings alone were studied as illustrated in The correctly classified burrow objects were divided into the temporal occupancy groups  buffered with a distance of 5 m from polygons buffered with a distance of 15 m. Apart from the mean NDVI values determined for every image, several derivative, temporal variables were also calculated for both the polygons and the donuts of the various sizes. These variables include the absolute temporal differences in NDVI values, such as |(NDVI August minus NDVI July)| and the normalized temporal differences in NDVI variables, such as NDVI August minus NDVI July divided by|(NDVI August plus NDVI April)|. For a full list of the variables used see The  burrows , Fig. 2,ee and oo object groups were split into a training set and validation set, by taking a random sample. The training set contained equal numbers of oo and ee objects (both 77 objects) to have equal a-priori probabilities for both groups. The validation set contained 77 oo and 329 ee objects. Classification was carried out using Random Forests. A Random Forest is a robust statistical classifier that makes a prediction of a test set based on observations from a training set, using multiple decision trees . These values were then fed into the Student t-test.To determine if the full polygons and the donuts performed differently a Student 2.6To be able to predict plague, it is essential to have good predictions of the occupancy level within a region. The percentage of occupied burrows is thus an important variable to derive from the burrow occupancy classification. We used the ratio between the observed occupancy and the predicted occupancy as an accuracy indicator of the estimation. For the optimal polygon set, this ratio was calculated for each Random Forest.2.7To ensure reliable classification accuracies, each of the polygon sets was classified ten times, each time using randomly drawn 50% training and validation samples. Mean producer’s, user’s and overall accuracies and their standard deviations were calculated from these ten runs. Both the producer’s accuracies and user’s accuracies for occupied and empty burrows were calculated. Current plague prediction models use the percentage of occupied burrows for a region and the ultimate objective is therefore an accurate prediction of regional occupancy. The ratio between predicted and observed occupancy was calculated to validate this .33.1oo), 406 continuously empty (ee), 31 occupied-empty (oe) and 269 empty-occupied (eo). The mean NDVI values for the images, calculated using all pixels in the sector   and continuously empty  burrows (based on 9 m radius of the circles) showed a similar pattern from April to June, although the ee burrows had higher and the oo burrows lower mean values. Between June and August there was a slight further deviation from the overall trend: the ee NDVI values decreased less rapidly.In total, 872 burrows were mapped in both April and September 2013, of which 166 were continuously occupied  , showed oo burrows had lower values of mean NDVI than the ee burrows . Of the ten Random Forests built for this polygon-set, the best results were obtained using the variables NDVI April, NDVI May and NDVI August. This resulted in producer’s and user’s accuracies of occupied burrows of 64.3% and 63.3%, respectively. The producer’s and user’s accuracies for empty burrows were 62.7% and 64.0%. Overall accuracy of the classification was 63.5%. All classification accuracies for the ten Random Forests are given in Table S.2 in the Supplementary material. The standard deviation of the overall classification accuracy of the ten runs is 3.8%, which shows that the method is robust, even when slightly different variables are used.The highest classification accuracies were obtained when using full polygons without a buffer  and (NDVI June–NDVI August)normalized (3 out of 10).In total, eight different NDVI variables were selected by the Random Forests in the ten runs. Most often selected were: NDVI April (9 out of 10 runs), NDVI August (8 out of 10), NDVI May (3 out of 10), (NDVI May–NDVI August)t-test, p = 0.81). The accuracies of the full polygons with buffer and the accuracies of the donuts are shown in Table S.1.No significant differences were found between the classification accuracies of objects and donuts . Unfortunately, the set-up of our study does not support an extensive characterization of the spatial variation.As an indication of the spatial variation in the results of the classification, the classification results are shown for two areas in 3.4The regional occupancy level is an essential variable to predict plague outbreaks. The full results for the ratios of predicted occupancy to observed occupancy are shown in Tables S.1 and S.2. Summarizing, the ratios calculated based on only validation data show that occupancy level can be predicted very well: a mean ratio of 1.02 for the optimum polygon with a range among the 10 Random Forests from 0.82 to 1.17.4A gerbil-burrow-occupancy threshold (i.e. a percentage of burrows occupied) exists for plague to spread and persist across large regions of Central Asia . MonitorEstimating animal abundance is important in many areas of ecology, including disease ecology, where the abundance of animal hosts influences the dynamics of infectious agents . Animal ee burrows (unoccupied throughout the study period), the NDVI values were higher than the sector-mean NDVI. This may be due to past occupation by gerbils making the soil more fertile, as it appears to have done for burrowing sand rats (Psammomys obesus) in Egypt  as well as false-negative (missed burrow) errors occur. The burrows that are most likely to result in a false-negative error are the empty burrows, because they show less contrast with the surroundings . This maThe producer’s and user’s accuracies for occupied  and empty burrows  were not as high as for burrow locations irrespective of occupancy , but were nonetheless encouraging given the increased difficulty of the task. However, if the focus is shifted from individual burrows to occupancy levels, which are important because they relate directly to the spread of plague , then thoe and eo classes will be small, and therefore the differences between occupied and empty burrows will be more pronounced. The years when it is more difficult to predict occupancy with high accuracy are those when the overall population of great gerbils is expanding (as was the case here) or contracting. Nonetheless, even in such cases, our estimates are valuable since in practice, previous studies of abundance thresholds for plague spread (In general, it is likely that when occupancies are stable for a number of years, they can be better predicted, because the e spread  have useIn practical terms, based on our results, we recommend the following method for mapping occupancy in future studies. Satellite images should be acquired in April, May and August. Whether the same Random Forest with the training data from 2013 can be used in subsequent years depends on the NDVI values. From year to year the NDVI can vary due to variations in for example precipitation. To account for this, the sector-mean NDVI values should first be compared with the sector-mean NDVI values obtained from the new satellite images, preferably both in April and in August. If the new NDVI values resemble the NDVI of the 2013 images, the Random Forest used for the 2013 data can in theory be used to predict occupancy. If not, for example a transformation could be applied to the NDVI in the new images to account for this.To conclude, occupied and empty burrows have different NDVI signatures and this characteristic has been used to classify occupied burrows, which are a proxy for gerbil abundance, and from which the occupancy level can be derived. This study was possible because, although the great gerbils make daily foraging movements, they have a stable residence, which can be monitored from space. Classifying occupied burrows can most likely also be applied to the estimation of abundance of other burrowing rodents in environments with short vegetation. More importantly, the occupancy level of burrows is an important predictor of plague outbreaks and the spatial distribution of occupied burrows can be used to more accurately model the spatial dynamics of plague. This is an encouraging step towards a monitoring approach for the future that can predict plague outbreaks because fluctuations in abundance of the wildlife host can be monitored remotely over extensive areas.


Diversities in structure and function of ACBP were discussed in this review. ACBP are important proteins that could transport newly synthesized fatty acid, activated into -coA, from plastid to endoplasmic reticulum, where oil in the form of triacylglycerol occurs. ACBP were detected in various animal and plants species, which indicated their importance in biological function. In fact, involvement of ACBP in important process such as lipid metabolism, regulation of enzyme and gene expression, and in response to plant stresses has been proven in several studies. In this review, findings on ACBP of 11 well-known oil crops were reviewed to comprehend diversity, comparative analyses on ACBP structure were made, and link between structure and function, tissue expression and subcellular location of ACBP were also observed. Incomplete reports in some species were mentioned, which might be encouraging to start or to perform deeper studies. Similar characteristics were found in paralogs ACBP, and orthologs ACBP had different functions, despite the high identity in amino acid sequence. At the end, it is confirmed that ortholog proteins could not necessarily display the same function, even from closely related species. The acyl-coA binding proteins (ACBP) could bind, convey, and maintain intracellular acyl-coA pool , soybean (Glycine max), rapeseed (Brassica napus), sunflower (Helianthus annuus), peanut (Arachis hypogaea), cotton (Gossypium hirsutum), coconut (Cocos nucifera), and olive (Olea europaea), respectively . In this review, ACBP from these important oil crops were reviewed, except for the peanut, coconut, and palm, of which ACBP could not be found on database. Besides, six other crops that could also provide oil, and of which ACBP are available on database, were added to this review, including rice (Oryza sativa), maize (Zea mays), tung tree (Vernicia fordii), physic nut (Jatropha curcas), turnip (Brassica rapa) and broccoli (Brassica oleracea). Note that B. napus was produced from hybridization of these two last species B. rapa and B. oleracea. Thus, in total, 11 well-known and important oil crops' ACBP were reviewed. Current findings were described, and incomplete investigations were pointed out, which might encourage to start or to add more researches. Additionally, conserved and separated functions in orthologs and paralogs ACBPs were discussed.Oil crops are plants that are highly valued because of edible and industrial oils that they can provide from their seeds, fruits and nuts. The USDA reported that the most produced and consumed vegetable oils from years 2013/2014 to years 2017/2018 in the world were produced by eight oil crops: palm , which allowed to enrich our knowledge on ACBP functions. Thus, it is worth to sum up findings on AtACBP before reviewing those of the oil crops. So, AtACBP are divided into four separate classes according to their structure, their binding affinity, their subcellular localization, their expression and their function.As mentioned above, most of reports