Semantic Entities

Entity retrieval has seen a lot of interest from the research community over the past decade. Ten years ago, the expertise retrieval task gained popularity in the research community during the TREC Enterprise Track [10]. It has remained relevant ever since, while broadening to social media, to tracking the dynamics of expertise [1-5, 8, 11], and, more generally, to a range of entity retrieval tasks. In the talk, which will be given by the second author, we will point out that existing methods to entity or expert retrieval fail to address key challenges: (1) Queries and expert documents use different representations to describe the same concepts [6, 7]. Term mismatches between entities and experts [7] occur due to the inability of widely used maximum-likelihood language models to make use of semantic similarities between words [9]. (2) As the amount of available data increases, the need for more powerful approaches with greater learning capabilities than smoothed maximum-likelihood language models is obvious [13]. (3) Supervised methods for entity or expertise retrieval [5, 8] were introduced at the turn of the last decade. However, the acceleration of data availability has the major disadvantage that, in the case of supervised methods, manual annotation efforts need to sustain a similar order of growth. This calls for the further development of unsupervised methods. (4) According to some entity or expertise retrieval methods, a language model is constructed for every document in the collection. These methods lack efficient query capabilities for large document collections, as each query term needs to be matched against every document [2]. In the talk we will discuss a recently proposed solution [12] that has a strong emphasis on unsupervised model construction, efficient query capabilities and, most importantly, semantic matching between query terms and candidate entities. We show that the proposed approach improves retrieval performance compared to generative language models mainly due to its ability to perform semantic matching [7]. The proposed method does not require any annotations or supervised relevance judgments and is able to learn from raw textual evidence and document-candidate associations alone. The purpose of the proposal is to provide insight in how we avoid explicit annotations and feature engineering and still obtain semantically meaningful retrieval results. In the talk we will provide a comparative error analysis between the proposed semantic entity retrieval model and traditional generative language models that perform exact matching, which yields important insights in the relative strengths of semantic matching and exact matching for the expert retrieval task in particular and entity retrieval in general. We will also discuss extensions of the proposed model that are meant to deal with scalability and dynamic aspects of entity and expert retrieval

Aconitate decarboxylase 1 participates in the control of pulmonary Brucella infection in mice

Brucellosis is one of the most widespread bacterial zoonoses worldwide. Here, our aim was to identify the effector mechanisms controlling the early stages of intranasal infection with Brucella in C57BL/6 mice. During the first 48 hours of infection, alveolar macrophages (AMs) are the main cells infected in the lungs. Using RNA sequencing, we identified the aconitate decarboxylase 1 gene ( Acod1 ;also known as Immune responsive gene 1), as one of the genes most upregulated in murine AMs in response to B .melitensis infection at 24 hours post-infection. Upregulation of Acod1 was confirmed by RT-qPCR in lungs infected with B .melitensis and B .abortus .We observed that Acod1 -/- C57BL/6 mice display a higher bacterial load in their lungs than wild-type (wt) mice following B .melitensis or B .abortus infection, demonstrating that Acod1 participates in the control of pulmonary Brucella infection. The ACOD1 enzyme is mostly produced in mitochondria of macrophages, and converts cis-aconitate, a metabolite in the Krebs cycle, into itaconate. Dimethyl itaconate (DMI), a chemically-modified membrane permeable form of itaconate, has a dose-dependent inhibitory effect on Brucella growth in vitro .Interestingly, structural analysis suggests the binding of itaconate into the binding site of B .abortus isocitrate lyase. DMI does not inhibit multiplication of the isocitrate lyase deletion mutant Δ aceA B .abortus in vitro .Finally, we observed that, unlike the wt strain, the Δ aceA B .abortus strain multiplies similarly in wt and Acod1 -/- C57BL/6 mice. These data suggest that bacterial isocitrate lyase might be a target of itaconate in AMs.info:eu-repo/semantics/publishe

High body mass index is not associated with atopy in schoolchildren living in rural and urban areas of Ghana

