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In open ocean the accuracy of tide models is considerably higher than in shallow-water, coastal and polar regions. This is due to physical factors, such as a complex dynamic conditions and compound tidal regimes, or data limitations like low resolution of bathymetry models and poor-quality (or quantity) altimetric data.The DGFI-TUM is working on an updated version of the Empirical Ocean Tide (EOT) model whose focus is to improve the accuracy of tidal estimation at the coast. EOT is a gridded, semi-empirical model that exploits altimetric observations to compute residual tidal harmonic constants from sea level anomaly values. The model is based on multi-mission data that include circa 26 years of observations, from 1992 to 2018.In this work we compare global state-of-the-art tide models to identify the regions with most critical differences and errors, aiming to characterize the uncertainties of the single models or model types (empirical, assimilation, hydrodynamic models). The model differences are evaluated through root-mean-square and absolute-median errors against in-situ observations, as well as pairwise differences and standard deviations for single constituents. Only constituents included in at least three models are considered.In this analysis a preliminary version of the Empirical Ocean Tide (EOT19p) model is also included. The development of EOT is focused on improving tidal estimations in the aforementioned critical areas, together with the delivery of realistic and standard error estimates in view of its exploitation for gravimetry applications. The assessment of EOT will be considered for further improving the model, and for the development of a global version. [ABSTRACT FROM AUTHOR]

In oncology, comprehensive omics and functional enrichment studies have led to an extensive profiling of (epi)genetic and neurobiological alterations that can be mapped onto a single tumor's clinical phenotype and divergent clinical phenotypes expressing common pathophysiological pathways. Consequently, molecular pathway-based therapeutic interventions for different cancer typologies, namely tumor type- and site-agnostic treatments, have been developed, encouraging the real-world implementation of a paradigm shift in medicine. Given the breakthrough nature of the new-generation translational research and drug development in oncology, there is an increasing rationale to transfertilize this blueprint to other medical fields, including psychiatry and neurology. In order to illustrate the emerging paradigm shift in neuroscience, we provide a state-of-the-art review of translational studies on the β-site amyloid precursor protein cleaving enzyme (BACE) and its most studied downstream effector, neuregulin, which are molecular orchestrators of distinct biological pathways involved in several neurological and psychiatric diseases. This body of data aligns with the evidence of a shared genetic/biological architecture among Alzheimer's disease, schizoaffective disorder, and autism spectrum disorders. To facilitate a forward-looking discussion about a potential first step towards the adoption of biological pathway-based, clinical symptom-agnostic, categorization models in clinical neurology and psychiatry for precision medicine solutions, we engage in a speculative intellectual exercise gravitating around BACE-related science, which is used as a paradigmatic case here. We draw a perspective whereby pathway-based therapeutic strategies could be catalyzed by highthroughput techniques embedded in systems-scaled biology, neuroscience, and pharmacology approaches that will help overcome the constraints of traditional descriptive clinical symptom and syndrome-focused constructs in neurology and psychiatry., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)