Building a framework for fake news detection in the health domain.

Disinformation in the medical field is a growing problem that carries a significant risk. Therefore, it is crucial to detect and combat it effectively. In this article, we provide three elements to aid in this fight: 1) a new framework that collects health-related articles from verification entities and facilitates their check-worthiness and fact-checking annotation at the sentence level; 2) a corpus generated using this framework, composed of 10335 sentences annotated in these two concepts and grouped into 327 articles, which we call KEANE (faKe nEws At seNtence lEvel); and 3) a new model for verifying fake news that combines specific identifiers of the medical domain with triplets subject-predicate-object, using Transformers and feedforward neural networks at the sentence level. This model predicts the fact-checking of sentences and evaluates the veracity of the entire article. After training this model on our corpus, we achieved remarkable results in the binary classification of sentences (check-worthiness F1: 0.749, fact-checking F1: 0.698) and in the final classification of complete articles (F1: 0.703). We also tested its performance against another public dataset and found that it performed better than most systems evaluated on that dataset. Moreover, the corpus we provide differs from other existing corpora in its duality of sentence-article annotation, which can provide an additional level of justification of the prediction of truth or untruth made by the model.

Negation-based transfer learning for improving biomedical Named Entity Recognition and Relation Extraction.

BACKGROUND AND OBJECTIVES: Named Entity Recognition (NER) and Relation Extraction (RE) are two of the most studied tasks in biomedical Natural Language Processing (NLP). The detection of specific terms and entities and the relationships between them are key aspects for the development of more complex automatic systems in the biomedical field. In this work, we explore transfer learning techniques for incorporating information about negation into systems performing NER and RE. The main purpose of this research is to analyse to what extent the successful detection of negated entities in separate tasks helps in the detection of biomedical entities and their relationships. METHODS: Three neural architectures are proposed in this work, all of them mainly based on Bidirectional Long Short-Term Memory (Bi-LSTM) networks and Conditional Random Fields (CRFs). While the first architecture is devoted to detecting triggers and scopes of negated entities in any domain, two specific models are developed for performing isolated NER tasks and joint NER and RE tasks in the biomedical domain. Then, weights related to negation detection learned by the first architecture are incorporated into those last models. Two different languages, Spanish and English, are taken into account in the experiments. RESULTS: Performance of the biomedical models is analysed both when the weights of the neural networks are randomly initialized, and when weights from the negation detection model are incorporated into them. Improvements of around 3.5% of F-Measure in the English language and more than 7% in the Spanish language are achieved in the NER task, while the NER+RE task increases F-Measure scores by more than 13% for the NER submodel and around 2% for the RE submodel. CONCLUSIONS: The obtained results allow us to conclude that negation-based transfer learning techniques are appropriate for performing biomedical NER and RE tasks. These results highlight the importance of detecting negation for improving the identification of biomedical entities and their relationships. The explored techniques show robustness by maintaining consistent results and improvements across different tasks and languages.

White spot syndrome virus impact on the expression of immune genes and gut microbiome of black tiger shrimp Penaeus monodon.

The gut microbiome plays an essential role in the immune system of invertebrates and vertebrates. Pre and pro-biotics could enhance the shrimp immune system by increasing the phenoloxidase (PO), prophenoloxidase (ProPO), and superoxide dismutase activities. During viral infection, the host immune system alteration could influence the gut microbiome composition and probably lead to other pathogenic infections. Since the JAK/STAT pathway is involved in white spot syndrome virus (WSSV) infection, we investigated the intestine immune genes of STAT-silenced shrimp. During WSSV infection, expression levels of PmVago1, PmDoral, and PmSpätzle in PmSTAT-silenced shrimp were higher than normal. In addition, the transcription levels of antimicrobial peptides, including crustinPm1, crustinPm7, and PmPEN3, were higher in WSSV-challenged PmSTAT-silenced shrimp than the WSSV-infected normal shrimp. Meanwhile, PmSTAT silencing suppressed PmProPO1, PmProPO2, and PmPPAE1 expressions during WSSV infection. The microbiota from four shrimp tested groups (control group, WSSV-infected, PmSTAT-silenced, and PmSTAT-silenced infected by WSSV) was significantly different, with decreasing richness and diversity due to WSSV infection. The relative abundance of Bacteroidetes, Actinobacteria, and Planctomycetes was reduced in WSSV-challenged shrimp. However, at the species level, P. damselae, a pathogen to human and marine animals, significantly increased in WSSV-challenged shrimp. In constrast, Shewanella algae, a shrimp probiotic, was decreased in WSSV groups. In addition, the microbiota structure between control and PmSTAT-silenced shrimp was significantly different, suggesting the importance of STAT to maintain the homeostasis interaction with the microbiota.

