Fast PCA for processing calcium-imaging data from the brain of Drosophila melanogaster

<p>Abstract</p> <p>Background</p> <p>The calcium-imaging technique allows us to record movies of brain activity in the antennal lobe of the fruitfly <it>Drosophila melanogaster</it>, a brain compartment dedicated to information about odors. Signal processing, e.g. with source separation techniques, can be slow on the large movie datasets.</p> <p>Method</p> <p>We have developed an approximate Principal Component Analysis (PCA) for fast dimensionality reduction. The method samples relevant pixels from the movies, such that PCA can be performed on a smaller matrix. Utilising <it>a priori </it>knowledge about the nature of the data, we minimise the risk of missing important pixels.</p> <p>Results</p> <p>Our method allows for fast approximate computation of PCA with adaptive resolution and running time. Utilising <it>a priori </it>knowledge about the data enables us to concentrate more biological signals in a small pixel sample than a general sampling method based on vector norms.</p> <p>Conclusions</p> <p>Fast dimensionality reduction with approximate PCA removes a computational bottleneck and leads to running time improvements for subsequent algorithms. Once in PCA space, we can efficiently perform source separation, e.g to detect biological signals in the movies or to remove artifacts.</p

Semi-supervised Instance Segmentation with a Learned Shape Prior

To date, most instance segmentation approaches are based on supervised learning that requires a considerable amount of annotated object contours as training ground truth. Here, we propose a framework that searches for the target object based on a shape prior. The shape prior model is learned with a variational autoencoder that requires only a very limited amount of training data: In our experiments, a few dozens of object shape patches from the target dataset, as well as purely synthetic shapes, were sufficient to achieve results en par with supervised methods with full access to training data on two out of three cell segmentation datasets. Our method with a synthetic shape prior was superior to pre-trained supervised models with access to limited domain-specific training data on all three datasets. Since the learning of prior models requires shape patches, whether real or synthetic data, we call this framework semi-supervised learning

EDISA: extracting biclusters from multiple time-series of gene expression profiles

<p>Abstract</p> <p>Background</p> <p>Cells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consisting of co-regulated genes. A growing pool of publicly available microarray datasets allows the identification of modules by monitoring expression changes over time. These time-series datasets can be searched for gene expression modules by one of the many clustering methods published to date. For an integrative analysis, several time-series datasets can be joined into a three-dimensional <it>gene-condition-time </it>dataset, to which standard clustering or biclustering methods are, however, not applicable. We thus devise a probabilistic clustering algorithm for <it>gene-condition-time </it>datasets.</p> <p>Results</p> <p>In this work, we present the EDISA (Extended Dimension Iterative Signature Algorithm), a novel probabilistic clustering approach for 3D <it>gene-condition-time </it>datasets. Based on mathematical definitions of gene expression modules, the EDISA samples initial modules from the dataset which are then refined by removing genes and conditions until they comply with the module definition. A subsequent extension step ensures gene and condition maximality. We applied the algorithm to a synthetic dataset and were able to successfully recover the implanted modules over a range of background noise intensities. Analysis of microarray datasets has lead us to define three biologically relevant module types: 1) We found modules with independent response profiles to be the most prevalent ones. These modules comprise genes which are co-regulated under several conditions, yet with a different response pattern under each condition. 2) Coherent modules with similar responses under all conditions occurred frequently, too, and were often contained within these modules. 3) A third module type, which covers a response specific to a single condition was also detected, but rarely. All of these modules are essentially different types of biclusters.</p> <p>Conclusion</p> <p>We successfully applied the EDISA to different 3D datasets. While previous studies were mostly aimed at detecting coherent modules only, our results show that coherent responses are often part of a more general module type with independent response profiles under different conditions. Our approach thus allows for a more comprehensive view of the gene expression response. After subsequent analysis of the resulting modules, the EDISA helped to shed light on the global organization of transcriptional control. An implementation of the algorithm is available at http://www-ra.informatik.uni-tuebingen.de/software/IAGEN/.</p

Applying generic landscape-scale models of natural pest control to real data: Associations between crops, pests and biocontrol agents make the difference

Managing agricultural land to maximize the supply of natural pest control can help reduce pesticide use. Tools that are able to represent the relationship between landscape structure, field management and natural pest control can help in deciding which management practices should be used and where. However, the reliability and the predictive power of generic models of natural pest control is largely unknown. We applied an existing generic model of natural pest control potential based on landscape structure to nine sites in five European countries and tested the resulting values against field measurements of natural pest control. Subsequently, we added information on local level factors to test the possibility of improving model performance and predictive power. The results showed that there is generally little or no evidence of correlation between modeled and field-measured values of natural pest control. Moreover, we found high variability in the results, depending on the associations of crops, pests and biocontrol agents considered (e.g. Oilseed rape-Pollen beetle-Parasitoids) and on the different case studies. Factors at the local level, such as conservation tillage, had an overall positive effect on natural pest control, and their inclusion in the models typically increased their predictive power. Our results underline the importance of developing predictive models of natural pest control which are tailored towards specific associations between crops, pests and biocontrol agents, consider local level factors and are trained using field measurements. They would serve as important tools within farmers' decision making, ultimately supporting the shift toward a low-pesticide agriculture

