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Quantitative determination of Cr (Ⅵ) in coal ash is very important for controlling the emission of highly toxic Cr (Ⅵ) produced by coal combustion. Available quantification determination methods are mainly used for detecting high concentration Cr (Ⅵ) in water, soil and solid waste, which are not suitable for analyzing low concentration Cr (Ⅵ) in coal ash. The sample processing methods are time consuming and the detection limits are relatively high. Thus, it is necessary to develop a convenient, effective and highly sensitive determination method for Cr (Ⅵ) in coal ash. In this study, alkaline solvent and the microwave digestion system were used for pretreatment of coal ash. Through the experiments, the digestion conditions, which included sample amount, microwave digestion time, and microwave digestion temperature were optimized. The chemical co-precipitation method was used to separate Cr (Ⅲ) and Cr (Ⅵ) in the extracted solution. The concentration of Cr (Ⅵ) was determined by Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES). The results show that the optimum digestion condition is 0.2 g sample digested under 9℃ for 60 minutes. The determination limit is 0.00134 μg/mL (in Table 1), and the average matrix spike recovery of practical samples is 87.2% (in Table 2). The detection limit of this method is 0.00033 μg/mL (in Table 1), suggesting that the sensitivity of Cr (Ⅵ) was improved by 10 times than that of the traditional 1, 5-diphenylcarbohydrazide spectrophotometric method (0.001 μg/mL).

Leptin is a hormone that reduces excitability in some hypothalamic neurons via leptin receptor activation of the JAK2 and PI3K intracellular signaling pathways. We hypothesized that leptin receptor activation in other neuronal subtypes would have anticonvulsant activity and that intranasal leptin delivery would be an effective route of administration. We tested leptin's anticonvulsant action in 2 rodent seizure models by directly injecting it into the cortex or by administering it intranasally. Focal seizures in rats were induced by neocortical injections of 4-aminopyridine, an inhibitor of voltage-gated [K.sup.+] channels. These seizures were briefer and less frequent upon coinjection of 4-aminopyridine and leptin. In mice, intranasal administration of leptin produced elevated brain and serum leptin levels and delayed the onset of chemical convulsant pentylenetetrazole-induced generalized convulsive seizures. Leptin also reduced neuronal spiking in an in vitro seizure model. Leptin inhibited [alpha]-amino-3-hydroxy-5-methyl-4-isoxazole proprionic acid (AMPA) receptor--mediated synaptic transmission in mouse hippocampal slices but failed to inhibit synaptic responses in slices from leptin receptor--deficient db/db mice. JAK2 and PI3K antagonists prevented leptin inhibition of AMPAergic synaptic transmission. We conclude that leptin receptor activation and JAK2/PI3K signaling may be novel targets for anticonvulsant treatments. Intranasal leptin administration may have potential as an acute abortive treatment for convulsive seizures in emergency situations., Introduction There is a pressing need for new anticonvulsants for children and adults with medically refractory epilepsy. Neuropeptides such as neuropeptide Y (NPY) and galanin are promising because they have [...]

Klinefelter syndrome (KS) is one of the most frequent genetic abnormalities and the leading genetic cause of nonobstructive azoospermia. The breeding and study of KS mouse models are essential to advancing our knowledge of the underlying pathological mechanism. Karyotyping and fluorescence in situ hybridization are reliable methods for identifying chromosomal contents. However, technical issues associated with these methods can decrease the efficiency of breeding KS mouse models and limit studies that require rapid identification of target mice. To overcome these limitations, we developed three polymerase chain reaction-based assays to measure specific genetic information, including presence or absence of the sex determining region of chromosome Y (Sry), copy number of amelogenin, X-linked (Amelx), and inactive X specific transcripts (Xist) levels. Through a combined analysis of the assay results, we can infer the karyotype of target mice. We confirmed the utility of our assays with the successful generation of KS mouse models. Our assays are rapid, inexpensive, high capacity, easy to perform, and only require small sample amounts. Therefore, they facilitate the breeding and study of KS mouse models and help advance our knowledge of the pathological mechanism underlying KS. [ABSTRACT FROM AUTHOR]

Deep learning algorithms have shown superior performance than traditional algorithms when dealing with computationally intensive tasks in many fields. The algorithm model based on deep learning has good performance and can improve the recognition accuracy in relevant applications in the field of computer vision. TensorFlow is a flexible opensource machine learning platform proposed by Google, which can run on a variety of platforms, such as CPU, GPU, and mobile devices. TensorFlow platform can also support current popular deep learning models. In this paper, an image recognition toolkit based on TensorFlow is designed and developed to simplify the development process of more and more image recognition applications. The toolkit uses convolutional neural networks to build a training model, which consists of two convolutional layers: one batch normalization layer before each convolutional layer, and the other pooling layer after each convolutional layer. The last two layers of the model use the full connection layer to output recognition results. Batch gradient descent algorithm is adopted in the optimization algorithm, and it integrates the advantages of both the gradient descent algorithm and the stochastic gradient descent algorithm, which greatly reduces the number of convergence iterations and has little influence on the convergence effect. The total training parameters of the toolkit model reach 1.7 million. In order to prevent overfitting problems, the dropout layer before each full connection layer is added and the threshold of 0.5 is set in the design. The convolution neural network model is trained and tested by the MNIST set on TensorFlow. The experimental result shows that the toolkit achieves the recognition accuracy of 99% on the MNIST test set. The development of the toolkit provides powerful technical support for the development of various image recognition applications, reduces its difficulty, and improves the efficiency of resource utilization. [ABSTRACT FROM AUTHOR]