Concomitant pulmonary and thyroid tumors identified by FDG PET/CT and immunohistochemical techniques

<p>Abstract</p> <p>Background</p> <p>The exact diagnosis of double primary papillary adenocarcinoma of thyroid and lung is even rarer, to our knowledge no report in the literature by [¹⁸F]-2-fluoro-2-deoxy-D-glucose-positron emission tomography/X-ray CT(FDG PET/CT) with surgical specimens immunohistochemistry(IHC). We report a patient with abnormal FDG PET/CT in thyroid and lung, this unusual presentation may lead to misdiagnosis without surgical specimens IHC.</p> <p>Case presentation</p> <p>A 56-year-old man with coughing three months. FDG PET/CT was performed, and resection specimens of lung and thyroid were detected by hematoxylin eosin staining (HE) and IHC. PET/CT: lung tumor SUVmax: 3.69, delay: 5.17; and thyroid tumor SUVmax 19.97. HE reveal papillary adenocarcinoma, but histological differentiation of primary pulmonary adenocarcinoma from metastatic adenocarcinoma is sometimes difficult because of their phenotypic similarities. So IHC was performed, the IHC of lung tumor: cytokeratin 20 (CK20)(-), thyroglobulin(Tg)(-), cytokeratin7(CK7)(+), thyroid transcription factor-1 (TTF-1)(+); thyroid tumor: CK7(+), TTF-1(+), thyroglobulin (+), CK20(-). Therefore, the final diagnosis was double primary adenocarcinomas of thyroid and lung.</p> <p>Conclusion</p> <p>FDG PET/CT has preliminary diagnostic capacity of multiple primary tumors; the final diagnosis should be adopted for specimens after tumor-specific markers IHC to obtain. Consequently, effective therapeutic approaches can be designed and conducted.</p

Characteristics of visibility and particulate matter (PM) in an urban area of Northeast China

AbstractThe visibility data from 2010 to 2012 were obtained at Shenyang in Northeast China and the relations between visibility, PM mass concentration and meteorological variables were statistically analyzed. These results demonstrate that the monthly–averaged visibility over Shenyang was higher in March and September with values of approximately 19.0±4.3 km and 17.1±4.3 km, respectively. Low visibility over Shenyang occurred in January at approximately 11.0±4.7 km. Among the meteorological variables considered, wind speed was the main meteorological factor that influenced visibility and PM mass concentrations. The relation between visibility and PM indicates that fine particles are already a main source of pollutants, the existence of which is the most important factor in the deterioration of visibility in an urban area of Northeast China. The study also shows an obvious diurnal variation and weekend effects of visibility and PM, which are mainly caused by human activities. Results of this study highlight the significant impact of fine particles on air pollution and visibility in an urban area of Northeast China

The "double-edged sword" effects of career support mentoring on newcomer turnover: How and when it helps or hurts.

Research on mentoring programs has portrayed them almost exclusively beneficial for newcomer retention. Drawing from the social cognitive model of career management and the boundaryless career perspective, we depart from this predominant view and examine the "double-edged sword" effects of career support mentoring on newcomer turnover. We propose that career support mentoring received by newcomers is likely to elicit both internal proactive socialization and external career self-management, which act as countervailing forces driving newcomer turnover in opposite directions (i.e., the retention pathway and the unintended detrimental pathway). We further propose that the organizational role of the mentor-supervisor versus nonsupervisor-is critical in determining which pathway prevails. We conducted two multiwave newcomer studies to test our hypotheses. In Study 1 ( = 495), we found that received career support mentoring was associated with lower newcomer turnover probability through the serial mediation of internal proactive socialization and perceived internal marketability but higher newcomer turnover probability through the serial mediation of external career self-management and perceived external marketability. In Study 2 ( = 193), we found that received career support mentoring was associated with lower newcomer turnover intention through the serial mediation of internal career advancement expectation and internal proactive socialization but higher newcomer turnover intention through the serial mediation of external career advancement expectation and external career self-management. In both studies, the unintended detrimental pathway was significant only when a newcomer's mentor was not a supervisor. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.apenergy.2016.08.085 © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The series hybrid electric tracked bulldozer (HETB)’s fuel economy heavily depends on its energy management strategy. This paper presents a model predictive controller (MPC) to solve the energy management problem in an HETB for the first time. A real typical working condition of the HETB is utilized to develop the MPC. The results are compared to two other strategies: a rule-based strategy and a dynamic programming (DP) based one. The latter is a global optimization approach used as a benchmark. The effect of the MPC’s parameters (e.g. length of prediction horizon) is also studied. The comparison results demonstrate that the proposed approach has approximately a 6% improvement in fuel economy over the rule-based one, and it can achieve over 98% of the fuel optimality of DP in typical working conditions. To show the advantage of the proposed MPC and its robustness under large disturbances, 40% white noise has been added to the typical working condition. Simulation results show that an 8% improvement in fuel economy is obtained by the proposed approach compared to the rule-based one.Natural Sciences and Engineering Research Council of Canada (NSERC) || Ontario Research Fun

