Neuronal architectures comprise synaptically connected neurons distributed throughout the central nervous system, the coordinated activities of which orchestrate neurological functions ranging from breathing to movement and cognition. Disentangling these neuronal architectures and how they are disrupted in disease is a fundamental goal of neuroscience. Historically, this challenge has been addressed with a reductionist framework that translated hypotheses into the interrogation of discrete neuronal subpopulations based on a priori expectations. The advent of high-throughput methodologies, including whole–central nervous system imaging in rodent models and single-cell transcriptomic readouts, now enable the visualization and characterization of neuronal subpopulations throughout the central nervous system. Increases in scale further enable comparative experimental designs that can be navigated with computational frameworks. These advances augur a new era wherein neuronal architectures implicated in diverse neurological functions, yet obscured by the complexity of the central nervous system, can be exposed without bias and interrogated with genetically guided experimental manipulations.

We present an AI-based ecosystem simulator that uses three-dimensional models of the terrain and animal models controlled by deep reinforcement learning. The simulations take place in a game engine environment, which enables continuous visual observation of the ecosystem model. The terrain models are generated from geographic data with altitudes and land cover type. The animal models combine three-dimensional conformation models with animation schemes and decision-making mechanisms trained with deep reinforcement learning in increasingly complex environments (curriculum learning). We show how AI tools of this kind can be used for modeling the development of specific ecosystems with and without different forms of economic activities. In particular, we show how they might be used for modeling local biodiversity effects of land cover change, exploitation of natural resources, pollution, invasive species, and climate change.

A compound generated by artificial intelligence-based tools predicted to bind to the kappa-opioid receptor. Researchers are turning to artificial intelligence to create and optimize potential new drugs to help people with opioid addiction. It is estimated that about three million Americans suffer from opioid use disorder, and every year more than 80,000 Americans die from overdoses. Opioid drugs, such as heroin, fentanyl, oxycodone, and morphine, activate opioid receptors. Activating mu-opioid receptors leads to pain relief and euphoria, but also physical dependence and decreased breathing.

Artificial intelligence (AI) and big data are transforming healthcare with high-throughput analyses of complex diseases. Machine learning and sophisticated computational methods can be used to efficiently interpret human genomes and other biomarkers, providing insights for patient treatment and with major applications in diagnostics and preventive care. A personalised treatment plan may include preventive care for diseases that are at a higher risk of developing, for example increased screening for cancer if a patient possesses the BRCA 1 or BRCA 2 gene mutation. Additionally, AI can generate insights from genetic information, biomarkers, and other physiological data to predict how a patient will respond to different treatment options, which may help avoid adverse reactions, reduce the use of expensive or unnecessary treatments on patients that are unlikely to respond, and ultimately reduce hospitalisation and outpatient costs. For more information, GlobalData's latest report, Precision and Personalized Medicine – Thematic Research, provides insight into the most prevalent uses of personalised medicine, new applications, and the healthcare, macroeconomic, and technology themes driving growth.

Researchers have found that genetically and pharmacologically restoring the normal activity of the brain circuit improved anorexia, opening the possibility of developing a treatment strategy for affected individuals in the future. Researchers at Baylor College of Medicine, Louisiana State University and collaborating institutions has discovered that abnormal activity in a particular brain circuit underlies anorexia in an animal model of the condition. Genetically and pharmacologically restoring the normal activity of the brain circuit improved the condition, opening the possibility of developing a treatment strategy for affected individuals in the future. Anorexia has no approved treatment, and the underlying causes is unclear. The study was recently published in Nature Neuroscience.

Summary: A new theory suggests consciousness is a state tied to complex cognitive operations, and not a passive basic state that automatically prevails when we are awake. Two researchers at Ruhr-Universität Bochum (RUB) have come up with a new theory of consciousness. They have long been exploring the nature of consciousness, the question of how and where the brain generates consciousness, and whether animals also have consciousness. The new concept describes consciousness as a state that is tied to complex cognitive operations – and not as a passive basic state that automatically prevails when we are awake. Professor Armin Zlomuzica from the Behavioral and Clinical Neuroscience research group at RUB and Professor Ekrem Dere, formerly at Université Paris-Sorbonne, now at RUB, describe their theory in the journal Behavioural Brain Research.

In this work we propose a new biophysical computational model of brain regions relevant to Parkinson's Disease based on local field potential data collected from the brain of marmoset monkeys. Parkinson's disease is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the substantia nigra pars compacta, which affects the normal dynamics of the basal ganglia-thalamus-cortex neuronal circuit of the brain. Although there are multiple mechanisms underlying the disease, a complete description of those mechanisms and molecular pathogenesis are still missing, and there is still no cure. To address this gap, computational models that resemble neurobiological aspects found in animal models have been proposed. In our model, we performed a data-driven approach in which a set of biologically constrained parameters is optimised using differential evolution. Evolved models successfully resembled single-neuron mean firing rates and spectral signatures of local field potentials from healthy and parkinsonian marmoset brain data. As far as we are concerned, this is the first computational model of Parkinson's Disease based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys. Results show that the proposed model could facilitate the investigation of the mechanisms of PD and support the development of techniques that can indicate new therapies. It could also be applied to other computational neuroscience problems in which biological data could be used to fit multi-scale models of brain circuits.

The discovery of new drugs is an undeniably important undertaking and represents a massive global market. Statista indicates that the drug discovery market worldwide finds itself on an exponential trajectory, with the expected market value poised to reach 71 billion U.S. dollars by 2025. As of 2016, the market was valued at just 35.2 billion U.S. dollars. Of course, this comes as no surprise; the U.S. pharmaceutical industry, after all, was coined'Big Pharma' for a reason. By 2021, Big Pharma profits for prescription drugs are expected to reach $610 billion and, in 2015, Americans spent $457 billion on prescription drugs.

With the advent of pharmacogenomics, machine learning research is well underway to predict the patients' drug response that varies by individual from the algorithms derived from previously collected data on drug responses. Entering high-quality learning data that can reflect a person's drug response as much as possible is the starting point for improving the accuracy of the prediction. Previously, preclinical study of animal models were used which were relatively easier to obtain compared to human clinical data. In light of this, a research team led by Professor Sanguk Kim in the Department of Life Sciences at POSTECH is drawing attention by successfully increasing the accuracy of anti-cancer drug response predictions by using data closest to a real person's response. The team developed this machine learning technique through algorithms that learn the transcriptome information from artificial organoids derived from actual patients instead of animal models.

Researchers from the Pohang University of Science and Technology (POSTECH) in South Korea say they have successfully increased the accuracy of anti-cancer drug response predictions by using data closest to a human being's response. The team developed this machine learning technique through algorithms that learn transcriptome information from artificial organoids derived from actual patients instead of animal models. The team, led by Sanguk Kim, PhD, in the life sciences department, published its findings "Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients" in Nature Communications "Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models," write the investigators. "The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin.