A break in play during a Small Size League match. Today, 21 July, saw the competitions draw to a close in a thrilling finale. In the third and final of our round-up articles, we provide a flavour of the action from this last day. If you missed them, you can find our first two digests here: 19 July 20 July. My first port of call this morning was the Standard Platform League, where Dr Timothy Wiley and Tom Ellis from Team RedbackBots, RMIT University, Melbourne, Australia, demonstrated an exciting advancement that is unique to their team.

This is the second of our daily digests from RoboCup2024 in Eindhoven, The Netherlands. If you missed the first digest, which gives some background to RoboCup, you can find it here. Competitions continued across all the leagues today, with participants vying for a place in Sunday's finals. The RoboCup@Work league focusses on robots in work-related scenarios, utilizing ideas and concepts from other RoboCup competitions to tackle open research challenges in industrial and service robotics. I arrived at the arena in time to catch the advanced navigation test.

RoboCup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. As part of this initiative, a series of competitions and events are held throughout the year. The main showcase event is an international affair with teams travelling from far and wide to put their machines through their paces. This year, RoboCup is being held in three arenas in the Genneper Parken, Eindhoven, The Netherlands. The organisers are expecting over 2,000 participants, from 45 different countries, with around 300 teams signed up to take part in the various competitions.

Diseases that have a complex genetic architecture tend to suffer from considerable amounts of genetic variants that, although playing a role in the disease, have not yet been revealed as such. Here researchers present DiseaseCapsule, as a capsule-network-based approach that explicitly addresses to capture the hierarchical structure of the underlying genome data, and has the potential to fully capture the non-linear relationships between variants and disease. Researchers propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. Dynamics and conformational sampling are essential for linking protein structure to biological function. While challenging to probe experimentally, computer simulations are widely used to describe protein dynamics, but at significant computational costs that continue to limit the systems that can be studied.

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. It can be easily dropped into any web page with a single line of code and has no external dependencies. The viewer runs completely in the web browser, with no backend server and no data pre-processing required. Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics.

Nonalcoholic fatty liver (NAFL) and its sequelae are growing health problems. Researches performed a genome-wide association study of NAFL, cirrhosis and hepatocellular carcinoma, and integrated the findings with expression and proteomic data. The present study provides insights into the development of noninvasive evaluation of NAFL and new therapeutic options. Interactions between proteins help us understand how genes are functionally related and how they contribute to phenotypes. Experiments provide imperfect "ground truth" information about a small subset of potential interactions in a specific biological context, which can then be extended to the whole genome across different contexts, such as conditions, tissues, or species, through machine learning methods.

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Researchers developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity, their potential to induce proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response. The development of medical applications of machine learning has required manual annotation of data, often by medical experts. Yet, the availability of large-scale unannotated data provides opportunities for the development of better machine-learning models. In this Review, the authors highlight self-supervised methods and models for use in medicine and healthcare, and discuss the advantages and limitations of their application to tasks involving electronic health records and datasets of medical images, bioelectrical signals, and sequences and structures of genes and proteins.

The reliable prediction of chemical reactivity remains in the realm of knowledgeable synthetic chemists. Here, researchers propose a chemistry-motivated graph neural network called LocalTransform, which learns organic reactivity based on generalized reaction templates to describe the net changes in electron configuration between the reactants and products. The proposed concept dramatically reduces the number of reaction rules and exhibits state-of-the-art product prediction accuracy. Machine learning algorithms are a powerful tool in healthcare, but sometimes perform no better than traditional statistical techniques. Steps should be taken to ensure that algorithms are not overused or misused, in order to provide genuine benefit for patients.

Drug hunters are moving into the clinic with human-first'no-hypothesis' target discovery, applying the full force of machine learning to massive collections of human omics data. Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Here, using whole-genome, exome and transcriptome sequencing of 2,754 childhood patients with ALL, researchers find that, despite a generally low mutation burden, ALL cases harbor a median of four putative somatic driver alterations per sample, with 376 putative driver genes identified varying in prevalence across ALL subtypes. Statistical and scientific programming has quickly become a necessary skill in the sciences, often conducted in the programming language, R. Here, the authors focus specifically on self-learning the programming language, R. These 10 rules will not provide technical instruction for using R--as there are many wonderful and thorough resources for that --but rather provide a nonexhaustive list of practical strategies for building or honing R programming skills.

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. Researchers developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell's marker expression profile and, when needed, its spatial information. High-quality visualization of biological networks often requires both manual curation for proper alignment and programming to map external data to the graphical components. Nezzle is a network visualization software written in Python, which provides programmable and interactive interfaces for facilitating both manual and automatic curation of the graphical components of networks to create high-quality figures.