Body conformation traits in early-lactation associated with clinical mastitis and lameness in lactating Chinese holstein cows.

BACKGROUND: Comprehending the correlation between body conformation traits of cows at the early stages of lactation and prevalent lactation diseases might facilitate the execution of selection and feeding strategies that prioritize cow health. This study aimed to evaluate the impact of body conformation traits on the incidence of clinical mastitis and lameness in Chinese Holstein cows. From a pasture herd of 1472 early lactating Chinese Holstein cows, we evaluated 20 body conformation traits. During lactation, this pasture herd was visited weekly to gather clinical mastitis and lameness data. A nine-point scale was used to determine the conformation traits of cows to clarify their linear characters, including frame capacity, rump (RU), feet and leg (FL), mammary system (MS), and dairy character. A longitudinal binary disease (0 = healthy; 1 = diseased) data structure was created by allocating disease records to adjacent official test dates. The impact of body conformation traits on the risk of developing diseases (clinical mastitis and lameness) was analyzed using the logistic regression models. RESULTS: Compared to cows with low total scores (75-79 points), those with high total scores (80-85 points) of body conformation traits had a significantly lower risk of mastitis (P < 0.001). The disease status (0 or 1: binary variable) of clinical mastitis in lactating cows was significantly impacted negatively by age (P < 0.05). The fore udder attachment (FUA), angularity, rear attachment height (RAH), and rear teat placement (RTP) were all significantly associated with clinical mastitis during lactation (P < 0.05). The rear leg-rear view (RLRV) was significantly correlated with correlated considerably (P < 0.05) with lameness during lactation. An ideal score of four points on the lameness risk dimension of the RLRV may indicate a low risk of lameness. Since the risk of mastitis decreased as this trait score increased, the RTP may be an ideal marker for mastitis risk. CONCLUSIONS: According to the study, clinical mastitis and lameness risks in cows can be estimated using their body conformation traits. Cows with more centrally located rear teats have a lower risk of mastitis. These results may help dairy farmers identify cows at high risk of disease early in lactation and aid in breeding for disease resistance in cows.

Characterization of T-cell subsets in response to foot-and-mouth disease bivalent inactivated vaccine in Chinese Holstein cows.

IMPORTANCE: Vaccination plays a crucial role in the prevention and control of FMD; however, outbreaks persist occurring worldwide. Assessing the immune response to FMD vaccines is essential for effective prevention of FMD. In this study, a seven-color flow cytometry protocol was developed to systematically evaluate the T-cell response of Chinese Holstein cows vaccinated with FMD bivalent inactivated vaccine. Our findings showed that while most T-cell subsets (%) decreased post-vaccination, a significant increase was observed in CD4+CD8+ DP T cells, which was consistent with the levels of specific foot-and-mouth disease virus (FMDV) antibodies. These findings suggested that CD4+CD8+ DP T cells could serve as a potential biomarker for the evaluation of cellular and humoral responses to FMDV vaccination. Additionally, we should be aware of the potential decline in cellular immunity among cattle during FMD vaccination, as this may increase the risk of other pathogen-related issues.

The combination of vaccines and adjuvants to prevent the occurrence of high incidence of infectious diseases in bovine.

As the global population grows, the demand for beef and dairy products is also increasing. The cattle industry is facing tremendous pressures and challenges. The expanding cattle industry has led to an increased risk of disease in cattle. These diseases not only cause economic losses but also pose threats to public health and safety. Hence, ensuring the health of cattle is crucial. Vaccination is one of the most economical and effective methods of preventing bovine infectious diseases. However, there are fewer comprehensive reviews of bovine vaccines available. In addition, the variable nature of bovine infectious diseases will result in weakened or even ineffective immune protection from existing vaccines. This shows that it is crucial to improve overall awareness of bovine vaccines. Adjuvants, which are crucial constituents of vaccines, have a significant role in enhancing vaccine response. This review aims to present the latest advances in bovine vaccines mainly including types of bovine vaccines, current status of development of commonly used vaccines, and vaccine adjuvants. In addition, this review highlights the main challenges and outstanding problems of bovine vaccines and adjuvants in the field of research and applications. This review provides a theoretical and practical basis for the eradication of global bovine infectious diseases.

Assessment of adaptive immune responses of dairy cows with Burkholderia contaminans-induced mastitis.

Burkholderia contaminans, an emerging pathogen related to cystic fibrosis, is known to cause potentially fatal infections in humans and ruminants, especially in immunocompromised individuals. However, the immune responses in cows following its infection have not been fully elucidated. In this study, T- and B-lymphocytes-mediated immune responses were evaluated in 15 B. contaminans-induced mastitis cows and 15 healthy cows with multi-parameter flow cytometry. The results showed that infection with B. contaminans was associated with a significant decrease in the number and percentage of B lymphocytes but with a significant increase in the proportion of IgG+CD27+ B lymphocytes. This indicated that humoral immune response may not be adequate to fight intracellular infection, which could contribute to the persistent bacterial infection. In addition, B. contaminans infection induced significant increase of γδ T cells and double positive (DP) CD4+CD8+ T cells but not CD4+ or CD8+ (single positive) T cells in blood. Phenotypic analysis showed that the percentages of activated WC1+ γδ T cells in peripheral blood were increased in the B. contaminans infected cows. Interestingly, intracellular cytokine staining showed that cattle naturally infected with B. contaminans exhibited multifunctional TNF-α+IFN-γ+IL-2+ B. contaminans-specific DP T cells. Our results, for the first time, revealed a potential role of IgG+CD27+ B cells, CD4+CD8+ T cells and WC1+ γδ T cells in the defense of B. contaminans-induced mastitis in cows.

