Fasting as key tone for COVID immunity - Nature Metabolism
SARS-CoV-2-induced anorexia triggers systemic metabolic alterations. In a study published in Nature, Karagiannis et al. show that the ketone body β-hydroxybutyrate (BHB) improves COVID-19 disease outcomes. Further, BHB metabolically and functionally reprograms CD4+ T cells, highlighting immunometabolic tuning of immunity in COVID-19.
Overall, this study has identified the ketone body BHB as an alternative carbon source to fuel mitochondrial OXPHOS, thereby metabolically reprogramming TH1 cells and improving antiviral immunity in conditions of infection-induced anorexia. Considering the diverse cellular signalling activities of BHB3, it is possible that BHB alters CD4+ T cell function through additional means such as transcriptional regulation or epigenetic modifications, consistent with a role for BHB in controlling CD8+ memory T cell development via epigenetic regulation15. As metabolic programs are crucial regulators of CD4+ and CD8+ T cell plasticity and heterogeneity9, additional studies are required to address whether BHB and other metabolites induced by a ketogenic diet have similar effects on other types of T cell during viral infection or in other nutrient-deprived contexts, including the tumour microenvironment. In summary, these important findings broaden our knowledge of dietary influence on antiviral immunity and provide new insights into and understanding of the variable morbidity associated with COVID-19.
Association of periodic fasting with lower severity of COVID-19 outcomes in the SARS-CoV-2 prevaccine era: an observational cohort from the INSPIRE registry
Objectives Intermittent fasting boosts some host defence mechanisms while modulating the inflammatory response. Lower-frequency fasting is associated with greater survival and lower risk from COVID-19-related comorbidities. This study evaluated associations of periodic fasting with COVID-19 severity and, secondarily, initial infection by SARS-CoV-2.
Design Prospective longitudinal observational cohort study.
Setting Single-centre secondary care facility in Salt Lake City, Utah, USA with follow-up across a 24-hospital integrated healthcare system.
Participants Patients enrolled in the INSPIRE registry in 2013–2020 were studied for the primary outcome if they tested positive for SARS-CoV-2 during March 2020 to February 2021 (n=205) or, for the secondary outcome, if they had any SARS-CoV-2 test result (n=1524).
Interventions No treatment assignments were made; individuals reported their personal history of routine periodic fasting across their life span.
Main outcome measures A composite of mortality or hospitalisation was the primary outcome and evaluated by Cox regression through February 2021 with multivariable analyses considering 36 covariables. The secondary outcome was whether a patient tested positive for SARS-CoV-2.
Results Subjects engaging in periodic fasting (n=73, 35.6%) did so for 40.4±20.6 years (max: 81.9 years) prior to COVID-19 diagnosis. The composite outcome occurred in 11.0% of periodic fasters and 28.8% of non-fasters (p=0.013), with HR=0.61 (95% CI 0.42 to 0.90) favouring fasting. Multivariable analyses confirmed this association. Other predictors of hospitalisation/mortality were age, Hispanic ethnicity, prior MI, prior TIA and renal failure, with trends for race, smoking, hyperlipidaemia, coronary disease, diabetes, heart failure and anxiety, but not alcohol use. In secondary analysis, COVID-19 was diagnosed in 14.3% of fasters and 13.0% of non-fasters (p=0.51).
Conclusions Routine periodic fasting was associated with a lower risk of hospitalisation or mortality in patients with COVID-19. Fasting may be a complementary therapy to vaccination that could provide immune support and hyperinflammation control during and beyond the pandemic.
Trial registration Clinicaltrials.gov, [NCT02450006][1] (the INSPIRE registry).
Data are available upon reasonable request. The data underlying this article cannot be shared publicly due to US privacy laws. Data are available upon reasonable request to the corresponding author.