on plant ACBP are about acbp4/acbp5/acbp6 in AtACBP6 produced important accumulation of 18:1-coA in the embryos and considerable decrease in seed weight  and farnesylated protein 6 (AtFP6) through ankyrin repeat  and AtACBP2 have 354 amino acids (38.5 kDa). Apart from the acyl-coA binding domain, a N-terminal membrane-associated domain and a C-terminal ankyrin repeats domain were found in their structure . A N-terminal membrane-associated domain was also seen in AtACBP3  at AtACBP4 was also reported.The kelch motifs AtACBP are AtACBP4 and AtACBP5 that shares 81.4% of identity: AtACBP4 contains 668 amino acids (73.3 kDa) and AtACBP5 has 648 amino acids (71 kDa), they both conserve five kelch motifs apart from the acyl-coA binding domain found on their structure , soybean (G. max), peanut (A. hypogaea), coconut (C. nucifera), palm (Areca sp.), olive (O. europea), turnip (B. rapa), and broccoli (B. oleracea). Therefore, this synopsis might encourage to initiate or to perform further studies.Breeding history of species is reflected on their genetic profile, which is also affected by environment fluctuation. Genes work together to support their development and adaption . Complex mechanisms are coordinated first in molecular level and then within cells, and gene products support species for survival, but this also affects their productivity. In the following paragraphs, major findings on ACBP in oil crops were assembled, the aim was to recognize their divergence in functions despite their similar belonging to the same family. To the best of our knowledge, very few studies have been done on ACBP in some species, and no report of ACBP function were found in maize (B. napus ACBP (BnACBP) was first isolated by Hills et al.  and found eight copies of which structure was very close to AtACBP4 and AtACBP5 . Structure, subcellular location and function were studied: this protein had molecular weight of 10.86 kDa, and as predicted in BnACBP and other ACBP, it displayed four-helix shape. HaACBP6 was expressed in vegetative tissues and strong expressions were observed in developing seeds and germinating cotyledons. This cytosolic protein was demonstrated to have high affinity for binding C16:0-coA, C18:0-coA, and C18:1-coA, rather than C18:2-coA, and its recombinant rHaACBP6 had affinity to bind dipalmitoyl-PC, dioleoyl-PC, and dilinoleoyl-PC, but no affinity for LPC, PA, and LPA were found  were characterized in tung tree (VfACBP3). They differed in structure notably in sequence, length, exon/intron architecture, and three-dimensional shape. VfACBP3 displayed similar expression in flowers and seed development, but one VfACBP3 (named VfACBP3A) was highly expressed in very young seeds, and stronger expression was also observed in leaves, compared to VfACBP3B. Then, 3D structure was predicted, and exposed the four alpha-helixes shapes of common ACBP in C-terminal side of protein, but two alpha-helixes were in addition in N-terminal side of proteins. Especially for VfACBP3B, C-terminal extension contained one long helix and two short helices. Subcellular localization inquiry resulted in the presence of these VfACBP3 in the ER, and it was reported that VfACBP3 had affinity for binding C18:1-coA and C20:4-coA  by now. It was cloned and characterized; the protein contained 92 amino acids and was presumed to have a molecular mass of 10.30 kDa. JcACBP displayed 96% identity with V. fordii VfACBP. Significant expression of JcACBP was observed in different organ, but the highest detected was in fruit, where expression level was in accordance with lipid accumulation , then Blastp tool of NCBI (www.ncbi.nlm.nih.gov) was used, with protein sequences of A. thaliana as model to find homologous proteins in each of the crop species. To make sure each protein had the acyl-coA binding domain (ACBD), protein domain analysis for each protein sequence was performed (results shown below). All accessions found in NCBI database, which had sequence similarity more than 60% with A. thaliana ACBP and had acyl-coA binding domain in their structure, were taken in this study. Thus, a total of 180 ACBP proteins for 11 oil crop species could be found in our analysis , seven ankyrin repeats proteins (339–352 Aa), 16 large proteins (208–381 Aa), and 16 kelch motifs proteins (254–670 Aa). In our previous study, 20 copies of ACBP were identified in B. napus, we cloned eight copies of kelch motif ACBPs from high oil content rapeseed , NCBI database conserves sequence from other cultivars, such as Zhongshuang11. G. hirsutum had 24 ACBP accessions: two accessions for each of small (88–89 Aa) and ankyrin repeats proteins , 12 large proteins (203–342 Aa), and eight kelch motifs proteins (650–679 Aa). In B. rapa and B. oleracea, the same numbers of accessions in each class of ACBP were found: three small ACBP, each , two ankyrin repeats ACBP, each , eight large ACBP, each , and seven kelch motifs ACBP, each . Thus, each of B. rapa and B. oleracea had a total of 20 ACBP accessions found in NCBI database. G. max had two accessions each for ankyrin repeats proteins (346–354 Aa) and large proteins (294–408 Aa), and seven kelch motifs proteins (575–663 Aa), which made a total of 11 accessions of ACBP. H. annuus had 10 accessions of ACBP: two accessions of small  and ankyrin repeats proteins (329–349 Aa), and three accessions for each of large (252–371 Aa) and kelch motifs proteins (658–662 Aa). For J. curcas, a total of six ACBP accessions were found: two accessions for each of small  and large proteins (274–387 Aa), and one accession for each of ankyrin repeats (366 Aa) and kelch motifs proteins (673 Aa). O. europaea had a total of nine accessions of ACBP: two accessions each for small and ankyrin repeats ACBP, with length of 88 and 96 Aa in small ACBP, and 314 and 357 Aa in ankyrin repeats ACBP. One large ACBP was found with 260 Aa, and four accessions of kelch motifs ACBP (625–677 Aa). V. fordii had only three ACBP accessions with one accession each of small (91 Aa), large (375 Aa), and kelch motifs proteins (669 Aa). Finally, for the monocots, O. sativa had a total of 10 ACBP accessions: four small proteins (91–155 Aa), two ankyrin repeats proteins (334–336 Aa), one large proteins (562 AA), and three kelch motifs proteins (536–656 Aa), and Z. mays had three accessions each for small (89–106 Aa) and large proteins (166–537 Aa), five ankyrin repeats proteins (267–330 Aa), and 11 kelch motifs proteins (421–783 Aa), for a total of 22 ACBP accessions. Note that small ACBP were missing in G. max, and ankyrin repeats were also absent in V. fordii. Besides, some small ACBP were much larger or much shorter compared to A. thaliana ACBP, but their sequence identity and their phylogenetic position (results shown below) led us to classify them as small or large ACBP. For example, Os03g0576600 (155 Aa) was classified as small ACBP in O. sativa, since it had 75% identity with A. thaliana small ACBP AT1G31812, against 34% with the large ACBP AT4G24230 (inquiry using Blastp of NCBI), also in the phylogenetic tree, Os03g0576600 was clustered in the small ACBP group, our analysis matched with reported by Meng et al.  or same clade (monocots O. sativa and Z. mays) displayed higher identity compared to others. However, it was surprising that amino acid sequence identity of numerous large ACBP were considerably lower (<40%) even within same species, family or clade. In small ACBP class, identity between J. curcas, V. fordii, and the monocots were much higher, as in H. annuus and G. hirsutum (XP_016755542) which displayed moderately higher identity with Brassicaceae species (more than 76%). Heat map of ankyrin repeats and kelch motifs ACBP displayed nearly similar profile, Brassicaceae species displayed moderate identity value to slightly higher identity with the other eudicots (about 40–65% in ankyrin repeats ACBP and 65–75% in kelch motifs ACBP), with relatively lower identity with monocots.For deeper observation, amino acid sequence identity was measured with Vector NTI software  were gathered side to side and diverged separately from the other species, as well as the Brassicaceae tribe . Also, proteins of the same species were clustered, except for large ACBP of O. europaea (XP_022844505) which was misplaced in the ankyrin repeats clade. Each cluster of the tree were well and moderately supported , except for the large proteins clade which lacked bootstrap support (BS < 50%).Phylogenetic relationship was analyzed to make a statement about resemblance level and affiliation in ACBP in oil crops. This is important to understand similarity or divergence in function of proteins based on their structure, but also might give a clue about function of ACBP in species which have not been defined yet. Therefore, phylogenetic tree was built based on structure using Neighbor-joining methods . This is not much amazing as in distantly related species such as V. fordii and J. curcas, but still had good bootstrap value, as in one large well supported assembly , they might have closely similar structures and could be expected to have similar functions. Amino acid sequence alignment revealed more 12 amino acids presents in J. curcas  compared to V. fordii , they shared 69% amino acid sequence identity. Deeper explanations could be obtained with the next analysis, which focused more on ACBP protein domain architecture.The aim of this analysis was to recognize the similarity and difference of ACBP structure in oil crops in order to compare their functions. Thus, referring to the tree, ACBP of same species or same family were grouped, which possibly indicated much similar in function or characteristics (expression and subcellular location) between them. Groups which were well supported, indicated that there were much more amino acid sequence similarities between them and they likely have similar functions, as for Domains of proteins are important pieces which not only characterize the structure of proteins, but also explain their functions  to 155 Aa (O. sativa Os03g0576600), the length of the acyl-coA binding domain varied from 69 Aa  to 98 Aa , as well as their location on the protein (between Aa 4 and Aa 101). Domain architecture of small ACBP were similar in all studied species. Second, ankyrin repeats ACBP size varied from 267 Aa (Z. mays AQK47322) to 367 Aa  and acyl-coA binding domain were located between Aa 87 and Aa 204, and their size varied from 27 Aa (Z. mays ADT92195) to 86 Aa . Besides, these proteins had additional domain which were the ankyrins located near the C-terminal side of proteins, one ankyrin domain were found in each of all proteins, they were located between Aa 154 and Aa 343, their size varied from 54 Aa (H. annuus XP_021977528) to 93 Aa . Third, the large ACBP had length that varied from 109 Aa (B. oleracea XP_013620900) to 525 Aa (O. sativa Os03g14000), the size of acyl-coA binding domain varied from 34 Aa (B. oleracea XP_013620900) to 87 Aa , and their locations were more or less near the C-terminal side of protein, which extended between Aa 35 and Aa 502, except for B. oleracea XP_013620900 of which acyl-coA binding domain was found on Aa 1, despite its short length and this position of acyl-coA binding domain, B. oleracea XP_013620900 was clustered in large ACBP group in the phylogenetic tree. Finally, the domain architecture of kelch motifs ACBP exposed one acyl-coA binding domain that contained 47 Aa  to 89 Aa  and which were located between Aa 12 and Aa 189. Additionally, one to five kelch were added to the architecture. For example, B. napus XP_013732400 had only