Multipath estimation based on modified ε-constrained rank-based differential evolution with minimum error entropy

Multipath is one of the dominant error sources for high-precision positioning systems, such as global navigation satellite systems (GNSS). The minimum mean square error (MSE) criterion is usually employed for multipath estimation under the assumption of Gaussian noise. For non-Gaussian noise as appeared in most practical applications, alternative solutions are required for multipath estimation. In this work, a multipath estimation algorithm is proposed based on the minimum error entropy (MEE) criterion under Gaussian or non-Gaussian noises. A key advantage of using MEE is that it can minimize the randomness of error signals, however, the shift-invariance characteristics in MEE may lead to a bias of the estimation result. To mitigate such a bias, an improved estimation strategy is proposed by integrating the second-order central moment of the estimation error together with the prior information of multipath parameters as a constraint. The multipath estimation problem is thus formulated as a constrained optimization problem. A modified ε-constrained rank-based differential evolution (εRDE) algorithm is developed to find the optimal solution. The effectiveness of the proposed algorithm, in terms of reducing the multipath estimation error and minimizing the randomness in the error signal, has been examined through case studies with Gaussian and non-Gaussian noises

Distressed yet bonded: A longitudinal investigation of the COVID-19 pandemic’s silver lining effects on life satisfaction.

It is a common understanding that the 2019 coronavirus pandemic (COVID-19) significantly harmed mental health. However, findings on changes in overall life satisfaction have been mixed and inconclusive. To address this puzzling phenomenon, we draw upon the domain specific perspective of well-being and research on catastrophe compassion and propose thatthe pandemic can have opposing effects on mental health and communal satisfaction, which then differently relate to people’s overall life satisfaction. Longitudinal analyses of the Household, Income and Labour Dynamics (HILDA) Survey of Australia (N = 12,093) showed that while there was a greater decrease in mental health in the first COVID-19 pandemic year (2019-2020) than in previous years (2017-2019), an increase in communal satisfaction also occurred, demonstrating a potential silver lining effect of the pandemic on people’s satisfaction with family, community and neighborhood. Moreover, consistent with socioemotional selectivity theory, changes in mental health, communal satisfaction and life satisfaction were related to age such that older adults generally reported less harmful and more beneficial psychological changes. We further found that age was associated with stronger associations of mental health and communal satisfaction with life satisfaction during the pandemic year. Overall, our findings speak to the importance of communal life in life satisfaction during the pandemic and age-related differences in the process, shedding light on the need to devise customized support to address inequalities in pandemic effects on public well-being

A Machine Learning Approach for Rating the Quality of Depression Treatment Web Pages

As health care information proliferates on the web, the content quality is varied and difficult to assess, partially due to the large volume and the dynamicity. This paper reports an automated approach in which the quality of depression treatment web pages is assessed according to evidence-based depression treatment guidelines. A supervised machine learning technique, specifically Naive Bayes classification, is used to identify the sentences that are consistent with the guidelines. The quality score of a depression treatment web page is the number of unique evidence-based guidelines covered in this page. Significant Pearson correlation (p<.001) was found between the quality rating results by the machine learning approach and the results by human raters on 31 depression treatment web pages in this case study. The semantic-based, machine learning quality rating method is promising and it may lead to an efficient and effective quality assessment mechanism for health care information on the Web.publishedye