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A glimpse into the early universe Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Dark Energy Spectroscopic Instrument (DESI) 05/02/25, 16:21 Last updated: Published: 08/07/23, 13:11 A glimpse into the early universe June 2023 marked the early release of data from the Dark Energy Spectroscopic Instrument (DESI). This instrument will study the nature of Dark Energy, an elusive addition to our cosmological equations that is thought to explain the accelerating expansion of the Universe. Current models estimate that Dark Energy comprises 68% of the total mass and energy of the universe and is distinct from matter and radiation in the sense that as space expands, its energy density remains constant rather than diluting. Imagine your favourite concentrated juice drink tasting the same regardless of how much water you add! DESI will investigate the large-scale structure of the Universe, obtaining spectra of around 40 million galaxies and using their redshift to create 3-D distance maps. The five-year observation effort has aptly been dubbed an experiment in “cosmic cartography”. (Redshift is the phenomenon wherein the light from objects moving away from us is stretched to longer and redder wavelengths.) The revolutionary engineering behind this instrument enables the measurement of light from more than 100,000 galaxies in a single night! This includes 5,000 optical fibres, each connected to a robotic positioner programmed to aim at galaxies from a specified target list. The survey is conducted on the 4-metre Mayall Telescope at the Kitt Peak National Observatory in Arizona. Another staggering feature DESI boasts is that the eventual sample size will outstrip the 20-year Sloan Digital Sky Survey by a factor of 10 in extra-galactic targets! The early release contains 80 Terabytes of data, representing 2% of the total dataset that should be available in 2026. See Figures 1 and 2. In 2005, the Sloan Digital Sky Survey found a signal that DESI will validate and make more precise. This signal is that of Baryonic Acoustic Oscillations (BAO). In the incredibly early universe, there were protons and neutrons, known as baryons, which existed in a hot, dense plasma with electrons. Photons were trapped in this plasma due to the extremely high probability of colliding with an electron. The universe was opaque. Only when the universe had cooled sufficiently so that protons and electrons could form neutral hydrogen atoms—an epoch known as recombination*—*did photons decouple from matter. The Cosmic Microwave Background is actually caused by these photons that were emitted after recombination. Before photons decoupled, the gravitational and high-pressure interactions in the plasma produced oscillations that radiated spherically outward from overdense regions, causing photons and baryons to travel through space together. However, as mentioned earlier, when the universe cooled and photons decoupled, the baryonic matter that was present in these oscillations became essentially frozen in space. The photons were free to stream throughout the now-transparent universe. This provided a so-called standard ruler, the distance that these baryons had travelled as an acoustic oscillation prior to recombination. Linking this back to Dark Energy requires the important detail that the radius of the spherical shell of baryons is tied to the expansion rate of the universe. As Dark Energy has propelled the Universe to expand, this standard ruler has expanded with it. See Figure 3. DESI's 3-D map of galaxies will provide a much clearer picture of the universe's large-scale structure, which is our only hope of finding the imprint of BAO. DESI will show (and has already shown) that there exists an overabundance of galaxies separated by a distance equivalent to the length of the standard ruler. Today, the size of this standard ruler is thought to be approximately 490 million lightyears. DESI represents an impressive step into the era of precision cosmology, and it will require the efforts of hundreds of scientists to make sense of the vast quantities of data we expect by 2026. Written by Joseph Brennan Project Gallery

Celebrating trailblazers in skin cancer, chemotherapy and cervical cancer cells Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link African-American women in cancer research 02/05/25, 11:32 Last updated: Published: 20/04/24, 11:05 Celebrating trailblazers in skin cancer, chemotherapy and cervical cancer cells We are going to be spotlighting the incredible contributions of three African-American women who have carved paths for future scientists while significantly advancing our knowledge in the relentless global battle against cancer. Jewel Plummer Cobb (1924-2017) As a distinguished cancer researcher, Jewel is known for her extensive work on melanoma, a serious form of skin cancer. Alongside her frequent collaborator, Jane Cooke Wright, Jewel evidenced the anticancer effects of the drug methotrexate in addressing skin and lung cancer, as well as childhood leukaemia. She is also recognised for her distinctive research examining the varying responses to chemotherapy drugs among cells from different racial and ethnic groups. This research led to the pivotal finding that melanin, a skin pigment, could serve as a protective shield against the damaging effects of sunlight associated with