Search Results (65)

Estrogen plays a crucial role in regulating reproductive and neuroendocrine functions, yet the molecular mechanisms underlying its effects on the hypothalamus remain incompletely understood. This study investigates the transcriptional and behavioral changes induced by ovariectomy (OVX) and estradiol (E2) supplementation in female C57BL/6J mice. RNA sequencing was performed to identify differentially expressed genes (DEGs) across control (CK), E2, OVX, and OVX+E2 groups, followed by functional enrichment and pathway analyses. Behavioral assessments, including open field, Y-maze, and elevated plus maze tests, were conducted to evaluate anxiety-like and cognitive behaviors. Results revealed significant alterations in GnRH signaling, neurotransmission, and inflammatory pathways, with key genes such as Elk1, Prkcb, and Camk2a differentially expressed in response to estrogen modulation. OVX-induced neuroendocrine disruptions were partially reversed by E2 treatment, as evidenced by transcriptomic and behavioral outcomes. Pearson correlation analysis further linked gene expression patterns with phenotypic traits, providing insights into estrogen’s regulatory mechanisms in the hypothalamus. These findings enhance our understanding of estrogen-mediated neuroendocrine regulation and may have implications for hormone replacement therapies in postmenopausal disorders. Full article

Optimization algorithms serve as a powerful instrument for tackling optimization issues and are highly valuable in the context of engineering design. The coati optimization algorithm (COA) is a novel meta-heuristic algorithm known for its robust search capabilities and rapid convergence rate. However, the effectiveness of the COA is compromised by the homogeneity of its initial population and its reliance on random strategies for prey hunting. To address these issues, a multi-strategy adaptive coati optimization algorithm (MACOA) is presented in this paper. Firstly, Lévy flights are incorporated into the initialization phase to produce high-quality initial solutions. Subsequently, a nonlinear inertia weight factor is integrated into the exploration phase to bolster the algorithm’s global search capabilities and accelerate convergence. Finally, the coati vigilante mechanism is introduced in the exploitation phase to improve the algorithm’s capacity to escape local optima. Comparative experiments with many existing algorithms are conducted using the CEC2017 test functions, and the proposed algorithm is applied to seven representative engineering design problems. MACOA’s average rankings in the three dimensions (30, 50, and 100) were 2.172, 1.897, and 1.759, respectively. The results show improved optimization speed and better performance. Full article

The banana pseudostem weevil (BPW), Odoiporus longicollis (Oliver), is one of the most destructive pests of bananas that is seriously affecting the yield and quality of bananas. We isolated pathogens from banana pseudostem weevils in Xishuangbanna and Dongchuan, Yunnan, China, and explored their biological characteristics. The pathogenicity of the strains was verified through laboratory and greenhouse inoculation experiments. The results showed that four strains of fungi were identified and confirmed as Beauveria caledonica (Bc) via ITS-rDNA sequencing. Optimal in vitro culture conditions were found to be a photoperiod of 24 h light, 25 °C temperature, and 18 days on potato dextrose agar (PDA) medium with insect meal. Under these conditions, the Cs-1 strain achieved a colony diameter of 65.17 ± 0.74 mm and spore production of 1.24 × 10⁸ cfu/cm². The Cs-1 strain had the shortest lethal time (LT₅₀) of 9.36 days at an inoculum of 1.00 × 10⁹ cfu/mL, with a lethality of 86.67% after 20 days. The Cs-3 strain showed 77.78% lethality at 1.00 × 10⁸ cfu/mL after 20 days. Despite variations in virulence, lethality did not correlate with major cuticle-degrading enzymes. The Cs-3 strain demonstrated effective biocontrol in greenhouse tests. Banana plants suffered significant damage without Bc-treated BPW, while the treated plantlets thrived. The mortality rate reached 82.78% after 35 days. This study marks the first identification of these entomopathogenic fungi (EPF) in Yunnan, China, highlighting B. caledonica’s potential for biocontrol application. Full article

In modern manufacturing, cutting tools are essential for cutting processes, and their wear state directly affects the processing accuracy, production efficiency, and product quality. Identification of the tool-wear state using a single sensor is insufficient to satisfy the requirements of high-precision, high-efficiency machining. To address this problem, this paper proposes a novel approach to identify the tool-wear state using information fusion technology and the sparrow search algorithm (SSA)–backpropagation (BP) neural network framework. This method uses a principal component analysis (PCA) to fuse multi-domain features extracted from three-way vibration signals, power signals, and temperature signals. Subsequently, the optimal initial threshold and weight of the BP neural network are optimized using the SSA to prevent the network from falling into the local optimum and accelerate the convergence of the algorithm. Lastly, a tool-wear-state identification model based on the SSA–BP neural network is constructed. Experimental results show that the proposed method has an identification accuracy of 98.33%, precision rate of 98.81%, recall rate of 97.96%, and F1 score of 98.36%. Full article

