Comparison of seed desiccation sensitivity amongst Castanea sativa, Quercus ilex and Q. cerris

The distribution and propagation by seed of species with recalcitrant seed storage behaviour requires knowledge of the lowest safe moisture content before desiccation damages seed survival. These values were comparatively high and varied amongst the forest tree species Castanea sativa (58%), Quercus cerris (40-47.5%) and Quercus ilex (44%) (Fagaceae) collected at one site in one year. Relations between lower seed moisture contents and viability (radicle emergence) were sigmoidal and quantified by logistic curves, with Q. ilex showing the smallest seed-to-seed variation

Interplay between astrocytic and neuronal networks during virtual navigation in the mouse hippocampus

Encoding of spatial information in hippocapal place cells is believed to contribute to spatial cognition during navigation. Whether the processing of spatial information is exclusively limited to neuronal cells or it involves other cell types, e.g. glial cells, in the brain is currently unknown. In this thesis work, I developed an analysis pipeline to tackle this question using statistical methods and Information Theory approaches. I applied these analytical tools to two experimental data sets in which neuronal place cells in the hippocampus were imaged using two-photon microscopy, while selectively manipulating astrocytic calcium dynamics with pharmacogenetics during virtual navigation. Using custom analytical methods, we observed that pharmacogenetic perturbation of astrocytic calcium dynamics, through clozapine-n-oxyde (CNO) injection, induced a significant increase in neuronal place field and response profile width compared to control conditions. The distributions of neuronal place field and response profile center were also significantly different upon perturbation of astrocytic calcium dynamics compared to control conditions. Moreover, we found contrasting effect of astrocytic calcium dynamics perturbation on neuronal content of spatial information in the two data sets. In the first data set, we found that CNO injection resulted in a significant increase in the average information content in all neurons. In the second data set, we instead found that mutual information values were not significantly different upon CNO application compared to controls. Although the presented results are still preliminary and more experiments and analyses are needed, these findings suggest that astrocytic calcium dynamics may actively control the way hippocampal neuronal networks encode spatial information during virtual navigation. These data thus suggest a complex and tight interplay between neuronal and astrocytic networks during higher cognitive functions

Simultaneous EEG Monitoring During Transcranial Direct Current Stimulation

Transcranial direct current stimulation (tDCS) is a technique that delivers weak electric currents through the scalp. This constant electric current induces shifts in neuronal membrane excitability, resulting in secondary changes in cortical activity. Although tDCS has most of its neuromodulatory effects on the underlying cortex, tDCS effects can also be observed in distant neural networks. Therefore, concomitant EEG monitoring of the effects of tDCS can provide valuable information on the mechanisms of tDCS. In addition, EEG findings can be an important surrogate marker for the effects of tDCS and thus can be used to optimize its parameters. This combined EEG-tDCS system can also be used for preventive treatment of neurological conditions characterized by abnormal peaks of cortical excitability, such as seizures. Such a system would be the basis of a non-invasive closed-loop device. In this article, we present a novel device that is capable of utilizing tDCS and EEG simultaneously. For that, we describe in a step-by-step fashion the main procedures of the application of this device using schematic figures, tables and video demonstrations. Additionally, we provide a literature review on clinical uses of tDCS and its cortical effects measured by EEG techniques

Spatially Guiding Unsupervised Semantic Segmentation Through Depth-Informed Feature Distillation and Sampling

Traditionally, training neural networks to perform semantic segmentation required expensive human-made annotations. But more recently, advances in the field of unsupervised learning have made significant progress on this issue and towards closing the gap to supervised algorithms. To achieve this, semantic knowledge is distilled by learning to correlate randomly sampled features from images across an entire dataset. In this work, we build upon these advances by incorporating information about the structure of the scene into the training process through the use of depth information. We achieve this by (1) learning depth-feature correlation by spatially correlate the feature maps with the depth maps to induce knowledge about the structure of the scene and (2) implementing farthest-point sampling to more effectively select relevant features by utilizing 3D sampling techniques on depth information of the scene. Finally, we demonstrate the effectiveness of our technical contributions through extensive experimentation and present significant improvements in performance across multiple benchmark datasets

Are They Worth Reading? An In-Depth Analysis of Online Trackers' Privacy Policies