skin cancer. Her 1979 article titled Filters for Women in Science recognised the low percentage of women working in scientific research and engineering, including the barriers that female scientists face in their professional journey. As a result, throughout her career, she often wrote about the experiences of black women in higher education. She also passionately championed for the advancement of black people and women working in the fields of science and medicine. In an interview, she stated that she would like to be remembered as “a black woman scientist who cared very much about what happens to young folks, particularly women, going into science”. Jane Cooke Wright (1919-2013) As the daughter of Harvard Medical School graduate, Louis Tompkins Wright, one of the first African American surgeons in the United States, Jane followed in her father’s footsteps and became a physician. Working together, they explored and compared the activity of possible anticancer compounds in both tissue cultures and in patients. This was revolutionary at the time, considering that chemotherapy guidelines were barely established. In collaboration with her father and six male doctors, the team established the American Society of Clinical Oncology (ASCO) to address the clinical needs of cancer patients. Later on, Jane led ASCO at just 33 years old, following her father’s death. Throughout her career, she conducted research in chemotherapy, publishing over 100 articles on the topic, aiming to fine-tune and tailor chemotherapeutic treatments for patients to ensure better survival outcomes. Like Jewel, she also played a key role in investigating and demonstrating how different racial and ethnic backgrounds respond to drugs used in chemotherapy. This has now become a field of its own, pharmacoethnicity, which studies the anticancer drug responses across people of different ethnicities and is advancing our knowledge on personalised chemotherapy treatment for patients. During an interview, her daughter, Alison Jones, described Jane as: A very ambitious person... she never let anything stand in the way of doing what she wanted to do. Henrietta Lacks (1920-1951) Although not a scientist herself, Henrietta has made a significant contribution to cancer research and medicine through her cervical cancer cells. Although, tragically, she did not know it. Henrietta was diagnosed with cervical cancer in 1951 and sadly passed away the same year. The cervical cancer cells obtained from her biopsy were found to have a unique ability to continuously grow and divide in vitro. Therefore, they could be grown into cell cultures and used in further research. As a result of this trait, researchers have investigated their behaviour, including mutation, division, and carcinogenesis, allowing them to study the effects of drugs and other treatments on these cells. The “immortal” cell line, termed HeLa, has played a pivotal role in the creation of the polio vaccine in the 1950s and medicines for conditions such as leukaemia, influenza, and Parkinson's disease. The HeLa cells also identified the Human papillomavirus (HPV), which later led to the finding that the virus can cause different types of cervical cancer, leading to the significant development of the HPV vaccine used today. It is estimated that over 110,000 research publications have used HeLa cells, emphasising their demand in research. Were it not for Henrietta Lacks, the HeLa cell line would not have been discovered, which has revolutionised our understanding of cancer and medical advancements. In conclusion, the remarkable journey of these pioneering African American women in cancer research serves not only as an inspiration but also a testament to their perseverance, courage, and dedication. They have championed diversity within science, pushed boundaries, and shaped the field of cancer research, allowing for the progress of scientific research in curing cancer and beyond. Written by Meera Solanki Related articles: Women leading the charge in biomedical engineering / The foremothers of gynaecology / Sisterhood in STEM REFERENCES American Society of Clinical Oncology (2016). Society History. [online] ASCO. Available at: https://old-prod.asco.org/about-asco/overview/society-history . Blood Cancer UK (2023). Blood Cancer UK | The story of Dr Jane C Wright, pioneer of blood cancer research. [online] Blood Cancer UK. Available at: https://bloodcancer.org.uk/news/the-story-of-jane-c-wright-pioneer-of-blood-cancer- research/. Boshart, M., Gissmann, L., Ikenberg, H., Kleinheinz, A., Scheurlen, W. and zur Hausen, H. (1984). A new type of papillomavirus DNA, its presence in genital cancer biopsies and in cell lines derived from cervical cancer. The EMBO Journal, 3(5), pp.1151–1157. doi: https://doi.org/10.1002/j.1460-2075.1984.tb01944.x . Cobb, J.P. (1956). Effect of in Vitro X Irradiation on Pigmented and Pale Slices of Cloudman S91 Mouse Melanoma as Measured by Subsequent Proliferation in Vivo234. JNCI: Journal of the National Cancer Institute, [online] 17(5). doi: https://doi.org/10.1093/jnci/17.5.657 . Cobb, J.P. (1979). Filters for Women in Science. Annals of the New York Academy of Sciences, 323(1 Expanding the), pp.236–248. doi: https://doi.org/10.1111/j.1749- 6632.1979.tb16857.x. Ferry, G. (2022). Jane Cooke Wright: innovative oncologist and leader in medicine. The Lancet, [online] 400(10360). doi: https://doi.org/10.1016/S0140-6736(22)01940-7 . Hyeraci, M., Papanikolau, E.S., Grimaldi, M., Ricci, F., Pallotta, S., Monetta, R., Minafò, Y.A., Di Lella, G., Galdo, G., Abeni, D., Fania, L. and Dellambra, E. (2023). Systemic Photoprotection in Melanoma and Non-Melanoma Skin Cancer. Biomolecules, [online] 13(7), p.1067. doi: https://doi.org/10.3390/biom13071067 . King, T., Fukishima, L., Donlon, T., Hieber, D. and Shimabukuro, K. (2000). Correlation between growth control, neoplastic potential and endogenous connexin43 expression in HeLa cell lines: implications for tumor progression. Carcinogenesis, [online] 21(2), pp.311–315. doi: https://doi.org/10.1093/carcin/21.2.311 . National Institutes of Health (2022). Significant Research Advances Enabled by HeLa Cells - Office of Science Policy. [online] Office of Science Policy. Available at: https://osp.od.nih.gov/hela-cells/significant-research-advances-enabled-by-hela- cells/. Pathak, S., Zajac, K.K., Manjusha Annaji, Manoj Govindarajulu, Nadar, R.M., Bowen, D., R. Jayachandra Babu and Muralikrishnan Dhanasekaran (2023). Clinical outcomes of chemotherapy in cancer patients with different ethnicities. Cancer Reports, 6(1). doi: https://doi.org/10.1002/cnr2.1830 . 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

Explaining and predicting the behaviour of serial killers Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The new age of forensic neurology 17/02/25, 14:43 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 article: serial killers in healthcare Project Gallery

How your gut influences your mood and behaviour Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The dopamine connection 10/05/24, 10:34 Last updated: Published: 25/03/24, 12:01 How your gut influences your mood and behaviour Introduction to dopamine Dopamine is a neurotransmitter derived from an amino acid called phenylalanine, which must be obtained through the diet, through foods such as fish, meat, dairy and more. Dopamine is produced and released by dopaminergic neurons in the central nervous system and can be found in different brain regions. The neurotransmitter acts via two mechanisms: wiring transmission and volume transmission. In wiring transmission, dopamine is released to the synaptic cleft and acts on postsynaptic dopamine receptors. In volume transmission, extracellular dopamine arrives at neurons other than postsynaptic ones. Through methods such as diffusion, dopamine then reaches receptors in other neurons that are not in direct contact with the cell that has released the neurotransmitter. In both mechanisms, dopamine binds to the receptors, transmitting signals between neurons and affecting mood and behaviour. The link between dopamine and gut health Dopamine has been known to result in positive emotions, including pleasure, satisfaction and motivation, which can be influenced by gut health. Therefore, what you eat and other factors, including motivation, could impact your mood and behaviour. This was proven by a study (Hamamah et al., 2022), which looked at the bidirectional gut-brain connection. The study found that gut microbiota was important in maintaining the concentrations of dopamine via the gut-brain connection, also known as the gut microbiota-brain axis or vagal gut-to-brain axis. This is the communication pathway between the gut microbiota and the brain facilitated by the vagus nerve, and it is important in the neuronal reward pathway, which regulates motivational and emotional states. Activating the vagal gut-to-brain axis, which leads to dopamine release, suggests that modulating dopamine levels could be a potential treatment approach for dopamine-related disorders. Some examples of gut microbiota include Prevotella, Bacteroides, Lactobacillus, Bifidobacterium, Clostridium, Enterococcus, and Ruminococcus , and they can affect dopamine by modulating dopaminergic activity. These gut microbiota are able to produce neurotransmitters, including dopamine, and their functions and bioavailability in the central nervous system and periphery are influenced by the gut-brain axis. Gut dysbiosis is the disturbance of the healthy intestinal flora, and it can lead to dopamine-related disorders, including Parkinson's disease, ADHD, depression, anxiety, and autism. Gut microbes that produce butyrate, a short-chain fatty acid, positively impact dopamine and contribute to reducing symptoms and effects seen in neurodegenerative disorders. Dopamine as a treatment It is important to understand the link between dopamine and gut health, as this could provide information about new therapeutic targets and improve current methods that have been used to prevent and restore deficiencies in dopamine function in different disorders. Most cells in the immune system contain dopamine receptors, allowing processes such as antigen presentation, T-cell activation, and inflammation to be regulated. Further research into this could open up a new possibility for dopamine to be used as a medication to treat diseases by changing the activity of dopamine receptors. Therefore, dopamine is important in various physiological processes, both in the central nervous and immune systems. For example, studies have shown that schizophrenia can be treated with antipsychotic medications which target dopamine neurotransmission. In addition, schizophrenia has also been treated by targeting the dysregulation (decreasing