Bullet characterization using Photon-Counting detector CT: A phantom study with intact bullets.

Objectives: Photon-counting detector CT (PCD-CT) is expected to substantially improve and expand CT-imaging applicability due to its intrinsic spectral capabilities, increased spatial resolution, reduced electronic noise, and improved image contrast. The current study aim is to evaluate PCD-CT efficacy in characterizing bullets based on their dimensions, shape, and material composition.

Materials And Methods: This is an observational phantom study examining 11 unfired, intact bullets of various common calibers, placed in ballistic gelatin.

Elevated airborne radioactivity downwind of a Colorado oil refinery.

Airborne radioactivity from fossil fuel production systems is poorly characterized, but a recent study showed elevated ambient levels with proximity to oil and gas production wells. Here, we report year-long, high temporal resolution monitoring results of airborne alpha radioactivity from both radon gas and radon progeny attached to particulates immediately northeast of an oil refinery in Commerce City, Colorado, USA, in an environmental justice community of concern. Gas and particle-associated radioactivity contributed nearly evenly to the total alpha radioactivity.

Biophysics-inspired spike rate adaptation for computationally efficient phenomenological nerve modeling.

This study introduces and evaluates the PHAST+ model, part of a computational framework designed to simulate the behavior of auditory nerve fibers in response to the electrical stimulation from a cochlear implant. PHAST+ incorporates a highly efficient method for calculating accommodation and adaptation, making it particularly suited for simulations over extended stimulus durations. The proposed method uses a leaky integrator inspired by classic biophysical nerve models.

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics.

We present IOHexperimenter, the experimentation module of the IOHprofiler project. IOHexperimenter aims at providing an easy-to-use and customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolutionary and genetic algorithms, and Bayesian optimization techniques. IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other modules of the IOHprofiler environment.

Evolutionary Algorithms for Parameter Optimization-Thirty Years Later.

Thirty years, 1993-2023, is a huge time frame in science. We address some major developments in the field of evolutionary algorithms, with applications in parameter optimization, over these 30 years. These include the covariance matrix adaptation evolution strategy and some fast-growing fields such as multimodal optimization, surrogate-assisted optimization, multiobjective optimization, and automated algorithm design.

Iterative Design, Feasibility, and Preliminary Efficacy Testing for the Development of a Cooperative Card Game Intervention to Reduce Loneliness and Foster Social Connection.

To present the design, development, and pilot testing of , an empirically derived cooperative card game intervention to reduce loneliness and enhance connection. Theory and empirical evidence from domains such as self-disclosure, interpersonal closeness, and serious games informed the design of this game. Iterative design was used to develop the intervention, followed by feasibility and preliminary efficacy pilot testing.

Optimizing stimulus energy for cochlear implants with a machine learning model of the auditory nerve.

Performing simulations with a realistic biophysical auditory nerve fiber model can be very time-consuming, due to the complex nature of the calculations involved. Here, a surrogate (approximate) model of such an auditory nerve fiber model was developed using machine learning methods, to perform simulations more efficiently. Several machine learning models were compared, of which a Convolutional Neural Network showed the best performance.

Structured reporting in radiology: a systematic review to explore its potential.

Objectives: Structured reporting (SR) in radiology reporting is suggested to be a promising tool in clinical practice. In order to implement such an emerging innovation, it is necessary to verify that radiology reporting can benefit from SR. Therefore, the purpose of this systematic review is to explore the level of evidence of structured reporting in radiology.

T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting.

Background: In the era of datafication, it is important that medical data are accurate and structured for multiple applications. Especially data for oncological staging need to be accurate to stage and treat a patient, as well as population-level surveillance and outcome assessment. To support data extraction from free-text radiological reports, Dutch natural language processing (NLP) algorithm was built to quantify T-stage of pulmonary tumors according to the tumor node metastasis (TNM) classification.

[Restoration choices for endodontically treated posterior teeth].

There aren't any generally accepted guidelines for the restoration of an endodontically treated tooth. With a questionnaire among dental general practitioners and endodontists, several restorative treatment options for endodontically treated molars and premolars were identified. The questionnaire inventoried the influence of various parameters on treatment preferences.

CT in relation to RT-PCR in diagnosing COVID-19 in The Netherlands: A prospective study.

Introduction: Early differentiation between emergency department (ED) patients with and without corona virus disease (COVID-19) is very important. Chest CT scan may be helpful in early diagnosing of COVID-19. We investigated the diagnostic accuracy of CT using RT-PCR for SARS-CoV-2 as reference standard and investigated reasons for discordant results between the two tests.

Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening.

There has been an increase in the use of machine learning and artificial intelligence (AI) for the analysis of image-based cellular screens. The accuracy of these analyses, however, is greatly dependent on the quality of the training sets used for building the machine learning models. We propose that unsupervised exploratory methods should first be applied to the data set to gain a better insight into the quality of the data.

Natural Language Processing in Dutch Free Text Radiology Reports: Challenges in a Small Language Area Staging Pulmonary Oncology.

Reports are the standard way of communication between the radiologist and the referring clinician. Efforts are made to improve this communication by, for instance, introducing standardization and structured reporting. Natural Language Processing (NLP) is another promising tool which can improve and enhance the radiological report by processing free text.

Redefining the structure of structured reporting in radiology.

Structured reporting is advocated as a means of improving reporting in radiology to the ultimate benefit of both radiological and clinical practice. Several large initiatives are currently evaluating its potential. However, with numerous characterizations of the term in circulation, "structured reporting" has become ambiguous and is often confused with "standardization," which may hamper proper evaluation and implementation in clinical practice.

Improving Comprehension Efficiency of High Content Screening Data Through Interactive Visualizations.

In this study, an experiment is conducted to measure the performance in speed and accuracy of interactive visualizations. A platform for interactive data visualizations was implemented using Django, D3, and Angular. Using this platform, a questionnaire was designed to measure a difference in performance between interactive and noninteractive data visualizations.

glUCModel: a monitoring and modeling system for chronic diseases applied to diabetes.

Chronic patients must carry out a rigorous control of diverse factors in their lives. Diet, sport activity, medical analysis or blood glucose levels are some of them. This is a hard task, because some of these controls are performed very often, for instance some diabetics measure their glucose levels several times every day, or patients with chronic renal disease, a progressive loss in renal function, should strictly control their blood pressure and diet.

Development and implementation of the guiding stars nutrition guidance program.

PURPOSE . To describe the collaborative process between a grocery retailer and a panel of nutrition experts used to develop a nutrition guidance system (Guiding Stars) that evaluates the nutrient profile of all edible products in the supermarket, and to report the results of the food and beverage ratings. DESIGN .

Assessment of CFTR function in homozygous R117H-7T subjects.

Background: R117H is a frequent missense mutation included in most CFTR mutation panels. However knowledge about the residual function of R117H-CFTR channels in cystic fibrosis-affected organs, e.g.

The connection between art, healing, and public health: a review of current literature.

This review explores the relationship between engagement with the creative arts and health outcomes, specifically the health effects of music engagement, visual arts therapy, movement-based creative expression, and expressive writing. Although there is evidence that art-based interventions are effective in reducing adverse physiological and psychological outcomes, the extent to which these interventions enhance health status is largely unknown. Our hope is to establish a foundation for continued investigation into this subject and to generate further interest in researching the complexities of engagement with the arts and health.

Bridging the knowledge-action gap in diabetes: information technologies, physician incentives and consumer incentives converge.

The gap between current medical knowledge and its application in chronic disease management is especially apparent in diabetes care. Although research over the last decade has shown that adherence to standards of care can prevent or delay the onset of devastating diabetic complications, little more than one-third of patients achieve adequate glycaemic control. Obstacles to better care include 'system' factors such as inadequate record-keeping and reimbursement policies that reimburse amply for illness but poorly for diabetes education and interventions via telephone and computer.