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Cardiovascular diseases (CVDs) are the main cause of death among patients with type 2 diabetes mellitus (T2DM), particularly in low- and middle-income countries. To effectively prevent the development of CVDs in T2DM, considerable effort has been made to explore novel preventive approaches, individualized glycemic control and cardiovascular risk management (strict blood pressure and lipid control), together with recently developed glucose-lowering agents and lipid-lowering drugs. This review mainly addresses the important issues affecting the choice of antidiabetic agents and lipid, blood pressure and antiplatelet treatments considering the cardiovascular status of the patient. Finally, we also discuss the changes in therapy principles underlying CVDs in T2DM.

Organic Light-Emitting Diode (OLED) is a high-performance display technology. Its performance and lifespan are extremely sensitive to the operating temperature. The existing temperature control methods, such as the traditional Proportional-Integral-Derivative (PID) controller, are difficult to meet the requirements of OLED for precise temperature control, especially in systems with significant nonlinear and time-varying characteristics. To solve this problem, the study proposes an improved PID controller based on the Long Short-Term Memory (LSTM) optimized by the Whale Optimization Algorithm (WOA). This method combines the global optimization ability of WOA and the timing analysis ability of LSTM. By optimizing the parameters of the PID controller, the accuracy and adaptability of temperature control are improved. Meanwhile, the effectiveness of the proposed controller is verified by constructing a thermodynamic model and combining experimental data. In the experimental results, compared with the traditional PID controller, the overshoot of the WOA-LSTM-PID controller was reduced from 8.5°C to 0.3°C, the steady-state error was reduced from 1.2°C to 0.2°C, the regulation time was shortened from 42.5 seconds to 20.2 seconds, and the response time was shortened from 70.5 seconds to 21.9 seconds. Furthermore, the root mean square error has been reduced from 5.23°C of the traditional PID to 0.78°C. The research results show that the WOA-LSTM-PID controller can significantly improve the accuracy and stability of OLED temperature control, while reducing the regulation time and response time. This controller effectively addresses the nonlinear and time-varying characteristics in OLED temperature control by optimizing the PID parameters. The innovation of the research lies in the combination of the WOA and the LSTM network. By optimizing the parameters of the PID controller, high-precision control of the OLED temperature has been achieved. This study not only proposes a new theoretical optimization method but also verifies its significant performance improvement in experiments. Furthermore, this method has strong universality and can be applied to other temperature-sensitive systems.

Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field of intelligent fault diagnosis. The traditional fault diagnosis methods rely on the manual feature extraction of engineers with prior knowledge. To effectively identify an incipient fault in rotating machinery, this paper proposes a novel method, namely improved the convolutional neural network-support vector machine (CNN-SVM) method. This method improves the traditional convolutional neural network (CNN) model structure by introducing the global average pooling technology and SVM. Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data. Finally, the extracted sparse representative feature vectors are input into SVM for fault classification. The proposed method is applied to the diagnosis multichannel vibration signal monitoring data of a rolling bearing. The results confirm that the proposed method is more effective than other existing intelligence diagnosis methods including SVM, K-nearest neighbor, back-propagation neural network, deep BP neural network, and traditional CNN.

In order to optimize the energy management strategy and solve the problem of the power quality degradation of fuel cell hybrid electric ships, a particle swarm optimization algorithm based energy management strategy is proposed in this paper. Taking a fuel cell ship as the target ship, a system simulation model is built in Matlab/Simulink to verify the proposed energy management strategy. Through simulations and comparisons, the bus voltage curve of the optimized hybrid power system fluctuates more gently, and the voltage sag is smaller. The amplitude of the voltage fluctuation under maneuvering conditions is reduced by 55% compared with that of the original ship. The charging and discharging process of the composite energy storage system is optimized under maneuvering conditions, the power quality of the marine power grid is improved, and the use of the energy management strategy can extend the service life of the battery.

