Learning and a spectrum of other behavioral competencies allow organisms to rapidly adapt to dynamically changing environmental variations. The emerging field of diverse intelligence seeks to understand what systems, besides ones with complex brains, exhibit these capacities. Here, we tested predictions of a general computational framework based on the free energy principle in neuroscience but applied to aneural biological process as established previously, by demonstrating and manipulating pattern recognition in a simple aneural organism, the green algae Volvox. Our studies of the adaptive photoresponse in Volvox reveal that aneural organisms can distinguish between patterned and randomized inputs and indicate how this is achieved mechanistically.
Microelectromechanical systems (MEMS) are tiny devices that integrate various components, such as miniature sensors, electronics and actuators, onto a single chip. These small devices have proved highly promising for precisely detecting biological signals, acceleration, force and other measurements.
Most of the MEMS developed to date are made of silicon and silicon nitride. While some of these devices have achieved promising results, their material composition and design limit their sensitivity and versatility, for instance limiting their use in wet environments.
In a recent Nature Electronicspaper, researchers at Ecole Polytechnique Fédérale de Lausanne (EPFL) introduced an innovative cantilever design for MEMS based on a polymer, a semiconductor and ceramic. Cantilevers are tiny flexible beams that can adapt their shape in response to external forces or molecular interactions, thus potentially serving as sensors or actuators.
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”
Prior to this role, Dr. Burnett was a Research Associate in the Department of Plant Sciences, and a former David MacKay Research Associate at Darwin College and Cambridge Zero where her work focused on understanding the response of maize plants to high light and cold temperature stresses, and the genetic basis for stress tolerance, so that breeders can produce plants which are better able to withstand environmental stress.
Dr. Burnett’s background is in plant physiology. She holds a BA from the University of Cambridge and a PhD from the University of Sheffield, where she was awarded the inaugural PhD studentship from the Society for Experimental Biology. Before commencing her role at the University of Cambridge, she worked as a postdoctoral research associate at Brookhaven National Laboratory in the USA and as a Consultant at the Food and Agriculture Organization of the United Nations in Italy.
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.
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.
