Utilizacion de grasas de origen vegetal en raciones de vacas lecheras: rendimentos productivos, reproductivos y composición de la grasa

El objetivo del presente trabajo fue estudiar los efectos de la incorporación de aceites vegetales con distinto grado de saturación en raciones de vacas lecheras, sobre la producción, composición de la leche y de la grasa, y sobre los rendimientos reproductivos. Las raciones experimentales fueron: CONTROL (sin grasa añadida, TC), SOJA (con un 4% de aceite de soja en el concentrado, TS) y LINAZA (con un 4% de aceite de linaza en el concentrado, TL). La incorporación de aceite no afectó la producción de leche, pero redujo el contenido en grasa y proteína. Las raciones con aceite de SOJA y LINAZA presentaron un mayor contenido en ácido Vaccénico y CLA; y una mejor relación poli insaturados/saturados y n6/n3 que las raciones control. Las raciones con aceite de LINAZA dieron lugar a una mayor producción de leche al pico y tardaron menos días en alcanzar el pico de producción que las dietas CONTROL y SOJA. En general, la incorporación de aceite en las raciones dio lugar a una mejora significativa en los índices reproductivos

Modeling Long-term Dependencies and Short-term Correlations in Patient Journey Data with Temporal Attention Networks for Health Prediction

Building models for health prediction based on Electronic Health Records (EHR) has become an active research area. EHR patient journey data consists of patient time-ordered clinical events/visits from patients. Most existing studies focus on modeling long-term dependencies between visits, without explicitly taking short-term correlations between consecutive visits into account, where irregular time intervals, incorporated as auxiliary information, are fed into health prediction models to capture latent progressive patterns of patient journeys. We present a novel deep neural network with four modules to take into account the contributions of various variables for health prediction: i) the Stacked Attention module strengthens the deep semantics in clinical events within each patient journey and generates visit embeddings, ii) the Short-Term Temporal Attention module models short-term correlations between consecutive visit embeddings while capturing the impact of time intervals within those visit embeddings, iii) the Long-Term Temporal Attention module models long-term dependencies between visit embeddings while capturing the impact of time intervals within those visit embeddings, iv) and finally, the Coupled Attention module adaptively aggregates the outputs of Short-Term Temporal Attention and Long-Term Temporal Attention modules to make health predictions. Experimental results on MIMIC-III demonstrate superior predictive accuracy of our model compared to existing state-of-the-art methods, as well as the interpretability and robustness of this approach. Furthermore, we found that modeling short-term correlations contributes to local priors generation, leading to improved predictive modeling of patient journeys.Comment: 10 pages, 4 figures, accepted at ACM BCB 202

Combining Evidence, Specificity, and Proximity towards the Normalization of Gene Ontology Terms in Text

Structured information provided by manual annotation of proteins with Gene Ontology concepts represents a high-quality reliable data source for the research community. However, a limited scope of proteins is annotated due to the amount of human resources required to fully annotate each individual gene product from the literature. We introduce a novel method for automatic identification of GO terms in natural language text. The method takes into consideration several features: (1) the evidence for a GO term given by the words occurring in text, (2) the proximity between the words, and (3) the specificity of the GO terms based on their information content. The method has been evaluated on the BioCreAtIvE corpus and has been compared to current state of the art methods. The precision reached 0.34 at a recall of 0.34 for the identified terms at rank 1. In our analysis, we observe that the identification of GO terms in the “cellular component†subbranch of GO is more accurate than for terms from the other two subbranches. This observation is explained by the average number of words forming the terminology over the different subbranches

A New Combination Method Based on Adaptive Genetic Algorithm for Medical Image Retrieval

Medical image retrieval could be based on the text describing the image as the caption or the title. The use of text terms to retrieve images have several disadvantages such as term-disambiguation. Recent studies prove that representing text into semantic units (concepts) can improve the semantic representation of textual information. However, the use of conceptual representation has other problems as the miss or erroneous semantic relation between two concepts. Other studies show that combining textual and conceptual text representations leads to better accuracy. Popularly, a score for textual representation and a score for conceptual representation are computed and then a combination function is used to have one score. Although the existing of many combination methods of two scores, we propose in this paper a new combination method based on adaptive version of the genetic algorithm. Experiments are carried out on Medical Information Retrieval Task of the ImageCLEF 2009 and 2010. The results confirm that the combination of both textual and conceptual scores allows best accuracy. In addition, our approach outperforms the other combination methods

