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The rapid development of deep learning technology has led to the emergence of diffusion models as a promising generative model with diverse applications. These include image generation, audio and video synthesis, molecular design, and text generation. The distinctive generation mechanism and exceptional generation quality of diffusion models have made them a valuable tool in these diverse fields. However, with the extensive deployment of diffusion models in the domain of image generation, concerns pertaining to data privacy, data security, and artistic ethics have emerged with increasing prominence. Given the accelerated pace of development in the field of diffusion models, the majority of extant surveys are deficient in two respects: firstly, they fail to encompass the latest advances in diffusion-based image synthesis; and secondly, they seldom consider the potential social implications of diffusion models. In order to address these issues, this paper presents a comprehensive survey of the most recent applications of diffusion models in the field of image generation. Furthermore, it provides an in-depth analysis of the potential social impacts that may result from their use. Firstly, this paper presents a systematic survey of the background principles and theoretical foundations of diffusion models. Subsequently, this paper provides a detailed examination of the most recent applications of diffusion models across a range of image generation subfields, including style transfer, image completion, image editing, super-resolution, and beyond. Finally, we present a comprehensive examination of these social issues, addressing data privacy concerns, such as the potential for data leakage and the implementation of protective measures during model training. We also analyse the risk of malicious exploitation of the model and the defensive strategies employed to mitigate such risks. Additionally, we examine the implications of the authenticity and originality of generated images on artistic creativity and copyright protection. [ABSTRACT FROM AUTHOR]

For light-element materials, X-ray phase contrast imaging provides better contrast compared to absorption imaging. While the Fourier transform method has a shorter imaging time, it typically results in lower image quality; in contrast, the phase-shifting method offers higher image quality but is more time-consuming and involves a higher radiation dose. To rapidly reconstruct low-dose X-ray phase contrast images, this study developed a model based on Generative Adversarial Networks (GAN), incorporating custom layers and self-attention mechanisms to recover high-quality phase contrast images. We generated a simulated dataset using Kaggle's X-ray data to train the GAN, and in simulated experiments, we achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). To further validate our method, we applied it to fringe images acquired from three phase contrast systems: a single-grating phase contrast system, a Talbot-Lau system, and a cascaded grating system. The current results demonstrate that our method successfully restored high-quality phase contrast images from fringe images collected in experimental settings, though it should be noted that these results were achieved using relatively simple sample configurations. [ABSTRACT FROM AUTHOR]

The region along the Great Wall is a typical dryland agricultural zone, serving as both a potential area for staple grain production and a key region for specialty crops like coarse grains and cool-climate vegetables. Studying the characteristics of drought during the spring sowing period is crucial for developing diversified planting strategies and ensuring food security. This study analyzes the drought conditions along the Great Wall from 1960 to 2023, revealing the spatial and temporal distribution of drought in the region and quantifying the impact of climate change on drought frequency and intensity. By doing so, it fills a gap in the existing drought research, which often lacks the long-term, multi-dimensional analysis of spring sowing drought characteristics. Using daily meteorological data from April 20 to May 20 during the spring sowing period between 1960 and 2023, the study employs the Meteorological Composite Drought Index (MCI) to quantitatively identify drought conditions and examine the spatial and temporal evolution of drought in the region. The results show that, on a daily scale, the frequency of mild and moderate droughts is 60.45% and 25.19%, respectively, with no occurrences of severe or extreme drought. On an annual scale, the intensity of drought and the ratio of affected stations show an increasing trend, with a decrease in mild drought frequency and an increase in moderate and severe drought occurrences. Additionally, the spatial distribution of drought frequency follows a pattern of "higher in the east than in the west" and "higher in the north than in the south". The study also finds that the migration of drought frequency centers shows a clear temporal evolution, with the center shifting southwestward from the 1960s to the 2000s, and then moving northeastward from the 2000s to 2023. These findings provide critical data support for optimizing agricultural drought resistance strategies and offer new insights for future research on the relationship between drought and climate change. It is suggested that agricultural practices and water resource management policies should be adjusted according to the spatial migration of drought centers, with a particular focus on optimizing drought mitigation measures during the spring sowing period. [ABSTRACT FROM AUTHOR]

Effect of saturation magnetization (Ms) of the free layer (FL) on the switching current is analyzed for spin transfer torque (STT) magnetic random access memory (MRAM). For in-plane FL, critical switching current (Ic0) decreases as Ms decreases. However, reduction in Ms also results in a low thermal stability factor (Δ), which must be compensated through increasing shape anisotropy, thus limiting scalability. For perpendicular FL, Ic0 reduction by using low-Ms materials is actually at the expense of data retention. To save energy consumed by STT current, two electric field (EF) controlled switching methods are proposed. Our simulation results show that elliptical FL can be switched by an EF pulse with a suitable width. However, it is difficult to implement this type of switching in real MRAM devices due to the distribution of the required switching pulse widths. A reliable switching method is to use an Oersted field guided switching. Our simulation and experimental results show that the bi-directional magnetization switching could be realized by an EF with an external field as low as  ±5 Oe if the offset field could be removed. [ABSTRACT FROM AUTHOR]

