Preweaning piglet mortality in relation to placental efficiency

The relationship between placental efficiency (PLEFF, i.e., the ratio of birth weight [BWB] to placental weight [PLW]) and neonatal pig vitality as measured by the probability of preweaning death of live born piglets was examined for 1,036 live born piglets of 118 litters. The data were first analyzed to establish whether the relationships between PLEFF, PLW, and BWB were affected by parity (first vs. higher). Furthermore, the data collected were used to establish whether PLEFF is a better predictor of the risk of neonatal pig mortality before weaning than BWB and PLW. The relationships of BWB to PLW and PLEFF to PLW differed (P <0.01 and P <0.05, respectively) between piglets from gilts and sows. This difference appeared to be due mainly to an additional population of piglets with very large placentas in sows that were not present in gilts. Despite being significant, the courses of the relationships were essentially similar for piglets in gilts and in sows. For the curvilinear relationship of BWB to PLW, up to a certain threshold value, an increase of PLW resulted in an increase in BWB, and thereafter BWB did not change. A consequence of this is that PLEFF at relatively high PLW does not give the same information as PLEFF at relatively low PLW. For the second-order relationship of PLEFF to BWB, PLEFF increased with an increase in BWB, until BWB = 1,657 g, and decreased thereafter. The PLEFF decreased linearly with PLW. A change in PLW had a much larger impact on the value of PLEFF than a change in BWB. Although BWB and PLW were negatively associated with the chance of dying before weaning (P <0.001 and P <0.01, respectively), only PLEFF tended to be negatively associated with the chance of dying only before weaning (P = 0.08). Its underlying trait, BWB, played a greater role on the effect of PLEFF on the chance of preweaning death than PLW. In conclusion, PLEFF in swine is a complicated trait that should be treated with care. It is merely a mathematical derivative of BWB and PLW, whereby the extent to which BWB depends on PLW depends on the value of PLW. Placental functioning and fetal growth capacity, however, also have their effects on the value of BWB. It is concluded that, of the three traits (BWB, PLW, and PLEFF), the best predictor for the chance of preweaning mortality, which also happens to be easiest to measure, remains BWB

Queries on Semantic Building Digital Twins for Robot Navigation

Autonomous mobile robots are starting to be deployed in complex built environments where they need to navigate to complete the given tasks. In order to navigate, autonomous mobile robots often rely on environmental maps. In this paper, we explore a novel approach to automatically create topological and metric environmental maps from BIM data exported to a graph database. We define queries on the exported graph data-base which retrieve the data needed to create the maps automatically. We validate our approach by applying standard path planning algorithms such as A* on the generated maps showing that they are suitable for computing optimal paths. We regard this work as a first step to connect linked data methods to robotics algorithms and use-cases. The results show the feasibility and potential of exploiting the semantic richness of the data available from BIM

Preweaning piglet mortality in relation to placental efficiency

The relationship between placental efficiency (PLEFF, i.e., the ratio of birth weight [BWB] to placental weight [PLW]) and neonatal pig vitality as measured by the probability of preweaning death of live born piglets was examined for 1,036 live born piglets of 118 litters. The data were first analyzed to establish whether the relationships between PLEFF, PLW, and BWB were affected by parity (first vs. higher). Furthermore, the data collected were used to establish whether PLEFF is a better predictor of the risk of neonatal pig mortality before weaning than BWB and PLW. The relationships of BWB to PLW and PLEFF to PLW differed (P <0.01 and P <0.05, respectively) between piglets from gilts and sows. This difference appeared to be due mainly to an additional population of piglets with very large placentas in sows that were not present in gilts. Despite being significant, the courses of the relationships were essentially similar for piglets in gilts and in sows. For the curvilinear relationship of BWB to PLW, up to a certain threshold value, an increase of PLW resulted in an increase in BWB, and thereafter BWB did not change. A consequence of this is that PLEFF at relatively high PLW does not give the same information as PLEFF at relatively low PLW. For the second-order relationship of PLEFF to BWB, PLEFF increased with an increase in BWB, until BWB = 1,657 g, and decreased thereafter. The PLEFF decreased linearly with PLW. A change in PLW had a much larger impact on the value of PLEFF than a change in BWB. Although BWB and PLW were negatively associated with the chance of dying before weaning (P <0.001 and P <0.01, respectively), only PLEFF tended to be negatively associated with the chance of dying only before weaning (P = 0.08). Its underlying trait, BWB, played a greater role on the effect of PLEFF on the chance of preweaning death than PLW. In conclusion, PLEFF in swine is a complicated trait that should be treated with care. It is merely a mathematical derivative of BWB and PLW, whereby the extent to which BWB depends on PLW depends on the value of PLW. Placental functioning and fetal growth capacity, however, also have their effects on the value of BWB. It is concluded that, of the three traits (BWB, PLW, and PLEFF), the best predictor for the chance of preweaning mortality, which also happens to be easiest to measure, remains BWB