Collocation analysis for UMLS knowledge-based word sense disambiguation

BACKGROUND: The effectiveness of knowledge-based word sense disambiguation (WSD) approaches depends in part on the information available in the reference knowledge resource. Off the shelf, these resources are not optimized for WSD and might lack terms to model the context properly. In addition, they might include noisy terms which contribute to false positives in the disambiguation results. METHODS: We analyzed some collocation types which could improve the performance of knowledge-based disambiguation methods. Collocations are obtained by extracting candidate collocations from MEDLINE and then assigning them to one of the senses of an ambiguous word. We performed this assignment either using semantic group profiles or a knowledge-based disambiguation method. In addition to collocations, we used second-order features from a previously implemented approach.Specifically, we measured the effect of these collocations in two knowledge-based WSD methods. The first method, AEC, uses the knowledge from the UMLS to collect examples from MEDLINE which are used to train a Naïve Bayes approach. The second method, MRD, builds a profile for each candidate sense based on the UMLS and compares the profile to the context of the ambiguous word.We have used two WSD test sets which contain disambiguation cases which are mapped to UMLS concepts. The first one, the NLM WSD set, was developed manually by several domain experts and contains words with high frequency occurrence in MEDLINE. The second one, the MSH WSD set, was developed automatically using the MeSH indexing in MEDLINE. It contains a larger set of words and covers a larger number of UMLS semantic types. RESULTS: The results indicate an improvement after the use of collocations, although the approaches have different performance depending on the data set. In the NLM WSD set, the improvement is larger for the MRD disambiguation method using second-order features. Assignment of collocations to a candidate sense based on UMLS semantic group profiles is more effective in the AEC method.In the MSH WSD set, the increment in performance is modest for all the methods. Collocations combined with the MRD disambiguation method have the best performance. The MRD disambiguation method and second-order features provide an insignificant change in performance. The AEC disambiguation method gives a modest improvement in performance. Assignment of collocations to a candidate sense based on knowledge-based methods has better performance. CONCLUSIONS: Collocations improve the performance of knowledge-based disambiguation methods, although results vary depending on the test set and method used. Generally, the AEC method is sensitive to query drift. Using AEC, just a few selected terms provide a large improvement in disambiguation performance. The MRD method handles noisy terms better but requires a larger set of terms to improve performance

Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction

The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist healthcare professionals in clinical decision-making. Existing methods focus on measuring the similarity between patients using deep neural networks to capture the hidden feature structures. However, the higher-order relationships are ignored, such as patient characteristics (e.g., diagnosis codes) and their causal effects on downstream clinical predictions. In this paper, we propose a novel Hypergraph Convolutional Network that allows the representation of non-pairwise relationships among diagnosis codes in a hypergraph to capture the hidden feature structures so that fine-grained patient similarity can be calculated for personalized mortality risk prediction. Evaluation using a publicly available eICU Collaborative Research Database indicates that our method achieves superior performance over the state-of-the-art models on mortality risk prediction. Moreover, the results of several case studies demonstrated the effectiveness of constructing graph networks in providing good transparency and robustness in decision-making.Comment: 7 pages, 2 figures, submitted to IEEE BIBM 202

Employment Expectations and Gross Flows by Type of Work Contract

There is growing interest in understanding firms’ temporary and permanent employment practices and how institutional changes shape them. Using data on Spanish establishments, we examine: (a) how employers adjust temporary and permanent job and worker flows to prior employment expectations, and (b) how the 1994 and 1997 labour reforms promoting permanent employment affected establishments’ employment practices. Generally, establishments’ prior employment expectations are realized through changes in all job and worker flows. However, establishments uniquely rely on temporary hires as a buffer to confront diminishing long-run employment expectations. None of the reforms significantly affected establishments’ net temporary or permanent employment flows.http://deepblue.lib.umich.edu/bitstream/2027.42/40032/3/wp646.pd

