A fundus image dataset for intelligent retinopathy of prematurity system.

Image-based artificial intelligence (AI) systems stand as the major modality for evaluating ophthalmic conditions. However, most of the currently available AI systems are designed for experimental research using single-central datasets. Most of them fell short of application in real-world clinical settings. In this study, we collected a dataset of 1,099 fundus images in both normal and pathologic eyes from 483 premature infants for intelligent retinopathy of prematurity (ROP) system development and validation. Dataset diversity was visualized with a spatial scatter plot. Image classification was conducted by three annotators. To the best of our knowledge, this is one of the largest fundus datasets on ROP, and we believe it is conducive to the real-world application of AI systems.

Proteomic, single-cell and bulk transcriptomic analysis of plasma and tumor tissues unveil core proteins in response to anti-PD-L1 immunotherapy in triple negative breast cancer.

BACKGROUND: Anti-PD-1/PD-L1 treatment has achieved durable responses in TNBC patients, whereas a fraction of them showed non-sensitivity to the treatment and the mechanism is still unclear. METHODS: Pre- and post-treatment plasma samples from triple negative breast cancer (TNBC) patients treated with immunotherapy were measured by tandem mass tag (TMT) mass spectrometry. Public proteome data of lung cancer and melanoma treated with immunotherapy were employed to validate the findings. Blood and tissue single-cell RNA sequencing (scRNA-seq) data of TNBC patients treated with or without immunotherapy were analyzed to identify the derivations of plasma proteins. RNA-seq data from IMvigor210 and other cancer types were used to validate plasma proteins in predicting response to immunotherapy. RESULTS: A random forest model constructed by FAP, LRG1, LBP and COMP could well predict the response to immunotherapy. The activation of complement cascade was observed in responders, whereas FAP and COMP showed a higher abundance in non-responders and negative correlated with the activation of complements. scRNA-seq and bulk RNA-seq analysis suggested that FAP, COMP and complements were derived from fibroblasts of tumor tissues. CONCLUSIONS: We constructe an effective plasma proteomic model in predicting response to immunotherapy, and find that FAP+ and COMP+ fibroblasts are potential targets for reversing immunotherapy resistance.

Ketamine induces insomnia-like symptom of zebrafish at environmentally relevant concentrations by mediating GABAergic synapse.

Although the stimulative effects on the normal behaviors of fish posed by ketamine (KET) were well-studied, the adverse effects on the behavioral functions induced by KET at nighttime were unknown. Here, we used zebrafish larvae as a model exposed to KET (10, 50, 100, and 250 ng/L) at environmental levels for 21 days. The behavioral functions at nighttime, morphological changes during exposure stage, and alterations on the associated genes transcriptional levels of fish were determined. The difficultly initiating sleep was found in the fish exposed to KET, while the sleep duration of the animals was at the normal levels in exposure groups. The significant suppressions of the developmentally relevant genes, including bmp2, bmp4, and pth2ra were consistent with the developmental abnormalities of fish found in exposure groups. Moreover, the expression of γ-aminobutyric acid (GABA) receptor increased and melatonin (MTN) receptor decreased while the levels of GABA and MTN remained unchanged after exposure, by gene expression analysis and molecular docking. In addition, the transcriptional expression of apoptotic genes, including tp53, aifm1, and casp6, was significantly upregulated by KET. After a 7-day recovery, the insomnia-like behaviors (shorter sleep duration) were observed in zebrafish from the 250 ng/L-KET group. Accordingly, the adverse outcome pathway framework of KET was constructed by prognostic assessment of zebrafish larvae. This study suggested that the adverse outcomes of KET on the sleep health of organisms at environmentally relevant concentrations should be concerned.

Multidimensional Refinement Graph Convolutional Network With Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition.

