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"Han, Ju"
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The impossible fairy tale : a novel
\"The Impossible Fairy Tale is the story of two unexceptional grade-school girls. Mia is \"lucky\"--she is spoiled by her mother and, as she explains, her two fathers. She gloats over her exotic imported color pencils and won't be denied a coveted sweater. Then there is the Child who, by contrast, is neither lucky nor unlucky. She makes so little impression that she seems not even to merit a name. At school, their fellow students, whether lucky or luckless or unlucky, seem consumed by an almost murderous rage. Adults are nearly invisible, and the society the children create on their own is marked by cruelty and soul-crushing hierarchies. Then, one day, the Child sneaks into the classroom after hours and adds ominous sentences to her classmates' notebooks. This sinister but initially inconsequential act unlocks a series of events that end in horrible violence. But that is not the end of this eerie, unpredictable novel. A teacher, who is also this book's author, wakes from an intense dream. When she arrives at her next class, she recognizes a student: the Child, who knows about the events of the novel's first half, which took place years before. The Impossible Fairy Tale is a fresh and terrifying exploration of the ethics of art making and of the stinging consequences of neglect.\" -- Publisher's website
Book
Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT
PurposeTo develop a deep learning–based computer-aided diagnosis (CAD) system for use in the CT diagnosis of cervical lymph node metastasis (LNM) in patients with thyroid cancer.MethodsA total of 995 axial CT images that included benign (n = 647) and malignant (n = 348) lymph nodes were collected from 202 patients with thyroid cancer who underwent CT for surgical planning between July 2017 and January 2018. The datasets were randomly split into training (79.0%), validation (10.5%), and test (10.5%) datasets. Eight deep convolutional neural network (CNN) models were used to classify the images into metastatic or benign lymph nodes. Pretrained networks were used on the ImageNet and the best-performing algorithm was selected. Class-specific discriminative regions were visualized with attention heatmap using a global average pooling method.ResultsThe area under the ROC curve (AUROC) for the tested algorithms ranged from 0.909 to 0.953. The sensitivity, specificity, and accuracy of the best-performing algorithm were all 90.4%, respectively. Attention heatmap highlighted important subregions for further clinical review.ConclusionA deep learning–based CAD system could accurately classify cervical LNM in patients with thyroid cancer on preoperative CT with an AUROC of 0.953. Whether this approach has clinical utility will require evaluation in a clinical setting.Key Points• A deep learning–based CAD system could accurately classify cervical lymph node metastasis. The AUROC for the eight tested algorithms ranged from 0.909 to 0.953.• Of the eight models, the ResNet50 algorithm was the best-performing model for the validation dataset with 0.953 AUROC. The sensitivity, specificity, and accuracy of the ResNet50 model were all 90.4%, respectively, in the test dataset.• Based on its high accuracy of 90.4%, we consider that this model may be useful in a clinical setting to detect LNM on preoperative CT in patients with thyroid cancer.
Journal Article
Acenocoumarol Exerts Anti-Inflammatory Activity via the Suppression of NF-κB and MAPK Pathways in RAW 264.7 Cells
The repurposing of already-approved drugs has emerged as an alternative strategy to rapidly identify effective, safe, and conveniently available new therapeutic indications against human diseases. The current study aimed to assess the repurposing of the anticoagulant drug acenocoumarol for the treatment of chronic inflammatory diseases (e.g., atopic dermatitis and psoriasis) and investigate the potential underlying mechanisms. For this purpose, we used murine macrophage RAW 264.7 as a model in experiments aimed at investigating the anti-inflammatory effects of acenocoumarol in inhibiting the production of pro-inflammatory mediators and cytokines. We demonstrate that acenocoumarol significantly decreases nitric oxide (NO), prostaglandin (PG)E2, tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1β levels in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. Acenocoumarol also inhibits the expression of NO synthase (iNOS) and cyclooxygenase (COX)-2, potentially explaining the acenocoumarol-induced decrease in NO and PGE2 production. In addition, acenocoumarol inhibits the phosphorylation of mitogen-activated protein kinases (MAPKs), c-Jun N terminal kinase (JNK), p38 MAPK, and extracellular signal-regulated kinase (ERK), in addition to decreasing the subsequent nuclear translocation of nuclear factor κB (NF-κB). This indicates that acenocoumarol attenuates the macrophage secretion of TNF-α, IL-6, IL-1β, and NO, inducing iNOS and COX-2 expression via the inhibition of the NF-κB and MAPK signaling pathways. In conclusion, our results demonstrate that acenocoumarol can effectively attenuate the activation of macrophages, suggesting that acenocoumarol is a potential candidate for drug repurposing as an anti-inflammatory agent.