It is key to develop novel economical and effective non-noble metal bifunctional catalysts for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in water splitting cells. Herein, a series of novel two-dimensional (2D) ultrathin donor-acceptor Co-based metal organic framework nanoplates (U-DA-CoMOF-NPs) as bifunctional HER/OER catalysts have been designed and fabricated based on a donor-acceptor strategy. Density functional theory calculations demonstrate that the fabricated U-DA-CoMOF-NPs are fine donor-acceptor pairs having HOMO-LUMO localizations and a HOMO-LUMO gap of 2.60 eV and give the Gibbs free energy for a reasonable OER mechanism. Additionally, changes in anions can affect the physicochemical characterizations and HER/OER performance of the fabricated U-DA-CoMOF-NPs. As a result, the fabricated U-DA-CoMOF-NPs-1 using NO 3 − as an anion exhibits the optimal HER/OER performance in alkaline solution thanks to the thinnest nanoplate thickness, the largest surface area and hierarchical mesoporous structure. Moreover, a self-assembled overall water splitting cell with U-DA-CoMOF-NPs-1/NF as both cathode and anode achieves a low potential of 1.65 V and a high cycling stability at 10 mA cm−2 for at least 15 h. The present study offers an option to design and fabricate other cost-effective donor-acceptor MOF-based catalysts for water splitting cells. • A series of U-DA-CoMOF-NPs have been fabricated with a donor-acceptor strategy. • The HOMO positioning is on Tdc, LUMO localization is at Tpt and a gap is 2.6 eV. • Co–N has more positive catalytic ability for OER than pyridinic-N and thiophene-S. • The representative U-DA-CoMOF-NPs-1 exhibits the optimal HER/OER performance. • A U-DA-CoMOF-NPs-1/NF cell shows a low potential and high stability at 10 mA cm−2. [ABSTRACT FROM AUTHOR]

Benefiting from the effective exploration of the valuable topological pair-wise relationship of data points across multiple views, multi-view subspace clustering (MVSC) has received increasing attention in recent years. However, we observe that existing MVSC approaches still suffer from two limitations that need to be further improved to enhance the clustering effectiveness. Firstly, previous MVSC approaches mainly prioritize extracting multi-view consistency, often neglecting the cross-view discrepancy that may arise from noise, outliers, and view-inherent properties. Secondly, existing techniques are constrained by their reliance on pair-wise sample correlation and pair-wise view correlation, failing to capture the high-order correlations that are enclosed within multiple views. To address these issues, we propose a novel MVSC framework via joiNt crOss-view discrepancy discOvery anD high-order correLation dEtection (NOODLE), seeking an informative target subspace representation compatible across multiple features to facilitate the downstream clustering task. Specifically, we first exploit the self-representation mechanism to learn multiple view-specific affinity matrices, which are further decomposed into cohesive factors and incongruous factors to fit the multi-view consistency and discrepancy, respectively. Additionally, an explicit cross-view sparse regularization is applied to incoherent parts, ensuring the consistency and discrepancy to be precisely separated from the initial subspace representations. Meanwhile, the multiple cohesive parts are stacked into a three-dimensional tensor associated with a tensor-Singular Value Decomposition (t-SVD) based weighted tensor nuclear norm constraint, enabling effective detection of the high-order correlations implicit in multi-view data. Our proposed method outperforms state-of-the-art methods for multi-view clustering on six benchmark datasets, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

