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The aerodynamic noise of a landing gear is an important source of airframe noise. The analysis of its noise characteristics plays an important role in the design of a low-noise landing gear. Based on the FL-52 acoustic wind tunnel test technology, the coupled scale adaptive model and the acoustic disturbance equation, the results on aerodynamic noise of a full-scale landing gear model are analyzed. The high-fidelity model includes transverse strut, torsion arm, piston rod, wheel and other parts. The characteristics of static pressure distribution, power spectrum density of pulsating pressure, aerodynamic noise source distribution and directivity of overall sound pressure level are analyzed. The noise characteristics of the far-field microphone are compared with the local microphone installed in the wheel cavity. In this way, we characterize the directivity of pure tone in the wheel cavity and understand its contribution to the far-field noise. The results show that the aerodynamic noise of the landing gear can be quantified accurately by the hybrid numerical method. The pure tone has two frequencies inside and outside the wheel of the landing gear: 560 Hz and 960 Hz. The peak of the loudest sound pressure level reaches 136 dB, and the pure tone radiates to the surface of the non-separation area of the wheel of the landing gear. However, the wall pressure spectrum of the points located in the turbulence region shows a wide-frequency characteristic, and there is no obvious pure tone. From the point of view of the far-field noise directivity, the forward noise of the landing gear is larger than the rear noise, and there is a small overall sound pressure level area at the points of 65 and 110 degrees respectively. When the monitoring points are far away, the far-field noise of the landing gear shows the characteristics of wide frequency, and no obvious pure tone appears. The method can provide the technical support for predicting the aeroacoustics of a landing gear and designing a low-noise land gear.

Simple Summary: In this study, a Common Objects in Context dataset of ovine pulmonary adenocarcinoma pathological images was constructed based on 7167 annotated typical lesions from 61,063 lung pathological images of ovine pulmonary adenocarcinoma. This study aimed to develop a mask regional convolutional neural network model for the localization and pathological diagnosis of ovine pulmonary adenocarcinoma lesions. The model achieved a mean average specificity of 0.573 and an average sensitivity of 0.745, with consistency rates of 100% for junior pathologists and 96.5% for senior pathologists in the diagnosis of ovine pulmonary adenocarcinoma. The successful development of this model not only facilitates the rapid diagnosis of ovine pulmonary adenocarcinoma by different personnel in practical applications but also lays a foundation for the transition from traditional pathology to digital pathology in the livestock industry. Ovine pulmonary adenocarcinoma (OPA) is a contagious lung tumour caused by the Jaagsiekte Sheep Retrovirus (JSRV). Histopathological diagnosis is the gold standard for OPA diagnosis. However, interpretation of traditional pathology images is complex and operator dependent. The mask regional convolutional neural network (Mask R-CNN) has emerged as a valuable tool in pathological diagnosis. This study utilized 54 typical OPA whole slide images (WSI) to extract 7167 typical lesion images containing OPA to construct a Common Objects in Context (COCO) dataset for OPA pathological images. The dataset was categorized into training and test sets (8:2 ratio) for model training and validation. Mean average specificity (mASp) and average sensitivity (ASe) were used to evaluate model performance. Six WSI-level pathological images (three OPA and three non-OPA images), not included in the dataset, were used for anti-peeking model validation. A random selection of 500 images, not included in the dataset establishment, was used to compare the performance of the model with assessment by pathologists. Accuracy, sensitivity, specificity, and concordance rate were evaluated. The model achieved a mASp of 0.573 and an ASe of 0.745, demonstrating effective lesion detection and alignment with expert annotation. In Anti-Peeking verification, the model showed good performance in locating OPA lesions and distinguished OPA from non-OPA pathological images. In the random 500-image diagnosis, the model achieved 92.8% accuracy, 100% sensitivity, and 88% specificity. The agreement rates between junior and senior pathologists were 100% and 96.5%, respectively. In conclusion, the Mask R-CNN-based OPA diagnostic model developed for OPA facilitates rapid and accurate diagnosis in practical applications. [ABSTRACT FROM AUTHOR]

DJ‐1, also known as Parkinson's disease protein 7 (Park7), is a multifunctional protein that regulates oxidative stress and mitochondrial function. Dysfunction of DJ‐1 is implicated in the pathogenesis of Parkinson's disease (PD). Hyperhomocysteinemia is associated with an increased risk of PD. Here we show that homocysteine thiolactone (HTL), a reactive thioester of homocysteine (Hcy), covalently modifies DJ‐1 on the lysine 182 (K182) residue in an age‐dependent manner. The N‐homocysteinylation (N‐hcy) of DJ‐1 abolishes its neuroprotective effect against oxidative stress and mitochondrial dysfunction, exacerbating cell toxicity. Blocking the N‐hcy of DJ‐1 restores its protective effect. These results indicate that the N‐hcy of DJ‐1 abolishes its neuroprotective effect and promotes the progression of PD. Inhibiting the N‐hcy of DJ‐1 may exert neuroprotective effect against PD. [ABSTRACT FROM AUTHOR]