Graph convolutional networks (GCNs) have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of interclass data. Moreover, the noisy data from pose extraction increase the challenge of fine-grained recognition. In this work, we propose a flexible attention block called channel-variable spatial-temporal attention (CVSTA) to enhance the discriminative power of spatial-temporal joints and obtain a more compact intraclass feature distribution. Based on CVSTA, we construct a multidimensional refinement GCN (MDR-GCN) that can improve the discrimination among channel-, joint-, and frame-level features for fine-grained actions. Furthermore, we propose a robust decouple loss (RDL) that significantly boosts the effect of the CVSTA and reduces the impact of noise. The proposed method combining MDR-GCN with RDL outperforms the known state-of-the-art skeleton-based approaches on fine-grained datasets, FineGym99 and FSD-10, and also on the coarse NTU-RGB + D 120 dataset and NTU-RGB + D X-view version. Our code is publicly available at https://github.com/dingyn-Reno/MDR-GCN.

Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system.

Purpose: We developed an Infant Retinal Intelligent Diagnosis System (IRIDS), an automated system to aid early diagnosis and monitoring of infantile fundus diseases and health conditions to satisfy urgent needs of ophthalmologists. Methods: We developed IRIDS by combining convolutional neural networks and transformer structures, using a dataset of 7697 retinal images (1089 infants) from four hospitals. It identifies nine fundus diseases and conditions, namely, retinopathy of prematurity (ROP) (mild ROP, moderate ROP, and severe ROP), retinoblastoma (RB), retinitis pigmentosa (RP), Coats disease, coloboma of the choroid, congenital retinal fold (CRF), and normal. IRIDS also includes depth attention modules, ResNet-18 (Res-18), and Multi-Axis Vision Transformer (MaxViT). Performance was compared to that of ophthalmologists using 450 retinal images. The IRIDS employed a five-fold cross-validation approach to generate the classification results. Results: Several baseline models achieved the following metrics: accuracy, precision, recall, F1-score (F1), kappa, and area under the receiver operating characteristic curve (AUC) with best values of 94.62% (95% CI, 94.34%-94.90%), 94.07% (95% CI, 93.32%-94.82%), 90.56% (95% CI, 88.64%-92.48%), 92.34% (95% CI, 91.87%-92.81%), 91.15% (95% CI, 90.37%-91.93%), and 99.08% (95% CI, 99.07%-99.09%), respectively. In comparison, IRIDS showed promising results compared to ophthalmologists, demonstrating an average accuracy, precision, recall, F1, kappa, and AUC of 96.45% (95% CI, 96.37%-96.53%), 95.86% (95% CI, 94.56%-97.16%), 94.37% (95% CI, 93.95%-94.79%), 95.03% (95% CI, 94.45%-95.61%), 94.43% (95% CI, 93.96%-94.90%), and 99.51% (95% CI, 99.51%-99.51%), respectively, in multi-label classification on the test dataset, utilizing the Res-18 and MaxViT models. These results suggest that, particularly in terms of AUC, IRIDS achieved performance that warrants further investigation for the detection of retinal abnormalities. Conclusions: IRIDS identifies nine infantile fundus diseases and conditions accurately. It may aid non-ophthalmologist personnel in underserved areas in infantile fundus disease screening. Thus, preventing severe complications. The IRIDS serves as an example of artificial intelligence integration into ophthalmology to achieve better outcomes in predictive, preventive, and personalized medicine (PPPM / 3PM) in the treatment of infantile fundus diseases. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00350-y.

Multiplatform tear proteomic profiling reveals novel non-invasive biomarkers for diabetic retinopathy.

