AIMC Topic: Humans

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[Ethical, legal and social requirements for assistive robots in healthcare : Viewpoint of management personnel in hospitals and nursing homes].

Zeitschrift fur Gerontologie und Geriatrie
BACKGROUND: In 2030 there will be 4 million people in need of care in Germany; however, the nursing care sector is already suffering from a shortage of skilled workers. Innovative approaches are thus required. One possible solution with high future p...

A Machine Learning-Assisted Nanoparticle-Printed Biochip for Real-Time Single Cancer Cell Analysis.

Advanced biosystems
Cancers are a complex conglomerate of heterogeneous cell populations with varying genotypes and phenotypes. The intercellular heterogeneity within the same tumor and intratumor heterogeneity within various tumors are the leading causes of resistance ...

Complete abdomen and pelvis segmentation using U-net variant architecture.

Medical physics
PURPOSE: Organ segmentation of computed tomography (CT) imaging is essential for radiotherapy treatment planning. Treatment planning requires segmentation not only of the affected tissue, but nearby healthy organs-at-risk, which is laborious and time...

A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition.

Sensors (Basel, Switzerland)
Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML ...

Deep neural networks detect suicide risk from textual facebook posts.

Scientific reports
Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56-0.58). In this study, Artificial Neural Network (ANN) models were constructed to pred...

Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance.

Scientific reports
Gene ontology (GO) is an eminent knowledge base frequently used for providing biological interpretations for the analysis of genes or gene sets from biological, medical and clinical problems. Unfortunately, the interpretation of such results is chall...

Sequence-to-function deep learning frameworks for engineered riboregulators.

Nature communications
While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules....

Reconstructing lost BOLD signal in individual participants using deep machine learning.

Nature communications
Signal loss in blood oxygen level-dependent (BOLD) functional neuroimaging is common and can lead to misinterpretation of findings. Here, we reconstructed compromised fMRI signal using deep machine learning. We trained a model to learn principles gov...

A deep learning approach to programmable RNA switches.

Nature communications
Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced ...