Introgression of wheat DNA markers from A, B and D genomes in early generation progeny of Aegilops cylindrica Host × Triticum aestivum L. hybrids

Introgression from allohexaploid wheat (Triticum aestivum L., AABBDD) to allotetraploid jointed goatgrass (Aegilops cylindrica Host, CCDD) can take place in areas where the two species grow in sympatry and hybridize. Wheat and Ae. cylindrica share the D genome, issued from the common diploid ancestor Aegilops tauschii Coss. It has been proposed that the A and B genome of bread wheat are secure places to insert transgenes to avoid their introgression into Ae. cylindrica because during meiosis in pentaploid hybrids, A and B genome chromosomes form univalents and tend to be eliminated whereas recombination takes place only in D genome chromosomes. Wheat random amplified polymorphic DNA (RAPD) fragments, detected in intergeneric hybrids and introgressed to the first backcross generation with Ae. cylindrica as the recurrent parent and having a euploid Ae. cylindrica chromosome number or one supernumerary chromosome, were assigned to wheat chromosomes using Chinese Spring nulli-tetrasomic wheat lines. Introgressed fragments were not limited to the D genome of wheat, but specific fragments of A and B genomes were also present in the BC1. Their presence indicates that DNA from any of the wheat genomes can introgress into Ae. cylindrica. Successfully located RAPD fragments were then converted into highly specific and easy-to-use sequence characterised amplified regions (SCARs) through sequencing and primer design. Subsequently these markers were used to characterise introgression of wheat DNA into a BC1S1 family. Implications for risk assessment of genetically modified wheat are discusse

Size and Content of the Sex-Determining Region of the Y Chromosome in Dioecious <i>Mercurialis annua</i>, a Plant with Homomorphic Sex Chromosomes.

Dioecious plants vary in whether their sex chromosomes are heteromorphic or homomorphic, but even homomorphic sex chromosomes may show divergence between homologues in the non-recombining, sex-determining region (SDR). Very little is known about the SDR of these species, which might represent particularly early stages of sex-chromosome evolution. Here, we assess the size and content of the SDR of the diploid dioecious herb &lt;i&gt;Mercurialis annua&lt;/i&gt; , a species with homomorphic sex chromosomes and mild Y-chromosome degeneration. We used RNA sequencing (RNAseq) to identify new Y-linked markers for &lt;i&gt;M. annua.&lt;/i&gt; Twelve of 24 transcripts showing male-specific expression in a previous experiment could be amplified by polymerase chain reaction (PCR) only from males, and are thus likely to be Y-linked. Analysis of genome-capture data from multiple populations of &lt;i&gt;M. annua&lt;/i&gt; pointed to an additional six male-limited (and thus Y-linked) sequences. We used these markers to identify and sequence 17 sex-linked bacterial artificial chromosomes (BACs), which form 11 groups of non-overlapping sequences, covering a total sequence length of about 1.5 Mb. Content analysis of this region suggests that it is enriched for repeats, has low gene density, and contains few candidate sex-determining genes. The BACs map to a subset of the sex-linked region of the genetic map, which we estimate to be at least 14.5 Mb. This is substantially larger than estimates for other dioecious plants with homomorphic sex chromosomes, both in absolute terms and relative to their genome sizes. Our data provide a rare, high-resolution view of the homomorphic Y chromosome of a dioecious plant

Task 2: ShARe/CLEF eHealth evaluation lab 2014

This paper reports on Task 2 of the 2014 ShARe/CLEF eHealth evaluation lab which extended Task 1 of the 2013 ShARe/CLEF eHealth evaluation lab by focusing on template lling of disorder attributes. The task was comprised of two subtasks: attribute normalization (task 2a) and cue identication (task 2b).We instructed participants to develop a system which either kept or updated a default attribute value for each task. Participant systems were evaluated against a blind reference standard of 133 discharge summaries using Accuracy (task 2a) and F-score (task 2b). In total, ten teams participated in task 2a, and three teams in task 2b. For task 2a and 2b, the HITACHI team systems (run 2) had the highest performances, with an overall average average accuracy of 0.868 and F1-score (strict) of 0.676, respectively

Association of Diabetic Ketoacidosis and HbA1c at Onset with Year-Three HbA1c in Children and Adolescents with Type 1 Diabetes: Data from the International SWEET Registry

Objective: To establish whether diabetic ketoacidosis (DKA) or HbA1c at onset is associated with year-three HbA1c in children with type 1 diabetes (T1D). Methods: Children with T1D from the SWEET registry, diagnosed <18 years, with documented clinical presentation, HbA1c at onset and follow-up were included. Participants were categorized according to T1D onset: (a) DKA (DKA with coma, DKA without coma, no DKA); (b) HbA1c at onset (low [<10%], medium [10 to <12%], high [≥12%]). To adjust for demographics, linear regression was applied with interaction terms for DKA and HbA1c at onset groups (adjusted means with 95% CI). Association between year-three HbA1c and both HbA1c and presentation at onset was analyzed (Vuong test). Results: Among 1420 children (54% males; median age at onset 9.1 years [Q1;Q3: 5.8;12.2]), 6% of children experienced DKA with coma, 37% DKA without coma, and 57% no DKA. Year-three HbA1c was lower in the low compared to high HbA1c at onset group, both in the DKA without coma (7.1% [6.8;7.4] vs 7.6% [7.5;7.8], P = .03) and in the no DKA group (7.4% [7.2;7.5] vs 7.8% [7.6;7.9], P = .01), without differences between low and medium HbA1c at onset groups. Year-three HbA1c did not differ among HbA1c at onset groups in the DKA with coma group. HbA1c at onset as an explanatory variable was more closely associated with year-three HbA1c compared to presentation at onset groups (P = .02). Conclusions: Year-three HbA1c is more closely related to HbA1c than to DKA at onset; earlier hyperglycemia detection might be crucial to improving year-three HbA1c.info:eu-repo/semantics/publishedVersio

