The presence of cracks reduces egg quality and safety, and can easily cause food safety hazards to consumers. Machine vision-based methods for cracked egg detection have achieved significant success on in-domain egg data. However, the performance of deep learning models usually decreases under practical industrial scenarios, such as the different egg varieties, origins, and environmental changes. Existing researches that rely on improving network structures or increasing training data volumes cannot effectively solve the problem of model performance decline on unknown egg testing data in practical egg production. To address these challenges, a novel and robust detection method is proposed to extract max domain-invariant features to enhance the model performance on unknown test egg data. Firstly, multi-domain egg data are built on different egg origins and acquisition devices. Then, a multi-domain trained strategy is established by using Maximum Mean Discrepancy with Normalized Squared Feature Estimation (NSFE-MMD) to obtain the optimal matching egg training domain. With the NSFE-MMD method, the original deep learning model can be applied without network structure improvements, which reduces the extremely complex tuning process and hyperparameter adjustments. Finally, robust cracked egg detection experiments are carried out on several unknown testing egg domains. The YOLOV5 (You Only Look Once v5) model trained by the proposed multi-domain training method with NSFE-MMD has a detection mAP of 86.6% on the unknown test Domain 4, and the YOLOV8 (You Only Look Once v8) model has a detection mAP of 88.8% on Domain 4, which is an increase of 8% and 4.4% compared to the best performance of models trained on a single domain, and an increase of 4.7% and 3.7% compared to models trained on all domains. In addition, the YOLOV5 model trained by the proposed multi-domain training method has a detection mAP of 87.9% on egg data of the unknown testing Domain 5. The experimental results demonstrate the robustness and effectiveness of the proposed multi-domain training method, which can be more suitable for the large quantity and variety of egg detection production.

UNASSIGNED: The present study aims to investigate the levels of illness uncertainty in patients with moyamoya disease and to determine the association of socio-demographic characteristics, perceived social support and resilience with illness uncertainty in patients with moyamoya disease.
UNASSIGNED: A cross-sectional survey using convenience sampling was conducted in two hospitals in China from August to December 2023. A socio-demographic characteristics questionnaire, the Chinese versions of Mishel\'s Unsurety in Disease Scale (MUIS), the Chinese version of Connor-Davidson Resilience Scale (CD-RISC), and the Chinese version of Multidimensional Scale of Perceived Social Support (MSPSS) were used to perform this research. The collected data were analyzed using SPSS 24.0 statistical software. The t-test, one-way analysis of variance (ANOVA), pearson correlation analysis and hierarchical regression analysis were used to identify associated factors.
UNASSIGNED: A total of 263 patients with moyamoya disease were recruited in this survey. The score of illness uncertainty was at a moderate level of (100.03 ± 18.59). The present study identified a negative correlation between illness uncertainty with resilience perceived social support. Hierarchical regression analysis showed that gender, occupation, education level, resilience and perceived social support were the related factors of illness uncertainty.
UNASSIGNED: Patients with moyamoya disease experienced moderate disease uncertainty on average, which was related to gender, occupation, education level, resilience and perceived social support. Future research is needed to better explore the complex relationships between illness uncertainty, resilience, and perceived social support with different types of moyamoya disease using longitudinal research.

The aim of this study was at building an effective machine learning model to contribute to the prediction of stroke recurrence in adult stroke patients subjected to moyamoya disease (MMD), while at analyzing the factors for stroke recurrence.
The data of this retrospective study originated from the database of JiangXi Province Medical Big Data Engineering & Technology Research Center. Moreover, the information of MMD patients admitted to the second affiliated hospital of Nanchang university from January 1st, 2007 to December 31st, 2019 was acquired. A total of 661 patients from January 1st, 2007 to February 28th, 2017 were covered in the training set, while the external validation set comprised 284 patients that fell into a scope from March 1st, 2017 to December 31st, 2019. First, the information regarding all the subjects was compared between the training set and the external validation set. The key influencing variables were screened out using the Lasso Regression Algorithm. Furthermore, the models for predicting stroke recurrence in 1, 2, and 3 years after the initial stroke were built based on five different machine learning algorithms, and all models were externally validated and then compared. Lastly, the CatBoost model with the optimal performance was explained using the SHapley Additive exPlanations (SHAP) interpretation model.
In general, 945 patients suffering from MMD were recruited, and the recurrence rate of acute stroke in 1, 2, and 3 years after the initial stroke reached 11.43%(108/945), 18.94%(179/945), and 23.17%(219/945), respectively. The CatBoost models exhibited the optimal prediction performance among all models; the area under the curve (AUC) of these models for predicting stroke recurrence in 1, 2, and 3 years was determined as 0.794 (0.787, 0.801), 0.813 (0.807, 0.818), and 0.789 (0.783, 0.795), respectively. As indicated by the results of the SHAP interpretation model, the high Suzuki stage, young adults (aged 18-44), no surgical treatment, and the presence of an aneurysm were likely to show significant correlations with the recurrence of stroke in adult stroke patients subjected to MMD.
In adult stroke patients suffering from MMD, the CatBoost model was confirmed to be effective in stroke recurrence prediction, yielding accurate and reliable prediction outcomes. High Suzuki stage, young adults (aged 18-44 years), no surgical treatment, and the presence of an aneurysm are likely to be significantly correlated with the recurrence of stroke in adult stroke patients subjected to MMD.

