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The researchers counted over 100,000 neurons and over a billion connections between them within this small cube of brain tissue. To find all the neurons and reconstruct the neural network, researchers had to slice the mouse brain 25,000 times. The issue is Go back Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Can a human brain be linked to a computer? Last updated: 06/11/24 Published: 28/12/22 Scientists in the US have succeeded in mapping the three-dimensional structure of the network of neurons in one cubic millimetre of mouse brain- a feat that would require two petabytes of storage. The human brain contains approximately 100 billion neurons, which is one million times the number of neurons found in a cubic millimetre of mouse brain. The researchers counted over 100,000 neurons and over a billion connections between them within this small cube of brain tissue. To find all the neurons and reconstruct the neural network, researchers had to slice the mouse brain 25,000 times. The issue is that the amount of data to store would kill any single computer. Memory and experiences that would have defined people later would be lost if they tried to store their minds too early. Using a computer too late may result in the accumulation of a mind with dementia, which would not work so well. Human tissue would have to be cut into zillions of thin slices using techniques compatible with dying and cutting. Local electrical changes that travel down dendrites and axons allow neurons to communicate with one another. However, when reconstructing the 3D structure, this may not be possible. After we die, our brains undergo significant chemical and anatomical changes. At the age of 20, they begin to lose 85,000 neurons per day due to apoptosis, or programmed cell death. Many memories that would have shaped a person later would be lost if he or she tried to store their mind too early. There are numerous steps involved in developing a computer capable of storing and processing human-level intelligence. It may be impossible for an artificial intelligence to produce sensations and actions identical to those provided and produced by your biological body. Bots are susceptible to hacking and hardware failure. Connecting sensors to the AI's digital mind would also be difficult. Written by Jeevana Thavarajah Related articles: The evolution of AI / Brain metastasis / AI in genetic diagnoses

AI can process data sets and identify patterns and biomarkers Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link AI: the next step in diagnosis and treatment of genetic diseases 08/07/25, 16:19 Last updated: Published: 23/03/24, 17:59 AI can process data sets and identify patterns and biomarkers With the development of more intricate Artificial Intelligence (AI) software, which has rapidly grown from the chaotic chatbots to the more well-formed ChatGPT, it is easy to think we are seeing the rise of powerful artificial intelligence that could potentially replace us all. However, there is one problem. Originality does not exist for AI, at least not complete originality. At its most basic, an AI program is trained on a set of data, whether this be an entire search engine’s worth of data, as is the case for ChatGPT, or a few images and phrases gathered from the internet. Therefore, an AI does not know any more than what it can quote or infer from the provided data, which means that a piece of art, a picture of a family, or any short story AI is asked to produce is often a replica of techniques or a chaotic and terrifying mess of images it has been given to use. However, here also lies its strength. AI can take in thousands of images and data sets and notice minor changes and differences the average person could not. It is, therefore, not AI’s ability to create the unique, but instead its ability to recognise the mundane that we can utilise, even in diagnosing and treating genetic disorders. Diagnosis By analysing PET, MRI, fMRI and genetic data, AI can process enormous data sets and identify subtle patterns and biomarkers that often elude human observations, enabling earlier and more precise diagnosis. When looking at examples of the application of AI in the diagnosis of genetic disorders, a good reference is the so-far successful use of AI in diagnosing Huntington’s disease. Huntington’s disease diagnosis using AI Huntington’s disease symptoms present as patients experience involuntary movements and a decline in decision-making processes. Huntington's disease is a genetic disorder, meaning it is caused by a faulty gene, in this case, a fault in the Huntingtin gene (Htt). The Huntington’s disease mutation in Htt results from CAG trinucleotide repeats, a highly polymorphic expansion of Htt consisting of the CAG (cytosine, adenine, guanine) nucleotides (DNA building blocks). Whilst CAG repeats are common and often normal and unharmful, individuals with Huntington’s disease possess an abnormally high number of these CAG repeats (more than 36). When an individual has an abnormally high number of CAG repeats, their Htt proteins do not fold into their proper shape, causing them to bond with other proteins and become toxic to a cell, which ultimately causes cell death in crucial medium spiny neurons (MSN) in the basal ganglia. Basal ganglia are brain structures responsible for the fine-tuning of our motor processes, which they do by essentially allowing neurons to respond in a preferred direction (a target muscle) rather than a null direction using MSNs. So, it is clear how Huntington's disease symptoms occur; mutant Htt leads to cell death in MSNs, leading to the basal ganglia’s inability to control movement, which causes characteristic involuntary behaviours, among other symptoms. Because we identified these changes in Htt and loss of MSN in the basal ganglia, PET, MRI, and fMRI scans are often used in the diagnosis of Huntington’s disease, in addition to genetic and mobility tests. By collecting and extracting clinical and genetic data, certain AI algorithms can analyse the broad range of Huntington’s disease clinical manifestations, identify differences, including even minute changes in the basal ganglia that a doctor may not have, and make an earlier diagnosis. One branch of AI that has proved effective is machine learning. Machine learning models in diagnosis Machine learning uses data and algorithms to imitate the way humans learn. For Huntington's disease diagnosis, this involves the identification of biomarkers and patterns in medical images, gene studies and mobility tests, and detecting subtle changes between data sets, distinguishing Huntington’s disease patients from healthy controls. While machine learning in Huntington’s disease diagnosis comes in many forms, the decision tree model, where the AI uses a decision tree as illustrated in the Project Gallery, has proven very effective. A decision tree model looks at decisions and their possible consequences and breaks them into subsets branching downward, going from decision to effect. Recent research using AI in Huntington’s disease diagnosis has utilised this model to analyse gait dynamics data. This data looks at variation in stride length, how unsteady a person is while walking, and the degree to which one stride interval (the time between strides) differs from any previous and any subsequent strides. For an individual, it is widely accepted that if they have abnormal variations in stride (their walking speed is reduced, their stance is widened), then they are exhibiting symptoms of Huntington’s disease. Therefore, by using this gait data, and having the machine learning model come up with a mean value for stride variation for trial patients, it will be able to discern which patients have stride variation associated with Huntington’s disease (a higher variation in stride) and those that do not. Researchers found that using this method of diagnosis, they were able to accurately identify which gaits belonged to Huntington's disease patients, with an accuracy of up to 100%. Furthermore, researchers also found decision tree models useful when identifying whether a gene links with Huntington’s disease when comparing patients' genetic information with prefrontal cortex samples, with this method’s accuracy being 90.79%. With these results and even more models showing incredible promise, AI is already proving itself useful when it comes to identifying and diagnosing sufferers of genetic disorders, such as those with Huntington’s disease. But this leads us to ask, can AI even help in the treatment of those suffering from genetic disorders? Treatment- current studies in cystic fibrosis While AI models can be applied diagnostically for disorders such as Huntington's disease, they may also be relied upon in disease treatment. The use of AI in tailored treatment is the focus of current research, with one even looking at improving the lives of those suffering from cystic fibrosis. Around 10,800 people are recorded as having cystic fibrosis in the UK, and this debilitating disorder results in a buildup of thick mucus, leading to persistent infections and other organ complications. The most common cause of cystic fibrosis is a mutation in the gene coding for the protein CFTR, resulting from a deletion in its coding gene, causing improper folding in the protein CFTR, as we saw in Huntington’s disease. This misfolding leads to its retention in the wrong place in a cell, so it can no longer maintain a balance of salt and water on body surfaces. Because of the complex symptoms arising from this imbalance, this disease is very difficult to manage, but there is hope, and hope comes as SmartCare. SmartCare involved home monitoring and followed 150 people with cystic fibrosis for six months, having them monitor their lung function, pulse, oxygen saturation and general wellness and upload recorded data to an app. Subsequently, researchers at the University of Cambridge used machine learning to create a predictive algorithm that used this lung, pulse, and oxygen saturation data, identifying patterns that were associated with a decline in a patient's condition, and then predicted this decline much faster than the patient of their doctor could. On average, this model could predict a decline in patient condition 11 days earlier than when the patient would typically start antibiotics, allowing health providers to respond quicker and patients to feel less restricted by their health. This project was, in fact, so successful that the US CF Foundation is now supporting a clinical implementation study, called Breath, which began in 2019 and continues to this day. Although there is a long way to go, using AI, the future can seem brighter. In Huntington’s disease and cystic fibrosis, we can see its effectiveness in both disease diagnosis and treatment. With the usage of AI predicted to increase in the future, there is a great outlook for patients and an opportunity for greater quality of care. This ultimately could ease patient suffering and prevent patient deaths. All this positive research tells us AI is our friend (although science fiction would often persuade us otherwise), and it will guide us through the tricky diagnosis and treatment of our most challenging diseases, even those engrained in our DNA. Written by Faye Boswell Related articles: AI in drug discovery / Can a human brain be linked to a computer? / AI in medicinal chemistry Project Gallery

