• how my new mannequin can spot liars and counter disinformation
    Mathematic

    how my new mannequin can spot liars and counter disinformation

    Understanding the human thoughts and behavior lies on the core of the self-discipline of psychology. However to characterise how folks’s behaviour adjustments over time, I consider psychology alone is inadequate – and that further mathematical concepts have to be introduced ahead.

    My new mannequin, revealed in Frontiers in Psychology, is impressed by the work of the Twentieth-century American mathematician, Norbert Wiener. At its coronary heart is how we alter our perceptions over time when tasked with making a alternative from a set of options. Such adjustments are sometimes generated by restricted data, which we analyse earlier than making selections that decide our behavioural patterns.

    To know these patterns, we’d like the arithmetic of knowledge processing. Right here, the state of an individual’s thoughts is represented by the probability it assigns to totally different options – which product to purchase; which college to ship your youngster to; which candidate to vote for in an election; and so forth.

    As we collect partial data, we turn into much less unsure – for instance, by studying buyer critiques we turn into extra sure about which product to purchase. This psychological updating is expressed in a mathematical formulation labored out by the 18th-century English scholar, Thomas Bayes. It primarily captures how a rational thoughts makes selections by assessing varied, unsure options.

    how my new mannequin can spot liars and counter disinformation
    The equation reveals the movement of knowledge over time, t. X is a random variable representing totally different chances akin to totally different options. If we assume that the data is revealed at a continuing price σ, and that the noise that obscures the data is B ((described by a idea referred to as Brownian movement, which is random), then the equation can provide us the data movement.
    Creator supplied

    When combining this idea with the arithmetic of knowledge (particularly sign processing), courting again to the Nineteen Forties, it might assist us perceive the behaviour of individuals, or society, guided by how data is processed over time. It’s only not too long ago that my colleagues and I realised how helpful this strategy could be.

    To date, we’ve got efficiently utilized it to mannequin the behaviour of monetary markets (market contributors reply to new data, which results in adjustments in inventory costs), and the behaviour of inexperienced vegetation (a flower processes details about the situation of the solar and turns its head in direction of it).

    I’ve additionally proven it may be used to mannequin the dynamics of opinion ballot statistics related to an election or a referendum, and drive a formulation that offers the precise chance of a given candidate profitable a future election, primarily based on immediately’s ballot statistics and the way data shall be launched sooner or later.

    On this new “information-based” strategy, the behaviour of an individual – or group of individuals – over time is deduced by modelling the movement of knowledge. So, for instance, it’s potential to ask what’s going to occur to an election end result (the probability of a proportion swing) if there may be “pretend information” of a given magnitude and frequency in circulation.

    However maybe most surprising are the deep insights we will glean into the human decision-making course of. We now perceive, as an example, that one of many key traits of the Bayes updating is that each various, whether or not it’s the proper one or not, can strongly affect the way in which we behave.

    If we don’t have a preconceived concept, we’re drawn to all of those options regardless of their deserves, and gained’t select one for a very long time with out additional data. That is the place the uncertainty is best, and a rational thoughts will want to scale back the uncertainty so {that a} alternative could be made.

    But when somebody has a really sturdy conviction on one of many options, then regardless of the data says, their place will hardly change for a very long time –it’s a nice state of excessive certainty.

    Such behaviour is linked to the notion of “affirmation bias” – decoding data as confirming your views even when it really contradicts them. That is seen in psychology as opposite to the Bayes logic, representing irrational behaviour. However we show it’s, in reality, a wonderfully rational characteristic appropriate with the Bayes logic – a rational thoughts merely desires excessive certainty.

    The rational liar

    The strategy may even describe the behaviour of a pathological liar. Can arithmetic distinguish mendacity from a real misunderstanding? It seems that the reply is “sure”, not less than with a excessive degree of confidence.

    If an individual genuinely thinks an alternate that’s clearly true is very unlikely – which means they’re misunderstanding – then in an setting wherein partial details about the reality is steadily revealed, their notion will slowly shift in direction of the reality, albeit fluctuating over time. Even when they’ve a robust perception in a false various, their view will very slowly converge from this false various to the true one.