Discovering HIV related information by means of association rules and machine learning.

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts.

Agavin induces beneficial microbes in the shrimp microbiota under farming conditions.

Prebiotics and probiotics have shown a number of beneficial impacts preventing diseases in cultured shrimps. Complex soluble carbohydrates are considered ideal for fostering microbiota biodiversity by fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPS). Here we evaluated the growth performance and microbiota composition of the white shrimp Litopenaeus vannamei after dietary intervention using agavin as a FODMAP prebiotic under farming conditions. Adult L. vannamei were raised at a shrimp farm and the effect of agavin supplemented at 2% (AG2) or 10% (AG10) levels were compared to an agavin-free basal diet (BD). After 28 days-trial, the feed conversion ratio, total feed ingested, and protein efficiency ratio was significantly improved on animals fed with AG2. At the same time, no effect on growth performance was observed in AG10. Surprisingly, after sequencing the V3-V4 regions of the 16S rRNA gene a higher microbial richness and diversity in the hepatopancreas and intestine was found only in those animals receiving the AG10 diet, while those receiving the AG2 diet had a decreased richness and diversity, both diets compared to the BD. The beta diversity analysis showed a clear significant microbiota clustering by agavin diets only in the hepatopancreas, suggesting that agavin supplementation had a more substantial deterministic effect on the microbiota of hepatopancreas than on the intestine. We analyzed the literature to search beneficial microbes for shrimp's health and found sequences for 42 species in our 16S data, being significantly increased Lactobacillus pentosus, Pseudomonas putida and Pseudomonas synxantha in the hepatopancreas of the AG10 and Rodopseudomonas palustris and Streptococcus thermophiles th1435 in the hepatopancreas of the AG2, both compared to BD. Interestingly, when we analyzed the abundance of 42 beneficial microbes as a single microbial community "meta-community," found an increase in their abundance as agavin concentration increases in the hepatopancreas. In addition, we also sequenced the DNA of agavin and found 9 of the 42 beneficial microbes. From those, Lactobacillus lactis and Lactobacillus delbrueckii were found in shrimps fed with agavin (both AG2 and AG10), and Lysinibacillus fusiformis in AG10 and they were absent the BD diet, suggesting these three species could be introduced with the agavin to the diet. Our work provides evidence that agavin supplementation is associated with an increase of beneficial microbes for the shrimp microbiota at farming conditions. Our study provides the first evidence that a shrimp prebiotic may selectively modify the microbiota in an organ-dependent effect.

Depthgram: Visualizing outliers in high-dimensional functional data with application to fMRI data exploration.

Functional magnetic resonance imaging (fMRI) is a non-invasive technique that facilitates the study of brain activity by measuring changes in blood flow. Brain activity signals can be recorded during the alternate performance of given tasks, that is, task fMRI (tfMRI), or during resting-state, that is, resting-state fMRI (rsfMRI), as a measure of baseline brain activity. This contributes to the understanding of how the human brain is organized in functionally distinct subdivisions. fMRI experiments from high-resolution scans provide hundred of thousands of longitudinal signals for each individual, corresponding to brain activity measurements over each voxel of the brain along the duration of the experiment. In this context, we propose novel visualization techniques for high-dimensional functional data relying on depth-based notions that enable computationally efficient 2-dim representations of fMRI data, which elucidate sample composition, outlier presence, and individual variability. We believe that this previous step is crucial to any inferential approach willing to identify neuroscientific patterns across individuals, tasks, and brain regions. We present the proposed technique via an extensive simulation study, and demonstrate its application on a motor and language tfMRI experiment.

Semi-supervised incremental learning with few examples for discovering medical association rules.

BACKGROUND: Association Rules are one of the main ways to represent structural patterns underlying raw data. They represent dependencies between sets of observations contained in the data. The associations established by these rules are very useful in the medical domain, for example in the predictive health field. Classic algorithms for association rule mining give rise to huge amounts of possible rules that should be filtered in order to select those most likely to be true. Most of the proposed techniques for these tasks are unsupervised. However, the accuracy provided by unsupervised systems is limited. Conversely, resorting to annotated data for training supervised systems is expensive and time-consuming. The purpose of this research is to design a new semi-supervised algorithm that performs like supervised algorithms but uses an affordable amount of training data. METHODS: In this work we propose a new semi-supervised data mining model that combines unsupervised techniques (Fisher's exact test) with limited supervision. Starting with a small seed of annotated data, the model improves results (F-measure) obtained, using a fully supervised system (standard supervised ML algorithms). The idea is based on utilising the agreement between the predictions of the supervised system and those of the unsupervised techniques in a series of iterative steps. RESULTS: The new semi-supervised ML algorithm improves the results of supervised algorithms computed using the F-measure in the task of mining medical association rules, but training with an affordable amount of manually annotated data. CONCLUSIONS: Using a small amount of annotated data (which is easily achievable) leads to results similar to those of a supervised system. The proposal may be an important step for the practical development of techniques for mining association rules and generating new valuable scientific medical knowledge.

A keyphrase-based approach for interpretable ICD-10 code classification of Spanish medical reports.

BACKGROUND AND OBJECTIVES: The 10th version of International Classification of Diseases (ICD-10) codification system has been widely adopted by the health systems of many countries, including Spain. However, manual code assignment of Electronic Health Records (EHR) is a complex and time-consuming task that requires a great amount of specialised human resources. Therefore, several machine learning approaches are being proposed to assist in the assignment task. In this work we present an alternative system for automatically recommending ICD-10 codes to be assigned to EHRs. METHODS: Our proposal is based on characterising ICD-10 codes by a set of keyphrases that represent them. These keyphrases do not only include those that have literally appeared in some EHR with the considered ICD-10 codes assigned, but also others that have been obtained by a statistical process able to capture expressions that have led the annotators to assign the code. RESULTS: The result is an information model that allows to efficiently recommend codes to a new EHR based on their textual content. We explore an approach that proves to be competitive with other state-of-the-art approaches and can be combined with them to optimise results. CONCLUSIONS: In addition to its effectiveness, the recommendations of this method are easily interpretable since the phrases in an EHR leading to recommend an ICD-10 code are known. Moreover, the keyphrases associated with each ICD-10 code can be a valuable additional source of information for other approaches, such as machine learning techniques.

Gut dsDNA virome shows diversity and richness alterations associated with childhood obesity and metabolic syndrome.

Changes in the human gut microbiome are associated with obesity and metabolic syndrome, but the role of the gut virome in both diseases remains largely unknown. We characterized the gut dsDNA virome of 28 school-aged children with healthy normal-weight (NW, n = 10), obesity (O, n = 10), and obesity with metabolic syndrome (OMS, n = 8), using metagenomic sequencing of virus-like particles (VLPs) from fecal samples. The virome classification confirmed the bacteriophages' dominance, mainly composed of Caudovirales. Notably, phage richness and diversity of individuals with O and OMS tended to increase, while the VLP abundance remained the same among all groups. Of the 4,611 phage contigs composing the phageome, 48 contigs were highly prevalent in ≥80% of individuals, suggesting high inter-individual phage diversity. The abundance of several contigs correlated with gut bacterial taxa; and with anthropometric and biochemical parameters altered in O and OMS. To our knowledge, this gut phageome represents one of the largest datasets and suggests disease-specific phage alterations.

Censored functional data for incomplete follow-up studies.