Development and implementation of blood pressure screening and referral guidelines for German community pharmacists

Involvement of community pharmacists in the detection and control of hypertension improves patient care. However, current European or North‐American guidelines do not provide specific guidance how to implement collaboration between pharmacists and physicians, especially when and how to refer patients with undetected or uncontrolled hypertension to a physician. The German Society of Cardiology and the ABDA – Federal Union of German Associations of Pharmacists developed and tested referral recommendations for community pharmacists, embedded in two guideline worksheets. The project included a guideline‐directed blood pressure (BP) measurement and recommendations when patients should be referred to their physician. A “red flag” referral within 4 weeks was recommended when SBP was >140 mm Hg or DBP >90 mm Hg (for subjects 160 mm Hg or >90 mm Hg (≥80 years) in undetected individuals, or >130 mm Hg or >80 mm Hg (140 mm Hg or >80 mm Hg (≥65 years) in treated patients. BP was measured in 187 individuals (86 with known hypertension, mean [±SD] age 62 ± 15 years, 64% female, and 101 without known hypertension, 47 ± 16 years, 75% female) from 17 community pharmacies. In patients with hypertension, poorly controlled BP was detected in 55% (n = 47) and were referred. A total of 16/101 subjects without a history of hypertension were referred to their physician because of uncontrolled BP. Structured BP testing in pharmacies identified a significant number of subjects with undetected/undiagnosed hypertension and patients with poorly controlled BP. Community pharmacists could play a significant role in collaboration with physicians to improve the management of hypertension

Remarks on the pion-nucleon sigma-term

The pion-nucleon $\sigma$-term can be stringently constrained by the combination of analyticity, unitarity, and crossing symmetry with phenomenological information on the pion-nucleon scattering lengths. Recently, lattice calculations at the physical point have been reported that find lower values by about $3\sigma$ with respect to the phenomenological determination. We point out that a lattice measurement of the pion-nucleon scattering lengths could help resolve the situation by testing the values extracted from spectroscopy measurements in pionic atoms.Comment: 5 pages, 2 figures; version published in PL

Riparian reforestation on the landscape scale – Navigating trade‐offs among agricultural production, ecosystem functioning and biodiversity

1. Stream–riparian networks are subject to multiple human pressures that threaten key functions of aquatic and terrestrial ecosystems, drive habitat and diversity losses, affect riparian connectivity and cause stakeholder conflicts. Designing riparian landscapes in a way that they can simultaneously meet multiple competing demands requires a clear understanding of existing trade-offs, and a landscape-scale perspective on the planning of reforestation measures. 2. This study applied a landscape optimization algorithm for allocating riparian forest management measures in the intensively used agricultural catchment of the Zwalm River (Belgium). We optimized forest allocation to improve stream ecological quality (EPT index), functional diversity (diatoms) and riparian carbon processing (cotton-strip assay), while minimizing losses in agricultural production potential. Regression models were developed to predict the target indicators for 489 segments of the Zwalm riparian corridor, using spatial variables on three different scales. For each riparian segment, we developed spatially explicit management measures, representing different intensities of riparian reforestation. The allocation and combination of these measures in the riparian corridor were optimized to identify (a) trade-offs among the target indicators, (b) priority regions for reforestation actions and (c) the required reforestation intensity. 3.The results showed that all target indicators were affected by the area share of riparian forests and its landscape-scale configuration. Reforestation of the Zwalm riparian corridor could significantly improve indicators for biodiversity and ecosystem functioning (e.g. up to +96% for EPT index), but would lead to a strong trade-off with agricultural production. By optimizing the placement of management measures, we showed how these trade-offs could be best balanced. 4. The headwater regions of the Zwalm were identified as priority regions for reforestation actions. Facilitating connectivity among and further expansion of existing forest patches in the Zwalm headwaters showed to improve ecosystems with minimized trade-offs. 5. Synthesis and applications. This study demonstrates, for the first time, the potential of landscape optimization algorithms to support the management and design of multifunctional stream–riparian networks. We identified riparian reforestation solutions that minimized trade-offs between specific natural values and societal needs. Our spatially explicit approach allows for an integration into spatial planning and can inform policy design and implementation