Brewing byproduct may be a key sustainable secret ingredient. Breakthroughs, discoveries, and DIY tips sent every weekday. Brewing beer relies on a very simple living thing-brewer's yeast. The microorganisms thrive on mashed grains, converting sugars into both alcohol and carbon dioxide along the way. But there's not much use for yeast after the pints are poured .

Automating biological experimentation remains challenging due to the need for millimeter-scale precision, long and multi-step experiments, and the dynamic nature of living systems. Current liquid handlers only partially automate workflows, requiring human intervention for plate loading, tip replacement, and calibration. Industrial solutions offer more automation but are costly and lack the flexibility needed in research settings. Meanwhile, research in autonomous robotics has yet to bridge the gap for long-duration, failure-sensitive biological experiments. We introduce RoboCulture, a cost-effective and flexible platform that uses a general-purpose robotic manipulator to automate key biological tasks. RoboCulture performs liquid handling, interacts with lab equipment, and leverages computer vision for real-time decisions using optical density-based growth monitoring. We demonstrate a fully autonomous 15-hour yeast culture experiment where RoboCulture uses vision and force feedback and a modular behavior tree framework to robustly execute, monitor, and manage experiments. Video demonstrations of RoboCulture can be found at https://ac-rad.github.io/roboculture.

Sepsis is a life-threatening condition which requires rapid diagnosis and treatment. Traditional microbiological methods are time-consuming and expensive. In response to these challenges, deep learning algorithms were developed to identify 14 bacteria species and 3 yeast-like fungi from microscopic images of Gram-stained smears of positive blood samples from sepsis patients. A total of 16,637 Gram-stained microscopic images were used in the study. The analysis used the Cellpose 3 model for segmentation and Attention-based Deep Multiple Instance Learning for classification. Our model achieved an accuracy of 77.15% for bacteria and 71.39% for fungi, with ROC AUC of 0.97 and 0.88, respectively. The highest values, reaching up to 96.2%, were obtained for Cutibacterium acnes, Enterococcus faecium, Stenotrophomonas maltophilia and Nakaseomyces glabratus. Classification difficulties were observed in closely related species, such as Staphylococcus hominis and Staphylococcus haemolyticus, due to morphological similarity, and within Candida albicans due to high morphotic diversity. The study confirms the potential of our model for microbial classification, but it also indicates the need for further optimisation and expansion of the training data set. In the future, this technology could support microbial diagnosis, reducing diagnostic time and improving the effectiveness of sepsis treatment due to its simplicity and accessibility. Part of the results presented in this publication was covered by a patent application at the European Patent Office EP24461637.1 "A computer implemented method for identifying a microorganism in a blood and a data processing system therefor".

Although DNA foundation models have advanced the understanding of genomes, they still face significant challenges in the limited scale and diversity of genomic data. This limitation starkly contrasts with the success of natural language foundation models, which thrive on substantially larger scales. Furthermore, genome understanding involves numerous downstream genome annotation tasks with inherent data heterogeneity, thereby necessitating more efficient and robust finetuning methods tailored for genomics. Lingo further accommodates numerous, heterogeneous downstream fine-tune tasks by an adaptive rank sampling method that prunes and stochastically reintroduces pruned singular vectors within small computational budgets. Adaptive rank sampling outperformed existing fine-tuning methods on all benchmarked 14 genome understanding tasks, while requiring fewer than 2% of trainable parameters as genomic-specific adapters. Impressively, applying these adapters on natural language foundation models matched or even exceeded the performance of DNA foundation models. Lingo presents a new paradigm of efficient and scalable genome understanding via genomic-specific adapters on language models. DNA foundation models, such as DNABERT [1], DNABERT-2 [2], and Nucleotide Transformer (NT) [3], have made significant progress in decoding the linguistic intricacies of the genome. An important paradigm of utilizing such DNA foundation models is "pre-training+finetuning", i.e., pre-training on unlabeled genomic sequences, and then adaptation to a particular genome understanding task. A critical aspect of genome annotation and downstream tasks is their considerable number and diversity. For example, state-of-the-art deep learning models in epigenetics alone can encompass nearly 22,000 individual tasks [4].

