Marching at the front and dragging behind: differential αVβ3-integrin turnover regulates focal adhesion behavior

Integrins are cell–substrate adhesion molecules that provide the essential link between the actin cytoskeleton and the extracellular matrix during cell migration. We have analyzed αVβ3-integrin dynamics in migrating cells using a green fluorescent protein–tagged β3-integrin chain. At the cell front, adhesion sites containing αVβ3-integrin remain stationary, whereas at the rear of the cell they slide inward. The integrin fluorescence intensity within these different focal adhesions, and hence the relative integrin density, is directly related to their mobility. Integrin density is as much as threefold higher in sliding compared with stationary focal adhesions. High intracellular tension under the control of RhoA induced the formation of high-density contacts. Low-density adhesion sites were induced by Rac1 and low intracellular tension. Photobleaching experiments demonstrated a slow turnover of β3-integrins in low-density contacts, which may account for their stationary nature. In contrast, the fast β3-integrin turnover observed in high-density contacts suggests that their apparent sliding may be caused by a polarized renewal of focal contacts. Therefore, differential acto-myosin–dependent integrin turnover and focal adhesion densities may explain the mechanical and behavioral differences between cell adhesion sites formed at the front, and those that move in the retracting rear of migrating cells

A talin-dependent LFA-1 focal zone is formed by rapidly migrating T lymphocytes

Cells such as fibroblasts and endothelial cells migrate through the coordinated responses of discrete integrin-containing focal adhesions and complexes. In contrast, little is known about the organization of integrins on the highly motile T lymphocyte. We have investigated the distribution, activity, and cytoskeletal linkage of the integrin lymphocyte function associated antigen-1 (LFA-1) on human T lymphocytes migrating on endothelial cells and on ligand intercellular adhesion molecule-1 (ICAM-1). The pattern of total LFA-1 varies from low expression in the lamellipodia to high expression in the uropod. However, high affinity, clustered LFA-1 is restricted to a mid-cell zone that remains stable over time and over a range of ICAM-1 densities. Talin is essential for the stability and formation of the LFA-1 zone. Disruption of the talin–integrin link leads to loss of zone integrity and a substantial decrease in speed of migration on ICAM-1. This adhesive structure, which differs from the previously described integrin-containing attachments displayed by many other cell types, we have termed the “focal zone.

Paleoenvironments, δ13C and δ18O signatures in the Neoproterozoic carbonates of the Comba Basin, Republic of Congo: Implications for regional correlations and Marinoan event

The Ediacaran Schisto-Calcaire Group is a similar to 1300 m-thick succession belonging to the West Congo Supergroup in Central Africa. In the Comba Basin, it consists of three carbonate-dominated units defined as formations (SCI to SCIII) that are unconformably overlain by clastic deposits (Mpioka Group) interpreted as a molassic formation associated with the Panafrican Orogen. The underlying Upper Tillite and Cap Carbonate (SCIa) units, considered as markers of the Snowball Earth event were studied in three sections. We investigated the carbonates of the Schisto-Calcaire Group by defining new microfacies (MF1-MF7) and we performed C and O isotopic analyses in order to constraint the depositional and diagenetic events directly after the Marinoan interval. Stratigraphic variations of the stable isotopes are important in the series with lighter delta O-18 values (>1.5 parts per thousand) than those of the Neoproterozoic ocean in the SCIc unit. According to regional stratigraphy a temperature effect can be dismissed and a freshwater surface layer is the origin of such negative delta O-18 values in this unit. The negative delta C-13 anomaly (-3.5 parts per thousand on average) of the Cap Carbonate is similarly to the delta O-18 values (-6.4 parts per thousand on average) in the range of the marine domain during postglacial sea level rise. The sample suite as a whole (SCII and SCIII formations) displays heavier delta O-18 and delta C-13 than those of the lower part (SCI unit) of the Schisto-Calcaire Group. The comparison with the Lower Congo (Democratic Republic of Congo) and Nyanga (Gabon) basins shows that the meteoric flushing in SCIc unit of the Schisto-Calcaire Group was regional and not local, and could be derived from a climatic evolution. Although an overall overprint is present, our isotopic relationships argue against overall diagenetic resetting of primary compositions and suggest that with careful examination combined with detailed petrographic analysis general depositional and diagenetic controls can be discerned in oxygen and carbon isotopic data in the Schisto-Calcaire Group

Gully erosion susceptibility mapping using four machine learning methods in Luzinzi watershed, eastern Democratic Republic of Congo

peer reviewedSoil erosion by gullying causes severe soil degradation, which in turn leads to severe socio-economic and environmental damages in tropical and subtropical regions. To mitigate these negative effects and guarantee sustainable management of natural resources, gullies must be prevented. Gully management strategies start by devising adequate assessment tools and identification of driving factors and control measures. To achieve this, machine learning methods are essential tools to assist in the identification of driving factors to implement site-specific control measures. This study aimed at assessing the effectiveness of four machine learning methods (Random Forest (RF), Maximum of Entropy (MaxEnt), Artificial Neural Network (ANN), and Boosted Regression Tree (BRT)) to identify gully's driving factors, and predict gully erosion susceptibility in the Luzinzi watershed, in eastern Democratic Republic of Congo (DRC)

Novel machine learning approaches for modelling the gully erosion susceptibility

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help future researchers to select models from this perspective. This type of outcome should help planners and local stakeholders to implement appropriate land and water conservation measures

Improvement of data quality for Diffusion Kurtosis Imaging and application to clinical neurological research

