Conditional and inducible transgene expression in endothelial and hematopoietic cells using Cre/loxP and tetracycline-off systems

In the present study, the tetracycline-off and Cre/loxP systems were combined to gain temporal and spatial control of transgene expression. Mice were generated that carried three transgenes: Tie2-tTA, tet-O-Cre and either the ZEG or ZAP reporter. Tie2-tTA directs expression of tetracycline-controlled transactivator (tTA) in endothelial and hematopoietic cells under the control of the Tie2 promoter. Tet-O-Cre produces Cre recombinase from a minimal promoter containing the tet-operator (tetO). ZEG or ZAP contains a strong promoter and a loxP-flanked stop sequence, followed by an enhanced green fluorescence protein (EGFP) or human placental alkaline phosphatase (hPLAP) reporter. In the presence of tetracycline, the tTA transactivator produced by Tie-2-tTA is disabled and Cre is not expressed. In the absence of tetracycline, the tTA binds tet-O-Cre to drive the expression of Cre, which recombines the loxP sites of the ZEG or ZAP transgene and results in reporter gene expression. In the present study, the expression of the ZEG or ZAP reporter genes in embryos and adult animals with and without tetracycline treatment was examined. In the presence of tetracycline, no reporter gene expression was observed. When tetracycline was withdrawn, Cre excision was activated and the reporter genes were detected in endothelial and hematopoietic cells. These results demonstrate that this system may be used to bypass embryonic lethality and access adult phenotypes.

Total Variation Blind Deconvolution: The Devil Is in the Details

In this paper we study the problem of blind deconvolution. Our analysis is based on the algorithm of Chan and Wong [2] which popularized the use of sparse gradient priors via total variation. We use this algorithm because many methods in the literature are essentially adaptations of this framework. Such algorithm is an iterative alternating energy minimization where at each step either the sharp image or the blur function are reconstructed. Recent work of Levin et al. [14] showed that any algorithm that tries to minimize that same energy would fail, as the desired solution has a higher energy than the no-blur solution, where the sharp image is the blurry input and the blur is a Dirac delta. However, experimentally one can observe that Chan and Wong's algorithm converges to the desired solution even when initialized with the no-blur one. We provide both analysis and experiments to resolve this paradoxical conundrum. We find that both claims are right. The key to understanding how this is possible lies in the details of Chan and Wong's implementation and in how seemingly harmless choices result in dramatic effects. Our analysis reveals that the delayed scaling (normalization) in the iterative step of the blur kernel is fundamental to the convergence of the algorithm. This then results in a procedure that eludes the no-blur solution, despite it being a global minimum of the original energy. We introduce an adaptation of this algorithm and show that, in spite of its extreme simplicity, it is very robust and achieves a performance comparable to the state of the art.

Blind Deconvolution via Lower-Bounded Logarithmic Image Priors

In this work we devise two novel algorithms for blind deconvolution based on a family of logarithmic image priors. In contrast to recent approaches, we consider a minimalistic formulation of the blind deconvolution problem where there are only two energy terms: a least-squares term for the data fidelity and an image prior based on a lower-bounded logarithm of the norm of the image gradients. We show that this energy formulation is sufficient to achieve the state of the art in blind deconvolution with a good margin over previous methods. Much of the performance is due to the chosen prior. On the one hand, this prior is very effective in favoring sparsity of the image gradients. On the other hand, this prior is non convex. Therefore, solutions that can deal effectively with local minima of the energy become necessary. We devise two iterative minimization algorithms that at each iteration solve convex problems: one obtained via the primal-dual approach and one via majorization-minimization. While the former is computationally efficient, the latter achieves state-of-the-art performance on a public dataset.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

Discussion: Food security and sustainable food systems: The role of soil

There is a general consensus that healthy soils are pivotal for food security. Food production is one of the main ecosystem services provided by and thus dependent on well-functioning soils. There are also intrinsic connections between the four pillars of food security: food availability, access, utilization, and stability; with how soils are managed, accessed and secured, in particular by food insecure and vulnerable populations. On the other hand, socio-political and economic processes that precipitate inequalities and heighten vulnerabilities among poor populations often increase pressure on soils due to unsustainable forms of land use and poor agricultural practises. This has often led to scenarios that can be described as: ‘poor soils, empty stomachs (hungry people) and poor livelihoods.' In 2015, in particular, as we head towards approval of the ‘Sustainable Development Goals' (SDGs), the role of Financing for Development is debated and agreed upon and a new climate pact is signed – these three political dimensions define how a new post-2015 agenda needs to be people-smart as well as resource-smart. For proposed SDG 2 (Food Security and Hunger), there can be so resolution without addressing people, policies and institutions.

