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As the Chosen, Tormod knows he'll have all the Gifts heaven and earth can bestow, but he still struggles to gain control of his visions and powers as men of the French King seek the relics he's bound to protect.

LinkedIn has been slapped with three digital privacy class actions contending that it illegally intercepted users' sensitive health care information to use in targeted advertising.

The lawsuit accuses Google of abusing its \"monopoly power\" to exert unchecked control over the \"quantity of ad inventory.\"

Apple has alleged gender-based pay discrimination, biases against women in its performance evaluation system, and maintaining a hostile work environment.

We introduce T5Gemma 2, the next generation of the T5Gemma family of lightweight open encoder-decoder models, featuring strong multilingual, multimodal and long-context capabilities. T5Gemma 2 follows the adaptation recipe (via UL2) in T5Gemma -- adapting a pretrained decoder-only model into an encoder-decoder model, and extends it from text-only regime to multimodal based on the Gemma 3 models. We further propose two methods to improve the efficiency: tied word embedding that shares all embeddings across encoder and decoder, and merged attention that unifies decoder self- and cross-attention into a single joint module. Experiments demonstrate the generality of the adaptation strategy over architectures and modalities as well as the unique strength of the encoder-decoder architecture on long context modeling. Similar to T5Gemma, T5Gemma 2 yields comparable or better pretraining performance and significantly improved post-training performance than its Gemma 3 counterpart. We release the pretrained models (270M-270M, 1B-1B and 4B-4B) to the community for future research.

We present TranslateGemma, a suite of open machine translation models based on the Gemma 3 foundation models. To enhance the inherent multilingual capabilities of Gemma 3 for the translation task, we employ a two-stage fine-tuning process. First, supervised fine-tuning is performed using a rich mixture of high-quality large-scale synthetic parallel data generated via state-of-the-art models and human-translated parallel data. This is followed by a reinforcement learning phase, where we optimize translation quality using an ensemble of reward models, including MetricX-QE and AutoMQM, targeting translation quality. We demonstrate the effectiveness of TranslateGemma with human evaluation on the WMT25 test set across 10 language pairs and with automatic evaluation on the WMT24++ benchmark across 55 language pairs. Automatic metrics show consistent and substantial gains over the baseline Gemma 3 models across all sizes. Notably, smaller TranslateGemma models often achieve performance comparable to larger baseline models, offering improved efficiency. We also show that TranslateGemma models retain strong multimodal capabilities, with enhanced performance on the Vistra image translation benchmark. The release of the open TranslateGemma models aims to provide the research community with powerful and adaptable tools for machine translation.

We introduce MedGemma 1.5 4B, the latest model in the MedGemma collection. MedGemma 1.5 expands on MedGemma 1 by integrating additional capabilities: high-dimensional medical imaging (CT/MRI volumes and histopathology whole slide images), anatomical localization via bounding boxes, multi-timepoint chest X-ray analysis, and improved medical document understanding (lab reports, electronic health records). We detail the innovations required to enable these modalities within a single architecture, including new training data, long-context 3D volume slicing, and whole-slide pathology sampling. Compared to MedGemma 1 4B, MedGemma 1.5 4B demonstrates significant gains in these new areas, improving 3D MRI condition classification accuracy by 11% and 3D CT condition classification by 3% (absolute improvements). In whole slide pathology imaging, MedGemma 1.5 4B achieves a 47% macro F1 gain. Additionally, it improves anatomical localization with a 35% increase in Intersection over Union on chest X-rays and achieves a 4% macro accuracy for longitudinal (multi-timepoint) chest x-ray analysis. Beyond its improved multimodal performance over MedGemma 1, MedGemma 1.5 improves on text-based clinical knowledge and reasoning, improving by 5% on MedQA accuracy and 22% on EHRQA accuracy. It also achieves an average of 18% macro F1 on 4 different lab report information extraction datasets (EHR Datasets 2, 3, 4, and Mendeley Clinical Laboratory Test Reports). Taken together, MedGemma 1.5 serves as a robust, open resource for the community, designed as an improved foundation on which developers can create the next generation of medical AI systems. Resources and tutorials for building upon MedGemma 1.5 can be found at https://goo.gle/MedGemma.

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Background Social inequities are widening globally, contributing to growing health and health care inequities. Health inequities are unjust differences in health and well-being between and within groups of people caused by socially structured, and thus avoidable, marginalizing conditions such as poverty and systemic racism. In Canada, such conditions disproportionately affect Indigenous persons, racialized newcomers, those with mental health and substance use issues, and those experiencing interpersonal violence. Despite calls to enhance equity in health care to contribute to improving population health, few studies examine how to achieve equity at the point of care, and the impacts of doing so. Many people facing marginalizing conditions experience inadequate and inequitable treatment in emergency departments (EDs), which makes people less likely to access care, paradoxically resulting in reliance on EDs through delays to care and repeat visits, interfering with effective care delivery and increasing human and financial costs. EDs are key settings with potential for mitigating the impacts of structural conditions and barriers to care linked to health inequities. Methods EQUIP is an organizational intervention to promote equity. Building on promising research in primary health care, we are adapting EQUIP to emergency departments, and testing its impact at three geographically and demographically diverse EDs in one Canadian province. A mixed methods multisite design will examine changes in key outcomes including: a) a longitudinal analysis of change over time based on structured assessments of patients and staff, b) an interrupted time series design of administrative data (i.e., staff sick leave, patients who leave without care being completed), c) a process evaluation to assess how the intervention was implemented and the contextual features of the environment and process that are influential for successful implementation, and d) a cost-benefit analysis. Discussion This project will generate both process- and outcome-based evidence to improve the provision of equity-oriented health care in emergency departments, particularly targeting groups known to be at greatest risk for experiencing the negative impacts of health and health care inequities. The main deliverable is a health equity-enhancing framework, including implementable, measurable interventions, tested, refined and relevant to diverse EDs. Trial registration Clinical Trials.gov # NCT03369678 (registration date November 18, 2017).

The mechanical properties of dietary items are known to influence skull morphology, either through evolution or by phenotypic plasticity. Here, we investigated the impact of supplementary feeding of peanuts on the morphology of red squirrels (Sciurus vulgaris) from five populations in Britain (North Scotland, Borders, Jersey and two temporally distinct populations from Formby (Merseyside)). Stable isotope analysis confirmed dietary ecology in 58 specimens. Geometric morphometrics were used to analyse three-dimensional and two-dimensional shape variation across 113 crania and 388 mandibles, respectively. Nitrogen isotope ratios (δ15N) were lower in the 1990s and 2010s Formby squirrels (suggesting a diet with an increased proportion of peanuts), and higher in other populations. Significant differences in cranio-mandibular shape were found between all populations, with 1990s Formby red squirrels exhibiting a morphology associated with reduced masticatory efficiency. This effect was partially reversed following a reduction in supplementary feeding of peanuts. We propose that these morphological changes are related to the reduced mechanical effort needed to process peanuts relative to naturally occurring food items. This could be an example of diet-induced plastic changes to the skeleton in non-muroid wild mammals, although further research is needed to exclude other driving factors such as genetics.