Molecular characteristics and phylogenetic analysis of pigeon paramyxovirus type 1 isolates from pigeon meat farms in Shanghai (2009-2012).

The majority of pigeon paramyxovirus type 1 (PPMV-1) strains are generally non-pathogenic to chickens; however, they can induce severe illness and high mortality rates in pigeons, leading to substantial economic repercussions. The genomes of 11 PPMV-1 isolates from deceased pigeons on meat pigeon farms during passive monitoring from 2009 to 2012 were sequenced and analyzed using polymerase chain reaction and phylogenetic analysis. The complete genome lengths of 11 isolates were approximately 15,192 nucleotides, displaying a consistent gene order of 3'-NP-P-M-F-HN-L-5'. ALL isolates exhibited the characteristic motif of 112RRQKRF117 at the fusion protein cleavage site, which is characteristic of velogenic Newcastle disease virus. Moreover, multiple mutations have been identified within the functional domains of the F and HN proteins, encompassing the fusion peptide, heptad repeat region, transmembrane domains, and neutralizing epitopes. Phylogenetic analysis based on sequences of the F gene unveiled that all isolates clustered within genotype VI in class II. Further classification identified at least two distinct sub-genotypes, with seven isolates classified as sub-genotype VI.2.1.1.2.2, whereas the others were classified as sub-genotype VI.2.1.1.2.1. This study suggests that both sub-genotypes were implicated in severe disease manifestation among meat pigeons, with sub-genotype VI.2.1.1.2.2 displaying an increasing prevalence among Shanghai's meat pigeon population since 2011. These results emphasize the value of developing pigeon-specific vaccines and molecular diagnostic tools for monitoring and proactively managing potential PPMV-1 outbreaks.

Multiclass classification of whole-body scintigraphic images using a self-defined convolutional neural network with attention modules.

PURPOSE: A self-defined convolutional neural network is developed to automatically classify whole-body scintigraphic images of concern (i.e., the normal, metastasis, arthritis, and thyroid carcinoma), automatically detecting diseases with whole-body bone scintigraphy. METHODS: A set of parameter transformation operations are first used to augment the original dataset of whole-body bone scintigraphic images. A hybrid attention mechanism including the spatial and channel attention module is then introduced to develop a deep classification network, Dscint, which consists of eight weight layers, one hybrid attention module, two normalization modules, two fully connected layers, and one softmax layer. RESULTS: Experimental evaluations conducted on a set of whole-body scintigraphic images show that the proposed deep classification network, Dscint, performs well for automated detection of diseases by classifying the images of concerns, achieving the accuracy, precision, recall, specificity, and F-1 score of 0.9801, 0.9795, 0.9791, 0.9933, and 0.9792, respectively, on the test data in the augmented dataset. A comparative analysis of Dscint and several classical deep classification networks (i.e., AlexNet, ResNet, VGGNet, DenseNet, and Inception-v4) reveals that our self-defined network, Dscint, performs best on classifying whole-body scintigraphic images on the same dataset. CONCLUSIONS: The self-defined deep classification network, Dscint, can be utilized to automatically determine whether a whole-body scintigraphic image is either normal or contains diseases of concern. Specifically, better performance of Dscint is obtained on images with lesions that are present in relatively fixed locations like thyroid carcinoma than those with lesions occurring in nonfixed locations of bone tissue.

dSPIC: a deep SPECT image classification network for automated multi-disease, multi-lesion diagnosis.

