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This study examined the association between event-related disclosure and posttraumatic growth (PTG). Specifically, it targeted the Japanese adult, assessed attitudes toward event-related disclosure, and examined event-related rumination as a mediating variable. A cross-sectional online survey was conducted among Japanese adults aged 20–59. Participants completed measures of demographic characteristics, stressful life events attributes, attitudes toward disclosure, event-related rumination, and PTG. Analysis of data from 480 individuals revealed that neither willingness to disclose nor resistance to disclose was directly associated with PTG. However, both willingness to disclose and resistance to disclose were positively associated with PTG through deliberate rumination and negatively associated with PTG through intrusive rumination. The effect sizes for willingness to disclose were approximately three times greater than those for resistance to disclose. These findings suggest that event-related disclosure may enhance PTG by promoting deliberate meaning-making processes, while also potentially hindering it by reinforcing involuntary negative thinking. Furthermore, these effects may be stronger when individuals are more proactive and disclose more frequently. These results have important implications for interventions aimed at enhancing PTG through event-related disclosure.

To detect errors in patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), we proposed an error detection method based on dose distribution analysis using unsupervised deep learning approach and analyzed 161 prostate VMAT beams measured with a cylindrical detector. For performing error simulation, in addition to error-free dose distribution, dose distributions containing nine types of error, including multileaf collimator (MLC) positional errors, gantry rotation errors, radiation output errors and phantom setup errors, were generated. Only error-free data were employed for the model training, and error-free and error data were employed for the tests. As a deep learning model, the variational autoencoder (VAE) was adopted. The anomaly of test data was quantified by calculating Mahalanobis distance based on the feature vectors acquired from a trained encoder. Based on this anomaly, test data were classified as ‘error-free’ or ‘any-error.’ For comparison with conventional approaches, gamma (γ)-analysis was performed, and supervised learning convolutional neural network (S-CNN) was constructed. Receiver operating characteristic curves were obtained to evaluate their performance with the area under the curve (AUC). For all error types, except systematic MLC positional and radiation output errors, the performance of the methods was in the order of S-CNN ˃ VAE-based ˃ γ-analysis (only S-CNN required error data for model training). For example, in random MLC positional error simulation, the AUC of our method, S-CNN and γ-analysis were 0.699, 0.921 and 0.669, respectively. Our results showed that the VAE-based method has the potential to detect errors in patient-specific VMAT QA.

Japanese firms are accelerating their engagement in horizontal collaboration through unprecedented inter-firm combinations that allow organizations to respond flexibly and quickly to changes in the external environment. However, existing research has not sufficiently examined trust formation and individual interaction processes in the initial stages of such inter-organizational collaboration. This study examines a newly established value-creation consortium led by the private sector that uses state-of-the-art artificial intelligence (AI) technology to solve social issues. We interviewed consortium members in different positions; the steps for coding and theorization (SCAT) were used to analyze individuals' interactions in the initial stage of forming inter-organizational collaboration. The results showed that the members' willingness to collaborate increased due to the leader exhibiting trustworthy behavior. Furthermore, uncertainty caused by AI's technological specificity led to insecurity, creating role ambiguity and role conflicts, which leaders and members overcame to form interdependent relationships among individuals. The indication of such a process is a new finding, the practical implications of which are discussed.

This paper aimed to identify factors that promote innovation in Japan’s radio industry, where innovation has stagnated for many years. First, we examined previous studies on top management leadership characteristics, organizational unit coordination mechanisms, senior management teams, and environmental dynamism as key concepts for innovation creation. We also interviewed top managers in the Japanese radio industry to develop specific hypotheses. We tested our hypotheses by conducting a questionnaire survey with 157 middle managers of Japanese radio stations; we used multiple regression analysis to examine the effect of each variable on exploratory and exploitative innovation. Our main findings revealed that management, by exception of transactional leadership, formalization and connectedness of organizational unit coordination mechanisms, and perceived environmental dynamism, positively affect exploratory and exploitative innovation in the radio industry. In contrast, the inspirational motivation of transformational leadership and social integration of the senior team negatively affected exploratory innovation. This study’s academic contribution is to identify the unique organizational factors that drive innovation in the Japanese radio industry by quantitatively testing the original hypothesis derived from the interview survey. Our results showed an urgent need to develop organizational human resources for senior management. Finally, the limitations of this study and future research were discussed.