With the wide deployment of the Internet of Things (IoT), large volumes of incomplete multiview data that violates data integrity is generated by various applications, which inevitably produces negative impacts on the quality of service of IoT systems. Incomplete multiview clustering (IMC), as an essential technique of data processing, has the potential for mining patterns of incomplete IoT data. However, previous methods utilize notion-strong distances that can only measure differences between distributions at the overlap of data manifolds in fusing complementary information of data for pattern mining. They may suffer from biased estimation and information loss in capturing intrinsic structures of incomplete multiview data. To address these challenges, a semidiscrete multiview optimal transport (SD-MOT) is defined for IMC, which utilizes distances with weak notions to capture intrinsic structures of incomplete multiview data. Specifically, IMC is recast as an equivalent optimal transport between continuous incomplete multiview data and discrete clustering centroids, to avoid the strict assumption on overlap between manifolds in pattern mining. Then, SD-MOT is instantiated as a deep incomplete contrastive clustering network to remedy biased estimation and information loss on intrinsic structures of incomplete multiview data. Afterwards, a variational solution to SD-MOT is derived to effectively train the network parameters for pattern mining. Finally, extensive experiments on four representative incomplete multiview datasets verify the superiority of SD-MOT in comparison with nine baseline methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simple Summary: Our study provides an insightful look into the molecular interaction mechanisms of calcitonin and calcitonin gene-related peptide in chickens. The study highlights distinct pathways activated by these receptors and demonstrates that the calcitonin receptor and calcitonin receptor-like receptors have unique sensitivities to their ligands, which can be influenced by receptor activity-modifying proteins. Furthermore, an extensive expression analysis across chicken tissue revealed diverse roles for calcitonin and calcitonin gene-related peptide in avian biology. Our findings provide a fresh perspective on the evolutionary variations in hormone signaling between chickens and mammals, broadening the scope of knowledge in vertebrate endocrine systems. Calcitonin (CT) and calcitonin gene-related peptide (CGRP) are critical regulators of calcium balance and have extensive implications for vertebrate physiological processes. This study explores the CT and CGRP signaling systems in chickens through cloning and characterization of the chicken calcitonin receptor (CTR) and calcitonin receptor-like receptor (CLR), together with three receptor activity-modifying proteins (RAMPs). We illuminated the functional roles for chickens between the receptors examined alone and in RAMP-associated complexes using luciferase reporter assays. Chicken CTRs and CLRs stimulated the cAMP/PKA and MAPK/ERK signaling pathways, signifying their functional receptor status, with CT showing appreciable ligand activity at nanomolar concentrations across receptor combinations. Notably, it is revealed that chicken CLR can act as a functional receptor for CT without or with RAMPs. Furthermore, we uncovered a tissue-specific expression profile for CT, CGRP, CTR, CLR, and RAMPs in chickens, indicating the different physiological roles across various tissues. In conclusion, our data establish a clear molecular basis to reveal information on CT, CGRP, CTR, CLR, and RAMPs in chickens and contribute to understanding the conserved or divergent functions of this family in vertebrates. [ABSTRACT FROM AUTHOR]

Deep graph clustering, efficiently dividing nodes into multiple disjoint clusters in an unsupervised manner, has become a crucial tool for analyzing ubiquitous graph data. Existing methods have acquired impressive clustering effects by optimizing the clustering network under the parametric condition—predefining the true number of clusters (Ktr). However, Ktr is inaccessible in pure unsupervised scenarios, in which existing methods are incapable of inferring the number of clusters (K), causing limited feasibility. This article proposes the first Parameter-Agnostic Deep Graph Clustering method (PADGC), which consists of two core modules: K-guidence clustering and topological-hierarchical inference, to infer K efficiently and gain impressive clustering predictions. Specifically, K-guidence clustering is employed to optimize the cluster assignments and discriminative embeddings in a mutual promotion manner under the latest updated K, even though K may deviate from Ktr. In turn, such optimized cluster assignments are utilized to explore more accurate K in the topological-hierarchical inference, which can split the dispersive clusters and merge the coupled ones. In this way, these two modules are complementarily optimized until generating the final convergent K and discriminative cluster assignments. Extensive experiments on several benchmarks, including graphs and images, can demonstrate the superiority of our method. The mean values of our inferred K, in 11 out of 12 datasets, deviates from Ktr by less than 1. Our method can also achieve competitive clustering effects with existing parametric deep graph clustering. [ABSTRACT FROM AUTHOR]

Multi-view clustering aims to partition unlabeled patterns into disjoint clusters using consistent and complementary information derived from features of patterns in multiple views. Downstream methods perform this clustering sequentially: estimation of individual or consistent similarity matrices, spectral embedding, and clustering. In this article, we present an approach that can address some of the shortcomings of previous multiview clustering methods. We propose a single objective function whose optimization can jointly provide the consistent graph matrix for all views, the unified spectral data representation, the cluster assignments, and the view weights. We propose a new constraint term that sets the cluster index matrix to the convolution of the consistent spectral projection matrix over the consistent graph. Our proposed scheme has two interesting properties that the recent works do not have simultaneously. First, the cluster assignments can be estimated directly without the need for an additional clustering phase, which depends heavily on initialization. Second, the soft cluster assignments are directly linked to the kernel representation of the features of the views. Moreover, our method automatically computes the weights of each view, requiring fewer hyperparameters. We have conducted a series of experiments on real datasets. These demonstrate the effectiveness of the proposed approach, which compares favorably to many competing multi-view clustering methods. [ABSTRACT FROM AUTHOR]