the amount) of dopamine transmission. Studies have shown promising results regarding dopamine being used as a form of treatment. Nevertheless, further research is needed to understand the interactions between dopamine, motivation and gut health and explore how this knowledge can be used to create medications to treat conditions. Conclusion The bidirectional gut-brain connection shows the importance of gut microbiota in controlling dopamine levels. This connection influences mood and behaviour but also has the potential to lead to new and innovative dopamine-targeted treatments being developed (for conditions including dopamine-related disorders). For example, scientists could target and manipulate dopamine receptors in the immune system to regulate the above mentioned processes: antigen presentation, T-cell activation, and inflammation. While current research has shown some promising results, further investigations are needed to better comprehend the connection between gut health and dopamine levels. Nevertheless, through consistent studies, scientists can gain a deeper understanding of this mechanism to see how changes in gut microbiota could affect dopamine regulation and influence mood and behaviour. Written by Naoshin Haque Related articles: the gut microbiome / Crohn's disease Project Gallery

Treatment based on the individual's genetics Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Personalised medicine 30/01/25, 12:35 Last updated: Published: 29/04/24, 10:44 Treatment based on the individual's genetics In modern medicine, the concept of genetic risk factors is well understood. Certain individuals will be predisposed to disease based on their family history and DNA. Similar to how we inherit traits like eye colour from our parents, susceptibility to conditions such as diabetes or cancer can also be inherited. However, it is only recently that we have begun to understand that an individual's genetic makeup will affect not only their risk for disease but also their reaction to treatment. Understanding risk factors is crucial for diagnosing disease and implementing preventative measures to maintain a patient's health. Utilising a person’s unique DNA could provide insights into their genetic predisposition towards different health conditions, thus accelerating the diagnostic process. Giving patients the ability to make informed decisions about their health based on their genetic risk could help them prevent disease. For example, women carrying the BRCA1 gene may opt for mastectomies to reduce the risk of breast cancer later in life. Personalised medicine doesn’t only focus on risk; it can also directly influence how treatments are administered. Genomic data can indicate which medicines are most likely to be effective and whether there may be associated side effects. The Human Genome Project has made tremendous advancements in the last decade. Combining this data with medical records could provide doctors with insights into the molecular-level interactions of different drugs with individual patients. Personalised medicine in practice Cancer serves as the best example of the importance of personalised medicine. Patients have a unique combination of risk factors from their DNA and lifestyle. However, the same treatments are often offered to everyone with the same type of cancer. The specific mutations that cause a cell to become cancerous are unique to each patient. The genetic makeup of cancer cells may determine which treatment should be focused on, and this is where personalised medicine plays a critical role. An example of personalised medicine already in use is for lung cancer, particularly for cancers with mutated Epidermal Growth Factor Receptors (EGFRs). EGFRs are surface proteins involved in cell growth and division. If there is a mutation, it can result in unpredictable and uncontrollable cell proliferation. There are drugs specifically designed to treat lung cancer cells carrying this EGFR mutation, with their mechanism of action based on this. These drugs would likely be ineffective for lung cancers with different mutations, as they have different mechanisms of action. Personalised medicine tailors treatment to the genetic makeup of a person to achieve a bespoke and hopefully improved outcome. Transcriptomics, the study of RNA and its alterations instead of DNA, may be a future avenue of investigation in understanding cancer biology. Tumours can arise due to mutated RNA or abnormal transcription events, indicating that DNA is not the only genetic material relevant to oncology. There have been promising innovations in personalised vaccines tailored to each patient. Tissue from an individual is biopsied and studied, and using identified biomarkers, a custom mRNA vaccine can prime the immune system to attack cancer cells. Future potential Genetic variation in a patient’s response to drugs can significantly affect their reactions to treatment. By combining genomic data and AI technology, scientists are developing predictive algorithms to create individualised medication plans for patients, potentially eliminating the guesswork in prescriptions. Personalised precision medication holds great potential. However, the primary limitation currently lies in the cost of treatment. Medical services are stretched thin across the population, making bespoke treatments currently unfeasible. Personalised medicine is expected to improve as new genetic biomarkers are discovered and catalogued, leading to more sophisticated genomic databases over time. As sequencing technology becomes more mainstream, associated costs are likely to decrease, possibly making personalised medicine standard practice in the future. Written by Charlotte Jones Related article: mRNA vaccines Project Gallery

