Results 61 to 70 of about 33,273 (264)

Unveiling the Taxonomic Diversity and Unprecedented Biosynthetic Treasure of the Phylum Myxococcota

Advanced Science, EarlyView.
Natural products remain vital sources of therapeutics, and the phylum Myxococcota constitutes an especially rich reservoir for them. Based on decades of microbiological efforts, we present 154 new Myxococcota genomes and revise taxonomy quadrupling the number of families and genera.
Amay Ajaykumar Agrawal, Ronald Garcia, Guangyi Chen, Chantal D. Bader, Peter Sullivan, Alexander Popoff, Daniel Krug, Maja Hunter, Sebastian Walesch, Emilia Oueis, Christine Frank, Haowen Zhao, Joachim J. Hug, Sebastian Keller, Pascal Hirsch, Azat Tagirdzhanov, Tatiana Malygina, Mary Victory E. Gutierrez, Alexey Gurevich, Judith Boldt, Boyke Bunk, Jörg Overmann, Ulrich Nübel, Andreas Keller, Olga V. Kalinina, Rolf Müller   +25 more
wiley   +1 more source

Hydrochemical evolution of karst groundwater under the mining influence in Beiya Mine, Northwest Yunnan Province

Carsologica Sinica
The Beiya Gold Mine in Yunnan Province, a karst water-filled mine, is a typical skarn-porphyry type deposit in China. Its groundwater has been imbalanced for a long time caused by mining activities, which have affected or damaged the aquifer to varying ...
Xiang HE, Chao YANG, Xuelan DONG, Xiaojiao GUO, Haifeng YANG, Jiahuai LI, Fengji YANG   +6 more
doaj   +1 more source

Data mining fool’s gold [PDF]

Journal of Information Technology, 2020
The scientific method is based on the rigorous testing of falsifiable conjectures. Data mining, in contrast, puts data before theory by searching for statistical patterns without being constrained by prespecified hypotheses. Artificial intelligence and machine learning systems, for example, often rely on data-mining algorithms to construct models with ...
openaire   +1 more source

Livestock Multi‐Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation

Advanced Science, EarlyView.
A three‐tier livestock multi‐omics framework resolves four typical analytical pitfalls. Moving from statistical association through machine learning preprocessing to triple‐modal causal inference, it converts omics results into genomic selection and gene editing strategies to achieve One Health, underpinned by multi‐omics data, multimodal sequencing ...
Jiying Wen, Zhongyu Wang, Jieping Huang, Fen Li, Ningbo Chen, Yun Ma   +5 more
wiley   +1 more source

Deep Learning‐Driven Discovery and Engineering of an Efficient PETase for Depolymerization and Detoxification of PET Microplastics Under Physiological Conditions

Advanced Science, EarlyView.
A deep learning–driven pipeline mining 246 million protein sequences uncovers AhPETase, an evolutionarily distinct PET hydrolase. Engineered variant AhPETaseM1 degrades post‐consumer PET microplastics under physiological conditions and reverses microplasticinduced cytotoxicity in human lung and colon cells, establishing enzymatic microplastic ...
Yuxuan Wang, Shijie He, Yuheng Chang, Sheng Mao, Jianan Canal Li, Binbin Chen, Hongxun Gao, Mingchun Xu, Chenxu Liu, Yajie Wang   +9 more
wiley   +1 more source

Unveiling the Uncharted Potential of 2D Materials in Li/Na–S Batteries: A Paradigm Shift From Graphene

Advanced Science, EarlyView.
This review explores emerging 2D materials beyond graphene, including graphdiyne, phosphorene, borophene, siloxene, MBene, antimonene, and germanene for Li/Na–S batteries. It analyzes their roles as sulfur hosts, metallic anode protectors, separators, and electrolyte fillers, emphasizing polysulfide suppression, dendrite inhibition, and interfacial ...
Naveen Kumar T. R, Xuebing Zhu, Yao‐Jie Lei, Lei Zhang, Hua Kun Liu, Weihong Lai, Guoxiu Wang, Yun‐Xiao Wang   +7 more
wiley   +1 more source

Limitations of Foundation Models in Energy Materials Simulations: A Case Study in Polyanion Sodium Cathode Materials

Advanced Intelligent Discovery, EarlyView.
Several simulation techniques are used to explore static and dynamic behavior in polyanion sodium cathode materials. The study reveals that universal machine learning interatomic potentials (MLIPs) struggle with system‐specific chemistry, emphasizing the need for tailored datasets.
Martin Hoffmann Petersen, Chiku Parida, Jonas Busk, Jin Hyun Chang, Arghya Bhowmik, Juan Maria Garcia Lastra   +5 more
wiley   +1 more source

Smart Flexible Tactile Sensors: Recent Progress in Device Designs, Intelligent Algorithms, and Multidisciplinary Applications

Advanced Intelligent Discovery, EarlyView.
Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang, Ming Dong, Rongrong Bao, Caofeng Pan   +3 more
wiley   +1 more source

Discovery of Novel Materials with Giant Dielectric Constants via First‐Principles Phonon Calculations and Machine Learning

Advanced Intelligent Discovery, EarlyView.
We discovered novel materials with giant dielectric constants by combining first‐principles phonon calculations and machine learning. Screening 525 perovskites identified six candidates. RbNbO3 was synthesized under pressure and showed ε ≈ 800–1000. This validates our framework as a powerful tool for high‐performance dielectric materials discovery.
Hiroki Moriwake, Kazuki Shitara, Rie Yokoi, Takeharu Kato, Isao Tanaka, Ayako Yamamoto, Kimitoshi Murase, Yosuke Hamasaiki, Yoshitaka Ehara, Mitsuru Itoh   +9 more
wiley   +1 more source

Accelerating Biosensor Discovery: A Computationally‐Driven Pipeline for Microplastics Monitoring

Advanced Intelligent Discovery, EarlyView.
A computationally guided pipeline unites molecular simulation, synthetic biology, electrochemical engineering, and machine learning to accelerate biosensor discovery. A Bacillus anthracis carbohydrate‐binding module is used to develop a high‐performance micro‐ and nanoplastics sensor with greatly reduced error and variability.
Gabriel X. Pereira, Marcos de C. Leite, Pedro H. M. Zanineli, Juliana N. Y. Costa, Débora M. Cunha, Sophia P. Serra, Pedro H. Sophia, Flávio M. Shimizu, Yasmin W. Moura, Célio C. Oliveira, Gabriel R. Schleder, Gabriela F. Persinoti, Leandro Merces, Renato S. Lima   +13 more
wiley   +1 more source