The clinical significance of human S100A8/A9 (h-S100A8/A9) in patients with inflammatory bowel disease (IBD) is poorly understood. To clarify whether serum S100A8/A9 is a sensitive biomarker for IBD. Serum specimens from outpatients with IBD (n = 101) and healthy volunteers (HVs) (n = 101) were used in this study. Enzyme-linked immunosorbent assays for h-S100A8/A9 and inflammatory cytokines were performed using these specimens. Further, correlation analysis was performed to investigate the significance of h-S100A8/A9 fluctuation in patients with IBD. The average of serum h-S100A8/A9 concentration in outpatients with IBD was significantly higher than that in HVs. The concentration of h-S100A8/A9 in patients with IBD was barely correlated with that of CRP and inflammatory cytokines. Despite that finding, the serum level of h-S100A8/A9 in patients with ulcerative colitis (UC) was correlated with the severity of IBD, compared with other inflammatory proteins. Serum h-S100A8/A9 is superior to CRP as a sensitive biomarker for IBD.

This study aimed to develop a deep learning (DL)-based deliverable whole pelvic volumetric arc radiation therapy (VMAT) for patients with gynecologic cancer using a prototype DL-based automated planning support system, named RatoGuide, to evaluate its clinical validity. In our hospital, 110 patients with gynecologic cancer were registered. The prescribed dose was 50.4 Gy/28 fr. A DL-based three-dimensional dose prediction model was first trained by the dose distribution and structure data of whole pelvic VMAT ( n  = 100) created on the Monaco treatment planning system (TPS). The structure data of the test data ( n  = 10) were then input to RatoGuide, and RatoGuide predicted the dose distribution of the whole pelvic VMAT plan (PreDose). We established deliverable plans with Monaco and Eclipse TPS (DeliDose) based on PreDose and vendor-supplied optimization objectives. Medical physicists then manually developed plans (CliDose) for the test data. Finally, we evaluated and compared the dose distribution and dose constraints of PreDose, DeliDose, and CliDose. DeliDose, in both Eclipse and Monaco, was comparable to PreDose in most Dose constraints, planning target volume (PTV) coverage, and Dmax of the bladder, rectum, and bowel bag were better for DeliDose than for PreDose. Additionally, DeliDose demonstrated no significant difference from CliDose in most dose constraints. The blinded average scores of radiation oncologists for DeliDose and CliDose were 4.2 ± 0.4 and 4.3 ± 0.5, respectively, in Eclipse, and 4.0 ± 0.6 and 3.9 ± 0.5, respectively, in Monaco (5 is the max score and 3 is clinically acceptable). We indicated that RatoGuide can eliminate variations in plan quality between hospitals in whole pelvic VMAT irradiation and help develop VMAT plans in a short time.

This study aims to evaluate the dosimetric accuracy of a deep learning (DL)-based deliverable volumetric arc radiation therapy (VMAT) plan generated using DL-based automated planning assistant system (AIVOT, prototype version) for patients with prostate cancer. The VMAT data (cliDose) of 68 patients with prostate cancer treated with VMAT treatment (70–74 Gy/28–37 fr) at our hospital were used (n = 55 for training and n = 13 for testing). First, a HD-U-net-based 3D dose prediction model implemented in AIVOT was customized using the VMAT data. Thus, a predictive VMAT plan (preDose) comprising AIVOT that predicted the 3D doses was generated. Second, deliverable VMAT plans (deliDose) were created using AIVOT, the radiation treatment planning system Eclipse (version 15.6) and its vender-supplied objective functions. Finally, we compared these two estimated DL-based VMAT treatment plans—i.e. preDose and deliDose—with cliDose. The average absolute dose difference of all DVH parameters for the target tissue between cliDose and deliDose across all patients was 1.32 ± 1.35% (range: 0.04–6.21%), while that for all the organs at risks was 2.08 ± 2.79% (range: 0.00–15.4%). The deliDose was superior to the cliDose in all DVH parameters for bladder and rectum. The blinded plan scoring of deliDose and cliDose was 4.54 ± 0.50 and 5.0 ± 0.0, respectively (All plans scored ≥4 points, P = 0.03.) This study demonstrated that DL-based deliverable plan for prostate cancer achieved the clinically acceptable level. Thus, the AIVOT software exhibited a potential for automated planning with no intervention for patients with prostate cancer.