one kelch motif on its structure which extended from Aa 186 to Aa 227, and G. max XP_014627431 had five kelch motifs in Aa 183–224, Aa 299–349, Aa 351–398, and Aa 389–426, respectively. Also, some supplementary domains were found in this class of ACBP that might affect these ACBP functions  presented almost the same architecture, which was not surprising because they were closer than the other species. More attention should be focused on amino acid sequences of these acyl-coA binding domains to study more about similarity and difference of these ACBP structures, which would be developed more in the next paragraph.A special attention was given to the difference of proteins in the same class of ACBP, among the oil crops studied. Again, similarity or difference in their structure might explain or predict their function. Position and size of domains were observed to be different . A representative of each accession of ACBP in every class was taken for the analysis. Alpha-helix spaced by extended strands and random coils were obvious in ACBP secondary structure, but extent and proportion varied within species and class of ACBP  to 64.15% (Z. mays AQK41346), and 32.08% (Z. mays AQK41346) to 48.35% (V. fordii AFZ62125), respectively. In ankyrin repeats ACBP, random coils were rather dominant compared to alpha-helix: 45.20 and 42.09%, respectively in G. max KRH53542, to 50.55 and 38.25%, respectively in J. curcas KDP35833. Exceptions were obvious in Z. mays ACG24390 and H. annuus XP_022012478, of which 43.33% of random coils and 47.88% alpha-helix, and 42.69% of random coils and 53.58% of alpha-helix, respectively, were found. Shape of large ACBP displayed similar pattern as in small ACBP, which were mainly composed of alpha-helix and random coils: 47.49% (Z. mays ONL95885) to 55.22%  of alpha-helix, and 37.09%  to 44.00% (V. fordii AFZ62129) of random coils. Exceptions were also found of which random coils were more dominant, as in H. annuus XP_022011286  and G. max XP_003550311 , and in J. curcas XP_012068250, large ACBP were composed equally of 43.41% of alpha-helix and random coils. Extended strands represented <17% of proteins in small, large, and ankyrin repeats ACBP. However, in kelch motifs ACBP, extended strands were between 16.86% (Z. mays AQK61749) and 23.08% (G. max XP_006606527). In fact, random coils composed the main structure of kelch motifs ACBP, which were of 45.92% (H. annuus XP_021993755) to 50.19% (Z. mays AQK61749). Alpha-helix constituted 29.66% (B. oleracea XP_013616076) to 35.06% (O. sativa XP_015629756).Analysis of protein's secondary structure allows to know the local structural conformation, it is determined by the hydrogen bonds which exist within strands, and which contribute to protein stability and amino acids fold in repeating form. If these hydrogen bonds exist inside a single strand, the protein exhibits an alpha-helix shape, but if two strands are involved, beta-sheet shape is displayed. In our report, secondary structures of ACBP were analyzed using GOR version IV : a batch processing analysis was first performed , ACBP shape were then predicted based on homology with pre-existing protein on Phyre2 that serves as model. A portion of ACBP protein could be model, and not the total. Then, to highlight the conserved domains of each protein, results obtained from Phyre 2 were submitted for analysis to VAST tool of NCBI , and visualized with Cn3D macromolecular structure viewer , by using of protein sequences provided with PDB file which were generated from Phyre2. Thus, model of 3D structure of ACBP are represented on Figure A. thaliana, B. napus, B. oleracea, and G. hirsutum: four alpha-helixes were found, three of them were predicted to be the acyl-coA binding domain by SMART analysis. The second model  had four alpha-helixes of which the first domain (pink) had three alpha-helixes and was not recognized by SMART, and the fourth alpha-helix with the second domain (blue) was confirmed by SMART as an ankyrin domain. A2 (Z. mays) had five alpha-helixes which were all predicted as ankyrin domains by SMART despite their label as two different protein domains (pink and blue) by VAST. A3  showed five alpha-helixes of three domains in VAST: the first domain (gray) was not recognized by SMART, the second (pink) and third (blue) domains were predicted as ankyrin domains in SMART. A4 (B. rapa and O. europeae) displayed six alpha-helixes with two domains, however, the first domain (pink) was predicted as containing both an acyl-coA binding domain and an ankyrin domain, and the second domain (blue) was also an ankyrin domain. Note that in model A1–A4, beta-hairpin shapes were noticeable with alpha-helixes shapes. The model A5 (B. napus) contained 17 alpha-helixes of which nine were labeled as domain 1 (pink) containing the acyl-coA binding domain, and eight were domain 2 (blue) with ankyrin domains. A6 (G. max) showed 19 alpha-helixes with four domains: the first (gray) and second (pink) domains were displayed in one and three alpha-helixes, respectively, and they were not recognized by SMART, but the third domain (blue) was the acyl-coA binding domain, and the last domain (brown) was the ankyrin domains. In the model A7 (J. curcas), 17 alpha-helixes with four domains were displayed, three alpha-helix was highlighted by the first domain (pink) which contain transmembrane region, the second domain (blue) with seven alpha-helixes were the acyl-coA binding domain, the third domain (gray) was not recognized and the last domain (brown) was the ankyrin domain. A8 (G. hirsutum) had 18 alpha-helixes of three domains: the first domain (pink) of three alpha-helix contained transmembrane domain, the second domain (blue) of seven alpha-helixes had the acyl-coA binding domain and the third domain (brown) of eight alpha-helixes were the ankyrin domains. The model A9 (B. oleraceae and H. annuus) had 16 alpha-helixes with three domains, the first domain (pink) of four alpha-helixes were not recognized by SMART, the second domain (blue) of seven alpha-helixes were the acyl-coA binding domain and the last domain of six alpha-helixes were the ankyrin domain. Finally in kelch motifs ACBP, four models could be found, which exclusively had beta-sheets, except for the model K4: in fact, the model K1 (Z. mays) had three domains of kelch domains, K2 (G. max) had five domains that correspond all to kelch domains unless for the last domain (gray) which was not recognized by SMART, K3  had four domains  corresponding to kelch motifs, and K4 (B. rapa) had five domains, of which the first domain (pink) had five alpha-helix and some beta-sheets containing the acyl-coA binding domain, and the remaining four domains had all beta-sheets in which three domains  correspond to kelch domains and the last one (gray) was not recognized by SMART.The three-dimensional shapes or tertiary structures of ACBP were also elucidated in oil crops. Tertiary structure is the general folding of helical protein to 3D shape, and it is determined by attractions that offer maximum stability, and lowest energy state. Interaction bound between side-chain group of amino acids contributes to this stability of protein. 3D shapes of ACBP were predicted using Phyre2  which predict the location based on presence of N-terminal pre-sequences related to chloroplast, mitochondria, or secretory pathway signal peptide  which analyzed proteins in a large scale giving more accurate and robust results  and Z. mays (AQK61749) were likely to be located in mitochondria as well. Similarly, ankyrin repeats and large ACBP in all oil crops were predicted to be located in ER.Subcellular locations of ACBP were predicted using two different tools: the first tool was TargetP1.1 , G. max (KRH53542), J. curcas (KDP35833) might be located in outer cell, in golgi apparatus and in plasma membrane, large ACBP of H. annuus (XP_022011286) and V. fordii (AFZ62129) might be outer cell and in vacuole, and in plasma membrane also for V. fordii (AFZ62129), and extracellular for large ACBP of Z. mays ONL95885. Kelch motifs ACBP were also found in chloroplast and mitochondria for some species: B. napus (AIS76197) and V. fordii (AFZ62126) in chloroplast, Z. mays (AQK61749) in mitochondria, and H. annuus (XP_021993755) in both chloroplast and mitochondria.Furthermore, based on MultiLoc2 analysis, ankyrin repeats ACBP of A thaliana, B. napus, H. annuus, V. fordii, and O. sativa, in vitro analyses of ACBP subcellular localization were already performed as cited above, and compared to our analysis, consistencies were found in small and kelch motifs ACBP, which were located in cytoplasm, and large and ankyrin repeats ACBP in ER and plasma membrane. However, MultiLoc2 neither displayed significant presence of A. thaliana large ACBP in outer cell, nor O. sativa kelch motifs ACBP in peroxysome. At the end, findings from in-vitro analysis are always more trustworthy than from in silico analysis. Our findings might encourage to perform in-vitro analysis of subcellular localization of oil crops ACBP.Subcellular localizations of ACBP were predicted based on amino acid sequences. In Several reports gave a comprehensive review on structure-function relationship in proteins , and paralogous ACBP had similar function . Difference was not limited only in function, but also in subcellular localization and expression. No fixed subcellular location was found, but as known in Thus, based on our analysis, orthologs ACBP rarely conserved similar function in oil crops. It has been demonstrated in some studies that orthologs functional differences were larger than expected and might be at the same level of difference as in paralogs with different sequences (Studer and Robinson-Rechavi, Besides, functional similarity in paralogs ACBP was also observed in our analysis. Paralogous genes emerged after duplication event, many duplicated genes diverged that no sequence similarity was found, they formed gene families Zhang, , as in Ain-vitro/in-vivo analyses are far better than in-silico analyses, it is greatly needed to perform experimental studies related to ACBP structure, subcellular location and function. Moreover, ACBP from other oil crops as palm, peanut, and coconut should be isolated and characterized, and function of ACBP in broccoli, turnip, and maize should be studied. Additional studies in these oil crops ACBP are greatly encouraged to encounter new functions, in the purpose of altering crops to benefit valuable traits.In conclusion, this review aimed to comprehend diversity in ACBP of 11 selected oil crops. ACBP were subdivided into four classes according to their domain structure. Separate inquiries on these ACBP enriched our knowledge on their function, subcellular location, tissues expression, and structure. In overall, ACBP in oil crops have important function in lipid metabolism, membrane biosynthesis and repair, cell signaling, plant development, stress management, and disease resistance. Though, structure comparison displayed low and high similarity among oil crops ACBPs, however, orthologs ACBP diverged in function, and paralogs had analogous functions, rare cases were found in which they had same function or same tissue expression, but in majority, these ACBPs of dissimilar protein domain displayed similar tissue expression and might act for the same role. It is uncertain whether specified structure could be assigned to a fixed function, which might weaken the concept of function prediction based on structure. Ultimately, it is confirmed that proteins from closely related species could not