Identification of copper metabolism and cuproptosis-related subtypes for predicting prognosis tumor microenvironment and drug candidates in hepatocellular carcinoma.

Copper (Cu) is an essential element of organisms, which can affect the survival of cells. However, the role of copper metabolism and cuproptosis on hepatic carcinoma is still unclear. In this study, the TCGA database was used as the test set, and the ICGC database and self-built database were used as the validation set. We screened out a class of copper metabolism and cuproptosis-related genes (CMCRGs) that could influence hepatic carcinoma prognosis by survival analysis and differential comparison. Based on CMCRGs, patients were divided into two subtypes by cluster analysis. The C2 subtype was defined as the high copper related subtype, while the C1 subtype was defied as the low copper related subtype. At the clinical level, compared with the C1 subtype, the C2 subtype had higher grade pathological features, risk scores, and worse survival. In addition, the immune response and metabolic status also differed between C1 and C2. Specifically, C2 subtype had a higher proportion of immune cell composition and highly expressed immune checkpoint genes. C2 subtype had a higher TIDE score with a higher proportion of tumor immune dysfunction and exclusion. At the molecular level, the C2 subtype had a higher frequency of driver gene mutations (TP53 and OBSCN). Mechanistically, the single nucleotide polymorphisms of C2 subtype had a very strong transcriptional strand bias for C>A mutations. Copy number variations in the C2 subtype were characterized by LOXL3 CNV gain, which also showed high association with PDCD1/CTLA4. Finally, drug sensitivity responsiveness was assessed in both subtypes. C2 subtype had lower IC50 values for targeted and chemotherapeutic agents (sorafenib, imatinib and methotrexate, etc.). Thus, CMCRGs related subtypes showed poor response to immunotherapy and better responsiveness to targeted agents, and the results might provide a reference for precision treatment of hepatic carcinoma.

Ceriodaphnia (Cladocera: Daphniidae) in China: Lineage diversity, phylogeography and possible interspecific hybridization.

The distribution and species/lineage diversity of freshwater invertebrate zooplankton remains understudied in China. Here, we explored the species/lineage diversity and phylogeography of Ceriodaphnia species across China. The taxonomy of this genus is under-explored. Seven morphospecies of Ceriodaphnia (C. cornuta, C. laticaudata, C. megops, C. pulchella, C. quadrangula, C. rotunda and C. spinata) were identified across 45 of 422 water bodies examined. Rather little morphological variation was observed within any single morphospecies regardless of country of origin. Nevertheless, we recognized that some or all of these morphospecies might represent species complexes. To investigate this, phylogenetic relationships within and among these morphospecies were investigated based on mitochondrial (partial cytochrome c oxidase subunit I gene) and nuclear (partial 28S rRNA gene) markers. The mitochondrial marker placed these populations in nine lineages corresponding to the morphospecies: C. laticaudata and C. pulchella were each represented by two lineages, suggesting that both are species complexes. The remaining five morphospecies were each represented by a single mtDNA lineage. Three of the nine mitochondrial lineages (belonging to C. pulchella, C. rotunda and C. megops) are newly reported and exhibited a restricted distribution within China. The nuclear-DNA phylogeny also recognized seven Ceriodaphnia taxa within China. We detected occasional mito-nuclear discordances in Ceriodaphnia taxa across China, suggesting interspecific introgression and hybridization. Our study contributes to an understanding of the species/lineage diversity of Ceriodaphnia, a genus with understudied taxonomy.

Neural Compatibility Modeling with Probabilistic Knowledge Distillation.

In modern society, clothing matching plays a pivotal role in people's daily life, as suitable outfits can beautify their appearance directly. Nevertheless, how to make a suitable outfit has become a daily headache for many people, especially those who do not have much sense of aesthetics. In the light of this, many research efforts have been dedicated to the task of complementary clothing matching and have achieved great success relying on the advanced data-driven neural networks. However, most existing methods overlook the rich valuable knowledge accumulated by our human beings in the fashion domain, especially the rules regarding clothing matching, like "coats go with dresses" and "silk tops cannot go with chiffon bottoms". Towards this end, in this work, we propose a knowledge-guided neural compatibility modeling scheme, which is able to incorporate the rich fashion domain knowledge to enhance the performance of the compatibility modeling in the context of clothing matching. To better integrate the huge and implicit fashion domain knowledge into the data-driven neural networks, we present a probabilistic knowledge distillation (PKD) method, which is able to encode vast knowledge rules in a probabilistic manner. Extensive experiments on two real-world datasets have verified the guidance of rules from different sources and demonstrated the effectiveness and portability of our model. As a byproduct, we released the codes and involved parameters to benefit the research community.

Aesthetics-Guided Graph Clustering With Absent Modalities Imputation.

Accurately clustering Internet-scale Internet users into multiple communities according to their aesthetic styles is a useful technique in image modeling and data mining. In this paper, we present a novel partially supervised model which seeks a sparse representation to capture photo aesthetics. It optimally fuzes multi-channel features, i.e., human gaze behavior, quality scores, and semantic tags, each of which could be absent. Afterward, by leveraging the KL-divergence to distinguish the aesthetic distributions between photo sets, a large-scale graph is constructed to describe the aesthetic correlations between users. Finally, a dense subgraph mining algorithm which intrinsically supports outliers (i.e., unique users not belong to any community) is adopted to detect aesthetic communities. The comprehensive experimental results on a million-scale image set grabbed from Flickr have demonstrated the superiority of our method. As a byproduct, the discovered aesthetic communities can enhance photo retargeting and video summarization substantially.

Scene Categorization Using Deeply Learned Gaze Shifting Kernel.

Accurately recognizing sophisticated sceneries from a rich variety of semantic categories is an indispensable component in many intelligent systems, e.g., scene parsing, video surveillance, and autonomous driving. Recently, there have emerged a large quantity of deep architectures for scene categorization, wherein promising performance has been achieved. However, these models cannot explicitly encode human visual perception toward different sceneries, i.e., the sequence of humans sequentially allocates their gazes. To solve this problem, we propose deep gaze shifting kernel to distinguish sceneries from different categories. Specifically, we first project regions from each scenery into the so-called perceptual space, which is established by combining color, texture, and semantic features. Then, a novel non-negative matrix factorization algorithm is developed which decomposes the regions' feature matrix into the product of the basis matrix and the sparse codes. The sparse codes indicate the saliency level of different regions. In this way, the gaze shifting path from each scenery is derived and an aggregation-based convolutional neural network is designed accordingly to learn its deep representation. Finally, the deep representations of gaze shifting paths from all the scene images are incorporated into an image kernel, which is further fed into a kernel SVM for scene categorization. Comprehensive experiments on six scenery data sets have demonstrated the superiority of our method over a series of shallow/deep recognition models. Besides, eye tracking experiments have shown that our predicted gaze shifting paths are 94.6% consistent with the real human gaze allocations.

Learning Latent Stable Patterns for Image Understanding With Weak and Noisy Labels.

This paper focuses on weakly supervised image understanding, in which the semantic labels are available only at image-level, without the specific object or scene location in an image. Existing algorithms implicitly assume that image-level labels are error-free, which might be too restrictive. In practice, image labels obtained from the pretrained predictors are easily contaminated. To solve this problem, we propose a novel algorithm for weakly supervised segmentation when only noisy image labels are available during training. More specifically, a semantic space is constructed first by encoding image labels through a graphlet (i.e., superpixel cluster) embedding process. Then, we observe that in the semantic space, the distribution of graphlets from images with a same label remains stable, regardless of the noises in image labels. Therefore, we propose a generative model, called latent stability analysis, to discover the stable patterns from images with noisy labels. Inferring graphlet semantics by making use of these mid-level stable patterns is much more secure and accurate than directly transferring noisy image-level labels into different regions. Finally, we calculate the semantics of each superpixel using maximum majority voting of its correlated graphlets. Comprehensive experimental results show that our algorithm performs impressively when the image labels are predicted by either the hand-crafted or deeply learned image descriptors.

Deep Active Learning with Contaminated Tags for Image Aesthetics Assessment.

Image aesthetic quality assessment has becoming an indispensable technique that facilitates a variety of image applications, e.g., photo retargeting and non-realistic rendering. Conventional approaches suffer from the following limitations: 1) the inefficiency of semantically describing images due to the inherent tag noise and incompletion, 2) the difficulty of accurately reflecting how humans actively perceive various regions inside each image, and 3) the challenge of incorporating the aesthetic experiences of multiple users. To solve these problems, we propose a novel semi-supervised deep active learning (SDAL) algorithm, which discovers how humans perceive semantically important regions from a large quantity of images partially assigned with contaminated tags. More specifically, as humans usually attend to the foreground objects before understanding them, we extract a succinct set of BING (binarized normed gradients) [60]-based object patches from each image. To simulate human visual perception, we propose SDAL which hierarchically learns human gaze shifting path (GSP) by sequentially linking semantically important object patches from each scenery. Noticeably, SDLA unifies the semantically important regions discovery and deep GSP feature learning into a principled framework, wherein only a small proportion of tagged images are adopted. Moreover, based on the sparsity penalty, SDLA can optimally abandon the noisy or redundant low-level image features. Finally, by leveraging the deeply-learned GSP features, a probabilistic model is developed for image aesthetics assessment, where the experience of multiple professional photographers can be encoded. Besides, auxiliary quality-related features can be conveniently integrated into our probabilistic model. Comprehensive experiments on a series of benchmark image sets have demonstrated the superiority of our method. As a byproduct, eye tracking experiments have shown that GSPs generated by our SDAL are about 93% consistent with real human gaze shifting paths.