We analyzed the privacy policies of 75 online tracking companies with the goal of assessing whether they contain information relevant for users to make privacy decisions. We compared privacy policies from large companies, companies that are members of self-regulatory organizations, and nonmember companies and found that many of them are silent with regard to important consumer-relevant practices including the collection and use of sensitive information and linkage of tracking data with personally-identifiable information. We evaluated these policies against self-regulatory guidelines and found that many policies are not fully compliant. Furthermore, the overly general requirements established in those guidelines allow companies to have compliant practices without providing transparency to users. Few companies disclose their data retention times or offer users the opportunity to access the information collected about them. The lack of consistent terminology to refer to affiliate and non-affiliate partners, and the mix of practices for first-party and third-party contexts make it challenging for users to clearly assess the risks associated with online tracking. We discuss options to improve the transparency of online tracking companies’ privacy practices

Are They Worth Reading? An In-Depth Analysis of Online Trackers’ Privacy Policies

We analyzed the privacy policies of 75 online tracking companies with the goal of assessing whether they contain information relevant for users to make privacy decisions. We compared privacy policies from large companies, companies that are members of self-regulatory organizations, and nonmember companies and found that many of them are silent with regard to important consumer-relevant practices including the collection and use of sensitive information and linkage of tracking data with personally-identifiable information. We evaluated these policies against self-regulatory guidelines and found that many policies are not fully compliant. Furthermore, the overly general requirements established in those guidelines allow companies to have compliant practices without providing transparency to users. Few companies disclose their data retention times or offer users the opportunity to access the information collected about them. The lack of consistent terminology to refer to affiliate and non-affiliate partners, and the mix of practices for first-party and third-party contexts make it challenging for users to clearly assess the risks associated with online tracking. We discuss options to improve the transparency of online tracking companies’ privacy practices

Data types as a more ergonomic frontend for Grammar-Guided Genetic Programming

Genetic Programming (GP) is an heuristic method that can be applied to many Machine Learning, Optimization and Engineering problems. In particular, it has been widely used in Software Engineering for Test-case generation, Program Synthesis and Improvement of Software (GI). Grammar-Guided Genetic Programming (GGGP) approaches allow the user to refine the domain of valid program solutions. Backus Normal Form is the most popular interface for describing Context-Free Grammars (CFG) for GGGP. BNF and its derivatives have the disadvantage of interleaving the grammar language and the target language of the program. We propose to embed the grammar as an internal Domain-Specific Language in the host language of the framework. This approach has the same expressive power as BNF and EBNF while using the host language type-system to take advantage of all the existing tooling: linters, formatters, type-checkers, autocomplete, and legacy code support. These tools have a practical utility in designing software in general, and GP systems in particular. We also present Meta-Handlers, user-defined overrides of the tree-generation system. This technique extends our object-oriented encoding with more practicability and expressive power than existing CFG approaches, achieving the same expressive power of Attribute Grammars, but without the grammar vs target language duality. Furthermore, we evidence that this approach is feasible, showing an example Python implementation as proof. We also compare our approach against textual BNF-representations w.r.t. expressive power and ergonomics. These advantages do not come at the cost of performance, as shown by our empirical evaluation on 5 benchmarks of our example implementation against PonyGE2. We conclude that our approach has better ergonomics with the same expressive power and performance of textual BNF-based grammar encodings

Are They Worth Reading? An In-Depth Analysis of Online Trackers’ Privacy Policies

We analyzed the privacy policies of 75 online tracking companies with the goal of assessing whether they contain information relevant for users to make privacy decisions. We compared privacy policies from large companies, companies that are members of self-regulatory organizations, and nonmember companies and found that many of them are silent with regard to important consumer-relevant practices including the collection and use of sensitive information and linkage of tracking data with personally-identifiable information. We evaluated these policies against self-regulatory guidelines and found that many policies are not fully compliant. Furthermore, the overly general requirements established in those guidelines allow companies to have compliant practices without providing transparency to users. Few companies disclose their data retention times or offer users the opportunity to access the information collected about them. The lack of consistent terminology to refer to affiliate and non-affiliate partners, and the mix of practices for first-party and third-party contexts make it challenging for users to clearly assess the risks associated with online tracking. We discuss options to improve the transparency of online tracking companies’ privacy practices