5.1 µm Ion-Regulated Rigid Quasi-Solid Electrolyte Constructed by Bridging Fast Li-Ion Transfer Channels for Lithium Metal Batteries.

An ultra-thin quasi-solid electrolyte (QSE) with dendrite-inhibiting properties is a requirement for achieving high energy density quasi-solid lithium metal batteries (LMBs). Here, a 5.1 µm rigid QSE layer is directly designed on the cathode, in which Kevlar (poly(p-phenylene terephthalate)) nanofibers (KANFs) with negatively charged groups bridging metal-organic framework (MOF) particles are served as a rigid skeleton, and non-flammable deep eutectic solvent is selected to be encapsulated into the MOF channels, combined with in situ polymerization to complete safe electrolyte system with high rigidness and stability. The QSE with constructed topological network demonstrates high rigidity (5.4 GPa), high ionic conductivity (0.73 mS cm-1 at room temperature), good ion-regulated properties, and improved structural stability, contributing to homogenized Li-ion flux, excellent dendrite suppression, and prolonged cyclic performance for LMB. Additionally, ion regulation influences the Li deposition behavior, exhibiting a uniform morphology on the Li-metal surface after cycling. According to density-functional theory, KANFs bridging MOFs as hosts play a vital function in the free-state and fast diffusion dynamics of Li-ions. This work provides an effective strategy for constructing ultrathin robust electrolytes with a novel ionic conduction mode.

Schisandrin B enhances embryo competence and potentially mitigates endoplasmic reticulum stress during porcine preimplantation development.

Endoplasmic reticulum (ER) stress induced by agents such as tunicamycin (TM) substantially impedes the developmental progression of porcine embryos. Lignan compounds such as Schisandrin B (Sch-B), may have the potential to mitigate this stress. However, there are few studies on the effects of Sch-B on embryo development. To address this research gap, this study evaluates the protective efficacy of Sch-B against TM-induced ER stress during pivotal stages of porcine embryogenesis. Notably, embryos treated with Sch-B exhibited pronounced resistance to TM-induced developmental arrest, particularly at the 4-cell stage, facilitating progression to the 8-cell stage and subsequent blastocyst formation. It was also observed that Sch-B effectively reduced reactive oxygen species (ROS) levels and improved mitochondrial membrane potential (MMP). Furthermore, Sch-B positively influenced the expression of several stress-related genes. These findings highlight the promising role of Sch-B in improving porcine embryo development and mitigating ER stress.

Real-time water quality prediction in water distribution networks using graph neural networks with sparse monitoring data.

Ensuring the safety and reliability of drinking water supply requires accurate prediction of water quality in water distribution networks (WDNs). However, existing hydraulic model-based approaches for system state prediction face challenges in model calibration with limited sensor data and intensive computing requirements, while current machine learning models are lack of capacity to predict the system states at sites that are not monitored or included in model training. To address these gaps, this study proposes a novel gated graph neural network (GGNN) model for real-time water quality prediction in WDNs. The GGNN model integrates hydraulic flow directions and water quality data to represent the topology and system dynamics, and employs a masking operation for training to enhance prediction accuracy. Evaluation results from a real-world WDN demonstrate that the GGNN model is capable to achieve accurate water quality prediction across the entire WDN. Despite being trained with water quality data from a limited number of sensor sites, the model can achieve high predictive accuracies (Mean Absolute Error = 0.07 mg L-1 and Mean Absolute Percentage Error = 10.0 %) across the entire network including those unmonitored sites. Furthermore, water quality-based sensor placement significantly improves predictive accuracy, emphasizing the importance of careful sensor location selection. This research advances water quality prediction in WDNs by offering a practical and effective machine learning solution to address challenges related to limited sensor data and network complexity. This study provides a first step towards developing machine learning models to replace hydraulic models in WDN modelling.

Gated graph neural networks for identifying contamination sources in water distribution systems.