Telocytes (TCs) are distinctive interstitial cells due to their characteristic structures and heterogeneity. They are suggested to participate in tissue repair/regeneration. TCs have been identified in many organs of various mammals. However, data on TCs in lower animals are still very limited. In this work, TCs were identified in the myocardium of the bullfrog (Rana catesbeiana) by light and transmission electron microscopy (TEM). The structural relationships between TCs and neighbouring cell types were measured using the ImageJ (FiJi) morphometric software. TCs with slender Tps (telepodes) were located around cardiomyocytes (CMC). TEM revealed TCs with long Tps in the stroma between CMC. The homocellular tight junctions were observed between the Tps. The Tps were also very close to the neighbouring CMC. The distance between Tps and CMC was 0.15 ± 0.08 μm. Notably, Tps were observed to adhere to the periphery of the satellite cells. The Tps and the satellite cells established heterocellular structural connections by tight junctions. Additionally, Tps were frequently observed in close proximity to mast cells (MCs). The distance between the Tps and the MCs was 0.19 ± 0.09 μm. These results confirmed that TCs are present in the myocardium of the bullfrog, and that TCs established structural relationships with neighbouring cell types, including satellite cells and MCs. These findings provide the anatomical evidence to support the note that TCs are involved in tissue regeneration. [ABSTRACT FROM AUTHOR]

Copyright of Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jilin University (Medicine Edition) is the property of Jilin University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Consensus clustering provides an elegant framework to aggregate multiple weak clustering results to learn a consensus one that is more robust and stable than a single result. However, most of the existing methods usually use all data for consensus learning, whereas ignoring the side effects caused by some unreliable or difficult data. To address this issue, in this article, we propose a novel self-paced consensus clustering method with adaptive bipartite graph learning to gradually involve data from more reliable to less reliable ones in consensus learning. At first, we construct an initial bipartite graph from the base results, where the nodes represent the clusters and instances, and the edges indicate that an instance belongs to a cluster. Then, we adaptively learn a structured bipartite graph from this initial one by self-paced learning, i.e., we automatically determine the reliability of each edge with adaptive cluster similarity measuring and involve the edges in bipartite graph learning in order of their reliability. At last, we obtain the final consensus result from the learned structured bipartite graph. We conduct extensive experiments on both toy and benchmark datasets, and the results show the effectiveness and superiority of our method. The codes of this article are released in http://Doctor-Nobody.github.io/codes/code_SCCABG.zip. [ABSTRACT FROM AUTHOR]

Graph-based Multi-View Clustering (GMVC) has received extensive attention due to its ability to capture the neighborhood relationship among data points from diverse views. However, most existing approaches construct similarity graphs from the original multi-view data, the accuracy of which heavily and implicitly relies on the quality of the original multiple features. Moreover, previous methods either focus on mining the multi-view commonality or emphasize on exploring the multi-view individuality, making the rich information contained in multiple features cannot be effectively exploited. In this work, we design a novel GMVC framework via cOmmoNality and Individuality discOvering in lateNt subspace (ONION), seeking for a robust and discriminative subspace representation compatible across multiple features for GMVC. To be specific, our method simultaneously formulates the unsupervised sparse feature selection and the robust subspace extraction, as well as the target graph learning in a unified optimization model, which can help the learning of the discriminative subspace representation and the target graph in a mutual reinforcement manner. Meanwhile, we manipulate the target graph by an explicit structural penalty, rendering the connected components in the graph directly reveal clusters. Experimental results on seven benchmark datasets demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Graph-based multi-view learning has attracted much attention due to the efficacy of fusing the information from different views. However, most of them exhibit high computational complexity. We propose an anchor-based bipartite graph embedding approach to accelerate the learning process. Specifically, different from existing anchor-based methods where anchors are obtained from key samples by clustering or weighted averaging strategies, in this article, the anchors are learned in a principled fashion which aims at constructing a distance-preserving embedding for each view from samples to their representations, whose elements are the weights of the edges linking corresponding samples and anchors. In addition, the consistency among different views can be explored by imposing a low-rank constraint on the concatenated embedding representations. We further design a concise yet effective feature collinearity guided feature selection scheme to learn tight multi-label classifiers. The objective function is optimized in an alternating optimization fashion. Both theoretical analysis and experimental results on different multi-label image datasets verify the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The anchor graph structure has been widely used to speed up large-scale multi-view clustering and exhibited promising performance. How to effectively integrate the anchor graphs on multiple views to achieve enhanced clustering performance still remains a challenging task. Existing fusing strategies ignore the structure diversity among anchor graphs and restrict the anchor generation to be same on different views, which degenerates the representation ability of corresponding fused consensus graph. To overcome these drawbacks, we propose a novel structural fusion framework to integrate the multi-view anchor graphs for clustering. Different from traditional integration strategies, we merge the anchors and edges of all the view-specific anchor graphs into a single graph for the structural optimal graph learning. Benefiting from the structural fusion strategy, the anchor generation of each view is not forced to be same, which greatly improves the representation capability of the target structural optimal graph, since the anchors of each view capture the diverse structure of different views. By leveraging the potential structural consistency among each anchor graph, a connectivity constraint is imposed on the target graph to indicate clusters directly without any postprocessing such as k-means in classical spectral clustering. Substantial experiments on real-world datasets are conducted to verify the superiority of the proposed method, as compared with the state-of-the-arts over the clustering performance and time expenditure. [ABSTRACT FROM AUTHOR]