Marine heatwaves (MHWs), which are discrete extreme oceanic warming events, have important impacts on the marine ecosystem, fishery resources, and social economy. Previous studies based on sea surface temperature suggest that MHWs in the tropical western Pacific Ocean are very weak. However, here we show that the MHWs observed by the Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network buoys in the tropical western Pacific Ocean are unexpectedly strong in the subsurface layer (50–300 m depth). The ensemble mean intensity of subsurface MHWs shows a peak of about 5.2 °C at 150 m, and the maximal mean intensity reaches 8.9 °C at 5° N, 137° E. Subsurface MHWs occur almost every year with an ensemble mean duration ranging from 13 to 22 days, and show no statistically significant correlation with the El Niño-Southern Oscillation index although the subsurface MHWs during La Niña events are slightly stronger and more frequent than during El Niño events. It seems that the subsurface MHWs are strong and frequent in April–June but relatively weaker and less frequent in September and October than in other months. Anomalous sea surface convergence and Ekman down-welling play an important role in the development of subsurface MHWs. Strong subsurface MHWs are likely to affect the fishery production of tropical western Pacific.

Autophagy is a dynamic and highly regulated process of self-digestion responsible for cell survival and reaction to oxidative stress. As oxidative stress is increased in uremia and is associated with vascular calcification, we studied the role of autophagy in vascular calcification induced by phosphate. In an in vitro phosphate-induced calcification model of vascular smooth muscle cells (VSMCs) and in an in vivo model of chronic renal failure, autophagy was inhibited by the superoxide dismutase mimic MnTMPyP, superoxide dismutase-2 overexpression, and by knockdown of the sodium-dependent phosphate cotransporter Pit1. Although phosphate-induced VSMC apoptosis was reduced by an inhibitor of autophagy (3-methyladenine) and knockdown of autophagy protein 5, calcium deposition in VSMCs was increased during inhibition of autophagy, even with the apoptosis inhibitor Z-VAD-FMK. An inducer of autophagy, valproic acid, decreased calcification. Furthermore, 3-methyladenine significantly promoted phosphate-induced matrix vesicle release with increased alkaline phosphatase activity. Thus, autophagy may be an endogenous protective mechanism counteracting phosphate-induced vascular calcification by reducing matrix vesicle release. Therapeutic agents influencing the autophagic response may be of benefit to treat aging or disease-related vascular calcification and osteoporosis.

Sea surface temperature (SST) variability associated with El Niño–Southern Oscillation (ENSO) slightly increased in the central Pacific Ocean but weakened significantly in the eastern Pacific at the beginning of twenty-first century relative to 1980–99. This decadal shift led to the greater prominence central Pacific (CP) El Niño events during the 2000s relative to the previous two decades, which were dominated by eastern Pacific (EP) events. To expand upon previous studies that have examined this shift in ENSO variability, temperature and temperature variance budgets are examined in the mixed layer of the Niño-3 (5°S–5°N, 150°–90°W) and Niño-4 (5°S–5°N, 160°E–150°W) regions from seven ocean model products spanning the period 1980–2010. This multimodel-product-based approach provides a robust assessment of dominant mechanisms that account for decadal changes in two key index regions. A temperature variance budget perspective on the role of thermocline feedbacks in the ENSO cycle based on recharge oscillator theory is also presented. As found in previous studies, thermocline and zonal advective feedbacks are the most important positive feedbacks for generating ENSO SST variance, and thermodynamic damping is the largest negative feedback for damping ENSO variance. Consistent with the shift toward more CP El Niños after 2000, thermocline feedbacks experienced a substantial reduction from 1980 to 1999 and into the 2000s, while zonal advective feedbacks were less affected. Negative feedbacks likewise weakened after 2000, particularly thermal damping in the Niño-3 region and the nonlinear sink of variance in both regions.

