לוח שנה משולב

TBA

We have shown that microorganisms (bacteria, yeast and fungi) were present in clouds and were metabolically active. As a consequence a new scientific question rose: are they able to modify the chemical composition of clouds and be an alternative route to radical chemistry?
In the past we have mainly studied the biotransformation of simple carbon compounds (acetate, succinate, formate, methanol, formaldehyde), and oxidants (H2O2). We showed that biodegradation rates were within the same range of order than photo-transformation rates.
More recently we investigated their potential biodegradation activity towards atmospheric pollutants.
Using GCxGC-HRMS technique we were able to detect and identify over 100 semi-volatile compounds in 3 cloud samples collected at the puy de Dôme station (1465 m, France). Among these compounds, 10 priority pollutants from the US EPA list were identified and quantified.
We focused our work on the biodegradation of phenol and catechol in clouds using two strategies.
1) A metatranscriptomic analysis showed in cloud activity of microorganisms. We detected transcripts of genes coding for phenol monooxygenases (and phenol hydroxylases) and catechol 1,2-dioxygenases. These enzymes were likely from Gamma-proteobacteria (Acinetobacter and Pseudomonas genera).
2) 145 bacterial strains isolated from cloud water were screened for their phenol degradation capabilities, 93% of them (mainly Pseudomonas and Rhodococcus strains) were positive. These findings highlighted the possibility of phenol degradation by microorganisms in clouds.
To go further we measured the biodegradation rates of Phenol and Catechol by one of the most active strain (Rhodococcus enclensis) and compared them with the transformation rates resulting from the reactivity of °OH and NO3°radicals. In the cloud water phase, both phenol transformation rates were within the same range of order, while biodegradation of catechol was ten times quicker than chemical transformation. The experimentally derived biodegradation rates were included in a multiphase box model to compare the chemical loss rates of phenol and catechol in both the gas and aqueous phases to their biodegradation rate in the aqueous phase under atmospheric conditions.
In conclusion our results suggest that cloud microorganisms could play a role in atmospheric chemistry.

The conversion of physical analog signals to the digital domain for further processing inevitably entails loss of information.The famous Shannon-Nyquist theorem has become a landmark in analog to digital conversion and the development of digital signal processing algorithms. However, in many modern applications, the signal bandwidths have increased tremendously, while the acquisition capabilities have not scaled sufficiently fast. Furthermore, the resulting high rate digital data requires storage, communication and processing at very high rates which is computationally expensive and requires large amounts of power. In this talk, we present a framework for sampling and processing a wide class of wideband analog signals at rates far below Nyquist by exploiting signal structure and the processing task. We then show how these ideas can be used to overcome fundamental resolution limits in optical microscopy, ultrasound imaging, quantum systems and more. We demonstrate the theory through several demos of real-time sub-Nyquist prototypes and devices operating beyond the standard resolution limits combining high spatial resolution with short integration time.

Eukaryotic initiation factor 1A (eIF1A) is a key translation initiation regulatory factor yet little is known about its exact role in the translation process of mammalian cells. Previous work in our lab have shown that eIF1A interacts with ribosomal proteins RPS3 and RPS10 and these interactions are disrupted by eIF1A cancer-associated mutants. As the activities of eIF1A are critically dependent on its ability to bind the ribosome, we targeted eIF1A-RPS10 complex to identify eIF1A inhibitors, using high throughput drug screen. We found 21 eIF1A inhibitors which affected eIF1A known translational roles and divided them to groups according to the protein they bind. Several inhibitors which can differentiate between eIF1A known functions were identified and inhibitor 1Ai-5662 showed dramatic affect in decreasing uveal melanoma cells viability. Our results show the benefits of using small molecules research approach.