Journal Article
Technology-driven initiating actions influence movement patterns in HMEAYC musical activities
This study examines the integration of technology into the Holistic Music Educational Approach for Young Children (HMEAYC) by analyzing children’s movement sequences during musical rhythm activities. Specifically, it investigates the effects of technology-enhanced music on children’s initiating actions and movement patterns, comparing those with prior music experience to those without. A total of 75 children (43 boys and 32 girls), aged 3–6 years and enrolled in central Taiwan kindergartens, participated in the study. They were categorized into two clusters: Cluster 1 (n = 23) with minimal prior music exposure and Cluster 2 (n = 52) with previous musical experience. Researchers analyzed over 10 h of video recordings using a coding scheme that classified behaviors into six categories: Observation, Execution, Correct Performance, Abandonment, and Restart. Sequential behavior analysis revealed that children in Cluster 2 demonstrated significantly higher adaptability and reflective learning, showing more effective transitions, particularly from Observation to Execution and from Execution to Correct Performance. In contrast, Cluster 1 children exhibited limited behavioral flexibility, frequently repeating actions without evident improvements. Individual Performance (IP) did not manifest a statistically significant disparity among the clusters; nevertheless, a trend-level variation was detected. This indicates a plausible differentiation in children’s independent rhythmic execution, which necessitates additional inquiry. These findings suggest that technology-integrated HMEAYC enhances young learners’ engagement and adaptability. Future studies should explore targeted interventions to support children with minimal musical experience and conduct longitudinal research to assess the long-term cognitive and social impacts of technology-enhanced music education.
Journal Article
Sex differences in the genetic architecture of depression
The prevalence and clinical characteristics of depressive disorders differ between women and men; however, the genetic contribution to sex differences in depressive disorders has not been elucidated. To evaluate sex-specific differences in the genetic architecture of depression, whole exome sequencing of samples from 1000 patients (70.7% female) with depressive disorder was conducted. Control data from healthy individuals with no psychiatric disorder (n = 72, 26.4% female) and East-Asian subpopulation 1000 Genome Project data (n = 207, 50.7% female) were included. The genetic variation between men and women was directly compared using both qualitative and quantitative research designs. Qualitative analysis identified five genetic markers potentially associated with increased risk of depressive disorder in females, including three variants (rs201432982 within
PDE4A
, and rs62640397 and rs79442975 within
FDX1L
) mapping to chromosome 19p13.2 and two novel variants (rs820182 and rs820148) within
MYO15B
at the chromosome 17p25.1 locus. Depressed patients homozygous for these variants showed more severe depressive symptoms and higher suicidality than those who were not homozygotes (i.e., heterozygotes and homozygotes for the non-associated allele). Quantitative analysis demonstrated that the genetic burden of protein-truncating and deleterious variants was higher in males than females, even after permutation testing. Our study provides novel genetic evidence that the higher prevalence of depressive disorders in women may be attributable to inherited variants.
Journal Article
Fault Diagnosis and Its Applications to Fault Tolerant Control of a Turbojet Engine
This paper presents a comprehensive study of model-based fault diagnosis (FD) and a fault-tolerant control (FTC) scheme for sensor and actuator faults of turbojet engines. For actuator FD, an unbiased estimation scheme with a modified Kalman filter (KF) was developed. For sensor FD, two approaches, the generalized likelihood ratio with robust KF and the pseudo actuator model with modified KF, were investigated in a comparative study. For fault detection and isolation, test statistics are commonly employed to detect fault behavior. For FTC, integral-type sliding mode control using control reconfiguration and the reconstruction of the sensor signal was adopted with the FD schemes. The effectiveness of the employed methods was demonstrated in this study and discussed with numerical simulations.
