לוח שנה משולב

Zoom Link: https://weizmann.zoom.us/j/93495966390?pwd=T3hDNXY1WFh6bFpIbDh3OEFxZlcwZz09

The fibre-matrix interface plays a vital role in the overall mechanical behaviour of a fibre-reinforced composite, but the classical approach to improving the interface through chemical sizing is limited by material properties. Achieving a simultaneous improvement in strength and toughness in a composite is a particular challenge since these properties are mutually exclusive, and the chemical modification of the interface often results in one property being improved at the expense of the other. In contrast, the geometrical modification of the fibre-matrix interface to allow for mechanisms such as mechanical interlocking of components is a promising approach to resolving this challenge. This study explores a novel type of topographical obstacle – polymer droplets at the fibre-matrix interface. Discrete epoxy droplets are deposited onto glass fibres and embedded in an epoxy matrix to form model composites. The effect of the interfacial epoxy droplets is investigated using single fibre experiments.

The nucleocytoplasmic transport factor importin β1 has critical roles in the transport of injury-regulated cargos in peripheral neurons. I employed biotinylation by antibody recognition (BAR) to identify axonal importin β1 cargos in primary neurons. Axon focused BAR proteomics suggests a new role of importin β1 in long-distance fatty acid signaling in injured neurons.

The maker movement (cheap electronics + sharing), automated microscopy, autonomous platforms and small footprint satellites have been revolutionizing oceanography, opening a variety of new avenues for research and requiring a different education model. In this talk I will summarize a few activities my lab has been involved in that are associated with these disruptive technologies and why I am very optimistic for the future of our field in the coming years.

We all capture the world around us through digital data such as images, videos and sound. However, in many cases, we are interested in certain properties of the data that are either not available or difficult to perceive directly from the input signal. My goal is to “Re-render Reality”, i.e., develop algorithms that analyze digital signals and then create a new version of it that allows us to see and hear better. In this talk, I’ll present a variety of methodologies aimed at enhancing the way we perceive our world through modified, re-rendered output. These works combine ideas from signal processing, optimization, computer graphics, and machine learning, and address a wide range of applications. More specifically, I’ll demonstrate how we can automatically associate objects in a video with complex scene elements that are related to th! em (e.g., associate objects with their shadows or reflections), or transfer the visual appearance of objects in one image to semantically related objects in a different image.

The immune system and the gut microbiome are becoming major players in chronic neurodegenerative conditions. One of the key interfaces between the brain and the immune system with an impact on brain function is the choroid plexus (CP). The CP interface is central to the maintenance of brain homeostasis by exerting a plethora of different biological processes. However, in aging and Alzheimer’s disease (AD), interferon type-I (IFN-I) signaling accumulates at the CP and impedes part of its beneficial function by inducing a CP-pro-aging signature. My research contributed to the finding that IFN-I signaling at the CP induces an aging-like signature in microglia and impedes cognitive abilities in middle-aged mice in a microglia-dependent manner. In addition, I demonstrated that the brain-specific enzyme, cholesterol 24-hydroxylase (CYP46A1), is expressed by the CP epithelium and that its product, 24-hydroxycholesterol (24-OH), downregulates CP-pro-inflammatory signatures. Furthermore, in AD, CP CYP46A1 protein levels were decreased in both mice and humans and overexpression of Cyp46a1 at the CP in 5xFAD mice reversed brain inflammation, microglial dysfunction signatures, and cognitive loss. Finally, while the pro-inflammatory cytokine TNF-α impaired CP Cyp46a1 expression in vitro, boosting systemic immunity in vivo increased its levels in an IFNGR2-dependent manner. These results highlight CYP46A1 at the CP as a remote regulator of brain inflammation, which diminishes with neurodegeneration, but is amenable to rescue. Focusing on the gut microbiome, I found that 5xFAD mice devoid of microbiome exhibited a striking decrease of long-term spatial memory deficit and increased synaptic and neuronal survival. Spatial memory deficit in 5xFAD mice kept in germ free (GF) or specific-pathogen free (SPF) conditions, negatively correlated with the abundance of 2-hydroxypyridine, while systemic, chronic supply of 2-hydroxypyridine in SPF 5xFAD mice improved spatial memory deficits in comparison to phosphate-buffered saline (PBS)-supplied 5xFAD mice. Overall, these findings demonstrate a microbiome-dependent effect on AD pathology in the 5xFAD mouse model and suggest a connection between 2-hydroxypyridine and AD manifestation. In general, this research thesis addresses novel aspects of choroid plexus and gut microbiome metabolism and their relation to AD progression.
Zoom link
https://weizmann.zoom.us/j/98658552127?pwd=ZkZmWTBkd1AxZ0xPbGlpU3FPUWpzUT09

Meeting ID:986 5855 2127
Password:495213

Decadal Climate Predictions Using Sequential Learning Algorithms
Ensembles of climate models are commonly used to improve climate predictions and assess the
uncertainties associated with them. Weighting the models according to their performances holds the promise of further improving their predictions. Using an ensemble of climate model simulations from the CMIP5 decadal experiments, we quantified the total uncertainty associated with these predictions and the relative importance of model and internal uncertainties. Sequential learning algorithms (SLAs) were used to reduce the forecast errors and reduce the model uncertainties. The reliability of the SLA predictions was also tested, and the advantages and limitations of the different performance measures are discussed. The spatial distribution of the SLAs performance showed that they are skillful and better than the other forecasting methods over large continuous regions. This finding suggests that, despite the fact that each of the ensemble models is not skillful, the models were able to capture some physical processes that resulted in deviations from the climatology and that the SLAs enabled the extraction of this additional information. If time permits I will also present a method for estimating the uncertainties associated with ensemble predictions and demonstrate the resulting improved reliability.

References:
1. Improvement of climate predictions and reduction of their uncertainties using learning algorithms, Atmospheric Chemistry and Physics 15, 8631-8641 (2015).
2. Decadal climate predictions using sequential learning algorithms, Journal of Climate 29, 3787-3809 (2016).
3. The contribution of internal and model variabilities to the uncertainty in CMIP5 decadal climate predictions, Climate Dynamics 49, 3221 (2017).
4. Quantifying the uncertainties in an ensemble of decadal climate predictions. Journal of Geophysical Research: Atmospheres 122, 13,191–13,200 (2017).
5. Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections. Nature Communications 11, 451 (2020).