<p>Abstract</p> <p>Background</p> <p>Factors which determine the development of atopy and the observed rural-urban gradient in its prevalence are not fully understood. High body mass index (BMI) has been associated with asthma and potentially atopy in industrialized countries. In developing countries, the transition from rural to urban areas has been associated with lifestyle changes and an increased prevalence of high BMI; however, the effect of high BMI on atopy remains unknown in this population. We therefore investigated the association between high BMI and atopy among schoolchildren living in rural and urban areas of Ghana.</p> <p>Methods</p> <p>Data on skin prick testing, anthropometric, parasitological, demographic and lifestyle information for 1,482 schoolchildren aged 6-15 years was collected. Atopy was defined as sensitization to at least one tested allergen whilst the Centres for Disease Control and Prevention (CDC, Atlanta) growth reference charts were used in defining high BMI as BMI ≥ the 85^thpercentile. Logistic regression was performed to investigate the association between high BMI and atopy whilst adjusting for potential confounding factors.</p> <p>Results</p> <p>The following prevalences were observed for high BMI [Rural: 16%, Urban: 10.8%, p < 0.001] and atopy [Rural: 25.1%, Urban: 17.8%, p < 0.001]. High BMI was not associated with atopy; but an inverse association was observed between underweight and atopy [OR: 0.57, 95% CI: 0.33-0.99]. Significant associations were also observed with male sex [Rural: OR: 1.49, 95% CI: 1.06-2.08; Urban: OR: 1.90, 95% CI: 1.30-2.79], and in the urban site with older age [OR: 1.76, 95% CI: 1.00-3.07], family history of asthma [OR: 1.58, 95% CI: 1.01-2.47] and occupational status of parent [OR: 0.33, 95% CI: 0.12-0.93]; whilst co-infection with intestinal parasites [OR: 2.47, 95% CI: 1.01-6.04] was associated with atopy in the rural site. After multivariate adjustment, male sex, older age and family history of asthma remained significant.</p> <p>Conclusions</p> <p>In Ghanaian schoolchildren, high BMI was not associated with atopy. Further studies are warranted to clarify the relationship between body weight and atopy in children subjected to rapid life-style changes associated with urbanization of their environments.</p

Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011-2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of -24 % and -35 % for particles with dry diameters > 50 and > 120 nm, as well as -36 % and -34 % for CCN at supersaturations of 0.2 % and 1.0 %, respectively. However, they seem to behave differently for particles activating at very low supersaturations (<0.1 %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N-3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2 % (CCN0.2) compared to that for N-3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40 % during winter and 20 % in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB -13 % and -22 % for updraft velocities 0.3 and 0.6 m s(-1), respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (partial derivative N-d/partial derivative N-a) and to updraft velocity (partial derivative N-d/partial derivative w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities partial derivative N-d/partial derivative N-a and partial derivative N-d/partial derivative w; models may be predisposed to be too "aerosol sensitive" or "aerosol insensitive" in aerosol-cloud-climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain intermodel biases on the aerosol indirect effect.Peer reviewe

Evaluation of Global Simulations of Aerosol Particle and Cloud Condensation Nuclei Number, with Implications for Cloud Droplet Formation

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011-2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of -24% and -35% for particles with dry diameters > 50 and > 120nm, as well as -36% and -34% for CCN at supersaturations of 0.2% and 1.0%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (< 0.1%) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN(0.2)) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40% during winter and 20% in summer

Antimicrobial protein and Peptide concentrations and activity in human breast milk consumed by preterm infants at risk of late-onset neonatal sepsis

Objective: We investigated the levels and antimicrobial activity of antimicrobial proteins and peptides (AMPs) in breast milk consumed by preterm infants, and whether deficiencies of these factors were associated with late-onset neonatal sepsis (LOS), a bacterial infection that frequently occurs in preterm infants in the neonatal period. Study design: Breast milk from mothers of preterm infants (≤32 weeks gestation) was collected on days 7 (n = 88) and 21 (n = 77) postpartum. Concentrations of lactoferrin, LL-37, beta-defensins 1 and 2, and alpha-defensin 5 were measured by enzyme-linked immunosorbent assay. The antimicrobial activity of breast milk samples against Staphylococcus epidermidis, Staphylococcus aureus, Escherichia coli, and Streptococcus agalactiae was compared to the activity of infant formula, alone or supplemented with physiological levels of AMPs. Samples of breast milk fed to infants with and without subsequent LOS were compared for levels of AMPs and inhibition of bacterial growth. Results: Levels of most AMPs and antibacterial activity in preterm breast milk were higher at day 7 than at day 21. Lactoferrin was the only AMP that limited pathogen growth >50% when added to formula at a concentration equivalent to that present in breast milk. Levels of AMPs were similar in the breast milk fed to infants with and without LOS, however, infants who developed LOS consumed significantly less breast milk and lower doses of milk AMPs than those who were free from LOS. Conclusions: The concentrations of lactoferrin and defensins in preterm breast milk have antimicrobial activity against common neonatal pathogens