A well-studied longevity gene is SIRT1 Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The Genetics of Ageing and Longevity 20/02/25, 11:55 Last updated: Published: 13/05/24, 15:20 A well-studied longevity gene is SIRT1 Ageing is a natural process inherent to all living organisms. Yet, its mechanisms remain somewhat enigmatic. While lifestyle factors undoubtedly influence longevity, recent advancements in genetic research have revealed the influence of our genomes on ageing. Through understanding these influences, we can unlock further knowledge on longevity, which can aid us in developing interventions to promote healthy ageing. This article delves into the world of ageing and longevity genetics and how we can use this understanding to our benefit. Longevity genes A number of longevity genes, such as APOE , FOXO3 , and CETP, have been identified. These genes influence various biological processes, including cellular repair, metabolism, and stress response mechanisms. A well-studied longevity gene is SIRT1 . Located on chromosome 10, SIRT1 encodes sirtuin 1, a histone deacetylase, transcription factor, and cofactor. Its roles include protecting cells against oxidative stress, regulating glucose and lipid metabolism, and promoting DNA repair and stability via deacetylation. Sirtuins are an evolutionarily conserved mediator of longevity in many organisms. One study looked at mice with knocked-out SIRT1 ; these mice had significantly lower lifespans when compared with WT mice1. The protective effects of SIRT1 are thought to be due to deacetylating p53, which promotes cell death2. SIRT1 also stimulates the cytoprotective and stress-resistance gene activator FoxO1A (see Figure 1 ), which upregulates catalase activity to prevent oxidative stress3. Genome-wide association studies (GWAS) have identified several genetic variants associated with ageing and age-related diseases. Such variants influence diverse aspects of ageing, such as cellular senescence, inflammation, and mitochondrial function. For example, certain polymorphisms in APOE are associated with an increased risk of age-related conditions like Alzheimer's and Parkinson’s disease4. These genes have a cumulative effect on the longevity of an organism. Epigenetics of ageing Epigenetic modifications, such as histone modifications and chromatin remodelling, regulate gene expression patterns without altering the DNA sequence. Studies have shown that epigenetic alterations accumulate with age and contribute to age-related changes in gene expression and cellular function. For example, DNA methylation is downregulated in human fibroblasts during ageing. Furthermore, ageing correlates with decreased nucleosome occupancy in human fibroblasts, thereby increasing the expression of genes unoccupied by nucleosomes. One specific marker of ageing in metazoans is H3K4me3, indicating the trimethylation of lysine 4 on histone 3; in fact, H3K4me3 demethylation extends lifespan. Similarly, H3K27me3 is also a marker of biological age. By using these markers as an epigenetic clock, we can predict biological age using molecular genetic techniques. As a rule of thumb, genome-wide hypomethylation and CpG island hypermethylation correlate with ageing, although this effect is tissue-specific5. Telomeres are regions of repetitive DNA at the terminal ends of linear chromosomes. Telomeres become shorter every time a cell divides (see Figure 2 ), and eventually, this can hinder their function of protecting the ends of chromosomes. As a result, cells have complex mechanisms in place to prevent telomere degradation. One of these is the enzyme telomerase, which maintains telomere length by adding G-rich DNA sequences. Another mechanism is the shelterin complex, which binds to ‘TTAGGG’ telomeric repeats to prevent degradation. Two major components of the shelterin complex are TRF1 and TRF2, which bind telomeric DNA. They are regulated by the chromatin remodelling enzyme BRM-SWI/SNF, which has been shown to be crucial in promoting genomic stability, preventing cell apoptosis, and maintaining telomeric integrity. BRM-SWI/SNF regulates TRF1/2, thereby, regulating the shelterin complex, by remodelling the TRF1/2 promoter region to convert it to euchromatin and increase transcription. BRM-SWI/SNF inactivating mutations have been shown to contribute to cancer and cellular ageing through telomere degradation6. Together, the mechanisms cells have in place to protect telomeres provide protection against cancer as well as cellular ageing. Future of anti-ageing drugs Anti-ageing drugs are big business in the biotechnology and cosmetics sector. For example, senolytics are compounds that decrease the number of senescent cells in an individual. Senescent cells are those that have permanently exited the cell cycle and now secrete pro-inflammatory molecules (see Figure 3); they are a major cause of cellular and organismal ageing. Senolytic drugs aim to provide anti-ageing benefits to an individual, whereby senescent cells are