PURPOSE: Dual velocity encoding PC-MRI can produce spurious artifacts when using high ratios of velocity encoding values (VENCs), limiting its ability to generate high-quality images across a wide range of encoding velocities. This study aims to propose and compare dual-VENC correction methods for such artifacts. THEORY AND METHODS: Two denoising approaches based on spatiotemporal regularization are proposed and compared with a state-of-the-art method based on sign correction. Accuracy is assessed using simulated data from an aorta and brain aneurysm, as well as 8 two-dimensional (2D) PC-MRI ascending aorta datasets. Two temporal resolutions (30,60) ms and noise levels (9,12) dB are considered, with noise added to the complex magnetization. The error is evaluated with respect to the noise-free measurement in the synthetic case and to the unwrapped image without additional noise in the volunteer datasets. RESULTS: In all studied cases, the proposed methods are more accurate than the Sign Correction technique. Using simulated 2D+T data from the aorta (60 ms, 9 dB), the Dual-VENC (DV) error 0.82±0.07$$ 0.82\pm 0.07 $$ is reduced to: 0.66±0.04$$ 0.66\pm 0.04 $$ (Sign Correction); 0.34±0.04$$ 0.34\pm 0.04 $$ and 0.32±0.04$$ 0.32\pm 0.04 $$ (proposed techniques). The methods are found to be significantly different (p-value <0.05$$ <0.05 $$ ). Importantly, brain aneurysm data revealed that the Sign Correction method is not suitable, as it increases error when the flow is not unidirectional. All three methods improve the accuracy of in vivo data. CONCLUSION: The newly proposed methods outperform the Sign Correction method in improving dual-VENC PC-MRI images. Among them, the approach based on temporal differences has shown the highest accuracy.

• MeSH
• Algorithms * MeSH
• Aorta * diagnostic imaging   MeSH
• Artifacts * MeSH
• Phantoms, Imaging MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Intracranial Aneurysm diagnostic imaging   MeSH
• Humans MeSH
• Magnetic Resonance Imaging * methods   MeSH
• Brain diagnostic imaging   MeSH
• Computer Simulation MeSH
• Image Processing, Computer-Assisted * methods   MeSH
• Signal-To-Noise Ratio * MeSH
• Reproducibility of Results MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Trigger finger (TF), also known as stenosing flexor tenosynovitis, is a common pathology of the fingers causing functional deficit of the hand. In recent years, new therapeutic approaches such as extracorporeal shock wave therapy (ESWT) and ultrasound-guided (USG) procedures have joined the most traditional conservative treatments as the adaptation of daily activities involving the affected hand and the orthosis. Likewise, the ultrasound (US) examination of the affected finger using modern high-frequency probes has progressively become part of the comprehensive assessment of patients with TF coupled with the medical history, the physical examination, and the functional scales. In this sense, considering the technological advances in both diagnostic and therapeutic fields, the non-surgical strategies have progressively grown defining a rehabilitation panel more complex than in the past. The present manuscript aims to provide an updated practical guide for clinicians and surgeons reviewing the state-of-art of both the assessment and the treatments of patients with TF to plan tailored rehabilitation management taking advantage of the matching of traditional and novel techniques.

• MeSH
• Ultrasonography, Interventional methods   MeSH
• Extracorporeal Shockwave Therapy methods   MeSH
• Humans MeSH
• Trigger Finger Disorder * diagnosis   rehabilitation   MeSH
• Treatment Outcome MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

Verifying the speaker of a speech fragment can be crucial in attributing a crime to a suspect. The question can be addressed given disputed and reference speech material, adopting the recommended and scientifically accepted likelihood ratio framework for reporting evidential strength in court. In forensic practice, usually, auditory and acoustic analyses are performed to carry out such a verification task considering a diversity of features, such as language competence, pronunciation, or other linguistic features. Automated speaker comparison systems can also be used alongside those manual analyses. State-of-the-art automatic speaker comparison systems are based on deep neural networks that take acoustic features as input. Additional information, though, may be obtained from linguistic analysis. In this paper, we aim to answer if, when and how modern acoustic-based systems can be complemented by an authorship technique based on frequent words, within the likelihood ratio framework. We consider three different approaches to derive a combined likelihood ratio: using a support vector machine algorithm, fitting bivariate normal distributions, and passing the score of the acoustic system as additional input to the frequent-word analysis. We apply our method to the forensically relevant dataset FRIDA and the FISHER corpus, and we explore under which conditions fusion is valuable. We evaluate our results in terms of log likelihood ratio cost (Cllr) and equal error rate (EER). We show that fusion can be beneficial, especially in the case of intercepted phone calls with noise in the background.

