Measuring the influence of concept detection on video retrieval

There is an increasing emphasis on including semantic concept detection as part of video retrieval. This represents a modality for retrieval quite different from metadata-based and keyframe similarity-based approaches. One of the premises on which the success of this is based, is that good quality detection is available in order to guarantee retrieval quality. But how good does the feature detection actually need to be? Is it possible to achieve good retrieval quality, even with poor quality concept detection and if so then what is the 'tipping point' below which detection accuracy proves not to be beneficial? In this paper we explore this question using a collection of rushes video where we artificially vary the quality of detection of semantic features and we study the impact on the resulting retrieval. Our results show that the impact of improving or degrading performance of concept detectors is not directly reflected as retrieval performance and this raises interesting questions about how accurate concept detection really needs to be

Next-generation sequencing-based genome diagnostics across clinical genetics centers: Implementation choices and their effects

Implementation of next-generation DNA sequencing (NGS) technology into routine diagnostic genome care requires strategic choices. Instead of theoretical discussions on the consequences of such choices, we compared NGS-based diagnostic practices in eight clinical genetic centers in the Netherlands, based on genetic testing of nine pre-selected patients with cardiomyopathy. We highlight critical implementation choices, including the specific contributions of laboratory and medical specialists, bioinformaticians and researchers to diagnostic genome care, and how these affect interpretation and reporting of variants. Reported pathogenic mutations were consistent for all but one patient. Of the two centers that were inconsistent in their diagnosis, one reported to have found 'no causal variant', thereby underdiagnosing this patient. The other provided an alternative diagnosis, identifying another variant as causal than the other centers. Ethical and legal analysis showed that informed consent procedures in all centers were generally adequate for diagnostic NGS applications that target a limited set of genes, but not for exome- and genome-based diagnosis. We propose changes to further improve and align these procedures, taking into account the blurring boundary between diagnostics and research, and specific counseling options for exome- and genome-based diagnostics. We conclude that alternative diagnoses may infer a certain level of 'greediness' to come to a positive diagnosis in interpreting sequencing results. Moreover, there is an increasing interdependence of clinic, diagnostics and research departments for comprehensive diagnostic genome care. Therefore, we invite clinical geneticists, physicians, researchers, bioinformatics experts and patients to reconsider their role and position in future diagnostic genome care

Global change effects on plant communities are magnified by time and the number of global change factors imposed

Global change drivers (GCDs) are expected to alter community structure and consequently, the services that ecosystems provide. Yet, few experimental investigations have examined effects of GCDs on plant community structure across multiple ecosystem types, and those that do exist present conflicting patterns. In an unprecedented global synthesis of over 100 experiments that manipulated factors linked to GCDs, we show that herbaceous plant community responses depend on experimental manipulation length and number of factors manipulated. We found that plant communities are fairly resistant to experimentally manipulated GCDs in the short term (<10 y). In contrast, long-term (≥10 y) experiments show increasing community divergence of treatments from control conditions. Surprisingly, these community responses occurred with similar frequency across the GCD types manipulated in our database. However, community responses were more common when 3 or more GCDs were simultaneously manipulated, suggesting the emergence of additive or synergistic effects of multiple drivers, particularly over long time periods. In half of the cases, GCD manipulations caused a difference in community composition without a corresponding species richness difference, indicating that species reordering or replacement is an important mechanism of community responses to GCDs and should be given greater consideration when examining consequences of GCDs for the biodiversity–ecosystem function relationship. Human activities are currently driving unparalleled global changes worldwide. Our analyses provide the most comprehensive evidence to date that these human activities may have widespread impacts on plant community composition globally, which will increase in frequency over time and be greater in areas where communities face multiple GCDs simultaneously

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns

Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike’s information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk

Sammenligninger mellem økologisk og konventionelt jordbrug bør være bedre, siger forskere