The photosynthetic system of maize (Zea mays) leaves is sensitive to low temperatures and suffers from irreversible damage induced by cold exposure, making cold stress a major factor limiting maize yield. Identifying genes that improve the recovery of photosynthesis from low temperatures in maize will help enhance the cold tolerance of this crop and ensure stable yields. Here, we demonstrate the role of starch phosphorylase 2 (ZmPHOH) in promoting photosynthetic recovery from cold damage. Chlorotic leaf3 (chl3), a null mutant of ZmPHOH, which undergoes chlorophyll degradation and chlorosis earlier than under normal growth conditions after brief exposure to 8 °C and restoration to normal. We determined that chl3 plants could not repair the damage to their photosynthetic system caused by short-term cold exposure after the temperature returned to normal. Metabolome and transcriptome profiling indicated that the soluble sugar content in chl3 leaves was significantly increased after cold treatment and could not be catabolized promptly, leading to repression of photosynthetic gene expression. Our results reveal that ZmPHOH enhances post-cold photosynthetic recovery by promoting the decomposition and metabolism of soluble sugars, thereby regulating the low-temperature resilience in maize, which provides new insights into the chilling tolerance mechanism of maize. Full article

Photoreceptor/retinal degeneration is the major cause of blindness. Induced and inherited mouse models of retinal degeneration are valuable tools for investigating disease mechanisms and developing therapeutic interventions. This study investigated the potential of the antioxidant resveratrol to relieve photoreceptor degeneration using mouse models. Clinical studies have shown a potential association between thyroid hormone (TH) signaling and age-related retinal degeneration. Excessive TH signaling induces oxidative stress/damage and photoreceptor death in mice. C57BL/6 (rod-dominant) and Nrl^−/− (cone-dominant) mice at postnatal day 30 (P30) received triiodothyronine (T3) via drinking water (20 µg/mL) with or without concomitant treatment with resveratrol via drinking water (120 µg/mL) for 30 days, followed by evaluation of photoreceptor degeneration, oxidative damage, and retinal stress responses. In experiments using Leber congenital amaurosis model mice, mother Rpe65^−/− and Rpe65^−/−/Nrl^−/− mice received resveratrol via drinking water (120 µg/mL) for 20 days and 10–13 days, respectively, beginning on the day when the pups were at P5, and pups were then evaluated for cone degeneration. Treatment with resveratrol significantly diminished the photoreceptor degeneration induced by T3 and preserved photoreceptors in Rpe65-deficient mice, manifested as preserved retinal morphology/outer nuclear layer thickness, increased cone density, reduced photoreceptor oxidative stress/damage and apoptosis, reduced upregulation of genes involved in cell death/inflammatory responses, and reduced macroglial cell activation. These findings demonstrate the role of oxidative stress in photoreceptor degeneration, associated with TH signaling and Rpe65 deficiency, and support the therapeutic potential of resveratrol/antioxidants in the management of retinal degeneration. Full article

To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of Agaricus bisporus, in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate Agaricus bisporus. The harvesting control system, using a Jetson Orin Nano as the main controller, adopted an S-curve acceleration and deceleration motor control algorithm. This algorithm controlled the robotic arm and the flexible manipulator to harvest Agaricus bisporus based on the identification and positioning results. To confirm the impact of vibration on the harvesting process, a stepper motor drive test was conducted using both trapezoidal and S-curve acceleration and deceleration motor control algorithms. The test results showed that the S-curve acceleration and deceleration motor control algorithm exhibited excellent performance in vibration reduction and repeat positioning accuracy. The recognition efficiency and harvesting effectiveness of the intelligent harvesting device were tested using recognition accuracy, harvesting success rate, and damage rate as evaluation metrics. The results showed that the Agaricus bisporus recognition algorithm achieved an average recognition accuracy of 96.72%, with an average missed detection rate of 2.13% and a false detection rate of 1.72%. The harvesting success rate of the intelligent harvesting device was 94.95%, with an average damage rate of 2.67% and an average harvesting yield rate of 87.38%. These results meet the requirements for the intelligent harvesting of Agaricus bisporus and provide insight into the development of intelligent harvesting robots in the industrial production of Agaricus bisporus. Full article

