Dietary N-carbamylglutamate supplementation improves ammonia tolerance of juvenile yellow catfish Pelteobagrus fulvidraco

Introduction: Ammonia has been of concern for its high toxicity to animals. N-carbamylglutamate (NCG) can reduce blood ammonia levels in mammals, but studies on ammonia tolerance in fish are insufficient.Methods: Juvenile yellow catfish were fed two levels of NCG (0.00% and 0.05%) for 84 days under three ammonia levels (0.00, 0.08, and 0.16 mg/L NH3).Results and Discussion: The results showed that survival rate (SUR), final body weight (FBW), weight gain (WG), and serum total protein (TP), triglycerides (TG), glucose (Glu), ornithine (Orn), citrulline (Cit) contents, and liver superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), arginase (ARG), ornithine transcarbamylase (OTC) activities decreased with the increase of ammonia levels, on the contrary, feed conversion ratio (FCR), hepatosomatic index (HSI), and serum ammonia, urea, alanine aminotransferase (ALT), aspartate aminotransferase (AST), glutamine (Gln), arginine (Arg) contents, and liver malondialdehyde (MDA), tumor necrosis factor (TNF), interleukin (IL) 1, IL 8 contents, and mRNA expressions of cu/zn sod, cat, gpx, gr, tnf ɑ, il 1, and il 8 were significantly increased. Dietary 0.05% NCG supplementation had higher SUR, FBW, WG, feed intake (FI), whole-body protein, and serum TP, total cholesterol (TC), Glu, citrulline (Cit) contents, and liver SOD, GPx, argininosuccinate synthetase (ASS), argininosuccinate lyase (ASL), inducible nitric oxide synthase (iNOS) activities compared to 0.00% NCG group, but had lower serum ammonia, urea, ALT, AST, Gln, Arg contents, and liver MDA, TNF, IL 1, IL 8 contents, and neuronal nitric oxide synthase activity. At the end of bacterial challenge, cumulative mortality (CM) increased with ammonia levels increased, but serum antibody titer (AT), lysozyme (LYZ) activity, 50% hemolytic complement, immunoglobulin (Ig) contents, respiratory burst (RB), phagocytic indices decreased with ammonia levels increased. CM in 0.05% NCG group was lower than that in 0.00% NCG group, but serum AT, LYZ activity, Ig content, RB in 0.05% NCG group were significantly higher. The correlation analysis found that iNOS was positively correlated with ASS activity. This study indicates that dietary NCG supplementation can improve the ammonia tolerance of yellow catfish, and ASS may also be the target of NCG to activate the urea cycle

An efficient and rapid method to detect and verify natural antisense transcripts of animal genes

AbstractHigh-throughput sequencing has identified a large number of sense-antisense transcriptional pairs, which indicates that these genes were transcribed from both directions. Recent reports have demonstrated that many antisense RNAs, especially lncRNA (long non-coding RNA), can interact with the sense RNA by forming an RNA duplex. Many methods, such as RNA-sequencing, Northern blotting, RNase protection assays and strand-specific PCR, can be used to detect the antisense transcript and gene transcriptional orientation. However, the applications of these methods have been constrained, to some extent, because of the high cost, difficult operation or inaccuracy, especially regarding the analysis of substantial amounts of data. Thus, we developed an easy method to detect and validate these complicated RNAs. We primarily took advantage of the strand specificity of RT-PCR and the single-strand specificity of S1 endonuclease to analyze sense and antisense transcripts. Four known genes, including mouse β-actin and Tsix (Xist antisense RNA), chicken LXN (latexin) and GFM1 (G elongation factor, mitochondrial 1), were used to establish the method. These four genes were well studied and transcribed from positive strand, negative strand or both strands of DNA, respectively, which represented all possible cases. The results indicated that the method can easily distinguish sense, antisense and sense-antisense transcriptional pairs. In addition, it can be used to verify the results of high-throughput sequencing, as well as to analyze the regulatory mechanisms between RNAs. This method can improve the accuracy of detection and can be mainly used in analyzing single gene and was low cost

A family of conjugate gradient methods for large-scale nonlinear equations

Abstract In this paper, we present a family of conjugate gradient projection methods for solving large-scale nonlinear equations. At each iteration, it needs low storage and the subproblem can be easily solved. Compared with the existing solution methods for solving the problem, its global convergence is established without the restriction of the Lipschitz continuity on the underlying mapping. Preliminary numerical results are reported to show the efficiency of the proposed method

Refining deep convolutional features for improving fine-grained image recognition

Abstract Fine-grained image recognition, a computer vision task filled with challenges due to its imperceptible inter-class variance and large intra-class variance, has been drawing increasing attention. While manual annotation can be utilized to effectively enhance performance in this task, it is extremely time-consuming and expensive. Recently, Convolutional Neural Networks (CNN) achieved state-of-the-art performance in image classification. We propose a fine-grained image recognition framework by exploiting CNN as the raw feature extractor along with several effective methods including a feature encoding method, a feature weighting method, and a strategy to better incorporate information from multi-scale images to further improve recognition ability. Besides, we investigate two dimension reduction methods and successfully merge them to our framework to compact the final image representation. Based on the discriminative and compact framework, we achieved the state-of-the-art performance in terms of classification accuracy on several fine-grained image recognition benchmarks based on weekly supervision

