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\"Modals and Quasi-modals in English reports the findings of a corpus-based study of the modals and a set of semantically-related 'quasi-modals' in English. The study is informed by recent developments in the study of modality, including grammaticalization and recent diachronic change. The selection of the parallel corpora used, representing British, American and Australian English, was designed to facilitate the exploration of both regional and stylistic variation.\"--Jacket.

Selective attention to visual stimuli can spread cross‐modally to task‐irrelevant auditory stimuli through either the stimulus‐driven binding mechanism or the representation‐driven priming mechanism. The stimulus‐driven attentional spreading occurs whenever a task‐irrelevant sound is delivered simultaneously with a spatially attended visual stimulus, whereas the representation‐driven attentional spreading occurs only when the object representation of the sound is congruent with that of the to‐be‐attended visual object. The current study recorded event‐related potentials in a space‐selective visual object‐recognition task to examine the exact roles of space‐based visual selective attention in both the stimulus‐driven and representation‐driven cross‐modal attentional spreading, which remain controversial in the literature. Our results yielded that the representation‐driven auditory Nd component (200–400 ms after sound onset) did not differ according to whether the peripheral visual representations of audiovisual target objects were spatially attended or not, but was decreased when the auditory representations of target objects were presented alone. In contrast, the stimulus‐driven auditory Nd component (200–300 ms) was decreased but still prominent when the peripheral visual constituents of audiovisual nontarget objects were spatially unattended. These findings demonstrate not only that the representation‐driven attentional spreading is independent of space‐based visual selective attention and benefits in an all‐or‐nothing manner from object‐based visual selection for actually presented visual representations of target objects, but also that although the stimulus‐driven attentional spreading is modulated by space‐based visual selective attention, attending to visual modality per se is more likely to be the endogenous determinant of the stimulus‐driven attentional spreading. The present study found that the representation‐driven attentional spreading was independent of space‐based visual selective attention. The stimulus‐driven attentional spreading was modulated by space‐based visual selective attention but still prominent when the visual constituents of audiovisual nontarget objects were spatially unattended. These findings suggest not only that the representation‐driven attentional spreading benefits in an all‐or‐nothing manner from object‐based visual selection for actually presented visual representations of target objects, but also that although the stimulus‐driven attentional spreading is modulated by space‐based visual selective attention, attending to visual modality per se is more likely to be the endogenous determinant of the stimulus‐driven attentional spreading.

Dual-energy information in computed tomography can be obtained through different technical approaches. Most available scanner designs acquire examination with two different X-ray spectra. Recently, the first detector-based approach became clinically available. Upfront, physical principles of dual-energy CT are reviewed, including the interaction of photons with matter in terms of the Photoelectric effect and Compton scattering. In addition, available concepts to dual energy computed tomography are described. Afterwards, the spectral detector CT system is described in detail. The design of the of the stacked detector design and its inherent technical advantages and disadvantages are discussed. Further, the principles of image reconstruction, their possibilities and limitations are referred. The increase in reconstructions and data pose some challenges to both, clinical and technological workflow which are hereafter addressed. Finally, the detector-based approach is discussed in light of other, emission-based DECT approaches.

Light Detection and Ranging (LiDAR), which applies light in the formation of a pulsed laser to estimate the distance between the LiDAR sensor and objects, is an effective remote sensing technology. Many applications use LiDAR including autonomous vehicles, robotics, and virtual and augmented reality (VR/AR). The 3D point cloud classification is now a hot research topic with the evolution of LiDAR technology. This research aims to provide a high performance and compatible real-world data method for 3D point cloud classification. More specifically, we introduce a novel framework for 3D point cloud classification, namely, GSV-NET, which uses Gaussian Supervector and enhancing region representation. GSV-NET extracts and combines both global and regional features of the 3D point cloud to further enhance the information of the point cloud features for the 3D point cloud classification. Firstly, we input the Gaussian Supervector description into a 3D wide-inception convolution neural network (CNN) structure to define the global feature. Secondly, we convert the regions of the 3D point cloud into color representation and capture region features with a 2D wide-inception network. These extracted features are inputs of a 1D CNN architecture. We evaluate the proposed framework on the point cloud dataset: ModelNet and the LiDAR dataset: Sydney. The ModelNet dataset was developed by Princeton University (New Jersey, United States), while the Sydney dataset was created by the University of Sydney (Sydney, Australia). Based on our numerical results, our framework achieves more accuracy than the state-of-the-art approaches.

“Interaction” represents a critical term in the augmented and mixed reality ecosystem. Today, in mixed reality environments and applications, interaction occupies the joint space between any combination of humans, physical environment, and computers. Although interaction methods and techniques have been extensively examined in recent decades in the field of human-computer interaction, they still should be reidentified in the context of immersive realities. The latest technological advancements in sensors, processing power and technologies, including the internet of things and the 5G GSM network, led to innovative and advanced input methods and enforced computer environmental perception. For example, ubiquitous sensors under a high-speed GSM network may enhance mobile users’ interactions with physical or virtual objects. As technological advancements emerge, researchers create umbrella terms to define their work, such as multimodal, tangible, and collaborative interactions. However, although they serve their purpose, various naming trends overlap in terminology, diverge in definitions, and lack modality and conceptual framework classifications. This paper presents a modality-based interaction-oriented diagram for researchers to position their work and defines taxonomy ground rules to expand and adjust this diagram when novel interaction approaches emerge.