It seems the usefulness of ChatGPT knows no bounds as even brewers are using the tool to make new beer. German brand Beck's is one of a number of companies to have turned to the clever AI chatbot to make a futuristic beverage, called Beck's Autonomous. ChatGPT not only came up with the beer's recipe but also its packaging, name, advertising campaign and even a design for the beer's website. Beck's is the first commercial brewery to work with ChatGPT, although other independent brew houses in North America have already done the same. MailOnline gave Beck's Autonomous a try to see how it compares with the brand's flagship lager.

Tiny robots packed with yeast speed up the fermentation of beer and eliminate the need to filter it before bottling. Using living yeast to convert sugar to alcohol is a key part of making beer, but it can be time consuming, and the yeast can spoil and ruin a whole batch of the drink. Martin Pumera at the Brno University of Technology in the Czech Republic and his colleagues thought that both issues could be addressed by swapping yeast for tiny, metallic yeast-filled bots.

In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a subset of all labels. In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. This option is useful in cases of uncertainty, where the learner does not feel confident enough on the entire label set. Adopting a decision-theoretic perspective, we propose a formal framework of MLC with partial abstention, which builds on two main building blocks: First, the extension of underlying MLC loss functions so as to accommodate abstention in a proper way, and second the problem of optimal prediction, that is, finding the Bayes-optimal prediction minimizing this generalized loss in expectation. It is well known that different (generalized) loss functions may have different risk-minimizing predictions, and finding the Bayes predictor typically comes down to solving a computationally complexity optimization problem. In the most general case, given a prediction of the (conditional) joint distribution of possible labelings, the minimizer of the expected loss needs to be found over a number of candidates which is exponential in the number of class labels. We elaborate on properties of risk minimizers for several commonly used (generalized) MLC loss functions, show them to have a specific structure, and leverage this structure to devise efficient methods for computing Bayes predictors. Experimentally, we show MLC with partial abstention to be effective in the sense of reducing loss when being allowed to abstain.

Artificial Intelligence has many benefits, but one of the biggest benefits of it is faster technological advancements. Artificial intelligence is now widely used in research, which means it will quickly learn how to find results for many questions that the world is exploring. This means that researchers will be free to devise new parameters and objectives. Artificial Intelligence keeps developing, and that raises the question: will AI or robotics one day replace us in the workplace? Or will AI replace developers?

Recommendation algorithms that predict what we'll want to watch, buy, and read are now ubiquitous, in part thanks to advances in computing power. But while today's data science tools can sift through mounds of data to unearth patterns at levels of scale and speed that humans alone could never achieve, our models remain inadequate in fully understanding data and its applications, especially when the data becomes messy in reflecting fickle human behaviors. Data science is a craft that relies on human intuition and creativity to understand multi-faceted problem spaces. Without human oversight, it operates on an incomplete picture, for which the implications have never been clearer in the present COVID-19 age as our algorithms struggle to grasp the reality that human behaviors don't follow mathematics. March 2020 marked the start of a series of behaviors that would have seemed unusual just weeks prior: As COVID-19 was declared a global pandemic, we started stockpiling toilet paper, Googling hand sanitizer, and searching for masks.

In 2017, a friend gave me some sourdough starter to make bread with, and ever since then, my life has changed. It sounds cheesy, but I discovered a hobby that has led me to buy almost 200 pounds of flour at a time (seriously), develop a biweekly pizza baking habit, and dream of what bread I'm going to make in the coming days! Because I spend a lot of time baking sourdough and experimenting with new formulas, I wanted to see if I could create an artificial intelligence-powered recipe generator that would predict something for me to make! One of my go-to websites for technique, tips and tricks has been the helpful bread baking forum, The Fresh Loaf, where people ask questions and post recipes. My idea was to scrape this website and get data to train a neural network to generate new bread recipes -- and that's what I did.