A new book explores how humans evolved to be wired for intimacy. It can save our lives. Intimate relationships provide stability, safety, and reassurance, especially when we are in pain. Breakthroughs, discoveries, and DIY tips sent every weekday. Adapted from THE INTIMATE ANIMAL by Justin Garcia, PhD. Used with permission of Little, Brown Spark, an imprint of Little, Brown and Company. Jen and Dave's second child was born in November 2002. Two weeks later, on a cold Thursday night, the phone rang.

Animal filial cannibalism has been documented in fish, insects, even domestic pets. Scientists still don't fully understand why some animals eat their own offspring. Breakthroughs, discoveries, and DIY tips sent every weekday. "In general, cannibalism of offspring is super widespread," says Aneesh Bose, a behavioral ecologist at the Swedish University of Agricultural Sciences in Uppsala, Sweden. Bose has long studied the phenomenon of animals who turn from child-rearing to child-eating, and in 2022, he authored a review of prior research on the topic .

Cancer treatment outcomes are influenced not only by clinical and demographic factors but also by the collaboration of healthcare teams. However, prior work has largely overlooked the potential role of human collaboration in shaping patient survival. This paper presents an applied AI approach to uncovering the impact of healthcare professionals' (HCPs) collaboration--captured through electronic health record (EHR) systems--on cancer patient outcomes. We model EHR-mediated HCP interactions as networks and apply machine learning techniques to detect predictive signals of patient survival embedded in these collaborations. Our models are cross validated to ensure generalizability, and we explain the predictions by identifying key network traits associated with improved outcomes. Importantly, clinical experts and literature validate the relevance of the identified crucial collaboration traits, reinforcing their potential for real-world applications. This work contributes to a practical workflow for leveraging digital traces of collaboration and AI to assess and improve team-based healthcare. The approach is potentially transferable to other domains involving complex collaboration and offers actionable insights to support data-informed interventions in healthcare delivery.

Clinical and genomic models are both used to predict breast cancer outcomes, but they are often combined using simple linear rules that do not account for how their risk scores relate, especially at the extremes. Using the METABRIC breast cancer cohort, we studied whether directly modeling the joint relationship between clinical and genomic machine learning risk scores could improve risk stratification for 5-year cancer-specific mortality. We created a binary 5-year cancer-death outcome and defined two sets of predictors: a clinical set (demographic, tumor, and treatment variables) and a genomic set (gene-expression $z$-scores). We trained several supervised classifiers, such as Random Forest and XGBoost, and used 5-fold cross-validated predicted probabilities as unbiased risk scores. These scores were converted to pseudo-observations on $(0,1)^2$ to fit Gaussian, Clayton, and Gumbel copulas. Clinical models showed good discrimination (AUC 0.783), while genomic models had moderate performance (AUC 0.681). The joint distribution was best captured by a Gaussian copula (bootstrap $p=0.997$), which suggests a symmetric, moderately strong positive relationship. When we grouped patients based on this relationship, Kaplan-Meier curves showed clear differences: patients who were high-risk in both clinical and genomic scores had much poorer survival than those high-risk in only one set. These results show that copula-based fusion works in real-world cohorts and that considering dependencies between scores can better identify patient subgroups with the worst prognosis.

We develop a Bayesian model for decision-making under time p ressure with endogenous information acquisition. In our model, the decisi on-maker decides when to observe (costly) information by sampling an underlying c ontinuous-time stochastic process (time series) that conveys informa tion about the potential occurrence/non-occurrence of an adverse event which will t erminate the decision-making process. In her attempt to predict the occurrence of t he adverse event, the decision-maker follows a policy that determines when to acquire information from the time series (continuation), and when to stop acquiring information and make a final prediction (stopping). We show that the optimal polic y has a " rendezvous" structure, i.e. a structure in which whenever a new informat ion sample is gathered from the time series, the optimal "date" for acquiring the ne xt sample becomes computable. The optimal interval between two information s amples balances a trade-off between the decision maker's "surprise", i.e. th e drift in her posterior belief after observing new information, and "suspense", i. e. the probability that the adverse event occurs in the time interval between two inf ormation samples. Moreover, we characterize the continuation and stopping re gions in the decision-maker's state-space, and show that they depend not only on th e decision-maker's beliefs, but also on the "context", i.e. the current realiza tion of the time series.