Functional data analysis plays an increasingly important role in medical research because patients are followed over time. Thus, the measurements of a particular biomarker for each patient are often registered as curves. Hence, it is of interest to estimate the mean function under certain conditions as an average of the observed functional data over a given period. However, this is often difficult as this type of follow-up studies are confronted with the challenge of some individuals dropping-out before study completion. Therefore, for these individuals, only a partial functional observation is available. In this study, we propose an estimator for the functional mean when the functions may be censored from the right, and thus, only partly observed. Unlike sparse functional data, the censored curves are observed until some (random) time and this censoring time may depend on the trajectory of the functional observations. Our approach is model-free and fully nonparametric, although the proposed methods can also be incorporated into regression models. The use of the functional structure of the data distinguishes our approach from the longitudinal data approaches. In addition, in this study, we propose a bootstrap-based confidence band for the mean function, examine the estimation of the covariance function, and apply our new approach to functional principal component analysis. Employing an extensive simulation study, we demonstrate that our method outperforms the only two existing approaches. Furthermore, we apply our new estimator to a real data example on lung growth, measured by changes in pulmonary function for girls in the United States.

Variable selection with P-splines in functional linear regression: Application in graft-versus-host disease.

This paper focuses on the problems of estimation and variable selection in the functional linear regression model (FLM) with functional response and scalar covariates. To this end, two different types of regularization (L1 and L2 ) are considered in this paper. On the one hand, a sample approach for functional LASSO in terms of basis representation of the sample values of the response variable is proposed. On the other hand, we propose a penalized version of the FLM by introducing a P-spline penalty in the least squares fitting criterion. But our aim is to propose P-splines as a powerful tool simultaneously for variable selection and functional parameters estimation. In that sense, the importance of smoothing the response variable before fitting the model is also studied. In summary, penalized (L1 and L2 ) and nonpenalized regression are combined with a presmoothing of the response variable sample curves, based on regression splines or P-splines, providing a total of six approaches to be compared in two simulation schemes. Finally, the most competitive approach is applied to a real data set based on the graft-versus-host disease, which is one of the most frequent complications (30% -50%) in allogeneic hematopoietic stem-cell transplantation.

ITERL: A Wireless Adaptive System for Efficient Road Lighting.

This work presents the development and construction of an adaptive street lighting system that improves safety at intersections, which is the result of applying low-power Internet of Things (IoT) techniques to intelligent transportation systems. A set of wireless sensor nodes using the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard with additional internet protocol (IP) connectivity measures both ambient conditions and vehicle transit. These measurements are sent to a coordinator node that collects and passes them to a local controller, which then makes decisions leading to the streetlight being turned on and its illumination level controlled. Streetlights are autonomous, powered by photovoltaic energy, and wirelessly connected, achieving a high degree of energy efficiency. Relevant data are also sent to the highway conservation center, allowing it to maintain up-to-date information for the system, enabling preventive maintenance.

Deep neural models for extracting entities and relationships in the new RDD corpus relating disabilities and rare diseases.

BACKGROUND AND OBJECTIVE: There is a huge amount of rare diseases, many of which have associated important disabilities. It is paramount to know in advance the evolution of the disease in order to limit and prevent the appearance of disabilities and to prepare the patient to manage the future difficulties. Rare disease associations are making an effort to manually collect this information, but it is a long process. A lot of information about the consequences of rare diseases is published in scientific papers, and our goal is to automatically extract disabilities associated with diseases from them. METHODS: This work presents a new corpus of abstracts from scientific papers related to rare diseases, which has been manually annotated with disabilities. This corpus allows to train machine and deep learning systems that can automatically process other papers, thus extracting new information about the relations between rare diseases and disabilities. The corpus is also annotated with negation and speculation when they appear affecting disabilities. The corpus has been made publicly accessible. RESULTS: We have devised some experiments using deep learning techniques to show the usefulness of the developed corpus. Specifically, we have designed a long short-term memory based architecture for disabilities identification, as well as a convolutional neural network for detecting their relationships to diseases. The systems designed do not need any preprocessing of the data, but only low dimensional vectors representing the words. CONCLUSIONS: The developed corpus will allow to train systems to identify disabilities in biomedical documents, which the current annotation systems are not able to detect. The system could also be trained to detect relationships between them and diseases, as well as negation and speculation, that can change the meaning of the language. The deep learning models designed for identifying disabilities and their relationships to diseases in new documents show that the corpus allows obtaining an F-measure of around 81% for the disability recognition and 75% for relation extraction.

A meta-analysis reveals the environmental and host factors shaping the structure and function of the shrimp microbiota.

