Results 191 to 200 of about 143,436 (230)

Effect of the S2' site cleavage on SARS-CoV-2 spike. [PDF]

Nat Commun
Shi W, Jonaid GM, Kibria MG, Allen J, Peng H, Rits-Volloch S, Zhu H, Wang S, Walsh RM, Lu J, Chen B.   +10 more
europepmc   +1 more source

Metagenomic 16S rRNA amplicon and shotgun sequencing in investigation of granulomatous lesions in layer chickens: a case report. [PDF]

Anim Microbiome
Yun CS, Kim JK, Kwon H, Her M, Moon JS.
europepmc   +1 more source

Expanding diversity of tick-borne phleboviruses (Phlebovirus mukawaense, Mudanjiang phlebovirus, Gomselga Virus, and Onega tick phlebovirus) in Russia. [PDF]

PLoS One
Kartashov MY, Kurushina VY, Yanshin AO, Zheleznova AS, Svirin KA, Tregubchak TV, Ternovoi VA, Agafonov AP, Gladysheva AV.   +8 more
europepmc   +1 more source

SHIELD: An AI Framework for Skeletal Health Intelligence and Early Lesion Detection to Improve Orthopedic Referrals. [PDF]

J Med Syst
Alili A, McKane AM, Demir MF, Emory CL, Gurcan MN.   +4 more
europepmc   +1 more source

A Simple and Fast Protocol to Detect Nucleophosmin 1 (<i>NPM1</i>) Mutation and Fms-like Tyrosine Kinase 3 Internal Tandem Duplication (<i>FLT3</i>/ITD): Optimizing Laboratory Routine. [PDF]

Methods Protoc
Hellwig AHDS, Ewald GM, Rodrigues GM, Matos WL, Bergmann J, Melo VH, Paiva RM, Pereira DC, Gomez EW.   +8 more
europepmc   +1 more source

In Silico Druggability Assessment of <i>Escherichia coli</i> FtsQ Reveals Tractable PPI Interfaces in the Divisome. [PDF]

Antibiotics (Basel)
Frlan R.
europepmc   +1 more source

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Byte embeddings for file fragment classification

Future Generation Computer Systems, 2022
Abstract In digital forensics, file carving is the process of recovering files on a storage media in part or in whole without any file system information. An important problem in file carving is the identification of fragment types. Many fragment classification studies in the literature employ inflexible and indiscernible feature selection methods ...
Mehmet Engin Tozal
exaly   +2 more sources

SIFT – File Fragment Classification Without Metadata

2023 3rd International Conference on Computing and Information Technology (ICCIT), 2023
A vital issue of file carving in digital forensics is type classification of file fragments when the filesystem metadata is missing. Over the past decades, there have been several efforts for developing methods to classify file fragments. In this research, a novel sifting approach, named SIFT (Sifting File Types), is proposed.
Shahid Alam
exaly   +3 more sources

Using NLP techniques for file fragment classification

Digital Investigation, 2012
Abstract The classification of file fragments is an important problem in digital forensics. The literature does not include comprehensive work on applying machine learning techniques to this problem. In this work, we explore the use of techniques from natural language processing to classify file fragments.
George Mathews
exaly   +2 more sources