The Measurement of rho‐independent Transcription Terminator Efficiency

The purpose of this RFC is to provide standard methodology for the measurement of the absolute strength of terminators in bacteria. Because we have characterized the performance of terminator in E. coli and used a simple equation model, it can be expressed in PoPS

An improved contrastive learning network for semi-supervised multi-structure segmentation in echocardiography

Cardiac diseases have high mortality rates and are a significant threat to human health. Echocardiography is a commonly used imaging technique to diagnose cardiac diseases because of its portability, non-invasiveness and low cost. Precise segmentation of basic cardiac structures is crucial for cardiologists to efficiently diagnose cardiac diseases, but this task is challenging due to several reasons, such as: (1) low image contrast, (2) incomplete structures of cardiac, and (3) unclear border between the ventricle and the atrium in some echocardiographic images. In this paper, we applied contrastive learning strategy and proposed a semi-supervised method for echocardiographic images segmentation. This proposed method solved the above challenges effectively and made use of unlabeled data to achieve a great performance, which could help doctors improve the accuracy of CVD diagnosis and screening. We evaluated this method on a public dataset (CAMUS), achieving mean Dice Similarity Coefficient (DSC) of 0.898, 0.911, 0.916 with 1/4, 1/2 and full labeled data on two-chamber (2CH) echocardiography images, and of 0.903, 0.921, 0.928 with 1/4, 1/2 and full labeled data on four-chamber (4CH) echocardiography images. Compared with other existing methods, the proposed method had fewer parameters and better performance. The code and models are available at https://github.com/gpgzy/CL-Cardiac-segmentation

Edge Prior Multilayer Segmentation Network Based on Bayesian Framework

In recent years, methods based on neural network have achieved excellent performance for image segmentation. However, segmentation around the edge area is still unsatisfactory when dealing with complex boundaries. This paper proposes an edge prior semantic segmentation architecture based on Bayesian framework. The entire framework is composed of three network structures, a likelihood network and an edge prior network at the front, followed by a constraint network. The likelihood network produces a rough segmentation result, which is later optimized by edge prior information, including the edge map and the edge distance. For the constraint network, the modified domain transform method is proposed, in which the diffusion direction is revised through the newly defined distance map and some added constraint conditions. Experiments about the proposed approach and several contrastive methods show that our proposed method had good performance and outperformed FCN in terms of average accuracy for 0.0209 on ESAR data set

Lifting Scheme-Based Deep Neural Network for Remote Sensing Scene Classification

Recently, convolutional neural networks (CNNs) achieve impressive results on remote sensing scene classification, which is a fundamental problem for scene semantic understanding. However, convolution, the most essential operation in CNNs, restricts the development of CNN-based methods for scene classification. Convolution is not efficient enough for high-resolution remote sensing images and limited in extracting discriminative features due to its linearity. Thus, there has been growing interest in improving the convolutional layer. The hardware implementation of the JPEG2000 standard relies on the lifting scheme to perform wavelet transform (WT). Compared with the convolution-based two-channel filter bank method of WT, the lifting scheme is faster, taking up less storage and having the ability of nonlinear transformation. Therefore, the lifting scheme can be regarded as a better alternative implementation for convolution in vanilla CNNs. This paper introduces the lifting scheme into deep learning and addresses the problems that only fixed and finite wavelet bases can be replaced by the lifting scheme, and the parameters cannot be updated through backpropagation. This paper proves that any convolutional layer in vanilla CNNs can be substituted by an equivalent lifting scheme. A lifting scheme-based deep neural network (LSNet) is presented to promote network applications on computational-limited platforms and utilize the nonlinearity of the lifting scheme to enhance performance. LSNet is validated on the CIFAR-100 dataset and the overall accuracies increase by 2.48% and 1.38% in the 1D and 2D experiments respectively. Experimental results on the AID which is one of the newest remote sensing scene dataset demonstrate that 1D LSNet and 2D LSNet achieve 2.05% and 0.45% accuracy improvement compared with the vanilla CNNs respectively

Determination of the binding mechanism of cobalt(II) meso-tetraphenyl porphyrin with plant-esterase

Plant-esterase (EC 3.1.1.X) has received much attention because plant esterase and acetylcholinesterase (AChE) share a similar sensitivity towards organophosphorus (OP) pesticides detection with the same inhibition mechanism. To improve the analytical performance, tetraphenyl metal porphyrin, as an indicator was introduced to combine with plant-esterase. The time of reach equilibrium in PBS solution was shortened after adding plant-esterase by assaying the intensify change of the porphyrin spectrum. Meanwhile, intensify of porphyrin spectrum with plant-esterase was increased compared with that of only the porphyrin spectrum in solution. Tetraphenyl metal porphyrin, such as cobalt(II) meso-tetraphenyl porphyrin, is a mixed reversible inhibitor of plant-esterase from kinetic parameters. The combination ratio of plant-esterase and porphyrin is 2:1. On the other hand, the interaction between CoTPPCl and plant-esterase is the strongest among all tested tetraphenyl metal porphyrin. And the mixed system (CoTPPCl-plant-esterase) showed the best sensitivity towards the tested pesticide. All these results indicated that a complex system composed of tetraphenyl metal porphyrin and plant-esterase was fit for detecting pesticides. They make meaningful guidance on the further design of sensing material in monitoring pesticides

Salt adaptability in a halophytic soybean (Glycine soja) involves photosystems coordination

Background Glycine soja is a halophytic soybean native to saline soil in Yellow River Delta, China. Photosystem I (PSI) performance and the interaction between photosystem II (PSII) and PSI remain unclear in Glycine soja under salt stress. This study aimed to explore salt adaptability in Glycine soja in terms of photosystems coordination. Results Potted Glycine soja was exposed to 300 mM NaCl for 9 days with a cultivated soybean, Glycine max, as control. Under salt stress, the maximal photochemical efficiency of PSII (Fv/Fm) and PSI (oMR/MR0) were significantly decreased with the loss of PSI and PSII reaction center proteins in Glycine max, and greater PSI vulnerability was suggested by earlier decrease in oMR/MR0 than Fv/Fm and depressed PSI oxidation in modulated 820 nm reflection transients. Inversely, PSI stability was defined in Glycine soja, as oMR/MR0 and PSI reaction center protein abundance were not affected by salt stress. Consistently, chloroplast ultrastructure and leaf lipid peroxidation were not affected in Glycine soja under salt stress. Inhibition on electron flow at PSII acceptor side helped protect PSI by restricting electron flow to PSI and seemed as a positive response in Glycine soja due to its rapid recovery after salt stress. Reciprocally, PSI stability aided in preventing PSII photoinhibition, as the simulated feedback inhibition by PSI inactivation induced great decrease in Fv/Fm under salt stress. In contrast, PSI inactivation elevated PSII excitation pressure through inhibition on PSII acceptor side and accelerated PSII photoinhibition in Glycine max, according to the positive and negative correlation of oMR/MR0 with efficiency that an electron moves beyond primary quinone and PSII excitation pressure respectively. Conclusion Therefore, photosystems coordination depending on PSI stability and rapid response of PSII acceptor side contributed to defending salt-induced oxidative stress on photosynthetic apparatus in Glycine soja. Photosystems interaction should be considered as one of the salt adaptable mechanisms in this halophytic soybean