An extensive collection of insightful reviews on the best STEM books available. Whether you're a student looking to deepen your knowledge or something to aid your revision and research, an educator seeking great resources for your classroom, or simply a curious mind passionate about science, technology, engineering, mathematics, medicine and more, you'll find something here to inspire and inform you.  Discover Your Next Great Read Deep Dive into STEM Books Here you can explore an extensive collection of insightful reviews on the best STEM books available. Whether you're a student looking to deepen your knowledge or something to aid or complement your revision and research, an educator seeking great resources for your classroom, or simply a curious mind passionate about science, technology, engineering, mathematics, medicine and more, you'll find something here to inspire and inform you. Our Curated Selections: Intern Blues by Robert Marion, M.D. The Emperor of All Maladies by Siddhartha Mukherjee The Molecule by Dr Rick Sax and Marta New

How they're used in treatments Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link A new model: miniature organs in biomedicine 23/10/25, 10:21 Last updated: Published: 16/10/23, 21:39 How they're used in treatments Introduction Within biomedicine, the study of diseases and understanding their mechanisms are crucial to the treatments we can develop for them. Before a treatment option can be rolled out to the general public, it must be tested for safety and efficacy. Usually, this testing takes place in the form of cell cultures or animal models. However, these methods cannot always accurately replicate the human body's complexity and physiological responses and are sometimes quite expensive and difficult to maintain. In the past few years, a new model has come to light known as organoids, allowing for a new realm of understanding into drug development, disease, and human biology. What Are Organoids? Organoids are self-organised, small, three-dimensional organ models which allow scientists and researchers to study different biological organs and tissues in a lab setting, including their physiological functions, development, and structure. These miniature organs are remarkable in their resemblance to actual organs and are obtained from stem cells, and they can undergo division to become any cell type. From their theoretical abilities, organoids may be able to serve utmost value in biomedicine and how we think about testing new treatments. Disease Modelling, Drug Development and Personalised Medicine One of the ways in which organoids can be used is to model diseases and test for potential drug targets and treatment programmes. In this way, researchers can replicate congenital and acquired conditions, such as cystic fibrosis and cancer, to study key target phenotypes and understand disease progression, which can help identify potential drug targets. From here, the efficacy of these therapeutics can be assessed quite quickly under different circumstances. As an example of this being used currently, scientists involved in cancer research have produced organoids from tumour cells stemming from cancer patients. These patient-derived organoids have been made for various cancers, including endometrium. They will allow for the ability to test chemotherapy drugs and determine which are most effective for individual patients whilst factoring in comorbidities and other unique factors to that person. Through this personalised approach, it is hoped that therapeutics will allow for a customised treatment programme which lowers the risk of side effects and improves the quality of care. Understanding Development and Function Another use of organoids is going into more depth and exploring our understanding of how an organ may develop and function. Using organoids can help us observe how different cells may work together and interact to organise themselves, allowing researchers to strengthen their knowledge of organogenesis by mimicking the natural growth conditions of the human environment. By combining tissue engineering with an appreciation of an organ's functional and developmental processes, organoid use can be extended to regenerative medicine to help fill research gaps in the molecular and cellular mechanisms of tissue regeneration. Techniques such as ELISA and immunofluorescent staining can help garner these critical details. By achieving this, organoids may produce entire organs for transplantation, addressing the organ donor shortage and lowering the risk