This is a 10-minute version with my picks on an hour-and-a-half interview on the longevity science made by Rhonda Patrick to Morgan Levine.
The link to the entire interview, which took place on April 12, 2022, is in the description of the video.
The interview took place on April 2022.
Amid the chaotic chains of events that ensue when protons smash together at the Large Hadron Collider in Europe, one particle has popped up that appears to go to pieces in a peculiar way.
All eyes are on the B meson, a yoked pair of quark particles. Having caught whiffs of unexpected B meson behavior before, researchers with the Large Hadron Collider beauty experiment (LHCb) have spent years documenting rare collision events featuring the particles, in hopes of conclusively proving that some novel fundamental particle or effect is meddling with them.
In their latest analysis, first presented at a seminar in March, the LHCb physicists found that several measurements involving the decay of B mesons conflict slightly with the predictions of the Standard Model of particle physics — the reigning set of equations describing the subatomic world. Taken alone, each oddity looks like a statistical fluctuation, and they may all evaporate with additional data, as has happened before. But their collective drift suggests that the aberrations may be breadcrumbs leading beyond the Standard Model to a more complete theory.
Making positive impacts on human longevity — sonia arrison, author, analyst, investor, entrepreneur.
Sonia Arrison (https://soniaarrison.com/) is a best-selling author, analyst, entrepreneur, and investor.
Sonia is founder of 100 Plus Capital (https://100pluscap.com/ — Investing in companies positively impacting human longevity), Chair of the Alliance for Longevity Initiatives (https://a4li.org/ — The first and only 501©(4) nonprofit organization founded with the goal of creating social and political action around the issues of combating age-related chronic conditions and increasing our number of healthy, disease-free years), co-founder of Unsugarcoat Media (acquired by Medium), and was an associate founder of Singularity University.
“I cannot take your philanthropy on climate change seriously when you have a massive short position against Tesla,” Musk told Gates, per the texts.
She gives a great analogy of slowing aging versus reversing aging, and I did not realize Yamanaka Factors were not so perfect in current use.
In this video Eleanor talks about the her view on Longevity Escape Velocity and reprogramming with Yamanaka factors and some of the issues around this technology.
Eleanor Sheekey graduated from Cambridge University with a masters degree in Biochemistry and is now studying for her PhD at the Cancer Research UK — Cambridge Institute. Eleanor is the person behind the Sheekey Science Show, a popular YouTube channel where she covers longevity and other topics with her deep knowledge of biochemistry.
Researchers have designed proteins that self-assemble into tiny machine parts for use in molecular engines.
Software from Brave and DuckDuckGo will automatically bypass Google AMP.
Brave and DuckDuckGo have rolled out updates to help users bypass a divisive web initiative from Google.
An awful lot of meetings lie ahead for roboticists and regulators to determine how machines and people will work together.
Machines will do the nasty jobs; human beings the nice ones | Science & technology.
The classical neurophysiological approach for calculating PCI, power spectral density, and spectral exponent relies on many epochs to improve the reliability of statistical estimates of these indices21. However, these methods are only suitable for investigating the averaged brain states and they can only clarify general neurophysiological aspects. Machine learning (ML) allows decoding and identifying specific brain states and discriminating them from unrelated brain signals, even in a single trial in real-time22. This can potentially transform statistical results at the group level into individual predictions9. A deep neural network, which is a popular approach in ML, has been employed to classify or predict brain states using EEG data23. Particularly, a convolutional neural network (CNN) is the most extensively used technique in deep learning and has proven to be effective in the classification of EEG data24. However, a CNN has the drawback that it cannot provide information on why it made a particular prediction25. Recently, layer-wise relevance propagation (LRP) has successfully demonstrated why classifiers such as CNNs have made a specific decision26. Specifically, the relevance score resulting from the LRP indicates the contribution of each input variable to the classification or prediction decision. Thus, a high score in a particular area of an input variable implies that the classifier has made the classification or prediction using this feature. For example, neurophysiological data suggest that the left motor region is activated during right-hand motor imagery27. The LRP indicates that the neural network classifies EEG data as right-hand motor imagery because of the activity of the left motor region28. Therefore, the relevance score was higher in the left motor region than in other regions. Thus, it is possible to interpret the neurophysiological phenomena underlying the decisions of CNNs using LRP.
In this work, we develop a metric, called the explainable consciousness indicator (ECI), to simultaneously quantify the two components of consciousness—arousal and awareness—using CNN. The processed time-series EEG data were used as an input of the CNN. Unlike PCI, which relies on source modeling and permutation-based statistical analysis, ECI used event-related potentials at the sensor level for spatiotemporal dynamics and ML approaches. For a generalized model, we used the leave-one-participant-out (LOPO) approach for transfer learning, which is a type of ML that transfers information to a new participant not included in the training phase24,27. The proposed indicator is a 2D value consisting of indicators of arousal (ECIaro) and awareness (ECIawa). First, we used TMS–EEG data collected from healthy participants during NREM sleep with no subjective experience, REM sleep with subjective experience, and healthy wakefulness to consider each component of consciousness (i.e., low/high arousal and low/high awareness) with the aim to analyze correlations between the proposed ECI and the three states, namely NREM, REM, and wakefulness. Next, we measured ECI using TMS–EEG data collected under general anesthesia with ketamine, propofol, and xenon, again with the aim to measure correlation with these three anesthetics. Before anesthesia, TMS–EEG data were also recorded during healthy wakefulness. Upon awakening, healthy participants reported conscious experience during ketamine-induced anesthesia and no conscious experience during propofol-and xenon-induced anesthesia. Finally, TMS–EEG data were collected from patients with disorders of consciousness (DoC), which includes patients diagnosed as UWS and MCS patients. We hypothesized that our proposed ECI can clearly distinguish between the two components of consciousness under physiological, pharmacological, and pathological conditions.
To verify the proposed indicator, we next compared ECIawa with PCI, which is a reliable index for consciousness. Then, we applied ECI to additional resting-state EEG data acquired in the anesthetized participants and patients with DoC. We hypothesize that if CNN can learn characteristics related to consciousness, it could calculate ECI accurately even without TMS in the proposed framework. In terms of clinical applicability, it is important to use the classifier from the previous LOPO training of the old data to classify the new data (without additional training). Therefore, we computed ECI in patients with DoC using a hold-out approach29, where training data and evaluation data are arbitrarily divided, instead of cross-validation. Finally, we investigated why the classifier generated these decisions using LRP to interpret ECI30.
