Abstract We investigate how firms adjust corporate pension plans in response to economic policy uncertainty (EPU). Using a sample of US-listed firms, we find that firms increase pension underfunding ...levels when facing higher EPU. The result is robust to controlling for pension portfolio returns, discount rates, plan sizes, pension liability, numbers of employees, other macroeconomic factors, difference-in-differences and instrumental variable estimation, and additional evidence of pension risk-shifting. Further analysis reveals that financial distress and information asymmetry induced through EPU are the potential channels. The effect is stronger for firms having CEOs being excessively paid, using cash flow as a performance metric in CEO compensation, paying high dividends, and having short-term institutional investors, whereas the presence of unions, positive corporate culture, and social capital alleviate the effect. Notably, managers, not shareholders, appear to be the party reaping the benefits. Our findings suggest that firms may shift risk to employees in response to heightened uncertainty and institutional characteristics play a moderating role in this crucial business ethics issue.
The new classification announced by the World Health Organization in 2016 recognized five molecular subtypes of diffuse gliomas based on isocitrate dehydrogenase (IDH) and 1p/19q genotypes in ...addition to histologic phenotypes. We aim to determine whether clinical MRI can stratify these molecular subtypes to benefit the diagnosis and monitoring of gliomas.
The data from 456 subjects with gliomas were obtained from The Cancer Imaging Archive. Overall, 214 subjects, including 106 cases of glioblastomas and 108 cases of lower grade gliomas with preoperative MRI, survival data, histology, IDH, and 1p/19q status were included. We proposed a three-level machine-learning model based on multimodal MR radiomics to classify glioma subtypes. An independent dataset with 70 glioma subjects was further collected to verify the model performance.
The IDH and 1p/19q status of gliomas can be classified by radiomics and machine-learning approaches, with areas under ROC curves between 0.922 and 0.975 and accuracies between 87.7% and 96.1% estimated on the training dataset. The test on the validation dataset showed a comparable model performance with that on the training dataset, suggesting the efficacy of the trained classifiers. The classification of 5 molecular subtypes solely based on the MR phenotypes achieved an 81.8% accuracy, and a higher accuracy of 89.2% could be achieved if the histology diagnosis is available.
The MR radiomics-based method provides a reliable alternative to determine the histology and molecular subtypes of gliomas.
.
Background
Deep learning–based segmentation algorithms usually required large or multi‐institute data sets to improve the performance and ability of generalization. However, protecting patient ...privacy is a key concern in the multi‐institutional studies when conventional centralized learning (CL) is used.
Purpose
To explores the feasibility of a proposed lesion delineation for stereotactic radiosurgery (SRS) scheme for federated learning (FL), which can solve decentralization and privacy protection concerns.
Study Type
Retrospective.
Subjects
506 and 118 vestibular schwannoma patients aged 15–88 and 22–85 from two institutes, respectively; 1069 and 256 meningioma patients aged 12–91 and 23–85, respectively; 574 and 705 brain metastasis patients aged 26–92 and 28–89, respectively.
Field Strength/Sequence
1.5T, spin‐echo, and gradient‐echo Correction added after first online publication on 21 August 2023. Field Strength has been changed to “1.5T” from “5T” in this sentence..
Assessment
The proposed lesion delineation method was integrated into an FL framework, and CL models were established as the baseline. The effect of image standardization strategies was also explored. The dice coefficient was used to evaluate the segmentation between the predicted delineation and the ground truth, which was manual delineated by neurosurgeons and a neuroradiologist.
Statistical Tests
The paired t‐test was applied to compare the mean for the evaluated dice scores (p < 0.05).
Results
FL performed the comparable mean dice coefficient to CL for the testing set of Taipei Veterans General Hospital regardless of standardization and parameter; for the Taichung Veterans General Hospital data, CL significantly (p < 0.05) outperformed FL while using bi‐parameter, but comparable results while using single‐parameter. For the non‐SRS data, FL achieved the comparable applicability to CL with mean dice 0.78 versus 0.78 (without standardization), and outperformed to the baseline models of two institutes.
Data Conclusion
The proposed lesion delineation successfully implemented into an FL framework. The FL models were applicable on SRS data of each participating institute, and the FL exhibited comparable mean dice coefficient to CL on non‐SRS dataset. Standardization strategies would be recommended when FL is used.