Contamination events in water distribution networks (WDN) pose significant threats to water supply and public health. Rapid and accurate contamination source identification (CSI) can facilitate the development of remedial measures to reduce impacts. Though many machine learning (ML) methods have been proposed for fast detection, there is a critical need for approaches capturing complex spatial dynamics in WDNs to enhance prediction accuracy. This study proposes a gated graph neural network (GGNN) for CSI in the WDN, incorporating both spatiotemporal water quality data and flow directionality between network nodes. Evaluated across various contamination scenarios, the GGNN demonstrates high prediction accuracy even with limited sensor coverage. Notably, directional connections significantly enhance the GGNN CSI accuracy, underscoring the importance of network topology and flow dynamics in ML-based WDN CSI approaches. Specifically, the method achieves a 92.27% accuracy in narrowing the contamination source to 5 points using just 2 h of sensor data. The GGNN showcases resilience under model and measurement uncertainties, reaffirming its potential for real-time implementation in practice. Moreover, our findings highlight the impact of sensor sampling frequency and measurement accuracy on CSI accuracy, offering practical insights for ML methods in water network applications.

The Influence of Laser Process Parameters on the Adhesion Strength between Electroless Copper and Carbon Fiber Composites Determined Using Response Surface Methodology.

Laser process technology provides a feasible method for directly manufacturing surface-metallized carbon fiber composites (CFCs); however, the laser's process parameters strongly influence on the adhesion strength between electroless copper and CFCs. Here, a nanosecond ultraviolet laser was used to fabricate electroless copper on the surface of CFCs. In order to achieve good adhesion strength, four key process parameters, namely, the laser power, scanning line interval, scanning speed, and pulse frequency, were optimized experimentally using response surface methodology, and a central composite design was utilized to design the experiments. An analysis of variance was conducted to evaluate the adequacy and significance of the developed regression model. Also, the effect of the process parameters on the adhesion strength was determined. The numerical analysis indicated that the optimized laser power, scanning line interval, scanning speed, and pulse frequency were 5.5 W, 48.2 µm, 834.0 mm/s, and 69.5 kHz, respectively. A validation test confirmed that the predicted results were consistent with the actual values; thus, the developed mathematical model can adequately predict responses within the limits of the laser process parameters being used.

Schisandrin B protects boar spermatozoa against oxidative damage and increases their fertilization ability during in vitro storage.

Oxidative stress due to low temperatures during in vitro preservation reduces boar spermatozoa quality. It has been proven that Schisandrin B (Sch-B) can act against oxidative stress in cells. Therefore, the purpose of this study was to investigate whether the treatment with Sch-B could improve the quality of boar sperm during storage at 17 °C. Semen samples were randomly divided into four groups and added to the Beltsville Thawing Solution containing different concentrations of Sch-B (0, 0.1, 0.5, and 1 mg/L) after collection. Each group was then preserved at 17 °C and the sperm motility, membrane integrity, and acrosome integrity were detected to determine the maximum available concentration of Sch-B for sperm. The optimal concentration was set at 0.1 mg/L and was used in subsequent experiments. Sperms treated with 0 and 0.1 mg/L Sch-B were evaluated for lipid peroxidation (MDA) and fertilization ability through in vitro fertilization. Finally, the quality of blastocysts which were formed by 0 and 0.1 mg/L Sch-B-treated sperm was determined. The results showed that compared with the control, the addition of 0.1 mg/L Sch-B improved boar sperm motility, and the addition of 0.1 and 0.5 mg/L Sch-B improved sperm membrane integrity and acrosome integrity. Treatment with 0.1 mg/L Sch-B reduced the level of MDA and increased the cleavage rate, blastocyst rate, and total cell number of blastocysts compared to the rate and number in the control group. However, no significant difference was observed in the ROS levels of blastocysts between the treatment and the control groups. The expression levels of CAT, SOD2, and Bcl-2 in IVF-blastocysts formed using sperm stored for one day at 17 °C were significantly higher than those in the control blastocysts. On day 4 of storage, CAT and Bcl-2 expression were significantly higher in IVF-blastocysts formed from sperm treated with 0.1 mg/L Sch-B than that in the control blastocysts. The ratio of Bax/Bcl-2 was also significantly higher in IVF-blastocysts formed using Sch-B-treated sperm. Our findings demonstrate that treatment with Sch-B can protect boar sperm from oxidative stress during liquid preservation and can increase the fertilization ability of the sperm.

Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter, multi-site water quality monitoring.