of donor rejection. Recent Breakthroughs Cardiovascular diseases are one of the leading causes of death around the world. The human heart is limited to regenerating damaged tissue; thus, research must explore using organoids and other cell-based therapies to encourage natural repair processes. By investigating this avenue, cardiomyocytes derived from human pluripotent stem cells are a promising source. These cell types have the potential to restore contractile functions in animal models as well as the ability to regenerate myocardial tissue. Researchers have developed a cardiac organoid with silicon nanowires that have significantly improved the medicinal efficacy of stem cell-derived cardiac organoids. Using these nano-wired organoids, electrical activity was shown to improve, which in turn supported improved contractility in ischemia-injured mice. Challenges and Future Directions While the promising nature of organoids must be acknowledged, they are not without limitations. Research is currently ongoing to improve the reproducibility and scalability of organoids and their cultures to make organoids more accessible and their use more widespread. Below are some summarised advantages and disadvantages of organoids. Conclusion In conclusion, the advent of organoids has created a revolutionary era within the scope of biomedicine. These miniature organs have remarkable potential in various research, development, and tissue engineering facets. Organoids provide scientists with precise modelling of diseases across a range of different organs, assuring their versatility. From understanding organ development to combating cardiovascular diseases and introducing personalised treatment for cancer patients, it is unclear why they are being more rapidly explored. While they hold their promise, there are still challenges surrounding their reproducibility, restricting them from being used in organ transplantation. However, with ongoing progress, organoids undoubtedly have the aptitude to tailor treatments and address complexities of tissue regeneration, heralding a groundbreaking era in healthcare. Written by Irha Khalid Related article: iPSCs and organoids / Animal testing ethics Project Gallery

A (possibly) new class of semiconductors Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The search for a room-temperature superconductor 14/07/25, 15:02 Last updated: Published: 13/01/24, 15:19 A (possibly) new class of semiconductors In early August, the scientific community was buzzing with excitement over the groundbreaking discovery of the first room-temperature superconductor. As some rushed to prove the existence of superconductivity in the material known as LK-99, others were sceptical of the validity of the claims. After weeks of investigation, experts have concluded that LK-99 was likely not the elusive room-temperature superconductor but rather a different type of magnetic material with interesting properties. But what if we did stumble upon a room-temperature superconductor? What could this mean for the future of technology? Superconductivity is a property of some materials at extremely low temperatures that allows the material to conduct electricity with no resistance. Classical physics cannot explain this phenomenon, and instead, we have to turn to quantum mechanics to provide a description of superconductors. Inside superconductors, electrons are paired up and can move through the structure of the material without experiencing any friction. The pairs of electrons are broken up by the thermal energy from temperature, so they will only exist for low temperatures. Therefore, this theory, known as BCS theory after the physicists who formulated it, does not explain the existence of a high-temperature superconductor. To describe high-temperature superconductors, such as those occurring at room temperature, more complicated theories are needed. The magic of superconductors lies in their property of zero resistance. Resistance is a cause of energy waste in circuits due to heating, which leads to the unwanted loss of power, making for inefficient operation. Physically, resistance is caused by electrons colliding with atoms in the structure of a material, causing energy to be lost in the process. The ability for electrons to move through superconductors without experiencing any collisions results in no resistance. Superconductors are useful as components in circuits as they cause no wasted power due to heating effects and are completely energy-efficient in this aspect. Normally, using superconductors requires complex methods of cooling them down to typical superconducting temperatures. For example, the temperature at which copper becomes superconducting is 35 K, or in other words, around 130 °C colder than the temperature at which water freezes. These methods are expensive to implement, which prevents them from being implemented on a wide scale. However, having a room-temperature superconductor would allow access to the beneficial properties of the material, such as its resistance, without the need for extreme cooling. The current record holders for highest-temperature superconductors are the cuprate superconductors at around −135 °C. These are a family of materials made up of layers of copper oxides alternating with layers of other metal oxides. As the mechanism for superconductivity is yet to be revealed, scientists are still scratching their heads over how this material can exhibit superconducting properties. Once this mechanism is discovered, it may be easier to predict and find high-temperature superconducting materials and may lead to the first room-temperature superconductor. Until then, the search continues to unlock the next frontier in low-temperature physics… For more information on superconductors: [1] Theory behind superconductivity [2] Video demonstration Written by Madeleine Hales Related articles: Semiconductor manufacturing / Semiconductor laser technology / Silicon hydrogel lenses / Titan Submersible Project Gallery