Knowledge-based biomedical word sense disambiguation: comparison of approaches

<p>Abstract</p> <p>Background</p> <p>Word sense disambiguation (WSD) algorithms attempt to select the proper sense of ambiguous terms in text. Resources like the UMLS provide a reference thesaurus to be used to annotate the biomedical literature. Statistical learning approaches have produced good results, but the size of the UMLS makes the production of training data infeasible to cover all the domain.</p> <p>Methods</p> <p>We present research on existing WSD approaches based on knowledge bases, which complement the studies performed on statistical learning. We compare four approaches which rely on the UMLS Metathesaurus as the source of knowledge. The first approach compares the overlap of the context of the ambiguous word to the candidate senses based on a representation built out of the definitions, synonyms and related terms. The second approach collects training data for each of the candidate senses to perform WSD based on queries built using monosemous synonyms and related terms. These queries are used to retrieve MEDLINE citations. Then, a machine learning approach is trained on this corpus. The third approach is a graph-based method which exploits the structure of the Metathesaurus network of relations to perform unsupervised WSD. This approach ranks nodes in the graph according to their relative structural importance. The last approach uses the semantic types assigned to the concepts in the Metathesaurus to perform WSD. The context of the ambiguous word and semantic types of the candidate concepts are mapped to Journal Descriptors. These mappings are compared to decide among the candidate concepts. Results are provided estimating accuracy of the different methods on the WSD test collection available from the NLM.</p> <p>Conclusions</p> <p>We have found that the last approach achieves better results compared to the other methods. The graph-based approach, using the structure of the Metathesaurus network to estimate the relevance of the Metathesaurus concepts, does not perform well compared to the first two methods. In addition, the combination of methods improves the performance over the individual approaches. On the other hand, the performance is still below statistical learning trained on manually produced data and below the maximum frequency sense baseline. Finally, we propose several directions to improve the existing methods and to improve the Metathesaurus to be more effective in WSD.</p

Wavelength-shifter coated polystyrene as an easy-to-build and low-cost plastic scintillator detector

We studied the light yield of a pure polystyrene slide coated with wavelength-shifter molecules, coupled to a photomultiplier, using beta particles from a 90-Sr source, as a possible easy-to-build, low-cost plastic scintillator detector. Comparison measurements were performed with an uncoated polystyrene slide as well as with uncoated and coated PMMA slides, the latter which can only produce Cherenkov light when being traversed by charged particles. The results with the single (double) coated polystyrene slides show about 4.9 (6.3) times higher detected photon yield compared to the uncoated slide. For comparison, the light yield of a polystyrene-based extruded plastic scintillator material doped with PTP and POPOP was measured as well. The absolute detected light yield motivates future studies for developing easy-to-build, low-cost polystyrene-based plastic scintillator detectors.Comment: 20 pages, 13 figure

Auxins seem promising as a tuning method for balancing sugars with acidity in grape musts from cv. Tempranillo, but not defoliation or application of magnesium to leaves

[EN] Global warming boosted by climate change affects grape quality, with increasing total soluble solids (TSS) content and decreasing total acidity (TA). However, current wine preferences increasingly include moderate alcohol content, higher acidity and the preservation of primary aromas reminiscent of grapes. Therefore, we hypothesised that applying phytohormones or mineral nutrients to leaves or carrying out defoliation can improve grape must properties in the face of climate warming and in accordance with current oenological trends. The effects of these three viticultural strategies were assessed independently from one another during three growing seasons in a Vitis vinifera L. cv. Tempranillo vineyard in northern Spain. Specifically, three 1-naphtaleneacetic acid (NAA) treatments, two early defoliations (ED; moderate and severe) and two foliar fertilisations with magnesium (Mg) were applied. Treatment with NAA was the most encouraging strategy for decreasing must TSS while increasing TA: it had slight effects on TSS in general and also slight effects on TA when applied close to veraison. The effects of the Mg treatments and moderate ED had null to slightly adverse effects. Finally, severe ED was clearly counter-productive. This study contributes to understanding the effects of both auxin and early defoliation treatments on grape must TSS, acidity and even yeast assimilable nitrogen (YAN) at harvest time. The favourable effects of NAA application are shown to be consistent though slight. Therefore, according to these results, the application of auxins may be an adequate choice for balancing sugars with acidity in grape musts. However, the results also suggest that more research needs to be undertaken to better characterise the effects of auxin treatments on grape must properties at harvest. In particular, different types of auxins, rates, concentrations and number of applications should be tested in the quest for more marked effects.SIThe authors are most grateful to both Consejo Regulador de la Denominación de Origen Ribera del Duero and Bodega and Viñedos Martín Berdugo, S.L., for assisting with this research project