OBJECTIVES: To investigate a comprehensive proteomic profile of the tear fluid in patients with diabetic retinopathy (DR) and further define non-invasive biomarkers. METHODS: A cross-sectional, multicentre study that includes 46 patients with DR, 28 patients with diabetes mellitus (DM), and 30 healthy controls (HC). Tear samples were collected with Schirmer strips. As for the discovery set, data-independent acquisition mass spectrometry was used to characterize the tear proteomic profile. Differentially expressed proteins between groups were identified, with gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis further developed. Classifying performance of biomarkers for distinguishing DR from DM was compared by the combination of three machine-learning algorithms. The selected biomarker panel was tested in the validation cohort using parallel reaction monitoring mass spectrometry. RESULTS: Among 3364 proteins quantified, 235 and 88 differentially expressed proteins were identified for DR when compared to HC and DM, respectively, which were fundamentally related to retina homeostasis, inflammation and immunity, oxidative stress, angiogenesis and coagulation, metabolism, and cellular adhesion processes. The biomarker panel consisting of NAD-dependent protein deacetylase sirtuin-2 (SIR2), amine oxidase [flavin-containing] B (AOFB), and U8 snoRNA-decapping enzyme (NUD16) exhibited the best diagnostic performance in discriminating DR from DM, with AUCs of 0.933 and 0.881 in the discovery and validation set, respectively. CONCLUSIONS: Tear protein dysregulation is comprehensively revealed to be associated with DR onset. The combination of tear SIR2, AOFB, and NUD16 can be a novel potential approach for non-invasive detection or pre-screening of DR. CLINICAL TRIAL REGISTRATION: Chinese Clinical Trial Registry Identifier: ChiCTR2100054263. https://www.chictr.org.cn/showproj.html?proj=143177 . Date of registration: 2021/12/12.

Platinum-palladium-on-reduced graphene oxide as bifunctional electrocatalysts for highly active and stable hydrogen evolution and methanol oxidation reaction.

In the context of the gradual depletion of global fossil fuel resources, it is increasingly necessary to explore new alternative energy. Hydrogen energy has attracted great interest from researchers because of its green and pollution-free characteristics. Moreover, the methanol oxidation reaction (MOR) can combine the hydrogen evolution reaction (HER), replacing the anode reaction (oxygen evolution reaction-OER) in overall water splitting and efficiently producing hydrogen. In this study, platinum-palladium nanoparticles on reduced graphene oxide (PtPd/rGO) were successfully synthesized as HER and MOR bifunctional electrocatalysts under alkaline conditions by the stepwise loading of Pt and Pd bimetallic nanoparticles on rGO using a simple liquid-phase reduction method. PtPd/rGO-2 with 0.99 wt% Pt and 2.86 wt% Pd in the HER has the lowest overpotential (87.16 mV at 100 mA cm-2), with the smallest Tafel slope (18.9 mV dec-1). The exceptional mass activity of PtPd/rGO-2 in the MOR reaches 10.75 A mg-1PtPd, which is 18.22 and 53.75 times greater than that of commercial Pt/C (Pt/C) and commercial Pd/C (Pd/C), respectively. PtPd/rGO-2 is 0.935 V lower in the coupling reaction of HER and MOR (MOR ∥ HER) compared to the overall water splitting (OER ∥ HER) without methanol (10 mA cm-2). This is probably because appropriate Pt and Pd loading exposes many more catalytic sites, and the synergistic interaction between Pt, Pd, and Pt-Pd enhances the catalytic performance. This strategy can be used for the synthesis of novel bifunctional electrocatalysts.

Research hotspots and trends in retinopathy of prematurity from 2003 to 2022: a bibliometric analysis.

Background: In order to understand the research hotspots and trends in the field of retinopathy of prematurity (ROP), our study analyzed the relevant publications from 2003 to 2022 by using bibliometric analysis. Methods: The Citespace 6.2.R3 system was used to analyze the publications collected from the Web of Science Core Collection (WoSCC) database. Results: In total, 4,957 publications were included in this study. From 2003 to 2022, the number of publications gradually increased and peaked in 2022. The United States was the country with the most publications, while Harvard University was the most productive institution. The top co-cited journal PEDIATRICS is published by the United States. Author analysis showed that Hellström A was the author with the most publications, while Good WV was the top co-cited author. The co-citation analysis of references showed seven major clusters: genetic polymorphism, neurodevelopmental outcome, threshold retinopathy, oxygen-induced retinopathy, low birth weight infant, prematurity diagnosis cluster and artificial intelligence (AI). For the citation burst analysis, there remained seven keywords in their burst phases until 2022, including ranibizumab, validation, trends, type 1 retinopathy, preterm, deep learning and artificial intelligence. Conclusion: Intravitreal anti-vascular endothelial growth factor therapy and AI-assisted clinical decision-making were two major topics of ROP research, which may still be the research trends in the coming years.