[1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT02450006&atom=%2Fbmjnph%2Fearly%2F2022%2F06%2F30%2Fbmjnph-2022-000462.atom
In the n=205 patients with COVID-19, 11.0% of fasters and 28.8% of non-fasters had hospitalisation/mortality (figure 2). This constituted a total of 46 composite study events, or 40 hospitalisations without death, 4 hospitalisations ending in death and 2 deaths without hospitalisation. The association of periodic fasting with the composite end point had HR=0.61 (CI 0.42 to 0.90; p=0.013). Fasting remained significant in all multivariable analyses (table 2), with a range of HR=0.61–0.65 depending on the covariables that were entered (p=0.015–0.036). Results for periodic fasting were similar in subjects <65 years (figure 3A) and ≥65 years of age (figure 3B), although splitting the population into the two subgroups (n=104 and n=101, respectively) reduced the statistical significance in both age groups.
A fundamental problem in distributed computing and multi-agent systems is to achieve overall system reliability in the presence of a number of faulty processes. This often requires coordinating processes to reach consensus, or agree on some data value that is needed during computation. Example applications of consensus include agreeing on what transactions to commit to a database in which order, state machine replication, and atomic broadcasts. Real-world applications often requiring consensus include cloud computing, clock synchronization, PageRank, opinion formation, smart power grids, state estimation, control of UAVs (and multiple robots/agents in general), load balancing, blockchain, and others.
A fundamental problem in distributed computing and multi-agent systems is to achieve overall system reliability in the presence of a number of faulty processes. This often requires coordinating processes to reach consensus, or agree on some data value that is needed during computation. Example applications of consensus include agreeing on what transactions to commit to a database in which order, state machine replication, and atomic broadcasts. Real-world applications often requiring consensus include cloud computing, clock synchronization, PageRank, opinion formation, smart power grids, state estimation, control of UAVs (and multiple robots/agents in general), load balancing, blockchain, and others.
Consensus algorithms enable network participants to agree on the contents of a blockchain in a distributed and trust-less manner.“Consensus decision-making is a group decision-making process in which group members develop, and agree to support a decision in the best interest of the whole. Consensus may be defined professionally as an acceptable resolution, one that can be supported, even if not the “favourite” of each individual. Consensus is defined by Merriam-Webster as, first, general agreement, and second, group solidarity of belief or sentiment.” Wikipedia
The proof-of-stake (PoS) mechanism works using an algorithm that selects participants with the highest stakes as validators, assuming that the highest stakeholders are incentivized to ensure a transaction is processed. The idea is that those with the most coins in circulation have the most to lose so they are positioned to work in the interest of the network. The amount of coins that a network may require changes just like the difficulty in PoW.In PoS, the blocks aren’t created by miners doing work, but by minters staking their tokens to “bet” on which blocks are valid. In the case of a fork, minters spend their tokens voting on which fork to support. Assuming most people vote on the correct fork, validators who voted on the wrong fork would “lose their stake” in the correct one.
The common argument against proof-of-stake is the Nothing at Stake problem. The concern is that since it costs validators almost no computational power to support a fork unlike PoW, validators could vote for both sides of every fork that happens. Forks in PoS could then be much more common than in PoW, which some people worry could harm the credibility of the currency.
PoW was originally invented as a means to combat spam (see hashcash)if you make it computationally expensive to send email then spamming would be cost prohibitive while still being almost free for a normal user to send email.Bitcoin, which made the blockchain technology popular, developed the so-called Proof of Work (PoW) algorithm. In principle, each participant on the Bitcoin network can participate in the block generation. In order to confirm the transaction and enter a block into the blockchain, a miner has to provide an answer, or a proof, to a specific challenge. Miners use PoW to validate transactions and mining new coins, but its main goal is to block potential cyber-attacks or suspicious activities within the network.
