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Intelligent shipping: integrating autonomous maneuvering and maritime knowledge in the Singapore-Rotterdam Corridor

Designing safe and reliable routes is the core of intelligent shipping. However, existing methods for industrial use are inadequate, primarily due to the lack of considering company preferences and ship maneuvering characteristics. To address these challenges, here we introduce a methodological framework that integrates maritime knowledge and autonomous maneuvering model. Based on historical maritime big data, the framework offers customized routes for companies with specific routing preferences. The autonomous maneuvering model then evaluates the safety and reliability of the routes by considering ship motion characteristics and ocean hydrodynamics. We validate its effectiveness on the world’s longest Green and Digital Shipping Corridor between Singapore and Rotterdam. Results demonstrate that our model can provide customized route design for companies and enhance safety for shipping. The framework could serve as a fundamental structure to build a fully digitalized platform for route customization and evaluation for global shipping, optimizing operational decision-making and safety assurance.

Artificial intelligence in clinical genetics

Artificial intelligence (AI) has been growing more powerful and accessible, and will increasingly impact many areas, including virtually all aspects of medicine and biomedical research. This review focuses on previous, current, and especially emerging applications of AI in clinical genetics. Topics covered include a brief explanation of different general categories of AI, including machine learning, deep learning, and generative AI. After introductory explanations and examples, the review discusses AI in clinical genetics in three main categories: clinical diagnostics; management and therapeutics; clinical support. The review concludes with short, medium, and long-term predictions about the ways that AI may affect the field of clinical genetics. Overall, while the precise speed at which AI will continue to change clinical genetics is unclear, as are the overall ramifications for patients, families, clinicians, researchers, and others, it is likely that AI will result in dramatic evolution in clinical genetics. It will be important for all those involved in clinical genetics to prepare accordingly in order to minimize the risks and maximize benefits related to the use of AI in the field.

Human neural dynamics of real-world and imagined navigation

The ability to form episodic memories and later imagine them is integral to the human experience, influencing our recollection of the past and envisioning of the future. While rodent studies suggest the medial temporal lobe, especially the hippocampus, is involved in these functions, its role in human imagination remains uncertain. In human participants, imaginations can be explicitly instructed and reported. Here we investigate hippocampal theta oscillations during real-world and imagined navigation using motion capture and intracranial electroencephalographic recordings from individuals with chronically implanted medial temporal lobe electrodes. Our results revealed intermittent theta dynamics, particularly within the hippocampus, encoding spatial information and partitioning navigational routes into linear segments during real-world navigation. During imagined navigation, theta dynamics exhibited similar patterns despite the absence of external cues. A statistical model successfully reconstructed real-world and imagined positions, providing insights into the neural mechanisms underlying human navigation and imagination, with implications for understanding memory in real-world settings.

Diverse real-life outcomes after intensive risk-adapted therapy for 1034 AML patients from the CETLAM Group

Given the heterogeneity of acute myeloid leukemia patients, it is necessary to identify patients considered fit for intensive therapy but who will perform poorly, and in whom alternative approaches deserve investigation. We analyzed 1034 fit adults ≤70 years intensively treated between 2012 and 2022 in the CETLAM group. Young adults ( ≤ 60 years) presented higher remission rates and improved survival than older adults above that age (CR 79% vs. 73%; p = 0.03 and 4-yr OS 53% vs. 33%; p < 0.001). Remission and survival outcomes varied among different genetic subsets. An especially adverse genetic group included complex, monosomal karyotype, TP53 alterations (deleted/mutated), and MECOMr. Transplant feasibility in this very adverse risk group was low, and OS and EFS at 4 years were 14% and 12%, in contrast to 70% and 57% in the favorable group and 38% and 32% in all other patients. We integrated clinical and genetic data into the Intensive Chemotherapy Score for AML (ICSA) with 6-risk categories with significantly different remission rates and OS, validated in another cohort of 581 AML patients from a previous CETLAM protocol. In summary, we identified groups of fit patients that benefit differently from an intensive approach which may be helpful in future treatment decisions.

Emotions and individual differences shape human foraging under threat

A common behavior in natural environments is foraging for rewards. However, this is often in the presence of predators. Therefore, one of the most fundamental decisions for humans, as for other animals, is how to apportion time between reward-motivated pursuit behavior and threat-motivated checking behavior. To understand what affects how people strike this balance, we developed an ecologically inspired task and looked at both within-participant dynamics (moods) and between-participant individual differences (questionnaires about real-life behaviors) in two large internet samples (n = 374 and n = 702) in a cross-sectional design. For the within-participant dynamics, we found that people regulate task-evoked stress homeostatically by changing behavior (increasing foraging and hiding). Individual differences, even in superficially related traits (apathy–anhedonia and anxiety–compulsive checking) reliably mapped onto unique behaviors. Worse task performance, due to maladaptive checking, was linked to gender (women checked excessively) and specific anxiety-related traits: somatic anxiety (reduced self-reported checking due to worry) and compulsivity (self-reported disorganized checking). While anhedonia decreased self-reported task engagement, apathy, strikingly, improved overall task performance by reducing excessive checking. In summary, we provide a multifaceted paradigm for assessment of checking for threat in a naturalistic task that is sensitive to both moods as they change throughout the task and clinical dimensions. Thus, it could serve as an objective measurement tool for future clinical studies interested in threat, vigilance or behavior–emotion interactions in contexts requiring both reward seeking and threat avoidance.

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