• MeSH
• Speech Acoustics MeSH
• Algorithms MeSH
• Humans MeSH
• Linguistics MeSH
• Likelihood Functions MeSH
• Speech MeSH
• Forensic Sciences * methods   MeSH
• Support Vector Machine MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

In today's biometric and commercial settings, state-of-the-art image processing relies solely on artificial intelligence and machine learning which provides a high level of accuracy. However, these principles are deeply rooted in abstract, complex "black-box systems". When applied to forensic image identification, concerns about transparency and accountability emerge. This study explores the impact of two challenging factors in automated facial identification: facial expressions and head poses. The sample comprised 3D faces with nine prototype expressions, collected from 41 participants (13 males, 28 females) of European descent aged 19.96 to 50.89 years. Pre-processing involved converting 3D models to 2D color images (256 × 256 px). Probes included a set of 9 images per individual with head poses varying by 5° in both left-to-right (yaw) and up-and-down (pitch) directions for neutral expressions. A second set of 3,610 images per individual covered viewpoints in 5° increments from -45° to 45° for head movements and different facial expressions, forming the targets. Pair-wise comparisons using ArcFace, a state-of-the-art face identification algorithm yielded 54,615,690 dissimilarity scores. Results indicate that minor head deviations in probes have minimal impact. However, the performance diminished as targets deviated from the frontal position. Right-to-left movements were less influential than up and down, with downward pitch showing less impact than upward movements. The lowest accuracy was for upward pitch at 45°. Dissimilarity scores were consistently higher for males than for females across all studied factors. The performance particularly diverged in upward movements, starting at 15°. Among tested facial expressions, happiness and contempt performed best, while disgust exhibited the lowest AUC values.

• MeSH
• Algorithms * MeSH
• Automated Facial Recognition * methods   MeSH
• Biometric Identification methods   MeSH
• Adult MeSH
• Head Movements physiology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Face anatomy & histology   MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Posture physiology   MeSH
• Facial Expression * MeSH
• Imaging, Three-Dimensional MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Improving patient care and advancing scientific discovery requires responsible sharing of research data, healthcare records, biosamples, and biomedical resources that must also respect applicable use conditions. Defining a standard to structure and manage these use conditions is a complex and challenging task. This is exemplified by a near unlimited range of asset types, a high variability of applicable conditions, and differing applications at the individual or collective level. Furthermore, the specifics and granularity required are likely to vary depending on the ultimate contexts of use. All these factors confound alignment of institutional missions, funding objectives, regulatory and technical requirements to facilitate effective sharing. The presented work highlights the complexity and diversity of the problem, reviews the current state of the art, and emphasises the need for a flexible and adaptable approach. We propose Digital Use Conditions (DUC) as a framework that addresses these needs by leveraging existing standards, striking a balance between expressiveness versus ambiguity, and considering the breadth of applicable information with their context of use.

• MeSH
• Humans MeSH
• Information Dissemination * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

BACKGROUND: Despite the ever-increasing offering of SMART technologies (ie, computer-controlled devices acting intelligently and capable of monitoring, analyzing or reporting), a wide gap exists between the development of new technological innovations and their adoption in everyday care for older adults. OBJECTIVE: This study aims to explore the barriers and concerns related to the adoption of SMART technologies among different groups of stakeholders. METHODS: Data from 4 sources were used: semistructured in-person or internet-based interviews with professional caregivers (n=12), structured email interviews with experts in the area of aging (n=9), a web-based survey of older adults (>55 years) attending the Virtual University of the Third Age (n=369), and a case study on the adoption of new technology by an older adult care facility. RESULTS: Although all stakeholders noted the potential of SMART technologies to improve older adult care, multiple barriers to their adoption were identified. Caregivers perceived older adults as disinterested or incompetent in using technology, reported preferring known strategies over new technologies, and noted own fears of using technology. Experts viewed technologies as essential but expressed concerns about cost, low digital competency of older adults, and lack of support or willingness to implement technologies in older adult care. Older adults reported few concerns overall, but among the mentioned concerns were lack of ability or interest, misuse of data, and limited usefulness (in specific subgroups or situations). In addition, older adults' ratings of the usefulness of different technologies correlated with their self-rating of digital competency (r=0.258; P<.001). CONCLUSIONS: Older adults appeared to have more positive views of various technologies than professional caregivers; however, their concerns varied by the type of technology. Lack of competence and lack of support were among the common themes, suggesting that educationally oriented programs for both older adults and their caregivers should be pursued.

