Mexican Americans are the largest subgroup of Hispanics, the largest minority population in the United States. Stroke is the leading cause of disability and third leading cause of death. The authors compared stroke incidence among Mexican Americans and non-Hispanic Whites in a population-based study. Stroke cases were ascertained in Nueces County, Texas, utilizing concomitant active and passive surveillance. Cases were validated on the basis of source documentation by board-certified neurologists masked to subjects’ ethnicity. From January 2000 to December 2002, 2,350 cerebrovascular events occurred. Of the completed strokes, 53% were in Mexican Americans. The crude cumulative incidence was 168/10,000 in Mexican Americans and 136/10,000 in non-Hispanic Whites. Mexican Americans had a higher cumulative incidence for ischemic stroke (ages 45–59 years: risk ratio = 2.04, 95% confidence interval: 1.55, 2.69; ages 60–74 years: risk ratio = 1.58, 95% confidence interval: 1.31, 1.91; ages ≥75 years: risk ratio = 1.12, 95% confidence interval: 0.94, 1.32). Intracerebral hemorrhage was more common in Mexican Americans (age-adjusted risk ratio = 1.63, 95% confidence interval: 1.24, 2.16). The subarachnoid hemorrhage age-adjusted risk ratio was 1.57 (95% confidence interval: 0.86, 2.89). Mexican Americans experience a substantially greater ischemic stroke and intracerebral hemorrhage incidence compared with non-Hispanic Whites. As the Mexican-American population grows and ages, measures to target this population for stroke prevention are critical.

Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants’ emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

To provide a scientific rationale for choosing an optimal stroke surveillance method, the authors compared active surveillance with passive surveillance. The methods involved ascertaining cerebrovascular events that occurred in Nueces County, Texas, during calendar year 2000. Active methods utilized screening of hospital and emergency department logs and routine visiting of hospital wards and out-of-hospital sources. Passive means relied on International Classification of Diseases, Ninth Revision (ICD-9), discharge codes for case ascertainment. Cases were validated by fellowship-trained stroke neurologists on the basis of published criteria. The results showed that, of the 6,236 events identified through both active and passive surveillance, 802 were validated to be cerebrovascular events. When passive surveillance alone was used, 209 (26.1%) cases were missed, including 73 (9.1%) cases involving hospital admission and 136 (17.0%) out-of-hospital strokes. Through active surveillance alone, 57 (7.1%) cases were missed. The positive predictive value of active surveillance was 12.2%. Among the 2,099 patients admitted to a hospital, passive surveillance using ICD-9 codes missed 73 cases of cerebrovascular disease and mistakenly included 222 noncases. There were 57 admitted hospital cases missed by active surveillance, including 13 not recognized because of human error. This study provided a quantitative means of assessing the utility of active and passive surveillance for cerebrovascular disease. More uniform surveillance methods would allow comparisons across studies and communities.

Background: Magnetic resonance imaging (MRI) and Computed tomography (CT) are crucial imaging techniques in both diagnostic imaging and radiation therapy. MRI provides excellent soft tissue contrast but lacks the direct electron density data needed to calculate dosage. CT, on the other hand, remains the gold standard due to its accurate electron density information in radiation therapy planning (RTP) but it exposes patients to ionizing radiation. Synthetic CT (sCT) generation from MRI has been a focused study field in the last few years due to cost effectiveness as well as for the objective of minimizing side‐effects of using more than one imaging modality for treatment simulation. It offers significant time and cost efficiencies, bypassing the complexities of co‐registration, and potentially improving treatment accuracy by minimizing registration‐related errors. In an effort to navigate the quickly developing field of precision medicine, this paper investigates recent advancements in sCT generation techniques, particularly those using machine learning (ML) and deep learning (DL). The review highlights the potential of these techniques to improve the efficiency and accuracy of sCT generation for use in RTP by improving patient care and reducing healthcare costs. The intricate web of sCT generation techniques is scrutinized critically, with clinical implications and technical underpinnings for enhanced patient care revealed.Purpose: This review aims to provide an overview of the most recent advancements in sCT generation from MRI with a particular focus of its use within RTP, emphasizing on techniques, performance evaluation, clinical applications, future research trends and open challenges in the field.Methods: A thorough search strategy was employed to conduct a systematic literature review across major scientific databases. Focusing on the past decade's advancements, this review critically examines emerging approaches introduced from 2013 to 2023 for generating sCT from MRI, providing a comprehensive analysis of their methodologies, ultimately fostering further advancement in the field. This study highlighted significant contributions, identified challenges, and provided an overview of successes within RTP. Classifying the identified approaches, contrasting their advantages and disadvantages, and identifying broad trends were all part of the review's synthesis process.Results: The review identifies various sCT generation approaches, consisting atlas‐based, segmentation‐based, multi‐modal fusion, hybrid approaches, ML and DL‐based techniques. These approaches are evaluated for image quality, dosimetric accuracy, and clinical acceptability. They are used for MRI‐only radiation treatment, adaptive radiotherapy, and MR/PET attenuation correction. The review also highlights the diversity of methodologies for sCT generation, each with its own advantages and limitations. Emerging trends incorporate the integration of advanced imaging modalities including various MRI sequences like Dixon sequences, T1‐weighted (T1W), T2‐weighted (T2W), as well as hybrid approaches for enhanced accuracy.Conclusions: The study examines MRI‐based sCT generation, to minimize negative effects of acquiring both modalities. The study reviews 2013‐2023 studies on MRI to sCT generation methods, aiming to revolutionize RTP by reducing use of ionizing radiation and improving patient outcomes. The review provides insights for researchers and practitioners, emphasizing the need for standardized validation procedures and collaborative efforts to refine methods and address limitations. It anticipates the continued evolution of techniques to improve the precision of sCT in RTP.

Purpose – The purpose of this paper is to resolve a gap in the knowledge of how people with dyslexia interact with information retrieval (IR) systems, specifically an understanding of their information-searching behaviour. Design/methodology/approach – The dyslexia cognitive profile is used to design a logging system, recording the difference between two sets of participants: dyslexic and control users. A standard Okapi interface is used – together with two standard TREC topics – in order to record the information searching behaviour of these users. Findings – Using the log data, the differences in information-searching behaviour of control and dyslexic users, i.e. in the way the two groups interact with Okapi, are established and it also established that qualitative information collected (such as experience etc.) may not be able to account for these differences. Evidence from query variables was unable to distinguish between groups, but differences on topic for the same variables were recorded. Users who view more documents tended to judge more documents as being relevant, in terms of either the user group or topic. Session data indicated that there may be an important difference between the number of iterations used in a search between the user groups, as there may be little effect from the topic on this variable. Originality/value – This is the first study of the effect of dyslexia on information search behaviour, and it provides some evidence to take the field forward.