An Artificial Intelligence System for Screening and Recommending the Treatment Modalities for Retinopathy of Prematurity.

PURPOSE: The purpose of this study was to develop an artificial intelligence (AI) system for the identification of disease status and recommending treatment modalities for retinopathy of prematurity (ROP). METHODS: This retrospective cohort study included a total of 24,495 RetCam images from 1075 eyes of 651 preterm infants who received RetCam examination at the Shenzhen Eye Hospital in Shenzhen, China, from January 2003 to August 2021. Three tasks included ROP identification, severe ROP identification, and treatment modalities identification (retinal laser photocoagulation or intravitreal injections). The AI system was developed to identify the 3 tasks, especially the treatment modalities of ROP. The performance between the AI system and ophthalmologists was compared using extra 200 RetCam images. RESULTS: The AI system exhibited favorable performance in the 3 tasks, including ROP identification [area under the receiver operating characteristic curve (AUC), 0.9531], severe ROP identification (AUC, 0.9132), and treatment modalities identification with laser photocoagulation or intravitreal injections (AUC, 0.9360). The AI system achieved an accuracy of 0.8627, a sensitivity of 0.7059, and a specificity of 0.9412 for identifying the treatment modalities of ROP. External validation results confirmed the good performance of the AI system with an accuracy of 92.0% in all 3 tasks, which was better than 4 experienced ophthalmologists who scored 56%, 65%, 71%, and 76%, respectively. CONCLUSIONS: The described AI system achieved promising outcomes in the automated identification of ROP severity and treatment modalities. Using such algorithmic approaches as accessory tools in the clinic may improve ROP screening in the future.

Electroacupuncture at ST36 acupoint regulates stem cells during experimental autoimmune encephalomyelitis.

BACKGROUND: Electroacupuncture (EA) is given to assist in the treatment of MS, which is an effective therapeutic method. However, the therapy mechanism of EA related to stem cells in the treatment of MS is not yet known. In this study, we used a classic animal model of multiple sclerosis: experimental autoimmune encephalomyelitis (EAE) to evaluate the therapeutic effect of EA at Zusanli (ST36) acupoint in EAE and shed light on its potential roles in the effects of stem cells in vivo. METHODS: The EAE animal models were established. From the first day after immunization, EAE model mice received EA at ST36 acupoint, named the EA group. The weight and clinical score of the three groups were recorded for 28 days. The demyelination, inflammatory cell infiltration, and markers of neural stem cells (NSCs), hematopoietic stem cells (HSCs), and mesenchymal stem cells (MSCs) were compared. RESULTS: We showed that EAE mice treated with EA at ST36 acupoint, were suppressed in demyelination and inflammatory cell infiltration, and thus decreased clinical score and weight loss and mitigated the development of EAE when compared with the EAE group. Moreover, our data revealed that the proportions of NSCs, HSCs, and MSCs increased in the EA group compared with the EAE group. CONCLUSIONS: Our study suggested that EA at ST36 acupoint was an effective nonpharmacological therapeutic protocol that not only reduced the CNS demyelination and inflammatory cell infiltration in EAE disease but also increased the proportions of various stem cells. Further study is necessary to better understand how EA at the ST36 acupoint affects EAE.

Exosomes-regulated lipid metabolism in tumorigenesis and cancer progression.