    Nevertheless, if an individual is aware of the reality however refuses to just accept it – is a liar – then in accordance with the mannequin, their behaviour is radically totally different: they’ll quickly select one of many false options and confidently assert this to be the reality. (Actually, they might virtually consider on this false various that has been chosen randomly.) Then, as the reality is steadily revealed and this place turns into untenable, in a short time and assertively they’ll decide one other false various.

    Man taking a lie detector test.
    May eerratically altering views be a extra dependable signal of mendacity than lie detectors?
    Serhii Bobyk/Shuttestock

    Therefore a rational (within the sense of somebody following the Bayes logic) liar will behave in a quite erratic method, which may in the end assist us spot them. However they’ll have such a robust conviction that they are often convincing to those that have restricted information of the reality.

    For individuals who have recognized a constant liar, this behaviour might sound acquainted. After all, with out the entry to somebody’s thoughts, one can by no means be 100% positive. However mathematical fashions show that for such behaviour to come up from a real misunderstanding is statistically most unlikely.

    This information-based strategy is very efficient in predicting the statistics of individuals’s future behaviour in response to the unravelling of knowledge – or disinformation, for that matter. It may present us with a software to analyse and counter, particularly, the unfavourable ramifications of disinformation.

  • New mannequin for on-line Extension coaching launches
    ONLINE COURSES

    New mannequin for on-line Extension coaching launches

    Growers and technical workers within the greenhouse and managed surroundings business can achieve a brand new qualification in plant well being administration with collaboration from two award-winning on-line Extension coaching applications provided by the College of Florida and Michigan State College.

    Over 4,000 growers have already graduated from particular person programs by way of the UF/IFAS Extension Greenhouse Coaching On-line and the MSU Faculty of Data applications. Below the brand new collaboration, growers who go 5 programs in plant well being in these applications can get hold of a brand new certificates of completion that acknowledges their coaching, expertise, and dedication and can assist them develop of their careers.

    New mannequin for on-line Extension coaching launches

    “We’re creating a coaching curriculum for horticulture professionals already working within the business,” mentioned Paul Fisher, UF/IFAS environmental horticulture professor, and floriculture Extension specialist. “On-line works effectively for growers who’re already dwelling the ‘how-to’ of horticulture however profit from understanding the ‘why’ of their decision-making from the underlying science.”

    Heidi Lindberg, greenhouse and nursery educator with MSU Extension, emphasised the function she’s seen the Faculty of Data program play in constructing foundational information for newer business professionals, as effectively.

    “The net floriculture Faculty of Data program has offered a broad viewers from 39 nations and 46 U.S. states the introductory information in greenhouse manufacturing,” Lindberg mentioned. “It has been very profitable in coaching these new to the business or these altering careers. It gives an important basis that professionals can then use to construct real-life expertise.”

    The brand new collaboration additionally has the potential to succeed in even bigger audiences, thanks not solely to its on-line format but additionally to its bilingual presentation choices.

    “This program checks off a number of packing containers for contemporary Extension,” mentioned Saqib Mukhtar, the UF/IFAS affiliate dean for Extension, agriculture, and pure sources. “That features English and Spanish languages; on-line and obtainable any time of day; collaboration by main universities for nationwide and worldwide affect; and high-quality coaching by main consultants.”

    Growers should go 5 of six programs, which embrace:

    Growers join particular person programs by way of the UF (hort.ifas.ufl.edu/coaching) and MSU web sites (canr.msu.edu/online-college-of-knowledge). E-mail [email protected] as soon as 5 programs are efficiently accomplished, and growers can get hold of their certificates of completion at an annual commencement ceremony (the primary graduates are anticipated in July 2023).

    The primary course within the sequence for 2022 is Nutrient Administration Degree 1, which begins on July 11.

