Respiratory viral infections pose a global health burden, yet the cellular immune responses driving protection or pathology remain unclear. Natural infection cohorts often lack pre-exposure baseline data and structured temporal sampling. In contrast, inoculation and vaccination trials generate insightful longitudinal transcriptomic data. However, the scattering of these datasets across platforms, along with inconsistent metadata and preprocessing procedure, hinders AI-driven discovery. To address these challenges, we developed the Human Respiratory Viral Immunization LongitudinAl Gene Expression (HR-VILAGE-3K3M) repository: an AI-ready, rigorously curated dataset that integrates 14,136 RNA-seq profiles from 3,178 subjects across 66 studies encompassing over 2.56 million cells. Spanning vaccination, inoculation, and mixed exposures, the dataset includes microarray, bulk RNA-seq, and single-cell RNA-seq from whole blood, PBMCs, and nasal swabs, sourced from GEO, ImmPort, and ArrayExpress. We harmonized subject-level metadata, standardized outcome measures, applied unified preprocessing pipelines with rigorous quality control, and aligned all data to official gene symbols. To demonstrate the utility of HR-VILAGE-3K3M, we performed predictive modeling of vaccine responders and evaluated batch-effect correction methods. Beyond these initial demonstrations, it supports diverse systems immunology applications and benchmarking of feature selection and transfer learning algorithms. Its scale and heterogeneity also make it ideal for pretraining foundation models of the human immune response and for advancing multimodal learning frameworks. As the largest longitudinal transcriptomic resource for human respiratory viral immunization, it provides an accessible platform for reproducible AI-driven research, accelerating systems immunology and vaccine development against emerging viral threats.

Scientists at University of California San Diego School of Medicine have developed an artificial intelligence (AI)-based strategy for discovering high-affinity antibody drugs. In the study, published January 28, 2023 in Nature Communications, researchers used the approach to identify a new antibody that binds a major cancer target 17-fold tighter than an existing antibody drug. The authors say the pipeline could accelerate the discovery of novel drugs against cancer and other diseases such as COVID-19 and rheumatoid arthritis. In order to be a successful drug, an antibody has to bind tightly to its target. To find such antibodies, researchers typically start with a known antibody amino acid sequence and use bacterial or yeast cells to produce a series of new antibodies with variations of that sequence.

But however revolutionary their deaths might be for the universe of "The Last of Us," they still fall into well-worn gay death tropes. It seems that Bill is older than Frank, but Frank succumbs to an unspecified illness and ends up infirm, which ultimately prompts his suicide. If you grew up queer in the 80s and 90s, the image of one gay man pushing another in a wheelchair might look fiercely familiar from the early days of the AIDS crisis and the storytelling that came out of it. Many cis gay men of my generation believed this kind of death was inevitable, that they would die tended to by a lover or they would be the widower left behind. Bill rebels against this trope by dying alongside Frank, but as I watched (and cried) as Bill wheeled Frank around their house and handed him his pills, I thought of how many times I had seen this scene in other movies and television. I wondered why the show's creators chose to have Frank sicken to lead to Bill and Frank's deaths when one or both of their ages could have been the inciting factor.

An artificial intelligence can detect if YouTubers are infected with the omicron coronavirus variant with up to 80 per cent accuracy. Although vocal changes aren't considered a key symptom of any coronavirus infection, the researchers behind the AI argue their results suggest a subtle "Omicron-specific laryngitis".

Abstract: In this note, we propose empirical Bayes methods under heteroskedastic Gaussian location models, without assuming that the unknown location parameters are independent from the known scale parameters. We derive the finite-sample convergence rate of the mean-squared error regret of our method. We also derive a minimax regret lower bound that matches the upper bound up to logarithmic factors. Moreover, we link decision objectives of other economic problems to mean-squared error control. We illustrate our method with a simulation calibrated to the Opportunity Atlas (Chetty, Friedman, Hendren, Jones and Porter, 2018) and Creating Moves to Opportunity (Bergman, Chetty, DeLuca, Hendren, Katz and Palmer, 2019).

