Results 81 to 90 of about 134,347 (253)

Forecasting the Dialysis Burden in Japan: Validation‐Based Projections of Prevalence and Incidence Through 2050

Therapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Background Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a sustained post‐peak phase or whether recent declines partly reflect pandemic‐related disruptions remains uncertain.
Hatice Şahin, Berrak Itır Aylı, Nüket Paksoy   +2 more
wiley   +1 more source

CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy

BMC Cancer
Cervical cancer is a significant global health issue affecting women worldwide, necessitating prompt detection and effective management. According to the World Health Organization (WHO), approximately 660,000 new cases of cervical cancer and 350,000 ...
Zahra Taghados, Zohreh Azimifar, Malihezaman Monsefi, Mojgan Akbarzadeh Jahromi   +3 more
doaj   +1 more source

Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery

IEEE Open Journal of Signal Processing
We introduce Dagma-DCE, an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery use opaque proxies of “independence” to justify the inclusion
Daniel Waxman, Kurt Butler, Petar M. Djuric   +2 more
doaj   +1 more source

Organoids in pediatric cancer research

FEBS Letters, EarlyView.
Organoid technology has revolutionized cancer research, yet its application in pediatric oncology remains limited. Recent advances have enabled the development of pediatric tumor organoids, offering new insights into disease biology, treatment response, and interactions with the tumor microenvironment.
Carla Ríos Arceo, Jarno Drost
wiley   +1 more source

Large Causal Models for Temporal Causal Discovery

CoRR
Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for each individual dataset. Such an approach limits the potential of multi-dataset pretraining. The concept of large causal models (LCMs) envisions a class of pre-trained neural architectures specifically ...
Nikolaos Kougioulis, Nikolaos Gkorgkolis, MingXue Wang, Bora Caglayan, Dario Simionato, Andrea Tonon, Ioannis Tsamardinos   +6 more
openaire   +2 more sources

Reciprocal control of viral infection and phosphoinositide dynamics

FEBS Letters, EarlyView.
Phosphoinositides, although scarce, regulate key cellular processes, including membrane dynamics and signaling. Viruses exploit these lipids to support their entry, replication, assembly, and egress. The central role of phosphoinositides in infection highlights phosphoinositide metabolism as a promising antiviral target.
Marie Déborah Bancilhon, Bruno Mesmin
wiley   +1 more source

A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections – CORRIGENDUM

Environmental Data Science, 2023
Xavier-Andoni Tibau, Christian Reimers, Andreas Gerhardus, Joachim Denzler, Veronika Eyring, Jakob Runge   +5 more
doaj   +1 more source

Phosphatidylinositol 4‐kinase as a target of pathogens—friend or foe?

FEBS Letters, EarlyView.
This graphical summary illustrates the roles of phosphatidylinositol 4‐kinases (PI4Ks). PI4Ks regulate key cellular processes and can be hijacked by pathogens, such as viruses, bacteria and parasites, to support their intracellular replication. Their dual role as essential host enzymes and pathogen cofactors makes them promising drug targets.
Ana C. Mendes, Guilherme M. Azevedo, Amanda P. Barcellos, Diana Bahia   +3 more
wiley   +1 more source

Foundations of causal discovery on groups of variables

Journal of Causal Inference
Discovering causal relationships from observational data is a challenging task that relies on assumptions connecting statistical quantities to graphical or algebraic causal models.
Wahl Jonas, Ninad Urmi, Runge Jakob
doaj   +1 more source

Enhanced Causal Discovery for Autocorrelated Time Series via Adaptive Momentary Conditional Independence

Mathematics
Discovering causal relationships from time series data is essential for understanding complex dynamical systems across a range of domains. However, strong autocorrelation often limits the detection power of existing algorithms and increases the risk of ...
Minglong Gao, Yingchun Zhou
doaj   +1 more source