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It is necessary to assess the appropriate dietary protein level of the forest musk deer (FMD), as nutritional needs are unclear. The microbiome in gastrointestinal tracts plays an important role in regulating nutrient utilization, absorption and host growth or development. Thus, we aimed to evaluate growth performance, nutrient digestibility and fecal microbiome of growing FMD supplied with different protein levels of diets. Eighteen 6-month-old male FMD with an initial weight 5.0 ± 0.2 kg were used in a 62-day trial. The animals were randomly distributed to three groups, the dietary crude protein (CP) level was 11.51% (L), 13.37% (M), and 15.48% (H). The results showed that the CP digestibility decreased as dietary CP level increased (p p Ruminococcaceae_005, Ruminococcaceae_UCG-014 and uncultured_bacterium_f_Lachnospiraceae were significantly increased wtih rising CP, the proportions of Bacteroides and Rikenellaceae_RC9_gut_group were significantly decrease nevertheless at the genus level. The higher abundance of f_Prevotellaceae and g_Prevotellaceae_UCG_004 were found at M group by LEfSe analysis. The relative abundance of uncultured_bacterium_f_Ruminococcaceae was positively correlated with the average daily gain and feed conversion ratio (p Family_XIII_AD3011_group was negatively correlated with feed conversion ratio (p

Supplemental material, sj-pdf-1-imr-10.1177_03000605231174313 for Capillary leak syndrome presenting as refractory ascites in a patient with lymphoma: A rare case report by Xu Han, Huanjun Huang, Mingyu Zhang, Yue Wen, Shuhui Wang, Zheng Huang, Dean Tian and Yan Yu in Journal of International Medical Research

Research Data, sj-pdf-3-imr-10.1177_03000605231174313 for Capillary leak syndrome presenting as refractory ascites in a patient with lymphoma: A rare case report by Xu Han, Huanjun Huang, Mingyu Zhang, Yue Wen, Shuhui Wang, Zheng Huang, Dean Tian and Yan Yu in Journal of International Medical Research

Few-shot image generation aims to generate data of an unseen category based on only a few samples. Apart from basic content generation, a bunch of downstream applications hopefully benefit from this task, such as low-data detection and few-shot classification. To achieve this goal, the generated images should guarantee category retention for classification beyond the visual quality and diversity. In our preliminary work, we present an ``editing-based'' framework Attribute Group Editing (AGE) for reliable few-shot image generation, which largely improves the generation performance. Nevertheless, AGE's performance on downstream classification is not as satisfactory as expected. This paper investigates the class inconsistency problem and proposes Stable Attribute Group Editing (SAGE) for more stable class-relevant image generation. SAGE takes use of all given few-shot images and estimates a class center embedding based on the category-relevant attribute dictionary. Meanwhile, according to the projection weights on the category-relevant attribute dictionary, we can select category-irrelevant attributes from the similar seen categories. Consequently, SAGE injects the whole distribution of the novel class into StyleGAN's latent space, thus largely remains the category retention and stability of the generated images. Going one step further, we find that class inconsistency is a common problem in GAN-generated images for downstream classification. Even though the generated images look photo-realistic and requires no category-relevant editing, they are usually of limited help for downstream classification. We systematically discuss this issue from both the generative model and classification model perspectives, and propose to boost the downstream classification performance of SAGE by enhancing the pixel and frequency components.

Supplemental material, sj-pdf-2-imr-10.1177_03000605231174313 for Capillary leak syndrome presenting as refractory ascites in a patient with lymphoma: A rare case report by Xu Han, Huanjun Huang, Mingyu Zhang, Yue Wen, Shuhui Wang, Zheng Huang, Dean Tian and Yan Yu in Journal of International Medical Research

Supplemental material, sj-pdf-1-imr-10.1177_03000605231174313 for Capillary leak syndrome presenting as refractory ascites in a patient with lymphoma: A rare case report by Xu Han, Huanjun Huang, Mingyu Zhang, Yue Wen, Shuhui Wang, Zheng Huang, Dean Tian and Yan Yu in Journal of International Medical Research

With the recent worldwide COVID-19 pandemic, almost everyone wears a mask daily, leading to severe degradation in the accuracy of conventional face recognition systems. Several works improve the performance of masked faces by adopting synthetic masked face images for training. However, such methods often cause performance degradation on unmasked faces, raising the contradiction between the face recognition system's accuracy on unmasked and masked faces. In this paper, we propose a dual-proxy face recognition training method to improve masked faces' performance while maintaining unmasked faces' performance. Specifically, we design two fully-connected layers as the unmasked and masked feature space proxies to alleviate the significant difference between the two data distributions. The cross-space constraints are adopted to ensure the intra-class compactness and inter-class discrepancy. Extensive experiments on popular unmasked face benchmarks and masked face benchmarks, including real-world mask faces and the generated mask faces, demonstrate our method's superiority over the state-of-the-art methods on masked faces without incurring a notable accuracy degradation on unmasked faces.