Multi-view clustering, which aims at boosting the clustering performance by leveraging the individual information and the common information of multi-view data, has gained extensive consideration in recent years. However, most existing multi-view clustering algorithms either focus on extracting the multi-view individuality or emphasize on exploring the multi-view commonality, neither of which can fully utilize the comprehensive information from multiple views. To this end, we propose a novel algorithm named View-specific and Consensus Graph Alignment (VCGA) for multi-view clustering, which simultaneously formulates the multi-view individuality and the multi-view commonality into a unified framework to effectively partition data points. To be specific, the VCGA model constructs the view-specific graphs and the shared graph from original multi-view data and hidden latent representation, respectively. Furthermore, the view-specific graphs of different views and the consensus graph are aligned into an informative target graph, which is employed as a crucial input to the standard spectral clustering method for clustering. Extensive experimental results on six benchmark datasets demonstrate the superiority of our method against other state-of-the-art clustering algorithms. [ABSTRACT FROM AUTHOR]

The methyltransferase like 3 (METTL3) has been generally recognized as a nuclear protein bearing oncogenic properties. We find predominantly cytoplasmic METTL3 expression inversely correlates with node metastasis in human cancers. It remains unclear if nuclear METTL3 is functionally distinct from cytosolic METTL3 in driving tumorigenesis and, if any, how tumor cells sense oncogenic insults to coordinate METTL3 functions within these intracellular compartments. Here, we report an acetylation-dependent regulation of METTL3 localization that impacts on metastatic dissemination. We identify an IL-6-dependent positive feedback axis to facilitate nuclear METTL3 functions, eliciting breast cancer metastasis. IL-6, whose mRNA transcript is subjected to METTL3-mediated m6A modification, promotes METTL3 deacetylation and nuclear translocation, thereby inducing global m6A abundance. This deacetylation-mediated nuclear shift of METTL3 can be counterbalanced by SIRT1 inhibition, a process that is further enforced by aspirin treatment, leading to ablated lung metastasis via impaired m6A methylation. Intriguingly, acetylation-mimetic METTL3 mutant reconstitution results in enhanced translation and compromised metastatic potential. Our study identifies an acetylation-dependent regulatory mechanism determining the subcellular localization of METTL3, which may provide mechanistic clues for developing therapeutic strategies to combat breast cancer metastasis. METTL3 catalyzes mRNA m6A deposition. The authors identify an acetylation-mediated regulation of METTL3 subcellular localization and compartment-specific functions, a process that is fine-tuned by anti-inflammatory and pro-inflammatory signals, which ultimately determine breast cancer metastasis. [ABSTRACT FROM AUTHOR]

Corticotropin (ACTH) is a pituitary hormone playing important roles in stress response within the hypothalamus–pituitary–adrenal (HPA) axis. The biosynthesis and secretion of ACTH are controlled by multiple factors, including corticotropin-releasing hormone (CRH). As a key hypothalamus-derived regulator, CRH binds to corticotropin-releasing hormone receptor 1 (CRHR1) in the anterior pituitary gland to regulate ACTH synthesis and release. Thus, CRH-binding protein (CRHBP), which binds CRH with high affinity to inhibit CRH-induced ACTH secretion from pituitary cells, draws wide attention. In contrast to the extensive investigation of CRHBP in mammals and other lower vertebrates, the gene structure, tissue expression and physiological functions of CRHBP in birds remain largely unknown. In the present study, using chicken (c-) as our animal model, we examined the gene structure, tissue expression and functionality of CRHBP. Our results showed that: (1) cCRHBP cDNA encodes a 345 amino acid precursor, which shares high sequence identity with that of mammals, reptiles, frogs and fish; (2) cCRHBP is abundantly expressed in the brain (cerebrum and hypothalamus), pituitary and ovary; (3) cCRHBP inhibits the signaling of cCRHRs induced by cCRH, thus reducing the cCRH-induced ACTH secretion from cultured chick pituitary cells; (4) stress mediators (e.g., glucocorticoids) and stress significantly upregulate CRHBP mRNA expression in chickens, supporting its role as a negative feedback regulator in the HPA axis. The present study enriches our understanding of the conserved roles of CRHBP across vertebrates. In addition, chicken is an important poultry animal with multiple economic traits which are tightly controlled by the HPA axis. The characterization of the chicken CRHBP gene helps to reveal the molecular basis of the chicken HPA axis and is thus beneficial to the poultry industry. [ABSTRACT FROM AUTHOR]