removed, therefore, decreasing inflammation. Currently, researchers are certain that removing senescent cells would have an anti-ageing effect, although senolytic drugs currently on the market are understudied, and so their side effects are unknown. Speculative drugs could include those that enhance telomerase or SIRT1 activity. Evidently, ageing is not purely determined by lifestyle and environmental factors alone but also by genetics. While longevity genes are hereditary, epigenetic modifications may be influenced by external factors. Therefore, we can attribute the complex interplay between various external factors and an individual’s genome to understanding the role of genetics in ageing. Perhaps we will see a new wave of anti-ageing treatments in the coming years, developed on the genetics of ageing. Written by Malintha Hewa Batage Related articles: An introduction to epigenetics / Schizophrenia, inflammation and ageing / Ageing and immunity REFERENCES Cilic, U et al., (2015) ‘A Remarkable Age-Related Increase in SIRT1 Protein Expression against Oxidative Stress in Elderly: SIRT1 Gene Variants and Longevity in Human’, PLoS One , 10(3). Alcendor, R et al., (2004) ‘Silent information regulator 2alpha, a longevity factor and class III histone deacetylase, is an essential endogenous apoptosis inhibitor in cardiac myocytes’, Circulation Research , 95(10):971-80. Alcendor, R et al., (2007) ‘Sirt1 regulates aging and resistance to oxidative stress in the heart’, Circulation Research , 100(10):1512-21. Yin, Y & Wang, Z, (2018) ‘ApoE and Neurodegenerative Diseases in Aging’, Advances in Experimental Medicine and Biology , 1086:77-92. Wang, K et al., (2022) ‘Epigenetic regulation of aging: implications for interventions of aging and diseases’, Signal Transduction and Targeted Therapy , 7(1):374. Images made using BioRender. Project Gallery

Transforming agriculture with computer science Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Digital innovation in rural farming 27/03/25, 11:20 Last updated: Published: 21/07/23, 09:58 Transforming agriculture with computer science With their rich agricultural heritage and significant contribution to the national economy, rural farming communities have always been at the forefront of agricultural innovation. Today, as the world undergoes rapid digital transformation, the integration of computer science has emerged as a game-changer in the agricultural sector. By harnessing the power of emerging technologies and data-driven approaches, farmers can enhance productivity, optimize resource allocation, and foster sustainable farming practices. This article delves into the role of computer science in revolutionising agriculture and farming practices in rural areas. From precision agriculture and data analytics to the utilisation of IoT, drones, and decision support tools, we explore how technology-driven solutions are shaping a new era of agriculture, promising increased efficiency, reduced environmental impact, and improved livelihoods for farmers. A recent report revealed that farmers in various regions, specifically rural and eastern regions such as Punjab, India have faced significant challenges, including crop failures, leading to distress and financial difficulties. It is important to address these issues and prevent the associated consequences. Digitalisation within the farming industry can play a vital role in mitigating these challenges and fostering resilience. So how exactly can rural farming benefit from digitalisation? Precision agriculture and data analytics: the implementation of precision agriculture techniques, supported by data analytics, can enable farmers to optimise resource utilisation, improve crop management, and mitigate agricultural risks. By analysing data related to weather patterns, soil conditions, and crop health, farmers can make informed decisions, enhance productivity, and reduce the incidence of crop failures. Market intelligence and price forecasting: computer science tools can facilitate better market intelligence and price forecasting, empowering farmers to make informed decisions about crop selection, timing of harvest, and market strategies. Access to real-time market data, coupled with predictive analytics, can help farmers negotiate fair prices and reduce financial vulnerability caused by market instability. Remote sensing and drone technology: utilising remote sensing and drone technology can enable efficient crop monitoring, early detection of diseases, and targeted interventions. High-resolution imagery and computer vision algorithms can identify crop stress, nutrient deficiencies, or pest outbreaks, allowing farmers to take timely action, reduce crop losses, and enhance yield. Decision support systems: the introduction of decision support systems can provide customised recommendations to farmers, incorporating data from multiple sources such as weather forecasts, market trends, and agronomic best practices. These systems can assist farmers in making well-informed decisions regarding crop selection, input usage, and resource allocation, ultimately improving their profitability, and reducing financial distress. The integration of computer science offers promising avenues for addressing the complex challenges faced by farmers in rural areas. By harnessing the power of data analytics, IoT, drones, and decision support tools, farmers can benefit from enhanced agricultural practices, improved market access, and financial stability. However, it is crucial to ensure the accessibility and affordability of these technologies, coupled with comprehensive support systems and policy reforms, to truly empower farmers and create sustainable change. Written by Jaspreet Mann Related articles: Revolutionising sustainable agriculture through AI / Plant diseases and nanoparticles Project Gallery