necessarily display the same function. Because findings from NR wrote the manuscript. ML, BW, and LY supervised the work and revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. In detail, first, we adopt a bidirectional metric to improve the symmetry of the energy functional, whose variables are two reciprocal deformations. Secondly, we slack these two deformations into two independent variables and introduce a reciprocal regularizer to assure the deformations being the exact diffeomorphism. Then, we utilize an alternating iterative strategy to decouple the model into two minimizing subproblems, where a new closed form for the approximate velocity of deformation is calculated. Finally, we compare our proposed algorithm on two data sets of real brain MR images with two relative and conventional methods. The results validate that our proposed method improves accuracy and robustness of registration, as well as the gained bidirectional deformations are actually reciprocal. Magnetic Resonance Imaging (MRI) technique plays more and more important roles in the study of brain structure and its function because it offers amount of reliable information by a non-invasive approach . Due to In recent decades, a variety of effective registration methods are developed for constructing the common reference and calculating the deformations between different images. Especially, as a kind of fundamental methods, pairwise registration plays important roles in variants of registration problems. According to the differences between representations of model and algorithm, these methods are divided into two classes. One is the parametrization methods, in which the deformation between two images is approximated in some finite dimensional spaces. For example, Shen and Davatzikos proposed the HAMMER algorithm based on the process of feature detection and hierarchical deformation mechanism  and the Although these algorithms can deal with the nonlinear image registration, there are still two issues. One is the invertibility of the deformation field between two images in practice and the other is the accuracy of registration. Due to various approximations in the model and calculation, the deformation is always not an exact diffeomorphism. That is, the composition of the deformation and its inverse deformation is not an exact identity . To dealThe remainder of this paper is organized as follows. In Section 2, we first recall some relative materials for diffeomorphic registration methods, and then we extend the model of Diffeomorphic Demons by using a bi-direction metric and introducing the reversible constraint. Then, in Section 3, we propose a solution of such new model and form a novel registration algorithm. To validate the efficiency of proposed algorithm, we compare it with the conventional Diffeomorphic Demons and symmetric Diffeomorphic Demons methods in Section 4. Finally, the whole paper is concluded in Section 5.In this section, we first recall the diffeomorphic model for the nonlinear image registration and the Diffeomorphic Demons method. Then, we introduce a symmetric metric, which is widely used in computer vision, and a group of symmetrically reversible constraint to the model.n is always 2 or 3), and then an image is always represented by a BV function on this region. For a fixed image F and a moving image M, the aim of registration is to find the best deformation filed s from M to F, and the general model of nonlinear registration is described as follows.s|s, s−1 ∈ C∞} is the set of all invertible and smooth deformations from the region Ω to itself, Dist is the distance to measure the similarity between two images, Reg(s) is the regular term of the deformation s, s ⋅ M means the deformation s acting on the image M and is defined by (s ⋅ M)(x) = M(s(x)), ∀x ∈ Ω, and λ is a balancing parameter. It should be pointed out that there are several selections of the similarity metric and the regularity term. In this paper, we adopt an L2 metric (SSD between two images) as the similarity metric, which is defined bys) is selected as the Laplacian regularizer and defined byDenote the region of the image by s−1 is the deformation from F to M and should be the inverse of s. In [s and its opposite to approximate the deformations s and s−1, and hence the composition of deformations from F to M and from M to F is not exact identity. Therefore, in order to make the deformation being a exact diffeomorphism and keep the advantages of Diffeomorphic Demons, we slack the symmetric registration model by replacing the s−1 by an independent deformation t and add a Reciprocal constraint s ∘ t = t ∘ s = Id to the model. Then, the registration model is translated to the following minimization problem.λ is a positive parameter, ∘ is the composite operator of two deformations and Id is the identity deformation.To improve the precision of registration and consider the inverse deformation field, Vercauteren et al. introduced a bi-direction metric instead of the conventional SSD in . That isof s. In , to makeCombining the similarity metric in μ is the Lagrange multiplier. It should be pointed out that we only use one multiplier here because of the dependence of two constraints. That is, one constraint includes the other one. Therefore, in practice, we only use one constraint.To design the algorithm, we first penalize the constraint to the objective function. Then, problems –8) is t is t8) iμ in the objective function in It is clear that there are two independent variables except for (S1) Fixing current deformation sk, we solve the kth deformation tk from F to M by(S2) Given the deformation tk, we calculate the (k + 1)th deformation sk+1 from M to F byIt is worth mentioning that the first two terms of Eqs  and 11)11) are sc, and σ is a positive parameter used to describe the weight of similarity between c and s. Then, to deal with such optimization problem with two independent variables, we can also adopt an efficient two-step process alternatively as follows.We consider the subproblem (R1) Fixing current fixed deformation sk,l, we solve the intermediate variable ck,l by(R2) Given the the intermediate variable ck,l, we calculate the sk,l+1 byk and l to represent the indices of the outer iteration for Eqs k and l t for Eqs  and 13)k and l th enough . Then, bTo make two parts of the functional unified with respect to the order of magnitudes in the objective function in f on Diff(Ω) at a neighborhood of the deformation s as follows.f at s.To solve By this approximation, the objective function in L2 for u. Therefore, to make the equation short, we rewrite it as follows,It is seen that this approximated objective function is u.Moreover, minimizing x ∈ Ω as follows,According to the generalized inverse and the Sherman–Morrison formula, we obtain a closed form of the velocity vector at the point K⋆ is a Gaussian kernel K acting on the deformation, andSimilarly, we can solve subproblem Therefore, we summarize the algorithm as follows.Algorithm 1. Constrained Symmetric AlgorithmStep 0. Initialization.ϵ1, ϵ2 > 0 and initial deformation fields s1 and t1.   Given the precision tk,0 = tk,Step 1. Solving the minimization vk,l by  1.1 Calculating vk,l ← K⋆vk,l, 1.2 Smoothing the updated field by a Gaussian Kernel: tk,l+1 = tk,l ∘ exp, 1.3 Set vk,l‖ > ϵ1 then set l ⇐ l + 1 and goto Step 1.1. Otherwise let tk+1 = tk,l and goto Step 2; 1.4 If ‖sk,0 = sk,Step 2. Solving the minimization uk,l by  2.1 Calculating uk,l ← K⋆uk,l, 2.2 Smoothing the updated field by a Gaussian Kernel: sk,l+1 = sk,l ∘ exp, 2.3 Set uk,l‖ > ϵ1 then set l ⇐ l + 1 and goto Step 2.1. Otherwise let sk+1 = sk,l and goto Step 3; 2.4 If ‖E > ϵ2, then k ⇐ k + 1 and goto Step 1. Otherwise output the optimal deformations s* = sk+1 and t* = tk+1.Step 3. If u. If it is too large, we choose a positive integer N such that u/2N is significantly small. Then, exp(u/2N) ≈ Id + u/2N, and It is remarkable that the deformation is updated by combining the last deformation and the exponential map of a velocity field. In fact, the exponential map of a vector field is difficult to be calculated, especially when the velocity field is large. Here, we use a kind of Arsigny’s approximate method. For the other approximate methods for the exponential map (or geodesic), we refer to , 33. FirOn the other hand, to accelerate the algorithm and avoid the local minima as possible, a multiscale strategy is used. We estimate the deformation between two images from the low to high resolution, and the deformation field in the low resolution is used as the initial deformation for the high resolution.In this section, we evaluate the performance of our proposed registration algorithm on two real datasets of LONI LPBA40 MR images and IXI MR images. For the sake of comparison, we also use the Diffeomorphic Demons  and the http://www.loni.usc.edu/atlases/Atlas_Detail.php?atlas_id=12). In each subject, 56 structures (ROIs) were manually labeled and brain masks were constructed from the manual labels and projected back to the labeling space to produce brain only MRI volumes.In order to test our registration algorithm, we compare with the Diffeomorphic Demons and Symmetric-log-Demons algorithms on a public LONI dataset, which is widely used for testing registration algorithms. There are totally 40 MR images shown in By using two conventional nonlinear registration methods and our method, one group of results are shown in Figs In On the other hand, we show the combinations of the straight and backward deformation fields in Finally, to quantitatively evaluate the registration accuracy, we calculate the Dice ratios of 56 ROIs by three methods shown in http://brain-development.org/ixi-dataset/. Here, we use 30 MR images in the following experiment. Each image has 83 manually delineated ROIs and its size of is 256 × 256 × 198 and the voxel spacing is 1 × 1 × 1mm3. Slices of images are shown in In this part, we conduct the same experiment on the data of IXI MR images, which can be downloaded at By using two conventional nonlinear registration methods and our method, one group of results are shown in Figs Furthermore, we show the combinations of the straight and backward deformation fields in Finally, to quantitatively evaluate the registration accuracy, we calculate the Dice ratios of 83 ROIs by three methods shown in In general, from the numerical results, our proposed method not only improves the accuracy of the registration slightly, but also assures that the deformation is an actual diffeomorphism. In addition, by our method, we can get the deformation field and its inverse between two images at once.In this paper, we considered the nonlinear image registration problem, especially, the invertibility of deformation field. First, we improve the registration model by using the bi-direction metric and introducing a regular constraint to assure that the deformation is a real diffeomorphism. Second, we decompose the model into two subproblems by the alternative iteration strategy. Then, inspired by the Diffeomorphic Demons algorithm, we established a novel registration algorithm, in which a new closed form for the velocity of deformation in each step is calculated as well as a multi-scale method is adopted. Finally, several numerical results on two real data validated that the propose method not only improved the accuracy of registration slightly, but also assured that the deformations between images are the actually invertible. In addition, by our method, we can gain the deformation field and its inverse between two images simultaneously.


