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This manuscript evaluates forecasts of laboratory-confirmed influenza hospital admissions, a new target for influenza forecasting in the United States. Across two influenza seasons, the FluSight ensemble is robust compared to submitted models.

Divangahi and colleagues report that β-glucan pretreatment before influenza A virus challenge can increase survival by inducing long-term reprogramming of neutrophils, promoting disease tolerance irrespective of viral load.

Boolean networks allow a simplified representation of interactions. Here, the authors systematically analyze regulation in dozens of biological Boolean networks, finding mathematical regularities that suggest biological systems could be controlled through a relatively small number of components.

Alzheimer’s disease is heterogeneous in its neuroimaging and clinical phenotypes. Here the authors present a semi-supervised deep learning method, Smile-GAN, to show four neurodegenerative patterns and two progression pathways providing prognostic and clinical information.

In this study the authors identify a possible link between the gene FAM222A and brain atrophy. The protein it encodes is found to accumulate in plaques seen in Alzheimer’s disease, and functional analysis suggests it interacts with amyloid-beta.

Systematic characterization of longitudinal tau variability in human Alzheimer’s disease using an unbiased subtyping algorithm reveals four trajectories of tau deposition with distinct clinical features.

Sun et al. report human lifespan changes in the brain’s functional connectome in 33,250 individuals, which highlights critical growth milestones and distinct maturation patterns and offers a normative reference for development, aging and diseases.

Late-onset Alzheimer's disease (LOAD) is a complex multi-factorial disorder. Here, the authors perform a data-driven analysis of LOAD progression, including multimodal brain imaging, plasma and CSF biomarkers, and find vascular dysfunction is among the earliest and strongest altered events.

This study finds that sST2 is a disease-causing factor for Alzheimer’s disease. Higher sST2 levels impair microglial Aβ clearance in APOE4⁺ female individuals. A genetic variant, rs1921622, is associated with a reduction in sST2 level and protects against AD in APOE4⁺ female individuals.

In Alzheimer’s disease (AD) tau and neurodegeneration have complex regional relationships. Here, the authors show neuronal hypometabolism discordant with tau burden defines functional resilience or susceptibility to Alzheimer’s pathology via limbic/cortical axes. Susceptible groups have faster cognitive decline and evidence of non-Alzheimer’s pathologies.

The interplay between amyloid and tau pathology in Alzheimer’s disease is still not well understood. Here, the authors show that amyloid-related increased in soluble p-tau is related to subsequent accumulation of tau aggregates and cognitive decline in early stage of the disease.

Alzheimer’s disease has been associated with increased structural brain aging. Here the authors describe a model that predicts brain aging from resting state functional connectivity data, and demonstrate this is accelerated in individuals with pre-clinical familial Alzheimer’s disease.

Lakes are essential components of the hydrological and biogeochemical cycles. Here, Pi et al develop a global lake dataset called GLAKES via high-resolution satellite images and deep learning to examine global lake changes over four decades.

Progressive diseases tend to be heterogeneous in their underlying aetiology mechanism, disease manifestation, and disease time course. Here, Young and colleagues devise a computational method to account for both phenotypic heterogeneity and temporal heterogeneity, and demonstrate it using two neurodegenerative disease cohorts.

The KL-VS haplotype of the Klotho gene has been associated with reduced risk of Alzheimer’s disease and dementia. Here the authors show an association between the KL-VS haplotype and amyloid-dependent tau accumulation using PET data.

Tau accumulation is associated with disease progression in Alzheimer’s disease. Here the authors use resting state fMRI and tau-PET to demonstrate that baseline connectivity in Alzheimer's disease is associated with tau spreading.

The BIN1 SNP rs744373 is associated with higher CSF tau and phosphorylated tau levels. Here the authors show, using PET imaging, that this SNP is associated with tau accumulation in the brain as well as impaired memory in older individuals without dementia.

Whether Alzheimer’s disease originates in basal forebrain or entorhinal cortex remains highly debated. Here the authors use structural magnetic resonance data from a longitudinal sample of participants stratified by cerebrospinal biomarker and clinical diagnosis to show that tissue volume changes appear earlier in the basal forebrain than in the entorhinal cortex.

Brain-iron elevation is implicated in Alzheimer’s disease (AD), but the impact of the metal on disease outcomes has not been analysed in a longitudinal study. Here, the authors examine the association between the levels of ferritin, an iron storage protein, in the cerebrospinal fluid (CSF) of AD patients and show that CSF ferritin levels predict AD outcomes.

The tau protein is theorized to spread transneuronally in Alzheimers disease, though this theory remains unproven in humans. Our simulations of epidemic-like protein spreading across human brain networks support this theory, and suggest the spreading dynamics are modified by β-amyloid

Several studies show that APOE-ε4 coding variants are associated with Alzheimer’s disease (AD) risk. Here, Zhou et al. perform fine-mapping of the APOE region and find AD risk haplotypes with non-coding variants in the PVRL2 and APOC1 regions that are associated with relevant endophenotypes.

The diurnal rhythm of glucocorticoid is shown to be a master regulator of the bladder clock in mice.

Zhou et al. utilise deep learning to improve polygenic risk analysis for Alzheimer’s disease. Their computational approach outperforms existing statistical methods and helps to identify potential biological mechanisms of Alzheimer’s disease risk.