We discuss the ionization of aligned hydrogen molecules into their ionic ground state by 200 eV electrons. Using a reaction microscope, the complete electron scattering kinematics is imaged over a large solid angle. Simultaneously, the molecular alignment is derived from postcollision dissociation of the residual ion. It is found that the ionization cross section is maximized for small angles between the internuclear axis and the momentum transfer. Fivefold differential cross sections (5DCSs) reveal subtle differences in the scattering process for the distinct alignments. We compare our observations with theoretical 5DCSs obtained with an adapted molecular three-body distorted wave model that reproduces most of the results, although discrepancies remain.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1063/1.3457155 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Enhancing the efficacy of near-infrared photoimmunotherapy through intratumoural delivery of CD44-targeting antibody-photoabsorber conjugates.
EBioMedicine
January 2025
Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Osaka, Japan; Department of Immunopathology, World Premier International Research Center, Initiative, Immunology, Frontier Research Center, Osaka University, Osaka, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka, Japan; Center for Infectious Diseases for Education and Research, Osaka University, Osaka, Japan; Japan Agency for Medical Research and Development - Core Research for Evolutional Science and Technology, Osaka University, Osaka, Japan; Center for Advanced Modalities and DDS, Osaka University, Osaka, Japan. Electronic address:
Background: Photoimmunotherapy (PIT) is a potent modality for cancer treatment. The conventional PIT regimen involves the systemic delivery of an antibody-photoabsorber conjugate, followed by a 24-h waiting period to ensure adequate localisation on the target cells. Subsequent exposure to near-infrared (NIR) light selectively damages the target cells.
View Article and Find Full Text PDFUtilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases.
Front Neurol
January 2025
Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Objective: To develop a machine learning-based clinical and/or radiomics model for predicting the primary site of brain metastases using multiparametric magnetic resonance imaging (MRI).
Materials And Methods: A total of 202 patients (87 males, 115 females) with 439 brain metastases were retrospectively included, divided into training sets (brain metastases of lung cancer [BMLC] = 194, brain metastases of breast cancer [BMBC] = 108, brain metastases of gastrointestinal tumor [BMGiT] = 48) and test sets (BMLC = 50, BMBC = 27, BMGiT = 12). A total of 3,404 quantitative image features were obtained through semi-automatic segmentation from MRI images (T1WI, T2WI, FLAIR, and T1-CE).
Glaucoma detection and staging from visual field images using machine learning techniques.
PLoS One
January 2025
School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.
Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.
Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.
Support Care Cancer
January 2025
Oral Diagnosis Department, Faculdade de Odontolodia de Piracicaba, Universidade de Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.
View Article and Find Full Text PDFCellMsg: graph convolutional networks for ligand-receptor-mediated cell-cell communication analysis.
Brief Bioinform
November 2024
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
The role of cell-cell communications (CCCs) is increasingly recognized as being important to differentiation, invasion, metastasis, and drug resistance in tumoral tissues. Developing CCC inference methods using traditional experimental methods are time-consuming, labor-intensive, cannot handle large amounts of data. To facilitate inference of CCCs, we proposed a computational framework, called CellMsg, which involves two primary steps: identifying ligand-receptor interactions (LRIs) and measuring the strength of LRIs-mediated CCCs.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!