miR-181a increases FoxO1 acetylation and promotes granulosa cell apoptosis via SIRT1 downregulation.

Oxidative stress impairs follicular development by inducing granulosa cell (GC) apoptosis, which involves enhancement of the transcriptional activity of the pro-apoptotic factor Forkhead box O1 (FoxO1). However, the mechanism by which oxidative stress promotes FoxO1 activity is still unclear. Here, we found that miR-181a was upregulated in hydrogen peroxide (

A Modified Method to Generate Typical Meteorological Years from the Long-Term Weather Database

A modified typical meteorological year (TMY) method is proposed for generating TMY from practical measured weather data. A total of eleven weather indices and novel assigned weighting factors are applied in the processing of forming the TMY database. TMYs of 35 cities in China are generated based on the latest and accurate measured weather data (dry bulb temperature, relative humidity, wind velocity, atmospheric pressure, and daily global solar radiation) in the period of 1994–2010. The TMY data and typical solar radiation data are also investigated and analyzed in this paper, which are important in the utilizations of solar energy systems

Meta-learning based infrared ship object detection model for generalization to unknown domains

Infrared images exhibit considerable variations in probability distributions, stemming from the utilization of distinct infrared sensors and the influence of diverse environmental conditions. The variations pose great challenges for deep learning models to detect ship objects and adapt to unseen maritime environments. To address the domain shift problem, we propose an end-to-end infrared ship object detection model based on meta-learning neural network to improve domain adaptation for target domain where data is not available at training phase. Different from existing domain generalization methods, the novelty of our model lies in the effective exploitation of meta-learning and domain adaptation, ensuring that the extracted domain-independent features are meaningful and domain-invariant at the semantic level. Firstly, a double gradient-based meta-learning algorithm is designed to solve the common optimal descent direction between different domains through two gradient updates in the inner and outer loops. The algorithm enables extraction of domain-invariant features from the pseudo-source and pseudo-target domain data. Secondly, a domain discriminator with dynamic-weighted gradient reversal layer (DWGRL) is designed to accurately classify domain-invariant features and provide additional global supervision information. Finally, a multi-scale feature aggregation method is proposed to improve the extraction of multi-scale domain-invariant features. It can effectively fuse local features at different scales and global features of targets. Extensive experimental results conducted in real nighttime water surface scenes demonstrate that the proposed model achieves very high detection accuracy on target domain data, even no target domain data was used during the training phase. Compared to the existing methods, our method not only improves the detection accuracy of infrared ships by 18%, but also exhibits the smallest standard deviation with a value of 0.93, indicating its superior generalization performance

Different Angiogenic Potentials of Mesenchymal Stem Cells Derived from Umbilical Artery, Umbilical Vein, and Wharton's Jelly

Human mesenchymal stem cells derived from the umbilical cord (UC) are a favorable source for allogeneic cell therapy. Here, we successfully isolated the stem cells derived from three different compartments of the human UC, including perivascular stem cells derived from umbilical arteries (UCA-PSCs), perivascular stem cells derived from umbilical vein (UCV-PSCs), and mesenchymal stem cells derived from Wharton’s jelly (WJ-MSCs). These cells had the similar phenotype and differentiation potential toward adipocytes, osteoblasts, and neuron-like cells. However, UCA-PSCs and UCV-PSCs had more CD146+ cells than WJ-MSCs (P<0.05). Tube formation assay in vitro showed the largest number of tube-like structures and branch points in UCA-PSCs among the three stem cells. Additionally, the total tube length in UCA-PSCs and UCV-PSCs was significantly longer than in WJ-MSCs (P<0.01). Microarray, qRT-PCR, and Western blot analysis showed that UCA-PSCs had the highest expression of the Notch ligand Jagged1 (JAG1), which is crucial for blood vessel maturation. Knockdown of Jagged1 significantly impaired the angiogenesis in UCA-PSCs. In summary, UCA-PSCs are promising cell populations for clinical use in ischemic diseases

The role of autophagy in cardiovascular disease: Cross-interference of signaling pathways and underlying therapeutic targets

Autophagy is a conserved lysosomal pathway for the degradation of cytoplasmic proteins and organelles, which realizes the metabolic needs of cells and the renewal of organelles. Autophagy-related genes (ATGs) are the main molecular mechanisms controlling autophagy, and their functions can coordinate the whole autophagic process. Autophagy can also play a role in cardiovascular disease through several key signaling pathways, including PI3K/Akt/mTOR, IGF/EGF, AMPK/mTOR, MAPKs, p53, Nrf2/p62, Wnt/β-catenin and NF-κB pathways. In this paper, we reviewed the signaling pathway of cross-interference between autophagy and cardiovascular diseases, and analyzed the development status of novel cardiovascular disease treatment by targeting the core molecular mechanism of autophagy as well as the critical signaling pathway. Induction or inhibition of autophagy through molecular mechanisms and signaling pathways can provide therapeutic benefits for patients. Meanwhile, we hope to provide a unique insight into cardiovascular treatment strategies by understanding the molecular mechanism and signaling pathway of crosstalk between autophagy and cardiovascular diseases

Pengaruh sense of school belonging terhadap student's misbehavior

Penelitian ini bertujuan untuk mengetahu pengaruh sense of school belonging terhadap student’s misbehavior. Penelitian ini merupakan penelitian korelasional dengan menggunakan teknnik pengumpulan data berupa skala likert yaitu skala sense of school belonging dan skala student’s misbehavior masing masing terdiri dari 30 aitem yang sudah melalui uji coba. Skala sense of school belonging memiliki reabilitas sebesar 0,899 sedangkan skala student’s misbehavior memiliki reabilitas sebesar 0,924. Subjek penelitian berjumlah 144 siswa dari jumlah populasi sebesar 576 siswa. Pengambilan data menggunakan simple random sampling. Hasil penelitian menujukkan bahwa terdapat pengaruh sense of school belonging terhadap student’s misbehavior dengan nilai signifikansi 0,000 < 0,05. Dalam table model summary pada analisis regresi linier sederhana, sense of school belonging memberikan pengaruh sebesar 17,7% terhadap student’s misbehavior. Pada table correlation, terdapat nilai koerfisien korelasi sebesar -0,420 yang berarti semakin tinggi sense of school belonging maka semakin rendah student’s misbehavior yang dilakukan oleh siswa

Closed-Loop Supply Chain Network Equilibrium Model with Subsidy on Green Supply Chain Technology Investment

The green supply chain (GSC) can effectively reduce the waste of resources and avoid environmental pollution. For a closed-loop supply chain network consisting of multiple manufacturers, multiple retailers, and multiple consumer and recycling markets, we assume that retailers are responsible for the recycling of used products, manufacturers use raw materials to produce new products and recycled products for remanufacturing, and government departments subsidize all manufacturers and retailers for GSC technology investment. Then, the equilibrium conditions of manufacturers, retailers, demand markets, and recycling markets are obtained by using the variational inequality method, complementarity theorem, and Nash equilibrium theory, and the variational inequality model of the closed-loop supply chain network multiphase equilibrium is established. Based on numerical simulation, the optimal technology investment decision of green supply chain under different government subsidy rates, and the influence of market structure and enterprise cost asymmetry on the equilibrium solution of supply chain network are analyzed. The results show that government subsidies can effectively promote enterprises to upgrade their level of GSC technology investment. The intensification of enterprise competition and the asymmetry of enterprise costs will affect the composition of enterprise profits and the allocation of profits between enterprises, and the former will weaken the effect of government subsidies