A spreadsheet-based program (Good-Enough RFLP Matcher or GERM) is presented that matches unknown restriction fragment length polymorphism (RFLP) patterns of ectomycorrhizal fungi to a database of known ectomycorrhizal fungi. The program uses three simple methods to determine whether a sample matches a known: (1) Forward Matching: whether every band in the unknown is present in a known sample within a given error range; (2) Backward Matching: whether every band in the known sample is present in the unknown within a given error range; (3) Sum of Bands: whether the sum of all bands in the known and unknown are similar within a given error range. The program is available through the web page of this journal.
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http://dx.doi.org/10.1007/s00572-003-0225-x | DOI Listing |
J Chem Phys
January 2025
Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Charlottenburg, Germany.
We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for charge-neutral iso-electronic diatomics at fixed interatomic distance d0. To account for variations in distance, we combine AHA with this ansatz for the electronic binding potential, E(d)=(Eu-Es)Ec-EsEu-Esd/d0+Es, where Eu, Ec, Es correspond to the energies of the united atom, calibration at d0, and the sum of infinitely separated atoms, respectively. Our model covers the two-dimensional electronic potential energy surface spanned by distances of 0.
View Article and Find Full Text PDFDigit Health
January 2025
School of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Objective: Machine learning (ML) has enabled healthcare discoveries by facilitating efficient modeling, such as for cancer screening. Unlike clinical trials, real-world data used in ML are often gathered for multiple purposes, leading to bias and missing information for a specific classification task. This challenge is especially pronounced in healthcare because of stringent ethical considerations and resource constraints.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Background: Echocardiography can conveniently, rapidly, and economically evaluate the structure and function of the heart, and has important value in the diagnosis and evaluation of cardiovascular diseases (CVDs). However, echocardiography still exhibits significant variability in image acquisition and diagnosis, with a heavy dependency on the operator's experience. Image quality affects disease diagnosis in the later stage, and even image quality assessment still has variability in human evaluation.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Department of Rehabilitation Sciences, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
Background: Quantitative ultrasound imaging is a popular technique to assess the structural properties of the intrinsic and extrinsic foot muscles. Although several studies examined test-retest reliability, specific gaps remain in assessing inter-rater reliability, particularly distinguishing between image acquisition and muscle measurement. Additionally, these studies utilized equipment that may not be generalizable across both clinical and research settings and often involved small sample sizes without prior sample size calculations.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Department of Imaging Medicine and Nuclear Medicine, Shandong Second Medical University, Weifang, China.
Background: Rapid kilovolt (kV)-switching dual-energy computed tomography (DECT) has been increasingly applied to the measurement of lumbar spine bone mineral density (BMD) in humans and animal models. The objective of this study was to investigate the optimal parameters for the measurement of vertebral BMD. The BMD of the spinal model was measured by means of DECT in combination with different noise index (NI) and preset adaptive statistical iterative reconstruction Veo (ASiR-V) levels.
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