Elements, compounds, and mixtures make up the building blocks of materials that shape our world. Read on to uncover the latest contributions in chemistry, such as advances in mass spectrometry and quantum chemistry. Chemistry Articles Elements, compounds, and mixtures make up the building blocks of materials that shape our world. Read on to uncover the latest contributions in chemistry, such as advances in mass spectrometry and quantum chemistry. You may also like: Medicine , Pharmacology Advances in mass spectrometry Analytical chemistry Bioorthogonal chemistry Chemical reactions with high yields Polypharmacy Multiple medications Plastics and their environmental impact The same property that makes plastics so strong endangers the environment Quantum chemistry A relatively new field of chemistry Nanomedicine and targeted drug delivery An overview as to why nanoparticles are suitable for drug delivery Nanogels Smarter drug delivery The importance of symmetry in chemistry Symmetry in spectroscopy, reaction mechanisms and bonding Previous

How diseases start and spread, the body’s defence system, vaccines, policies, and public opinion: unravel the maze of infection and immunity with these articles. Immunology Articles How diseases start and spread, the body’s defence system, vaccines, policies, and public opinion: unravel the maze of infection and immunity with these articles. You may also like: Biology , Medicine , Neuroscience , Chemistry COVID-19 misconceptions Common misconceptions during the COVID-19 pandemic Glossary of COVID-19 terms Key terms used during the COVID-19 pandemic A vaccine for malaria? A new hope for a vaccine for malaria The world vs. the next pandemic Can we see it coming? What steps do we need to take? Are pandemics becoming more severe? Arguments for and against Natural substances And how they can tackle infectious diseases A treatment for HIV? Can the CRISPR-Cas9 system be used as a potential treatment? The mast cell Key cells in the immune system Origins of COVID -19 How COVID-19 caused a pandemic Mechanisms of pathogen invasion How pathogens avoid detection by the immune system Astronauts in space How does little gravity affect the immune system? Ageing and immunity Ageing and its association with immune decline The impacts of global warming on dengue fever Dengue fever is a mosquito-borne Neglected Tropical Disease (NTD) Is the immune system 'selfish'? 'Selfish' genes from a Dawkins perspective, and the Modern Evolutionary Synthesis

Explaining and predicting the behaviour of serial killers Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The new age of forensic neurology 14/07/25, 14:58 Last updated: Published: 23/08/23, 16:16 Explaining and predicting the behaviour of serial killers Background Nobody can argue that true crime has taken the media by storm in recent years. In 2021, the search to find Gabby Petito inflamed social media, with the r/gabbypetito subreddit having 120,000 members at its peak. Tiktok ‘psychics’ would amass millions of views by attempting to predict how the case would progress, with predictably terrible results. A small solace remains, however; the fact that increased media presence of murder cases increases the rate at which research into murderers is published. The increase in both research and media attention toward true crime continued through 2022, invigorated by the release of Monster: the Dahmer Story on Netflix, which was viewed on Netflix for over 1 billion hours by its user base. It could be argued that the popularity of this show and others depicting serial killers also increased the publication of research on the neurology of serial killers. The neurological basis of the serial killer refractory period Dilly (2021) encompasses some very interesting correlational research into the neurological factors at play in the evocation of the serial killer refractory period. Following analysis of the refractory periods of ten American serial killers, a metaanalysis of prior research was performed to establish which prior theory most thoroughly explained the patterns derived. The American serial killers utilised in this investigation were: The Golden State Killer, Joseph James DeAngelo. Jeffrey Dahmer. Ted Bundy. John Wayne Gacy. The Night Stalker, Richard Ramirez. The BTK Killer, Dennis Rader. The I-5 Killer, Randall Woodfield. Son of Sam, David Berkowitz. The Green River Killer, Gary Ridgway. The Co-Ed Killer, Edmund Kemper III. Theory no. 1 While this research is purely speculative due to the lack of real-time neurological imaging of the killers both during refractory periods and their murderous rampages, this research was demonstrated to lend