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As a global issue, water shortage has attracted much attention from the society. Artificial rain enhancement (ARE) is an effective way to exploit cloud water resources and solve water shortage, but the timing of operation is always a key problem that ARE is facing. The fluctuating properties of water vapor content (WVC) are intricately tied to the choice of operational timing, so accurately predicting the evolution of WVC holds paramount importance when determining the optimal operational timing. However, most of the proposed forecasting methods are limited to simple time series forecasting, and do not pay attention to the complex characteristics of the original data and the shortcomings of a single model prediction. Therefore, the prediction accuracy is difficult to meet the requirements of increasingly refined meteorological services. To tackle this challenge, a new hybrid prediction model, including data reconstruction strategy, benchmark model and improved multi-objective optimization algorithm, is proposed in our research by combining advanced theoretical research of artificial intelligence and data preprocessing ideas. The microwave radiometer WVC observation data at high altitude of Qilian Mountains in China is taken as a case study. By comparing 12 mainstream models, it can be concluded that: The model developed in this study achieves the highest prediction accuracy, and the mean MAPE of the three data sets at 2, 4, 6 and 8 prediction steps is 1.23%, 1.33%, 1.37% and 1.52%, respectively. This result verifies the superiority and practical value of the proposed model in predicting WVC under complex terrain conditions, and provides an excellent solution for accurate prediction of WVC. [ABSTRACT FROM AUTHOR]

Petroleum-based general-purpose polymers, like polyethylene and polypropylene, have become the most economical and practical solution for packaging applications due to their characteristics, such as low cost, excellent optical, mechanical, and barrier properties. However, the significant environmental issues associated with the extensive use of these synthetic polymers have prompted searches for new sustainable alternatives. In pursuit of a carbon–neutral economy, the aim of this work is to develop a nanocomposite film combining high barrier property and transparency, suitable for packaging purpose, utilizing commercial cellulose derivatives (carboxymethyl cellulose, CMC) and 2D non-toxic layered double hydroxide nanosheets (LDH-NSs). Employing a simple, eco-friendly method, the nanocomposite films (CMC/LDH-NS) are fabricated by mixing and casting a mixed suspension from a CMC solution and an LDH-NS suspension, facilitated by a non-toxic exfoliation method in an aqueous amino acid solution. The effects of different ratios of LDH-NS on the barrier properties and transparency of the nanocomposite films are studied, along with the related mechanism through comprehensive analysis of micromorphology, X-ray diffraction, and Fourier-transform infrared spectroscopy. The CMC/LDH-NS films present a remarkable 252-fold increase of oxygen barrier and a 51% improvement in water vapor barrier performance compared to the pure CMC film. Moreover, the nanocomposite films maintain high light transmittance, with values exceeding 80% at 600 nm, even at a 50% LDH-NS:CMC ratio. These outstanding attributes underscore the potential of CMC/LDH-NS nanocomposite films for broad application in food and pharmaceutical packaging, as well as in electronic encapsulation, marking a significant step forward in the development of sustainable packaging solutions. [ABSTRACT FROM AUTHOR]

Shear thickening fluid (STF), a type of non-Newtonian fluid, becomes more viscous as the strain rate rises. Due to their unique rheological properties, STFs have outstanding vibration-damping and energy-absorbing capabilities and have tremendous engineering applications. To obtain a comprehensive understanding of STFs, a review of aspects from the principle and composition, theoretical expression to application of STFs is presented on what are in this paper. In terms of principle of STFs, the thickening theory and thickening mechanism are explained firstly. The rheological characteristics and mechanical properties of STFs are reviewed. Subsequently, typical composition and preparation methods of STFs are enumerated. In terms of theoretical expression, theoretical modelling methods towards the viscosity of STFs are summarized. In terms of application, influencing factors on the thickening properties are examined. Furthermore, current engineering applications of STFs are presented. Finally, future perspectives on the research of STFs are put forward in the conclusion. The work of this article provides a systematic study and thorough knowledge of STFs, which is beneficial for numerous potential engineering applications of STFs. [ABSTRACT FROM AUTHOR]

With the exponential growth of the volume of scientific literature, it is particularly important to grasp the research frontier. Predicting emerging research topics will help research institutions and scholars promptly discover promising research topics. However, previous studies mainly focused on identifying and detecting emerging research topics and lacked a method to efficiently represent and predict the emerging degree of research topics. Therefore, this study proposes a novel deep learning-based method to predict the emerging degree of research topics. First, a new indicator, the emerging index, is proposed based on the emerging attributes such as novelty, growth, and impact to quantitatively measure the emerging degree of research topics. Second, new features reflecting the emerging attributes of the research topics are extracted by constructing heterogeneous networks of bibliographic entities in the research domain. Finally, a deep learning-based time series model was employed to predict the future emerging index based on these new features. Data from the neoplasms and metabolism research domains in the PubMed Central database were used to validate the proposed method. The experimental results showed that the emerging index proposed effectively measures the emerging degree of the research topics. Furthermore, the deep learning-based model demonstrates superior performance to other models in predicting the emerging index, as evidenced by both error-based and rank-based metrics. [ABSTRACT FROM AUTHOR]

As a fundamental energy infrastructure, the power system has a direct impact on people's daily lives and production. If the security of the electrical power technology is compromised by malicious network attacks, it can result in widespread blackouts and have significant consequences on every aspect of society. Therefore, to ensure power control system security, this study introduces a network security risk prediction algorithm for power control systems. The algorithm is based on categorization-constrained Boltzmann machine and Markov time-varying model. It classifies network security risk states using classification-constrained Boltzmann machine and predicts security risk using Markov time-varying model. The experimental results demonstrate that the classification-constrained Boltzmann machine achieved higher average accuracy (78.4%), average precision (76%), and average recall (73.4%) for the network risk state compared to the Hidden Markov Model, Multi-kernel Support Vector Machines, and Bayesian Networks. Therefore, it is evident that the classification-constrained Boltzmann machine is better suited for the power control system. Additionally, when considering the prediction of security risks, the categorization-constrained Boltzmann machine and Markov time-varying model produced impressive results with a combined accuracy, precision, F-score, and consistency index of 0.964, 0.947, 0.96, and 0.974, respectively. These results demonstrate that this particular security risk prediction method has high accuracy and is adaptable to complex equipment and network environments found in power control systems. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the potential of big data technology for spectral feature extraction rice plants under different water management. In this study, we performed reflectance spectral analysis of rice grown under different water management schedules based on big data and machine learning approaches. We applied water management at the tillering, jointing, and heading stages of rice growth and collected visible to near-infrared reflectance spectra to analyze the spectral characteristics of rice grown under different water management schedules. Then, we constructed a data mining model based on the spectral analysis results using the Hadoop and Spark frameworks, and analyzed the characteristic bands of rice grown under each schedule using a parallel machine learning algorithm run in both local and cluster modes. The ChiSqSelector algorithm showed characteristic bands in the range of 400–1000 nm, with bands at approximately 474, 558, 632, 735, and 855 nm for water management in the tillering stage; 472, 551, 627, 721, and 843 nm in the jointing stage; and 471, 545, 642, 725, and 849 nm in the heading stage. By contrast, the UnivariateFeatureSelector algorithm showed characteristic bands at approximately 462, 665, 755, 833, and 937 nm in the tillering stage; 475, 671, 744, 848, and 932 nm in the jointing stage; and 470, 678, 757, 857, and 942 nm in the heading stage. These results demonstrate the feasibility and efficiency of applying big data and data mining technologies for spectral analysis of rice grown under different water management schedules. [ABSTRACT FROM AUTHOR]