Ferroptosis is a form of regulated cell death with roles in degenerative diseases and cancer. Excessive iron-catalyzed peroxidation of membrane phospholipids, especially those containing the polyunsaturated fatty acid arachidonic acid (AA), is central in driving ferroptosis. Here, we reveal that an understudied Golgi-resident scaffold protein, MMD, promotes susceptibility to ferroptosis in ovarian and renal carcinoma cells in an ACSL4- and MBOAT7-dependent manner. Mechanistically, MMD physically interacts with both ACSL4 and MBOAT7, two enzymes that catalyze sequential steps to incorporate AA in phosphatidylinositol (PI) lipids. Thus, MMD increases the flux of AA into PI, resulting in heightened cellular levels of AA-PI and other AA-containing phospholipid species. This molecular mechanism points to a pro-ferroptotic role for MBOAT7 and AA-PI, with potential therapeutic implications, and reveals that MMD is an important regulator of cellular lipid metabolism.

Spurious correlations allow flexible models to predict well during training but poorly on related test populations. Recent work has shown that models that satisfy particular independencies involving correlation-inducing nuisance variables have guarantees on their test performance. Enforcing such independencies requires nuisances to be observed during training. However, nuisances, such as demographics or image background labels, are often missing. Enforcing independence on just the observed data does not imply independence on the entire population. Here we derive MMD estimators used for invariance objectives under missing nuisances. On simulations and clinical data, optimizing through these estimates achieves test performance similar to using estimators that make use of the full data.

Objective: This study aimed to identify immune infiltration characteristics and new immunological diagnostic biomarkers in the cerebrovascular tissue of moyamoya disease (MMD) using bioinformatics analysis. Methods: GSE189993 and GSE141022 were downloaded from the GEO database. Differentially expressed gene and PPI analysis were performed. After performing WGCNA, the most significant module associated with MMD was obtained. Next, functional pathways according to GSEA, GO, and KEGG were enriched for the aforementioned core genes obtained from PPI and WGCNA. Additionally, immune infiltration, using the CIBERSORT deconvolution algorithm, immune-related biomarkers, and the relationship between these genes, was further explored. Finally, diagnostic accuracy was verified with ROC curves in the validation dataset GSE157628. Results: A total of 348 DEGs were screened, including 89 downregulated and 259 upregulated genes. The thistlel module was detected as the most significant module associated with MMD. Functional analysis of the core genes was chiefly involved in the immune response, immune system process, protein tyrosine kinase activity, secretory granule, and so on. Among 13 immune-related overlapping genes, 4 genes (BTK, FGR, PTPN11, and SYK) were identified as potential diagnostic biomarkers, where PTPN11 showed the highest specificity and sensitivity. Meanwhile, a higher proportion of eosinophils, not T cells or B cells, was demonstrated in the specific immune infiltration landscape of MMD. Conclusion: Immune activities and immune cells were actively involved in the progression of MMD. BTK, FGR, PTPN11, and SYK were identified as potential immune diagnostic biomarkers. These immune-related genes and cells may provide novel insights for immunotherapy in the future.

As industrial development increases, electric machine systems are more widely used in industrial production. Rolling bearings play a key role in machine systems and so the prevention of faults in rolling bearings is more important than ever before. Recently, with the development of artificial intelligence, neural networks have been used to monitor the remaining useful life of rolling bearings. However, there are two problems with this technique. First, a network trained by data for a single operating condition (source domain) cannot predict the remaining useful life of bearings under a different operating condition (target domain), such as a different load or speed. Second, a large number of labeled data are needed for network training, but the acquisition of labeled data for different operating conditions is a challenging task. To address these problems, this paper proposes a domain-adaptive adversarial network, in which a transfer learning strategy and maximum mean discrepancy algorithm are used for network optimization, so that remaining useful life can be predicted without labeled data in target domain training. Our results confirm that a model trained by source domain data alone cannot predict the remaining useful life of bearings under different conditions, but the domain-adaptive adversarial network can accurately predict remaining useful life for varying operating conditions. The method proposed also exhibits good performance even if there are noises in the signals.