A type of anaemia Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Iron deficiency anaemia 07/02/25, 16:23 Last updated: Published: 27/06/23, 17:10 A type of anaemia This article is no. 2 of the anaemia series. Next article: anaemia of chronic disease . Previous article: Anaemia . Aetiology Iron deficiency anaemia (IDA) is the most frequent in children due to rapid growth (adolescence) and poor diets (infants), and in peri and post -menopausal women due to rapid growth (pregnancy) and underlying conditions. Anaemia typically presents, in around 50% of cases as headache, lethargy and pallor depending on the severity. Less common side effects include organomegaly and Pica which occurs in patients with zinc and iron deficiency and is defined by the eating of things with little to no nutritional value. Pathophysiology Iron is primarily sourced through diet, as haem (Fe2+) and non-haem iron (Fe3+). Fe2+ is sourced through meat, fish, and other animal-based products, Fe2+ can be absorbed directly through the enterocyte via the haem carrier protein1 (HCP1). Fe3+ is less easily absorbed and is mostly found in plant-based products. Fe3+ must be reduced and transported through the duodenum by the enzyme duodenal cytochrome B (DcytB) and the divalent metal transporter 1 (DMT1), respectively. Diagnosis As with any diagnosis, the first test to run would be a full blood count and this will occur with all the anaemias. In suspected cases of anaemia, the Haemoglobin (Hb) levels would be lower than 130 in males and 120 in females. The mean cell volume (MCV) is a starting point for pinpointing the type of anaemia, for microcytic anaemias you would expect to see an MCV < 80. Iron studies are best for diagnosing anaemias, for IDA you would expect most of the results to be low. A patient with IDA has little to no available iron so the body would halt the mechanism’s for storing iron. As ferratin is directly related to storage, low ferratin can be a lone diagnostic of IDA. Total iron-binding capacity (TIBC) would be expected to be raised, as transferrin transports iron throughout the body, the higher it is the more iron it would be capable of binding to. Elliptocytes (tear drop) are elongated RBC, often described as pencil like in structure and are regularly seen in IDA and other anaemias. Typically, one would see hypochromic RBC as they contain less Hb than normal cells, the Hb is what gives red cells their pigment. It’s not uncommon to see other changes in RBC such as target cells, given their name due to the bullseye appearance. Target cells are frequently seen in cases with blood loss. Summary IDA is the most frequent anaemia affecting patients of all age ranges and usually presents with lethargy and headaches. Dietary iron from animal derivatives are the most efficient source of iron uptake. Diagnosis of IDA is through iron studies, red cell morphological investigations alongside clinical presentation, to rule out other causes. Written by Lauren Kelly Project Gallery

Landing on the moon (again!) Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Artemis: the Lunar South Pole Base 29/01/25, 15:45 Last updated: Published: 13/01/24, 15:44 Landing on the moon (again!) Humans have not visited the moon since 1972, but that’s about to change. Thanks to NASA’s Artemis missions, we have already taken the first small step towards our own lunar home for astronauts. NASA has established the second generation of its lunar missions- “Artemis”, fittingly named after the ancient Greek Goddess of the Moon, and Apollo’s twin. The ultimate aim of the Artemis missions is to solidify a stepping stone to Mars. Technologies will be developed, tested, and perfected, before confidence is built to travel on to Mars. NASA has to consider the natural conditions of the Moon, since doing so will allow astronauts to limit their reliance on resources from Earth, and increase their length of stay and therefore potential for research. The amount achieved would be extremely limited if a lunar mission relied solely on resources from Earth, due to the limitation of rocket payloads. This is known as In-Situ Resource Utilisation, and in addition to extended lunar stays, its success on the Moon is essential if we hope to one day establish a base on Mars. As a priority, astronauts need to have access to energy and water. Luckily, the conditions at the lunar south pole may be ideal for this. Unlike Earth, where we experience seasons due to its 23.5° tilt, the Moon’s tilt is tiny, at only 1.5°. This means some areas at the lunar poles are almost always exposed to sunlight, providing a reliable source of solar energy generation for a potential Artemis Base Camp. And since the Sun is always near the horizon at the poles, there are even areas in deep craters that never see the light. These areas of “eternal darkness” can reach temperatures of -235°, possibly allowing astronauts access to water ice. Aside from access to resources, Artemis has