Seasonal and spatial variations of heavy metalsin surface sediments collected from the BaoxiangRiver in the Dianchi Watershed, China

To explore potential ecological hazards due to heavy metals in the Dianchi Lake Watershed, a three-stage European Community Bureau of Reference (BCR) sequential extraction procedure was applied to examine the spatial distributions and relative speciation ratios of Zn, Cu, Ni, Pb, and Cr in Baoxiang River sediments during wet and dry seasons. The metal species have similar spatial variations during different seasons. In the upstream reaches of the Baoxiang River, heavy metals reside primarily in the non-extractable residual fraction (72&ndash;90%). In the midstream, the residual fraction (35&ndash;89%) remains dominant, but the extractable fraction increases, featuring especially notable increases in the reducible fraction (5&ndash;40%). Downstream, the Cu, Ni, Pb, and Cr residual fractions remain high (46&ndash;80%) and the extractable fractions increase rapidly; the Zn extractable fraction is quite high (65.5%). Anthropogenic sources drive changes in heavy metal speciation. Changes in the river environment, such as pH and oxidation-reduction potential, also affect speciation. The reducible fraction of heavy metals in Baoxiang River sediments is most sensitive to pH. Potential ecological risk assessments for these five elements indicate that risks from Zn and Pb are mild to moderate in the middle and lower reaches of the river.<br style="line-height: normal; text-align: -webkit-auto; text-size-adjust: auto;" /

A nanoporous graphene analog for superfast heavy metal removal and continuous-flow visible-light photoredox catalysis

A metal-free conjugated porous polymer offers remarkable capabilities crucial for green and sustainable technologies, including removing 99.9% of lead from water within minutes, catalyzing quantitatively the Knoevenagel reaction in water, donor–acceptor units boosting photocatalytic activity and implementation in a continuous flow reactor.</p

Effects of Neutrophil Extracellular Traps on Bovine Mammary Epithelial Cells in vitro

Bovine mastitis is a common infectious disease which causes huge economic losses in dairy cattle. Bovine mammary epithelial cell (BMEC) damage usually directly causes the decrease of milk production, which is one of the most important causes of economic loss. NETs, novel effector mechanisms, are reported to exacerbate the pathogenesis of several inflammatory diseases. NETs formation has also been observed in the milk and mammary glands of sheep. However, the effects and detailed mechanisms of NETs on BMEC damage remain unclear. Thus, we aim to examine the effects of NETs on BMECs in vitro, and further to investigate the detail mechanism. In this study, the cytotoxicity of NETs on BMECs was determined using lactic dehydrogenase (LDH) levels in culture supernatants. Histone-induced BMEC damage was examined by flow cytometry and immunofluorescence analysis. The activities of caspase 1, caspase 3, caspase 11, and NLRP3 was detected using western blotting and immunohistochemical analysis. The results showed that NETs and their component histone significantly increased cytotoxicity to BMECs, suggesting the critical role of NETs, and their component histone in BMEC damage. In addition, histone could also induce necrosis, pyroptosis, and apoptosis of BMECs, and the mechanisms by which histone leads to BMEC damage occurred via activating caspase 1, caspase 3, and NLRP3. Altogether, NETs formation regulates inflammation and BMEC damage in mastitis. Inhibiting excess NETs formation may be useful to ameliorate mammary gland damage associated with mastitis