Short timescale variability is often associated with a black hole system. The consequence of an electromagnetic outflow suddenly generated near a Kerr black hole is considered assuming that it is described by a solution of a force-free field with a null electric current. We compute charged particle acceleration induced by the burst field. We show that the particle is instantaneously accelerated to the relativistic regime by the field with a very large amplitude, which is characterized by a dimensionless number κ. Our numerical calculation demonstrates how the trajectory of the particle changes with κ. We also show that the maximum energy increases with κ2/3. The typical maximum energy attained by a proton for an event near a super massive black hole is Emax∼100 TeV, which is enough observed high-energy flares.

In this study, we propose two methods for representing virtual transparent objects convincingly on an optical see-through head-mounted display without the use of an attenuation function or shielding environmental light. The first method represents the shadows and caustics of virtual transparent objects as illusionary images. Using this illusion-based approach, shadows can be represented without blocking the luminance produced by the real environment, and caustics are represented by adding the luminance of the environment to the produced shadow. In the second method, the visual effects that occur in each individual image of a transparent object are represented as surface, refraction, and reflection images by considering human binocular movement. The visual effects produced by this method reflect the disparities among the vergence and defocus of accommodation associated with the respective images. When reproducing the disparity, each parallax image is calculated in real time using a polygon-based method, whereas when reproducing the defocus, image processing is applied to blur each image and consider the user’s gaze image. To validate these approaches, we conducted experiments to evaluate the realism of the virtual transparent objects produced by each method. The results revealed that both methods produced virtual transparent objects with improved realism.

ObjectiveNardilysin (NRDC), a zinc peptidase, exhibits multiple localisation-dependent functions including as an enhancer of ectodomain shedding in the extracellular space and a transcriptional coregulator in the nucleus. In this study, we investigated its functional role in exocrine pancreatic development, homeostasis and the formation of pancreatic ductal adenocarcinoma (PDA).DesignWe analysed Ptf1a-Cre; Nrdcflox/flox mice to investigate the impact of Nrdc deletion. Pancreatic acinar cells were isolated from Nrdcflox/flox mice and infected with adenovirus expressing Cre recombinase to examine the impact of Nrdc inactivation. Global gene expression in Nrdc-cKO pancreas was analysed compared with wild-type pancreas by microarray analysis. We also analysed Ptf1a-Cre; KrasG12D; Nrdcflox/flox mice to investigate the impact of Nrdc deletion in the context of oncogenic Kras. A total of 51 human samples of pancreatic intraepithelial lesions (PanIN) and PDA were examined by immunohistochemistry for NRDC.ResultsWe found that pancreatic deletion of Nrdc leads to spontaneous chronic pancreatitis concomitant with acinar-to-ductal conversion, increased apoptosis and atrophic pancreas in mice. Acinar-to-ductal conversion was observed mainly through a non-cell autonomous mechanism, and the expression of several chemokines was significantly increased in Nrdc-null pancreatic acinar cells. Furthermore, pancreatic deletion of Nrdc dramatically accelerated KrasG12D -driven PanIN and subsequent PDA formation in mice. These data demonstrate a previously unappreciated anti-inflammatory and tumour suppressive functions of Nrdc in the pancreas in mice. Finally, absence of NRDC expression was observed in a subset of human PanIN and PDA.ConclusionNrdc inhibits pancreatitis and suppresses PDA initiation in mice.