We present a collection of minimalist binary vectors for transformation through ATMT applicable to several fungi species. pLUO plasmid binary vectors consist of a reporter module containing fluorescent proteins, mCherry or eGFP, flanked by a multiple cloning site and a transcription terminator site. They also present a synthetic gene allowing resistance to Hygromicin B flanked by alternate promoters, one for yeast and another for filamentous fungi. Left and right borders were added for Agrobacterium tumefaciens recognition, and a minimal broad-host range RK2 replication origin. Transformation was validated in the pathogenic fungus Paracoccidioides lutzii. Hence, we developed an efficient and reliable molecular tool for fungal transformation: minimalist, synthetic, modular, and available in four different versions, and these can still be readily modified using a few primers and few cloning steps.
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http://dx.doi.org/10.1590/1678-4685-GMB-2018-0221 | DOI Listing |
Sci Rep
January 2025
Crop and Horticultural Science Research Department, Mazandaran Agricultural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Tajrish, Iran.
Plum fruit fresh weight (FW) estimation is crucial for various agricultural practices, including yield prediction, quality control, and market pricing. Traditional methods for estimating fruit weight are often destructive, time-consuming, and labor-intensive. In this study, we addressed the problem of predicting plum FW using artificial intelligence (AI) methods based on fruit dimensions.
View Article and Find Full Text PDFRadiography (Lond)
January 2025
Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany.
Background: Facial recognition technology in medical imaging, particularly with head scans, poses privacy risks due to identifiable facial features. This study evaluates the use of facial recognition software in identifying facial features from head CT scans and explores a defacing pipeline using TotalSegmentator to reduce re-identification risks while preserving data integrity for research.
Methods: 1404 high-quality renderings from the UCLH EIT Stroke dataset, both with and without defacing were analysed.
Alzheimers Dement
December 2024
Douglas Mental Health University Institute, Montreal, QC, Canada.
Background: White matter hyperintensities (WMHs) are increasingly recognized for their role in cognitive decline and the progression of neurodegenerative conditions including Alzheimer's disease (AD). Despite advances in imaging technologies, the exact contribution of WMHs to disease processes remains a subject of ongoing research. This study aims to apply machine learning approaches to determine critical features of AD-related neuropathologies in vivo.
View Article and Find Full Text PDFSci Rep
January 2025
Institute of Visual Informatics, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
Worldwide, Cancer remains a significant health concern due to its high mortality rates. Despite numerous traditional therapies and wet-laboratory methods for treating cancer-affected cells, these approaches often face limitations, including high costs and substantial side effects. Recently the high selectivity of peptides has garnered significant attention from scientists due to their reliable targeted actions and minimal adverse effects.
View Article and Find Full Text PDFFront Hum Neurosci
December 2024
Department of Biomedical Engineering, Izmir Katip Celebi University, Izmir, Türkiye.
Introduction: Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms.
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