Preoperative prediction of visceral pleural invasion (VPI) is important because it enables thoracic surgeons to choose appropriate surgical plans. This study aimed to develop and validate a multivariate logistic regression model incorporating the maximum standardized uptake value (SUVmax) and valuable computed tomography (CT) signs for the non-invasive prediction of VPI status in subpleural clinical stage IA lung adenocarcinoma patients before surgery.PURPOSEPreoperative prediction of visceral pleural invasion (VPI) is important because it enables thoracic surgeons to choose appropriate surgical plans. This study aimed to develop and validate a multivariate logistic regression model incorporating the maximum standardized uptake value (SUVmax) and valuable computed tomography (CT) signs for the non-invasive prediction of VPI status in subpleural clinical stage IA lung adenocarcinoma patients before surgery.A total of 140 patients with subpleural clinical stage IA peripheral lung adenocarcinoma were recruited and divided into a training set (n = 98) and a validation set (n = 42), according to the positron emission tomography/CT examination temporal sequence, with a 7:3 ratio. Next, VPI-positive and VPI-negative groups were formed based on the pathological results. In the training set, the clinical information, the SUVmax, the relationship between the tumor and the pleura, and the CT features were analyzed using univariate analysis. The variables with significant differences were included in the multivariate analysis to construct a prediction model. A nomogram based on multivariate analysis was developed, and its predictive performance was verified in the validation set.METHODSA total of 140 patients with subpleural clinical stage IA peripheral lung adenocarcinoma were recruited and divided into a training set (n = 98) and a validation set (n = 42), according to the positron emission tomography/CT examination temporal sequence, with a 7:3 ratio. Next, VPI-positive and VPI-negative groups were formed based on the pathological results. In the training set, the clinical information, the SUVmax, the relationship between the tumor and the pleura, and the CT features were analyzed using univariate analysis. The variables with significant differences were included in the multivariate analysis to construct a prediction model. A nomogram based on multivariate analysis was developed, and its predictive performance was verified in the validation set.The size of the solid component, the consolidation-to-tumor ratio, the solid component pleural contact length, the SUVmax, the density type, the pleural indentation, the spiculation, and the vascular convergence sign demonstrated significant differences between VPI-positive (n = 40) and VPI-negative (n = 58) cases on univariate analysis in the training set. A multivariate logistic regression model incorporated the SUVmax [odds ratio (OR): 1.753, P = 0.002], the solid component pleural contact length (OR: 1.101, P = 0.034), the pleural indentation (OR: 5.075, P = 0.041), and the vascular convergence sign (OR: 13.324, P = 0.025) as the best combination of predictors, which were all independent risk factors for VPI in the training group. The nomogram indicated promising discrimination, with an area under the curve value of 0.892 [95% confidence interval (CI), 0.813-0.946] in the training set and 0.885 (95% CI, 0.748-0.962) in the validation set. The calibration curve demonstrated that its predicted probabilities were in acceptable agreement with the actual probability. The decision curve analysis illustrated that the current nomogram would add more net benefit.RESULTSThe size of the solid component, the consolidation-to-tumor ratio, the solid component pleural contact length, the SUVmax, the density type, the pleural indentation, the spiculation, and the vascular convergence sign demonstrated significant differences between VPI-positive (n = 40) and VPI-negative (n = 58) cases on univariate analysis in the training set. A multivariate logistic regression model incorporated the SUVmax [odds ratio (OR): 1.753, P = 0.002], the solid component pleural contact length (OR: 1.101, P = 0.034), the pleural indentation (OR: 5.075, P = 0.041), and the vascular convergence sign (OR: 13.324, P = 0.025) as the best combination of predictors, which were all independent risk factors for VPI in the training group. The nomogram indicated promising discrimination, with an area under the curve value of 0.892 [95% confidence interval (CI), 0.813-0.946] in the training set and 0.885 (95% CI, 0.748-0.962) in the validation set. The calibration curve demonstrated that its predicted probabilities were in acceptable agreement with the actual probability. The decision curve analysis illustrated that the current nomogram would add more net benefit.The nomogram integrating the SUVmax and the CT features could non-invasively predict VPI status before surgery in subpleural clinical stage IA lung adenocarcinoma patients.CONCLUSIONThe nomogram integrating the SUVmax and the CT features could non-invasively predict VPI status before surgery in subpleural clinical stage IA lung adenocarcinoma patients.