Background: The impact of gene mutations typically associated with myelodysplastic syndrome (MDS) in acute myeloid leukemia (AML) with NPM1 mutation is unclear. Methods: Using a cohort of 107 patients with NPM1-mutated AML treated with risk-adapted therapy, we compared survival outcomes of patients without MDS-related gene mutations (group A) with those carrying concurrent FLT3-ITD (group B) or with MDS-related gene mutations (group C). Minimal measurable disease (MMD) status assessed by multiparameter flow cytometry (MFC), polymerase chain reaction (PCR), and/or next-generation sequencing (NGS) were reviewed. Results: Among the 69 patients treated intensively, group C showed significantly inferior progression-free survival (PFS, p < 0.0001) but not overall survival (OS, p = 0.055) compared to group A. Though groups A and C had a similar MMD rate, group C patients had a higher relapse rate (p = 0.016). Relapse correlated with MMD status at the end of cycle 2 induction (p = 0.023). Survival of group C patients was similar to that of group B. Conclusion: MDS-related gene mutations are associated with an inferior survival in NPM1-mutated AML.

The Berlin Grading System assesses clinical severity of moyamoya angiopathy (MMA) by combining MRI, DSA, and cerebrovascular reserve capacity (CVRC). Our aim was to validate this grading system using [15O]H2O PET for CVRC. We retrospectively identified bilateral MMA patients who underwent [15O]H2O PET examination and were treated surgically at our department. Each hemisphere was classified using the Suzuki and Berlin Grading System. Preoperative symptoms and perioperative ischemias were collected, and a logistic regression analysis was performed. A total of 100 hemispheres in 50 MMA patients (36 women, 14 men) were included. Using the Berlin Grading System, 2 (2.8%) of 71 symptomatic hemispheres were categorized as grade I, 14 (19.7%) as grade II, and 55 (77.5%) as grade III. The 29 asymptomatic hemispheres were characterized as grade I in 7 (24.1%) hemispheres, grade II in 12 (41.4%), and grade III in 10 (34.5%) hemispheres. Berlin grades were independent factors for identifying hemispheres as symptomatic and higher grades correlated with increasing proportion of symptomatic hemispheres (p < 0.01). The Suzuki grading did not correlate with preoperative symptoms (p = 0.26). Perioperative ischemic complications occurred in 8 of 88 operated hemispheres. Overall, complications did not occur in any of the grade I hemispheres, but in 9.1% (n = 2 of 22) and 9.8% (n = 6 of 61) of grade II and III hemispheres, respectively. In this study, we validated the Berlin Grading System with the use of [15O]H2O PET for CVRC as it could stratify preoperative symptomatology. Furthermore, we highlighted its relevance for predicting perioperative ischemic complications.

Background: Globally, efforts to curtail the HIV pandemic are growing. The Joint United Nations Programme on HIV and AIDS (UNAIDS) and partners set the 95-95-95 targets to be achieved by 2025. Tanzania\'s ongoing transition from single-month ARV to longer multi-month dispensing (MMD) involves significant planning and shifts in existing resources, including health commodities, clinical staff and storage space. This study aimed at evaluating the costs and efficiency gains of rolling out MMD compared to the prior monthly dispending (MD) standard of care before the new guidelines.Methods: The analysis employed a health provider perspective utilising prior costing data collected to estimate cost of treatment for HIV/AIDS, including salaries, laboratory costs, antiretroviral drugs, other supplies and overhead costs. The projections were run from 2018 to 2030 using the Spectrum package for Tanzania.Results: Our model estimated that total treatment cost without MMD (including salaries, laboratory costs, antiretroviral drugs, other supplies, and overhead costs) is estimated to rise from USD 189 million in 2018 to USD 244 million in 2030. The introduction of a six-month MMD would lead to the total annual facility-based treatment costs being reduced to USD 205 million in 2030. When comparing MD to a six-month MMD, the total savings over the 13-year period would be USD 425 million. The introduction of six-month MMD for stable patients would reduce the average cost from USD 180 to USD 156 per patient per year if stable patients were only required to make six-monthly visit.Conclusions: The introduction of differentiated service delivery models (DSDMs) and MMD is already contributing to significant cost savings for Tanzania and will continue to do so as the country puts more stable patients on MMD. The potential gains from MMD implantation could further be harnessed if retention of treatment and viral suppression monitoring are prioritised.