Proof of Stake (POS) / Proof of Presence (PoP) - consensus
Reward for generating blocks (Proof-of-Stake, POS). This involves running a full node, unlocked and with the user's stake applied to generate blocks. Users who run a block generating node generally need to have at least a moderate amount of token on their account
Proof of History is a sequence of computation that can provide a way to cryptographically verify passage of time between two events. It uses a cryptographically secure function written so that output cannot be predicted from the input, and must be completely executed to generate the output. The function is run in a sequence on a single core, its previous output as the current input, periodically recording the current output, and how many times its been called. The output can then be re-computed and verified by external computers in parallel by checking each sequence segment on a separate core. Data can be timestamped into this sequence by appending the data (or a hash of some data) into the state of the function. The recording of the state, index and data as it was appended into the sequences provides a timestamp that can guarantee that the data was created sometime before the next hash was generated in the sequence. This design also supports horizontal scaling as multiple generators can synchronize amongst each other by mixing their state into each others sequences.
First it performs a semantic search with k=1 to find the document (e.g. a Sentence, Paragraph, Article, etc.) which is most likely to contain the answer. This step has no certainty threshold and as long as at least one document is present, it will be fetched and selected as the one most likely containing the answer. In a second step, Weaviate creates the required prompt as an input to an external call made to the OpenAI Completions endpoint. Weaviate uses the most relevant documents to establish a prompt for which OpenAI extracts the answer
In economics and game theory, a participant is considered to have superrationality (or renormalized rationality) if they have perfect rationality (and thus maximize their utility) but assume that all other players are superrational too and that a superrational individual will always come up with the same strategy as any other superrational thinker when facing the same problem. Applying this definition, a superrational player playing against a superrational opponent in a prisoner's dilemma will cooperate while a rationally self-interested player would defect.
Parallax is a displacement or difference in the apparent position of an object viewed along two different lines of sight and is measured by the angle or half-angle of inclination between those two lines. Due to foreshortening, nearby objects show a larger parallax than farther objects, so parallax can be used to determine distances.
Parallax is a displacement or difference in the apparent position of an object viewed along two different lines of sight and is measured by the angle or half-angle of inclination between those two lines.[1][2] Due to foreshortening, nearby objects show a larger parallax than farther objects, so parallax can be used to determine distances.
Proof of work (PoW) is a form of cryptographic proof in which one party (the prover) proves to others (the verifiers) that a certain amount of a specific computational effort has been expended.[1] Verifiers can subsequently confirm this expenditure with minimal effort on their part. The concept was invented by Moni Naor and Cynthia Dwork in 1993 as a way to deter denial-of-service attacks and other service abuses such as spam on a network by requiring some work from a service requester, usually meaning processing time by a computer. The term "proof of work" was first coined and formalized in a 1999 paper by Markus Jakobsson and Ari Juels.[2][3]
Proof of work (PoW) is a form of cryptographic proof in which one party (the prover) proves to others (the verifiers) that a certain amount of a specific computational effort has been expended.[1] Verifiers can subsequently confirm this expenditure with minimal effort on their part. The concept was invented by Moni Naor and Cynthia Dwork in 1993 as a way to deter denial-of-service attacks and other service abuses such as spam on a network by requiring some work from a service requester, usually meaning processing time by a computer. The term "proof of work" was first coined and formalized in a 1999 paper by Markus Jakobsson and Ari Juels.
An explanation of the proof-of-stake consensus protocol and its role in Ethereum.
Proof-of-stake underlies certain consensus mechanisms used by blockchains to achieve distributed consensus. In proof-of-work, miners prove they have capital at risk by expending energy. Ethereum uses proof-of-stake, where validators explicitly stake capital in the form of ETH into a smart contract on Ethereum. This staked ETH then acts as collateral that can be destroyed if the validator behaves dishonestly or lazily. The validator is then responsible for checking that new blocks propagated over the network are valid and occasionally creating and propagating new blocks themselves.
A direct public offering (DPO) or direct listing[disputed  – discuss] is a method by which a company can offer an investment opportunity directly to the public.