In order to increase the capacity of landfills while ensuring a certain degree of stability of such structures, an engineered berm is typically constructed at the front slope of the landfill. For this type of landfill slopes, this paper primarily focuses on the construction and verification of stability assessment models for such structures. Initially, the calculation models of the safety factor were established, considering over and under berm failure modes separately. Subsequently, through error analysis, it was determined that it is feasible to evaluate the stability of this type of landfills by substituting the true safety factor with the average safety factor obtained from the calculation model. The analysis for parameters and slip surfaces was then conducted to investigate the impact of parameters associated with the engineered berm on the landfill slope stability. Finally, a visual comparison and brief discussion were conducted on the average safety factors under translational and composite failure modes. Thus, the critical failure modes under specific working conditions can be reasonably ascertained, which holds significant practical implications for enhancing the reliability of stability assessment of such landfill slopes. Full article

An arc fault is a potential hazard in power systems, capable of causing serious safety accidents such as fires. Therefore, the timely detection of arc faults and implementation of circuit-breaking measures are crucial for ensuring safety, preventing fires, and maintaining the stable operation of power systems. Although existing studies have made progress in improving the accuracy of their detection, most methods have not proposed effective solutions that address the cost-sensitive problem of feature selection. Thus, a multi-feature method is proposed by combining time-domain, frequency-domain, energy, and spatial features, which are integrated into a CBN (conditional batch normalization) convolutional neural network for detection. The experimental results show that the proposed method outperforms traditional models in terms of its accuracy and misjudgment rate while maintaining a lower computational cost, demonstrating its superior detection performance. This provides an effective improvement for arc fault detection. Full article

In mammals, the melatonin (Mel) concentration in the gastrointestinal tract is 400 times greater than in the pineal gland. However, the origin of Mel in the gastrointestinal tract and its role in reproductive regulation remains unclear. Therefore, we analyzed three potential Mel sources (feed, microorganisms, and the rumen wall) for their contribution to high Mel levels in the rumen and their biological effects. The feed contained high Mel concentrations, and Mel in rumen fluid and blood peaked two hours after feeding. Rumen microbial analysis showed a strong positive correlation between Mel and specific microbes, including Megasphaera, Butyrivibrio, Acetobacter, and Olsenella. In vitro experiments indicated that rumen microorganisms synthesized Mel from tryptophan. The rumen wall also contains key enzymes, AANAT and HIOMT, which catalyze Mel synthesis and membrane receptors MT1 and MT2 that mediate the function of Mel, suggesting that the rumen wall synthesizes Mel. Mel peaked in both rumen fluid and blood two hours after feeding. Feeding also altered blood levels of Mel, Gonadotropin-releasing hormone (GnRH), Luteinizing hormone (LH), Follicle-stimulating hormone (FSH), progesterone (P4), and Estradiol (E2), with a correlation between Mel and fluctuations in GnRH, LH, P4, and E2 levels. Our findings suggest that feed is the primary source of high Mel levels in the rumen and impacts reproductive hormone fluctuations. This study elucidates the origin of high rumen Mel concentrations and reveals that food intake affects the natural secretion of various hormones, offering a new perspective on food sources for regulating reproductive physiology. Full article

Based on the transition state theory, a continuum plasticity theory is developed for metallic materials. Moreover, the nature of local stress fluctuation within a material point is considered by incorporating the probability distribution of the stresses. The model is applied to investigate the mechanical behaviors of 316 L stainless steel under various loading cases. The simulated results closely match the results obtained by the polycrystal plasticity model and experiments. The mechanical behaviors associated with strain rate sensitivity, temperature dependence, stress relaxation, and strain creep are correctly captured by the model. Furthermore, the proposed model successfully characterizes the Bauschinger effect, which is challenging to capture with a conventional continuum model without additional assumptions. The proposed model could be further employed in the design, manufacturing, and service of engineering components. Full article

Low-voltage arc fault detection can effectively prevent fires, electric shocks, and other accidents, reducing potential risks to human life and property. The research on arc fault circuit interrupters (AFCIs) is of great significance for both safety in production scenarios and daily living disaster prevention. Considering the diverse characteristics of loads between the normal operational state and the arc fault condition, a parallel neural network structure is proposed for arc fault recognition, which is based on a convolutional neural network (CNN) and a Transformer. The network uses convolutional layers and Transformer encoders to process the low-frequency current and high-frequency components, respectively. Then, it uses Softmax classification to perform supervised learning on the concatenated features. The method combines the advantages of both networks and effectively reduces the required depth and computational complexity. The experimental results show that the accuracy of this method can reach 99.74%, and with the threshold-moving method, the erroneous judgment rate can be lower. These results indicate that the parallel neural network can definitely detect arc faults and also improve recognition efficiency due to its lean structure. Full article

