Munroenevoldsen8442

Fifteen fresh, frozen whole cadaveric femora specimens (age 72.1 ± 15.0 years old, 10 male, 5 female) were scanned on a clinical 3-T MRI using a dual-echo UTE sequence. read more Specimens were then scanned on a clinical CT scanner to measure volumetric BMD (vBMD) and then non-destructively mechanically tested in a sideways fall configuration. The PI in the cortical shaft demonstrated strong correlations with bone stiffness (r = -0.82, P = 0.0014), CT-derived vBMD (r = -0.64, P = 0.0149), and with average cortical thickness (r = -0.60, P = 0.0180). Furthermore, a hierarchical regression showed that PI was a strong predictor of bone stiffness which was independent of the other parameters. The findings from this study validate the MRI-derived porosity index as a useful measure of whole-bone mechanical integrity and stiffness.Periapical Radiographs are commonly used to detect several anomalies, like caries, periodontal, and periapical diseases. Even considering that digital imaging systems used nowadays tend to provide high-quality images, external factors, or even system limitations can result in a vast amount of radiographic images with low quality and resolution. Commercial solutions offer tools based on interpolation methods to increase image resolution. However, previous literature shows that these methods may create undesirable effects in the images affecting the diagnosis accuracy. One alternative is using deep learning-based super-resolution methods to achieve better high-resolution images. Nevertheless, the amount of data for training such models is limited, demanding transfer learning approaches. In this work, we propose the use of super-resolution generative adversarial network (SRGAN) models and transfer learning to achieve periapical images with higher quality and resolution. Moreover, we evaluate the influence of using the transfer learning approach and the datasets selected for it in the final generated images. For that, we performed an experiment comparing the performance of the SRGAN models (with and without transfer learning) with other super-resolution methods. Considering Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Opinion Score (MOS), the results of SRGAN models using transfer learning were better on average. This superiority was also verified statistically using the Wilcoxon paired test. In the visual analysis, the high quality achieved by the SRGAN models, in general, is visible, resulting in more defined edges details and fewer blur effects.Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Besides, the public, who is the data source, always expects the, ethics clearance, consent management, medical innovations, and developments in the health domain.Although previous research has shown that exposure to violent video games is related to aggression, little is known about the relationship between the player's perspective (player vs. observer) and aggression. In this experimental study, we tested the short-term effects of actively playing versus passively observing the same type of video games on aggressive cognitions and aggressive behaviors. A total of 192 12-year-old children (50% girls) were randomly assigned to one of four conditions (a) violent game player (active violent players), (b) violent game observer (passive violent observers), (c) neutral game player (active neutral players), or (d) neutral game observer (passive neutral observers). After either playing or observing the designated games, each participant completed a lexical decision task and a competitive reaction time task to measure their aggressive cognitions and behaviors. Results showed that players displayed more aggressive cognitions and behaviors than observers. Boys displayed more aggressive cognitions and behaviors than girls, but this trend was observed only in the violent game play condition. Mediational analysis suggested that aggressive cognitions partially mediated the effect of violent video games on aggressive behaviors.It has been argued that children implicitly acquire the rules relating to the structure of music in their environment using domain-general mechanisms such as statistical learning. Closely linked to statistical learning is the ability to form expectations about future events. Whether children as young as 5 years can make use of such internalized regularities to form expectations about the next note in a melody is still unclear. The possible effect of the home musical environment on the strength of musical expectations has also been under-explored. Using a newly developed melodic priming task that included melodies with either "expected" or "unexpected" endings according to rules of Western music theory, we tested 5- and 6-year-old children (N = 46). The stimuli in this task were constructed using the information dynamics of music (IDyOM) system, a probabilistic model estimating the level of "unexpectedness" of a note given the preceding context. Results showed that responses to expected versus unexpected tones were faster and more accurate, indicating that children have already formed robust melodic expectations at 5 years of age. Aspects of the home musical environment significantly predicted the strength of melodic expectations, suggesting that implicit musical learning may be influenced by the quantity of informal exposure to the surrounding musical environment.Recent evidence suggests that infants engage in selective prosocial behavior toward some individuals over others; the ways in which infants are selective can illuminate the origins of prosocial behaviors. Here, we explored selective helping behavior, investigating whether a target recipient's prior adherence to, or defiance of, social conventions affects infants' subsequent likelihood of helping the target individual. 19-month-old infants (N = 120) participated in an interaction with an experimenter who correctly labeled common objects, incorrectly labeled objects, or labeled objects with nonsense English-like labels. Infants' rates of helping were higher when the experimenter adhered to labeling conventions than when she defied labeling conventions by either labeling objects incorrectly or using unfamiliar nonsense labels. link2 The current study provides evidence that infants use information about adhering to conventions to guide their helping behavior. These findings help to document the ways in which infants are selective in their helping behavior as well as possible origins of prosocial obligations toward ingroup members.Edible wild plant/mushroom gathering, an essential food acquisition and outdoor recreation activity in rural areas, has declined in the area near the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident in eastern Japan. The present study first evaluated the spatial distribution of potential gathering sites of various edible wild plant/mushroom species before the accident by administering a face-to-face questionnaire survey to local gatherers as well as utilizing the group analytical hierarchy process (AHP) and geographic information systems (GIS). Then, the damage to and future reusability of previous gathering sites were estimated from the perspective of the external radiation dose by overlaying maps of potential gathering sites and the time-series air dose rate (ADR) up to 2050 incorporating different gathering frequency scenarios. The study area is located in Kawauchi village in the eastern Fukushima prefecture, at 12-30 km southwest of FDNPP. The spatial distributions of gathering sites before the accwild plants/mushrooms.