to consider the dangers that come from living in space. Away from the safety of Earth’s protective atmosphere and magnetosphere, astronauts would be exposed to harsh solar winds and cosmic rays. To combat this, NASA hopes to make use of the terrain surrounding the base, highlighting another advantage of the hilly south pole [3]. The exact location for the Artemis Base is currently undecided. We just know it will most likely be near a crater rim by the south pole, and on the Earth-facing side to allow for communication to and from Earth. Not only is the south pole ideal from a practical standpoint, it is also an area of exciting scientific interest. Scientists will have access to the South Pole–Aitken basin, not only the oldest and largest confirmed impact crater on the Moon, but the second largest confirmed impact crater in the entire Solar System. With a depth of up to 8.2 km, and diameter of 2500 km, it is thought this huge crater will contain exposed areas of lower crust and mantle, providing an insight into the Moon’s history and formation. Additionally, thanks to areas of “eternal darkness” the ice water found deep within craters at the south pole may hold trapped volatiles up to 3.94 billion years old, which, although not as ancient as previously expected, can still provide an insight into the evolution of the Moon. The scientific potential of the Artemis Base Camp extends far beyond location specific investigations to our most fundamental understanding of physics, from Quantum Physics to General Relativity. Not to mention the astronauts themselves, as well as “model organisms” which will be the focus of physiological studies into the effects of extreme space environments. Artemis Timeline Overview: Artemis 1 launched on 16th November 2022. It successfully tested the use of two key elements of the Artemis mission- Orion and the Space Launch System (SLS)- with an orbit around the moon. Orion, named after the Goddess Artemis' hunting partner, is the spacecraft that will carry the Artemis crew into lunar orbit. It is carried by the SLS, NASA’s super heavy-lift rocket, one of the most powerful rockets in the world. Artemis 2 plans to launch late 2024 and will be the first crewed Artemis mission, with a lunar flyby bringing four astronauts further than humans have ever travelled beyond Earth. Artemis 3 plans to launch the following year. It will be the historic moment that will see humans step foot on the surface of the moon for the first time since we left in 1972. The mission will be the first use of another key element of the Artemis missions- the Human Landing System (HLS). Astronauts will use a lunar version of SpaceX’s Starship rocket as the HLS for Artemis 3 and 4. (Starship is currently in its test stage, with its second test launch carried out very recently on the 18th November 2023.) Two astronauts will stay on the lunar surface for about a week, beating the current record of 75 hours on the Moon by Apollo 17. Artemis 4 plans to launch in 2028. The mission will include the first use of Gateway, another key element to the Artemis missions. Gateway will be a multifunctional lunar space station, designed to transfer astronauts between Orion and HLS, as well as hosting astronauts to live and research in lunar orbit. Gateway will be constructed over Artemis 4-6 , with each mission completing an additional module. NASA plans to have Artemis missions extending for years beyond this, with over 10 proposed and more expected. Eventually we will have a working base on the Moon with astronauts able to stay for months at a time. Having already started a year ago, Artemis will continue to expand our horizons. We can look forward to uncovering long held secrets of the Moon, and soon, setting our sights confidently on Mars. Written by Imo Bell Related articles: Exploring Mercury / Fuel for the colonisation of Mars / Nuclear fusion REFERENCES How could we live on the Moon? - Institute of Physics. Available at: https://www.iop.org/explore-physics/moon/how-could-we-live-on-the-moon Understanding Physical Sciences on the Moon - NASA. Available at: https://science.nasa.gov/lunar-science/focus-areas/understanding-physical-sciences-on-themoon NASA’s Artemis Base Camp on the moon will need light, water, elevation - NASA. Available at: https://www.nasa.gov/humans-in-space/nasas-artemis-base-camp-on-the-moon-will-need-ligh t-water-elevation Zuber, M.T. et al. (1994) ‘The shape and internal structure of the Moon from the Clementine Mission’, Science, 266(5192), pp. 1839–1843. doi:10.1126/science.266.5192.1839. Flahaut, J. et al. (2020) ‘Regions of interest (ROI) for future exploration missions to the Lunar South Pole’, Planetary and Space Science, 180, p. 104750. doi:10.1016/j.pss.2019.104750. Moriarty, D.P. et al. (2021) ‘The search for lunar mantle rocks exposed on the surface of the Moon’, Nature Communications, 12(1). doi:10.1038/s41467-021-24626-3. Estimates of water ice on the Moon get a ‘dramatic’ downgrade - Physics World. Available at: https://physicsworld.com/a/estimates-of-water-ice-on-the-moon-get-a-dramatic-downgrade Biological Systems in the lunar environment - NASA. Available at: https://science.nasa.gov/lunar-science/focus-areas/biological-systems-in-the-lunar-environme Https://www.nasa.gov/wp-content/uploads/static/artemis/NASA : Artemis - NASA. Available at: https://www.nasa.gov/specials/artemis Project Gallery