Lifeboat Foundation

Coherent X-ray imaging has emerged as a powerful tool for studying both nanoscale structures and dynamics in condensed matter and biological systems. The nanometric resolution together with chemical sensitivity and spectral information render X-ray imaging a powerful tool to understand processes such as catalysis, light harvesting or mechanics.

Unfortunately these processes might be random or stochastic in nature. In order to obtain freeze-frame images to study stochastic dynamics, the X-ray fluxes must be very high, potentially heating or even destroying the samples.

Also, detectors acquisition rates are insufficient to capture the fast nanoscale processes. Stroboscopic techniques allow imaging ultrafast repeated processes. But only mean dynamics can be extracted, ruling out measurement of stochastic processes, where the system evolves through a different path in phase space during each measurement. These two obstacles prevent coherent imaging from being applied to complex systems.

Synthetic Plants For A Sustainable Future — Dr. Angie Burnett, Ph.D. — Program Director, Advanced Research + Invention Agency (ARIA)

Dr. Angie Burnett, Ph.D. is Program Director at the Advanced Research and Invention Agency (ARIA — https://www.aria.org.uk/), a UK organization created by an Act of Parliament, and sponsored by the Department for Science, Innovation, and Technology, to fund projects across a full spectrum of R\&D disciplines, approaches, and institutions, per the ARIA mission statement to “Look beyond what exists today to the breakthroughs we’ll need tomorrow”

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A specially coated foil removed more than 99% of E. coli bacteria from water in laboratory tests.

Alternative splicing is a genetic process where different segments of genes are removed, and the remaining pieces are joined together during transcription to messenger RNA (mRNA). This mechanism increases the diversity of proteins that can be generated from genes, by assembling sections of genetic code into different combinations. This is believed to enhance biological complexity by allowing genes to produce different versions of proteins, or protein isoforms, for many different uses.

And, if its in trees, guess where else it is, Crisis Yet? or nah.

It is well known that more and more plastic waste is ending up in soil and bodies of water. Researchers are particularly concerned about tiny micro-and nano-sized particles. It remains unclear how and to what extent they are able to enter living organisms—and what effect they may have on metabolism.

In his new book, to be published in September, neuroscientist Francisco Aboitiz links consciousness back to the earliest days of biological life.

Abstract. Feelings of love are among the most significant human phenomena. Love informs the formation and maintenance of pair bonds, parent-offspring attachments, and influences relationships with others and even nature. However, little is known about the neural mechanisms of love beyond romantic and maternal types. Here, we characterize the brain areas involved in love for six different objects: romantic partner, one’s children, friends, strangers, pets, and nature. We used functional magnetic resonance imaging (fMRI) to measure brain activity, while we induced feelings of love using short stories. Our results show that neural activity during a feeling of love depends on its object. Interpersonal love recruited social cognition brain areas in the temporoparietal junction and midline structures significantly more than love for pets or nature. In pet owners, love for pets activated these same regions significantly more than in participants without pets. Love in closer affiliative bonds was associated with significantly stronger and more widespread activation in the brain’s reward system than love for strangers, pets, or nature. We suggest that the experience of love is shaped by both biological and cultural factors, originating from fundamental neurobiological mechanisms of attachment.

Organoid intelligence (OI) is an emerging scientific field aiming to create biocomputers where lab-grown brain organoids serve as ‘biological hardware’

In their article, published in Frontiers in Science, Smirnova et al., outline the multidisciplinary strategy needed to pursue this vision: from next-generation organoid and brain-computer interface technologies, to new machine-learning algorithms and big data infrastructures.

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Have you ever wonder how SNNs work and their difference from traditional neural networks? Or how SNNs play an important role in computing beyond the Moore’s Law?

What is SNN?
Spiking neural network (SNN) is a new form of neural networks with biologically realistic mechanisms designed to emulate the efficiency and effectiveness of the biological brain.