The objectives of our study were 1) to characterize culling and retention patterns in parities 0 to 6 in served females and farrowed sows in two herd groups, and 2) to quantify the factors associated with by-parity culling risks for both groups in commercial herds. Lifetime data from first-service to removal included 465,947 service records of 94,691 females served between 2008 and 2013 in 98 Spanish herds. Herds were categorized into two groups based on the upper 25th percentile of the herd means of annualized lifetime pigs weaned per sow: high-performing (> 24.7 pigs) and ordinary herds (≤ 24.7 pigs). Two-level log-binomial regression models were used to examine risk factors and relative risk ratios associated with by-parity culling risks.P < 0.05). Furthermore, sows in parities 1 to 6 that farrowed 3 or more stillborn piglets had 2.2–4.8% higher culling risk than for sows that did not farrow any stillborn piglets (P < 0.05). For served sows, culling risk in parity 1 to 6 sows with a weaning-to-first-service interval (WSI) of 7 days or more were 2.2–3.9% higher than equivalent parity sows with WSI 0–6 days (P < 0.05). With regard to relative risk ratios, served sows with WSI 7 days or more were 1.56–1.81 times more likely to be culled than those with WSI 0–6 days.Mean by-parity culling risks (± SE) for served females and farrowed sows were 5.9 ± 0.03 and 12.4 ± 0.05%, respectively. Increased culling risks were associated with sows that farrowed 8 or fewer pigs born alive (PBA). Also, farrowed sows in high-performing herds in parities 2 to 6 had 1.5–5.6% higher culling risk than equivalent parity sows in ordinary herds  contains supplementary material, which is available to authorized users. Culling decisions in everyday practice are critical for retention patterns and financial performance in breeding herds. Annual culling rates are between 35.7 and 49.5% in the U.S.A., Spain, Sweden and Japan –3. FurthCulling policies for farrowed sows should be different from pregnant pigs in commercial herds. However, culling risk and retention patterns have not been well reported for either served pigs or farrowed sows in consecutive parities from 0 to 6. Also, there is little reported information about factors relating to culling risks for either served females or farrowed sows at consecutive parities, nor about lifetime records from first-served gilts to their removal.It has been recommended that log-binomial regression modeling with relative risk ratios is a better method to use in cohort studies, rather than logistic regression modeling with odds ratios . In thisA veterinary consultancy firm  has requested all client producers to mail their data files on a regular basis and has accumulated a database. By the end of 2013, 98 Spanish client herds had allowed their herd data to be used for research purposes. The present study’s data, collected in 2013 with data for 2008–2013, included approximately 0.5% of all Spanish herds, with approximately 4% of female inventories in Spain; the country had 19,630 breeding herds and 2,568,450 females in December of 2013 .Average herd size (± SEM) of the studied herds between 2008 and 2013 was 699 ± 64.3 females with a range between 81 and 3222 females. These studied herds used mechanical or natural ventilation in their breeding, gestation and farrowing barns. Their lactation and gestation diets were formulated using cereals  and soybean meal. Also, all the studied herds used artificial insemination; double or triple inseminations of sows during an estrous period are practiced for breeding management. Replacement gilts in the herds were either purchased from breeding companies or were home-produced through internal multiplication programs.The present study was designed as an epidemiological study coordinating reproductive data collected from the 98 Spanish herds. Gilts, first-served between 2008 and 2013, were observed until their removal or until the end of 2013 using the PigCHAMP recording system. When the data were collected, 4842 (4.9%) of the 99,533 sows had not yet been removed, and so they were excluded. Thus, the initial data contained 465,947 first-served records and 94,691 lifetime records in the 98 herds. Three datasets were created: Dataset 1 for calculating culling risks and retention rates, and Datasets 2 and 3 for log-binomial models of served females and farrowed sows, respectively. In Dataset 2, service records were omitted as missing records if they met any of the following criteria: total number of pigs born was 0 pigs or 26 pigs or more ; lactatBy-parity culling risk (%) for pigs served and sows farrowed were defined as the number of culled pigs divided by the number of female pigs served and sows farrowed, respectively, at that parity × 100. By-parity retention rate was defined as the number of sows that successfully reached farrowing at the next parity divided by the number of gilts first-served. A gilt was defined as a female pig that was entered into a herd but had not yet farrowed, and a sow was a female pig that had farrowed at least once.To avoid the linearity assumption of the independent variable and to clearly describe an association, the herds were categorized into two herd groups, basis of the upper 25th percentile of the herd means of annualized lifetime pigs weaned per sow: high-performing herds (> 24.7 pigs) and ordinary herds (≤ 24.7 pigs). Herd size (± SEM) in high-performing and ordinary herds were 1095 ± 169 and 571 ± 59 females, respectively. Two WSI groups were formed: 0–6 days and 7 days or more.Reasons for culling were recorded by producers when females were culled. Culling reasons were grouped into four categories: ‘reproductive failure,’ ‘lameness,’ ‘high parity’ and ‘others.’ Reproductive failure included no estrus, failure to farrow, found not pregnancy and abortion. The high parity culling category was restricted to sows of parity ≥4 at culling, because the median culling parity was 4, and so a planned culling for “high parity” could not be the reason for culling in a low parity . TherefoDescriptive statistics were obtained using SAS version 9.3 . Dataset 1 was used for culling risks and retention rates. A log-binomial regression model was applied to the binary outcome in Dataset 2, i.e. whether or not a female pig was culled (1 or 0), by using the GLIMMIX procedure with a log link function with binomial distribution . The ILINK (inverse link function) was used to convert the logarithm to a probability . Two-levp-value was <0.05. Pairwise multiple comparisons were performed using the Tukey-Kramer test.To select the most suitable final models, individual risk factors and possible interactions were examined. The model with the lowest pseudo-Akaike Information Criterion (pseudo-AIC) was selected as final models. The random effects for herds were obtained from residual log-pseudo-Likelihood using the COVTEST function. Additionally, the associations were considered significant when the The intraclass correlation coefficients (ICC) were calculated by the following eq.  to assesπ2/3 is the assumed variance at the individual record level.in which Descriptive statistics of lifetime performance and by-parity reproductive performance of sows are shown in Table Table P < 0.05).Tables P < 0.05). Also, the culling risks of sows in parities 1 to 6 that had WSI 7 days or more were 2.2–3.9% higher than equivalent parity sows which had WSI 0–6 days (P < 0.05). Furthermore, there was a two-way interaction for culling risk between the herd groups and WSI groups in parity 2. Sows with WSI 7 days or more in ordinary herds had 2.0% higher culling risk than those in high-performing herds (P < 0.05). However, there was no difference between the herd groups for culling risk in sows with WSI 0–6 days (P ≥ 0.05).Table P < 0.05). Also, sows that farrowed 8 or fewer pigs in parities 1 to 6 had 2.8–19.7% higher culling risks than for sows that farrowed 16 pigs or more (P < 0.05). Culling risks of sows that farrowed 3 or more stillborn piglets from parities 1 to 6 were 2.2–4.8% higher than for sows that did not farrow any stillborn piglets (P < 0.05).Table P < 0.05). But, there were no differences between herd groups for sows that farrowed 16 or more PBA (P ≥ 0.05). Table P < 0.05). In contrast, across the PBA groups, sows that farrowed 3 or more stillborn piglets had 1.3–5.7% higher culling risks than sows that did not farrow any stillborn piglets (P < 0.05). With regard to the ICC, the random herd effect explained 3.0–11.1% of the total variance values for culling risks for served and farrowed females.Table Our study showing a 72% retention rate at parity 3 is within the range reported in previous studies in Sweden and Japan of 70 and 77%, respectively , 17. In More farrowed sows are culled due to high age than served females, but in contrast more served females are culled due to reproductive failure than farrowed sows. In fact, our study shows that approximately 40% of the served gilts and sows were culled due to reproductive failure, without any successful farrowing. A Swedish report showed only 26.9% of culling was due to reproductive failure , so our Our study also showed 1.4–2.3% lower culling risk for served sows from parities 4 to 6 in high-performing herds than those in ordinary herds. This can be explained by higher farrowing rates, lower return risks, and better management for pregnant sows in high-performing herds , 24. AlsHigh-performing herds appear to relentlessly cull farrowed sows from parity 2 or higher without subsequent service. Culling risks in farrowed sows in parities 2 to 6 were 1.5–5.6% higher in high-performing herds than in ordinary herds. This is because it is better to cull farrowed sows without subsequent service rather than served sows, because culling pregnant sows increases culling interval and non-productive days.Our present study showing a higher culling risk in sows that farrowed 8 or fewer PBA than in sows that farrowed 16 or more PBA is consistent with a previous study in Japan, and shows that producers tend to cull sows that farrowed few PBA . It appeThe increased culling risks in served sows with WSI 7 days or more in our present study correspond with previous studies showing a high culling risk in sows with prolonged WSI . Sows wiIn our study, the higher culling risks for sows that farrowed 3 or more stillborn piglets compared with sows that farrowed 0 stillborn piglets could be explained by an increased likelihood of infectious events or manual intervention during farrowing difficulties . Sows thTo our knowledge, this is the first study applying log-binomial regression models with relative risk ratios to culling risks of female pigs in commercial herds. The convergence failure in our parity 6 models can be explained by the fact that a log-binomial model is less stable than logistic models, and there have been some cases where log-binomial models failed to converge .Finally, there are some limitations that should be noted when interpreting the results of this observational study using commercial herd data. Our studied herds were not randomly selected from all Spanish herds. Also, our analyses did not take account of health status, nutritional programs, genotype or housing types. However, even with such limitations, this research provides valuable information about culling and retention patterns, and the quantitative relationship between production factors and culling risks that should help swine producers and practicing veterinarians to maximize their sows’ reproductive potential.To achieve high retention in low parity and improve longevity, it is recommended that producers provide appropriate management for sows farrowing stillborn piglets or having prolonged WSI. Also, culling policy in all herds should be reconsidered for parity 2 to 4 sows that farrowed 8 or fewer PBA. Finally, to reduce non-productive days, producers should cull sows after weaning, not after service or during pregnancy.Additional file 1:Appendix A) Estimates of factors in the log-binomial regression model for culling risks of served gilts. Appendix B) Estimates of factors in the log-binomial regression models for culling risks of served sows. Appendix C) Estimates of factors in the log-binomial regression models for culling risks of farrowed sows. (DOCX 94 kb)


