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Simple Summary: In the complex world of cancer, iron and copper play essential roles as trace metal ions that are crucial for cancer cell survival. Disruption in their metabolic functions can be lethal to cancer cells, triggering ferroptosis and cuproptosis, respectively. Given an accelerated proliferation rate, cancer cells exhibit a heightened dependence on iron and copper, exposing vulnerabilities that could potentially be exploited to reverse drug resistance. Notably, mitochondria, the cellular powerhouses, play a crucial role in regulating both ferroptosis and cuproptosis. This review focuses on elucidating the key mechanisms behind ferroptosis and cuproptosis and summarizes recent clinical applications targeting dysfunctional iron and copper metabolic pathways. Drug resistance is a hallmark of cancer development that underscores a critical need to address it, underscoring the critical need to explore novel approaches. Understanding and targeting these metal-related processes offers promising approaches for developing innovative cancer therapies, making use of vulnerabilities specific to cancer cells. Iron (Fe) and copper (Cu), essential transition metals, play pivotal roles in various cellular processes critical to cancer biology, including cell proliferation, mitochondrial respiration, distant metastases, and oxidative stress. The emergence of ferroptosis and cuproptosis as distinct forms of non-apoptotic cell death has heightened their significance, particularly in connection with these metal ions. While initially studied separately, recent evidence underscores the interdependence of ferroptosis and cuproptosis. Studies reveal a link between mitochondrial copper accumulation and ferroptosis induction. This interconnected relationship presents a promising strategy, especially for addressing refractory cancers marked by drug tolerance. Harnessing the toxicity of iron and copper in clinical settings becomes crucial. Simultaneous targeting of ferroptosis and cuproptosis, exemplified by the combination of sorafenib and elesclomol-Cu, represents an intriguing approach. Strategies targeting mitochondria further enhance the precision of these approaches, providing hope for improving treatment outcomes of drug-resistant cancers. Moreover, the combination of iron chelators and copper-lowering agents with established therapeutic modalities exhibits a synergy that holds promise for the augmentation of anti-tumor efficacy in various malignancies. This review elaborates on the complex interplay between ferroptosis and cuproptosis, including their underlying mechanisms, and explores their potential as druggable targets in both cancer research and clinical settings. [ABSTRACT FROM AUTHOR]

The support vector machine (SVM) combined with the genetic algorithm (GA) has been utilized for the fault diagnosis of transformers since its high accuracy. In addition to the fault diagnosis, the condition assessment of transformer oil-immersed insulation conveys the crucial engineering significance as well. However, the approaches for getting GA-SVM used to the moisture prediction of oil-immersed insulation have been rarely reported. In view of this issue, this paper pioneers the application of GA-SVM and frequency domain spectroscopy (FDS) to realize the moisture prediction of transformer oil-immersed insulation. In the present work, a method of constructing a GA-SVM multi-classifier for moisture diagnosis based on the fitting analysis model is firstly reported. Then, the feasibility and reliability of the reported method are proved by employing the laboratory and field test experiments. The experimental results indicate that the reported prediction model might be serviced as a potential tool for the moisture prediction of transformer oil-immersed polymer insulation. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL), which excels at solving a wide variety of Atari and board games, is an area of machine learning that combines the deep learning approach and reinforcement learning (RL). However, to the authors' best knowledge, there seem to be few studies that apply the latest DRL algorithms on real-world powertrain control problems. If there are any, the requirement of classical model-free DRL algorithms typically for a large number of random exploration in order to realize good control performance makes it almost impossible to implement directly on a real plant. Unlike most of the other DRL studies, whose control strategies can only be trained in a simulation environment—especially when a control strategy has to be learned from scratch—in this study, a hybrid end-to-end control strategy combining one of the latest DRL approaches—i.e., a dueling deep Q-network and traditional Proportion Integration Differentiation (PID) controller—is built, assuming no fidelity simulation model exists. Taking the boost control of a diesel engine with a variable geometry turbocharger (VGT) and cooled (exhaust gas recirculation) EGR as an example, under the common driving cycle, the integral absolute error (IAE) values with the proposed algorithm are improved by 20.66% and 9.7% respectively for the control performance and generality index, compared with a fine-tuned PID benchmark. In addition, the proposed method can also improve system adaptiveness by adding another redundant control module. This makes it attractive to real plant control problems whose simulation models do not exist, and whose environment may change over time. [ABSTRACT FROM AUTHOR]

Background: The identification of asymptomatic structural and functional cardiac abnormalities can help us to recognize early and intervene in patients at pre-heart failure (HF). However, few studies have adequately evaluated the associations of renal function and left ventricular (LV) structure and function in patients at high risk of cardiovascular diseases (CVD). Methods: Patients undergoing coronary angiography and/or percutaneous coronary interventions were enrolled from the Cardiorenal ImprovemeNt II (CIN-II) cohort study, and their echocardiography and renal function were assessed at admission. Patients were divided into five groups according to their estimated glomerular filtration rate (eGFR). Our outcomes were LV hypertrophy and LV systolic and diastolic dysfunction. Multivariable logistic regression analyses were conducted to investigate the associations of eGFR with LV hypertrophy and LV systolic and diastolic dysfunction. Results: A total of 5610 patients (mean age: 61.6 ± 10.6 years; 27.3% female) were included in the final analysis. The prevalence of LV hypertrophy assessed by echocardiography was 29.0%, 34.8%, 51.9%, 66.7%, and 74.3% for the eGFR categories >90, 61–90, 31–60, 16–30, and ≤15 mL/min per 1.73 m2 or for patients needing dialysis, respectively. Multivariate logistic regression analysis showed that subjects with eGFR levels of ≤15 mL/min per 1.73 m2 or needing dialysis (OR: 4.66, 95% CI: 2.96–7.54), as well as those with eGFR levels of 16–30 (OR: 3.87, 95% CI: 2.43–6.24), 31–60 (OR: 2.00, 95% CI: 1.64–2.45), and 61–90 (OR: 1.23, 95% CI: 1.07–1.42), were significantly associated with LV hypertrophy. This reduction in renal function was also significantly associated with LV systolic and diastolic dysfunction (all P for trend <0.001). In addition, a per one unit decrease in eGFR was associated with a 2% heightened combined risk of LV hypertrophy and systolic and diastolic dysfunction. Conclusions: Among patients at high risk of CVD, poor renal function was strongly associated with cardiac structural and functional abnormalities. In addition, the presence or absence of CAD did not change the associations. The results may have implications for the pathophysiology behind cardiorenal syndrome. [ABSTRACT FROM AUTHOR]