Landbrugets og fødevareproduktionens miljøpåvirkning er et meget aktuelt emne, som diskuteres flittigt verden over. Men den mest anvendte sammenligningsmetode (LCA) mangler ofte nogle meget afgørende faktorer, påpeger forskere fra Aarhus Universitet, Chalmers University of Technology og INRAE. LCA metoden inkluderer ofte ikke faktorer som biodiversitet, jordkvalitet og pesticidpåvirkninger, der har stor betydning i forhold til landbrugets påvirkning af miljøet. I en artikel til tidskriftet Nature Sustainability beskriver de ud fra en dybdegående analyse deres bekymring for, at den nuværende anvendelse af metoden kan føre til forkerte konklusioner om henholdsvis konventionelt og økologisk jordbrug

Surveillance of animal diseases through implementation of a Bayesian spatio-temporal model: A simulation example with neurological syndromes in horses and West Nile Virus

A potentially sensitive way to detect disease outbreaks is syndromic surveillance, i.e. monitoring the number of syndromes reported in the population of interest, comparing it to the baseline rate, and drawing conclusions about outbreaks using statistical methods. A decision maker may use the results to take disease control actions or to initiate enhanced epidemiological investigations. In addition to the total count of syndromes there are often additional pieces of information to consider when assessing the probability of an outbreak. This includes clustering of syndromes in space and time as well as historical data on the occurrence of syndromes, seasonality of the disease, etc. In this paper, we show how Bayesian theory for syndromic surveillance applies to the occurrence of neurological syndromes in horses in France. Neurological syndromes in horses may be connected e.g. to West Nile Virus (WNV), a zoonotic disease of growing concern for public health in Europe. A Bayesian method for spatio-temporal cluster detection of syndromes and for determining the probability of an outbreak is presented. It is shown how surveillance can be performed simultaneously for a specific class of diseases (WNV or diseases similar to WNV in terms of the information available to the system) and a non-specific class of diseases (not similar to WNV in terms of the information available to the system). We also discuss some new extensions to the spatio-temporal models and the computational algorithms involved. It is shown step-by-step how data from historical WNV outbreaks and surveillance data for neurological syndromes can be used for model construction. The model is implemented using a Gibbs sampling procedure, and its sensitivity and specificity is evaluated. Finally, it is illustrated how predictive modelling of syndromes can be useful for decision making in animal health surveillance

Making agricultural Life Cycle Assessment tools effective

Customized life cycle assessment (LCA) tools are potentially valuable for facilitating eco-design in agriculture, but if not effective are prone to being under-utilised. We drew on the experiences of tool developers to identify the key challenges and opportunities for making tools effective (based on effectiveness criteria defined in earlier research), and to propose practical recommendations to inform future tool development. Priority recommendation are online hosting, uncertainty analysis, data input from farm data systems, results categorised by practices, consensus best-practice methods, accounting for diverse practices, regionalized analysis, and capitalizing on agriculturist knowledge

Life cycle assessment data of French organic agricultural products

International audienceEnvironmental data on organic products are needed to assess their environmental performance. The purpose of the ACV Bio project reported here was to generate environmental data as life cycle assessment (LCA) data for a sample of French organic production systems including cropping systems (annual crops, intercrops, forages), grassland, wine grapes, cow milk, calves, beef cattle, sheep, pigs, broilers and eggs. LCA was used to estimate environmental impacts of products from these systems. Recommended uses are to characterize part of the diversity of French organic farming systems and some of their environmental impacts, identify areas for improvement, perform eco-design and sensitivity analysis, and/or make system choices in a given context. However, these data do not represent average French organic products and should not be used as such. The MEANS-InOut web application was used to generate life cycle inventories (LCI). Impact assessment was performed using SimaPro v9 software. The Environmental Footprint 2.0 characterisation method was used to generate LCA data. These data were supplemented with three LCA indicators: cumulative energy demand, land competition (CML-IA non-baseline) and biodiversity loss. Three non-LCA indicators were also calculated for certain systems: diversity of crop families (for cropping systems), agro-ecological in- frastructure (for sheep) and pesticide treatment frequency index (for grapes). In total, 173 products were modelled. LCA and non-LCA data are available in the Microsoft (R) Excel file at Data INRAE (https://doi.org/10.15454/TTR25S). LCI data are available in the AGRIBALYSE database and can be accessed using SimaPro and openLCA software. Farmer-practice data are available on demand