Preweaning piglet mortality in relation to placental efficiency

The relationship between placental efficiency (PLEFF, i.e., the ratio of birth weight [BWB] to placental weight [PLW]) and neonatal pig vitality as measured by the probability of preweaning death of live born piglets was examined for 1,036 live born piglets of 118 litters. The data were first analyzed to establish whether the relationships between PLEFF, PLW, and BWB were affected by parity (first vs. higher). Furthermore, the data collected were used to establish whether PLEFF is a better predictor of the risk of neonatal pig mortality before weaning than BWB and PLW. The relationships of BWB to PLW and PLEFF to PLW differed (P <0.01 and P <0.05, respectively) between piglets from gilts and sows. This difference appeared to be due mainly to an additional population of piglets with very large placentas in sows that were not present in gilts. Despite being significant, the courses of the relationships were essentially similar for piglets in gilts and in sows. For the curvilinear relationship of BWB to PLW, up to a certain threshold value, an increase of PLW resulted in an increase in BWB, and thereafter BWB did not change. A consequence of this is that PLEFF at relatively high PLW does not give the same information as PLEFF at relatively low PLW. For the second-order relationship of PLEFF to BWB, PLEFF increased with an increase in BWB, until BWB = 1,657 g, and decreased thereafter. The PLEFF decreased linearly with PLW. A change in PLW had a much larger impact on the value of PLEFF than a change in BWB. Although BWB and PLW were negatively associated with the chance of dying before weaning (P <0.001 and P <0.01, respectively), only PLEFF tended to be negatively associated with the chance of dying only before weaning (P = 0.08). Its underlying trait, BWB, played a greater role on the effect of PLEFF on the chance of preweaning death than PLW. In conclusion, PLEFF in swine is a complicated trait that should be treated with care. It is merely a mathematical derivative of BWB and PLW, whereby the extent to which BWB depends on PLW depends on the value of PLW. Placental functioning and fetal growth capacity, however, also have their effects on the value of BWB. It is concluded that, of the three traits (BWB, PLW, and PLEFF), the best predictor for the chance of preweaning mortality, which also happens to be easiest to measure, remains BWB

Trichuris suis induces human non-classical patrolling monocytes via the mannose receptor and PKC: implications for multiple sclerosis

Introduction: The inverse correlation between prevalence of auto-immune disorders like the chronic neuro-inflammatory disease multiple sclerosis (MS) and the occurrence of helminth (worm) infections, suggests that the helminth-trained immune system is protective against auto-immunity. As monocytes are regarded as crucial players in the pathogenesis of auto-immune diseases, we explored the hypothesis that these innate effector cells are prime targets for helminths to exert their immunomodulatory effects. Results: Here we show that soluble products of the porcine nematode Trichuris suis (TsSP) are potent in changing the phenotype and function of human monocytes by skewing classical monocytes into anti-inflammatory patrolling cells, which exhibit reduced trans-endothelial migration capacity in an in vitro model of the blood-brain barrier. Mechanistically, we identified the mannose receptor as the TsSP-interacting monocyte receptor and we revealed that specific downstream signalling occurs via protein kinase C (PKC), and in particular PKC delta. Conclusion: This study provides comprehensive mechanistic insight into helminth-induced immunomodulation, which can be therapeutically exploited to combat various auto-immune disorder

Input files underlying the the publication "PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model”

The folder "pcrglobwb2_input" contains global-extent input files, e.g. meteorological forcing data and model parameters, for running PCR-GLOBWB model (Sutanudjaja et al., 2018, ). PCR-GLOBWB is a hydrology and water resources model developed at Department of Physical Geography, Utrecht University, intended for global and regional applications. Using the input files in the folder "pcrglobwb2_input", global-extent PCR-GLOBWB model runs can be performed at the spatial resolutions of 5 arcmin (~10 km at the equator) and 30 arcmin (50 km). As examples, some 5 arcmin simulation output files are given in the folder "example_output". The model codes of PCR-GLOBWB are open source and available on . Please refer to Sutanudjaja et al. (2018) and its reference list for further explanations about how the data were derived. The input files in the folder "pcrglobwb2_input" are basically the same as the ones shared on (Sutanudjaja et al., 2017). Yet, we have converted all PCRaster files to NetCDF format so that all input files can be accessed using OPeNDAP