credence to a prior theory proposed by Simkin and Roychowdhury (2014). This research, titled Stochastic Modelling of a Serial Killer , theorised based on their own collated data that the refractory period of serial killers functions identically to that of the refractory period of neurons. This theory is based upon the idea that murder precipitates the release of a powerful barrage of neurotransmitters, culminating in widespread neurological activation. In line with neurological refractory periods, it is believed that this extreme change in state of activation is followed by a period of time wherein another global activation event cannot occur. Theory no. 2 Hamdi et al. (2022) delineates the extent to which the subject’s murderous impulses were derived from Fregoli syndrome, rather than his comorbid schizophrenia. This research elucidated how schizophrenic symptoms can synergise with symptoms of delusional identification syndromes (DIS) to create distinct behaviours and thought patterns that catalyse sufferers to engage in homicidal impulses. DIS include a range of disorders wherein sufferers experience issues identifying objects, people, places or events; Fregoli Syndrome is a DIS characterised by the delusional belief that people around the sufferer are familiar figures in disguise. The subject’s Fregoli Syndrome caused the degeneration of his trust of those around him, which quickly led to an increase in aggressive behaviours. The killer attacked each member of his family multiple times before undertaking his first homicide- excluding his father, whom reportedly ‘scared him very much’. Unsurprisingly then, his victim cohort of choice for murder were older men. The neurobiological explanation of Fregoli Syndrome asserts that the impairment of facial identification, wherein cerebrocortical hyperactivity catalyses delusional identification of unfamiliar faces as familiar ones. Conclusion Forensic neurology has been a key element in expanding the understanding of serial killers, with the research of Raine et al. (1997) popularising the use of neurology to answer the many questions posed by the existence of serial killers. Since Raine, Buchsbaum and LaCasse of the 1997 study first used brain scanning techniques to study and understand serial killers, the use of brain scanning techniques to study this population has become a near-perfect art, becoming ever more of a valid option for use both in understanding and predicting serial killer behaviour. In all likelihood, future innovations in forensic neurology research will continue to bring about positive change, reducing homicidal crime with the invention and use of different methods and systems to predict and stop the crimes before they happen. Summarised from a full investigation. Written by Aimee Wilson Related articles: Serial killers in healthcare / Brain of a bully Project Gallery

How do they work? Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link A concise introduction to Markov chain models 20/03/25, 11:59 Last updated: Published: 09/03/24, 18:16 How do they work? Introduction A Markov chain is a stochastic process that models a system that transitions from one state to another, where the probability of the next state only depends on the current state and not on the previous history. For example, assuming that X 0 is the current state of a system or process, the probability of a state, X 1 , depends only on X 0 which is of course the current state of the system as stated. P ( X 1 ) = f ( P ( X 0 )) It may be hard to think of any real-life processes that follow this behaviour because there is the belief that all events happen in a sequence because of each other. Here are some examples: Games e.g. chess - If your king is in a certain spot on a chess board, there will be a maximum of 4 transition states that can be achieved that all depend on the initial position of chess piece. The parameters for the Markov model will obviously vary depending on your position on the board which is the essence of the Markov process. Genetics - The genetic code of an organism can be modelled as a Markov chain, where each nucleotide (A, C, G, or T) is a state, and the probability of the next nucleotide depends only on the current one. Text generation - Consider the current state as the most recent word. The transition states would be all possible words which could follow on from said word. Next word prediction algorithms can utilize a first-order Markov process to predict the next word in a sentence based on the most recent word. The text generation example is particularly interesting because only considering the previous word when trying to predict the next word sentence would lead to a very random sentence. That is where we can change things up using various mathematical techniques. k-Order Markov Chains (adding more steps) In a first-order Markov chain, we only consider the immediately preceding state to predict the next state. However, in k-order Markov chains, we broaden our perspective. Here’s how it works: Definition: a k-order Markov chain considers the previous states (or steps) when predicting the next state. It’s like looking further back in time to inform our predictions. Example: suppose we’re modelling the weather. In a first-order Markov chain, we’d only look at today’s weather to predict tomorrow’s weather. But