Few-shot image generation is a challenging task even using the state-of-the-art Generative Adversarial Networks (GANs). Due to the unstable GAN training process and the limited training data, the generated images are often of low quality and low diversity. In this work, we propose a new editing-based method, i.e., Attribute Group Editing (AGE), for few-shot image generation. The basic assumption is that any image is a collection of attributes and the editing direction for a specific attribute is shared across all categories. AGE examines the internal representation learned in GANs and identifies semantically meaningful directions. Specifically, the class embedding, i.e., the mean vector of the latent codes from a specific category, is used to represent the category-relevant attributes, and the category-irrelevant attributes are learned globally by Sparse Dictionary Learning on the difference between the sample embedding and the class embedding. Given a GAN well trained on seen categories, diverse images of unseen categories can be synthesized through editing category-irrelevant attributes while keeping category-relevant attributes unchanged. Without re-training the GAN, AGE is capable of not only producing more realistic and diverse images for downstream visual applications with limited data but achieving controllable image editing with interpretable category-irrelevant directions., CVPR2022

Additional file 1. Additional figures and tables.

Referring video object segmentation aims to segment the object referred by a given language expression. Existing works typically require compressed video bitstream to be decoded to RGB frames before being segmented, which increases computation and storage requirements and ultimately slows the inference down. This may hamper its application in real-world computing resource limited scenarios, such as autonomous cars and drones. To alleviate this problem, in this paper, we explore the referring object segmentation task on compressed videos, namely on the original video data flow. Besides the inherent difficulty of the video referring object segmentation task itself, obtaining discriminative representation from compressed video is also rather challenging. To address this problem, we propose a multi-attention network which consists of dual-path dual-attention module and a query-based cross-modal Transformer module. Specifically, the dual-path dual-attention module is designed to extract effective representation from compressed data in three modalities, i.e., I-frame, Motion Vector and Residual. The query-based cross-modal Transformer firstly models the correlation between linguistic and visual modalities, and then the fused multi-modality features are used to guide object queries to generate a content-aware dynamic kernel and to predict final segmentation masks. Different from previous works, we propose to learn just one kernel, which thus removes the complicated post mask-matching procedure of existing methods. Extensive promising experimental results on three challenging datasets show the effectiveness of our method compared against several state-of-the-art methods which are proposed for processing RGB data. Source code is available at: https://github.com/DexiangHong/MANet., Comment: Accepted by ACM MM 2022

Additional file 3: Table S1. Association of CARM1 level with clinicopathological parameters of patients with gastric cancer.

Additional file 5: Table S3. Antibodies and regents used in experiments.

Additional file 4: Table S2. Sequences of primers and targets for siRNA.

Clinical treatment of triple negative breast cancer (TNBC) is very poor for lack of effective treatment combination selection. Protein C receptor (PROCR) is a novel cancer stem marker in TNBC patients tumor tissues. Developed based on peptide BP10 with affinity to PROCR as a targeting element, constructing a peptide drug conjugate of BP10 covalently coupling doxorubicin with disulfide bonds. This study demonstrated that the constructed BP10-DOX can selectively target Triplenegative breast cancer cells expressing PROCR and controlled release of DOX in response to the GSH environment. Moreover, BP10-DOX improves the therapeutic efficiency on MDA-MB-231 cells in vitro. Further evidence obtained from in vivo xenograft experiments revealed that administration of BP10-DOX enhanced the antitumor efficacy. This study developed a promising chemotherapy strategy for TNBC.

Detecting faults in axial piston pumps is of significance to enhance the reliability and security of hydraulic systems. However, it is difficult to detect multiple faults in the hydraulic electromechanical coupling systems because the fault mechanism of some faults is unclear. In this paper, a method using deep belief networks (DBNs) is proposed to detect multiple faults in axial piston pumps. Firstly, for each individual fault, all the data indicators extracted from the raw signals in time domain, frequency domain and time-frequency domain are calculated to construct training and testing samples. Then, the constructed samples are fed into DBNs to classify the multiple faults in axial piston pumps. With restricted Boltzmann machine (RBM) stacked layer by layer, DBNs can automatically learn fault features. Numerical simulations using the benchmark data of five faults in rolling bearings are classified by the present method to select the relative optimal combination of indicators. The classification results are also compared with those commonly used support vector machine (SVM) and artificial neural network (ANN) to manifest the classification accuracy of the present method. Experimental investigations are performed to classify four faults in an axial piston pump. The classification accuracy ratio is 97.40%, which confirms the feasibility and effectiveness of multiple faults detection in axial piston pumps using DBNs.