In recent years, the issue of aircraft icing has gained widespread recognition. The breaking and detachment of dynamic ice can pose a threat to flight safety. However, the shedding and fracture mechanisms of dynamic ice are unclear and cannot meet the engineering needs of ice-shedding hazard assessment. Therefore, studying the fracture toughness of ice bodies has extremely important practical significance. To address this issue, this article uses a centrally cracked Brazilian disk (CCBD) specimen to measure the pure mode I toughness and pure mode II fracture toughness of freshwater ice at different loading rates. The mixed-mode (I–II) fracture characteristics of ice are discussed, and the experimental results are compared and analyzed with the theoretical values of the generalized maximum tangential stress (GMTS) criterion considering the influence of T-stress. The results indicated that as the loading rate increases, the pure mode I toughness and pure mode II fracture toughness of freshwater ice decrease, and the fracture toughness of freshwater ice is more sensitive to the loading rate. In terms of fracture criteria, the theoretical value of the ratio of pure mode II fracture toughness to pure mode I fracture toughness based on the GMTS criterion is in good agreement with the experimental value, while the theoretical value based on the maximum tangential stress (MTS) criterion deviates significantly from the experimental value, indicating that the GMTS criterion considering the influence of T-stress can better predict the experimental results. [ABSTRACT FROM AUTHOR]

The characteristics of residual errors in GNSS positioning are crucial for fault detection and integrity monitoring. Despite the wide use of the zero-mean Gaussian assumption in the navigation community, studies highlight non-Gaussian traits and heavy-tailed patterns in residual errors. The problem will be even more challenging for users in difficult environments where residual errors consist of a combination of multiple modes with high complexity and cannot be fitted with known distributions or empirical models. To address these issues, our work introduces a novel approach leveraging the Wasserstein distance for assessing the performance of error characterization and fault modeling. However, relying solely on the Wasserstein distance value for direct similarity assessment is hindered by its dependency on dimensionality. We propose a second-order Gaussian Wasserstein distance-based precision metric to offer a quantitative evaluation of GNSS error models in terms of both goodness-of-fit and underlying assumptions. We also establish a robust scoring criterion to distinguish between various GNSS error models, ensuring comprehensive evaluation. The proposed method is validated through a known high-dimensional Gaussian model, achieving a score of 99.95 over 100 with a sample size of 10,000. To demonstrate the capability in dealing with complexity, two multivariate complex GNSS models incorporating copula functions to capture intricate inter-dimensional correlations are established and assessed by our approach. Experimental results show that the method can effectively deliver the evaluation of goodness-of-fault models using the establishment of a universal criteria with different dimensions. It provides a quantitative measure on the goodness of fittings and enhances the modeling to reflect the reality, therefore solving the problems raised above. In addition, with this technique, the close-to-reality fault models can be chosen to generate simulated faulty datasets, thus benefiting algorithm testing and improvement. This is also beneficial to more accurate integrity risk assessment to avoid overbounding- or underbounding-resulted false or missed alert. [ABSTRACT FROM AUTHOR]

Globally, the transportation industry has become one of the leading sectors in carbon emission, and all countries are committed to environmental protection and energy conservation while experiencing rapid development. Under China's "dual-carbon" goal, the carbon emission problem hinders the construction of China's green transportation system and affects the high-quality development of transportation, so it is of great significance to study the spatial pattern of carbon emission efficiency in the transportation industry and the factors affecting it. Firstly, this paper measures the carbon emission value of transportation in 30 provinces in China from 2010 to 2020 based on the IPCC method and measures the carbon emission efficiency through the super-efficiency slack-based measurement model. Secondly, spatial autocorrelation analysis was conducted to determine the spatial clustering characteristics of the efficiency values. Finally, two spatial Durbin models are constructed to measure the spatial spillover effects and analyze the short-term immediate effects of each influencing factor on the static model and the long-term effects of the dynamic model considering the time lag of the transportation carbon emission efficiency. The results of the study show that (1) the average value of efficiency in the central and eastern regions is basically higher than 0.5; in the western and northeastern regions, it is basically lower than 0.3.The overall efficiency of carbon emission in the region shows a fluctuating upward trend but with increasing regional differences. (2) The number of regions with positive spatial correlation increased from 21 to 25 during the study period, and the degree of provincial transportation carbon emission efficiency agglomeration increased. (3) Although urbanization and energy intensity have a large detrimental influence on transportation carbon emission efficiency, environmental regulation has a major favorable effects on it both long and short term. Population scale, opening level, and urbanization all have significant spatial spillover effects. Accordingly, relevant policy recommendations are put forward to provide theoretical guidance for promoting the realization of low-carbon transportation. [ABSTRACT FROM AUTHOR]

Immunotherapy has attracted considerable attention as a therapeutic strategy for cancers including acute myeloid leukemia (AML). In this study, we found that the development of several aggressive subtypes of AML is slower in Rag2−/− mice despite the lack of B and T lymphocytes, even compared to the immunologically normal C57BL/6 mice. Furthermore, an orally active p53-activating drug shows stronger antileukemia effect on AML in Rag2−/− mice than C57BL/6 mice. Intriguingly, Natural Killer (NK) cells in Rag2−/− mice are increased in number, highly express activation markers, and show increased cytotoxicity to leukemia cells in a coculture assay. B2m depletion that triggers missing-self recognition of NK cells impairs the growth of AML cells in vivo. In contrast, NK cell depletion accelerates AML progression in Rag2−/− mice. Interestingly, immunogenicity of AML keeps changing during tumor evolution, showing a trend that the aggressive AMLs generate through serial transplantations are susceptible to NK cell-mediated tumor suppression in Rag2−/− mice. Thus, we show the critical role of NK cells in suppressing the development of certain subtypes of AML using Rag2−/− mice, which lack functional lymphocytes but have hyperactive NK cells. Natural Killer (NK) cells play a role in suppressing the development of certain subtypes of acute myeloid leukemia as shown by employing Rag2−/− mice as model system, which lack functional lymphocytes but have hyperactive NK cells. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC), a prevalent cause of cancer-related deaths, is insensitive to traditional treatments. At different time intervals, the combined antitumor effects of DC-TEX and the programmed death protein 1 (PD-1) antibody (Ab) have not been investigated. In this study, HCC models were established and treated at different time intervals with DC-TEX alone or in combination with PD-1 Ab. In addition, we developed an orthotopic HCC model in BALB/c nude mice and restored T cells. Results demonstrated that the PD-1 + CD8 + T-cell population also increased significantly after DC-TEX treatment, in addition to the increased number of CD8 + T cells. The number of CD8 + T cells increased 72 h after DC-TEX administration. Similar observations were made for PD-1 + CD8 + T cells. Subsequently, PD-1 Ab was administered in combination with DC-TEX at different time points (0, 24, 72, 96, 120, or 168 h). Surprisingly, the combination treatment demonstrated a strong antitumor effect, which was very prominent when PD-1 Ab was administered at 72 h. PD-1 Ab significantly reversed the proliferative ability of PD-1 + CD8 + T cells at 72 h in vitro. The combined antitumor effects of PD-1 Ab and DC-TEX occurred mainly by stimulating CD8 + T cell proliferation and inhibiting T cell exhaustion. In conclusion, our results indicate that the combination of DC-TEX and PD-1 Ab significantly inhibits tumor growth in a murine HCC model and that the timing of PD-1 Ab administration impacts the antitumor effect. [ABSTRACT FROM AUTHOR]