A DPO is similar to an initial public offering (IPO) in that securities, such as stock or debt, are sold to investors. But unlike an IPO, a company uses a DPO to raise capital directly and without a "firm underwriting" from an investment banking firm or broker-dealer. A DPO may have a sponsoring FINRA broker, but the broker does not guarantee full subscription of the offering. In a DPO, the broker merely assures compliance with all applicable securities laws and assists with organizing the offering. Following compliance with federal and state securities laws, a company can sell its shares directly to anyone, even non-accredited investors, including customers, employees, suppliers, distributors, family, friends, and others.
Monarchy, where the divine right of kings establishes the political legitimacy of the rule of the monarch (king or queen); legitimacy also derives from the popular perception (tradition and custom) and acceptance of the monarch as the rightful ruler of nation and country. Contemporarily, such divine-right legitimacy is manifest in the absolute monarchy of the House of Saud (est. 1744), a royal family who have ruled and governed Saudi Arabia since the 18th century. Moreover, constitutional monarchy is a variant form of monarchic political legitimacy which combines traditional authority and legal–rational authority, by which means the monarch maintains nationalist unity (one people) and democratic administration (a political constitution)
In political science, legitimacy is the right and acceptance of an authority, usually a governing law or a regime. Whereas authority denotes a specific position in an established government, the term legitimacy denotes a system of government—wherein government denotes "sphere of influence"
Arrow's impossibility theorem, the general possibility theorem or Arrow's paradox is an impossibility theorem in social choice theory that states that when voters have three or more distinct alternatives (options), no ranked voting electoral system can convert the ranked preferences of individuals into a community-wide (complete and transitive) ranking while also meeting the specified set of criteria: unrestricted domain, non-dictatorship, Pareto efficiency, and independence of irrelevant alternatives. The theorem is often cited in discussions of voting theory as it is further interpreted by the Gibbard–Satterthwaite theorem. The theorem is named after economist and Nobel laureate Kenneth Arrow, who demonstrated the theorem in his doctoral thesis and popularized it in his 1951 book Social Choice and Individual Values. The original paper was titled "A Difficulty in the Concept of Social Welfare".[1]
Arrow's impossibility theorem, the general possibility theorem or Arrow's paradox is an impossibility theorem in social choice theory that states that when voters have three or more distinct alternatives (options), no ranked voting electoral system can convert the ranked preferences of individuals into a community-wide (complete and transitive) ranking while also meeting the specified set of criteria: unrestricted domain, non-dictatorship, Pareto efficiency, and independence of irrelevant alternatives. The theorem is often cited in discussions of voting theory as it is further interpreted by the Gibbard–Satterthwaite theorem.
The Bondareva–Shapley theorem, in game theory, describes a necessary and sufficient condition for the non-emptiness of the core of a cooperative game in characteristic function form. Specifically, the game's core is non-empty if and only if the game is balanced. The Bondareva–Shapley theorem implies that market games and convex games have non-empty cores. The theorem was formulated independently by Olga Bondareva and Lloyd Shapley in the 1960s.
The Bondareva–Shapley theorem, in game theory, describes a necessary and sufficient condition for the non-emptiness of the core of a cooperative game in characteristic function form. Specifically, the game's core is non-empty if and only if the game is balanced. The Bondareva–Shapley theorem implies that market games and convex games have non-empty cores. The theorem was formulated independently by Olga Bondareva and Lloyd Shapley in the 1960s.
In game theory, the Nash equilibrium, named after the mathematician John Nash, is the most common way to define the solution of a non-cooperative game involving two or more players. In a Nash equilibrium, each player is assumed to know the equilibrium strategies of the other players, and no one has anything to gain by changing only one's own strategy.[1] The principle of Nash equilibrium dates back to the time of Cournot, who in 1838 applied it to competing firms choosing outputs.[2]
In game theory, the Nash equilibrium, named after the mathematician John Nash, is the most common way to define the solution of a non-cooperative game involving two or more players. In a Nash equilibrium, each player is assumed to know the equilibrium strategies of the other players, and no one has anything to gain by changing only one's own strategy.[1] The principle of Nash equilibrium dates back to the time of Cournot, who in 1838 applied it to competing firms choosing outputs
Training Your Own Dense Passage Retrieval Model | Haystack
Learn about training a Dense Passage Retrieval model and the data needed to do so.
DPR is standardly trained using a method known as in-batch negatives.
This means that positive contexts for a given query are treated as negative contexts for the other queries in the batch.
Doing so allows for a high degree of computational efficiency, thus allowing the model to be trained on large amounts of data.
Average SaaS Growth Rate: Brief Guide for Startups
Essential knowledge on how to benchmark, calculate, and forecast the growth rate — industry studies findings & tips for startups.
It's typical for many startups to grow fast in the early stage, with the ARR growth by 144% on average. As the company matures, the growth rate slows down and falls into the 15% to 45% year-to-year growth range.
In physical chemistry, the Arrhenius equation is a formula for the temperature dependence of reaction rates. The equation was proposed by Svante Arrhenius in 1889, based on the work of Dutch chemist Jacobus Henricus van 't Hoff who had noted in 1884 that the van 't Hoff equation for the temperature dependence of equilibrium constants suggests such a formula for the rates of both forward and reverse reactions. This equation has a vast and important application in determining the rate of chemical reactions and for calculation of energy of activation. Arrhenius provided a physical justification and interpretation for the formula.[1][2][3][4] Currently, it is best seen as an empirical relationship.[5]: 188  It can be used to model the temperature variation of diffusion coefficients, population of crystal vacancies, creep rates, and many other thermally-induced processes/reactions. The Eyring equation, developed in 1935, also expresses the relationship between rate and energy.
In physical chemistry, the Arrhenius equation is a formula for the temperature dependence of reaction rates. The equation was proposed by Svante Arrhenius in 1889, based on the work of Dutch chemist Jacobus Henricus van 't Hoff who had noted in 1884 that the van 't Hoff equation for the temperature dependence of equilibrium constants suggests such a formula for the rates of both forward and reverse reactions. This equation has a vast and important application in determining the rate of chemical reactions and for calculation of energy of activation. Arrhenius provided a physical justification and interpretation for the formula.[1][2][3][4] Currently, it is best seen as an empirical relationship.[5]: 188 It can be used to model the temperature variation of diffusion coefficients, population of crystal vacancies, creep rates, and many other thermally-induced processes/reactions. The Eyring equation, developed in 1935, also expresses the relationship between rate and energy.
Activation Energy: Why Getting Started Is the Hardest Part - Farnam Street
Sometimes we all need a little more energy than we thought to get going. This is the mental model of activation energy and it can help you solve problems.
Returning to the example of fire, our intuitive knowledge of activation energy keeps us safe. Many chemical reactions have high activation energy requirements, so they do not proceed without an additional input. We all know that a book on a desk is flammable, but will not combust without heat application. At room temperature, we need not see the book as a fire hazard. If we light a candle on the desk, we know to move the book away.
If chemical reactions did not have reliable activation energy requirements, we would live in a dangerous world.
Energy can have two dimensions. One is motivated, going somewhere, a goal somewhere, this moment is only a means and the goal is going to be the dimension of activity, goal oriented-then everything is a means, somehow it has to be done and you have to reach the goal, then you will relax. But for this type of energy, the goal never comes because this type of energy goes on changing every present moment into a means for something else, into the future. The goal always remains on the horizon. You go on running, but the distance remains the same. No, there is another dimension of energy: that dimension is unmotivated celebration. The goal is here, now; the goal is not somewhere else. In fact, you are the goal. In fact, there is no other fulfillment than that of this moment–consider the lilies. When you are the goal and when the goal is not in the future, when there is nothing to be achieved, rather you are just celebrating it, then you have already achieved it, it is there. This is relaxation, unmotivated energy