In contrast, the structural intervention approach predicts that object relative clauses with two full NP arguments mismatching in number  are comprehended earlier and more accurately than those with number-matching NPs . These approaches were tested in two steps. First, we ran a corpus analysis to ensure that object relative clauses with number-mismatching NPs are not more frequent than object relative clauses with number-matching NPs in child directed speech. Next, the comprehension of these structures was tested experimentally in 3-, 4-, and 5-year-olds respectively by means of a color naming task. By comparing the predictions of the two approaches within the same participant groups, we were able to uncover that the effects predicted by the input frequency and by the structural intervention approaches co-exist and that they both influence the performance of children on transitive relative clauses, but in a manner that is modulated by age. These results reveal a sensitivity to animacy mismatch already being demonstrated by 3-year-olds and show that animacy is initially deployed more reliably than number to interpret relative clauses correctly. In all age groups, the animacy mismatch appears to explain the performance of children, thus, showing that the comprehension of frequent object relative clauses is enhanced compared to the other conditions. Starting with 4-year-olds but especially in 5-year-olds, the number mismatch supported comprehension—a facilitation that is unlikely to be driven by input frequency. Once children fine-tune their sensitivity to verb agreement information around the age of four, they are also able to deploy number marking to overcome the intervention effects. This study highlights the importance of testing experimentally contrasting theoretical approaches in order to characterize the multifaceted, developmental nature of language acquisition.The predictions of two contrasting approaches to the acquisition of transitive relative clauses were tested within the same groups of German-speaking participants aged from 3 to 5 years old. The input frequency approach predicts that object relative clauses with inanimate heads (e.g.,  Child language acquisition is a multifaceted process, which is likely to be influenced by several factors including structural rule learning, statistical learning, and social learning  will be reviewed. Next, the hypotheses made by the two theoretical approaches will be tested by means of a corpus study and an experimental study. A discussion of these results and of the co-existence of frequency and structural factors on the development of complex sentences will conclude the paper.At the core of the input frequency approach lies the question of which environmental factors influence the emergence of children's early linguistic knowledge and its usage  around?]. Diessel and Tomasello (the car that she borrowed had a low tyre) rather than with two animate NPs as verb arguments . This pattern appeared to be fairly robust across  languages, such as English , a “lexical restriction” as Friedmann et al. called it. This overt noun on the embedded subject NP (dog) intervenes between the head noun (cat) and the position where this noun is interpreted as an object of the verb chase. This position and the one where the noun is pronounced as relative clause head are argued to be connected via syntactic movement. According to Friedmann et al. . Children were more accurate on those conditions where only one of the two verb arguments contained an overt noun , compared to object relatives with two full NPs. Hence, the prediction that the presence of an overt noun in the full NP hinders the comprehension of object relative clauses was borne out. By testing Italian- and English-speaking children, Adani et al. (The cat that is washing the goats/that the goats are washing has climbed onto the stool) were understood significantly more accurately than the same structures without number dissimilarity . This result suggests that NP-internal features, like number, are relevant in the computation of structural intervention. Moreover, Adani et al. . Italian and Hebrew crucially differ with respect to gender-marking: in Italian, it is only marked on nouns  while in Hebrew it is marked on the noun as well as on the verb via subject-verb agreement. Belletti et al. The man that the boy is scratching (OR:AN-AN)The man that the boys are scratching (OR:SG-PL)4. The man that is scratching the boy (SR:AN-AN)5. The man that is scratching the boys (SR:SG-PL)In order to address further predictions of the two approaches, two types of subject relative clauses were tested as well. Based on the previous studies conducted in the spirit of the input frequency approach, OR:IN-AN are expected to be as accurate (or even more accurate) than subject relative clauses with two animate NPs . On the other hand, the structure intervention approach predicts the number marking facilitation to be specific for object relative clauses. Hence, when comparing SR:AN-AN with subject relative clauses with a singular head and a plural embedded NP , the structural intervention account predicts no difference between the two:Crucially, however, it is not clear from the published literature whether a number facilitation in these contexts could also be predicted by the input frequency approach. According to the structural intervention account, object relative clauses with one singular NP and one plural NP are expected to be easier to interpret than object relative clauses with two verb arguments of the same number. Number is a movement-triggering feature and helps to reduce intervention between the moved head NP (the object) and the embedded NP (the subject). A facilitation in the same direction would be predicted by the input frequency account only if object relative clauses with one singular NP and one plural NP were more frequent in the input than object relative clauses with two verb arguments of the same number. To our knowledge, the question of how frequent relative clauses with number dissimilarity are (compared to relative clauses with number match), has not been examined yet in the existing literature. In order to set the basis for our experimental study, we report the data of a corpus study in which this question was addressed.der, die, das, den, wer, was, welcher, welches, welche, and wo were extracted for a total of 307 utterances. All sentences were coded by the first author and subsequently checked by a native speaker with a linguistic background who was blind to the purpose of the analysis. All utterances were first classified as subject  or object  relative clauses. Among subject relative clauses, the ones containing the copular verb sein (to be) , an intransitive verb , or a reflexive verb  were excluded from further analysis, leaving us with a total of 32 transitive subject relative clauses. For all transitive relative clauses, we analyzed whether the two NPs displayed (a) the same number; (b) a different number. Moreover, the two NPs were further analyzed in terms of their animacy properties, whether they are: (c) both animate; (d) the head noun is inanimate and the embedded NP is animate; (e) the head noun is animate and the embedded NP is inanimate; (f) both inanimate. These results are summarized in Table In order to examine the input frequency of relative clauses with and without number mismatch between the head NP and the embedded NP, the speech of adults interacting with three German-speaking children from the CHILDES corpus  and both subject- and object relative clauses with number mismatch are rarer than their number match counterparts .The distribution of NP types in object relative clauses was analyzed statistically with respect to two relevant comparisons: the occurrence of inanimate head nouns and the occurrence of number mismatch. Object relative clauses with inanimate heads were more frequent than object relative clauses with two animate NPs , difficulty in distinguishing the depicted characters (N = 1), color blindness, as indicated in the parental questionnaire (N = 1), failure to name three colors during the pre-test (N = 1), history of speech therapy (N = 2). This study was reviewed and approved by ethic committee at the University of Potsdam and it was carried out with parental written informed consent from all participants, in accordance with the Declaration of Helsinki. The study was piloted with a group of 4- to 5-year-olds and a group of adults , 25 four-year-olds , and 25 five-year-olds . Six additional children were tested but later excluded for one following reasons: lack of task completion  and subject  relative clauses, for a total of 20 trials (four items per condition). Differently from English, both subject and object relative clauses display the finite verb in clause final position in German, thus creating minimal pairs between the two extraction types, which are not confounded by overt word order differences. In addition to the test sentences, 16 relative clauses with intransitive verbs, e.g., (11), and prepositional phrases, e.g., (12), were included as fillers, for a total of 36 trials per list.6. Welche Farbe hat der Mann, den die Jungen kratzen? (OR:SG-PL)Which color has the man whom the boys scratch7. Welche Farbe hat der Mann, den der Junge kratzt? (OR:AN-AN)Which color has the man whom the boy scratches8. Welche Farbe hat der Pulli, den der Mann kratzt? (OR:IN-AN)Which color has the pullover whom the man scratches9. Welche Farbe hat der Mann, der den Jungen kratzt? (SR:AN-AN)Which color has the man who scratches the boy?10. Welche Farbe hat der Mann, der die Jungen kratzt? (SR:SG-PL)Which color has the man who scratches the boys?11. Welche Farbe hat der Mann, der trinkt? Which color has the man who drinks?12. Welche Farbe hat der Pulli an der Wäscheleine? Which color has the pullover on the clothesline?All trials were pseudo-randomized, with one filler after maximally three test sentences and it was never the case that two relative clauses of the same type followed each