Clean water is an indispensable essential resource on which humans and other living beings depend. Therefore, the establishment of a water quality prediction model to predict future water quality ...conditions has a significant social and economic value. In this study, a model based on an artificial neural network (ANN), discrete wavelet transform (DWT), and long short-term memory (LSTM) was constructed to predict the water quality of the Jinjiang River. Firstly, a multi-layer perceptron neural network was used to process the missing values based on the time series in the water quality dataset used in this research. Secondly, the Daubechies 5 (Db5) wavelet was used to divide the water quality data into low-frequency signals and high-frequency signals. Then, the signals were used as the input of LSTM, and LSTM was used for training, testing, and prediction. Finally, the prediction results were compared with the nonlinear auto regression (NAR) neural network model, the ANN-LSTM model, the ARIMA model, multi-layer perceptron neural networks, the LSTM model, and the CNN-LSTM model. The outcome indicated that the ANN-WT-LSTM model proposed in this study performed better than previous models in many evaluation indices. Therefore, the research methods of this study can provide technical support and practical reference for water quality monitoring and the management of the Jinjiang River and other basins.
Programmed cell death (PCD)—apoptosis, autophagy and programmed necrosis—is any pathological form of cell death mediated by intracellular processes. Ototoxic drugs, ageing and noise exposure are some ...common pathogenic factors of sensorineural hearing loss (SNHL) that can induce the programmed death of auditory hair cells through different pathways, and eventually lead to the loss of hair cells. Furthermore, several mutations in apoptotic genes including DFNA5, DFNA51 and DFNB74 have been suggested to be responsible for the new functional classes of monogenic hearing loss (HL). Therefore, in this review, we elucidate the role of these three forms of PCD in different types of HL and discuss their guiding significance for HL treatment. We believe that further studies of PCD pathways are necessary to understand the pathogenesis of HL and guide scientists and clinicians to identify new drug targets for HL treatment.
The authors review accumulating evidence pointing out that programmed cell death pathways, including apoptosis, autophagy and necrosis, play key roles in ultimate fates of auditory hair cells, when cells suffer adverse factors. These three forms of PCD may jointly decide occurrence of hearing loss.
Skin wound healing is an intractable problem that represents an urgent clinical need. To solve this problem, a large number of studies have focused on the use of exosomes (EXOs) derived from ...adipose‐derived stem cells (ADSCs). This review describes the mechanisms whereby ADSCs‐EXOs regulate wound healing and their clinical application. In the wound, ADSCs‐EXOs modulate immune responses and inflammation. They also promote angiogenesis, accelerate proliferation and re‐epithelization of skin cells, and regulate collagen remodelling which inhibits scar hyperplasia. Compared with ADSCs therapeutics, ADSCs‐EXOs have highly stability and are easily stored. Additionally, they are not rejected by the immune system and have a homing effect and their dosage can be easily controlled. ADSCs‐EXOs can improve fat grafting and promote wound healing in patients with diabetes mellitus. They can also act as a carrier and combined scaffold for treatment, leading to scarless cutaneous repair. Overall, ADSCs‐EXOs have the potential to be used in the clinic to promote wound healing.
This review describes the mechanisms whereby exosomes (EXOs) derived from adipose‐derived stem cells (ADSCs) regulate wound healing and their clinical application. In the wound, ADSCs‐EXOs modulate immune responses and inflammation, promote angiogenesis, accelerate proliferation and re‐epithelization of skin cells and regulate collagen remodelling, which inhibits scar hyperplasia. ADSCs‐EXOs have the potential to be used in the clinic, as they can improve fat grafting, promote wound healing of diabetic patients and act as a carrier and combined scaffold for treatment, leading to scarless cutaneous repair.
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•Bromodomain-containing protein 4 (BRD4) is a cancer therapeutic target for synergistic combinations of drugs.•Dual-target small-molecule inhibitors of BRD4 have been discovered as ...cancer therapies.•We describe strategies for designing dual-target inhibitors of BRD4.•Dual-target inhibition of BRD4 is a promising strategy for drug development.
Bromodomain-containing protein 4 (BRD4) is emerging as a therapeutic target that acts synergistically with other targets of small-molecule drugs in cancer. Therefore, the discovery of potential new dual-target inhibitors of BRD4 may be a promising strategy for cancer therapy. In this review, we highlight a series of strategies to design therapeutic dual-target inhibitors of BRD4 that focus on the synergistic functions of this protein. Drug combinations that exploit synthetic lethality, protein–protein interactions, functional complementarity, and blocking of resistance mechanisms could ultimately overcome the barriers inherent to the development of BRD4 inhibitors as future cancer drugs.