Methanol and ammonia are important chemical materials in the chemical industry. During the production of methanol and ammonia, a large amount of waste heat is released. The waste heat can be used to save energy and reduce CO2 emissions. In this study, pinch analysis is used to design the heat exchanger network (HEN) of pulverized coke (PC) chemical looping gasification coupled with coke-oven gas (COG) to methanol and ammonia (PCCLHG-CGTMA). The heat integration process is accomplished in two ways, as mentioned below. (1) The HENs in each of the three heat exchange units are designed individually; (2) the HENs of the three heat exchange units are treated as a whole and designed simultaneously. Compared to the HEN designed individually, when the HENs are designed as a whole, a total of 112.12 MW of hot and cold utilities are saved. In the HENs designed as a whole, the reduction in operating cost is sufficient to offset the increase in capital cost; the total annual cost (TAC) is reduced by 10.9%. These results reveal that the HENs designed as a whole have more scope for energy saving, which can be a reference for new HEN design and modification to realize more heat recovery and lower investment. [ABSTRACT FROM AUTHOR]

Copyright of Inorganic Chemicals Industry is the property of Editorial Office of Inorganic Chemicals Industry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Design and development of cost-efficient multifunctional three-dimensional (3D) metal organic frameworks (MOFs) towards oxygen reduction reaction (ORR), oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) are very significant for green energy devices. Herein, a scalable one-pot solvothermal method is developed to obtain a series of multifunctional 3D flower-like MOFs. In addition, systematic studies are also conducted on the effects of various metal cations and N-containing ligands on the structures, compositions, and multifunctional performance of the obtained MOFs. As a result, 3D flower-like Co-MOFs using Co2+ as a metal cation and 2,2':6′,2″-terpyridine as a N-containing ligand exhibit the highest multifunctional performance towards ORR, OER and HER. The scalable method provides a new prospect to design and develop other MOFs-based multifunctional catalysts. • F–Co-MOFs have been developed via a scalable one-pot solvothermal method. • The effect factors of the obtained MOFs are studied in detail. • F–Co-MOFs display high ORR, OER and HER performance in alkaline medium. • The 3D flower-like structure and MOFs ensure high multifunctional performance. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN), with the capacity for feature inference and nonlinear mapping, has demonstrated its effectiveness in end-to-end fault diagnosis. However, the intermediate learning process of the DNN architecture is invisible, making it an uninterpretable black-box model. In this paper, a stacked residual multi-attention network (SRMANet) is proposed as a means of feature extraction of vibration signals, and visualizing the model training process, designing Squeeze-excitation residual (SE-Res) blocks to obtain additive features with minimal redundancy and sparsity. This study recommends the use of the attention fusion unit to ensure the interpretability of the model and ultimately to obtain representative features. By feeding the output gradient of the attention layer back to the original signal, the key feature components in the time domain signal can be effectively captured. Finally, the interpretability, identification accuracy and adaptability of the model under different operating conditions are verified on 12 different fault tasks in the planetary gearbox. [ABSTRACT FROM AUTHOR]

Generative models have been applied in many fields and can be evaluated with many methods. In the evaluation of generative models, the proper evaluation metric varies with the application field. Therefore, the evaluation of generative adversarial networks is inherently challenging. In this study, conditional deep convolutional generative adversarial networks were applied in mechanical fault diagnosis and then evaluated. We proposed three evaluation metrics of conditional deep convolutional generative adversarial networks: Jensen-Shannon divergence, kernel maximum mean discrepancy, and the 1-nearest neighbor classifier which were used to distinguish generated samples from real samples, test mode collapsing and detect overfitting based on the dataset of Electronic Engineering Laboratory of Case Western Reserve University and the planetary gearbox dataset measured in the laboratory. The Jensen-Shannon divergence could not well distinguish generated samples from real samples. However, the two metrics (maximum mean discrepancy and 1-nearest neighbor classifier) well-distinguished generated samples from real samples, thus verifying the applicability of conditional deep convolutional generative adversarial networks in the field of mechanical diagnosis. [ABSTRACT FROM AUTHOR]

A large number of tailings are produced in the process of mineral processing, which not only wastes resources, but also pollutes the environment. Therefore, it is the only way for the development and utilization of mineral resources in the future to carry out the research on the comprehensive recovery and utilization technology of low-tail and tailless resources and improve the utilization rate of resources. In view of a complex polymetallic ore in China, through the analysis of the genetic attributes, occurrence and dissemination relationship of minerals, the separation and recovery of tantalum-niobium, calcite, lithium minerals, mica, feldspar and quartz were carried out, and the full recovery and utilization of other valuable components except a small amount of mud and iron filings were realized. It provides technical ideas and reference for comprehensive recovery and utilization of the same type of complex polymetallic ore in the future. [ABSTRACT FROM AUTHOR]

What makes speeches effective has long been a subject for debate, and until today there is broad controversy among public speaking experts about what factors make a speech effective as well as the roles of these factors in speeches. Moreover, there is a lack of quantitative analysis methods to help understand effective speaking strategies. In this paper, we propose E-ffective, a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. From interviews with domain experts and investigating existing literature, we identified important factors to consider in inspirational speeches. We obtained the generated factors from multi-modal data that were then related to effectiveness data. Our system supports rapid understanding of critical factors in inspirational speeches, including the influence of emotions by means of novel visualization methods and interaction. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information). In our evaluation we studied the influence of our system on experts' domain knowledge about speech factors. We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness. [ABSTRACT FROM AUTHOR]

Copyright of Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ultraviolet (UV) photodetectors have attracted much attention due to their important applications in many fields. Improving of the photoelectric performance of ultraviolet detectors is the key challenge. One solution is to fabricate UV photodetectors based on a wide bandgap semiconductor material—zinc oxide (ZnO). Here, ZnO nanorods with pure surface and high crystallization are prepared by laser ablation in liquid combined with hydrothermal method. The bandgap of ZnO products calculated from UV–vis reflection spectra is 3.43 eV, which means the ZnO nanorods synthesized in this work are suitable for UV detection. Moreover, Al nanoparticles with localized surface plasmon resonance (LSPR) are also prepared by laser ablation in liquid. The UV photodetector based on the ZnO nanorods and Al nanoparticles is fabricated. It is found that the photoelectric performance of ZnO-based UV photodetector is significantly increased after the addition of Al nanoparticles. The mechanism is that the LSPR happens when laser irradiated on the ZnO nanorods with Al nanoparticles, so the absorption is enhanced. Therefore, the ZnO nanorods get more light energy, which means more photo-induced carriers are generated and the current will increase. [ABSTRACT FROM AUTHOR]

Differential responses of diatoms, an important group of marine primary producers to ocean acidification, have been well documented. However, studies so far are based on limited representative strains from key species. Investigation of strain level responses will help us better understand the contrasting discrepancy in diatom responses to ocean acidification. Here, we selected four strains of the model diatom Phaeodactylum tricornutum isolated from different regions of the global ocean, representing all genotypes based on internal transcribed spacer 2 sequences, and investigated strain‐specific responses to ocean acidification. In response to ocean acidification, changes in carbon metabolism varied among strains, although no significant effects of ocean acidification on growth rates or pigments were observed in any strains. The expression of genes encoding plasma membrane bicarbonate transporters was downregulated in strain Pt4, reflecting a potential decrease in active HCO3− uptake, which was not observed in the other strains. Reduction of CO2 concentrating mechanism efficiency was also indicated by the regulated expression of genes encoding carbonic anhydrases that catalyze the interconversion of HCO3− and CO2 in the pyrenoids and pyrenoid‐penetrating thylakoid, which exhibited different patterns among the strains. Under ocean acidification conditions, C4‐like metabolism appeared to redistribute carbon flux to gluconeogenesis in strain Pt1, and lipid synthesis in strains Pt8 and Pt11, rather than participating in net photosynthetic carbon fixation. These variations were incompletely correlated with phylogenetic relationship in different strains, implying that the habitat‐adapted imprints of the different strains could also be responsible for their differential responses to ocean acidification. [ABSTRACT FROM AUTHOR]

Monolithic luminescent glass‐ceramic (GC) embedded with fluoride‐based nanocrystals (NCs) has drawn much attention as it is stable and possesses long fluorescent lifetime, while only small partial of optically active ions could enter the fluoride NCs prepared by conventional crystallization process. In this work, YF3:Eu3+ embedded GCs have been controllably synthesized by Spark Plasma Sintering at a relatively low sintering temperature (960°C) within 10 minutes. The GC samples show typical sharp reddish‐orange emissions peaking at λ = 590 nm and 620 nm, which can be ascribed to the 5D0 → 7Fj transitions of Eu3+ located in the tetrahedral coordination sites of the YF3 NCs. Significantly, a small R/O ratio (photoluminescence intensity ratio of 5D0 → 7F2/5D0 → 7F1) suggests that majority of Eu3+ ions are well preserved in YF3 NCs, which is confirmed by the EDS and TEM results that highly crystallized YF3:Eu3+ NCs are homogeneously dispersed into the silica glass matrix without interfacial reaction. Hence, the lifetime of GC sample is prolonged to 6.8 ms These results demonstrate that Eu3+ could be well protected and resided in YF3 low phonon crystal by this method to fabricate GC composites with high optical performance. [ABSTRACT FROM AUTHOR]

Arterial stiffness plays a key role in the pathogenesis of cardiovascular disease. However, the relationship between lipid levels and arterial stiffness is controversial. We aimed to investigate the association between lipid parameters and brachial–ankle pulse-wave velocity (baPWV) in Chinese patients with hypertension. A total of 14 071 participants with hypertension in the China Stroke Primary Prevention Trial (CSPPT) were enrolled in the present study. Patients were assigned to 4 equal groups according to their baPWV. Participants in the highest baPWV group were older with a higher prevalence of stroke and diabetes mellitus as well as higher body mass index (BMI), blood pressure, fasting plasma glucose, uric acid, total cholesterol (TC), triglycerides (TG), homocysteine (Hcy), and vitamin B12 levels (P <.001). After adjusting for age, sex, BMI, and other cardiovascular risks, high-density lipoprotein cholesterol (HDL-C) was negatively related to baPWV (β = −0.22, P =.012), TC (β = 0.08, P = 0.001), TG (β = 0.14, P =.001); non-HDL-C (β = 0.12, P =.001) and positively related to baPWV. The effect was not observed for low-density lipoprotein cholesterol (LDL-C; β = 0.12, P =.335).These results suggested that non-HDL-C, TG, and TC were associated with arterial stiffness in a Chinese population with hypertension. HDL-C was inversely associated with arterial stiffness. [ABSTRACT FROM AUTHOR]

Understanding of the molecular responses underpinning diatom responses to ocean acidification is fundamental for predicting how important primary producers will be shaped by the continuous rise in atmospheric CO2. In this study, we have analyzed global transcriptomic changes of the model diatom Phaeodactylum tricornutum following growth for 15 generations in elevated p CO2 by strand-specific RNA sequencing (ssRNA-seq). Our results indicate that no significant effects of elevated p CO2 and associated carbonate chemistry changes on the physiological performance of the cells were observed after 15 generations whereas the expression of genes encoding histones and other genes involved in chromatin structure were significantly down-regulated, while the expression of transposable elements (TEs) and genes encoding histone acetylation enzymes were significantly up-regulated. Furthermore, we identified a series of long non-protein coding RNAs (lncRNAs) specifically responsive to elevated p CO2, suggesting putative regulatory roles for these largely uncharacterized genome components. Taken together, our integrative analyses reveal that epigenetic elements such as TEs, histone modifications and lncRNAs may have important roles in the acclimation of diatoms to elevated p CO2 over short time scales and thus may influence longer term adaptive processes in response to progressive ocean acidification. [ABSTRACT FROM AUTHOR]

Pulsed laser ablation in liquid (PLAL) is gradually becoming an attractive approach for nanomaterial fabrication because it is a chemically simple and clean method with high product purity. We studied the laser interaction with bulk target and particle products in detail, including mechanism process, target morphology and nanoparticle products. We captured three oscillations of one bubble after laser ablates the bulk target and calculated the variation of pressure and temperature inside the bubble. The results show that the first bubble oscillation has greatest impact on the nanomaterial synthesis, and the most powerful stages for the material synthesis during all the bubble oscillations are the beginning of each expansions and the end of each shrinks. Nanomaterial releases from the bubble at the end of each oscillations. In addition, based on the analysis of ablation cavity on the target, it is found that the cavity depth increases with the number of laser pulses, and then the depth tends to be under saturation status, which means it is difficult to obtain great improvement of the nanomaterial productivity just by prolonging the laser irradiation time. More importantly, the strong interaction between laser and particle products is presented clearly by time-resolved shadowgraphy, which can contributed to the modification of nanoparticle products. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The 2-aminophenylaluminum dihydride (2-TMP-C6H4)AlH2 ( 2) has been prepared and characterized for the first time. Compound 2 features an intramolecular N⋅⋅⋅Al donor-acceptor bond. 2 reacted with N-methylpyrrole and N-methylindole (both at 50 °C) by means of the elusive AlH C(sp2)−H dehydroalumination to aluminum heteroaryls ( 3 and 4). Moreover, 2 reacted with PhCCSiMe3 (at room temperature) and Ph2CCNR (R= iPr or 2,6- iPr2C6H3, at −30 to 20 °C ) to yield aluminaindene heterocycle ( 8) and alumina-aza-naphthalene heterocycle ( 9 and 10), respectively. These reactions underwent hydroalumination followed by AlH C(sp2)−H dehydroalumination. The reaction mechanism has been studied by combining experiment and quantum chemical calculations, with the result that the key heteroarene or arene C(sp2)−H bond activation is involved under cooperative interaction by the inherent N/Al donor/acceptor pair. The reported reactions open a straightforward route to heteroaryl and unique heterocyclic aluminum compounds. [ABSTRACT FROM AUTHOR]