Lifeboat Foundation

Solid-liquid filtration is a ubiquitous process found in industrial and biological systems. Although implementations vary widely, almost all filtration systems are based on a small set of fundamental separation mechanisms, including sieve, cross-flow, hydrosol, and cyclonic separation. Anatomical studies showed that manta rays have a highly specialized filter-feeding apparatus that does not resemble previously described filtration systems. We examined the fluid flow around the manta filter-feeding apparatus using a combination of physical modeling and computational fluid dynamics. Our results indicate that manta rays use a unique solid-fluid separation mechanism in which direct interception of particles with wing-like structures causes particles to “ricochet” away from the filter pores. This filtration mechanism separates particles smaller than the pore size, allows high flow rates, and resists clogging.

Several fundamental mechanisms for solid-fluid separation have been described in the biological and engineering literature, including sieve (1, 2), cross-flow (3–6), hydrosol , and cyclonic separation. Sieve filtration passes a mixture of particles and fluid through a structure with regularly sized pores, causing the particles to be retained while the fluid is drained. Although effective, sieve filters must have pore sizes smaller than the particle size, and they inevitably clog in use (2, 8, 9). Cross-flow filtration is similar to sieving, except that the incoming flow runs parallel rather than perpendicular to the filter. This configuration shears captured particles off the filter’s surface, which reduces but does not eliminate clogging (5, 6). Unlike sieve and cross-flow filters, hydrosol and cyclonic filtration do not require regularly sized pores.

A recent experiment may have placed living organisms in a state of quantum entanglement.

• By Jonathan O’Callaghan on October 29, 2018

Despite the simplicity of their visual system, fruit flies are able to reliably distinguish between individuals based on sight alone. This is a task that even humans who spend their whole lives studying Drosophila melanogaster struggle with. Researchers have now built a neural network that mimics the fruit fly’s visual system and can distinguish and re-identify flies. This may allow the thousands of labs worldwide that use fruit flies as a model organism to do more longitudinal work, looking at how individual flies change over time. It also provides evidence that the humble fruit fly’s vision is clearer than previously thought.

In an interdisciplinary project, researchers at Guelph University and the University of Toronto, Mississauga combined expertise in fruit fly biology with machine learning to build a biologically-based algorithm that churns through low-resolution videos of fruit flies in order to test whether it is physically possible for a system with such constraints to accomplish such a difficult task.

Fruit flies have small compound eyes that take in a limited amount of visual information, an estimated 29 units squared (Fig. 1A). The traditional view has been that once the image is processed by a fruit fly, it is only able to distinguish very broad features (Fig. 1B). But a recent discovery that fruit flies can boost their effective resolution with subtle biological tricks (Fig. 1C) has led researchers to believe that vision could contribute significantly to the social lives of flies. This, combined with the discovery that the structure of their visual system looks a lot like a Deep Convolutional Network (DCN), led the team to ask: “can we model a fly brain that can identify individuals?”

Using engineered nanocomposite structures called metamaterials, a City College of New York-led research team reports the ability to measure a significant increase in the energy transfer between molecules. Reported in the journal ACS Photonics, this breakthrough breaks the F\xF6rster resonance energy transfer (FRET) distance limit of ~10–20 nanometers, and leads to the possibility of measuring larger molecular assemblies.

And since FRET is a staple technique in many biological and biophysical fields, this new development could benefit pharmaceuticals, for instance.

“Energy transfer between molecules plays a central role in phenomena such as photosynthesis and is also used as a spectroscopic ruler for identifying structural changes of molecules,” said Vinod Menon, professor of physics in City College’s Division of Science. “However, the process of energy transfer is usually limited in the distance over which it occurs, typically reaching 10 to 20 nm.”

In support of the NAD+ Mouse Project over at Lifespan.io, Dr. David Sinclair will be doing an AMA on Reddit Futurology Tuesday, October 23, 2018 from 11:00 – 12:00 AM EDT. Dr. Sinclair will be answering questions from the community about his work with NAD+ biology, Sirtuins, and why the NAD+ Mouse Project is important for aging research. To ask your question please visit the AMA thread on Reddit Futurology here.

For those not familiar with NAD+ biology we did the NAD+ World series recently which explores this area of the biology of aging. We also took a look at why NAD+ appears to decline as we age and what is one of the most likely reasons for this.

Continue reading “Dr. David Sinclair AMA at Reddit Futurology” »

The Silicon Valley mindset combines biology and engineering: The body is a machine to be hacked and improved.

Futurists at some of the biggest tech firms want to reshape the way we think about death.

Exciting visitor at the Real Bodies (https://www.realbodies.it/) exhibit!

The lovely Ms. Chiara Bordi (https://www.facebook.com/Chiara-Bordi-474572166390000/), Miss Italia 3rd place runner up (aka the “Bionic Beauty”) stopping by to visit our associates at HealthQE (www.healthqe.cloud), and QantiQa (https://www.qantiqa.com/), to test out their new Musyke device

Bio-mechanics and Bio-acoustics

Two critical components in the regeneration, repair, and rejuvenation equation, and part of the integrated age-reversal paradigm of Embrykinesis at Bioquark Inc.- (www.bioquark.com)

MIT has launched the Stephen A. Schwarzman College of Computing, a $1 billion center dedicated to “reshaping its academic program” around AI. The idea, said MIT president L. Rafael Reif, is to use AI, machine learning and data science with other academic disciplines to “educate the bilinguals of the future,” defining bilingual as those working in biology, chemistry, politics, history and linguistics with computing skills that can be used in their field.

What happens when a new technology is so precise that it operates on a scale beyond our characterization capabilities? For example, the lasers used at INRS produce ultrashort pulses in the femtosecond range (10^-15 s), which is far too short to visualize. Although some measurements are possible, nothing beats a clear image, says INRS professor and ultrafast imaging specialist Jinyang Liang. He and his colleagues, led by Caltech’s Lihong Wang, have developed what they call T-CUP: the world’s fastest camera, capable of capturing 10 trillion (10¹³) frames per second (Fig. 1). This new camera literally makes it possible to freeze time to see phenomena—and even light—in extremely slow motion.

In recent years, the junction between innovations in non-linear optics and imaging has opened the door for new and highly efficient methods for microscopic analysis of dynamic phenomena in biology and physics. But harnessing the potential of these methods requires a way to record images in real time at a very short temporal resolution—in a single exposure.

Using current imaging techniques, measurements taken with ultrashort laser pulses must be repeated many times, which is appropriate for some types of inert samples, but impossible for other more fragile ones. For example, laser-engraved glass can tolerate only a single laser pulse, leaving less than a picosecond to capture the results. In such a case, the imaging technique must be able to capture the entire process in real time.