Understanding human brain function and dysfunction is one of the major challenges of our century. One of the most popular methods to achieve this goal is in vivo magnetic resonance imaging. In particular, diffusion-weighted (DW) imaging has become a standard tool to non-invasively study white matter structure in vivo. The main contributions of this work can be divided in two parts. The first part deals with the development of pre-processing methods to improve image quality and the accuracy of diffusion tensor and diffusion kurtosis-derived parameters. First, we describe and evaluate a novel method to correct data misalignment due to subject motion. Using an iterative model-based approach, individual diffusion images are realigned to their own theoretical pair, rather than to the unweighted image. A recently developed advanced measure of tensor distance was used as a stopping criterion. The accuracy of the method is tested via a simulated diffusion tensor imaging data set. We have shown here that our procedure is a reliable and efficient way to correct subject motion during DW acquisitions, and that with a proper acquisition setup, it performs better than standard coregistration procedures. Next, we studied the influence of noise in diffusion kurtosis imaging. Two noise correction approaches are proposed and applied to a pool of 25 subjects to evaluate inter-subject variability and the impact of noise correction. Additionally, data were acquired on a single subject with different head positions within the coil and different acquisition scheme to evaluate the impact of noise correction on within-subject variability. Results show a strong impact of noise correction on the estimated mean kurtosis, while the estimation of fractional anisotropy and mean diffusivity were less affected. Both within- and between-subject signal-to-noise (SNR) related variability of the mean kurtosis estimate is considerably reduced after correction for the noise bias, leading to more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves the accuracy of diffusion kurtosis metrics. Diffusion kurtosis imaging acquisitions at higher spatial resolution are made possible, which increases the chances to make valuable inferences in group analysis.The second part of this thesis deals with a clinical application of these methods. A large group of patients with early-stage Parkinson’s disease was investigated with diffusion kurtosis imaging and compared with a group of age- and sex-matched healthy volunteers using voxel-based analysis. Kurtosis metrics show more sensitivity to white matter changes than standard diffusion metrics. Significant differences were found in posterior cerebral areas as well as subcortical regions like the putamen, and are therefore promising results.Comprendre le fonctionnement et le dysfonctionnement du cerveau humain est l’un des grands défis de ce siècle. Pour atteindre ce but, l’imagerie par résonance magnétique (IRM) in vivo est devenue une technique incontournable. En particulier, l’IRM de diffusion est aujourd’hui un outil standard et non invasif pour étudier la structure de la matière blanche in vivo. Les principales constributions de ce travail de thèse se divisent en deux parties. Dans une première partie, deux nouvelles méthodes pour le prétraitement des images sont développées afin d’améliorer la qualité de celles-ci. Ces méthodes permettront également d’augmenter la reproductibilité et la précision des paramètres dérivés des tenseurs de diffusion et de kurtosis. Tout d’abord, nous présentons et évaluons une nouvelle méthode pour recaler les images, initialement décalées à cause des mouvements du sujet. Via une approche itérative et qui repose sur un modèle, chaque image de diffusion est recalée individuellement sur sa propre paire théorique plutôt que sur l’image non pondérée en diffusion. Comme critère d’arrêt, nous avons utilisé une measure de distance entre deux tenseurs. Un set de données de tenseurs de diffusion a été simulé pour tester la performance de cette méthode. Nous avons démontré que notre procédure est une technique fiable et efficace pour corriger les effets dus aux mouvements du sujet pendant l’acquisition des images de diffusion. Nous avons également mis en évidence que cette méthode, utilisée avec des paramètres d’acquisition adéquats, permet d’obtenir de meilleurs résultats par rapport aux méthodes standard de recalage. Ensuite, toujours pour améliorer la qualité des images, nous avons étudié l’influence du bruit dans le cadre de l’imagerie du tenseur de kurtosis. Deux méthodes de correction du bruit sont proposées et appliquées sur les données acquises sur 25 sujets afin d’évaluer la variabilité inter-sujets et l’impact de la correction du bruit sur cette variabilité. De plus, plusieurs sets de données ont été acquis sur un mˆeme sujet, en faisant varier d’une part la position de la tˆete à l’intérieur de l’antenne et d’autre part les paramètres d’acquisition, afin d’étudier l’impact de la correction du bruit sur la variabilité intra-sujet. Les résultats montrent un effet très important du bruit sur l’estimation du coefficient de kurtosis moyen. Par contre cet effet est relativement plus faible sur l’estimation de l’anisotropie fractionnelle et de la diffusivité moyenne. Après correction du bruit, la dépendance du coefficient moyen de kurtosis avec le rapport signal sur bruit, ainsi que de la variabilité intra- et inter-sujets, sont considérablement réduites, conduisant à des mesures plus justes et reproductibles. Nous avons donc proposé ici une méthode simple qui améliore la justesse et la précision des métriques dérivées des tenseurs de kurtosis, indépendemment du niveau de bruit. Il est donc possible d’augmenter la résolution spatiale et ainsi d’augmenter les chances de trouver des différences en comparant deux groupes de sujets. Dans une deuxième partie, nous avons appliqués les méthodes développées dans la première partie à une étude de recherche clinique. Un groupe de patients diagnostiqués à un stade pécoce de la maladie de Parkinson a suivi un protocole d’acquisition d’imagerie du tenseur de kurtosis et les données ont été comparées voxel par voxel avec celles acquises dans un groupe de sujets sains, avec une répartition semblable de l’ˆage et du sexe. Les paramètres dérivés du tenseur de kurtosis sont plus sensibles aux changements de la structure de la matière blanche que les paramètres standard dérivés du tenseur de diffusion. Des différences significatives ont été trouvées dans les régions cérébrales postérieures ainsi que dans les régions sous-corticales comme le putamen. Les résultats sont donc prometteurs.Methods in Neuroimagin