The shrimp or prawn is the most valuable traded marine product in the world market today and its microbiota plays an essential role in its development, physiology, and health. The technological advances and dropping costs of high-throughput sequencing have increased the number of studies characterizing the shrimp microbiota. However, the application of different experimental and bioinformatics protocols makes it difficult to compare different studies to reach general conclusions about shrimp microbiota. To meet this necessity, we report the first meta-analysis of the microbiota from freshwater and marine shrimps using all publically available sequences of the 16S ribosomal gene (16S rRNA gene). We obtained data for 199 samples, in which 63.3% were from marine (Alvinocaris longirostris, Litopenaeus vannamei and Penaeus monodon), and 36.7% were from freshwater (Macrobrachium asperulum, Macrobrachium nipponense, Macrobranchium rosenbergii, Neocaridina denticulata) shrimps. Technical variations among studies, such as selected primers, hypervariable region, and sequencing platform showed a significant impact on the microbiota structure. Additionally, the ANOSIM and PERMANOVA analyses revealed that the most important biological factor in structuring the shrimp microbiota was the marine and freshwater environment (ANOSIM R = 0.54, P = 0.001; PERMANOVA pseudo-F = 21.8, P = 0.001), where freshwater showed higher bacterial diversity than marine shrimps. Then, for marine shrimps, the most relevant biological factors impacting the microbiota composition were lifestyle (ANOSIM R = 0.341, P = 0.001; PERMANOVA pseudo-F = 8.50, P = 0.0001), organ (ANOSIM R = 0.279, P = 0.001; PERMANOVA pseudo-F = 6.68, P = 0.001) and developmental stage (ANOSIM R = 0.240, P = 0.001; PERMANOVA pseudo-F = 5.05, P = 0.001). According to the lifestyle, organ, developmental stage, diet, and health status, the highest diversity were for wild-type, intestine, adult, wild-type diet, and healthy samples, respectively. Additionally, we used PICRUSt to predict the potential functions of the microbiota, and we found that the organ had more differentially enriched functions (93), followed by developmental stage (12) and lifestyle (9). Our analysis demonstrated that despite the impact of technical and bioinformatics factors, the biological factors were also statistically significant in shaping the microbiota. These results show that cross-study comparisons are a valuable resource for the improvement of the shrimp microbiota and microbiome fields. Thus, it is important that future studies make public their sequencing data, allowing other researchers to reach more powerful conclusions about the microbiota in this non-model organism. To our knowledge, this is the first meta-analysis that aims to define the shrimp microbiota.

A novel predictive approach for GVHD after allogeneic SCT based on clinical variables and cytokine gene polymorphisms.

Despite considerable advances in our understanding of the pathophysiology of graft-versus-host disease (GVHD), its prediction remains unresolved and depends mainly on clinical data. The aim of this study is to build a predictive model based on clinical variables and cytokine gene polymorphism for predicting acute GVHD (aGVHD) and chronic GVHD (cGVHD) from the analysis of a large cohort of HLA-identical sibling donor allogeneic stem cell transplant (allo-SCT) patients. A total of 25 SNPs in 12 cytokine genes were evaluated in 509 patients. Data were analyzed using a linear regression model and the least absolute shrinkage and selection operator (LASSO). The statistical model was constructed by randomly selecting 85% of cases (training set), and the predictive ability was confirmed based on the remaining 15% of cases (test set). Models including clinical and genetic variables (CG-M) predicted severe aGVHD significantly better than models including only clinical variables (C-M) or only genetic variables (G-M). For grades 3-4 aGVHD, the correct classification rates (CCR1) were: 100% for CG-M, 88% for G-M, and 50% for C-M. On the other hand, CG-M and G-M predicted extensive cGVHD better than C-M (CCR1: 80% vs. 66.7%, respectively). A risk score was calculated based on LASSO multivariate analyses. It was able to correctly stratify patients who developed grades 3-4 aGVHD (P < .001) and extensive cGVHD (P < .001). The novel predictive models proposed here improve the prediction of severe GVHD after allo-SCT. This approach could facilitate personalized risk-adapted clinical management of patients undergoing allo-SCT.

Co-occurrence graphs for word sense disambiguation in the biomedical domain.