    For extra info:
    UF/IFAS
    [email protected]
    www.ifas.ufl.edu      

     

  • Mathematical model predicts best way to build muscle
    Mathematic

    Mathematical model predicts best way to build muscle

    Mathematical model predicts best way to build muscle
    Figure 1. The “textbook” hierarchy in the anatomy of skeletal muscle. The overall muscle is characterized by its cross-sectional area (CSA), which contains a certain number (Nc) of muscle fibers (the muscle cells with multiple nuclei or multinucleate myocytes). A given muscle has a nearly fixed number of myocytes: between Nc ≈ 1000 for the tensor tympani and Nc > 1,000,000 for large muscles (gastrocnemius, temporalis, etc. Credit: DOI: 10.1016/j.bpj.2021.07.023

    Researchers have developed a mathematical model that can predict the optimum exercise regimen for building muscle.

    The researchers, from the University of Cambridge, used methods of theoretical biophysics to construct the model, which can tell how much a specific amount of exertion will cause a muscle to grow and how long it will take. The model could form the basis of a software product, where users could optimize their exercise regimens by entering a few details of their individual physiology.

    The model is based on earlier work by the same team, which found that a component of muscle called titin is responsible for generating the chemical signals which affect muscle growth.

    The results, reported in the Biophysical Journal, suggest that there is an optimal weight at which to do resistance training for each person and each muscle growth target. Muscles can only be near their maximal load for a very short time, and it is the load integrated over time which activates the cell signaling pathway that leads to synthesis of new muscle proteins. But below a certain value, the load is insufficient to cause much signaling, and exercise time would have to increase exponentially to compensate. The value of this critical load is likely to depend on the particular physiology of the individual.

    We all know that exercise builds muscle. Or do we? “Surprisingly, not very much is known about why or how exercise builds muscles: there’s a lot of anecdotal knowledge and acquired wisdom, but very little in the way of hard or proven data,” said Professor Eugene Terentjev from Cambridge’s Cavendish Laboratory, one of the paper’s authors.

    When exercising, the higher the load, the more repetitions or the greater the frequency, then the greater the increase in muscle size. However, even when looking at the whole muscle, why or how much this happens isn’t known. The answers to both questions get even trickier as the focus goes down to a single muscle or its individual fibers.

    Muscles are made up of individual filaments, which are only 2 micrometers long and less than a micrometer across, smaller than the size of the muscle cell. “Because of this, part of the explanation for muscle growth must be at the molecular scale,” said co-author Neil Ibata. “The interactions between the main structural molecules in muscle were only pieced together around 50 years ago. How the smaller, accessory proteins fit into the picture is still not fully clear.”

    This is because the data is very difficult to obtain: people differ greatly in their physiology and behavior, making it almost impossible to conduct a controlled experiment on muscle size changes in a real person. “You can extract muscle cells and look at those individually, but that then ignores other problems like oxygen and glucose levels during exercise,” said Terentjev. “It’s very hard to look at it all together.”

    Terentjev and his colleagues started looking at the mechanisms of mechanosensing—the ability of cells to sense mechanical cues in their environment—several years ago. The research was noticed by the English Institute of Sport, who were interested in whether it might relate to their observations in muscle rehabilitation. Together, they found that muscle hyper/atrophy was directly linked to the Cambridge work.

    In 2018, the Cambridge researchers started a project on how the proteins in muscle filaments change under force. They found that main muscle constituents, actin and myosin, lack binding sites for signaling molecules, so it had to be the third-most abundant muscle component—titin—that was responsible for signaling the changes in applied force.

    Whenever part of a molecule is under tension for a sufficiently long time, it toggles into a different state, exposing a previously hidden region. If this region can then bind to a small molecule involved in cell signaling, it activates that molecule, generating a chemical signal chain. Titin is a giant protein, a large part of which is extended when a muscle is stretched, but a small part of the molecule is also under tension during muscle contraction. This part of titin contains the so-called titin kinase domain, which is the one that generates the chemical signal that affects muscle growth.

    The molecule will be more likely to open if it is under more force, or when kept under the same force for longer. Both conditions will increase the number of activated signaling molecules. These molecules then induce the synthesis of more messenger RNA, leading to production of new muscle proteins, and the cross-section of the muscle cell increases.