Ruthenium Poly(ethylenimine)/Gold Nanoparticles Immobilized on Dendritic Mesoporous Silica Nanoparticles for a CA15‑3 Electrochemiluminescence Immunosensor via Cu₂O@PDA Dual Quenching

The development of a specifically sensitive approach for CA15-3 detection is of great significance for the early diagnosis and treatment monitoring of breast cancer. In the present work, an electrochemiluminescence (ECL) immunosensor was constructed for the sensitive and selective detection of CA15-3 based on a dual-quenching strategy. Ru­(dcbpy)32+, poly­(ethylenimine) (PEI), and gold nanoparticles (AuNPs) were immobilized on dendritic mesoporous silica nanoparticles (DMSNs) (Ru-PEI/AuNPs@DMSNs) with high ECL efficiency due to the high loading amounts of Ru­(dcbpy)32+, the shortened electron-transfer path between the luminophore and coreactant, and the excellent conductivity and localized surface plasmon resonance effect of AuNPs. In the presence of CA15-3, a Cu2O nanoparticles coated with poly­(dopamine) (Cu2O@PDA) nanocomposite was introduced to the synthesized Ru-PEI/AuNPs@DMSNs through antigen–antibody interaction, resulting in a remarkable ECL quenching due to the dual quenchers of Cu2O and PDA. Under the optimal conditions, the fabricated sensor was used to detect CA15-3 in a wide linear range of 5.0 × 10–5–6.0 × 102 U mL–1 with a low limit of detection of 2.4 × 10–6 U mL–1. The dual-quenching ECL immunosensor was successfully applied for the determination of CA15-3 in patient serum, indicating the potential applicability of the present immuosensor for the clinical determination of CA15-3 and other cancer biomarkers

Detection and Prediction of Peripheral Arterial Plaque Using Vessel Wall MR in Patients with Diabetes

Objectives. To evaluate the predictive performance of a newly developed delay alternating with nutation for tailored excitation (DANTE) pulse sequence for detecting lower extremity artery wall morphology and distribution in patients with peripheral artery disease (PAD) with diabetes. Methods. Seventy-four PAD patients diagnosed according to 2011 WHO criteria were enrolled, who has diabetic diagnosis by 1999 WHO diabetes criteria. All patients received sequential DANTE, T2WI, DANTE-enhance, and CE-MRA scans. The images consisted of three parts: the iliac artery (segment 1), femoral artery (segment 2), and popliteal artery (segment 3). Regions of interest (ROIs) were drawn on vessels, muscle, and background, and multiple imaging metrics compared between modalities, including image quality score, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). In the images with a score greater than 2, the lumen area (LA), total vessel area (TVA), and vessel thickness (VT) can be identified using semiautomatic image analysis vessel morphology parameters. Results. All 222 arterial segments were successfully analyzed from 71 patients, after exclusion of three subjects with poor image quality (IQ<2) in segment 3. There were 54 diabetic and 17 nondiabetic patients. Quantitative analysis shows that the CNR difference between diabetic patients and nondiabetic patients was statistically significant for the same segment, while there was no significant difference among the three segments of SNR and CNR. There were a total of 54 diabetics with plaque distribution data, which showed that LA of segments 1 and 2 was higher than that of segment 3. The VWI of segments 1 and 2 was lower than segment 3. Diabetic was associated with vascular WT 3 and WA3, which increased by 0.23 and 0.83 units on average compared without diabetic foot, respectively. Diabetic foot was associated with vascular WT 3, which increased by 0.37 units on average compared without diabetic foot. The incidence of segment 3 plaques was higher than that of segment 1. The incidence of the left and right plaques was different. Conclusions. MR imaging using the DANTE and multicontrast sequence could evaluate plaque morphology, and distribution of lower extremities and the occurrence of diabetic foot development are closely related; it may predict occurrence of PAD with diabetic foot

Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network

Objectives. To evaluate the application of a deep learning architecture, based on the convolutional neural network (CNN) technique, to perform automatic tumor segmentation of magnetic resonance imaging (MRI) for nasopharyngeal carcinoma (NPC). Materials and Methods. In this prospective study, 87 MRI containing tumor regions were acquired from newly diagnosed NPC patients. These 87 MRI were augmented to >60,000 images. The proposed CNN network is composed of two phases: feature representation and scores map reconstruction. We designed a stepwise scheme to train our CNN network. To evaluate the performance of our method, we used case-by-case leave-one-out cross-validation (LOOCV). The ground truth of tumor contouring was acquired by the consensus of two experienced radiologists. Results. The mean values of dice similarity coefficient, percent match, and their corresponding ratio with our method were 0.89±0.05, 0.90±0.04, and 0.84±0.06, respectively, all of which were better than reported values in the similar studies. Conclusions. We successfully established a segmentation method for NPC based on deep learning in contrast-enhanced magnetic resonance imaging. Further clinical trials with dedicated algorithms are warranted