Journal Article
Genetically proxied glucagon-like peptide-1 receptor perturbation and risk of mood disorders: a Mendelian randomization study
Background
Glucagon-like peptide-1 receptor (GLP1R) agonists have gained attention for their role in diabetes treatment along with their diverse effects, such as appetite suppression, suggesting potential psychiatric benefits. This study aimed to assess the effect of GLP1R perturbation on mood disorders based on protein and biomarker levels using Mendelian randomization (MR) approach.
Methods
We conducted two-sample MR using summary statistics for GLP1R plasma levels (
n
= 3,301) from the INTERVAL study, glycated hemoglobin (HbA1c) levels (
n
= 128,610) from the Meta-Analyses of Glucose and Insulin-related traits Consortium, and bipolar disorder (BD: 371 cases/360,823 controls) and major depressive disorder (MDD: 776 cases/360,418 controls) incidences from the UK Biobank. Genetic variants associated with the plasma levels of GLP1R and HbA1c were used as proxies for the variation in GLP1R.
Results
GLP1R level was significantly associated with a reduced risk of MDD (odds ratio [OR] = 0·9988, 95% confidence interval [CI] = 0·9978-0·9999,
P
= 0·0291) and of BD (OR = 0·9990, 95% CI = 0·9982-0·9998,
P
= 0·0182). GLP1R’s HbA1c level-lowering effect was significantly associated with a decreased risk of BD (OR = 0·9786, 95% CI = 0·9613-0·9962,
P
= 0·0175) but not with MDD.
Conclusions
GLP1R perturbation may have protective effects on MDD and BD through different mechanisms, although additional clinical trials are required to determine the therapeutic implications.
Trial registration
Clinical trial number not applicable.
Journal Article
Safety Helmet Detection Based on YOLOv5 Driven by Super-Resolution Reconstruction
High-resolution image transmission is required in safety helmet detection problems in the construction industry, which makes it difficult for existing image detection methods to achieve high-speed detection. To overcome this problem, a novel super-resolution (SR) reconstruction module is designed to improve the resolution of images before the detection module. In the super-resolution reconstruction module, the multichannel attention mechanism module is used to improve the breadth of feature capture. Furthermore, a novel CSP (Cross Stage Partial) module of YOLO (You Only Look Once) v5 is presented to reduce information loss and gradient confusion. Experiments are performed to validate the proposed algorithm. The PSNR (peak signal-to-noise ratio) of the proposed module is 29.420, and the SSIM (structural similarity) reaches 0.855. These results show that the proposed model works well for safety helmet detection in construction industries.
Journal Article
Clinicopathologic significance of MYD88 L265P mutation in diffuse large B-cell lymphoma: a meta-analysis
The precise clinicopathologic significance of
myeloid differentiation primary response gene
(
MYD88)
L265P mutation in diffuse large B-cell lymphomas (DLBCLs) remains elusive. To investigate the frequency and clinicopathologic significance of the
MYD88
L265P mutation in DLBCLs, we conducted a meta-analysis of 40 published studies on 2736 DLBCL patients. We collected relevant published research findings identified using the PubMed and Embase databases. The effect sizes of outcome parameters were calculated using a random-effects model. In this meta-analysis, the
MYD88
L265P mutation in DLBCL showed a significant difference according to tumor sites. The overall incidence of the
MYD88
L265P mutation in DLBCLs, excluding the central nervous system and testicular DLBCLs, was 16.5%. Notably, the
MYD88
L265P mutation rates of CNS and testicular DLBCL patients were 60% and 77%, respectively. Interestingly, the
MYD88
L265P mutation was more frequently detected in activated B-cell-like (ABC) or non-germinal center B-cell-like (GCB) than GCB subtype (OR = 3.414, p < 0.001). The
MYD88
L265P mutation was significantly associated with old age and poor overall survival, but not with sex and clinical stage. This pooled analysis demonstrates that the
MYD88
L265P mutation is significantly associated with the tumor sites and molecular subtypes in DLBCL patients.
Journal Article