Level of Evidence
4
Technical Efficacy
Stage 1
Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for ...the clinical management of patients based on intensive imaging follow-up. Our objective in this study was to establish an algorithm by which to automate the volumetric measurement of vestibular schwannoma (VS) using a series of parametric MR images following radiosurgery. Based on a sample of 861 consecutive patients who underwent Gamma Knife radiosurgery (GKRS) between 1993 and 2008, the proposed end-to-end deep-learning scheme with automated pre-processing pipeline was applied to a series of 1290 MR examinations (T1W+C, and T2W parametric MR images). All of which were performed under consistent imaging acquisition protocols. The relative volume difference (RVD) between AI-based volumetric measurements and clinical measurements performed by expert radiologists were + 1.74%, - 0.31%, - 0.44%, - 0.19%, - 0.01%, and + 0.26% at each follow-up time point, regardless of the state of the tumor (progressed, pseudo-progressed, or regressed). This study outlines an approach to the evaluation of treatment responses via novel volumetric measurement algorithm, and can be used longitudinally following GKRS for VS. The proposed deep learning AI scheme is applicable to longitudinal follow-up assessments following a variety of therapeutic interventions.
Purpose Medulloblastoma (MB) is a highly malignant pediatric brain tumor. In the latest classification, medulloblastoma is divided into four distinct groups: wingless (WNT), sonic hedgehog (SHH), ...Group 3, and Group 4. We analyzed the magnetic resonance imaging radiomics features to find the imaging surrogates of the 4 molecular subgroups of MB. Material and methods Frozen tissue, imaging data, and clinical data of 38 patients with medulloblastoma were included from Taipei Medical University Hospital and Taipei Veterans General Hospital. Molecular clustering was performed based on the gene expression level of 22 subgroup-specific signature genes. A total 253 magnetic resonance imaging radiomic features were generated from each subject for comparison between different molecular subgroups. Results Our cohort consisted of 7 (18.4%) patients with WNT medulloblastoma, 12 (31.6%) with SHH tumor, 8 (21.1%) with Group 3 tumor, and 11 (28.9%) with Group 4 tumor. 8 radiomics gray-level co-occurrence matrix texture (GLCM) features were significantly different between 4 molecular subgroups of MB. In addition, for tumors with higher values in a gray-level run length matrix feature-Short Run Low Gray-Level Emphasis, patients have shorter survival times than patients with low values of this feature (p = 0.04). The receiver operating characteristic analysis revealed optimal performance of the preliminary prediction model based on GLCM features for predicting WNT, Group 3, and Group 4 MB (area under the curve = 0.82, 0.72, and 0.78, respectively). Conclusion The preliminary result revealed that 8 contrast-enhanced T1-weighted imaging texture features were significantly different between 4 molecular subgroups of MB. Together with the prediction models, the radiomics features may provide suggestions for stratifying patients with MB into different risk groups.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In daily life, mobility requires walking while performing a cognitive or upper-extremity motor task. Although previous studies have evaluated the effects of dual tasks on gait performance, few ...studies have evaluated cortical activation and its association with gait disturbance during dual tasks. In this study, we simultaneously assessed gait performance and cerebral oxygenation in the bilateral prefrontal cortices (PFC), premotor cortices (PMC), and supplemental motor areas (SMA), using functional near-infrared spectroscopy, in 17 young adults performing dual tasks. Each participant was evaluated while performing normal-pace walking (NW), walking while performing a cognitive task (WCT), and walking while performing a motor task (WMT). Our results indicated that the left PFC exhibited the strongest and most sustained activation during WCT, and that NW and WMT were associated with minor increases in oxygenation levels during their initial phases. We observed increased activation in channels in the SMA and PMC during WCT and WMT. Gait data indicated that WCT and WMT both caused reductions in walking speed, but these reductions resulted from differing alterations in gait properties. WCT was associated with significant changes in cadence, stride time, and stride length, whereas WMT was associated with reductions in stride length only. During dual-task activities, increased activation of the PMC and SMA correlated with declines in gait performance, indicating a control mechanism for maintaining gait performance during dual tasks. Thus, the regulatory effects of cortical activation on gait behavior enable a second task to be performed while walking.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background and purpose
A reliable neuroimaging biomarker to predict language improvement after neuromodulation in post‐stroke aphasia is lacking. It is hypothesized that aphasic patients with stroke ...injuries in the left primary language circuits but with sufficient right arcuate fasciculus (AF) integrity might respond to low‐frequency repetitive transcranial magnetic stimulation (LF‐rTMS), leading to language improvement. This study aimed to assess the microstructural indices of the right AF before LF‐rTMS treatment and further correlate with language improvement after the treatment.
Methods
Thirty‐three patients with at least 3 months after stroke in the left hemisphere and nonfluent aphasia were recruited in this randomized double‐blind study. All patients received real 1‐Hz LF‐rTMS (n = 16) or sham stimulation (n = 17) at the right pars triangularis for 10 consecutive weekdays. Fractional anisotropy, axial diffusivity, radial diffusivity and apparent diffusion coefficient of the right AF were extracted using diffusion tensor imaging before the rTMS treatment and correlated with the measured functional improvement by the Concise Chinese Aphasia Test.
