Histone Lactylation Participates in Psoriasis Progression by Regulating the Adiponectin Expression.

Background: Psoriasis is a chronic inflammatory skin disease characterized by erythema, papules, and plaques. Adiponectin (ADIPOQ) is an important protein hormone secreted by adipose tissue. Here, we aimed to explore the expression of ADIPOQ in psoriasis patients and the moderation effect of histone lactylation on ADIPOQ. Methods: The GSE78097 data set was downloaded from GEO database to analyze the differentially expressed genes (DEGs) in psoriasis. A total of 36 psoriasis patients were recruited to obtain the skin samples. The ADIPOQ protein levels, global lactylation and histone lactylation (H3K18lac) levels were detected by Western blot assay. Chromatin immunoprecipitation (CHIP) assay was performed to detect the combination between H3K18lac and promoter regions of the ADIPOQ. The receiver operating curve (ROC) analysis was used to evaluate the diagnostic value of ADIPOQ in psoriasis. Results: ADIPOQ was decreased in the skin tissues of psoriasis patients. In addition, the global lactylation and H3K18lac levels were significantly decreased in the skin tissues of psoriasis patients. In HaCaT cells, promoting the global lactylation and H3K18lac levels increased the ADIPOQ protein levels, while si-LDHA transfection decreased the ADIPOQ protein levels. The CHIP results indicated that lactylation promoted the binding of promoter regions of the ADIPOQ and H3K18lac. Finally, the ROC analysis showed that ADIPOQ exhibited diagnostic value in psoriasis. Conclusion: This study demonstrated ADIPOQ was decreased in the skin tissues of psoriasis patients, and ADIPOQ has diagnostic value for psoriasis. Furthermore, down-regulation of H3K18lac levels inhibited the transcription of ADIPOQ, which was the key factor of decrease of ADIPOQ levels in psoriasis patients.

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion: Drawing Insights From Psychology, Engineering, and the Arts, This Article Provides a Comprehensive Overview of the Field of Emotion Analysis in Visual Media and Discusses the Latest Research, Systems, Challenges, Ethical Implications, and Potential Impact of Artificial Emotional Intelligence on Society.

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of "emotion," coupled with the inherently subjective nature of emotions and their intricate nuances. In this article, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a "Holy Grail" research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field.

Oxyberberine suppressed the carbon tetrachloride-induced liver fibrosis by inhibiting liver inflammation in a sirtuin 3-dependent manner in mice.

Previous studies have shown that oxyberberine (OBB), a novel gut microbiota metabolite of berberine, exhibited prominent protective property against acute liver injury and non-alcoholic fatty liver diseases, however, the effect of OBB on liver fibrosis and its potential mechanisms remain largely unknown. This study was aimed to study the effects of OBB on carbon tetrachloride (CCl4)-induced liver fibrosis and tried to clarify the potential mechanisms by focusing on regulating of sirtuin 3 (SIRT3)-mediated liver inflammation. OBB significantly alleviated the liver injury and fibrosis in CCl4-treated C57/BL6 mouse livers. OBB evidently down-regulated the expression of inflammatory factors and reduced the levels of inflammatory factors in CCl4-treated mouse livers. Noteworthy, CCl4-treated decreased the mRNA and protein expression of SIRT3, and treatment with OBB notably increased the expression of SIRT3 both in transcriptional and translational levels in CCl4-treated mice livers. OBB also suppressed the cell viability of TGF-ß1-stimulated JS-1 cells and inhibited the protein expression of α-SMA but increased the expression of SIRT3 in stimulated JS-1 cells. Moreover, depletion of SIRT3 weakened the anti-inflammatory effects of OBB in stimulated JS-1 cells. Interestingly, the anti-liver injury and anti-fibrotic effects of OBB could be available in CCl4-treated WT (129S1/SvImJ) mice but were unavailable in CCl4-treated SIRT3 knockout (KO) mice. In addition, the anti-inflammatory effect of OBB was only found in CCl4-treated WT mice but was not in SIRT3 KO mice. Collectively, these findings suggested that OBB suppressed the liver injury and fibrosis through inhibition of liver inflammation in a SIRT3-dependent manner in CCl4-treated mice.

Agricultural Food System Transformation on China's Food Security.