The aggregation of α‐synuclein plays a pivotal role in the pathogenesis of Parkinson's disease (PD). Epidemiological evidence indicates that high level of homocysteine (Hcy) is associated with an increased risk of PD. However, the molecular mechanisms remain elusive. Here, we report that homocysteine thiolactone (HTL), a reactive thioester of Hcy, covalently modifies α‐synuclein on the K80 residue. The levels of α‐synuclein K80Hcy in the brain are increased in an age‐dependent manner in the TgA53T mice, correlating with elevated levels of Hcy and HTL in the brain during aging. The N‐homocysteinylation of α‐synuclein stimulates its aggregation and forms fibrils with enhanced seeding activity and neurotoxicity. Intrastriatal injection of homocysteinylated α‐synuclein fibrils induces more severe α‐synuclein pathology and motor deficits when compared with unmodified α‐synuclein fibrils. Increasing the levels of Hcy aggravates α‐synuclein neuropathology in a mouse model of PD. In contrast, blocking the N‐homocysteinylation of α‐synuclein ameliorates α‐synuclein pathology and degeneration of dopaminergic neurons. These findings suggest that the covalent modification of α‐synuclein by HTL promotes its aggregation. Targeting the N‐homocysteinylation of α‐synuclein could be a novel therapeutic strategy against PD. [ABSTRACT FROM AUTHOR]

With the progress of science and technology, artificial intelligence represented by deep learning has gradually begun to be applied in the medical field. Artificial intelligence has been applied to benign gastrointestinal lesions, tumors, early cancer, inflammatory bowel disease, gallbladder, pancreas, and other diseases. This review summarizes the latest research results on artificial intelligence in digestive endoscopy and discusses the prospect of artificial intelligence in digestive system diseases. We retrieved relevant documents on artificial intelligence in digestive tract diseases from PubMed and Medline. This review elaborates on the knowledge of computer-aided diagnosis in digestive endoscopy. Artificial intelligence significantly improves diagnostic accuracy, reduces physicians' workload, and provides a shred of evidence for clinical diagnosis and treatment. Shortly, artificial intelligence will have high application value in the field of medicine. [ABSTRACT FROM AUTHOR]

Ovine pulmonary adenocarcinoma (OPA) is a contagious lung tumour caused by the Jaagsiekte Sheep Retrovirus (JSRV). Histopathological diagnosis is the gold standard for OPA diagnosis. However, interpretation of traditional pathology images is complex and operator dependent. The mask regional convolutional neural network (Mask R-CNN) has emerged as a valuable tool in pathological diagnosis. This study utilized 54 typical OPA whole slide images (WSI) to extract 7167 typical lesion images containing OPA to construct a Common Objects in Context (COCO) dataset for OPA pathological images. The dataset was categorized into training and test sets (8:2 ratio) for model training and validation. Mean average specificity (mASp) and average sensitivity (ASe) were used to evaluate model performance. Six WSI-level pathological images (three OPA and three non-OPA images), not included in the dataset, were used for anti-peeking model validation. A random selection of 500 images, not included in the dataset establishment, was used to compare the performance of the model with assessment by pathologists. Accuracy, sensitivity, specificity, and concordance rate were evaluated. The model achieved a mASp of 0.573 and an ASe of 0.745, demonstrating effective lesion detection and alignment with expert annotation. In Anti-Peeking verification, the model showed good performance in locating OPA lesions and distinguished OPA from non-OPA pathological images. In the random 500-image diagnosis, the model achieved 92.8% accuracy, 100% sensitivity, and 88% specificity. The agreement rates between junior and senior pathologists were 100% and 96.5%, respectively. In conclusion, the Mask R-CNN-based OPA diagnostic model developed for OPA facilitates rapid and accurate diagnosis in practical applications.

Backgrounds/aims: It has been previously demonstrated that vitamin D acts as a prognostic indicator of gastric cancer and may be correlated with the incidence risk of gastric cancer. However, the effect of 1,25-dihydroxyvitamin D3 on the apoptosis of human gastric cancer cells is unclear. The aim of this study was to determine whether 1,25-dihydroxyvitamin D3 induced the cellular apoptosis of BGC-823 gastric cancer cells and to determine the potential mechanism of action., Methodology: We demonstrate that 1,25-dihydroxyvitamin D3 induced the apoptosis of gastric cancer cells via the processing of PARP and cleavage of caspase 3. Additionally, an increase in BAX expression and a decrease in ERK1/2 and AKT phosphorylation were associated with 1,25-dihydroxyvitamin D3-induced apoptosis. The mRNA expression levels of VDR, CYP24A1, and p21 were increased significantly following 1,25-dihydroxyvitamin D3 treatment., Conclusions: These findings suggest that 1,25-dihydroxyvitamin D3 exerts tumor-suppressive effects on BGC-823 human gastric cancer cells.