The complete reference genome for grapevine (Vitis vinifera L.) genetics and breeding

Grapevine is one of the most economically important crops worldwide. However, the previous versions of the grapevine reference genome consisted of thousands of fragments with missing centromeres and telomeres, which limited the accessibility of the repetitive sequences, the centromeric and telomeric regions, and the inheritance of important agronomic traits in these regions. Here, we assembled a telomere-to-telomere (T2T) gap-free reference genome for the pinot noir cultivar (PN40024) using the PacBio HiFi long reads. The T2T reference genome (PN_T2T) was 69 Mb longer with 9026 more genes identified than the 12X.v2 version (Canaguier et al., 2017). We annotated 67% repetitive sequences, 19 centromeres and 36 telomeres, and incorporated gene annotations of previous versions into the PN_T2T. We detected a total of 377 gene clusters, which showed associations with complex traits, such as aroma and disease resistance. Even though the PN40024 sample had been selfed for nine generations, we still found nine genomic hotspots of heterozygous sites associated with biological processes, such as the oxidation-reduction process and protein phosphorylation. The fully annotated complete reference genome, therefore, provides important resources for grapevine genetics and breeding.This work was supported by the National Natural Science Fund for Excellent Young Scientists Fund Program (Overseas) to Yongfeng Zhou, the National Key Research and Development Program of China(grant2019YFA0906200), the Agricultural Science and Technology Innovation Program (CAAS-ZDRW202101), the Shenzhen Science and Technology Program (grant KQTD2016113010482651), the BMBF funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI). We thank Bianca Frommer, Marie Lahaye, David Navarro-Payá, Marcela K. Tello-Ruiz and Kapeel Chougule for their help in analyzing the RNA-Seq data and in running the gene annotation pipeline. This study is also based upon work from COST Action CA17111 INTEGRAPE and form COST Innovators Grant IG17111 GRAPEDIA, supported by COST (European Cooperation in Science and Technology).ViticultureT2Tgap-fregene clustercentromeretelomerePublishe

Dynamics underlying epileptic seizures: insights from a neural mass model

In this work, we propose an approach that allows to explore the potential pathophysiological mechanisms (at neuronal population level) of ictogenesis by combining clinical intracranial electroencephalographic (iEEG) recordings with a neural mass model. IEEG recordings from temporal lobe epilepsy (TLE) patients around seizure onset were investigated. Physiologically meaningful parameters (average synaptic gains of the excitatory, slow and fast inhibitory population, Ae, B and G) were identified during interictal to ictal transition. We analyzed the temporal evolution of four ratios, i.e. Ae/G, Ae/B, Ae/(B + G), and B/G. The excitation/inhibition ratio increased around seizure onset and decreased before seizure offset, suggesting the disturbance and restoration of balance between excitation and inhibition around seizure onset and before seizure offset, respectively. Moreover, the slow inhibition may have an earlier effect on the breakdown of excitation/inhibition balance. Results confirm the decrease in excitation/inhibition ratio upon seizure termination in human temporal lobe epilepsy, as revealed by optogenetic approaches both in vivo in animal models and in vitro. We further explored the distribution of the average synaptic gains in parameter space and their temporal evolution, i.e. the path through the model parameter space, in TLE patients. Results showed that the synaptic gain values located roughly on a plane before seizure onset, dispersed during ictal and returned when the seizure terminated. Cluster analysis was performed on seizure paths and demonstrated consistency in synaptic gain evolution across different seizures from individual patients. Furthermore, two patient groups were identified, each one corresponding to a specific synaptic gain evolution in the parameter space during a seizure. Results were validated by a bootstrapping approach based on comparison with random paths. The differences in the path revealed variations in EEG dynamics for patients despite showing an identical seizure onset pattern. Our approach may have the potential to classify the epileptic patients into subgroups based on different mechanisms revealed by subtle changes in synaptic gains and further enable more robust decisions regarding treatment strategy. The increase of excitation/inhibition ratios, i.e. Ae/G, Ae/B and Ae/(B+G), around seizure onset makes them potential cues for seizure detection. We explored the feasibility of a model based seizure detection algorithm. A simple thresholding method was employed. We evaluated the algorithm against the manual scoring of a human expert on iEEG samples from patients suffering from different types of epilepsy. Results suggest that Ae/(B+G), i.e. excitation/(slow + fast inhibition) ratio, allowed the best performance and that the algorithm best suited TLE patients. Leave-one-out cross-validation showed that the algorithm achieved 94.74% sensitivity for TLE patients. The median false positive rate was 0.16 per hour, and median detection delay was -1.0 s. Of interest, the values of the threshold determined by leave-one-out cross-validation for TLE patients were quite constant, suggesting a general excitation/inhibition balance baseline in background iEEG among TLE patients. Such a model-based seizure detection approach is of clinical interest and could also achieve good performance for other types of epilepsy provided that more appropriate model, i.e. better describe epileptic EEG waveforms for other types of epilepsy, is implemented. Altogether, this thesis contributes to the field of epilepsy research from two perspectives. Scientifically, it gives new insights into the mechanisms underlying interictal to ictal transition, and facilitates better understanding of epileptic seizures. Clinically, it provides a tool for reviewing EEG data in a more efficient and objective manner and offers an opportunity for on-demand therapeutic devices.Doctorat en Sciences de l'ingénieur et technologieinfo:eu-repo/semantics/nonPublishe

Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters

In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in terms of accuracy and tracking speed. However, their performance is not satisfactory in scenes with scale variation, deformation, and occlusion. In this paper, we propose a scene-aware adaptive updating mechanism for visual tracking via a kernel correlation filter (KCF). First, a low complexity scale estimation method is presented, in which the corresponding weight in five scales is employed to determine the final target scale. Then, the adaptive updating mechanism is presented based on the scene-classification. We classify the video scenes as four categories by video content analysis. According to the target scene, we exploit the adaptive updating mechanism to update the kernel correlation filter to improve the robustness of the tracker, especially in scenes with scale variation, deformation, and occlusion. We evaluate our tracker on the CVPR2013 benchmark. The experimental results obtained with the proposed algorithm are improved by 33.3%, 15%, 6%, 21.9% and 19.8% compared to those of the KCF tracker on the scene with scale variation, partial or long-time large-area occlusion, deformation, fast motion and out-of-view