Results
The Concise Chinese Aphasia Test change scores revealed a stronger language improvement in auditory/reading comprehension and expression in the rTMS group than in the sham group. Regression analysis showed that the pre‐treatment fractional anisotropy, axial diffusivity and apparent diffusion coefficient of the right AF significantly correlated with the expression abilities (R2 > 0.700, p < 0.044) and comprehension abilities (R2 > 0.702, p < 0.039) in the rTMS group.
Conclusions
It was concluded that the right AF could be a predictor in language recovery induced by LF‐rTMS after the injuries of primary language circuits.
High-cost orphan drugs are becoming increasingly available to treat rare diseases that affect a relatively small population. Little attention has been given to the prevalence of rare diseases and ...their health-related economic burden in Taiwan.
This study examined the national trends in the prevalence of rare diseases and their health-related economic burden (including medication costs) in Taiwan.
Rare disease-related claims data from 2003-2014 (12 years) from the National Health Insurance Research Database were used in this study. We used a time series analysis to assess trends in the yearly rates of treated patients with rare diseases, overall healthcare use, and expenditures, including drugs.
During the 12-year study period, the estimated prevalence of rare diseases increased from 10.57 to 33.21 per 100,000 population, an average rate of a 19.46% increase per year. Total health expenditures for treatment of rare diseases increased from US$18.65 million to US$137.44 million between 2003 and 2014, accounting for 0.68% of the total national health expenditures in 2014. Drug expenditures for treatment of rare diseases increased from US$13.24 million to US$121.98 million between 2003 and 2014, which accounted for 71.00% and 88.75% of the health expenditures for patients with rare diseases in 2003 and 2014, respectively. In 2014, we found a 20.43-fold difference in average health expenditures and a 69.46-fold difference in average drug expenditures between patients with rare diseases and the overall population.
The prevalence of rare diseases and the related economic burden have grown substantially in Taiwan over the past 12 years, and these trends are likely to continue. Drug expenditures accounted for almost 90% of health expenditures for rare diseases. Further analyses are underway to examine the economic burden of individual rare diseases.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This paper proposes an evolutionary fuzzy lead-lag control approach for coordinated control of flexible AC transmission system (FACTS) devices in a multi-machine power system. The FACTS devices used ...are a thyristor-controlled series capacitor (TCSC) and a static var compensator (SVC), both of which are equipped with a fuzzy lead-lag controller to improve power system dynamic stability. The fuzzy lead-lag controller uses a fuzzy controller (FC) to adaptively determine the parameters of two lead-lag controllers at each control step according to the deviations of generator rotor speeds. This paper proposes an Advanced Continuous Ant Colony Optimization (ACACO) algorithm to optimize all of the free parameters in the FC, which avoids the time-consuming task of parameter selection by human experts. The effectiveness and efficiency of the proposed evolutionary fuzzy lead-lag controller for oscillation damping control is verified through control of a multi-machine power system and comparisons with other lead-lag controllers and various population-based optimization algorithms.
The diagnosis of brain metastasis (BM) is commonly observed in non-small cell lung cancer (NSCLC) with poor outcomes. Accordingly, developing an approach to early predict BM response to Gamma Knife ...radiosurgery (GKRS) may benefit the patient treatment and monitoring. A total of 237 NSCLC patients with BMs (for survival prediction) and 256 patients with 976 BMs (for prediction of local tumor control) treated with GKRS were retrospectively analyzed. All the survival data were recorded without censoring, and the status of local tumor control was determined by comparing the last MRI follow-up in patients’ lives with the pre-GKRS MRI. Overall 1763 radiomic features were extracted from pre-radiosurgical magnetic resonance images. Three prediction models were constructed, using (1) clinical data, (2) radiomic features, and (3) clinical and radiomic features. Support vector machines with a 30% hold-out validation approach were constructed. For treatment outcome predictions, the models derived from both the clinical and radiomics data achieved the best results. For local tumor control, the combined model achieved an area under the curve (AUC) of 0.95, an accuracy of 90%, a sensitivity of 91%, and a specificity of 89%. For patient survival, the combined model achieved an AUC of 0.81, an accuracy of 77%, a sensitivity of 78%, and a specificity of 80%. The pre-radiosurgical radiomics data enhanced the performance of local tumor control and survival prediction models in NSCLC patients with BMs treated with GRKS. An outcome prediction model based on radiomics combined with clinical features may guide therapy in these patients.
PDF
