•The largest publicly dataset of breast cancer pathological images is released.•Dataset diversity alleviates relatively low accuracy of benign images classification.•Richer multilevel features make ...the image-wise feature fusion more sufficient.•The short-term and long-term correlations between patches are both preserved.•Our hybrid network outperformed other methods in pathological image classification.
Even with the rapid advances in medical sciences, histopathological diagnosis is still considered the gold standard in diagnosing cancer. However, the complexity of histopathological images and the dramatic increase in workload make this task time consuming, and the results may be subject to pathologist subjectivity. Therefore, the development of automatic and precise histopathological image analysis methods is essential for the field. In this paper, we propose a new hybrid convolutional and recurrent deep neural network for breast cancer histopathological image classification. Based on the richer multilevel feature representation of the histopathological image patches, our method integrates the advantages of convolutional and recurrent neural networks, and the short-term and long-term spatial correlations between patches are preserved. The experimental results show that our method outperforms the state-of-the-art method with an obtained average accuracy of 91.3% for the 4-class classification task. We also release a dataset with 3771 breast cancer histopathological images to the scientific community that is now publicly available at http://ear.ict.ac.cn/?page_id=1616. Our dataset is not only the largest publicly released dataset for breast cancer histopathological image classification, but it covers as many different subclasses spanning different age groups as possible, thus providing enough data diversity to alleviate the problem of relatively low classification accuracy of benign images.
Many visual and robotics tasks in real-world scenarios rely on robust handling of high speed motion and high dynamic range (HDR) with effectively high spatial resolution and low noise. Such stringent ...requirements, however, cannot be directly satisfied by a single imager or imaging modality, rather by multi-modal sensors with complementary advantages. In this paper, we address high performance imaging by exploring the synergy between traditional frame-based sensors with high spatial resolution and low sensor noise, and emerging event-based sensors with high speed and high dynamic range. We introduce a novel computational framework, termed Guided Event Filtering (GEF), to process these two streams of input data and output a stream of super-resolved yet noise-reduced events. To generate high quality events, GEF first registers the captured noisy events onto the guidance image plane according to our flow model. it then performs joint image filtering that inherits the mutual structure from both inputs. Lastly, GEF re-distributes the filtered event frame in the space-time volume while preserving the statistical characteristics of the original events. When the guidance images under-perform, GEF incorporates an event self-guiding mechanism that resorts to neighbor events for guidance. We demonstrate the benefits of GEF by applying the output high quality events to existing event-based algorithms across diverse application categories, including high speed object tracking, depth estimation, high frame-rate video synthesis, and super resolution/HDR/color image restoration.
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It is often assumed only limited amount of amorphous phase could generate in the oxide film of Ni-based alloy with H-participation. However, in this study, the formation of a ...remarkable amorphous layer on top of the oxide film of Ni-based Alloy 600 (Ni-15.5Cr-9.4Fe, wt.%) after oxidizing in high-temperature high-pressure water environment by in-situ H-charging condition is observed. A synergy of the film porosity and PH2O/PH2 is proposed as a plausible mechanism. Cavity layer appeared at the oxide and matrix interface, and the H-distribution are also discussed.
Parkinson’s disease (PD) is the second-most common neurodegenerative disease after Alzheimer’s disease. The most important pathological feature of PD is the irreversible damage of dopamine neurons, ...which is related to autophagy and neuroinflammation in the substantia nigra. Previous studies found that the activation of NAcht Leucine-rich repeat Protein 3 (NLRP3) inflammasome/pyroptosis and cell division protein kinase 5 (CDK5)-mediated autophagy played an important role in PD. Bioinformatics analyses further predicted that microRNA (miR)-188-3p potentially targets NLRP3 and CDK5. Adipose-derived stem cell (ADSC)-derived exosomes were found to be excellent vectors for genetic therapy. We assessed the levels of injury, autophagy, and inflammasomes in 1-methyl-4-phenyl-1,2,4,5-tetrahydropyridine (MPTP)-induced PD mice models and neurotoxin 1-methyl-4-phenylpyridinium (MPP+)-induced cell models after treating them with miR-188-3p-enriched exosomes. miR-188-3p-enriched exosome treatment suppressed autophagy and pyroptosis, whereas increased proliferation via targeting CDK5 and NLRP3 in mice and MN9D cells. It was revealed that mir-188-3p could be a new therapeutic target for curing PD patients.
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Guo and colleagues found that miR-188-3p is low expressed in the serum of PD patients. miR-188-3p-enriched exosomes derived from ADSCs ameliorated MPTP-induced Parkinson’s mice models and neurotoxin MPP+-induced cell models. miR-188-3p suppressed its target genes NLRP3 and CDK5, thereby inhibiting autophagy and pyroptosis both in vitro and in vivo.
•Supervised data mining-based methods for building energy systems are reviewed.•Unsupervised data mining-based methods for building energy systems are reviewed.•Strengths and shortcomings of the ...existing data mining-based methods are revealed.•Four important research tasks in the future are proposed.