    This realization led to the current work, started by Ibata, himself a keen athlete. “I was excited to gain a better understanding of both the why and how of muscle growth,” he said. “So much time and resources could be saved in avoiding low-productivity exercise regimens, and maximizing athletes’ potential with regular higher value sessions, given a specific volume that the athlete is capable of achieving.”

    Terentjev and Ibata set out to constrict a mathematical model that could give quantitative predictions on muscle growth. They started with a simple model that kept track of titin molecules opening under force and starting the signaling cascade. They used microscopy data to determine the force-dependent probability that a titin kinase unit would open or close under force and activate a signaling molecule.

    They then made the model more complex by including additional information, such as metabolic energy exchange, as well as repetition length and recovery. The model was validated using past long-term studies on muscle hypertrophy.

    “Our model offers a physiological basis for the idea that muscle growth mainly occurs at 70% of the maximum load, which is the idea behind resistance training,” said Terentjev. “Below that, the opening rate of titin kinase drops precipitously and precludes mechanosensitive signaling from taking place. Above that, rapid exhaustion prevents a good outcome, which our model has quantitatively predicted.”

    “One of the challenges in preparing elite athletes is the common requirement for maximizing adaptations while balancing associated trade-offs like energy costs,” said Fionn MacPartlin, Senior Strength & Conditioning Coach at the English Institute of Sport. “This work gives us more insight into the potential mechanisms of how muscles sense and respond to load, which can help us more specifically design interventions to meet these goals.”

    The model also addresses the problem of muscle atrophy, which occurs during long periods of bed rest or for astronauts in microgravity, showing both how long can a muscle afford to remain inactive before starting to deteriorate, and what the optimal recovery regimen could be.

    Eventually, the researchers hope to produce a user-friendly software-based application that could give individualized exercise regimens for specific goals. The researchers also hope to improve their model by extending their analysis with detailed data for both men and women, as many exercise studies are heavily biased towards male athletes.


    Body builders aren’t necessarily the strongest athletes


    More information:
    Neil Ibata et al, Why exercise builds muscles: titin mechanosensing controls skeletal muscle growth under load, Biophysical Journal (2021). DOI: 10.1016/j.bpj.2021.07.023

    Provided by
    University of Cambridge


    Citation:
    Mathematical model predicts best way to build muscle (2021, August 23)
    retrieved 19 September 2021
    from https://phys.org/news/2021-08-mathematical-muscle.html

    This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
    part may be reproduced without the written permission. The content is provided for information purposes only.

  • Mathematical model predicts best way to build muscle — ScienceDaily
    Mathematic

    Mathematical model predicts best way to build muscle — ScienceDaily

    Researchers have developed a mathematical model that can predict the optimum exercise regime for building muscle.

    The researchers, from the University of Cambridge, used methods of theoretical biophysics to construct the model, which can tell how much a specific amount of exertion will cause a muscle to grow and how long it will take. The model could form the basis of a software product, where users could optimise their exercise regimes by entering a few details of their individual physiology.

    The model is based on earlier work by the same team, which found that a component of muscle called titin is responsible for generating the chemical signals which affect muscle growth.

    The results, reported in the Biophysical Journal, suggest that there is an optimal weight at which to do resistance training for each person and each muscle growth target. Muscles can only be near their maximal load for a very short time, and it is the load integrated over time which activates the cell signalling pathway that leads to synthesis of new muscle proteins. But below a certain value, the load is insufficient to cause much signalling, and exercise time would have to increase exponentially to compensate. The value of this critical load is likely to depend on the particular physiology of the individual.

    We all know that exercise builds muscle. Or do we? “Surprisingly, not very much is known about why or how exercise builds muscles: there’s a lot of anecdotal knowledge and acquired wisdom, but very little in the way of hard or proven data,” said Professor Eugene Terentjev from Cambridge’s Cavendish Laboratory, one of the paper’s authors.

    When exercising, the higher the load, the more repetitions or the greater the frequency, then the greater the increase in muscle size. However, even when looking at the whole muscle, why or how much this happens isn’t known. The answers to both questions get even trickier as the focus goes down to a single muscle or its individual fibres.