WOCAT - World Overview of Conservation Approaches and Technologies - Preliminary results from Eastern and Southern Africa

The World Overview of Conservation Approaches and Technologies (WOCAT) is a program of the World Association of Soil and Water Conservation (WASWC), organized as a consortium of several international institutions. The overall goal of WOCAT is to contribute to sustainable utilization of soil and water. WOCAT collects and analyzes information on soil and water conservation (SWC) technologies and approaches world-wide, and presents the collected information in computer databases and decision support systems, and in the form of handbooks, reports and maps readily accessible to SWC specialists and policy-makers world-wide. WOCAT has prepared a framework for the evaluation of soil and water conservation and has started data collection. The paper presents preliminary results with promising SWC technologies and approaches used in Eastern and Southern Africa. The first finding is that hardly any promising SWC activities could be found on common grazing lands. Analysis of the cropland shows some of the bio-physical and socioeconomic conditions under which certain SWC technologies and approaches are used, including land use types, climatic zones and land tenure, and looks at issues such as participation and costs. Furthermore, classification criteria for SWC technologies and approaches are discussed.

GoCARB: Smartphone-App zur Kohlenhydratberechnung bei Typ-1-Diabetes

Smartphone-App zur Kohlenhydratberechnung Neue Technologien wie Blutzuckersensoren und moderne Insulinpumpen prägten die Therapie des Typ-1-Diabetes (T1D) in den letzten Jahren in wesentlichem Ausmaß. Smartphones sind aufgrund ihrer rasanten technischen Entwicklung eine weitere Plattform für Applikationen zur Therapieunterstützung bei T1D. GoCARB Hierbei handelt es sich um ein zur Kohlenhydratberechnung entwickeltes System für Personen mit T1D. Die Basis für Endanwender stellt ein Smartphone mit Kamera dar. Zur Berechnung werden 2 mit dem Smartphone aus verschiedenen Winkeln aufgenommene Fotografien einer auf einem Teller angerichteten Mahlzeit benötigt. Zusätzlich ist eine neben dem Teller platzierte Referenzkarte erforderlich. Die Grundlage für die Kohlenhydratberechnung ist ein Computer-Vision-gestütztes Programm, das die Mahlzeiten aufgrund ihrer Farbe und Textur erkennt. Das Volumen der Mahlzeit wird mit Hilfe eines dreidimensional errechneten Modells bestimmt. Durch das Erkennen der Art der Mahlzeiten sowie deren Volumen kann GoCARB den Kohlenhydratanteil unter Einbeziehung von Nährwerttabellen berechnen. Für die Entwicklung des Systems wurde eine Bilddatenbank von mehr als 5000 Mahlzeiten erstellt und genutzt. Resümee Das GoCARB-System befindet sich aktuell in klinischer Evaluierung und ist noch nicht für Patienten verfügbar.

Impact of Taylor-Aris diffusivity on analyte and system zone dispersion in CZE assessed by computer simulation and experimental validation.

Application of pressure-driven laminar flow has an impact on zone and boundary dispersion in open tubular CE. The GENTRANS dynamic simulator for electrophoresis was extended with Taylor-Aris diffusivity which accounts for dispersion due to the parabolic flow profile associated with pressure-driven flow. Effective diffusivity of analyte and system zones as functions of the capillary diameter and the amount of flow in comparison to molecular diffusion alone were studied for configurations with concomitant action of imposed hydrodynamic flow and electroosmosis. For selected examples under realistic experimental conditions, simulation data are compared with those monitored experimentally using modular CE setups featuring both capacitively coupled contactless conductivity and UV absorbance detection along a 50 μm id fused-silica capillary of 90 cm total length. The data presented indicate that inclusion of flow profile based Taylor-Aris diffusivity provides realistic simulation data for analyte and system peaks, particularly those monitored in CE with conductivity detection.