other. To neutralize order effects, the stimuli were administered in one order to half of the participants and in the reversed order to the other half. One stimulus list is reported in the Each visual display contained four pictures with characters of different colors. Only one picture displayed the target referent, e.g., a pullover in (8 and 12) or a man in the other conditions, performing the action expressed by the verb, e.g., scratching, and assuming the correct thematic role (either the agent or the patient). Each four-picture configuration was used to test one subject- and one object relative clause as well as two filler sentences, except in the animacy contrast, where subject relative clauses were not tested. Five additional characters in the scene were coded according to five non-target responses. In this paper, we decided not to report an analysis of non-target responses but the interested reader can find an overview in the Junge (boy), Baumstamm (log), Mann (man), Eimer (bucket), Gurt (belt), Schuh (shoe), Pulli (pullover), Frau (lady), the verbs: drücken , halten , kratzen (to scratch), tragen  and the colors: grün (green), lila (purple), blau (blue), orange (orange), gelb (yellow), rot (red) used in the experiment. The lexical items, and in particular, the verbs, were chosen in such a way that either an animate or an inanimate noun could be a plausible subject. The interaction between the experimenter and the child was mediated by a snail puppet named Bala who introduced the sentence-picture-matching task in the form of a color naming game, inspired by Arnon , 4-year-olds (97%), and 5-year-olds (99%). Percentage of correct responses in each experimental condition is illustrated in Figure %, 4-yearVisual inspection of Figure group and condition as categorical variable in its fixed effects structure. The three levels of group were contrasted in two steps: first, 4 year-olds (coded as 1) vs. 3 year-olds (coded as −1); next, 5 year-olds (coded as 1) vs. 4 year-olds (coded as −1). To address our research questions, we planned the following condition comparisons (the contrast coding is indicated in brackets):f1: OR:AN-AN (1) vs. OR:SG-PL (−1) −> Is there a number dissimilarity facilitation?f2: OR:IN-AN (1) vs. OR:AN-AN (−1) −> Is there an animacy dissimilarity facilitation?f3: SR:AN-AN (1) vs. OR:IN-AN (−1) −> Are OR with an inanimate head as easy as SR?f4: SR:SG-PL (1) vs. SR:AN-AN (−1) −> Is the number dissimilarity effect present also in SR?We analyzed the proportion of correct responses with generalized linear mixed-models within the lme4 package  than 4-year-olds (M = 62%), but the difference between 4- and 5-year-olds (M = 67%) does not reach significance.We found significant main effects of group and of three out of the four pre-planned comparisons. The significant main effect of group reveals that 3-year-olds perform significantly less accurately (M = 51%) than object relative clauses with two animate, singular NPs (M = 33%). A significant interaction between group and OR:AN-AN vs. OR:SG-PL and subsequent pairwise comparisons reveal that, for 4- and especially for 5-year-olds, object relative clauses with number dissimilarity are significantly more accurate than object relative clauses with two animate, singular NPs . In 3-year-olds, this effect shows a similar direction but it is not significant . Moreover, the effect of condition comparison OR:IN-AN vs. OR:AN-AN and the absence of an interaction with group reveals that object relative clauses with an inanimate head and an animate embedded subject (M = 57%) are more accurate that object relative clauses with two animate NPs (M = 33%) and that this facilitation holds across all age groups. The effect of condition comparison SR:AN-AN vs. OR:IN-AN reveals that subject relative clauses with two animate, singular NPs (M = 80%) are overall more accurate than object relative clauses with an inanimate head (M = 57%). A significant interaction between group and SR:AN-AN vs. OR:IN-AN and subsequent pairwise comparisons reveal that subject relative clauses with two animate, singular NPs are more accurate than object relative clauses with an inanimate head for 4- and 5-year-olds  but not for 3-year-olds . There was no significant difference between subject relative clauses with two animate, singular NPs (M = 80%) and subject relative clauses with number dissimilarity (M = 81%).The effect of the condition comparison OR:AN-AN vs. OR:SG-PL reveals that object relative clauses with number dissimilarity are more accurate  color out of six potential alternatives (100/6 = 16.6). The results of this pass/fail analysis are reported in Table In order to evaluate whether these group results reflect a response behavior that also holds at the individual level, we checked how many children named the correct color on at least 3 out of 4 trials per condition, within each age group. This corresponds to a probability of The response patterns of individual participants corroborate the group results along several dimensions. Starting with subject relative clauses, we found that the majority of the 3-year-olds already performed very accurately on both types (SR:AN-AN and SR:SG-PL). This rate increases in 4-year-olds and virtually all 5-year-olds performed correctly on both types of subject relative clauses. Moving to object relative clauses, we observe that the number of children who performed accurately on object relative clauses with two animate, singular NPs (OR:AN-AN) and with an inanimate head (OR:IN-AN) does not increase as a function of age. Rather, while only a restricted subgroup (12–20%) of children succeeds on OR:AN-AN, a larger subgroup of children (40–48%) performed accurately on the condition OR:IN-AN. What is nevertheless worth to emphasize is that these rates remain fairly steady across age groups. Finally, object relative clauses with number mismatch are correctly understood only by a restricted subgroup of 3-year-olds (22%). Crucially, for the research questions addressed in this study, the relative number of children who performed correctly on the OR with number dissimilarity was low in relation to the OR with an inanimate head . But by the age of 5 years, the relation between these two conditions flips its direction. While the number of children performing accurately on the OR with inanimate heads remains fairly steady, the number of passers on OR with number dissimilarity increases . The implications of the group and individual results will be discussed in the next section.A very thoroughly investigated question in the last decades' psycholinguistic research literature has been which type of information the human parser is relying on when processing filler-gap dependencies, of which relative clauses are a typical instance. Simplifying somehow a very multifaceted state-of-the-art, researchers have proposed several accounts, which capitalize on different sources of information that become crucial to achieve the correct interpretation of these sentences. For instance, some of these approaches emphasize the role of frequency  are less frequent than object relative clauses with two animate, singular NPs. This suggests that a potential facilitation in the former condition could only be explained under the structural intervention approach and not in terms of input frequency.Our experimental data reveal that object relative clauses with an inanimate head are more accurate than object relative clauses with two animate NPs, as the input frequency approach predicts. This response pattern is attested already in 3-year-olds and the facilitation sustains developmentally, as it is also attested in 4- and 5-year-olds. In 3-year-olds, however, the animacy dissimilarity is the main factor that enhances the accuracy on object relative clauses. These results are in contrast with the claim that only presentational, mono-clausal constructions and relative clauses with intransitive verbs are mastered by 3-year-olds, as Diessel and Tomasello , 2005 prDespite the early occurrence and the longitudinal robustness of an animacy effect, it is noted that the accuracy level in object relative clauses with an inanimate head and an embedded animate subject does not increase as the children grow older but remains around 57–58%. This response pattern is also reflected in the analysis of individual performances where we have noticed that around half of the group of 3-year-olds is already quite accurate in this condition, by performing on at least three out of four trials correctly. However, about half of the 5-year-olds still has some difficulties in interpreting these sentences fully correctly. At this point, we can only speculate that the animacy contrast information is immediately accessible even to very young children but that the successful deployment of this information to correct sentence interpretation is based on the application of some top-down, shallow processing heuristic rather than a bottom-up, deep processing of the sentence in the adult-like sense. Based on these results, we suggest that the sensitivity to input frequency information is not subject to development, meaning that it is available from very early on but it does not increase as the child's cognitive and linguistic development progresses.Coming to the second prediction of the input frequency approach, namely that object relative clauses with an inanimate head can become as easy as subject relative clauses, our data do not support this prediction. We have found that 4- and 5-year-olds are still significantly more accurate on subject relative clauses than on the frequent object relatives with inanimate heads. This difference is not attested in 3-year-olds, though. However, a similar performance on these two conditions in 3-year-olds has more to do with the low accuracy of subject relative clauses in the youngest group. For this reason, the difference with object relative clauses with an inanimate head fades away. This finding is similar to the one reported by Kidd et al.  where 3-Coming to the predictions of the structural intervention approach, we found that number dissimilarity enhances the correct interpretation of object relative clauses, when all age groups are taken together. Moreover, this effect emerges in 4-year-olds but it becomes stronger in 5-year-olds. This is in line with the literature that has tested the structural intervention approach in children with a mean age of 4;6 or older ones. In all these studies, a dissimilarity of features that are triggers of syntactic movement enhanced object relative clause comprehension. These features are, for instance, number dissimilarity in Italian and English  and it increases gradually in 4-year-olds  and in the 5-year-olds .The conclusions that can be drawn from our study are in agreement with most of the studies published by researchers that work with the input frequency approach and the structural intervention approach. The step forward that our study makes is to compare these two approaches directly, within the same children and across relatively large samples of participants belonging to different age groups. By doing so, we have discovered that input frequency