Word sense disambiguation is a key step for many natural language processing tasks (e.g. summarization, text classification, relation extraction) and presents a challenge to any system that aims to process documents from the biomedical domain. In this paper, we present a new graph-based unsupervised technique to address this problem. The knowledge base used in this work is a graph built with co-occurrence information from medical concepts found in scientific abstracts, and hence adapted to the specific domain. Unlike other unsupervised approaches based on static graphs such as UMLS, in this work the knowledge base takes the context of the ambiguous terms into account. Abstracts downloaded from PubMed are used for building the graph and disambiguation is performed using the personalized PageRank algorithm. Evaluation is carried out over two test datasets widely explored in the literature. Different parameters of the system are also evaluated to test robustness and scalability. Results show that the system is able to outperform state-of-the-art knowledge-based systems, obtaining more than 10% of accuracy improvement in some cases, while only requiring minimal external resources.

CMV-specific T-cell immunity in solid organ transplant recipients at low risk of CMV infection. Chronology and applicability in preemptive therapy.

OBJECTIVES: To characterize whether the CMV-specific cellular immune response can be used as a predictor of the control of CMV infection and disease and determine thresholds in solid organ transplant (SOT) recipients seropositive for CMV (R+). METHODS: The CMV-specific T-cell response was characterized using intracellular cytokine staining and the evolution of clinical and virological parameters were recorded during the first year after transplantation. RESULTS: Besides having positive CMV serology, only 28.4% patients had positive immunity (CD8+CD69+IFN-γ+ ≥0.25%) at 2 weeks after transplantation. These patients had less indication of preemptive treatment (p = 0.025) and developed less high grade (≥2000 IU/ml) CMV replication episodes (p = 0.006) than patients with no immunity. Of the 49 patients with a pretransplant sample, only 22.4% had positive immunity, and had a detectable immune response early after transplantation (median of 3.7 weeks). However, only 50% of patients with negative pretransplant immunity acquired a positive immune response and it was significantly later, at a median of 11 weeks (p < 0.001). Patients that developed CMV disease had no CMV-specific immunity. CONCLUSIONS: Having CMV-specific CD8+IFN-γ+ cells ≥0.25% before transplant; 0.15% at two weeks or 0.25% at four weeks after transplantation, identifies patients that may spontaneously control CMV infection and may require less monitoring.

Diastereoselective and Catalytic α-Alkylation of Chiral N-Acyl Thiazolidinethiones with Stable Carbocationic Salts.

Direct nickel-catalyzed alkylation of chiral N-acyl-4-isopropyl-1,3-thiazolidine-2-thiones using a commercially available nickel(II) complex, (Me3P)2NiCl2, has been developed for tropylium and trityl tetrafluoroborate salts. The reaction provides a single diastereomer of the corresponding adducts in good to high yields, which, in turn, can be easily converted into a wide array of enantiomerically pure compounds that are difficult to obtain by other asymmetric procedures.

Can multilinguality improve Biomedical Word Sense Disambiguation?

Ambiguity in the biomedical domain represents a major issue when performing Natural Language Processing tasks over the huge amount of available information in the field. For this reason, Word Sense Disambiguation is critical for achieving accurate systems able to tackle complex tasks such as information extraction, summarization or document classification. In this work we explore whether multilinguality can help to solve the problem of ambiguity, and the conditions required for a system to improve the results obtained by monolingual approaches. Also, we analyze the best ways to generate those useful multilingual resources, and study different languages and sources of knowledge. The proposed system, based on co-occurrence graphs containing biomedical concepts and textual information, is evaluated on a test dataset frequently used in biomedicine. We can conclude that multilingual resources are able to provide a clear improvement of more than 7% compared to monolingual approaches, for graphs built from a small number of documents. Also, empirical results show that automatically translated resources are a useful source of information for this particular task.

Stereoselective Synthesis of the C9-C19 Fragment of Peloruside A.

A concise synthesis of the C9-C19 fragment of peloruside A that is both highly stereoselective and efficient is described. Achieving an overall yield of 23% over 14 steps, this synthesis not only is high yielding but also involves four chromatography steps. This approach is based on the addition of metal enolates of chiral auxiliary scaffolds generated by either catalytic or stoichiometric amounts of nickel(II) or titanium(IV) Lewis acids.