The study of mental workload has attracted much interest in neuroergonomics, a frontier field of research. However, there appears no consensus on how to measure mental workload effectively because the mental workload is not only regulated by task difficulty but also affected by individual skill level reflected as mental schema. In this study, we investigated the alterations in the functional brain network induced by a 10-day simulated piloting task with different difficulty levels. Topological features quantifying global and local information communication and network organization were analyzed. It was found that during different tests, the global efficiency did not change, but the gravity center of the local efficiency of the network moved from the frontal to the posterior area; the small-worldness of the functional brain network became stronger. These results demonstrate the reconfiguration of the brain network during the development of mental schema. Furthermore, for the first two tests, the global and local efficiency did not have a consistent change trend under different difficulty levels, but after forming the developed mental schema, both of them decreased with the increase in task difficulty, showing sensitivity to the increase in mental workload. Our results demonstrate brain network reconfiguration during the motor learning process and reveal the importance of the developed mental schema for the accurate assessment of mental workload. We concluded that the efficiency of the brain network was associated with mental workload with developed mental schema. [ABSTRACT FROM AUTHOR]

Simple Summary: Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer. Studies have shown that P3H4 is a key gene underlying the malignant progression of LUAD. A potential biomarker and therapeutic target, P3H4 is involved in various cancers, but its molecular mechanism in LUAD remains unclear. Based on a series of experiments, we found that it significantly promoted the metastasis and proliferation of LUAD in vivo and in vitro. Lung cancer is associated with the greatest number of cancer-related deaths worldwide. Lung adenocarcinoma (LUAD) accounts for 85% of all cases of lung cancer. Despite recent advances in treatment, the 5-year survival rate remains less than 15%. Thus, the diagnostic and therapeutic role of LUAD remain to be further studied. The prolyl 3-hydroxylase family member 4 (P3H4) is involved in various cancers, but little is known about its role in LUAD. Our study demonstrated that the P3H4 gene was upregulated in LUAD. Clinically, the expression of P3H4 was positively correlated with an advanced TNM stage and shorter survival. Functionally, P3H4 plays a significant role in the metastasis and proliferation of LUAD both in vitro and in vivo. Mechanistically, P3H4 might interact with EGFR to regulate the metabolic substances. Our study indicated that P3H4 is a critical gene in the malignant progression of LUAD and represents a potential biomarker and therapeutic target. [ABSTRACT FROM AUTHOR]

Fertilizer is extremely essential to increasing the yield of vegetables. However, excessively using fertilizers has had a negative impact on the yield and quality of vegetables as well as soil environment in recent years. Non-heading Chinese cabbage 'yellow rose' was applied to determine the influence of organic manure and inorganic fertilizer on the character of rhizosphere soil, the growth and quality of plants. There were five treatments: conventional fertilization (NF), a total nitrogen reduction of 20% (NF20), a total nitrogen reduction of 30% (NF30), a total nitrogen reduction of 20% with 100 kg·667 m−2 bio-organic fertilizer (BNF20) and a total nitrogen reduction of 30% with 200 kg·667 m−2 bio-organic fertilizer (BNF30). The results show that the content of nitrate nitrogen, organic matter in rhizosphere soil treated by BNF20 and BNF30, was significantly enhanced compared with NF. The yield, Vc and soluble protein of plants treated by BNF20 and BNF30 increased by 30.11%, 17.26%, 5.66% and 15.90%, 16.02%, 5.37%, respectively, compared with NF. On the contrary, the nitrate content significantly decreased in plants of BNF20 and BNF30 by 47.87% and 40.98% compared with NF. The significantly positive correlation was observed between nitrate nitrogen content in rhizosphere soil and the yield (p < 0.05). In conclusion, reduced nitrogen with bio-organic fertilizer can improve the yield and quality of 'yellow rose' cabbage by improving the quality of rhizosphere soil. [ABSTRACT FROM AUTHOR]

Frequency-domain spectroscopy (FDS) is demonstrated to be affected by electrode polarization and conductance behavior in the low-frequency ranges, which causes the unreliable prediction results of transformer cellulose insulation. In order to solve this issue, a fingerprint database based on the dielectric modulus is reported to predict the degree of polymerization (DP) and moisture content of cellulose insulation. In the current work, the relevant fingerprints that characterize the insulation conditions are obtained by studying the dielectric modulus curves of cellulose insulation with various insulation conditions, as well as the DC conductivity of transformer oil. Then, the dielectric modulus fingerprint database is established in the lab, and the accuracy of the reported fingerprint database is later verified. As a potential tool, the dielectric modulus fingerprint database is tested by several samples, and the results demonstrate that the accuracy of this method is more than 80%. In that respect, an interesting discovery of this paper is that the dielectric modulus fingerprint database may be a helpful tool for conditions prediction of the transformer cellulose insulation system. [ABSTRACT FROM AUTHOR]