BACKGROUND: Functional imaging especially the SPECT bone scintigraphy has been accepted as the effective clinical tool for diagnosis, treatment, evaluation, and prevention of various diseases including metastasis. However, SPECT imaging is brightly characterized by poor resolution, low signal-to-noise ratio, as well as the high sensitivity and low specificity because of the visually similar characteristics of lesions between diseases on imaging findings. METHODS: Focusing on the automated diagnosis of diseases with whole-body SPECT scintigraphic images, in this work, a self-defined convolutional neural network is developed to survey the presence or absence of diseases of concern. The data preprocessing mainly including data augmentation is first conducted to cope with the problem of limited samples of SPECT images by applying the geometric transformation operations and generative adversarial network techniques on the original SPECT imaging data. An end-to-end deep SPECT image classification network named dSPIC is developed to extract the optimal features from images and then to classify these images into classes, including metastasis, arthritis, and normal, where there may be multiple diseases existing in a single image. RESULTS: A group of real-world data of whole-body SPECT images is used to evaluate the self-defined network, obtaining a best (worst) value of 0.7747 (0.6910), 0.7883 (0.7407), 0.7863 (0.6956), 0.8820 (0.8273) and 0.7860 (0.7230) for accuracy, precision, sensitivity, specificity, and F-1 score, respectively, on the testing samples from the original and augmented datasets. CONCLUSIONS: The prominent classification performance in contrast to other related deep classifiers including the classical AlexNet network demonstrates that the built deep network dSPIC is workable and promising for the multi-disease, multi-lesion classification task of whole-body SPECT bone scintigraphy images.

Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images.

SPECT nuclear medicine imaging is widely used for treating, diagnosing, evaluating and preventing various serious diseases. The automated classification of medical images is becoming increasingly important in developing computer-aided diagnosis systems. Deep learning, particularly for the convolutional neural networks, has been widely applied to the classification of medical images. In order to reliably classify SPECT bone images for the automated diagnosis of metastasis on which the SPECT imaging solely focuses, in this paper, we present several deep classifiers based on the deep networks. Specifically, original SPECT images are cropped to extract the thoracic region, followed by a geometric transformation that contributes to augment the original data. We then construct deep classifiers based on the widely used deep networks including VGG, ResNet and DenseNet by fine-tuning their parameters and structures or self-defining new network structures. Experiments on a set of real-world SPECT bone images show that the proposed classifiers perform well in identifying bone metastasis with SPECT imaging. It achieves 0.9807, 0.9900, 0.9830, 0.9890, 0.9802 and 0.9933 for accuracy, precision, recall, specificity, F-1 score and AUC, respectively, on the test samples from the augmented dataset without normalization.

Effect of Diethylene Glycol on the Inkjet Printability of Reactive Dye Solution for Cotton Fabrics.

Digital inkjet printing technology plays an increasingly important role in textile printing. The printing printability of reactive dye inks is the key to improving the quality of printed fabrics. In this study, an eco-friendly and simple method to improve the inkjet printability of reactive dye solutions was proposed. The influence of diethylene glycol on the surface tension, rheology, and dye molecule aggregation properties for three reactive dye solutions was investigated. The jetting performance of dye solutions was explored by observing droplet formation. Moreover, the color performance of printed cotton fabrics, including reactive dye solution penetration, colorimetric values, and color strength, was evaluated. Addition of diethylene glycol could change the aggregation of dye molecules by hydrophobic forces and hydrogen bonds. Diethylene glycol could inhibit formation of satellite droplets by changing the viscosity and surface tension of solutions, which made the pattern printed on cotton fabrics show regular edge sharpness. Furthermore, the dye solutions containing 10% DEG not only satisfied various properties of reactive dye inks but also had the highest color strength and the deepest and brightest colors.

Effects of alkanolamine solvents on the aggregation states of reactive dyes in concentrated solutions and the properties of the solutions.

The aggregation of dyes is a common phenomenon in solutions, particularly concentrated solutions, which seriously affects the dyeing and printing processes. In this study, the effects of alkylamine solvents on the reactive dye aggregation behavior in highly concentrated solutions was studied. Typical cases were conducted with two slightly toxic and environmentally friendly solvents, namely diethanolamine (DEA) and triethanolamine (TEA), and two reactive dyes, namely C. I. Reactive Red 218 (R-218) and C. I. Reactive Orange 13 (O-13). Aggregation states were studied by ultraviolet-visible (UV-Vis) absorption spectroscopy, Gaussian-peak-fitting method and fluorescence spectroscopy. The results showed that both the additives DEA and TEA could reduce the dye aggregation because the solvents, DEA and TEA, can break the iceberg structure and allow easy entry of the molecules into the dye aggregates. Also, the disaggregation caused by DEA was higher as compared with TEA, which may be caused by the weaker hydrogen bond and the relatively smaller steric hindrance effects of DEA. The schematic of disaggregation between R-218 and DEA was also discussed. For R-218, the dimers were disaggregated to monomer, while the higher-ordered aggregates were disaggregated to trimers and dimers for O-13. Moreover, physical properties such as viscosity and surface tension of the solutions were measured. This investigation is instructive for the further dyeing progress with organic bases in the textile industries.

Systematically Exploring Molecular Aggregation and Its Impact on Surface Tension and Viscosity in High Concentration Solutions.

The aggregation structure of dye molecules has a great influence on the properties of dye solutions, especially in high concentration. Here, the dye molecular aggregation structures were investigated systemically in aqueous solutions with high concentration using three reactive dyes (O-13, R-24:1 and R-218). O-13 showed stronger aggregation than R-24:1 and R-218. This is because of the small non-conjugate side chain and its ß-linked position on the naphthalene of O-13. Compared with R-218, R-24:1 showed relatively weaker aggregation due to the good solution of R-24:1. The change of different aggregate distributions in the solutions were also investigated by splitting the absorption curves. Moreover, it is found that the surface tension of solutions can be modified by the combined effect of both aggregation and the position of the hydrophilic group, which, however, also have an effect on viscosity. This exploration will provide guidance for the study of high concentration solutions.

Effect of ethylene glycol and its derivatives on the aggregation properties of reactive Orange 13 dye aqueous solution.

The aggregation behavior of dyes especially in the dyeing and printing of different textile materials is an important phenomenon which affects the process of dye adsorption and diffusion. In order to avoid the aggregation of dyes, scientists are looking for materials which can inhibit the aggregation process by fabricating the dye solution. Organic solvents have found important influence in the aggregation of dye molecules. Therefore, herein, we report the fabrication of reactive orange 13 dye solutions with the aid of ethylene glycol and its derivative organic solvents to investigate the aggregation behavior of dye molecules by UV-vis absorption spectrum, fluorescence spectrum, surface tension, rheological and particle size measurements. IR spectra were performed to understand the effect of hydrogen bonding on the aggregation behavior of dye molecules. Moreover, transmission electron microscopy was also tested to confirm the effect of organic solvents on the surface morphology of dye molecules. The results show that the reactive Orange 13 dye molecules show aggregation in terms of dimeric and multimeric structures at high dye concentrations due to π-π interaction of naphthalene rings. Moreover, on introducing the ethylene glycol and its derivatives, the dye molecules disaggregate by hydrophobic interactions of dye molecules and organic solvents which destroyed the ice-like structure between the dye molecules and the water molecules. Among the three organic solvents, DME solvent caused more disaggregation of reactive Orange 13 dye molecules due to extra hydrophobic methyl groups in its structure. The results also show that the interaction between Orange 13 dyes and ethylene glycol and its derivatives could decrease the surface tension and particle size of the dye, and increase the quantum yield and viscosity. This research will help to understand the aggregation behavior of dyes and help the textile industries to choose the suitable formulations of dye solutions for coloration of different textile substrates via dyeing and printing methods.

Novel H⁺-Ion Sensor Based on a Gated Lateral BJT Pair.

An H⁺-ion sensor based on a gated lateral bipolar junction transistor (BJT) pair that can operate without the classical reference electrode is proposed. The device is a special type of ion-sensitive field-effect transistor (ISFET). Classical ISFETs have the advantage of miniaturization, but  they are difficult to fabricate by a single fabrication process because of the bulky and brittle reference electrode materials. Moreover, the reference electrodes need to be separated from the sensor device in some cases. The proposed device is composed of two gated lateral BJT components, one of which had a silicide layer while the other was without the layer. The two components were operated under the metal-oxide semiconductor field-effect transistor (MOSFET)-BJT hybrid mode, which can be controlled by emitter voltage and base current. Buffer solutions with different pH values were used as the sensing targets to verify the characteristics of the proposed device. Owing to their different sensitivities, both components could simultaneously detect the H⁺-ion concentration and function as a reference to each other. Per the experimental results, the sensitivity of the proposed device was found to be approximately 0.175 µA/pH. This experiment demonstrates enormous potential to lower the cost of the ISFET-based sensor technology.