Results 241 to 250 of about 14,335,879 (294)

Paclitaxel induces NM2‐dependent cellular contraction through GEF‐H1 dissociation from microtubules and RhoA/ROCK activation in cancer cells

Molecular Oncology, EarlyView.
Taxanes are widely used chemotherapeutics whose effects on cellular mechanics remain poorly understood. We show that paclitaxel induces rapid cellular contraction by promoting GEF‐H1 dissociation from microtubules and non‐muscle myosin II activation through RhoA/ROCK.
Gloria Asensio‐Juárez, Rafael Pérez‐Díaz, Hugo Ramos‐Solano, Marina Garrido‐Casado, Vanessa C. Talayero, Miguel Vicente‐Manzanares   +5 more
wiley   +1 more source

Generalizing location-centric variations to enhance contactless human activity recognition. [PDF]

Front Comput Neurosci
Khan F, Yaseen Shah S, Ahmad J, Al Mazroa A, Zahid A, Ilyas M, Abbasi QH, Shah SA.   +7 more
europepmc   +1 more source

APOBEC3 activity and DNA polymerase‐ε deficiency are associated with distinct IDH1 R132 hotspot mutations

Molecular Oncology, EarlyView.
Isocitrate dehydrogenase 1 (IDH1) mutations are highly recurrent in multiple human cancer types, including cholangiocarcinoma and glioma. IDH1 R132C is the most common IDH1 mutation in cholangiocarcinoma and likely arises from APOBEC3A‐ or APOBEC3B‐mediated deamination.
Kelly E. Butler, Bilal A. Lone, Ecem Unal, A. Rouf Banday   +3 more
wiley   +1 more source

Implementing ensemble of deep learning model with optimization techniques for human activity recognition to assist individuals with disabilities. [PDF]

Sci Rep
Alqahtani H.
europepmc   +1 more source

p190A/ARHGAP35 and p190B/ARHGAP5 proteins in endometrial cancer: a novel cancer‐relevant paralog interplay

Molecular Oncology, EarlyView.
This study identifies ARHGAP5, in addition to the frequently mutated ARHGAP35, as significantly mutated in endometrial cancer. Mutations in both genes co‐occur and are associated with their correlated downregulation. Functional CRISPR studies show that both paralogs regulate similar pathways, including actin cytoskeleton organization.
Mathilde Pinault, Capucine Heraud, Mélissa Correia de Oliveira, Véronique Neaud, Valérie Valesco, Valérie Prouzet‐Mauleon, Béatrice Turcq, Jean‐William Dupuy, Anne‐Aurélie Raymond, Sabrina Croce, Frédéric Saltel, Valérie Lagrée, Violaine Moreau   +12 more
wiley   +1 more source

Enhancing human activity recognition with machine learning: insights from smartphone accelerometer and magnetometer data. [PDF]

PeerJ Comput Sci
Silva Zendron LA, Coelho PJ, Soares C, Pereira I, Pires IM.   +4 more
europepmc   +1 more source

A Comprehensive Methodological Survey of Human Activity Recognition Across Diverse Data Modalities. [PDF]

Sensors (Basel)
Shin J, Hassan N, Miah ASM, Nishimura S.
europepmc   +1 more source

Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach. [PDF]

Sensors (Basel)
Guo P, Nakayama M.
europepmc   +1 more source

Ensemble transfer learning for classifying physical examinations in GP consultation: a multi-model approach to human-object and human-to-human activity recognition. [PDF]

J Am Med Inform Assoc
Waheed M, Xiong H, Tong K, Lau AYS.
europepmc   +1 more source

Quantifying the uncertainty of human activity recognition using a Bayesian machine learning method: a prediction study. [PDF]

Phys Act Nutr
Mamiya H, Fuller D.
europepmc   +1 more source