Browse Articles
Discover research articles across all indexed journals
A semi-supervised domain adaptive medical image segmentation method based on dual-level multi-scale alignment
A neoantigen vaccine generates antitumour immunity in renal cell carcinoma
Increased bronchopulmonary dysplasia along with decreased mortality in extremely preterm infants
Multivitamin supplementation and its impact in metabolic dysfunction-associated steatotic liver disease
Diagnostic value of C-reactive protein/ albumin ratio and TMTC1 in intracranial atherosclerotic stenosis in patients with acute cerebral infarction
Impact of distal or pylorus preserving gastrectomy on postoperative quality of life in T1 stage middle third gastric cancer patients
Pulse width modulation for current source inverters with arbitrary number of phases
Specific visual expertise reduces susceptibility to visual illusions
Abstract Extensive exposure to specific kinds of imagery tunes visual perception, enhancing recognition and interpretation abilities relevant to those stimuli (e.g. radiologists can rapidly extract important information from medical scans). For the first time, we tested whether specific visual expertise induced by professional training also affords domain-general perceptual advantages. Experts in medical image interpretation (n = 44; reporting radiographers, trainee radiologists, and certified radiologists) and a control group consisting of psychology and medical students (n = 107) responded to the Ebbinghaus, Ponzo, Müller-Lyer, and Shepard Tabletops visual illusions in forced-choice tasks. Our results show that medical image experts were significantly less susceptible to all illusions except for the Shepard Tabletops, demonstrating superior perceptual accuracy. These findings could possibly be attributed to a stronger local processing bias, a by-product of learning to focus on specific areas of interest by disregarding irrelevant context in their domain of expertise.
Liver margin segmentation in abdominal CT images using U-Net and Detectron2: annotated dataset for deep learning models
Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture
Mesothelioma cell heterogeneity identified by single cell RNA sequencing
Author Correction: Neutralizing GDF-15 can overcome anti-PD-1 and anti-PD-L1 resistance in solid tumours
Author Correction: The value of the continuous genotyping of multi-drug resistant tuberculosis over 20 years in Spain
Trade-offs and synergies when balancing economic growth and globalization for sustainable development goals achievement
Abstract This study investigates the complex relationships between globalization, economic growth, urbanization, and ecological footprint in the context of advancing the United Nations Sustainable Development Goals (SDGs). Employing a club convergence framework, we evaluate global SDG Index from 2000 to 2023 for 149 countries with 3212 observations, identifying five converging clubs and one non-converging group. Our analysis demonstrates that higher GDP per capita and various dimensions of globalization positively impact SDG outcomes, whereas rapid urbanization and expansive ecological footprints exert negative influences. This research highlights the critical need for tailored policy interventions that address the distinct challenges encountered by different country clusters to bolster sustainable development efforts. Our findings reveal the multifaceted nature of sustainable development, indicating that economic growth and globalization can support SDG advancement if their detrimental effects are effectively mitigated. The study offers valuable insights for crafting national and global strategies to expedite progress towards the SDGs, emphasizing the importance of harmonizing economic, social, and environmental priorities.
Using mathematical modelling to highlight challenges in understanding trap counts obtained by a baited trap
Abstract Baited traps are routinely used in many ecological and agricultural applications, in particular when information about pest insects is required. However, interpretation of trap counts is challenging, as consistent methods or algorithms relating trap counts to the population abundance in the area around the trap are largely missing. Thus, interpretation of trap counts is usually relative rather than absolute, i.e., a larger average trap count is regarded as an indication of a larger population. In this paper, we challenge this assumption. We show that the key missing point is the animal movement behaviour, which is known to be modified in the presence of attractant (bait), in particular being dependent on the attractant strength. Using an individual-based simulation model of animal movement, we show that an increase in trap counts can happen simply because of changes in the animal movement behaviour even when the population size is constant or even decreasing. Our simulation results are in good qualitative agreement with some available field data. We conclude that, unless reliable biological information about the dependence of animal movement pattern on the type and strength of attractant is available, an increase in trap counts can send a grossly misleading message, resulting in wrong conclusions about the pest population dynamics and hence inadequate conservation or pest management decisions.