Water is a fundamental natural and strategic economic resource that plays a vital role in promoting economic and social development. With the accelerated urbanization and industrialization in China, ...the potential demand for water resources will be enormous. Therefore, accurate prediction of water resources demand is important for the formulation of industrial and agricultural policies, development of economic plans, and many other aspects. In this study, we develop a model based on principal component analysis (PCA) and back propagation (BP) neural network to predict water resources demand in Taiyuan, Shanxi Province, a city with severe water shortage in China. The prediction accuracy is then compared with PCA-ANN, ARIMA, NARX, Grey–Markov, serial regression, and LSTM models, and the results showed that the PCA-BP model outperformed other models in many evaluation factors. The proposed PCA-BP model reduces the dimensionality of high-dimensional variables by PCA and transformed them into uncorrelated composite data, which can make them easier to compute. More importantly, BP and weight threshold adjustment in model training further improve the prediction accuracy of the model. The model analysis will provide an important reference for water demand assessment and optimal water allocation in other regions. HIGHLIGHTS A water demand forecasting model based on principal component analysis (PCA) and back propagation (BP) neural network is proposed.; PCA is used to reduce the dimensionality of the data to reduce the computational complexity.; Compared with other existing models, the prediction accuracy of the PCA-BP model has been significantly improved.;
Tai Lake, the third largest freshwater lake in China, with a history of serious ecological pollution incidents.
Lake water quality prediction techniques are essential to ensure an early emergency ...response capability for sustainable water management. Herein, an effective data-driven ensemble model was developed for predicting lake dissolved oxygen (DO) based on meteorological factors, water quality indicators and spatial information. First, variation mode decomposition (VMD) was used to decompose data into multiple modal components and classify them into feature terms and self terms. The feature terms were combined with relevant external features for multivariate prediction by convolutional neural network (CNN) and a bi-directional long and short-term memory (BiLSTM) with attention mechanism (AT), as well as using the whale optimization algorithm (WOA) to optimize the model hyperparameters. The self terms form a secondary modal decomposition model. Finally, the groupings were linearly summed to obtain outcome.
The proposed model has the highest prediction accuracy in Tai Lake as well as the best prediction effect using 0.5 days as the period. This research also establishes a stepwise water temperature regulation mechanism, where the output of the target DO content value is achieved by changing the magnitude of water temperature and combining it with this prediction model, thereby strengthening the protection of water resources and the management of fishery production.
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•Combining meteorological, water quality and spatial information to predict DO content.•The secondary modal decomposition extracts more information of external feature terms.•Seeking model hyperparameters with WOA overcomes the randomness of subjective choices.•Establishing a stepped water temperature regulation mechanism to control DO content.•The proposed model has early warning capability in ultra-short time periods.
Metal-organic frameworks (MOF)-based gas sensors have garnered significant interest and are highly desirable for monitoring indoor air quality and mitigating various environmental reclamation ...challenges. Herein, we describe a unique approach for fabricating hydrogen sulfide (H2S) gas sensors based on MOF-derived cobalt oxide nanosheets (Co3O4 Ns) nanostructures. The surface morphology and porosity of the fabricated material were confirmed through comprehensive Brunauer-Emmett-Teller (BET) and electron microscopy analyses. Chemical composition and phase purity were identified using Energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD). The sensor exhibited remarkable sensitivity towards hydrogen sulfide in the range of 0.5–100 ppm, reaching a maximum response of 1702.61 % at an operating temperature of 250 °C for 100 ppm H2S gas. Furthermore, the sensor demonstrated a detection limit of 500 ppb with a response rate of 78.22 %. A consistent stability over 30 days was observed, validating its suitability for real-world applications. The response and recovery times of the H2S gas sensors were assessed with τres/τrec = 63.56/103.34 s, offering valuable insights into their real-world applicability.
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•Simple Coprecipitation synthesis technique producing Co3O4 nanosheets for gas sensors.•Co3O4 nanosheets show high selectivity and efficiency in H2S gas at 250 °C.•CTAB concentration improves ZIF-67-derived-Co3O4 nanomaterials porous topologies, improving gas sensing properties.•Co3O4 nanomaterials exhibit exceptional stability, ensuring long-term gas sensing reliability.
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