BioNTech has announced a planned acquisition of London-based InstaDeep in a deal that will see £362 million in cash and BioNTech shares, excluding the shares already owned by BioNTech with InstaDeep shareholders eligible to receive additional performance-based future milestone payments up to approximately £200 million. The move will allow the German biopharma company to rapidly incorporate a number of Instadeep's validated and novel BioNTech-trained AI- and ML-based models across BioNTech's discovery platforms and connected, through InstaDeep's DeepChain platform, to an integrated automated lab infrastructure. Ultimately, this means that BioNTech, which was focusing on cancer treatments prior to shooting to stardom through its partnership with pfizer, developing the omnipresent COVID-19 vaccine, can now develop more, faster, and perhaps even better than ever before. The acquisition is more of a formality at this point, as the two companies have a track record that extends back nearly four years now. In November 2020, the companies announced a collaboration and joint AI Innovation Lab that has been aimed at applying the latest advances in AI and ML technology to develop novel medicines for a range of cancers and infectious diseases.

Artificial intelligence (AI) is no longer just a buzzword. It is a rapidly growing industry that is changing the way we live and work. From developing COVID vaccines at unprecedented speeds to powering autonomous vehicles and understanding climate change, AI is at the heart of humanity's most transformative innovations. But, with any technological advancement comes concerns about the impact on the workforce. Will machines and robots replace jobs, leaving human workers unemployed?

Sia Partners is a next-generation consulting firm dedicated to creating state-of-the-art narratives for transformation and innovation and deploying them at scale. Our goal is to deliver superior value and tangible results to our clients as they navigate the digital revolution and achieve transformations which generate a positive impact. Our global footprint and expertise in more than 40 sectors and services allow us to enhance our clients' businesses worldwide. These are the six core values that guide all our actions. As an expression of our values, our Sia Village concept describes our commitment to fostering a sense of community within and among our offices.

IT Reseller spoke with leading analysts and vendors about current developments within the manufacturing technology space and what future innovations might emerge over the next few years. The world of manufacturing technology is changing, and digital is certainly the watchword. As Rowan Litter, research analyst, enterprise mobility, VDC Research, points out, the primary development within the manufacturing technology space is Digital Transformation. This, he explains, can be as simple as upgrading from outdated/legacy systems or pen-and-paper or enabling an entire smart factory with automation, and machine learning/AI capabilities. "As these technologies become tested and proven, manufacturers are realising that correctly incorporating these innovations will lead to increased operational efficiency and greater production to meet rises in consumer demand," he says. Litter believes a very important piece that needs to be talked about is the enabler of these innovations and technology. "That enabler comes from connectivity and networks; what will allow businesses to adopt and connect more technologies, process data faster and provide the best security from a rise in cybersecurity threats, as well as the overall risks with digitalisation," he says. "Private Wireless Networks have emerged as the enabler for manufacturers who are interested in implementing these new technologies. A challenge for many organisations comes from not knowing what to prioritise and where to start. With labour critical to support operations in many of these environments and organisations challenged with optimising workflows, we find that enabling the mobile worker with digital tools is the optimal jumping off point." In terms of drivers for change, Litter maintains that the impacts of COVID-19 highlighted inefficiencies in the manufacturing sector.

Chatbots are fast emerging at the forefront of user engagement across industries. In 2021, healthcare is undoubtedly being touted as one of the most important industries due to the noticeable surge in demand amid the pandemic and its subsequent waves. The Global Healthcare Chatbots Market is expected to exceed over US$ 314.63 Million by 2024 at a CAGR of 20.58%. Chatbots are being seen as those with high potential to revolutionize healthcare. They act as the perfect support system to agents on the floor by providing the first-step resolution to the customer, in terms of understanding intent and need, boost efficiency, and also improve the accuracy of symptom detection and ailment identification, preventive care, feedback procedures, claim filing and processing and more.