Background Pectin methylesterase (PME) is a hydrolytic enzyme that catalyzes the demethylesterification of homogalacturonans and controls pectin reconstruction, being essential in regulation of cell wall modification. During fruit ripening stage, PME-mediated cell wall remodeling is an important process to determine fruit firmness and softening. Strawberry fruit is a soft fruit with a short postharvest life, due to a rapid loss of firm texture. Hence, preharvest improvement of strawberry fruit rigidity is a prerequisite for extension of fruit refreshing time. Although PME has been well characterized in model plants, knowledge regarding the functionality and evolutionary property of PME gene family in strawberry remain limited. Results A total of 54 PME genes ( FvPMEs ) were identified in woodland strawberry ( Fragaria vesca ‘Hawaii 4’). Phylogeny and gene structure analysis divided these FvPME genes into four groups (Group 1–4). Duplicate events analysis suggested that tandem and dispersed duplications effectively contributed to the expansion of the PME family in strawberry. Through transcriptome analysis, we identified FvPME38 and FvPME39 as the most abundant-expressed PME s at fruit ripening stages, and they were positively regulated by abscisic acid. Genetic manipulation of FvPME38 and FvPME39 by overexpression and RNAi-silencing significantly influences the fruit firmness, pectin content and cell wall structure, indicating a requirement of PME for strawberry fruit softening. Conclusion Our study globally analyzed strawberry pectin methylesterases by the approaches of phylogenetics, evolutionary prediction and genetic analysis. We verified the essential role of FvPME38 and FvPME39 in regulation of strawberry fruit softening process, which provided a guide for improving strawberry fruit firmness by modifying PME level.

Significant strides have been made in understanding El Niño-Southern Oscillation (ENSO) dynamics, yet its long-lead prediction remains challenging, especially for the El Niño events after 2000. Sea surface salinity (SSS) is known to affect ENSO development and intensity by influencing ocean stratification and heat redistribution and therefore, when combined with sea surface temperature (SST) data, can potentially enhance ENSO forecast skill. In this study, we develop a deep learning (DL) model that incorporates a multiscale-pyramid structure and spatiotemporal feature extraction blocks, and the model successfully extends effective ENSO forecast lead time to 24 months for 2000–2021 with reduced effect of the spring predictability barrier (SPB). Interpretable methods are then applied to reveal the time-dependent roles of SST and SSS in ENSO forecast. More specifically, SST is critical for short-medium lead forecasts (<1 year), while SSS is important for medium-long lead forecasts (>6 months). Furthermore, we track global SST and SSS spatiotemporal shifts related to subsequent ENSO development, highlighting the importance of ocean inter-basin and tropics-extratropics interactions. With increasing availability of satellite SSS observations, our findings unveil unprecedented potential for advancing ENSO long-lead forecast skills.

The Makassar Strait throughflow (MST) constitutes a significant component of the Indonesian throughflow (ITF) and plays a pivotal role in the interbasin exchange between the Indian and Pacific Oceans. While previous studies have suggested that the buoyancy forcing plays a role in influencing the seasonality of the MST, the quantitative contribution of salinity effect on MST seasonality remains unclear. Here we use the measurements from the Monitoring ITF program and the Global Ocean Physics Reanalysis product to investigate the seasonality of MST and quantify the impact of the salinity effect. We find that the halosteric variability due to the salinity effect contributes to approximately (69.6 ± 11.7) % of the total seasonal variability of surface dynamic height gradient along the Makassar Strait, and dominates the seasonality of the upper layer MST. The primary drivers for freshwater forcing are horizontal advection through the Karimata Strait and precipitation in the Java Sea. Plain Language Summary The Indonesian throughflow (ITF) connects the tropical Pacific and Indian Oceans, playing a big role in moving heat and freshwater between them. The Makassar Strait throughflow (MST), which is a major part of the ITF, is important for both regional and global climate systems. Previous studies suggest that the freshwater flux can affect the transport and vertical structure of MST throughout the year. In this study, we used current observations in the Makassar Strait and other high‐resolution data to investigate how MST changes with each season, specifically focusing on how salinity changes affect it. The impact of salinity changes on the seasonal variations in MST overwhelms that of temperature changes. Most of the freshwater in the Makassar Strait comes from water flowing through the Karimata Strait, with some contribution from local rainfall in the Java Sea. Key Points The Makassar Strait throughflow (MST) shows significant seasonality associated with the along‐strait sea surface height gradient Halosteric dynamic height (DH) dominates the seasonal variability of the sea surface DH gradient and MST Advection of freshwater from the Karimata Strait and local precipitation constitute the major freshwater forcing for the MST