Fair classification is an important field in machine learning, especially in ensemble learning research. However, traditional machine learning methods neither consider the bias in datasets nor the unfairness while training the ensemble model. Therefore, in this paper, a novel fairness-aware ensemble model (FAEM) based on hybrid sampling and modified two-layer stacking is proposed to achieve more equitable predictive performance. To reduce the bias caused by the imbalanced dataset, a new hybrid sampling-based bias-alleviation method is proposed, which removes majority samples through cross-validation-based under-sampling and adds generated minority samples through sensitive attribute-based over-sampling. The fairness of the proposed FAEM is further improved by the proposed new two-layer stacking-based fairness-aware ensemble learning method, which modifies the individual prediction results of the base classifiers in the first layer of stacking to alleviate the bias. Four datasets and five evaluation metrics were used to evaluate the classification performance and fairness of the model. The experiment results show that the proposed FAEM can effectively trade off accuracy for fairness and is 54.8% better than benchmark models in fairness metrics in average. [ABSTRACT FROM AUTHOR]

Wind power generation helps to save energy and reduce emissions. Accurate wind speed prediction can ensure the safe integration of wind power into the energy grid, but due to the intermittent and volatile nature of wind, it brings great difficulties to wind speed prediction. To solve this issue, this paper develops a hybrid forecasting model called WT-VMD-LSTM-TCN, where the wavelet transform (WT) is used to decompose the original wind speed into multiple detailed components and an approximate component, then the variational mode decomposition (VMD) is employed to further decompose the first detailed component, and several intrinsic mode functions and a residual term are obtained. Subsequently, the LSTM-TCN model is established on each component obtained by WT and VMD, where the component is first input into the long short-term memory (LSTM), and then the output sequence of LSTM is input into the temporal convolutional network (TCN) for processing. Ultimately, the predicted results of all components from WT-VMD are reconstructed to obtain the predicted value of wind speed. The performance of the model is verified by simulation experiments with three datasets. The experimental results show that: (a) The developed LSTM-TCN model integrates the feature extraction methods of LSTM and TCN, and the feature extraction ability is more comprehensive, which can improve the forecasting performance of a single model; (b) VMD is used to decompose the subsequence with the largest error, which reduces the difficulty of the prediction of this subsequence; (c) WT-VMD-LSTM-TCN is superior to other comparison models in three datasets, which verifies that the model has good applicability. [ABSTRACT FROM AUTHOR]

Objective: To explore the frequency and effect of extreme temperature on the non-accidental death rate in Hulunbuir, a Chinese ice city. Methods: From 2014 to 2018, mortality data of residents residing in Hulunbuir City were collected. The lag and cumulative effects of extreme temperature conditions on non-accidental death and respiratory and circulatory diseases were analyzed by distributed lag non-linear models (DLNM). Results: The risk of death was the highest during high-temperature conditions, the RR value was 1.111 (95% CI 1.031 ~ 1.198). The effect was severe and acute. The risk of death during extreme low-temperature conditions peaked on the fifth day, (RR 1.057; 95% CI 1.012 ~ 1.112), then decreased and was maintained for 12 days. The cumulative RR value was 1.289 (95% CI 1.045 ~ 1.589). Heat significantly influenced the incidence of non-accidental death in both men (RR 1.187; 95% CI 1.059–1.331) and women (RR 1.252; 95% CI 1.085–1.445). Conclusions: Regardless of the temperature effect, the risk of death in the elderly group (≥ 65 years) was significantly higher than that of the young group (0–64 years). High-temperature and low-temperature conditions can contribute to the increased number of deaths in Hulunbei. While high-temperature has an acute effect, low-temperature has a lagging effect. Elderly and women, as well as people with circulatory diseases, are more sensitive to extreme temperatures. [ABSTRACT FROM AUTHOR]

Polyhedral distortion, associated closely with the atomic arrangement and interatomic interactions, drives many unique behaviors in solids, such as phase transition and negative thermal expansion. In thermoelectric heteroanionic oxides, the anionic polyhedra are widely present, but their effect on thermal transport is rarely investigated. Here, we report an anomalous thermal conduction induced by local symmetry distortion in layered oxyselenides via solving the Boltzmann transport equation based on first-principles calculations. We found interestingly that lighter BiCuSeO exhibits lower thermal conductivity than heavier BiAgSeO. Due to the different distorted degrees of CuSe4 and AgSe4 tetrahedrons, Cu prefers the in-plane vibration, while Ag has more tendency of out-of-plane vibration. Thus, the heat-carrying phonons dominated by the rattling-like vibration of Cu are significantly suppressed, resulting in lower thermal conductivity of BiCuSeO. This study highlights the importance of polyhedral distortion in regulating thermal conduction in layered heteroanionic materials. [ABSTRACT FROM AUTHOR]

Background: Janagliflozin is a novel sodium–glucose cotransport-2 inhibitor. Despite its remarkable effect in glycemic control, no systematic research has evaluated the effect of renal impairment (RI) on its pharmacokinetics and pharmacodynamics. Methods: Here, patients with T2DM (n = 30) were divided into normal renal function (eGFR ≥ 90 mL/min/1.73 m2), mild RI (eGFR between 60 and 89 mL/min/1.73 m2), moderate RI-I (eGFR between 45 and 59 mL/min/1.73 m2), and moderate RI-II (eGFR between 30 and 44 mL/min/1.73 m2) groups. They were administered 50 mg janagliflozin orally, and plasma and urine samples were collected for the determination of janagliflozin concentration. Results: Following oral administration, janagliflozin was rapidly absorbed, with the time to Cmax of 2–6 h for janagliflozin and 3–6 h for its metabolite XZP-5185. Plasma exposure levels were similar for janagliflozin in T2DM patients with or without RI but decreased for the metabolite XZP-5185 in T2DM patients with eGFR between 45 and 89 mL/min/1.73 m2. Janagliflozin significantly promoted the excretion of urinary glucose, even in patients with reduced eGFR. Janagliflozin was well tolerated in patients with T2DM with or without RI, and no serious adverse events (SAEs) occurred during this trial. Conclusions: The exposure levels of janagliflozin in T2DM patients were slightly increased with worsening of RI (i.e., 11% increase in the AUC in patients with moderate RI compared with the normal renal function group). Despite worsening of renal function, janagliflozin exerted a significant pharmacologic effect and was well tolerated, even in patients with moderate RI, implying a promising role in the treatment of patients with in T2DM. Registration: China Drug Trial register (http://www.chinadrugtrials.org.cn/I) identifier no.: CTR20192721. [ABSTRACT FROM AUTHOR]

Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems (SRS). However, the current deep model structures are limited in their ability to learn high-quality embeddings with insufficient data. Meanwhile, highly skewed long-tail distribution is very common in recommender systems. Therefore, in this paper, we focus on enhancing the representation of tail items to improve sequential recommendation performance. Through empirical studies on benchmarks, we surprisingly observe that both the ranking performance and training procedure are greatly hindered by the poorly optimized tail item embeddings. To address this issue, we propose a sequential recommendation framework named TailRec that enables contextual information of tail item well-leveraged and greatly improves its corresponding representation. Given the characteristics of the sequential recommendation task, the surrounding interaction records of each tail item are regarded as contextual information without leveraging any additional side information. This approach allows for the mining of contextual information from cross-sequence behaviors to boost the performance of sequential recommendations. Such a light contextual filtering component is plug-and-play for a series of SRS models. To verify the effectiveness of the proposed TailRec, we conduct extensive experiments over several popular benchmark recommenders. The experimental results demonstrate that TailRec can greatly improve the recommendation results and speed up the training process. The codes of our methods have been available. [ABSTRACT FROM AUTHOR]

Seasonal low-oxygen in marine ranching in the northern Yellow Sea has been one of the major environmental problems in coastal waters in recent years. Nitrogen (N) and phosphorus (P) are important nutrients, which are susceptible to the concentration of dissolved oxygen (DO). This article studied the effects of low-oxygen on nutrients represented by N and P fractions in marine ranching in the northern Yellow Sea. The results showed that there were significant layer differences in temperature and salinity during the low-oxygen period. In the seawater, the nutrient distribution in the death disaster zone of sea cucumbers and the non-disaster zone was similar, and DO had a strong positive correlation with dissolved inorganic nitrogen (DIN). In the sediment, significant regional differences existed in nutrient concentration, and the concentration of total phosphorus (TP) decreased significantly with the increase in DO content. The results showed that the sources and sinks of nitrogen and phosphorus nutrients were inconsistent in this zone, and migration and transformation of the existing form of nitrogen with the seasonal changes in the water environment was a main factor for N distribution. This study extended the understanding of the effects of seasonal low-oxygen on N and P. [ABSTRACT FROM AUTHOR]

As a resource-conserving and environmentally friendly manufacturing paradigm, remanufacturing with the potential to realize sustainability in production has been extensively investigated. Scheduling plays a significant role in achieving the remanufacturing benefits. However, the remanufacturing process involves intricate uncertainties because it takes end-of-life products with different qualities as workblanks, which increases the risk of rework and complicates remanufacturing scheduling. Though the traditional stochastic optimization methods or fuzzy theory have been employed to address uncertainties in the remanufacturing scheduling problem, they are constrained with the limited historical data which renders it difficult to describe uncertainties accurately and intuitively. Therefore, a new uncertain remanufacturing scheduling model with rework risk is proposed, in which the interval grey numbers are applied to describe the uncertainty clearly and consider the rework risk in remanufacturing process. To solve this model, a hybrid optimization algorithm that combines differential evolution and particle swarm optimization algorithms through an efficient representation scheme is proposed. Besides, this algorithm integrates multiple improvements to maintain the diversity of the population and enhance its performance. Simulation experiments are conducted on 18 sets of instances with different scales, and the results demonstrated that the proposed algorithm obtains a better optimal solution than other baseline algorithms on 17 sets of instances. The main finding of this study is providing a new method for solving uncertain remanufacturing scheduling problem with rework risk practically and effectively. [ABSTRACT FROM AUTHOR]

Thermoelectric materials can realize direct conversion between heat and electricity, showing excellent potential for waste heat recovery. Cu2Se is a typical superionic conductor thermoelectric material having extraordinary ZT values, but its superionic feature causes poor service stability and low mobility. Here, we reported a fast preparation method of self-propagating high-temperature synthesis to realize in situ compositing of BiCuSeO and Cu2Se to optimize the service stability. Additionally, using the interface design by introducing graphene in these composites, the carrier mobility could be obviously enhanced, and the strong phonon scatterings could lead to lower lattice thermal conductivity. Ultimately, the Cu2Se-BiCuSeO-graphene composites presented excellent thermoelectric properties with a ZTmax value of ~2.82 at 1000 K and a ZTave value of ~1.73 from 473 K to 1000 K. This work provides a facile and effective strategy to largely improve the performance of Cu2Se-based thermoelectric materials, which could be further adopted in other thermoelectric systems. Here, the authors devise a synthesis strategy to optimize the stability and thermoelectric performance of Cu2Se-based materials. They obtain a maximum ZT value of ~2.82 at 1000 K on Cu2Se-BiCuSeO-graphene composites. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motor (PMSM) is widely used in new energy vehicles. At present, to make electric vehicles have a wider speed range, the motor can reach the rated speed above through the field-weakening control. However, when the traditional field-weakening control strategy is above the rated speed, the dynamic response ability of the vehicle declines. Problems such as torque oscillation and current jump occur. To solve these problems, based on a pigeon-inspired optimization (PIO) algorithm and optimized light gradient boosting machine (LightGBM), the dynamic response capability of the permanent magnet synchronous motor is improved. The robust adaptability of the control system to disturbances and parameter changes is also further improved. By collecting experimental data, the importance of relevant variables is analyzed, and the variable with the largest weight is selected as the input of the model. PIO is used to optimize LightGBM, and the loss function is optimized. Finally, the regression model is established. Simulation and experimental results show that the method is effective. [ABSTRACT FROM AUTHOR]

Water shortage is a major problem facing the world. Artificial precipitation enhancement is an effective way to improve precipitation conversion rate, but the selection of artificial precipitation enhancement operation timing is the main difficulty, and the precipitable water vapor(PWV) is a major index. The variation of PWV is nonlinear and unstable due to complex factors, especially in the Qilian mountains in the northeastern part of the Qinghai-Tibet Plateau, so it is difficult to predict it accurately. Therefore, based on the analysis of the observed data of microwave radiometer in Qilian Mountains, a new combined model is constructed which considers both data decomposition and prediction of several single models in this research. In the data preprocessing stage, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is used to decompose and de-noise the PWV sequence. In the prediction stage, four neural network with unique characteristics, back propagation neural network (BPNN), long short term memroy (LSTM), bidirectional gated recurrent unit (BiGRU) and temporal convolutional network (TCN), are selected to predict the decomposed data respectively. A variant of gray Wolf optimization algorithm (IGWO) is used to determine the optimal weight of the model, and finally the comprehensive predicted value is obtained by weighting calculation. Through the analysis of experimental results, in the longest 15-step prediction, compared with CEEMDAN-BP, CEEMDAN-LSTM, CEEMDAN-BiGRU, CEEMDAN-TCN, the prediction accuracy can be improved by 54.17%, 35.05%, 22.38%, 23.86%, respectively. Other step size prediction also achieves the highest prediction accuracy. [ABSTRACT FROM AUTHOR]

Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-labeled source domain to a related but unlabeled target domain. While the source model is a key avenue for acquiring target pseudolabels, the generated pseudolabels may exhibit source bias. In the conventional SFDA pipeline, a large data (e.g. ImageNet) pre-trained feature extractor is used to initialize the source model at the start of source training, and subsequently discarded. Despite having diverse features important for generalization, the pre-trained feature extractor can overfit to the source data distribution during source training and forget relevant target domain knowledge. Rather than discarding this valuable knowledge, we introduce an integrated framework to incorporate pre-trained networks into the target adaptation process. The proposed framework is flexible and allows us to plug modern pre-trained networks into the adaptation process to leverage their stronger representation learning capabilities. For adaptation, we propose the Co-learn algorithm to improve target pseudolabel quality collaboratively through the source model and a pre-trained feature extractor. Building on the recent success of the vision-language model CLIP in zero-shot image recognition, we present an extension Co-learn++ to further incorporate CLIP’s zero-shot classification decisions. We evaluate on 4 benchmark datasets and include more challenging scenarios such as open-set, partial-set and open-partial SFDA. Experimental results demonstrate that our proposed strategy improves adaptation performance and can be successfully integrated with existing SFDA methods. [ABSTRACT FROM AUTHOR]

Metal oxide nanoparticles (MONPs) such as TiO2 and ZnO have been engineered for various industrial manufacturing processes. Among MONPs, environmental exposure to ZnO nanoparticles may cause health concerns about their significant toxicity. This leads to a desire of efficient methods for their removal in the aqueous environment with facile detection. In this work, 3-aminopropyltriethoxysilane (APTES) was chosen to treat aqueous suspensions containing ZnO and other metal oxide nanoparticles. APTES first formed a thin surface layer on ZnO nanoparticles and continued in branching polymerization to link up with adjacent APTES-ZnO nanoparticles. Eventually, a web-like sediment settled down onto the bottom and was confirmed to be APTES-ZnO conjugation by FT-IR. A high removal efficiency over 99 % w of ZnO nanoparticles in aqueous suspension was attained using 2.0 % by volume of APTES. The sedimentation was selective towards ZnO nanoparticles due to electrostatic interaction with ZnO to form a sturdy condensation coagulate. Other MONPs (Al2O3, CaO, CeO2, CuO and TiO2) did not form any web-like sediment with APTES but could co-precipitate with the ZnO nanoparticles. Meso-tetra(carboxyphenyl)porphyrin (TCPP) could be added as an indicator to co-precipitate with the sediment, with its color changing from red purple to bright green during the formation of APTES-TCPP-ZnO sediment. A filter paper or membrane placed at the bottom greatly facilitated the disposal of all sediments at a low cost. [ABSTRACT FROM AUTHOR]

3-Aminopropyltriethoxysilane (APTES) is one of the most important silicon compounds in chemistry for the synthesis of materials. It has extensively been used as a silanization agent for the chemical modification of metal oxide nanoparticle (MONP) surfaces. The introduction of amine (NH2) enhances their dispersibility and anti-bacterial property. The APTES-MONPs have various direct applications in electrochemical sensors, catalysts and Pickering emulsions. In this review article, exemplary APTES modifications of MONPs for unique biomedical, industrial and scientific applications are highlighted. APTES modification provides a linkage for numerous organic, inorganic, or biochemical attachments that are essential to drug delivery, contaminants removal, catalyst immobilization, and medical imaging. [ABSTRACT FROM AUTHOR]

Dopamine plays important roles in motivational and social behaviors in mammals, and it has been implicated in several human neurological and psychiatric disorders. Rodents are used extensively as experimental models to study dopamine function in health and disease. However, interspecies differences of dopamine systems remain incompletely characterized. Here, we assessed whether the commonly referenced anatomical organization of dopamine systems in Mus musculus differs from another rodent species, Peromyscus californicus, which exhibits unique social behaviors such as biparental care. We applied tyrosine hydroxylase immunofluorescence labeling and high-throughput microscopy to establish whole-brain maps of dopamine systems in P. californicus. By comparing these maps to those from M. musculus, we identified unexpected anatomical similarity and difference between these two species. A sex difference in dopamine neurons at the anteroventral periventricular nucleus of hypothalamus, which has been implicated in regulating the maternal behaviors of the uniparental M. musculus, is similarly present in the biparental P. californicus. In contrast, major interspecies differences from M. musculus are found in the ventral midbrain and striatum of P. californicus, including the expansion of midbrain dopamine neurons into the ventral substantia nigra and the presence of an internal capsule-like white matter tract that demarcates a dorsomedial area from the rest of the striatum. These features identified in P. californicus resemble the anatomical organization of the primate brain more closely compared to those in M. musculus. Our findings suggest that P. californicus is a unique model organism for studying the evolution of dopamine systems in mammals and the disorders of dopamine systems. [ABSTRACT FROM AUTHOR]

We present a nonlocal phase-field framework aimed at modeling ductile fracture using a extended Gurson-type model. Both the intervoid necking and the void shearing mechanisms are introduced into the crack driving force for the phase-field evolution. Different from the conventional Gurson-type models, void coalescence is considered by a modified phenomenological function of the nonlocal damage introduced via a thermodynamic approach. In this paper, firstly, we detail the numerical implementation of the nonlocal regularization of the extended Gurson-type model and the phase-field model into the commercial software (e.g., ABAQUS), wherein several user interfaces are used in combination. Then, we present several numerical benchmark examples of ductile fracture under tension, shear, and mixed mode loadings in order to illustrate the predictive ability of the proposed model. After systematic experimental verification, the nonlocal simulations presented adequately demonstrate that the pathological mesh-dependency and the transferability problem are alleviated using the current model. [ABSTRACT FROM AUTHOR]

To solve the problem of regional water system evaluation, this paper proposes a system efficiency evaluation method based on the game cross-efficiency model and conducts an empirical analysis. First, autopoiesis is introduced as the theoretical basis. The characteristics of the authigenic system are combined with a regional water system, and the connotation and characteristics of the regional water system are defined. Second, based on the competitive relationship between regional water systems, the existing game crossover efficiency model is improved. A crossover efficiency model of other games is proposed to evaluate the efficiency of regional water systems. Then, the Pearl River Delta urban agglomeration is selected as the research object. The effects of four systematic evaluation methods based on the DEA method are compared horizontally to find the optimal system efficiency evaluation method. Finally, the characteristics of the regional water system in the Pearl River Delta are systematically analysed through the evaluation results, and the present situation of the regional water system is fully explained. [ABSTRACT FROM AUTHOR]

Based on data obtained from ERA-20C during 1900–2010, this study statistically conducts spatiotemporal characteristics and relations between boundary layer height (BLH) and aridity index (AI) in East Asian drylands, thus originally understanding dry–wet changes from perspective of atmospheric boundary layer. A high (low) BLH generally coincides with regions that have small (large) AI. The annual variability of BLH and AI is negatively correlated with correlation coefficients of − 0.469, − 0.880, and − 0.947 for hyper-arid, arid, and semiarid regions, respectively. This correlation is followed in 61.3, 55.9, and 55.9% of years, whereas in 12.6, 8.1, and 9.9% of years the changes in BLH are in advance of the changes in AI for hyper-arid, arid, and semiarid regions, respectively. The BLH and area of East Asian drylands have a positive correlation coefficient of 0.651. These results indicate that there is a connection between changes in BLH and dry–wet changes in the climate of arid regions, confirming the changes in BLH drive dry–wet changes. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ability to accurately and consistently discover anomalies in time series is important in many applications. Fields such as finance (fraud detection), information security (intrusion detection), healthcare, and others all benefit from anomaly detection. Intuitively, anomalies in time series are time points or sequences of time points that deviate from normal behavior characterized by periodic oscillations and long-term trends. For example, the typical activity on e-commerce websites exhibits weekly periodicity and grows steadily before holidays. Similarly, domestic usage of electricity exhibits daily and weekly oscillations combined with long-term season-dependent trends. How can we accurately detect anomalies in such domains while simultaneously learning a model for normal behavior? We propose a robust offline unsupervised framework for anomaly detection in seasonal multivariate time series, called AURORA. A key innovation in our framework is a general background behavior model that unifies periodicity and long-term trends. To this end, we leverage a Ramanujan periodic dictionary and a spline-based dictionary to capture both seasonal and trend patterns. We conduct experiments on both synthetic and real-world datasets and demonstrate the effectiveness of our method. AURORA has significant advantages over existing models for anomaly detection, including high accuracy (AUC of up to 0.98), interpretability of recovered normal behavior ( 100 % accuracy in period detection), and the ability to detect both point and contextual anomalies. In addition, AURORA is orders of magnitude faster than baselines. [ABSTRACT FROM AUTHOR]

Personnel performance is a key factor to maintain core competitive advantages. Thus, predicting personnel future performance is a significant research domain in human resource management (HRM). In this paper, to improve the performance, we propose a novel method for personnel performance prediction which helps decision-makers select high-potential talents. Specifically, for modeling the personnel performance, we first devise a GRU model to learn sequential information from personnel performance data without any expertise. Then, to better cluster the features, we exploit capsule network. Finally, to precisely make predictions, we further design one strategy, i.e., an improved loss function, and embed it into the capsule network. In addition, by introducing this strategy, our proposed model can well deal with the imbalanced data problem. Extensive experiments on real-world data clearly demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The legume plant alfalfa (Medicago sativa L.) is a widely cultivated perennial forage due to its high protein content, palatability, and strong adaptability to diverse agro-ecological zones. Alfalfa is a self-incompatible cross-pollinated autotetraploid species with tetrasomic inheritance. Therefore, maintaining excellent traits through seed reproduction is a prime challenge in alfalfa. However, the cutting propagation technology could enable consistent multiplication of quality plants that are genetically identical to the parent plant. The current study aimed to develop a simple, cost-effective, reproducible, and efficient hydroponic cutting method to preserve alfalfa plants and for molecular research. In this study, alfalfa landrace 'Wudi' was grown in hydroponics for 30 days and used as source material for cuttings. The top, middle and bottom sections of its stem were used as cuttings. The rooting rate, root length, and stem height of the different stem sections were compared to determine the best segment for alfalfa propagation in four nutrient treatments (HM, HM + 1/500H, HM + 1/1000H and d HM + 1/2000H). After 21 days of culture, the rooting rates of all the three stem types under four cutting nutrient solutions were above 78%. The rooting rate of the middle and bottom parts in HM + 1/1000 H and HM + 1/2000 H nutrient solutions reached more than 93%, with a higher health survey score (> 4.70). In conclusion, this study developed a de novo cutting propagation method that can be used to conserve and propagate germplasm in breeding programs and research. This method is a new report on the cutting propagation of alfalfa by hydroponics, which could supplement the existing cutting propagation methods. [ABSTRACT FROM AUTHOR]

Despite the well-developed history of research on typical TiO2 semiconductors, the modification of their photon-to-electron catalytic performance by incorporating graphene quantum dots (GQDs) which possesses extraordinary properties such as tuned band gap, high mobility and excellent separation of charges, great absorption capacity in the visible light region, and high surface area is still in the primary stage. Since 2013, the investigations regarding the photodriven applications of the versatile GQDs/TiO2 heterojunction has been harvesting the emergent research interests considering the significantly enhanced performance in a range of environment-, energy-, and bio-related areas. The present review aims to provide detailed information about the recent developments in this emerging field and to provide a timely overview of potential applications of the star composite GQDs/TiO2. The recent information regarding the application of versatile GQDs/TiO2 composites as photocatalytic degradation, H2 generation via water splitting, solar cells, and sensors has been discussed. Recent reports suggested that the incorporation of GQD into the TiO2 noticeably increases the degradation efficiency, H2 production, power conversion efficiency of solar cells, as well as sensitivity and selectivity in sensors owing to the extended photon absorption, facilitated charge transfer, and quench electron–hole pair recombination. Besides, the advanced response of GQDs/TiO2 composite as a gas sensor can be ascribed to the enhanced ability to detect gas molecules under UV light illumination besides thermal sensing. Moreover, the common trend in the development of GQDs/TiO2 composites and insights into future perspectives have also been discussed. [ABSTRACT FROM AUTHOR]

PIGK gene, encoding a key component of glycosylphosphatidylinositol (GPI) transamidase, was recently reported to be associated with inherited GPI deficiency disorders (IGDs). However, little is known about the specific downstream effects of PIGK on neurodevelopment due to the rarity of the disease and the lack of in vivo study. Here, we described 2 patients in a Chinese family presented with profound global developmental delay, severe hypotonia, seizures, and postnatal progressive global brain atrophy including hemisphere, cerebellar and corpus callosum atrophy. Two novel compound heterozygous variants in PIGK were identified via genetic analysis, which was proved to cause significant decrease of PIGK protein and reduced cell surface presence of GPI-APs in the patients. To explore the role of Pigk on embryonic and neuronal development, we constructed Pigk knock-down zebrafish and knock-in mouse models. Zebrafish injected with a small dose of morpholino oligonucleotides displayed severe developmental defects including small eyes, deformed head, curly spinal cord, and unconsumed yolk sac. Primary motor neuronal dysplasia and extensive neural cell apoptosis were further observed. Meanwhile, the mouse models, carrying the two variants respectively homologous with the patients, both resulted in complete embryonic lethality of the homozygotes, which suggested the intolerable effect caused by amino acid substitution of Asp204 as well as the truncated mutation. Our findings provide the in vivo evidence for the essential role of PIGK during the embryonic and neuronal development. Based on these data, we propose a basis for further study of pathological and molecular mechanisms of PIGK-related neurodevelopmental defects. [ABSTRACT FROM AUTHOR]

The recent advancements in single-cell technologies, including single-cell chromatin accessibility sequencing (scCAS), have enabled profiling the epigenetic landscapes for thousands of individual cells. However, the characteristics of scCAS data, including high dimensionality, high degree of sparsity and high technical variation, make the computational analysis challenging. Reference-guided approaches, which utilize the information in existing datasets, may facilitate the analysis of scCAS data. Here, we present RA3 (Reference-guided Approach for the Analysis of single-cell chromatin Accessibility data), which utilizes the information in massive existing bulk chromatin accessibility and annotated scCAS data. RA3 simultaneously models (1) the shared biological variation among scCAS data and the reference data, and (2) the unique biological variation in scCAS data that identifies distinct subpopulations. We show that RA3 achieves superior performance when used on several scCAS datasets, and on references constructed using various approaches. Altogether, these analyses demonstrate the wide applicability of RA3 in analyzing scCAS data. Methods for profiling differences between individual cells are constantly expanding. Here, the authors present a computational framework for the analysis of chromatin accessibility data at the single-cell level that takes into account previous knowledge and data-specific characteristics. [ABSTRACT FROM AUTHOR]

Membraneous nephropathy (MN) is one of the complicated kidney diseases associated with proteinuria. Mizoribine (MZR) is an emerging treatment option for nephrotic syndrome; however, its dosage and administration are yet lack of consensus. This study aims to evaluate the efficacy and safety of high-dose MZR pulse therapy for adult membraneous nephropathy. Sixty patients with membraneous nephropathy were recruited, and assigned to two treatment groups. One group received conventional treatment of steroid combining with cyclophosphamide (CPM), the other group received steroid combining with high-dose MZR pulse administration. Both groups were followed up for 1 year. Treatment efficacy and side effects were measured regularly. Fifty-nine patients completed the treatment courses. There was no significant difference in demographic and disease conditions prior to treatment between two treatment groups. Both groups showed significant decrease of urine proteins and increase of serum albumin levels after treatments with no severe side effects. After 6 months of treatment, MZR group has 71% reduction (compared to 74.4% reduction in CPM group) in urine protein compared to baseline after adjusting for age and gender. 89.7% of patients in CPM and 93.3% in MZR groups had partial/ complete remission after 12 months. This study demonstrated satisfactory safety and efficacy of high-dose mizoribine pulse administration combining with steroid treatment for adult patients with membranous nephropathy. [ABSTRACT FROM AUTHOR]

With the development of distribution logistics, the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) has garnered attention in recent years due to its higher capacity-usage rate and transportation efficiency. This study proposes a novel dynamic multi-stage failure-specific cooperative (DMS-FSC) recourse strategy for solving the route failure which occurs in the VRPSPD. In the case of simultaneous transportation, the proposed DMS-FSC strategy allows a pair of cooperative vehicles to revisit each other's customers with unmet demands by dividing the whole transportation route into multiple stages. An extended genetic algorithm with a new multi-stage paired vectors representation scheme is offered to more effectively deal with the proposed DMS-FSC strategy. It explores a more ideal a priori transportation plan for the VRPSPD by incorporating an acceptance criterion based on simulated annealing. Two sets of cases with middle and corner located depots are used to verify the proposed algorithm's effectiveness in solving the presented problem. Furthermore, a comparison between the DMS-FSC strategy and the baseline strategy, which involves no cooperation, is also made to evaluate the cost savings generated from the proposed DMS-FSC strategy. [ABSTRACT FROM AUTHOR]

Enlarged perivascular spaces (EPVS) are widely considered as a feature of cerebral small vessel diseases (SVD), but its underlying pathology is still under active investigation. The aim of this study was to explore the association between hemoglobin level and the severity of EPVS. Consecutive patients with acute ischemic stroke who underwent baseline MRI scan and hemoglobin testing were evaluated. EPVS in basal ganglia (BG) and central semiovale (CS) were rated with a validated 4-point semiquantitative scale (0 = none; 1 = 1–10; 2 = 11–20; 3 = 21–40; and 4 ≥ 40). Bivariate logistic regression models were used to identify the associations of hemoglobin with predefined high-degree (score > 1) CS-EPVS and BG-EPVS. Multinomial logistic regression models were used to analyze the associations between hemoglobin and CS-/BG-EPVS predominance patterns. A total of 401 patients were included in the final analysis, 94 patients (23.4%) had a high degree of CS-EPVS and 45 patients (11.2%) had a high degree of BG-EPVS. Compared with tertile 1 of hemoglobin, tertile 3 of hemoglobin was independently associated with high degree of CS-EPVS after adjusting for other features of SVD (odds ratio [OR] 2.399, 95% confidence interval [CI] 1.315–4.379, P = 0.004) and potential confounding factors (OR 2.611, 95% CI 1.346–5.066, P = 0.005). In multinomial logistic regression models, compared with tertile 1 of hemoglobin, tertile 2 (OR 2.463, 95% CI 1.195–5.075, P = 0.015) and tertile 3 (OR 2.625, 95% CI 1.102–6.251, P = 0.029) of hemoglobin were associated with higher odds of BG-EPVS = CS-EPVS pattern, and tertile 3 of hemoglobin (OR 2.576, 95% CI 1.004–6.608, P = 0.049) was associated with higher odds of BG-EPVS < CS-EPVS pattern. Elevated hemoglobin level was independently associated with high degree of CS-EPVS and higher odds of CS-EPVS predominance pattern, but not with BG-EPVS, which support that the topography of EPVS is characteristic. However, the pathogenesis linking hemoglobin and CS-EPVS is unclear and still needs further investigation. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Peroxidation damage induces sublethal injury to boar sperm during preservation. Rosmarinic acid (RA) has already been verified to efficiently protect cells from oxidant-induced injury and to produce significant effect on cryopreservation of semen. Through our experiments, we aim at investigating whether RA has a positive effect on the preservation of pig semen at room temperature. The semen collected from sexually mature Large White boars were preserved at 17 °C in Beltsville thawing solution (BTS) supplied. The boar sperm were exposed to 0, 25, 50, 75, 100, 125 and 150 μM RA in vitro and the sperm functions were examined. The sperm motility, the acrosome and plasma membrane integrity, the catalase activity (CAT), the total antioxidative capacity (T-AOC) activity and the malondialdehyde content (MDA) were examined at 0, 1, 3 and 5 days. The BTS diluent containing RA improved the sperm quality during the process of liquid preservation compared with the control treatment. After 5 days of liquid preservation, the addition of RA at 100 μM produced an optimal effect on the survival time as well as on the maintenance of motility, acrosome and plasma membrane integrity; T-AOC activity; CAT activity; and the MDA content. Besides, our results in the reproductive experiments showed that the addition of RA at 100 μM to the BTS diluent increased the pregnancy rate. These results suggest that the proper concentration of RA in boar semen extenders possibly improves the artificial insemination efficiency by reducing the sperm damage and the subsequent dysfunction during liquid preservation in swine production systems. [ABSTRACT FROM AUTHOR]

A suite of 33 lacustrine source rocks (Pr/Ph ranging from 0.29 to 0.84) from the Songliao Basin with uniform organic matter input have been examined to explore the 17α(H)-diahopane and 18α(H)-neohopane series distribution. The absolute concentrations of 17α(H)-diahopane and 18α(H)-neohopane were determined. In addition, an immature lacustrine source rock was chosen to conduct a thermal simulation experiment to further study the relationship between these components and maturity. Analysis results of geological samples show that the absolute concentrations of 17α(H)-diahopanes have an excellent correlation with 18α(H)-neohopanes, whereas the relative abundance of two series of components exhibited a general correlation. Thermal simulation experiment results show that low absolute concentration and relative abundance of 17α(H)-diahopanes and 18α(H)-neohopanes are presented from low-maturity to high-maturity. However, high absolute concentration and relative abundance are merely distributed near the oil-generative peak. Moreover, with increasing of maturity, the absolute concentrations of both 17α(H)-diahopanes and 18α(H)-neohopanes display an initial increase and a following decrease. Interestingly, the relative abundances of 17α(H)-diahopanes and 18α(H)-neohopanes demonstrate an initial increase and a following decrease with maturity before the oil-generative peak, and then, the ratios display a finally gradual increase with increasing maturity. It suggested that the ratios of rearranged hopanes to hopanes could act as maturity parameter at the high maturity. [ABSTRACT FROM AUTHOR]

Skeletal muscle is one of the most important tissues of the human body necessary for sporting activities. The differentiation of muscle-derived satellite cells (MDSCs) plays an important role in the development and regeneration of skeletal muscles. Similarly, the Wnt/β-catenin signalling pathway plays an important role in the process of muscle differentiation. Wnt1-inducible signalling pathway protein-1 (WISP1), a downstream protein of the Wnt/β-catenin signalling pathway and a member of the CCN family that also plays an important role in the differentiation process, and its expression increase during the differentiation of bovine MDSCs. However, its role in MDSC differentiation is poorly understood. Therefore, we investigated the mechanisms regulating this process via Western blot and immunofluorescence staining. Immunoprecipitation and mass spectrometry detected annexin A1 (ANXA1), a protein that interacts with WISP1. To determine whether WISP1 influences TGF-β signalling and differentiation independently of ANXA1, the latter was knocked down, while WISP1 was activated. WISP1 expression increased significantly during bovine MDSC differentiation. However, WISP1 did not affect the TGF-β signalling pathway protein marker when ANXA1 was inhibited. Taken together, WISP1 regulates the TGF-β signalling pathway through ANXA1 recruitment, thereby promoting bovine MDSC differentiation, suggesting the Wnt/β-catenin signalling pathway as another target to promote cell differentiation. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) is commonly associated with sleep disturbance. However, the relationship between the trajectory of sleep quality and short-term residual renal function is not clear. Thus, this study aimed to investigate such relationship. Methods: In total, 132 patients with CKD stage 3–5 were prospectively enrolled. All participants were followed-up for 6 months. The Pittsburgh Sleep Quality Index (PSQI) questionnaire was used to assess sleep quality. The longitudinal PSQI and estimated glomerular filtration rate (eGFR) were measured at baseline, the 3rd month and 6th month. The participants were stratified into three groups according to the PSQI trajectories. The primary outcome was set as the eGFR change among 6 months less than the median. Results: Sixty nine participants showed PSQI ≤ 5 at baseline and 15 patients had increased scores > 5 at 3rd month among them. 63 participants showed PSQI > 5 at baseline and 11 patients had decreased scores ≤ 5 at 3rd month. Only in patients whose baseline PSQI ≤ 5 but increased to > 5 at 3rd month presented a longitudinal decline in eGFR at both 3rd month and 6th month compared with baseline eGFR. Linear regression analysis for eGFR change showed no significant association between eGFR change and PSQI score. Logistic regression revealed worsen sleep quality will deteriorate renal function. Conclusion: A relationship was observed between worsening sleep quality and eGFR decline in non-dialysis CKD patients. [ABSTRACT FROM AUTHOR]

An electroanalytical method for determining the concentration of ZnO nanoparticles (< 50 nm) in aqueous dispersion has been developed. The nanoparticles are first allowed to coat a screen-printed electrode by air-drying, which provides a significant magnification of the reduction current for KCl electrolyte at negative potentials. Phenol is next added as a chemical probe for electrochemical analysis by cyclic voltammetry. Measurement of the reduction current at an applied potential of − 0.3 V provides adequate sensitivity for the indirect quantification of ZnO nanoparticles down to a low concentration of 0.1 mg/mL. Other nanoparticles such as TiO2 and CeO2 do not produce any similar reduction peak. Furthermore, the irreversible oxidation peak at + 0.6 V and the charge storage capacity are both linearly proportional to ZnO concentrations (below 0.2 mg/mL) and can afford a detection limit of 0.01 mg/mL nanoparticles. Hence, the new method comprising three simultaneous measurements is good for both the identification and quantification of ZnO nanoparticles. [ABSTRACT FROM AUTHOR]

Purpose: Sleep disturbances have a negative impact on the prognosis of chronic kidney disease (CKD). However, information on the prevalence and predictors is limited. This study aimed to evaluate the prevalence and explore clinical factors affecting the quality of sleep in patients with non-dialysis CKD. Methods: Participants included 152 adult non-dialysis patients with stage 3–5 CKD. Demographic and clinical data were collected. Sleep quality and depression were assessed using the Pittsburgh Sleep Quality Index (PSQI) and Beck Depression Inventory (BDI), respectively. Sleep disturbances were defined as a PSQI score ≥ 5. Logistic regression was conducted to explore the independent factors of sleep disturbances. Clinical parameters were correlated with BDI scores using linear regression models. Results: The total prevalence of patients with sleep disturbances was 66.4%. Older age, higher BDI scores, lower estimated glomerular filtration rate (eGFR) changes per month (△eGFR/m) before the study, and lower serum magnesium levels were found in patients with sleep disturbances. BDI scores (odds ratio [OR] 1.224, 95% confidence interval [CI] 1.091–1.373, p = 0.001) and age (OR 1.041, 95% CI 1.013–1.069, p = 0.003) were independent predictors of sleep disturbances. Serum uric acid levels (β − 0.629, 95% CI − 1.244 to − 0.013, p = 0.046), △eGFR/m before the study (β − 0.454, 95% CI − 0.885 to − 0.024, p = 0.039), and daily protein intake (β − 0.052, 95% CI − 0.102 to − 0.002, p = 0.043) were negatively associated with BDI scores. Conclusion: A high overall prevalence of sleep disturbances was found in patients with non-dialysis stage 3–5 CKD. Depression, as a manageable predictor, should be managed, especially in elderly patients. [ABSTRACT FROM AUTHOR]