in a second-order Markov chain, we’d consider both today’s and yesterday’s weather. Similarly, a third-order Markov chain would involve three days of historical data. By incorporating more context, k-order chains can capture longer-term dependencies and patterns. As k increases, the model becomes more complex, and we need more data to estimate transition probabilities accurately. See diagram below for a definition of higher order Markov chains. Markov chains for Natural Language Processing A Markov chain can generate text by using a dictionary of words as the states, and the frequency of words in a corpus of text as the transition probabilities. Given an input word, such as "How", the Markov chain can generate the next word, such as "to", by sampling from the probability distribution of words that follow "How" in the corpus. Then, the Markov chain can generate the next word, such as "use", by sampling from the probability distribution of words that follow "to" in the corpus. This process can be repeated until a desired length or end of sentence is reached. That is a basic example and for more complex NLP tasks we can employ more complex Markov models such as k-order, variable, n-gram or even hidden Markov models. Limitations of Markov models Markov models for tasks such as text generation will struggle because they are too simplistic to create text that is intelligent and sometimes even coherent. Here are some reasons why: Fixed Transition Probabilities: Markov models assume that transition probabilities are constant throughout. In reality, language is dynamic, and context can change rapidly. Fixed probabilities may not capture these nuances effectively. Local Dependencies: Markov chains have local dependencies, meaning they only consider a limited context (e.g., the previous word). They don’t capture long-range dependencies or global context. Limited Context Window: Markov models have a fixed context window (e.g., first-order, second order, etc.). If the context extends beyond this window, the model won’t capture it. Sparse Data : Markov models rely on observed data (transition frequencies) from the training corpus. If certain word combinations are rare or absent, the model struggles to estimate accurate probabilities. Lack of Learning: Markov models don’t learn from gradients or backpropagation. They’re based solely on observed statistics. Written by Temi Abbass Related articles: Latent space transformation s / Evolution of AI FURTHER READING 1. “Improving the Markov Chain Approach for Generating Text Used for…” : This work focuses on text generation using Markov chains. It highlights the chance based transition process and the representation of temporal patterns determined by probability over sample observations . 2 . “Synthetic Text Generation for Sentiment Analysis” : This paper discusses text generation using latent Dirichlet allocation (LDA) and a text generator based on Markov chain models. It explores approaches for generating synthetic text for sentiment analysis . 3. “A Systematic Review of Hidden Markov Models and Their Applications” : This review paper provides insights into HMMs, a statistical model designed using a Markov process with hidden states. It discusses their applications in various fields, including robotics, finance, social science, and ecological time series data analysis . Project Gallery

An individual may be restricted to a certain range of physical activities which they can participate in. Individuals are usually reliant on the surrounding environment and the maintenance of facilities. If they are not kept well maintained, individuals are usually discouraged. Go back Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Influence of different environmental factors on exercise Last updated: 30/01/25 Published: 10/02/23 The characteristics of environmental factors: - Chemical safety - Air pollution - Climate change and natural disasters - Diseases caused by microbes - Lack of access to health care - Infrastructure issues - Poor water quality - Global environmental issues What are the impacts of these environmental influences on physical activity? An individual may be restricted to a certain range of physical activities which they can participate in. Individuals are usually reliant on the surrounding environment and the maintenance of facilities. If they are not kept well maintained, individuals are usually discouraged. The physiological effect on training: Climate change will disproportionately affect the most vulnerable in our populations, including the very young, the very old, and those with pre-existing health conditions. Training adjustments to compensate for the influence of environmental factors on training: - Treatments for heat stress- stop exercising / move to a shaded or air-conditioned area / remove excess clothing or equipment / drink cold beverages / sit in front of a fan / put a cool piece of cloth around neck / place entire body in cool water e.g. cool bath or shower - Treatments for cold stress- move to a warm environment / remove cold and wet clothes / find access to warm air such as heaters, or fireplace / use electric or non-electric blankets / drink warm beverages Written by Kushwant Nathoo Related articles: Impacts of negligent exercise on physiology / Physical and mental health / Environmental impact of EVs