Increasing evidence highlights the role of lipid metabolism in tumorigenesis and tumor progression. Targeting the processes of lipid metabolism, including lipogenesis, lipid uptake, fatty acid oxidation, and lipolysis, is an optimal strategy for anti-cancer therapy. Beyond cell-cell membrane surface interaction, exosomes are pivotal factors that transduce intercellular signals in the tumor microenvironment (TME). Most research focuses on the role of lipid metabolism in regulating exosome biogenesis and extracellular matrix (ECM) remodeling. The mechanisms of exosome and ECM-mediated reprogramming of lipid metabolism are currently unclear. We summarize several mechanisms associated with the regulation of lipid metabolism in cancer, including transport of exosomal carriers and membrane receptors, activation of the PI3K pathway, ECM ligand-receptor interactions, and mechanical stimulation. This review aims to highlight the significance of these intercellular factors in TME and to deepen the understanding of the functions of exosomes and ECM in the regulation of lipid metabolism.

Nanoscale Insight into the Effect of Calcium on Early-Age Polymerization of CNASH Gels.

Sodium-containing calcium-alumino-silicate-hydrate (CNASH) gels, the primary binder phase of alkali-activated materials (AAMs), significantly impact the performance of the AAM. Although the effect of the calcium content on the AAM has been extensively studied in the past, few studies focus on the effect of calcium on the structure and performance of gels at a molecular scale. As an important element in gels, the effect of calcium in gels on its atomic-scale properties remains unclear. This study establishes a molecular model of the CNASH gel via reactive molecular dynamics (MD) simulation and verifies the feasibility of the gel model. By employing the reactive MD, the effect of calcium on the physicochemical properties of gels in the AAM is investigated. The simulation highlights that the condensation process of the system containing Ca is accelerated dramatically. This phenomenon is explained from the perspective of thermodynamics and kinetics. The increased calcium content enhances the thermodynamic stability and reduces the energy barrier of the reaction. Then, the phenomenon is further analyzed through the nanosegregation in the structure. It is proved that this behavior is driven by the weaker affinity of calcium for aluminosilicate chains than the particles in the aqueous environment. The difference in affinity leads to nanosegregation in the structure, making Si(OH)4 and Al(OH)3 monomers and oligomers closer for better polymerization.

Extracellular matrix remodeling in tumor progression and immune escape: from mechanisms to treatments.

The malignant tumor is a multi-etiological, systemic and complex disease characterized by uncontrolled cell proliferation and distant metastasis. Anticancer treatments including adjuvant therapies and targeted therapies are effective in eliminating cancer cells but in a limited number of patients. Increasing evidence suggests that the extracellular matrix (ECM) plays an important role in tumor development through changes in macromolecule components, degradation enzymes and stiffness. These variations are under the control of cellular components in tumor tissue via the aberrant activation of signaling pathways, the interaction of the ECM components to multiple surface receptors, and mechanical impact. Additionally, the ECM shaped by cancer regulates immune cells which results in an immune suppressive microenvironment and hinders the efficacy of immunotherapies. Thus, the ECM acts as a barrier to protect cancer from treatments and supports tumor progression. Nevertheless, the profound regulatory network of the ECM remodeling hampers the design of individualized antitumor treatment. Here, we elaborate on the composition of the malignant ECM, and discuss the specific mechanisms of the ECM remodeling. Precisely, we highlight the impact of the ECM remodeling on tumor development, including proliferation, anoikis, metastasis, angiogenesis, lymphangiogenesis, and immune escape. Finally, we emphasize ECM "normalization" as a potential strategy for anti-malignant treatment.

Stroke and Risk Factors in Antiphospholipid Syndrome.

Stroke is considered one of the most common and life-threatening manifestations of antiphospholipid syndrome (APS), which leads to high mortality and permanent disability. This study investigated the prevalence and the potential risk factors of stroke in APS. We enrolled 361 APS patients retrospectively from 2009 to 2022 at Peking University People's Hospital. Stroke was found in 25.8% (93/361) of the participants. The multivariate logistic regression showed that hypertension, diabetes, livedo reticularis, and other central nervous system involvements were significant related factors for stroke. The use of hydroxychloroquine appeared to relate to a lower incidence of stroke. During a median follow-up of 3.0 years, 11.8% (11/93) of the individuals with a previous stroke developed stroke recurrence, and thrombocytopenia seemed to be a predictor of stroke recurrence.