Whole-genome sequencing (WGS) has revealed numerous non-coding short variants whose functional impacts remain poorly understood. Despite recent advances in deep-learning genomic approaches, accurately predicting and prioritizing clinically relevant mutations in gene regulatory regions remains a major challenge. Here we introduce Deep VRegulome, a deep-learning method for prediction and interpretation of functionally disruptive variants in the human regulome, which combines 700 DNABERT fine-tuned models, trained on vast amounts of ENCODE gene regulatory regions, with variant scoring, motif analysis, attention-based visualization, and survival analysis. We showcase its application on TCGA glioblastoma WGS dataset in prioritizing survival-associated mutations and regulatory regions. The analysis identified 572 splice-disrupting and 9,837 transcription-factor binding site altering mutations occurring in greater than 10% of glioblastoma samples. Survival analysis linked 1352 mutations and 563 disrupted regulatory regions to patient outcomes, enabling stratification via non-coding mutation signatures. All the code, fine-tuned models, and an interactive data portal are publicly available.

Glioblastoma (GBM) is an aggressive primary brain tumor with a median survival of approximately 15 months. In clinical practice, the Stupp protocol serves as the standard first-line treatment. However, patients exhibit highly heterogeneous therapeutic responses which required at least two months before first visual impact can be observed, typically with MRI. Early prediction treatment response is crucial for advancing personalized medicine. Disease Progression Modeling (DPM) aims to capture the trajectory of disease evolution, while Treatment Response Prediction (TRP) focuses on assessing the impact of therapeutic interventions. Whereas most TRP approaches primarly rely on time-series data, we consider the problem of early visual TRP as a slice-to-slice translation model generating post-treatment MRI from a pre-treatment MRI, thus reflecting the tumor evolution. To address this problem we propose a Latent Diffusion Model with a concatenation-based conditioning from the pre-treatment MRI and the tumor localization, and a classifier-free guidance to enhance generation quality using survival information, in particular post-treatment tumor evolution. Our model were trained and tested on a local dataset consisting of 140 GBM patients collected at Centre François Baclesse. For each patient we collected pre and post T1-Gd MRI, tumor localization manually delineated in the pre-treatment MRI by medical experts, and survival information.

The rapid growth of digital pathology and advances in self-supervised deep learning have enabled the development of foundational models for various pathology tasks across diverse diseases. While multimodal approaches integrating diverse data sources have emerged, a critical gap remains in the comprehensive integration of whole-slide images (WSIs) with spatial tran-scriptomics (ST), which is crucial for capturing critical molecular heterogeneity beyond standard hematoxylin & eosin (H&E) staining. We introduce SPADE, a foundation model that integrates histopathology with ST data to guide image representation learning within a unified framework, in effect creating an ST-informed latent space. These authors contributed equally to this work. Pre-trained on the comprehensive HEST-1k dataset, SPADE is evaluated on 20 downstream tasks, demonstrating significantly superior few-shot performance compared to baseline models, highlighting the benefits of integrating morphological and molecular information into one latent space. Introduction High-resolution whole slide images (WSIs) have propelled the development of powerful deep learning foundation models in computational pathology, demonstrating robust performance across diverse tissue types and tasks [1, 2, 3, 4]. These models are typically trained using self-supervision, enabling learning from large unlabeled datasets and producing embeddings robust to institutional variations, including differences in staining procedures and other image-quality factors [5, 6, 7, 8]. By visually capturing cellular arrangement, WSIs enable the study of spatial organization and disorganization of cells in tissues, characterizations that are especially relevant in cancer research [9, 10]. In clinical settings, WSIs are commonly stained with hematoxylin & eosin (H&E), a two-color stain that highlights nuclei and cytoplasm but offers a limited view of molecular-level heterogeneity [11]. As tumor tissues are known to exhibit high variability within and across patients, deciphering the heterogeneity at the molecular level is critical for improving deep learning applications that can more precisely inform diagnosis, treatment, and prognosis [12, 13]. While H&E provides crucial morphological insights, its inability to capture molecular heterogeneity limits its utility in fully characterizing tissue complexity. Spatial transcriptomics addresses this gap by providing spatially resolved gene expression data, allowing for additional molecular context for a given tissue specimen. Although both ST and H&E data have independently proven useful in various applications, their combined potential for creating a more comprehensive representation learning framework remains unexplored. To this end, we introduce SPADE, a vision-ST foundation model that uses a mixture of experts, each trained via contrastive learning, to unify ST data and H&E images to produce slide representations that encompass both modalities.