Overview of the ShARe/CLEF eHealth evaluation lab 2013

Discharge summaries and other free-text reports in healthcare transfer information between working shifts and geographic locations. Patients are likely to have difficulties in understanding their content, because of their medical jargon, non-standard abbreviations, and ward-specific idioms. This paper reports on an evaluation lab with an aim to support the continuum of care by developing methods and resources that make clinical reports in English easier to understand for patients, and which helps them in finding information related to their condition. This ShARe/CLEFeHealth2013 lab offered student mentoring and shared tasks: identification and normalisation of disorders (1a and 1b) and normalisation of abbreviations and acronyms (2) in clinical reports with respect to terminology standards in healthcare as well as information retrieval (3) to address questions patients may have when reading clinical reports. The focus on patients' information needs as opposed to the specialised information needs of physicians and other healthcare workers was the main feature of the lab distinguishing it from previous shared tasks. De-identied clinical reports for the three tasks were from US intensive care and originated from the MIMIC II database. Other text documents for Task 3 were from the Internet and originated from the Khresmoi project. Task 1 annotations originated from the ShARe annotations. For Tasks 2 and 3, new annotations, queries, and relevance assessments were created. 64, 56, and 55 people registered their interest in Tasks 1, 2, and 3, respectively. 34 unique teams (3 members per team on average) participated with 22, 17, 5, and 9 teams in Tasks 1a, 1b, 2 and 3, respectively. The teams were from Australia, China, France, India, Ireland, Republic of Korea, Spain, UK, and USA. Some teams developed and used additional annotations, but this strategy contributed to the system performance only in Task 2. The best systems had the F1 score of 0.75 in Task 1a; Accuracies of 0.59 and 0.72 in Tasks 1b and 2; and Precision at 10 of 0.52 in Task 3. The results demonstrate the substantial community interest and capabilities of these systems in making clinical reports easier to understand for patients. The organisers have made data and tools available for future research and development

Clinical narrative analytics challenges

Precision medicine or evidence based medicine is based on the extraction of knowledge from medical records to provide individuals with the appropriate treatment in the appropriate moment according to the patient features. Despite the efforts of using clinical narratives for clinical decision support, many challenges have to be faced still today such as multilinguarity, diversity of terms and formats in different services, acronyms, negation, to name but a few. The same problems exist when one wants to analyze narratives in literature whose analysis would provide physicians and researchers with highlights. In this talk we will analyze challenges, solutions and open problems and will analyze several frameworks and tools that are able to perform NLP over free text to extract medical entities by means of Named Entity Recognition process. We will also analyze a framework we have developed to extract and validate medical terms. In particular we present two uses cases: (i) medical entities extraction of a set of infectious diseases description texts provided by MedlinePlus and (ii) scales of stroke identification in clinical narratives written in Spanish

Psychiatric co-morbidity is associated with increased risk of surgery in Crohn's disease

Psychiatric co-morbidity, in particular major depression and anxiety, is common in patients with Crohn's disease (CD) and ulcerative colitis (UC). Prior studies examining this may be confounded by the co-existence of functional bowel symptoms. Limited data exist examining an association between depression or anxiety and disease-specific endpoints such as bowel surgery.National Institutes of Health (U.S.) (NIH U54-LM008748)American Gastroenterological AssociationNational Institutes of Health (U.S.) (NIH K08 AR060257)Beth Isreal Deaconess Medical Center (Katherine Swan Ginsburg Fund)National Institutes of Health (U.S.) (NIH R01-AR056768)National Institutes of Health (U.S.) (NIH U01-GM092691)National Institutes of Health (U.S.) (NIH R01-AR059648)Burroughs Wellcome Fund (Career Award for Medical Scientists)National Institutes of Health (U.S.) (NIH K24 AR052403)National Institutes of Health (U.S.) (NIH P60 AR047782)National Institutes of Health (U.S.) (NIH R01 AR049880

Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing

Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection, the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000–­2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases

Profiling allele-specific gene expression in brains from individuals with autism spectrum disorder reveals preferential minor allele usage.

One fundamental but understudied mechanism of gene regulation in disease is allele-specific expression (ASE), the preferential expression of one allele. We leveraged RNA-sequencing data from human brain to assess ASE in autism spectrum disorder (ASD). When ASE is observed in ASD, the allele with lower population frequency (minor allele) is preferentially more highly expressed than the major allele, opposite to the canonical pattern. Importantly, genes showing ASE in ASD are enriched in those downregulated in ASD postmortem brains and in genes harboring de novo mutations in ASD. Two regions, 14q32 and 15q11, containing all known orphan C/D box small nucleolar RNAs (snoRNAs), are particularly enriched in shifts to higher minor allele expression. We demonstrate that this allele shifting enhances snoRNA-targeted splicing changes in ASD-related target genes in idiopathic ASD and 15q11-q13 duplication syndrome. Together, these results implicate allelic imbalance and dysregulation of orphan C/D box snoRNAs in ASD pathogenesis