Appealing to Individual Fears or Social Norms: How Can the Public Be Persuaded to Accept COVID-19 Vaccination through Risk Communication?

In the context of the COVID-19 pandemic, improving the public's understanding of the increased efficacy and safety of the COVID-19 vaccines through scientific risk communication campaigns, promoting the public's acceptance and willingness to receive COVID-19 vaccines, and forming collective actions at the social level will deeply impact on the effect of COVID-19 prevention in various countries, which is also a key factor that governments need to address urgently. Previous research on risk communication has mostly focused on microscopic perspectives of how to stimulate individual self-protection behaviors by awakening threat and efficacy perceptions; however, a lack of observation of social collective actions means there is a risk of failure regarding COVID-19 epidemic reduction and prevention. In this regard, this study was based on the issue of vaccination in the context of the COVID-19 epidemic through a highly regulated and controlled research experiment in China (n = 165), which was designed to examine the impact of two risk communication frameworks, appealing to individual fears and appealing to social norms, on the public's acceptance and recommendations of COVID-19 vaccines, thus outlining the path of action from individual protection to collective epidemic prevention. Both the "fear appeals" framework and the "social norms" framework were found to have a positive effect on the Chinese public's vaccination acceptance. Specifically, social norms information may increase vaccination acceptance by enhancing the public's perceptions of social responsibility, while fear appeals information may reduce their perceptions of threat and social pressure to get the vaccine. Female and highly educated groups were more likely to refuse to recommend vaccination after reading the risk communication information. These results can be a useful supplement to the theory and practice of risk communication.

Minimizing the Programming Power of Phase Change Memory by Using Graphene Nanoribbon Edge-Contact.

Nonvolatile phase-change random access memory (PCRAM) is regarded as one of the promising candidates for emerging mass storage in the era of Big Data. However, relatively high programming energy hurdles the further reduction of power consumption in PCRAM. Utilizing narrow edge-contact of graphene can effectively reduce the active volume of phase change material in each cell, and therefore realize low-power operation. Here, it demonstrates that the power consumption can be reduced to ≈53.7 fJ in a cell with ≈3 nm-wide graphene nanoribbon (GNR) as edge-contact, whose cross-sectional area is only ≈1 nm2 . It is found that the polarity of the bias pulse determines its cycle endurance in the asymmetric structure. If a positive bias is applied to the graphene electrode, the endurance can be extended at least one order longer than the case with a reversal of polarity. In addition, the introduction of the hexagonal boron nitride (h-BN) multilayer leads to a low resistance drift and a high programming speed in a memory cell. The work represents a great technological advance for the low-power PCRAM and can benefit in-memory computing in the future.

Robust pure spin current induced by the photogalvanic effect in half-silicane with spatial inversion symmetry.

The spin-dependent photogalvanic (PG) effect in low-dimensional spin semiconductors has attracted great interest recently. Here, we have studied the spin semiconducting feature and spin-dependent photocurrent in a two-dimensional (2D) silicene-based device with spatial inversion symmetrical half-hydrogenation, in which half of the silicene is hydrogenated on the upper surface and half is hydrogenated on the lower surface. Because of the unique spin semiconductor properties and symmetry of the system, pure spin current can be robustly produced in both the zigzag and armchair directions for linearly and elliptically polarized light. The behavior of the spin-dependent photoresponse in the spin PG effect is highly anisotropic and can be tuned by the polarization/phase angles or photon energy (Eph). Moreover, the produced pure spin current in such a half-silicane device with spatial inversion symmetry via the PG effect is several orders of magnitude larger than that obtained in metal/semiconductor/metal systems. These findings suggest a promising approach for generating pure spin current by the PG effect and provide a new possibility for the application of 2D half-silicane in spintronics.