Contamination events in water distribution networks (WDNs) can have a huge impact on water supply and public health; increasingly, online water quality sensors are deployed for real-time detection of contamination events. Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection; however, accurate extraction of spatial features in water quality signals remains challenging. This study proposed a contamination detection method based on generative adversarial networks (GANs). The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators. The model consists of two networks-a generator and a discriminator-the outputs of which are used to measure the degree of abnormality of water quality data at each time step, referred to as the anomaly score. Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores. Alarms are then generated from the fusion of single-site and multi-site models. The proposed method was tested on a WDN for various contamination events with different characteristics. Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes. Additionally, the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters, and water quality data from different sensor stations, highlighting its robustness and potential for practical application to real-time contamination events.

Water transfer infrastructure buffers water scarcity risks to supply chains.

Inter-basin water transfer (IBWT) infrastructure has been expanding to deliver water across China to meet water demands in populated and industrial areas. Water scarcity may threaten the ability to produce and distribute goods through supply chains. Yet, it is not clear if IBWTs transmit or buffer water scarcity throughout supply chains. Here we combine a national database of IBWT projects and multi-region input-output analysis to trace water transferred by IBWT and virtual scarce water (scarcity weighted water use) from IBWT sourcing basins to production sites then to end consumers. The results indicate that production and final consumption of sectoral products have been increasingly supported by IBWT infrastructure, with physically transferred water volumes doubling between 2007 and 2017. Virtual scarce water is about half of the virtual water supporting the supply chain of the nation. IBWT effectively reduced virtual scarce water supporting the supply chains of most provinces, with the exposure to water scarcity reduced by a maximum of 56.7% and 15.0% for production and final consumption, respectively. IBWT Infrastructure development can thus buffer water scarcity risk to the supply chain and should be considered in water management and sustainable development policy decisions.

Developing stacking ensemble models for multivariate contamination detection in water distribution systems.

This study presents a new stacking ensemble model for contamination event detection using multiple water quality parameters. The stacking model consists of a number of machine learning base predictors and a meta-predictor, and it is trained using cross-validation to capture different features in multiple water quality parameters and then used for water quality predictions. For each water quality parameter, the residuals between predicted and measured data are classified to identify anomalies with thresholds derived from the sequential model-based optimization method and detection probabilities updated using Bayesian analysis. Alarms derived from individual water quality parameters are fused to enhance the anomaly signals and improve the detection accuracy. The proposed stacking-based method is evaluated using a data set of six water quality parameters from a real water distribution system with randomly simulated events. The stacking-based method could detect 2496 events out of a total 2500 events without a false alarm. The results show that the stacking method outperforms an artificial neural network (ANN) benchmark method in contamination event detection. The stacking method has a higher true positive rate, lower false positive rate and higher F1 score than the ANN method. This implies that the stacking method has great promise of detecting contamination events in the water distribution system.

Evaluation validation of a qPCR curve analysis method and conventional approaches.

BACKGROUND: Reverse Transcription quantitative polymerase chain reaction (RT-qPCR) is a sensitive and reliable method for mRNA quantification and rapid analysis of gene expression from a large number of starting templates. It is based on the statistical significance of the beginning of exponential phase in real-time PCR kinetics, reflecting quantitative cycle of the initial target quantity and the efficiency of the PCR reaction (the fold increase of product per cycle). RESULTS: We used the large clinical biomarker dataset and 94-replicates-4-dilutions set which was published previously as research tools, then proposed a new qPCR curve analysis method--CqMAN, to determine the position of quantitative cycle as well as the efficiency of the PCR reaction and applied in the calculations. To verify algorithm performance, 20 genes from biomarker and partial data with concentration gradients from 94-replicates-4-dilutions set of MYCN gene were used to compare our method with various publicly available methods and established a suitable evaluation index system. CONCLUSIONS: The results show that CqMAN method is comparable to other methods and can be a feasible method which applied to our self-developed qPCR data processing and analysis software, providing a simple tool for qPCR analysis.

Unraveling the effect of inter-basin water transfer on reducing water scarcity and its inequality in China.

Securing water supply in the face of increasing water scarcity is one important challenge faced by humanity in sustainable development. Inter-basin water transfer is widely applied to provide water supply security in regions where water demand exceeds water availability. However, the effect of inter-basin water transfer on alleviating water scarcity and its inequality is poorly understood especially at the national scale. Based on a newly compiled database of inter-basin water transfer projects in China, here we report a first national assessment of their effect on securing water supply in different basins. We developed a number of indices to facilitate quantifying the effect of water transfer on water scarcity and its inequality. The capacity of inter-basin transfer projects has been steadily increased, which achieved ~48.5 billion m3 yr-1 by 2016 (equivalent to ~8% of the national water use). The results indicate that water transfer has impacted water supply of 43 sub-basins out of a total of 76 sub-basins, but it hardly changes a basin's water scarcity level (e.g., from water scarcity to low water scarcity). Approximately three quarters of people in China are affected by water transfer. More than a half of the national population (705 million) benefit from alleviated water scarcity, leading to the inequality coefficient reduced from 0.64 under natural water availability condition to 0.59 considering water transfer in 2016. However, 357 million people in water transfer source basins are subject to increased water scarcity, in which ~21% are from water stressed sub-basins. This study reveals for the first time water transfer induced water scarcity and inequality change across sub-basins in China, and highlights the challenges to secure water supply across basins.

Flow regime identification for air valves failure evaluation in water pipelines using pressure data.

Air valve failure can cause air accumulation and result in a loss of carrying capacity, pipe vibration and even in some situations a catastrophic failure of water transmission pipelines. Air is most likely to accumulate in downward sloping pipes, leading to flow regime transition in these pipes. The flow regime identification can be used for fault diagnosis of air valves, but has received little attention in previous research. This paper develops a flow regime identification method that is based on support vector machines (SVMs) to evaluate the operational state of air valves in freshwater/potable pipelines using pressure signals. The laboratory experiments are set up to collect pressure data with respect to the four common flow regimes: bubbly flow, plug flow, blow-back flow and stratified flow. Two SVMs are constructed to identify bubbly and plug flows and validated based on the collected pressure data. The results demonstrate that pressure signals can be used for identifying flow regimes that represent the operational state (functioning or malfunctioning) of air valves. Among several signal features, Power Spectral Density and Short-Zero Crossing Rate are found to be the best indictors to classify flow regimes by SVMs. The sampling rate and time of pressure signals have significant influence on the performance of SVM classification. With optimal SVM features and pressure sampling parameters the identification accuracies exceeded 93% in the test cases. The findings of this study show that the SVM flow regime identification is a promising methodology for fault diagnosis of air valve failure in water pipelines.

Assessing catchment scale flood resilience of urban areas using a grid cell based metric.

Urban flooding has become a global issue due to climate change, urbanization and limitation in the capacity of urban drainage infrastructures. To tackle the growing threats, it is crucial to understand urban surface flood resilience, i.e., how urban drainage catchments can resist against and recover from flooding. This study proposes a grid cell based resilience metric to assess urban surface flood resilience at the urban drainage catchment scale. The new metric is defined as the ratio of the number of unflooded grid cells to the total grid cell number in an urban drainage catchment. A two-dimensional Cellular Automata based model CADDIES is used to simulate urban surface flooding. This methodology is demonstrated using a case study in Dalian, China, which is divided into 31 urban drainage catchments for flood resilience analysis. Results show the high resolution resilience assessment identifies vulnerable catchments and helps develop effective adaptation strategies to enhance urban surface flood resilience. Comparison of the new metric with an existing metric reveals that new metric has the advantage of fully reflecting the changing process of system performance. Effectiveness of adaptation strategies for enhancing urban surface flood resilience is discussed for different catchments. This study provides a new way to characterize urban flood resilience and an in-depth understanding of flood resilience for urban drainage catchments of different characteristics, and thus help develop effective intervention strategies for sustainable sponge city development.

Pterostilbene alleviates hydrogen peroxide-induced oxidative stress via nuclear factor erythroid 2 like 2 pathway in mouse preimplantation embryos.

Pterostilbene (PTS) in blueberries is a phytoalexin with antioxidant properties. PTS exerts strong cytoprotective effects on various cells via Nuclear Factor Erythroid 2 like 2 (NFE2L2) pathway. We evaluated the antioxidant PTS treatment in mouse preimplantation embryos. In vitro culture media were supplemented with different concentrations of PTS. Treatment of zygotes with 0.25 µM PTS improved the development of day 4 blastocysts (P < 0.05). Moreover, H2O2 treatment significantly increased the reactive oxygen species level and reduced the glutathione level in mouse blastocyst, whereas PTS treatment counteracted these effects. The fluorescence intensity of apoptotic positive cell was higher in the H2O2 group than in the PTS group. Furthermore, PTS-treated embryos significantly increased the protein expression of NFE2L2 in the nucleus and decreased Kelch-like ECH-associated protein1 (KEAP1). PTS treatment significantly increased the expression of downstream target genes involved in the NFE2L2 pathway, such as catalase (CAT), heme oxygenase1 (HMOX1), glutathione peroxidase (GPX), and superoxide dismutase (SOD); these genes confer cellular protection. In addition, PTS treatment significantly increased the expression of anti-apoptotic B-cell lymphoma 2 (BCL2), with a concomitant reduction in the apoptotic Bcl-2-associated X protein (BAX) and Caspase-3 genes in the embryo. PTS treatment also increased the protein expression of BCL2 and reduced the protein expression of BAX in the mouse embryo. In conclusion, PTS activated NFE2L2 signaling pathway in the development of mouse embryos by altering downstream expression of genes involved in the antioxidant mechanisms and apoptosis.

Pterostilbene exerts a protective effect via regulating tunicamycin-induced endoplasmic reticulum stress in mouse preimplantation embryos.

Pterostilbene (PTS) mainly enriched in small fruits such as berries and grapes exerts an antioxidant effect. However, the protective effects of PTS against endoplasmic reticulum stress (ERS) have not yet been elucidated in mouse preimplantation embryo. ERS plays an important role in regulating the pathological and physiological processes, including embryonic development. We explored the protective effect of PTS on the tunicamycin (TM)-induced ERS in mouse preimplantation embryos. In vitro, culture medium was supplemented with different concentrations of TM and PTS. Our result indicated that treatment of zygotes with 0.5 µg/ml TM significantly decreased the development of day 4 blastocysts (P < 0.05), whereas 0.25 µM PTS supplementation improved the development rate of blastocysts. Moreover, TM treatment significantly increased (P < 0.05) the apoptotic index and reduced the total cell number of the blastocyst, whereas PTS treatment counteracted these effects. Additionally, TM potently increased expression levels of ERS-related proteins, such as GRP78, ATF6, PERK, p-Perk, IRE1, ATF4, and CHOP (P < 0.05). However, PTS and PTS + TM treatment decreased expression levels of ERS-related proteins (P < 0.05). Furthermore, expression level of the anti-apoptotic protein and gene BCL2 significantly decreased (P < 0.05) in TM-treated embryo but increased by PTS treatment (P < 0.05), whereas expression levels of the pro-apoptotic protein and gene BAX increased (P < 0.05) with TM but significantly decreased (P < 0.05) with co-treatment with PTS. In summary, PTS treatment significantly increased the development potential of mouse embryo by reduction of ERS.

Advanced glycation end products induce oxidative stress and mitochondrial dysfunction in SH-SY5Y cells.

This study aimed to investigate the direct effects of advanced glycation end products (AGEs) on the mitochondrial structure and function of SH-SY5Y cells and the possible molecular mechanism(s) underlying mitochondria dysfunction by AGEs. SH-SY5Y cells were cultured in 400 µg/ml of AGE-bovine serum albumin (BSA) for 24 h, and changes in the mitochondrial function of SH-SY5Y cells were analysed as follows. Reactive oxygen species (ROS) were detected using 2',7'-dichlorodihydrofluorescein diacetate molecular probes. Mitochondrial membrane potential (ΔΨm) was determined by flow cytometry using fluorescent probes. The expression of cytochrome c (Cyt c) protein level was assessed by Western blotting. Mitochondrial structures were observed by transmission electron microscopy. Our results showed that AGE-BSA induced an increase in ROS levels, a decrease in mitochondrial ΔΨm, and the release of Cyt c from mitochondria in SH-SY5Y cells. The mitochondria of SH-SY5Y cells showed remarkable swelling and vacuolisation, but these changes were recovered after pretreatment with neutralising anti-receptor for advanced glycation end products (RAGE) antibody. Our results suggested that AGE-BSA induced mitochondrial dysfunction in SH-SY5Y cells through RAGE pathways. Thus, AGEs are potential mechanistic links between diabetes mellitus and Alzheimer's disease.

Insulin inhibits Abeta production through modulation of APP processing in a cellular model of Alzheimer's disease.

OBJECTIVE: Amyloid-beta (Abeta) is a 36-43 amino acid peptide that is derived by processing of the beta-amyloid precursor protein (APP). Abeta plays a central role in the development of Alzheimer's disease (AD). Although growing evidence suggests that insulin has important functions in Abeta metabolism, the underlying mechanisms are still unknown. METHODS: Using an SH-SY5Y cell line overexpressing human APP Swedish mutant (APPsw), we evaluated the effect of insulin on APP processing and Abeta production by using western blot analysist. RESULTS: Our data showed that administration of insulin reduced the Abeta generation in culture media with a concomitant decreases in the levels of beta-secretase BACE1, secreted extracellular domain (sAPPbeta) and a fragment of 99 amino acids (C99) in APPsw cells. We further showed that insulin increased the levels of alpha-secretase ADAM10, a secreted extracellular domain secreted (sAPPa) and a fragment of 83 amino acids (C83) in APPsw cells. CONCLUSION: Our present data suggest that insulin could inhibit Abeta production through modulation of APP processing by increasing cleavage at the a-secretase site and decreased cleavage at the beta-secretase sites.

Streptozotocin-induced diabetes increases amyloid plaque deposition in AD transgenic mice through modulating AGEs/RAGE/NF-κB pathway.

BACKGROUND: An increasing number of studies have demonstrated of that diabetes mellitus (DM) is associated with an increased prevalence of Alzheimer disease (AD), the underlying mechanisms are still obscure. METHODS: We developed a streptozotocin (STZ)-induced diabetic AD transgenic mouse model and evaluated the effect of hyperglycemia on senile plaque formation. RESULTS: Our data showed that administration of STZ increased the level of blood glucose and increased the advanced glycation end products (AGEs) in brain tissue, and further enhanced the expression levels of the receptor for AGEs (RAGE) and the nuclear factor-kappa B (NF-κB) in the brain, and accelerated the senile plaque formation in the transgenic mice. Our results showed that STZ-induced insulin-deficient hyperglycemia caused the pathophysiology of AD in APP/PS1 transgenic mice by modulating the AGEs/RAGE/NF-κB pathway. CONCLUSIONS: Our study suggests that there is a close linkage of DM and cerebral amyloidosis in the pathogenesis of AD.

Tris(1,10-phenanthroline)nickel(II) dichromate tetra-hydrate.

The asymmetric unit of the title compound, [Ni(C(12)H(8)N(2))(3)][Cr(2)O(7)]·4H(2)O, contains one cation, one anion and four water mol-ecules, three of which are disordered over two sites with equal occupancies. In the cation, the metal centre is coordinated by six N atoms from three 1,10-phenanthroline ligands in a distorted octa-hedral geometry. The [Cr(2)O(7)](2-) anion exhibits a staggered conformation. The crystal packing is stabilized by inter-molecular O-H⋯O hydrogen bonds and π-π inter-actions, evidenced by short distances of 3.531 (5) Šbetween the centroids of aromatic rings.

Diaqua-bis(1,10-phenanthroline)magnesium dichromate(VI) 1,10-phenanthroline disolvate.

In the title compound, [Mg(C(12)H(8)N(2))(2)(H(2)O)(2)][Cr(2)O(7)]·2C(12)H(8)N(2), the cation and anion are situated on a twofold rotation axis. The Mg(II) ion is coordinated by four N atoms from two 1,10-phenanthroline ligands and two O atoms from coordinated water mol-ecules in a distorted octa-hedral geometry. Inter-molecular O-H⋯N and O-H⋯O hydrogen bonds and π-π inter-actions between the aromatic rings [shortest centroid-centroid separation = 3.527 (2) Å] link the cations, anions and 1,10-phenanthroline solvent mol-ecules into a hydrogen-bonded cluster.