Allergen-specific immunotherapy (ASIT) is currently the only therapy for allergic rhinitis (AR) that can induce immune tolerance to allergens. However, the course of ASIT is long and there is no objective biomarker to predict treatment efficacy. The present study aimed to explore potential biomarkers predictive of efficacy of AIT based on serum metabolomics profiles.

This prospective study recruited 72 consecutive eligible patients who were assigned to receive sublingual immunotherapy (SLIT). Serum samples were collected prior to SLIT and utilized to obtain metabolomics profiling by applying ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). Treatment response was determined 3years after SLIT, and patients were divided into effective group and ineffective group. Orthogonal partial least square-discriminate analysis (OPLS-DA) was performed to evaluate the metabolite differences between two groups.

Sixty-eight patients completed the whole SLIT, 39 patients were categorized into effat can reliably and accurately predict the efficacy of SLIT in AR patients. The discriminative metabolites and related metabolic pathways contributed to better understand the mechanisms of SLIT in AR patients.This study explores neural mechanisms underlying how prior knowledge gained from pre-listening transcript reading helps comprehend fast-rate speech in a second language (L2) and applies to L2 learning. Top-down predictive processing by prior knowledge may play an important role in L2 speech comprehension and improving listening skill. By manipulating the pre-listening transcript effect (pre-listening transcript reading [TR] vs. no transcript reading [NTR]) and type of languages (first language (L1) vs. L2), we measured brain activity in L2 learners, who performed fast-rate listening comprehension tasks during functional magnetic resonance imaging. Thereafter, we examined whether TR_L2-specific brain activity can predict individual learning success after an intensive listening training. link3 The left angular and superior temporal gyri were key areas responsible for integrating prior knowledge to sensory input. Activity in these areas correlated significantly with gain scores on subsequent training, indicating that brain activity related to prior knowledge-sensory input integration predicts future learning success.Although memory of past experiences is crucial for the ability to transfer knowledge to new situations, surprisingly little research has directly investigated the relationship between memory and generalization. The present study sought to investigate how the perceptual memory of a trained stimulus influences generalization to similar stimuli. Forty participants underwent a fear conditioning procedure on Day 1, and separate memory recall and generalization tests on Day 2. We focused on two aspects of perceptual memory namely memory bias (i.e., over- or underestimation of stimulus magnitude) and uncertainty. We found that memory bias predicted the pattern of generalized self-reported (expectancy ratings) and psychophysiological responses (fear-potentiated startle responses). Memory uncertainty was measured in two ways self-reported uncertainty ratings and variability in stimulus recall. We found that higher levels of self-reported memory uncertainty corresponded with a broader generalization gradient on US expectancy, while greater variability in memory recall was associated with a broader generalization gradient on fear-potentiated startle responses.