Three-dimensional perovskite nanowire array-based ultrafast resistive RAM with ultralong data retention.

Resistive random access memories (Re-RAMs) have transpired as a foremost candidate among emerging nonvolatile memory technologies with a potential to bridge the gap between the traditional volatile and fast dynamic RAMs and the nonvolatile and slow FLASH memories. Here, we report electrochemical metallization (ECM) Re-RAMs based on high-density three-dimensional halide perovskite nanowires (NWs) array as the switching layer clubbed between silver and aluminum contacts. NW Re-RAMs made of three types of methyl ammonium lead halide perovskites (MAPbX3; X = Cl, Br, I) have been explored. A trade-off between device switching speed and retention time was intriguingly found. Ultrafast switching speed (200 ps) for monocrystalline MAPbI3 and ~7 × 109 s ultralong extrapolated retention time for polycrystalline MAPbCl3 NW devices were obtained. Further, first-principles calculation revealed that Ag diffusion energy barrier increases when lattice size shrinks from MAPbI3 to MAPbCl3, culminating in the trade-off between the device switching speed and retention time.

Down-Scalable and Ultra-fast Memristors with Ultra-high Density Three-Dimensional Arrays of Perovskite Quantum Wires.

With strikingly high speed, data retention ability and storage density, resistive RAMs have emerged as a forerunning nonvolatile memory. Here we developed a Re-RAM with ultra-high density array of monocrystalline perovskite quantum wires (QWs) as the switching matrix with a metallic silver conducting pathway. The devices demonstrated high ON/OFF ratio of ∼107 and ultra-fast switching speed of ∼100 ps which is among the fastest in literature. The devices also possess long retention time of over 2 years and record high endurance of ∼6 × 106 cycles for all perovskite Re-RAMs reported. As a concept proof, we have also successfully demonstrated a flexible Re-RAM crossbar array device with a metal-semiconductor-insulator-metal design for sneaky path mitigation, which can store information with long retention. Aggressive downscaling to ∼14 nm lateral dimension produced an ultra-small cell effectively having 76.5 nm2 area for single bit storage. Furthermore, the devices also exhibited unique optical programmability among the low resistance states.

Predictive Hydration Model of Portland Cement and Its Main Minerals Based on Dissolution Theory and Water Diffusion Theory.

Efficient and accurate cement hydration simulation is an important issue for predicting and analyzing concrete's performance evolution. A large number of models have been proposed to describe cement hydration. Some models can simulate the test results with high accuracy by constructing reasonable functions, but they are based on mathematical regression and lack of physical background and prediction ability. Other models, such as the famous HYMOSTRUC model and CEMHYD3D model, can predict the hydration rate and microstructure evolution of cement based on its initial microstructure. However, this kind of prediction model also has some limitations, such as the inability to fully consider the properties of cement slurry, or being too complicated for use in finite element analysis (FEA). In this study, the hydration mechanisms of the main minerals in Portland cement (PC) are expounded, and the corresponding hydration model is built. Firstly, a modified particle hydration model of tricalcium silicate (C3S) and alite is proposed based on the moisture diffusion theory and the calcium silicate hydrate (C-S-H) barrier layer hypothesis, which can predict the hydration degree of C3S and alite throughout the age. Taking the hydration model of C3S as a reference, the hydration model of dicalcium silicate (C2S) is established, and the synergistic hydration effect of C3S and C2S is calibrated by analyzing the published test results. The hydration model of tricalcium aluminate(C3A)-gypsum system is then designed by combining the theory of dissolution and diffusion. This model can reflect the hydration characteristics of C3A in different stages, and quantify the response of the hydration process of C3A to different gypsum content, water-cement ratio, and particle size distribution. Finally, several correction coefficients are introduced into the hydration model of the main mineral, to consider the synergistic hydration effect among the minerals to some extent and realize the prediction of the hydration of PC.