Different countries and regions in the world are experiencing structural transformation of the agri-food system, which is represented by the increase of meat and feed consumption. Based on a detailed review of the global and China's agri-food system transformation, this paper constructs an analytical framework of the impact of agri-food system transformation on food security and discusses the food security issues in China after 2000. The results show that the proportion of the dimension of agri-food system transformation in the food security index has a significant downward trend, and its positive effect on food security is decreasing. At the same time, due to the growth in demand for meat during the transformation of the agri-food system, China imports a large number of feed grains such as soybean and corn, leading to a decline in domestic food self-sufficiency. Furthermore, the coupling coordination degree between China's agri-food system transformation and food quantity security decreases, and their development tends to deviate. In the future, increasing the consumption of grain-saving poultry and curbing table waste are feasible strategies for China to cope with the transformation of agri-food system and ensure food security. China's problems and solutions in facing the transformation of its agri-food system can provide some references for other developing countries.

Cobalt chloride exposure disturbs spindle assembly and decreases mouse oocyte development potential.

Cobalt is a kind of heavy metal which is widely used in petrochemical and biomedical industries. Animal studies have reported that cobalt would exert systemic toxicity, but its effects on the ovarian function in mammals, especially for oocyte quality remains unknown. In the present study, we report that cobalt chloride treatment affects ovary coefficient and follicular growth. Oocytes in cobalt chloride exposed mice exhibited a decreased development potential, with the evidence of decreased occurrence rate of germ vesicle breakdown and polar body extrusion. Besides, cobalt chloride disorganized meiotic spindle formation and movement, mechanically associated with affecting TACC3 and Ac-a-tubulin levels, and disturbing actin reorganization. In addition, cobalt chloride exposure result in mitochondrial cristae structures disappear, cluster distribution and potential depolarization. Altogether, these findings suggest that cobalt chloride impairs the ovarian follicle growth and affects oocyte development by disrupted spindle assembly and mitochondrial function.

How Has Climate Change Driven the Evolution of Rice Distribution in China?

Estimating the impact of climate change risks on rice distribution is one of the most important elements of climate risk management. This paper is based on the GEE (Google Earth Engine) platform and multi-source remote sensing data; the authors quantitatively extracted rice production distribution data in China from 1990 to 2019, analysed the evolution pattern of rice distribution and clusters and explored the driving effects between climatic and environmental conditions on the evolution of rice production distribution using the non-parametric quantile regression model. The results show that: The spatial variation of rice distribution is significant, mainly concentrated in the northeast, south and southwest regions of China; the distribution of rice in the northeast is expanding, while the distribution of rice in the south is extending northward, showing a spatial evolution trend of "north rising and south retreating". The positive effect of precipitation on the spatial distribution of rice has a significant threshold. This shows that when precipitation is greater than 800 mm, there is a significant positive effect on the spatial distribution of rice production, and this effect will increase with precipitation increases. Climate change may lead to a continuous northward shift in the extent of rice production, especially extending to the northwest of China. This paper's results will help implement more spatially targeted climate change adaptation measures for rice to cope with the changes in food production distribution caused by climate change.

Safety and biodistribution of exosomes derived from human induced pluripotent stem cells.

As a new cell-free therapy, exosomes have provided new ideas for the treatment of various diseases. Human induced pluripotent stem cells (hiPSCs) cannot be used in clinical trials because of tumorigenicity, but the exosomes derived from hiPSCs may combine the advantages of iPSC pluripotency and the nanoscale size of exosomes while avoiding tumorigenicity. Currently, the safety and biodistribution of hiPSC-exosomes in vivo are unclear. Here, we investigated the effects of hiPSC-exosomes on hemolysis, DNA damage, and cytotoxicity through cell experiments. We also explored the safety of vein injection of hiPSC-exosomes in rabbits and rats. Differences in organ distribution after nasal administration were compared in normal and Parkinson's disease model mice. This study may provide support for clinical therapy and research of intravenous and nasal administration of hiPSC-exosomes.

Acrylonitrile exposure triggers ovarian inflammation and decreases oocyte quality probably via mitochondrial dysfunction induced apoptosis in mice.

Acrylonitrile is an organic chemical synthetic monomer that is widely used in food packaging and manufacturing. Animal studies have reported that acrylonitrile is carcinogenic and toxic, but the effects on the female reproductive function in mammals are unknown. In the present study, we report that acrylonitrile treatment affects ovarian homeostasis in mice, resulting in impaired follicular development. Follicles in acrylonitrile-exposed mice exhibited high levels of inflammation and apoptosis, and acrylonitrile treatment interfered with oocyte development. Transcriptomics analysis showed that acrylonitrile altered the expression of oocyte genes related to apoptosis, oxidative stress, endoplasmic reticulum stress, and autophagy. Further molecular tests revealed that acrylonitrile induced early apoptosis, DNA damage, elevated levels of reactive oxygen species, endoplasmic reticulum abnormalities, and lysosomal aggregation. We also observed disruption of mitochondrial structure and distribution and depolarization of membrane potential. Finally, acrylonitrile treatment in female mice decreased the number and weight of offspring. Altogether, these findings suggest that acrylonitrile impairs the stability of the ovarian internal environment, which in turn affects oocyte development and reduces the number of offspring.

Captan exposure disrupts ovarian homeostasis and affects oocytes quality via mitochondrial dysfunction induced apoptosis.

Captan is a non-systematic fungicide widely used in agricultural production, and its residues have been found in the environment and daily diet. Previous studies confirmed that captan exerts several toxic effects on tissues, but its effect on the mammalian female reproductive system is unclear. In current study, we reported that captan affected mouse ovarian homeostasis and disrupted female hormone receptor expression, leading to impaired follicular development. Ovarian follicles from the captan exposure group showed an increased level of inflammation, endoplasmic reticulum stress and apoptosis. In addition, captan exposure disrupted oocyte development. Transcriptomic analysis indicated that captan changed multiple genes expression in oocytes, including autophagy and apoptosis. Further molecular testing showed that captan induced oxidative stress and mitochondrial dysfunction, as indicated by the increased level of reactive oxygen species, disrupted mitochondrial structure and distribution, and depolarized membrane potential. Furthermore, captan triggered DNA damage, autophagy and early apoptosis, as shown by the enhanced levels of γ-H2AX, LC3, and Annexin-V and increased expression of related genes. Taken together, these results indicated that captan exposure impairs ovarian homeostasis and subsequently affects oocyte development.

Personalized Image Aesthetics Assessment via Meta-Learning With Bilevel Gradient Optimization.

Typical image aesthetics assessment (IAA) is modeled for the generic aesthetics perceived by an "average" user. However, such generic aesthetics models neglect the fact that users' aesthetic preferences vary significantly depending on their unique preferences. Therefore, it is essential to tackle the issue for personalized IAA (PIAA). Since PIAA is a typical small sample learning (SSL) problem, existing PIAA models are usually built by fine-tuning the well-established generic IAA (GIAA) models, which are regarded as prior knowledge. Nevertheless, this kind of prior knowledge based on "average aesthetics" fails to incarnate the aesthetic diversity of different people. In order to learn the shared prior knowledge when different people judge aesthetics, that is, learn how people judge image aesthetics, we propose a PIAA method based on meta-learning with bilevel gradient optimization (BLG-PIAA), which is trained using individual aesthetic data directly and generalizes to unknown users quickly. The proposed approach consists of two phases: 1) meta-training and 2) meta-testing. In meta-training, the aesthetics assessment of each user is regarded as a task, and the training set of each task is divided into two sets: 1) support set and 2) query set. Unlike traditional methods that train a GIAA model based on average aesthetics, we train an aesthetic meta-learner model by bilevel gradient updating from the support set to the query set using many users' PIAA tasks. In meta-testing, the aesthetic meta-learner model is fine-tuned using a small amount of aesthetic data of a target user to obtain the PIAA model. The experimental results show that the proposed method outperforms the state-of-the-art PIAA metrics, and the learned prior model of BLG-PIAA can be quickly adapted to unseen PIAA tasks.

Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation.

Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However, because of the presence of domain shift, the learned knowledge of the well-trained DNNs cannot be well generalized to new domains or datasets that have few labels. Unsupervised domain adaptation (UDA) studies the problem of transferring models trained on one labeled source domain to another unlabeled target domain. In this article, we focus on UDA in visual emotion analysis for both emotion distribution learning and dominant emotion classification. Specifically, we design a novel end-to-end cycle-consistent adversarial model, called CycleEmotionGAN++. First, we generate an adapted domain to align the source and target domains on the pixel level by improving CycleGAN with a multiscale structured cycle-consistency loss. During the image translation, we propose a dynamic emotional semantic consistency loss to preserve the emotion labels of the source images. Second, we train a transferable task classifier on the adapted domain with feature-level alignment between the adapted and target domains. We conduct extensive UDA experiments on the Flickr-LDL and Twitter-LDL datasets for distribution learning and ArtPhoto and Flickr and Instagram datasets for emotion classification. The results demonstrate the significant improvements yielded by the proposed CycleEmotionGAN++ compared to state-of-the-art UDA approaches.

A Review of Single-Source Deep Unsupervised Visual Domain Adaptation.

Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain. Unfortunately, direct transfer across domains often performs poorly due to the presence of domain shift or dataset bias. Domain adaptation (DA) is a machine learning paradigm that aims to learn a model from a source domain that can perform well on a different (but related) target domain. In this article, we review the latest single-source deep unsupervised DA methods focused on visual tasks and discuss new perspectives for future research. We begin with the definitions of different DA strategies and the descriptions of existing benchmark datasets. We then summarize and compare different categories of single-source unsupervised DA methods, including discrepancy-based methods, adversarial discriminative methods, adversarial generative methods, and self-supervision-based methods. Finally, we discuss future research directions with challenges and possible solutions.

Deep Correlated Joint Network for 2-D Image-Based 3-D Model Retrieval.

In this article, we propose a novel deep correlated joint network (DCJN) approach for 2-D image-based 3-D model retrieval. First, the proposed method can jointly learn two distinct deep neural networks, which are trained for individual modalities to learn two deep nonlinear transformations for visual feature extraction from the co-embedding feature space. Second, we propose the global loss function for the DCJN, consisting of a discriminative loss and a correlation loss. The discriminative loss aims to minimize the intraclass distance of the extracted features and maximize the interclass distance of such features to a large margin within each modality, while the correlation loss focuses on mitigating the distribution discrepancy across different modalities. Consequently, the proposed method can realize cross-modality feature extraction guided by the defined global loss function to benefit the similarity measure between 2-D images and 3-D models. For a comparison experiment, we contribute the current largest 2-D image-based 3-D model retrieval dataset. Moreover, the proposed method was further evaluated on three popular benchmarks, including the 3-D Shape Retrieval Contest 2014, 2016, and 2018 benchmarks. The extensive comparison experimental results demonstrate the superiority of this method over the state-of-the-art methods.

Affective Image Content Analysis: Two Decades Review and New Perspectives.

Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this survey, we will comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges - the affective gap, perception subjectivity, and label noise and absence. We begin with an introduction to the key emotion representation models that have been widely employed in AICA and description of available datasets for performing evaluation with quantitative comparison of label noise and dataset bias. We then summarize and compare the representative approaches on (1) emotion feature extraction, including both handcrafted and deep features, (2) learning methods on dominant emotion recognition, personalized emotion prediction, emotion distribution learning, and learning from noisy data or few labels, and (3) AICA based applications. Finally, we discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.

Nickel sulfate exposure induces ovarian inflammation and fibrosis and decreases oocyte quality in mice.

Nickel is a heavy metal element extensively distributed in the environment and widely used in modern life. Divalent nickel is one of the most widespread forms of nickel and has been reported as toxic to various tissues. However, whether exposure to divalent nickel negatively affects ovarian homeostasis and oocyte quality remains unclear. In this study, we found that 3 weeks of nickel sulfate exposure affected body growth and decreased the weight and coefficient of the ovary, and increased atretic follicles exhibiting enhanced apoptosis in granulosa cells. Further studies have found that nickel sulfate triggered ovarian fibrosis and inflammation via transforming growth factor-ß1 and nuclear factor-κB pathways, and reduced oocyte development ability. In addition, nickel sulfate increased the level of reactive oxygen species, which induced DNA damage and early apoptosis. Moreover, it was found that nickel sulfate caused damage to the mitochondria showing aberrant morphology, distribution and membrane potential while decreased levels of histone methylation. To summarize, our results indicated that nickel sulfate exposure triggered ovarian fibrosis and inflammation and caused structural and functional disorders of mitochondria in oocytes, which consequently disturbed ovarian homeostasis and follicle development and decreased oocyte quality.

Thiamethoxam Exposure Induces Endoplasmic Reticulum Stress and Affects Ovarian Function and Oocyte Development in Mice.

Neonicotinoids are the most widely used insecticides in modern agriculture, and their residues have been found in the environment and food. Previous studies reported that neonicotinoids exert toxic effects in various tissues, but whether they interfered with the female reproductive process remains unknown. In our present research, thiamethoxam was selected as a representative neonicotinoid to establish a mouse toxicity model with gavage. We found that thiamethoxam decreased the ovarian coefficient and disrupted the expression of female hormone receptors, subsequently affecting follicle development. Ovarian granulosa cells from the thiamethoxam exposure group underwent a high level of apoptosis. Using transcriptome analysis, we showed that thiamethoxam exposure altered the expression of multiple oocyte genes related to inflammation, apoptosis, and endoplasmic reticulum stress. Thiamethoxam also adversely affected oocyte and embryo development. Western blotting and fluorescence staining results confirmed that thiamethoxam affected the integrity of DNA, triggered apoptosis, promoted oxidative stress and endoplasmic reticulum stress, and impaired mitochondrial function. Collectively, our results indicated that thiamethoxam exposure disrupts ovarian homeostasis and decreases oocyte quality via endoplasmic reticulum stress and apoptosis induction.

Electric-Field-Induced Pattern Formation in Layers of DNA Molecules at the Interface between Two Immiscible Liquids.

The concentration patterns of DNA molecules attached to the interface between two immiscible aqueous phases forming under an electric field are studied. The pattern formation is driven by hydrodynamic interactions between the molecules originating from the electro-osmotic flow due to the Debye layer around a molecule. A nonlinear integrodifferential equation is derived describing the time evolution of the concentration field at the liquid-liquid interface. A linear stability analysis of this equation shows that a mode of given wavelength is initially stable, but destabilizes after a critical time which is inversely proportional to the wavelength. The scaling behavior of the critical time with electric field strength and viscosity found in the experiments agrees with the predictions by the theoretical model.

Personality-assisted Multi-task Learning for Generic and Personalized Image Aesthetics Assessment.

Traditional image aesthetics assessment (IAA) approaches mainly predict the average aesthetic score of an image. However, people tend to have different tastes on image aesthetics, which is mainly determined by their subjective preferences. As an important subjective trait, personality is believed to be a key factor in modeling individual's subjective preference. In this paper, we present a personality-assisted multi-task deep learning framework for both generic and personalized image aesthetics assessment. The proposed framework comprises two stages. In the first stage, a multi-task learning network with shared weights is proposed to predict the aesthetics distribution of an image and Big-Five (BF) personality traits of people who like the image. The generic aesthetics score of the image can be generated based on the predicted aesthetics distribution. In order to capture the common representation of generic image aesthetics and people's personality traits, a Siamese network is trained using aesthetics data and personality data jointly. In the second stage, based on the predicted personality traits and generic aesthetics of an image, an inter-task fusion is introduced to generate individual's personalized aesthetic scores on the image. The performance of the proposed method is evaluated using two public image aesthetics databases. The experimental results demonstrate that the proposed method outperforms the state-of-the-arts in both generic and personalized IAA tasks.

Real-Time Multimedia Social Event Detection in Microblog.

Detecting events from massive social media data in social networks can facilitate browsing, search, and monitoring of real-time events by corporations, governments, and users. The short, conversational, heterogeneous, and real-time characteristics of social media data bring great challenges for event detection. The existing event detection approaches rely mainly on textual information, while the visual content of microblogs and the intrinsic correlation among the heterogeneous data are scarcely explored. To deal with the above challenges, we propose a novel real-time event detection method by generating an intermediate semantic level from social multimedia data, named microblog clique (MC), which is able to explore the high correlations among different microblogs. Specifically, the proposed method comprises three stages. First, the heterogeneous data in microblogs is formulated in a hypergraph structure. Hypergraph cut is conducted to group the highly correlated microblogs with the same topics as the MCs, which can address the information inadequateness and data sparseness issues. Second, a bipartite graph is constructed based on the generated MCs and the transfer cut partition is performed to detect the events. Finally, for new incoming microblogs, incremental hypergraph is constructed based on the latest MCs to generate new MCs, which are classified by bipartite graph partition into existing events or new ones. Extensive experiments are conducted on the events in the Brand-Social-Net dataset and the results demonstrate the superiority of the proposed method, as compared to the state-of-the-art approaches.

Instability of a rotating liquid ring.

It is shown numerically that a rotating inviscid liquid ring has a temporally oscillating state, where the radius of the ring varies periodically because of the competition between the centrifugal force and the centripetal force caused by the surface tension. Stability analysis reveals that an enlarging or shrinking ring is unstable to a varicose-type mode, which is affected by both the radial velocity and the radius ratio between the cross section and the ring. Furthermore, uniform rotation of a ring leads to a traveling unstable mode, whose frequency is determined by a simple sinuous mode, while the surface shape is modulated by the varicose mode and twisted by the rotation-induced Coriolis force.