Results 301 to 310 of about 3,816,755 (370)

Prospects and challenges of recombinant spider venom enzymes: insights from <i>Loxosceles</i> and <i>Phoneutria</i> venom protease expressions. [PDF]

Front Bioeng Biotechnol
Dresler J, Rodriguez I, Paas A, Cabrera-Orefice A, Vilcinskas A, Lüddecke T.   +5 more
europepmc   +1 more source

Unrolling of Syngonium podophyllum: Functional Anatomy, Morphology and Modelling of Its Peltate Leaves

Advanced Biology, EarlyView.
The unrolling of the peltate leaves in Syngonium podophyllum is analyzed and quantified (left‐hand side to center). These measurements serve to verify a mathematical model for leaf unrolling based on the model used in Schmidt (2007). An additional formula for obtaining a layer mismatch from a prescribed radius is derived.
Michelle Modert, Nick Seinsche, Sören Bartels, Tom Masselter, Thomas Speck   +4 more
wiley   +1 more source

Sustained response to larotrectinib in a pediatric patient with recurrent STRN3::NTRK2 fusion-positive pilocytic astrocytoma. [PDF]

CNS Oncol
Balasubramanian K, Fung KM, McNall-Knapp RY, Balsara K.   +3 more
europepmc   +1 more source

A Spatially Resolved View on the Aging Substantia nigra: An Exploratory Proteomic Study

Advanced Biology, EarlyView.
Although aging is the most important risk factor for several neurodegenerative diseases, the molecular effects of physiological aging are still understudied. By applying spatially‐resolved proteomic analyses of the human substantia nigra pars compacta, alterations in vesicular trafficking and mitochondrial proteins are observed, as well as reduced ...
Britta Eggers, Maximilian Hausherr, Michel Lim, Karin Schork, Bilhan Karacora, Robin Grugel, Martin Eisenacher, Isabel Gil Aldea, Peter Riederer, Manfred Gerlach, Katrin Marcus   +10 more
wiley   +1 more source

Development and internal validation of a mammography-based model fusing clinical, radiomics, and deep learning models for sentinel lymph node metastasis prediction in breast cancer. [PDF]

Front Med (Lausanne)
Liu X, Ruan Y, Cao S, Zhao M, Shi Z, Jin Y, Wang Y, Gao B.   +7 more
europepmc   +1 more source

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics

Advanced Engineering Materials, EarlyView.
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani, Aditi Singh, Behnam Ashrafi   +2 more
wiley   +1 more source

A case of GLI1-altered mesenchymal pleural tumour with novel gene fusion: a clinical perspective. [PDF]

J Surg Case Rep
Bahajjaj SIBZ, Chia CML, Farah BL.
europepmc   +1 more source

Comparative Wear and Friction Analysis of Sliding Surface Materials for Hydrostatic Bearing under Oil Supply Failure Conditions

Advanced Engineering Materials, EarlyView.
Hydrostatic bearings excel in high‐precision applications, but their performance hinges on a continuous external supply. This study evaluates various material combinations for sliding surfaces to mitigate damage during supply failures or misalignment and to discover the most effective materials identified for enhancing the reliability and efficiency of
Michal Michalec, Lukáš Daněk, Jan Foltýn, Petr Svoboda, Carsten Gachot, Martin Hartl, Ivan Křupka   +6 more
wiley   +1 more source

Preparation, expression, and anti-cancer effects of the novel fusion protein rONC-T7-pHLIP. [PDF]

Appl Microbiol Biotechnol
Yang G, Wang S, Wang Y, Zheng H, Du M, Wang Z, Chen Z, Chang C, Yu G.   +8 more
europepmc   +1 more source

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

Advanced Engineering Materials, EarlyView.
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi, Saeed Kamarian, Mahdi Bodaghi, Saeed Saber‐Samandari, Jung‐il Song   +4 more
wiley   +1 more source