    Muscles are made up of individual filaments, which are only 2 micrometres long and less than a micrometre across, smaller than the size of the muscle cell. “Because of this, part of the explanation for muscle growth must be at the molecular scale,” said co-author Neil Ibata. “The interactions between the main structural molecules in muscle were only pieced together around 50 years ago. How the smaller, accessory proteins fit into the picture is still not fully clear.”

    This is because the data is very difficult to obtain: people differ greatly in their physiology and behaviour, making it almost impossible to conduct a controlled experiment on muscle size changes in a real person. “You can extract muscle cells and look at those individually, but that then ignores other problems like oxygen and glucose levels during exercise,” said Terentjev. “It’s very hard to look at it all together.”

    Terentjev and his colleagues started looking at the mechanisms of mechanosensing — the ability of cells to sense mechanical cues in their environment — several years ago. The research was noticed by the English Institute of Sport, who were interested in whether it might relate to their observations in muscle rehabilitation. Together, they found that muscle hyper/atrophy was directly linked to the Cambridge work.

    In 2018, the Cambridge researchers started a project on how the proteins in muscle filaments change under force. They found that main muscle constituents, actin and myosin, lack binding sites for signalling molecules, so it had to be the third-most abundant muscle component — titin — that was responsible for signalling the changes in applied force.

    Whenever part of a molecule is under tension for a sufficiently long time, it toggles into a different state, exposing a previously hidden region. If this region can then bind to a small molecule involved in cell signalling, it activates that molecule, generating a chemical signal chain. Titin is a giant protein, a large part of which is extended when a muscle is stretched, but a small part of the molecule is also under tension during muscle contraction. This part of titin contains the so-called titin kinase domain, which is the one that generates the chemical signal that affects muscle growth.

    The molecule will be more likely to open if it is under more force, or when kept under the same force for longer. Both conditions will increase the number of activated signalling molecules. These molecules then induce the synthesis of more messenger RNA, leading to production of new muscle proteins, and the cross-section of the muscle cell increases.

    This realisation led to the current work, started by Ibata, himself a keen athlete. “I was excited to gain a better understanding of both the why and how of muscle growth,” he said. “So much time and resources could be saved in avoiding low-productivity exercise regimes, and maximising athletes’ potential with regular higher value sessions, given a specific volume that the athlete is capable of achieving.”

    Terentjev and Ibata set out to constrict a mathematical model that could give quantitative predictions on muscle growth. They started with a simple model that kept track of titin molecules opening under force and starting the signalling cascade. They used microscopy data to determine the force-dependent probability that a titin kinase unit would open or close under force and activate a signalling molecule.

    They then made the model more complex by including additional information, such as metabolic energy exchange, as well as repetition length and recovery. The model was validated using past long-term studies on muscle hypertrophy.

    “Our model offers a physiological basis for the idea that muscle growth mainly occurs at 70% of the maximum load, which is the idea behind resistance training,” said Terentjev. “Below that, the opening rate of titin kinase drops precipitously and precludes mechanosensitive signalling from taking place. Above that, rapid exhaustion prevents a good outcome, which our model has quantitatively predicted.”

    “One of the challenges in preparing elite athletes is the common requirement for maximising adaptations while balancing associated trade-offs like energy costs,” said Fionn MacPartlin, Senior Strength & Conditioning Coach at the English Institute of Sport. “This work gives us more insight into the potential mechanisms of how muscles sense and respond to load, which can help us more specifically design interventions to meet these goals.”

    The model also addresses the problem of muscle atrophy, which occurs during long periods of bed rest or for astronauts in microgravity, showing both how long can a muscle afford to remain inactive before starting to deteriorate, and what the optimal recovery regime could be.

    Eventually, the researchers hope to produce a user-friendly software-based application that could give individualised exercise regimes for specific goals. The researchers also hope to improve their model by extending their analysis with detailed data for both men and women, as many exercise studies are heavily biased towards male athletes.