Results 311 to 320 of about 1,743,586 (380)

Production of hydroxyectoine from biogas by an engineered strain of Methylomicrobium alcaliphilum using a novel Taylor‐flow bioreactor

Journal of Chemical Technology &Biotechnology, EarlyView.
Abstract BACKGROUND The production of compatible solutes, such as ectoine and hydroxyectoine, is of great interest due to their industrial and biotechnological applications. Methylomicrobium alcaliphilum was genetically engineered to replace a native gene with a heterologous one, aiming to enhance ectoine production.
Raquel Herrero‐Lobo, Nuria Fernández‐González, Eva Marcos, María Alejandra Martínez, Pedro García‐Encina, María del Rosario Rodero, Raúl Muñoz, Sergio Bordel   +7 more
wiley   +1 more source

Prediction of Proliferation Rate of Pre‐Osteoblasts Using Raman Spectroscopy and Machine Learning Models

Journal of Raman Spectroscopy, EarlyView.
The main processes involved in predicting the pre–osteoblast proliferation rate using Raman spectroscopy and machine learning. ABSTRACT This novel study focuses on using Raman spectroscopy and machine learning (ML) models to forecast the murine pre‐osteoblast (OB) proliferation rate.
Sai S. Sudha, Angshuman Baradwaz, Ahmad Y. Javaid, Ambalangodage C. Jayasuriya   +3 more
wiley   +1 more source

Discovery of anti-Mycobacterium tuberculosis desertomycins from Streptomyces flavofungini TRM90047 based on genome mining and HSQC-TOCSY. [PDF]

Sci Rep
Wang L, Reheman A, Wan C.
europepmc   +1 more source

Isolation and structure determination of a new lantibiotic cinnamycin B from Actinomadura atramentaria based on genome mining

, 2016
Shinya Kodani, Hisayuki Komaki, Sho Ishimura, Hikaru Hemmi, Mayumi Ohnishi‐Kameyama   +4 more
openalex   +2 more sources

A multi‐objective feature optimization strategy for developing high‐entropy alloys with optimal strength and ductility

Materials Genome Engineering Advances, Volume 3, Issue 1, March 2025.
Flowchart of the multi‐objective feature optimization strategy integrating traditional material features with elemental descriptor mining. Abstract Selecting appropriate material features is essential for effective data‐driven materials design. Here, we propose a multi‐objective feature optimization strategy that identifies feature subsets to improve ...
Yan Zhang, Shewei Xin, Wei Zhou, Xiao Wang, Yangyang Xu, Yanjing Su   +5 more
wiley   +1 more source

Genome mining of albocandins A-E from Streptomyces sp. YINM00030. [PDF]

RSC Adv
Zhang ZT, Ren Z, Su X, Xie TP, Yi M, Zhou H, Yin M, Ding ZT.   +7 more
europepmc   +1 more source

Data‐driven designing on mechanical properties of biodegradable wrought zinc alloys

Materials Genome Engineering Advances, EarlyView.
Based on a small dataset of biodegradable zinc alloy samples, machine learning (ML) methods were used to predict mechanical properties and the strain softening/hardening behaviors can be controlled. By these ML models, the optimization of high‐strength, strength/plasticity synergy, and high plasticity for biodegradable purpose were achieved. Abstract A
Zongqing Hu, Shaojie Li, Jianfeng Jin, Yuping Ren, Rui Hou, Lei Yang, Gaowu Qin   +6 more
wiley   +1 more source

Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS. [PDF]

Nat Commun
Yan D, Zhou M, Adduri A, Zhuang Y, Guler M, Liu S, Shin H, Kovach T, Oh G, Liu X, Deng Y, Wang X, Cao L, Sherman DH, Schultz PJ, Kersten RD, Clement JA, Tripathi A, Behsaz B, Mohimani H.   +19 more
europepmc   +1 more source

A knowledge graph attention network for the cold‐start problem in intelligent manufacturing: Interpretability and accuracy improvement

Materials Genome Engineering Advances, EarlyView.
SteelKGAT enhances prediction accuracy and interpretability. Notably, this work represents the first application of knowledge graph attention neural networks to address the cold‐start problem in steel rolling production. Abstract In the rolling production of steel, predicting the performance of new products is challenging due to the low variety of data
Ziye Zhou, Yuqi Zhang, Shuize Wang, David San Martin, Yongqian Liu, Yang Liu, Chenchong Wang, Wei Xu   +7 more
wiley   +1 more source

Genome mining of metabolic gene clusters in the Rubiaceae family. [PDF]

Comput Struct Biotechnol J
Correia de Lemos SM, Paschoal AR, Guyot R, Medema M, Domingues DS.   +4 more
europepmc   +1 more source