Two TPX2-Dependent Switches Control the Activity of Aurora A

Aurora A is an important oncogenic kinase for mitotic spindle assembly and a potentially attractive target for human cancers. Its activation could be regulated by ATP cycle and its activator TPX2. To understand the activation mechanism of Aurora A, a series of 20 ns molecular dynamics (MD) simulations were performed on both the wild-type kinase and its mutants. Analyzing the three dynamic trajectories (Aurora A-ATP, Aurora A-ADP, and Aurora A-ADP-TPX2) at the residue level, for the first time we find two TPX2-dependent switches, i.e., switch-1 (Lys-143) and switch-2 (Arg-180), which are tightly associated with Aurora A activation. In the absence of TPX2, Lys-143 exhibits a “closed” state, and becomes hydrogen-bonded to ADP. Once TPX2 binding occurs, switch-1 is forced to “open” the binding site, thus pulling ADP away from Aurora A. Without facilitation of TPX2, switch-2 exits in an “open” conformation which accompanies the outward-flipping movement of P·Thr288 (in an inactive conformation), leading to the crucial phosphothreonine exposed and accessible for deactivation. However, with the binding of TPX2, switch-2 is forced to undergo a “closed” movement, thus capturing P·Thr288 into a buried position and locking its active conformation. Analysis of two Aurora A (K143A and R180A) mutants for the two switches further verifies their functionality and reliability in controlling Aurora activity. Our systems therefore suggest two switches determining Aurora A activation, which are important for the development of aurora kinase inhibitors

The Application of Heterogeneous Information Fusion in Misalignment Fault Diagnosis of Wind Turbines

The misalignment of the drive system is one of the important factors causing damage to gears and bearings on the high-speed output end of the gearbox in doubly-fed wind turbines. How to use the obtained information to determine the types of the faults accurately has always been a challenging problem for researchers. Under the restriction that only one kind of signal is used in the current wind turbine fault diagnosis, a new method based on heterogeneous information fusion is presented in this paper. The collected vibration signal, temperature signal, and stator current signal are used as original sources. Their time domain, frequency domain and time-frequency domain information are extracted as fault features. Taking into account the correlation between the features, t-distributed Stochastic Neighbor Embedding (t-SNE) is used to reduce the dimensionality of the original combinations. Then, the fusion features are put into the Least Square Support Vector Machine (LSSVM), which is optimized by artificial bee colony (ABC) algorithm. The simulation tests show that this method has higher diagnostic accuracy than other methods

Metabolism in the tumor microenvironment: insights from single-cell analysis

The metabolism of both cancer and immune cells in the tumor microenvironment (TME) is poorly understood since most studies have focused on analysis in bulk samples and ex vivo cell culture models. Our recent analyses of single-cell RNA sequencing data suggest that the metabolic features of single cells within TME differ greatly from those of the bulk measurements. Here, we discuss some key findings about metabolism in cancer and immune cells and discuss possible relevance to immunotherapy

Extreme Learning Machine for Heartbeat Classification with Hybrid Time-Domain and Wavelet Time-Frequency Features

Automatic heartbeat classification via electrocardiogram (ECG) can help diagnose and prevent cardiovascular diseases in time. Many classification approaches have been proposed for heartbeat classification, based on feature extraction. However, the existing approaches face the challenges of high feature dimensions and slow recognition speeds. In this paper, we propose an efficient extreme learning machine (ELM) approach for heartbeat classification with multiple classes, based on the hybrid time-domain and wavelet time-frequency features. The proposed approach contains two sequential modules: (1) feature extraction of heartbeat signals, including RR interval features in the time-domain and wavelet time-frequency features, and (2) heartbeat classification using ELM based on the extracted features. RR interval features are calculated to reflect the dynamic characteristics of heartbeat signals. Discrete wavelet transform (DWT) is used to decompose the heartbeat signals and extract the time-frequency features of the heartbeat signals along the timeline. ELM is a single-hidden layer feedforward neural network with the hidden layer parameters randomly generated in advance and the output layer parameters calculated optimally using the least-square algorithm directly using the training samples. ELM is used as the heartbeat classification algorithm due to its high accuracy training accuracy, fast training speed, and good generalization ability. Experimental testing is carried out using the public MIT-BIH arrhythmia dataset to perform a 16-class classification. Experimental results show that the proposed approach achieves a superior classification accuracy with fast training and recognition speeds, compared with existing classification algorithms