Radiography is a field of medicine inherently intertwined with technology. The dependency on technology is very high for obtaining images in ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Although the reduction in radiation dose is not applicable in US and MRI, advancements in technology have made it possible in CT, with ongoing studies aimed at further optimization. The resolution and diagnostic quality of images obtained through advancements in each modality are steadily improving. Additionally, technological progress has significantly shortened acquisition times for CT and MRI. The use of artificial intelligence (AI), which is becoming increasingly widespread worldwide, has also been incorporated into radiography. This technology can produce more accurate and reproducible results in US examinations. Machine learning offers great potential for improving image quality, creating more distinct and useful images, and even developing new US imaging modalities. Furthermore, AI technologies are increasingly prevalent in CT and MRI for image evaluation, image generation, and enhanced image quality.

To develop and validate an automated computational tool for calculating a slice-specific volume computed tomography (CT) dose index (CTDI ), a water-equivalent diameter (D ), and size-specific dose estimates (SSDEs) from CT images, addressing limitations of conventional console-displayed values that provide only averaged values across scan regions. A custom ImageJ macro was developed based on methodologies proposed in American Association of Physicists in Medicine reports 220 and 293. The tool employs threshold-based body contour segmentation [-140 Hounsfield unit (HU)] to extract patient cross-sectional areas and calculates slice-specific D using mean CT numbers. Slice-specific CTDI values are estimated by normalizing scanner-displayed CTDI to individual slice exposure values from Digital Imaging and Communications in Medicine metadata. An SSDE was computed using appropriate correction factors for head and body examinations. Validation was performed using water phantoms, anthropomorphic phantoms, and clinical datasets from ≥30 patients. Two Siemens CT scanners were evaluated: SOMATOM go.Top , with console-displayed values, and SOMATOM Force , with Radimetrics software. Agreement was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Water phantom validation demonstrated excellent accuracy, with differences of <2.3% for both D and SSDEs. The macro required approximately 30 seconds per examination to complete the analysis. Bland-Altman plots confirmed clinically acceptable mean differences. Importantly, the slice-specific approach revealed substantial intra-scan dose variations not captured by console-reported averages, particularly in the chest phantom, where SSDEs ranged from 5.77 to 23.68 mGy despite identical average values. For the clinical dataset, ICC (3,1) values for Scanner A indicated good to excellent agreement across both head and chest/abdomen examinations (head CT-CTDI vol: 0.974, D : 0.893, SSDE: 0.965; chest/abdomen CT-CTDI : 1.000, D : 0.994, SSDE: 0.989). By contrast, Scanner B demonstrated near-perfect agreement for head CT in CTDI (0.996) and SSDE (0.967) but poor agreement for D (0.267). For chest/abdomen CT, however, Scanner B showed consistently high agreement, with ICC values ranging from 0.884 to 1.000. The developed ImageJ macro provides accurate, transparent, and low-cost open-source solution slice-specific CT dose estimation that correlates well with commercial systems while offering superior spatial resolution. This automated method overcomes the limitations of traditional dose reporting by providing detailed slice-by-slice dose variations, which are often overlooked in average summary values, allowing for more accurate and clinically meaningful dose assessments. This tool supports detailed dose evaluation across scan regions, helping optimize protocols and enhance radiation safety. Its slice-specific approach is especially useful in anatomically complex areas and research, offering clinicians more precise dose information to guide patient care.

Magnetic resonance imaging-proton density fat fraction (MRI-PDFF) is an emerging quantitative imaging biomarker that accurately measures the fat fraction of tissue by correcting factors influencing magnetic resonance signal intensity. Beyond fat quantification, it also measures R2* which is a direct measure of iron concentration. The utilization of MRI-PDFF in liver diseases is well established. In the present review, we focused on applications of MRI-PDFF in different body areas including pancreas, bone, muscle, spleen, testis, visceral, and subcutaneous adipose tissue. Future studies can enable tracking of quantitative fat fraction changes in different organs simultaneously, which can be critical in understanding fat metabolism.

To determine whether radiation exposure increased among different ages with chest computed tomography (CT) use during the coronavirus disease-2019 (COVID-19) pandemic. Patients with chest CT scans in an 8-month period of the pandemic between March 15, 2020, and November 15, 2020, and the same period of the preceding year were included in the study. Indications of chest CT scans were obtained from the clinical notes and categorized as infectious diseases, neoplastic disorders, trauma, and other diseases. Chest CT scans for infectious diseases during the pandemic were compared with those with the same indications in 2019. The dose-length product values were obtained from the protocol screen individually. The total number of chest CT scans with an indication of infectious disease was 21746 in 2020 and 4318 in 2019. Total radiation exposure increased by 573% with the use of chest CT for infectious indications but decreased by 19% for neoplasia, 12% for trauma, and 43% for other reasons. The mean age of the patients scanned in 2019 was significantly higher than those scanned during the pandemic (64.6 vs. 50.3 years). A striking increase was seen in the 10-59 age group during the pandemic ( < 0.001). The highest increase was seen in the 20-29 age group, being 18.6 fold. One death was recorded per 58 chest CT scans during the pandemic. Chest CT use was substantially higher at the beginning of the pandemic. Chest CT was excessively used during the COVID-19 pandemic. Young and middle-aged people were exposed more than others. The impact of COVID-19-pandemic-related radiation exposure on public health should be followed carefully in future years.