With the advent of the era of big data, buildings have become not only energy-intensive but also data-intensive. Data mining technologies have been widely utilized to release the values of massive amounts of building operation data with an aim of improving the operation performance of building energy systems. This paper aims at making a comprehensive literature review of the applications of data mining technologies in this domain. In general, data mining technologies can be classified into two categories, i.e., supervised data mining technologies and unsupervised data mining technologies. In this field, supervised data mining technologies are usually utilized for building energy load prediction and fault detection/diagnosis. And unsupervised data mining technologies are usually utilized for building operation pattern identification and fault detection/diagnosis. Comprehensive discussions are made about the strengths and shortcomings of the data mining-based methods. Based on this review, suggestions for future researches are proposed towards effective and efficient data mining solutions for building energy systems.
•The interaction of H and oxidation process in high temperature water were revealed.•The effects of H on oxidation was differentialized according to different chemical states of H.•H exhibited ...opposing impacts on the material’s reliability considering different oxidation sites.•A model of the interaction between H and oxidation in high temperature water was proposed.
The present research proposes the synergistic roles of H involved in the oxidation process of a Ni-based alloy in high temperature water environment, using an in-situ H-permeating method. The roles of H were differentiated according to the modification on the microstructure during oxidation in high-temperature water, as H is situated at multiple locations during permeating the material. The nature of the oxide was found to be modified significantly, however, it exhibited possibly opposing impacts on the reliability of the materials. The integrity of surface oxide film and matrix/oxide interface were degraded, yet grain boundary was protected by permeated H.
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•Three different roles of H were identified in structural modification of oxide film.•H modified the double-layer oxide film into a defective single-layer oxide film.•A redox chain ...reaction of Ni2+(NiO) → Ni0 → Ni2+(aq) selectively dissolved NiO.•Thickness of the oxide film was reduced by the in-situ charged H.•Growth of the oxide film under H charging followed a layer-by-layer mechanism.
The roles of H in the modification process of the oxide film of alloy 600 was investigated through a dual-exposed oxidation experiment with an in-situ H-charging method in a high temperature high pressure water environment. The selective dissolution of the NiO modified the oxide film into a defective and porous, single-layered Cr2O3 skeleton after oxidation with H charging. The modification process depended on three different chemical states of H with a varied distribution: i) H2 molecule in water, ii) neutral H in the grain boundary of the oxide film, and, iii) proton (H+) in the lattice of the oxide film.
Text-image cross-modal retrieval is a challenging task in the field of language and vision. Most previous approaches independently embed images and sentences into a joint embedding space and compare ...their similarities. However, previous approaches rarely explore the interactions between images and sentences before calculating similarities in the joint space. Intuitively, when matching between images and sentences, human beings would alternatively attend to regions in images and words in sentences, and select the most salient information considering the interaction between both modalities. In this paper, we propose Cross-modal Adaptive Message Passing (CAMP), which adaptively controls the information flow for message passing across modalities. Our approach not only takes comprehensive and fine-grained cross-modal interactions into account, but also properly handles negative pairs and irrelevant information with an adaptive gating scheme. Moreover, instead of conventional joint embedding approaches for text-image matching, we infer the matching score based on the fused features, and propose a hardest negative binary cross-entropy loss for training. Results on COCO and Flickr30k significantly surpass state-of-the-art methods, demonstrating the effectiveness of our approach.
Dynamic posttranslational modification of serine and threonine residues of nucleocytoplasmic proteins by β-N-acetylglucosamine (O-GlcNAc) is a regulator of cellular processes such as transcription, ...signaling, and protein-protein interactions. Like phosphorylation, O-GlcNAc cycles in response to a wide variety of stimuli. Although cycling of O-GlcNAc is catalyzed by only two highly conserved enzymes, O-GlcNAc transferase (OGT), which adds the sugar, and β-N-acetylglucosaminidase (O-GlcNAcase), which hydrolyzes it, the targeting of these enzymes is highly specific and is controlled by myriad interacting subunits. Here, we demonstrate by multiple specific immunological and enzymatic approaches that histones, the proteins that package DNA within the nucleus, are O-GlcNAcylated in vivo. Histones also are substrates for OGT in vitro. We identify O-GlcNAc sites on histones H2A, H2B, and H4 using mass spectrometry. Finally, we show that histone O-GlcNAcylation changes during mitosis and with heat shock. Taken together, these data show that O-GlcNAc cycles dynamically on histones and can be considered part of the histone code.
Little is known about the role of self-control in the relationship between depression and the meaning of life. In particular, when depression is viewed through the lens of symptom networks. It is ...also unclear how self-control (self-discipline and impulse control) and meaning in life relate to the symptoms of depression.
This study addressed this gap by estimating structural equation models and network analysis based on cross-sectional data representative of China (N = 936, surveyed from age 17 to 22, Mage = 18.27, SDage = 0.75, female = 65.7 %).
The findings revealed that depression is interrelated with self-control and meaning in life. Specifically, depression predicts levels of meaning in life through self-discipline, which is one component of self-control. There was a complex link between symptoms of depression, self-control, and meaning in life. However, there was no direct link between the core symptoms of depression and self-discipline or meaning in life.
Overall, the evidence from this study suggests that emphasizing the presence of meaning among young undergraduate students who are at high risk for depression may decrease their levels of depression. Moreover, if this emphasis is coupled with an improvement in their self-discipline, it may also be beneficial for depression.
•Self-discipline is a mediating variable in the relationship between depression and meaning in life.•At the core of the network structure of self-control, meaning in life, and depression in students is self-depreciation.•The core symptom of depression was not directly associated with self-discipline in network analyses.•Complex associations exist between depressive symptoms in young undergraduate students.
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