Unconfined compressive strength (UCS) is a critical property for assessing the engineering performances of sustainable materials, such as cement–fly ash mortar (CFAM), in the design of construction engineering projects. The experimental determination of UCS is time-consuming and expensive. Therefore, the present study aims to model the UCS of CFAM with boosting machine learning methods. First, an extensive database consisting of 395 experimental data points derived from the literature was developed. Then, three typical boosting machine learning models were employed to model the UCS based on the database, including gradient boosting regressor (GBR), light gradient boosting machine (LGBM), and Ada-Boost regressor (ABR). Additionally, the importance of different input parameters was quantitatively analyzed using the SHapley Additive exPlanations (SHAP) approach. Finally, the best boosting machine learning model’s prediction accuracy was compared to ten other commonly used machine learning models. The results indicate that the GBR model outperformed the LGBM and ABR models in predicting the UCS of the CFAM. The GBR model demonstrated significant accuracy, with no significant difference between the measured and predicted UCS values. The SHAP interpretations revealed that the curing time (T) was the most critical feature influencing the UCS values. At the same time, the chemical composition of the fly ash, particularly Al₂O₃, was more influential than the fly-ash dosage (FAD) or water-to-binder ratio (W/B) in determining the UCS values. Overall, this study demonstrates that SHAP boosting machine learning technology can be a useful tool for modeling and predicting UCS values of CFAM with good accuracy. It could also be helpful for CFAM design by saving time and costs on experimental tests. Full article

Ferroptosis has emerged as a potential mechanism for enhancing the efficacy of chemotherapy in cancer treatment. By suppressing nuclear factor erythroid 2-related factor 2 (Nrf2), cancer cells may lose their ability to counteract the oxidative stress induced by chemotherapy, thereby becoming more susceptible to ferroptosis. In this study, we investigate the potential of penexanthone A (PXA), a xanthone dimer component derived from the endophytic fungus Diaporthe goulteri, obtained from mangrove plant Acanthus ilicifolius, to enhance the therapeutic effect of cisplatin (CDDP) on colorectal cancer (CRC) by inhibiting Nrf2. The present study reported that PXA significantly improved the ability of CDDP to inhibit the activity of and induce apoptosis in CRC cells. Moreover, PXA was found to increase the level of oxidative stress and DNA damage caused by CDDP. In addition, the overexpression of Nrf2 reversed the DNA damage and ferroptosis induced by the combination of PXA and CDDP. In vivo experiments using zebrafish xenograft models demonstrated that PXA enhanced the therapeutic effect of CDDP on CRC. These studies suggest that PXA enhanced the sensitivity of CRC to CDDP and induce ferroptosis by targeting Nrf2 inhibition, indicating that PXA might serve as a novel anticancer drug in combination chemotherapy. Full article

Thyroid hormone (TH) plays an essential role in cell proliferation, differentiation, and metabolism. Experimental and clinical studies have shown a potential association between TH signaling and retinal degeneration. The suppression of TH signaling protects cone photoreceptors in mouse models of retinal degeneration, whereas excessive TH signaling induces cone degeneration, manifested as reduced light response and a loss of cones. This work investigates the genes/transcriptomic alterations that might be involved in TH-induced cone degeneration in mice using single-cell RNA sequencing (scRNAseq) analysis. One-month-old C57BL/6 mice received triiodothyronine (T3, 20 µg/mL in drinking water) for 4 weeks as a model of hyperthyroidism/excessive TH signaling. At the end of the experiments, retinal cells were dissociated, and cell viability was analyzed before being subjected to scRNAseq. The resulting data were analyzed using the Seurat package and visualized using the Loupe browser. Among 155,866 single cells, we identified 14 cell clusters, representing various retinal cell types, with rod and cone clusters comprising 76% and 4.1% of the total cell population, respectively. Cone cluster transcriptomes demonstrated the most alterations after the T3 treatment, with 450 differentially expressed genes (DEGs), accounting for 38.5% of the total DEGs. Statistically significant changes in the expression of genes in the cone cluster revealed that phototransduction and oxidative phosphorylation were impaired after the T3 treatment, along with mitochondrial dysfunction. A pathway analysis also showed the activation of the sensory neuronal/photoreceptor stress pathways after the T3 treatment. Specifically, the eukaryotic initiation factor-2 signaling pathway and the cAMP response element-binding protein signaling pathway were upregulated. Thus, excessive TH signaling substantially affects cones at the transcriptomic level. The findings from this work provide an insight into how excessive TH signaling induces cone degeneration. Full article