Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Daniel P, Russo"'
Autor:
Judy Malas, Daniel C. Russo, Olivier Bollengier, Michael J. Malaska, Rosaly M. C. Lopes, Fabien Kenig, D'Arcy R. Meyer-Dombard
Publikováno v:
Frontiers in Microbiology, Vol 15 (2024)
High hydrostatic pressure (HHP) is a key driver of life's evolution and diversification on Earth. Icy moons such as Titan, Europa, and Enceladus harbor potentially habitable high-pressure environments within their subsurface oceans. Titan, in particu
Externí odkaz:
https://doaj.org/article/b125b332869a41f6b3b9728817c21084
Autor:
Wenyi Wang, Xiliang Yan, Linlin Zhao, Daniel P. Russo, Shenqing Wang, Yin Liu, Alexander Sedykh, Xiaoli Zhao, Bing Yan, Hao Zhu
Publikováno v:
Journal of Cheminformatics, Vol 11, Iss 1, Pp 1-5 (2019)
Abstract To facilitate the development of new nanomaterials, especially nanomedicines, a novel computational approach was developed to precisely predict the hydrophobicity of gold nanoparticles (GNPs). The core of this study was to develop a large vi
Externí odkaz:
https://doaj.org/article/037788bcc5bc4cc395558efaeb0b618d
Autor:
Heather L. Ciallella, Daniel P. Russo, Swati Sharma, Yafan Li, Eddie Sloter, Len Sweet, Heng Huang, Hao Zhu
Publikováno v:
Environ Sci Technol
For hazard identification, classification, and labeling purposes, animal testing guidelines are required by law to evaluate the developmental toxicity potential of new and existing chemical products. However, guideline developmental toxicity studies
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9ce8b03ddfd97397ad5bdc4d5c09a4a
https://pubmed.ncbi.nlm.nih.gov/35451820
https://pubmed.ncbi.nlm.nih.gov/35451820
Autor:
Tom Chan, Concetta Tania Di Iorio, Simon de Lusignan, Daniel Lo Russo, Craig Kuziemsky, Siaw-Teng Liaw
Publikováno v:
Journal of Innovation in Health Informatics, Vol 23, Iss 3, Pp 627-632 (2016)
Sharing health and social care data is essential to the delivery of high quality health care as well as disease surveillance, public health, and for conducting research. However, these societal benefits may be constrained by privacy and data protecti
Externí odkaz:
https://doaj.org/article/3756915d980a46ccb7b08e213f4f798e
Publikováno v:
Environ Sci Technol
Traditional experimental testing to identify endocrine disruptors that enhance estrogenic signaling relies on expensive and labor-intensive experiments. We sought to design a knowledge-based deep neural network (k-DNN) approach to reveal and organize
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3a74d04d539a795f858f60e70d2f68b
https://doi.org/10.1021/acs.est.1c02656
https://doi.org/10.1021/acs.est.1c02656
Autor:
Andrea Amerio, Mario Amore, Andrea Aguglia, Daniel P. Russo, Vlasios Brakoulias, Alessandra Costanza, Norberto Miletto, Bernardo Dell'Osso, S. Nassir Ghaemi, Anna Odone, Sergio Barroilhet, Beatrice Benatti, Gianluca Serafini
Publikováno v:
Acta Psychiatrica Scandinavica
Objectives Polypharmacy is common in maintenance treatment of bipolar illness, but proof of greater efficacy compared to monotherapy is assumed rather than well known. We systematically reviewed the evidence from the literature to provide recommendat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e3f439863557cdeaf918d2ff1cf7476
https://doi.org/10.1111/acps.13312
https://doi.org/10.1111/acps.13312
Publikováno v:
Laboratory investigation; a journal of technical methods and pathology
As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)popula
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36077c2c2837df48f4939611baac2513
https://doi.org/10.1038/s41374-020-00477-2
https://doi.org/10.1038/s41374-020-00477-2
Publikováno v:
ACS Sustain Chem Eng
Compared to traditional experimental approaches, computational modeling is a promising strategy to efficiently prioritize new candidates with low cost. In this study, we developed a novel data mining and computational modeling workflow proven to be a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e0ce40ea32fe6bde67915667eeac67d
https://doi.org/10.1021/acssuschemeng.0c09139
https://doi.org/10.1021/acssuschemeng.0c09139
Publikováno v:
ACS Sustainable Chemistry & Engineering. 8:19096-19104
Artificial intelligence approaches, such as machine learning and deep learning, may predict nano–bio interactions. However, such a prediction is now hindered by the paucity of suitable nanodescript...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::169e3618eb85df1289c70389777b3ea7
https://doi.org/10.1021/acssuschemeng.0c07453
https://doi.org/10.1021/acssuschemeng.0c07453
Publikováno v:
Analytical Chemistry. 92:13971-13979
Digitalizing complex nanostructures into data structures suitable for machine learning modeling without losing nanostructure information has been a major challenge. Deep learning frameworks, particularly convolutional neural networks (CNNs), are espe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::797f9d5027f0d12653060db647e00f59
https://doi.org/10.1021/acs.analchem.0c02878
https://doi.org/10.1021/acs.analchem.0c02878