Additional file 1: Figure S1. Screening of reporter genes. Figure S2. Characterization of different green fluorescent proteins expressed under the promoter PUAS1B4-LEUm. Figure S3. Schematic of theTEF core promoter. Figure S4. Time course of fluorescence by Po1g PUAS1B4-LEUm + hrGFPO. Table S1. Primers used in PCR. Table S2. Plasmids used in this study. Table S3. Strains used in this study.

Due to the domain discrepancy in visual domain adaptation, the performance of source model degrades when bumping into the high data density near decision boundary in target domain. A common solution is to minimize the Shannon Entropy to push the decision boundary away from the high density area. However, entropy minimization also leads to severe reduction of prediction diversity, and unfortunately brings harm to the domain adaptation. In this paper, we investigate the prediction discriminability and diversity by studying the structure of the classification output matrix of a randomly selected data batch. We find by theoretical analysis that the prediction discriminability and diversity could be separately measured by the Frobenius-norm and rank of the batch output matrix. The nuclear-norm is an upperbound of the former, and a convex approximation of the latter. Accordingly, we propose Batch Nuclear-norm Maximization and Minimization, which performs nuclear-norm maximization on the target output matrix to enhance the target prediction ability, and nuclear-norm minimization on the source batch output matrix to increase applicability of the source domain knowledge. We further approximate the nuclear-norm by L_{1,2}-norm, and design multi-batch optimization for stable solution on large number of categories. The fast approximation method achieves O(n^2) computational complexity and better convergence property. Experiments show that our method could boost the adaptation accuracy and robustness under three typical domain adaptation scenarios. The code is available at https://github.com/cuishuhao/BNM., Comment: TPAMI under revivew. arXiv admin note: text overlap with arXiv:2003.12237

Additional file 2: Supplementary Figure S2. Principal component analysis (PCA) of the Chinese indigenous rabbit breeds based on autosome (PC2=4.10%, PC3=3.41%).

Additional file 1: Supplementary Figure S1. Principal component analysis (PCA) of the Chinese indigenous rabbit breeds based on autosome (PC1=5.59%, PC3=3.41%).

Vibration signals of faulty rolling element bearings usually exhibit non-linear and non-stationary characteristics caused by the complex working environment. It is difficult to develop a robust method to detect faults in bearings based on signal processing techniques. In this paper, convolutional neural network -based hidden Markov models (CNN HMMs) are presented to classify multi-faults in mechanical systems. In CNN HMMs, a CNN model is first employed to learn data features automatically from raw vibration signals. By utilizing the t-distributed stochastic neighbor embedding (t-SNE) technique, feature visualization is constructed to manifest the powerful learning ability of CNN. Then, HMMs are employed as a strong stability tool to classify faults. Both the benchmark data and experimental data are applied to the CNN HMMs. Classification results confirm the superior performance of the present combination model by comparing with CNN model alone, support vector machine (SVM) and back propagation (BP) neural network. It is shown that the average classification accuracy ratios are 98.125% and 98% for two data series with agreeable error rate reductions.

The fault diagnosis of axial piston pumps is of significance for enhancing the reliability and security of hydraulic systems. Most of the faults occurring in the mechanical components of piston pumps are exhibited as fault-excited impulses. However, the strong impact-induced natural periodic impulses under the common working conditions (i.e. reciprocating motion of pistons) inevitably cause interference that considerably affects the fault detection performance. In this study, a simulation-determined band pass filter is employed to improve the performance of minimum entropy deconvolution (MED) for the fault diagnosis of axial piston pump bearings. First, a finite element method (FEM) simulation is performed to determine the possible carrier frequency. Second, the carrier frequency is used as the center frequency in association with a fixed bandwidth to determine the band pass filter parameters. Finally, the MED technique is applied to enhance weak fault-excited impulses by means of kurtosis maximization. Thereafter, envelope spectrum analysis is applied to the enhanced signals to obtain faulty feature frequencies. Two case studies are conducted, using bearings with faults in the outer and inner races of an axial piston pumps under common working conditions. The case studies confirm the necessity and effectiveness of the proposed method for detecting bearings faults in axial piston pumps.

MicroRNAs (miRNAs) are small endogenous conserved RNAs regulating genes expression through base pairing with the 3'-untranslated region (3'-UTR) of target messenger RNAs. MiR-214-5p is a newly identified miRNA with its biological role largely unknown. In this study, we explored miR-214-5p expression status in 78 paired tumor and nontumor tissues obtained from patients with hepatocellular carcinoma (HCC) by RT-qPCR. The effects of miR-214-5p expression on HCC cell proliferation, cell cycle progression, and cell migration were measured by CCK-8 assay, flow cytometry, and wound-healing assay. A dual-luciferase activity assay was performed to identify whether KLF5 was a target of miR-214-5p. Kaplan-Meier curve and log-rank test were used to investigate the effects of miR-214-5p and KLF5 on overall survival and disease-free survival of patients with HCC. We found miR-214-5p expression was sharply reduced in HCC tissues and cell lines compared with the normal tissues and cell lines. Functional assay revealed that miR-214-5p overexpression could downregulate cell proliferation, cell migration, and arrested cell cycle at G0/G1 phase. Further, we validated Kruppel-like factor 5 (KLF5) as a direct target of miR-214-5p, and was upregulated in HCC and inversely correlated with the expression of miR-214-5p. Moreover, we found the low expression of miR-214-5p and high expression of KLF5 were correlated with tumor size, tumor stage, and poorer 5-year overall survival and disease-free survival of patients with HCC. In conclusion, our results suggested miR-214-5p functions as a tumor suppressor through targeting KLF5 in HCC. Also, miR-214-5p and KLF5 were identified as potential prognostic markers and might be therapeutic targets in HCC.

In video captioning task, the best practice has been achieved by attention-based models which associate salient visual components with sentences in the video. However, existing study follows a common procedure which includes a frame-level appearance modeling and motion modeling on equal interval frame sampling, which may bring about redundant visual information, sensitivity to content noise and unnecessary computation cost. We propose a plug-and-play PickNet to perform informative frame picking in video captioning. Based on a standard Encoder-Decoder framework, we develop a reinforcement-learning-based procedure to train the network sequentially, where the reward of each frame picking action is designed by maximizing visual diversity and minimizing textual discrepancy. If the candidate is rewarded, it will be selected and the corresponding latent representation of Encoder-Decoder will be updated for future trials. This procedure goes on until the end of the video sequence. Consequently, a compact frame subset can be selected to represent the visual information and perform video captioning without performance degradation. Experiment results shows that our model can use 6-8 frames to achieve competitive performance across popular benchmarks., Comment: 10 pages, 7 figures, 2 tables

Since the 1970s, international trade and investment patterns have occurred great changes, also the production organization of international enterprises had undergone profound changes. Intra-industry trade gradually replace the inter-industry trade, has become the main form of international trade. After 1980s, the multinational enterprises got unprecedented development. All the data are from the U.S. Bureau of Economic Analysis and the World Bank.

Since Sokolov[1] proposed the version of the Ultrasonic Microscope with an ultrasonic converter tube 40 years ago, many studies have been done. Smyth[2] produced the camera with the low-voltage electronic beam scanning. Jacobs[3] improved the effective aperture and increased the field of view of the tube using the discrete matching elements. Addison[4] invented the metal fiber faceplate which acts as a part of envelope and makes the construction of the tube’s inner target become outer target so that the chosing of the piezoelectric plate is easier. Nigam[5] adopted the foil-electret to make the condenser-microphone array. His tube has a large aperture up to 60. Brown[6] got the largest area of view of the tube up to 210 mm × 150 mm. Wardley[7] obtained the mechanically strong, long life Sokolov tube. The operating time can be up to 5000 hours. There are still many problems, such as resolution, sensitivity, effective aperture, large field of view, service life and so on, to be improved.

In order to develop a new vaccine candidate for equine infectious anemia virus (EIAV), gag gene of Chinese donkey leukocyte attenuated strain (EIAV DLV) and its parental virulent strain (EIAV LN) were inserted respectively into the TK region of the Tiantan strain (VV) of vaccinia virus by homologous recombination and the positive clone was confirmed by blue plaque assay. Protein expression was examined by Western blot. Prime and prime-boost procedures were used to immunize mice with two DNA vectors and two recombinant vaccinia viruses expressing EIAV Gag proteins. The results showed that the specific lysis of CTL responses in the DNA+rVV groups was stronger than those in the DNA groups, amounting to 31%. Although the levels of specific antibodies were not significantly different, we could conclude that the recombinant vaccinia virus could boost the cellular responses following DNA vector priming. There was no detectable difference between the immune responses induced by DLV and LN Gag proteins. This data demonstrates that the combined immunity of DNA vector and recombinant vaccinia virus expressing EIAV gag proteins, utilizing the prime-boost procedure, can drive immunized mice to produce powerful cellular responses. These results lay an important foundation for the development of a new EIAV genetic engineering vaccine.

A survey was conducted by the Sichuan Provincial Women's Federation from the winter of last year to the spring of this year. It was designed to probe into rural women's life in the province and covered ten townships in six counties with different levels of economic growth. Findings of the investigation indicate a general trend of decline in regard to the educational structure of female teenagers and young adults.