and structural intervention effects co-exist and that the emergence of these effects is modulated by age. In all age groups, the animacy mismatch appears to explain children's performance, thus, showing that the comprehension of frequent object relative clauses is enhanced. These results reveal a sensitivity to animacy mismatch already in 3-year-olds and show that animacy is initially deployed more reliably than number to interpret relative clauses correctly. Once children fine-tune their sensitivity to verb agreement information around age four, they are also able to deploy number marking to overcome the intervention effect. Future avenues of investigation that our study opens up are the use of implicit measures, for instance eye-tracking, to shed more light on the abilities of 3-year-olds, who might not be able to cope with the explicit task demands as efficiently as the older children do. Our study also highlights the importance of comparing predictions of different developmental approaches in combination with cross-sectional data to gain detailed insight into the dynamics of the acquisition process.FA and TN conceived the study and prepared the material; FA and MS acquired and analyzed the data. FA, MS and TN drafted, revised, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Phytate content (0.22–0.26 g/100 g) decreased significantly (p < 0.05) only with roasting. The tannins (0.01–0.30 g/100 g) and soluble proteins (4.0–29.0 g/100 g) significantly (p < 0.05) diminished with both treatments. There was a significant increase in the least gelation concentration (20–30 g/100 mL), a decrease in the swelling power (1.3–2.0 mL/mL), and consequently, reduction in the viscosity (range peak viscosity 18–210 cP) of the flour slurry after soaking and roasting. All these qualities—needed for producing nutritious flour for infants—highlighted the efficiency of these endogenous technologies.The effects of soaking and roasting on the physicochemical and pasting properties of soybean flour were evaluated. Soybean seeds were soaked overnight in tap water for 0–72 h, hand dehulled, dried, and part of the sample was roasted. Roasted and unroasted soy beans were milled into flour and analyzed. The results showed that the total carbohydrates (22.8–27.9 g/100 g), the ash content (3.5–3.6 g/100 g), and the total polyphenols (0.29–0.51 g/100 g) did not significantly change during both the soaking and roasting processes. However, the total proteins (35.8–46.0 g/100 g) and lipid contents (21.4–29.5 g/100 g) were significantly ( Soybean is an important source of proteins 40%), lipids 20%), minerals 5%), and B vitamins for human nutrition . There i0%, miner0%, lipid%, and B The major challenges in using soybean flour in infant food are the elimination of anti-nutrients, oligosaccharides, and beany flavor, and the reduction of the viscosity of the resulting porridge. Soaking and roasting have been applied to meet these objectives ,11. RoasThis study aims to determine the interaction effect of soaking and roasting on some physicochemical and pasting properties of soybean flour.The soybean seeds used in this study were purchased from local markets in Ngaoundere, Cameroon. The dried seeds (moisture content ≈ 11%) were carefully cleaned and sorted out to remove defective and small-sized seeds so as to obtain clean seeds of uniform size. The cleaned seeds were then processed into flour, as shown in Association of Official Analytical Chemists (AOAC) methods were used to determine the moisture, crude fat, and ash contents of the flours . Moistur2SO4 and quantified based on the formation of complex with Fe(III) ion at pH 1–2. Phenols were extracted in 80% ethanol followed by colorimetric quantification either directly in the extract  or after precipitation of tannins using polyvinylpyrrolidone (for non-tannin polyphenol determination). The tannin content of the sample was calculated as difference between total polyphenol and non-tannin polyphenol contents in the extract. Total phenolic and tannin contents were expressed as gallic acid and tannic acid equivalent, respectively.Phytic acid, total phenolic and tannin contents were determined as recently reported . Phytic The water absorption capacity (WAC) was determined at ambient temperature following the method described by Kaur and Singh . The oilData were expressed as mean ± standard deviation. Two-way analysis of variance with interaction was used to determine the effect of soaking and roasting on the properties. Where the effect of a factor was significant, Duncan Multiple Range test was carried out to further determine the differences between two means. The statistical analysis of the obtained data was carried out using the Statgraphics Centurion XVI version 16.1.18 statistical package.p < 0.02) decrease of moisture content, from a mean value of 7.2 to 5.7 g/100 g. A similar effect of soaking and roasting on the moisture content of maize flour has been reported [Moisture content varied from 5.1 g/100 g in roasted soybean soaked for 72 h to 8.9 g/100 g in unroasted and unsoaked soybean. The moisture content did not vary significantly with soaking time. Meanwhile, roasting the soybean led to a significant , with soaked soybean flours generally exhibiting lower protein content than raw ones. Loss of soluble proteins during soaking probably contributed to the decrease in protein content of soybean flour. By soaking and subsequent roasting, total protein content was reduced by 23%. Decrease in proteins and total sugars during soaking and roasting of maize has been reported [Rhizopus oligosporus. In this study, leaching induced loss of soluble nutrients, and the natural fermentation involving multiple microorganisms with variable metabolisms could have contributed to the decrease in proteins [The total protein and carbohydrate contents ranged from 35.5 ± 1.1–46.0 ± 2.1 g/100 g dry weight (dw) and 22.8 ± 1.6–27.9 ± 1.8 g/100 g dw, respectively. The total carbohydrate content did not vary significantly with soaking or roasting. However, only soaking significantly influenced the protein content . Generally, the crude lipid content of soybean flour increased with soaking by 20%–38% in roasted soybean flour and 13%–20% in unroasted soybean flour. This probably resulted as a consequence of the leaching of soluble components, causing a concentration of the lipids in the flour. Meanwhile, roasting the seeds induced a non-significant increase in the lipid content of the soybean flour by 0%–11%. An increase in oil content after roasting has been reported for cereal seeds including millet [The crude lipid content in the soybean flour samples varied between 21.4 ± 0.4 g/100 g dw (roasted-unsoaked) and 29.5 ± 1.2 g/100 g dw (roasted-72 h soaked). Only soaking induced a significant increase in the lipid content of soybean flour  decreased with roasting, while soaking had no significant effect. This observation is in agreement with earlier studies, which reported a significant decrease in phytate content during soaking and roasting of maize flour [ze flour ,23,25,27ze flour . Contrarze flour .p < 0.001) with both soaking and roasting. In addition, the soluble sugar content (3.4 ± 1.0–10.6 ± 0.9 g/100 g dw) decreased significantly (p < 0.01) with the soaking time, while roasting had no significant effect. Roasting induced a 33% to 66% decrease in soluble protein, while the reduction due to soaking ranged 64%–66% for soluble sugars and 67%–80% for soluble proteins. Egounlety and Aworh reported the major oligosaccharides in soybean as sucrose, raffinose, and stachyose, with respective contents of 4.91 g/100 g dw, 1.22 g/100 g dw, and 8.41 g/100 g dw [Rhizopus, while verbascose disappeared totally [The soluble protein and sugar contents of soybean flour as influenced by the treatment conditions are presented in 100 g dw . They re100 g dw . The mor100 g dw . In this totally . In the  totally . The sam totally . Similar totally . The red totally , and in p < 0.01) with only roasting. Roasting induced a 2% to 23% decrease in the WAC of soybean flour. These observations contrasted with some reports which showed either a non-significant change for maize [The Water absorption capacity (WAC) of soybean flour is presented in or maize  or an inor maize , but areor maize ,35. The or maize . In addior maize ,14.p < 0.001) diminished with soaking time and roasting. The decrease in swelling with soaking time was 10%–25% and 6%–13% for unroasted and roasted soybean flour, respectively. As for WAC, the swelling decreased with roasting by 13%–25%, and in general, 48 h soaking associated with roasting induced a 35% reduction in swelling power. The range of swelling values were 1.3–2.0 mL/mL—higher than that reported for maize flour [Compared to water absorption capacity, the oil absorption capacity (OAC) B did notze flour . Swellinp < 0.001) with soaking and roasting. While raw soybean flour had an LGC of 20 g/100 mL, the 72 h soaked and roasted soybean flour exhibited an LGC of 30 g/100 mL, equivalent to a 50% increase. A similar increase after 48–72 h soaking, although lower, was reported in cereal flours [Soaking and roasting are important traditional technologies applied to cereals and dry seeds for the production of infant foods. In this respect, gelation is a negative behavior needed for the flour. The least gelation concentration  is presented in l flours ,25.p < 0.05) with soaking, and no difference can be visualized between curves for 24, 48, and 72 h soaking. Similar observations were made for the viscosity of roasted soybean slurry. However, the behavior of roasted unsoaked soybean was typical to that of starch, with a clearly identified pasting point and peak viscosity. The pasting parameters of the soybean flours are reported in p < 0.05) decrease in the peak (74%), final (41%), hold (47%), and breakdown (94%) viscosities of the soybean flour slurry. Similar reductions of viscosity was reported in maize [The most important parameter to appreciate the increase in the energy density of the flour during treatment is the viscosity. Generally, flours with high gelling power exhibited high swelling ability, and the resulting slurry had high viscosity. The viscoamylographs of the soybean flour are presented in in maize  and a blin maize .This study reveals that the effect of soaking and roasting depends on the property studied. While soaking induced a decrease in total protein, soluble sugar and tannins, and an increase in lipid content, roasting led to a decrease in the tannin, phytate, and soluble protein contents of the soybean flour. In addition, the swelling index, gelling ability, and viscosity of the soybean flour diminished considerably with soaking and roasting, while the ability to bind and retain water only decreased with roasting. The reductions in these properties are more important when soaking is coupled with roasting. This study highlights the positive effect of endogenous technologies on the improvement of energy density of soy bean flour slurry. However, the nutritional and sensory properties and the microbiological quality of the foods made from soaked roasted soybean flour need to be investigated.