• MeSH
• Electronic Mail MeSH
• Humans MeSH
• Aged MeSH
• Aging MeSH
• Fear MeSH
• Technology * MeSH
• Quality Improvement * MeSH
• Check Tag
• Humans MeSH
• Aged MeSH
• Publication type
• Journal Article MeSH

• Keywords
• lidský kapitál, lidský faktor, green skills,
• MeSH
• Data Analysis MeSH
• Assertiveness MeSH
• Digital Technology MeSH
• Occupational Health * legislation & jurisprudence   MeSH
• Humans MeSH
• Teleworking MeSH
• Occupational Stress MeSH
• Green Chemistry Technology MeSH
• Technology methods   organization & administration   MeSH
• Accidents legislation & jurisprudence   MeSH
• Check Tag
• Humans MeSH

BACKGROUND: To say data is revolutionising the medical sector would be a vast understatement. The amount of medical data available today is unprecedented and has the potential to enable to date unseen forms of healthcare. To process this huge amount of data, an equally huge amount of computing power is required, which cannot be provided by regular desktop computers. These areas can be (and already are) supported by High-Performance-Computing (HPC), High-Performance Data Analytics (HPDA), and AI (together "HPC+"). OBJECTIVE: This overview article aims to show state-of-the-art examples of studies supported by the National Competence Centres (NCCs) in HPC+ within the EuroCC project, employing HPC, HPDA and AI for medical applications. METHOD: The included studies on different applications of HPC in the medical sector were sourced from the National Competence Centres in HPC and compiled into an overview article. Methods include the application of HPC+ for medical image processing, high-performance medical and pharmaceutical data analytics, an application for pediatric dosimetry, and a cloud-based HPC platform to support systemic pulmonary shunting procedures. RESULTS: This article showcases state-of-the-art applications and large-scale data analytics in the medical sector employing HPC+ within surgery, medical image processing in diagnostics, nutritional support of patients in hospitals, treating congenital heart diseases in children, and within basic research. CONCLUSION: HPC+ support scientific fields from research to industrial applications in the medical area, enabling researchers to run faster and more complex calculations, simulations and data analyses for the direct benefit of patients, doctors, clinicians and as an accelerator for medical research.

• MeSH
• Child MeSH
• Humans MeSH
• Computing Methodologies * MeSH
• Image Processing, Computer-Assisted MeSH
• Software * MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

INTRODUCTION: Loneliness and social isolation reduce physical and mental wellbeing. Older adults are particularly prone to social isolation due to decreased connection with previous social networks such as at workplaces. Social technology can decrease loneliness and improve wellbeing. The COVID-19 pandemic prompted quarantine and social distancing for many people, creating a context of widespread social isolation. METHOD: In the current study, we interviewed middle-aged and older adults' (n = 20) about their use of social technology when social isolation was common: during the early part of the pandemic while social isolation and masking were still required in the United States, between August 2020 and June 2021.We analyzed the data using three-phase coding. We compare our results against the model of the bidirectional and dynamic relationship between social internet use and loneliness. RESULTS: We found that during the COVID-19 pandemic, our participants experienced decreased social interaction and moved toward online interaction. Participant use of social technology supported the stimulation hypothesis - that is, they used it to maintain existing relationships and social connection. The findings also add novel evidence that the stimulation hypothesis endures for older adults during enforced isolation (in this case due to the COVID- 19 pandemic). DISCUSSION: Based on our data, we also propose adding the presence or realism of connection via social technology as a main factor to the model and engaging with construal level theory of social presence to fill in critical variables of this relationship. We further find that digital exclusion acts as a barrier to obtaining benefits from stimulation via social technology and recommend that further research examined digital exclusion in relation to the bidirectional and dynamic model. Finally, we discuss recommendations for improving social technology to benefit middle-aged and older adults.

• MeSH
• COVID-19 * epidemiology   MeSH
• Internet MeSH
• Middle Aged MeSH
• Humans MeSH
• Pandemics * MeSH
• Internet Use MeSH
• Aged MeSH
• Technology MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Aged MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH