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An early evaluating system for autism spectrum disorder (ASD) severity is crucial. Questionnaire survey is challenging for accurately assessing the severity levels for ASD in children. Offspring with ASD-like phenotypes were induced by treating pregnant mice with Poly (I:C) at GD12.5 and the placentae corresponding to the offspring were obtained by caesarean. The autism severity composite score (ASCS) for offspring was calculated through behavioral tests. HE staining and immunohistochemistry were used to observe the morphology of placenta. Candidate biomarkers were identified by weighted protein co-expression network analysis (WPCNA) combined with machine learning and further validated by ELISA. Sperman's was used to analyze the correlation between biomarkers and metabolome. The placental weight and mean vascular area of male offspring with ASD-like phenotypes were significantly decreased compared with typical mice. According to the WPCNA, four modules were identified and significantly correlated with ASCS of offspring. Two biomarkers (ASPG and DAD1) with high correlation with ASCS in offspring were identified. VEGF pathway may contribute to sexual dimorphism in placental morphology within mice with ASD-like phenotypes in term. The placental ASPG and DAD1 levels could reflect ASD-like symptom severity levels in male/female mice offspring. • Morphology of placenta within mice with ASD-like phenotypes in term are sexually dimorphic. • First study about placental biomarker for ASD-like symptom severity levels in mice offspring. • Placental factors affecting ASD-like symptom severity levels differ in female and male. [ABSTRACT FROM AUTHOR]

Referring video object segmentation (RVOS) aims to segment the target object in a video sequence described by a language expression. Typical multimodal Transformer based RVOS approaches process video sequence in a frame-independent manner to reduce the high computational cost, which however restricts the performance due to the lack of inter-frame interaction for temporal coherence modeling and spatio-temporal representation learning of the referred object. Besides, the absence of sufficient cross-modal interactions results in weak correlation between the visual and linguistic features, which increases the difficulty of decoding the target information and limits the performance of the model. In this paper, we propose a bidirectional correlation-driven inter-frame interaction Transformer, dubbed BIFIT, to address these issues in RVOS. Specifically, we design a lightweight and plug-and-play inter-frame interaction module in the Transformer decoder to efficiently learn the spatio-temporal features of the referred object, so as to decode the object information in the video sequence more precisely and generate more accurate segmentation results. Moreover, a bidirectional multi-level vision-language interaction module is implemented before the multimodal Transformer to enhance the correlation between the linguistic and multi-level visual features, thus facilitating the language queries to decode more precise object information from visual features and ultimately improving the segmentation performance. Extensive experimental results on four benchmarks validate the superiority of our BIFIT over state-of-the-art methods and the effectiveness of our proposed modules. The code is available in https://github.com/LANMNG/BIFIT. • A language query based multimodal RVOS Transformer outperforms the previous cutting-edge methods on several benchmarks. • Bidirectional multi-level vision-language interaction boosts correlation between cross-modal features. • Inter-frame interaction in Transformer decoder can model the temporal coherence. [ABSTRACT FROM AUTHOR]

The formation of heat conduction pathway is the key to fulfill the demand for versatile thermal management. However, constructing a three-dimensional (3D) thermal bridge in a single step is challenging and entails the critical co-dispersion of multiple fillers. Here we propose a simple and controllable method to assemble conjugated hybrids of 1D carbon nanotubes (CNTs) with 2D graphene through hydrothermal centrifugation. In addition to armoring CNTs through the conjugation effect, ionic liquid crystals (ILC) assisted the CNTs to exfoliate graphite, facilitating the co-dispersion of the binary material. Remarkably, the resulting graphene/CNTs (GC) hybrids acquire hydrophilicity from the ILC and manifest diverse states depending on the water content, including octopus-like blocks, self-limiting gels, high-concentration slurries, and dilute dispersions. Hybrid nanocomposite papers containing ANF nanofibers with 3D thermally conductive bridges yield exceptional photothermal conversion performance and Joule heating performance, rendering them suitable for flexible wearable applications. The multistate GC hybrids presented here afford a novel reference for the one-step construction of 3D thermally conductive bridges. Graphene/CNTs (GC) conjugated hybrids were successfully prepared by ILC-assisted hydrothermal centrifugation. Particularly, the three-dimensional network structure formed by the ILC-armored GC hybrids can absorb free water molecules, resulting in a non-free-flowing gel state that can be easily converted into self-healing blocks and dispersions. [Display omitted] • Multistate Graphene/CNTs conjugated hybrids were successfully prepared through ILC-assisted hydrothermal centrifugation. • GC hybrids exhibit excellent electrical conductivity, scalability and exceptional redispersibility even after storage. • Various GC-derived bulk forms from the ILC armored GC hybrids are realized by facile processing. • GC hybrids assembled nanocomposite papers yield exceptional photothermal conversion and Joule heating performance. [ABSTRACT FROM AUTHOR]

Technological accidents triggered by natural disasters (Natech) have become a significant threat to the safety of coastal energy infrastructure. The domino accidents involved could propagate rapidly in a short duration and amplify an accident exponentially. In previous studies of Natech-related domino accidents, the scope of natural disasters was mainly focused on the phase of primary events, and the subsequent accident cascades were entirely driven by the domino effect. This simplification essentially weakens the real-time intervention of ongoing disasters in the evolution of accidents, making it difficult to accurately model the evolutionary patterns of domino accidents. Accordingly, a Natech-related domino evolution graph (NT-DEG) is proposed in this paper for dynamic modeling of accident evolution under the real-time disturbance of natural disasters. In addition, a stochastic process and a deterministic process are innovatively used to control the generation of primary events and the update of the evolution network at each timestamp. Moreover, using a dynamic community detection algorithm and graph metrics, an identification method for critical units in dynamic domino accidents is proposed. The application of NT-DEG to typhoon-related Natech indicates that continuous-onset natural disasters may cause new failure units that could participate in the original domino accident at any moment. This real-time intervention noticeably changes the propagation path and scale of the original accident and accelerates accident evolution. In addition, the validation based on the integrated simulation reveals that the proposed identification method can accurately identify the critical units, and the safety measures against them can noticeably reduce the scale of accident cascades. [ABSTRACT FROM AUTHOR]

Abstract A new method for producing high permeability thin-gauge non-oriented electrical steels with 2.3 mm thick Fe-3.2% Si steel as-cast strip was studied. The evolution of microstructure and texture was characterized during the entire processing route. Furthermore, the effects of cold-rolling with large reduction and intermediate annealing process on magnetic properties were studied. The results indicated that the as-cast strip with coarse grains exhibited strong λ-fiber. Coarse equiaxed grains with Cube texture were obtained after first-stage cold-rolling with a reduction of 71.74% and intermediate annealing at 900 °C, which promoted the formation of λ-fiber and α-fiber components in the final cold-rolled sheets. {100}⟨0vw⟩ texture was partially retained from the intermediate annealed grains, and new Cube and Goss components were nucleated within {110}〈110〉 shear bands. The magnetic properties of 0.2 mm final sheets were greatly improved by strengthening λ-fiber component with two-stage rolling process, and best magnetic properties were B 50 = 1.758 T, P 15/50 = 2.065 W/kg, P 10/400 = 15.173 W/kg, P 10/1k = 59.48 W/kg. Highlights • Thin-gauge non-oriented silicon steel with high magnetic properties was produced by twin-roll strip casting. • The effects of cold-rolling with large reduction and intermediate annealing process on magnetic properties were studied. • Role of rolling methods in microstructure and texture evolution along the entire route was explored. • The magnetic properties were improved by strong {100}⟨0vw⟩ texture. [ABSTRACT FROM AUTHOR]

A 3.1 mm thick non-oriented Fe-1.0% Si steel as-cast strip was produced by twin-roll strip casting. Samples with/without strip annealing were cold rolled to 0.5 mm and then annealed. The development of microstructure, precipitate and texture was characterized along the entire processing route. It was found that annealing after strip casting had a minor influence on the average grain size and texture of the as-cast strip. A large number of particles precipitated and coarsened during the strip annealing process. Meanwhile, precipitates hindered dislocation slip and crystal rotation such that more significant in-grain shear bands were formed in cold rolled sheets. During recrystallization annealing process, the newly formed fine precipitates in directly cold-rolled sheets consumed stored strain energy and also hindered grain growth. But the initial precipitates in the annealed strip grew further and the effect of pinning of dislocations and grain boundary was lost. Therefore, annealed sheets with strip annealing process showed homogeneous equiaxed grains and the recrystallization texture was characterized by strong λ-fiber texture, which led to high permeability. [ABSTRACT FROM AUTHOR]

Porous Pt-doping heterojunctions, Pt-ZnO-Co 3 O 4 , Pt-ZnS-CoS, and Pt-Zn 3 P 2 -CoP were fabricated by firstly the oxidation, sulfurization, and phosphorization of ZnCo-zeolitic-imidazolate-framework (ZnCo-ZIF) and then the doping of Pt nanoparticles. They exhibit excellent photocatalytic activity towards hydrogen generation from water splitting. These bimetallic metal-organic-framework (MOF) derivatives maintain the porous framework skeleton of ZnCo-ZIF precursor, there by significantly enhancing the light utilization and simultaneously affording abundant exposed catalytic active sites. Most importantly, suitable band matching and strong electron coupling in these heterojunctions are achieved by using the bimetallic MOF template, which facilitate the efficient electron-hole separation and transportation. In addition, Pt nanoparticles distributed on the porous heterojunctions as electron traps can offer rich redox active sites for the hydrogen generation. Correspondingly, the hydrogen generation rate of Pt-ZnO-Co 3 O 4 , Pt-ZnS-CoS, and Pt-Zn 3 P 2 -CoP was up to ~7.80, ~8.21, and ~9.15 mmol h −1 g −1 , respectively, higher than those of respective ZIF-8 or ZIF-67-based derivatives. This work thus provides a new approach that using bimetallic MOF as template directs the fabrication of noble-metal doping heterojunctions to simultaneously enhance light absorption utilization, electro-hole separation, and transport, therefore promoting surface water oxidation reaction for efficient water splitting. [ABSTRACT FROM AUTHOR]

• We re-analyze the generation and utilization mechanism of temporal context in the semi-supervised VOS task and introduce the efficient tracklet data flow, based on which we propose a novel VOS model and achieve real-time running speed. Experimental results on three benchmark datasets show that our proposed model achieves a better trade-off between efficiency and accuracy. • We design a coherence-aware module to estimate the coherence of the predicted target and update the temporal context in a more robust way. • We devise a spatio-temporal context aggregation module to aggregate the spatial context and temporal context at each level of the decoder to alleviate the aliasing effect, and learn a robust and discriminative feature representation for the target object. Semi-supervised video object segmentation (VOS) is a highly challenging problem that has attracted much research attention in recent years. Temporal context plays an important role in VOS by providing object clues from the past frames. However, most of the prevailing methods directly use the predicted temporal results to guide the segmentation of the current frame, while ignoring the coherence of temporal context, which may be misleading and degrade the performance. In this paper, we propose a novel model named Coherence-aware Context Aggregator (CCA) for VOS, which consists of three modules. First, a coherence-aware module (CAM) is proposed to evaluate the coherence of the predicted result of the current frame and then fuses the coherent features to update the temporal context. CAM can determine whether the prediction is accurate, thus guiding the update of the temporal context and avoiding the introduction of erroneous information. Second, we devise a spatio-temporal context aggregation (STCA) module to aggregate the temporal context with the spatial feature of the current frame to learn a robust and discriminative target representation in the decoder part. Third, we design a refinement module to refine the coarse feature generated from the STCA module for more precise segmentation. Additionally, CCA uses a cropping strategy and takes small-size images as input, thus making it computationally efficient and achieving a real-time running speed. Extensive experiments on four challenging benchmarks show that CCA achieves a better trade-off between efficiency and accuracy compared to state-of-the-art methods. The code will be public. [ABSTRACT FROM AUTHOR]

The Bi 2 S 3 nanorods with abundant sulfur vacancies exhibit superior and sustainable photocatalytic nitrogen fixation activity under near-infrared irradiation and full solar irradiation. [Display omitted] • Controllable fabrication of sulfur-vacancy-rich Bi 2 S 3 nanorods were fabricated by a simple solvothermal route. • The abundant SVs can absorb nitrogen and decrease the photoinduced carriers recombination. • Bi 2 S 3 nanorods exhibit effective and sustainable photocatalytic N 2 fixation under near-infrared irradiation. Herein, orthorhombic phase Bi 2 S 3 nanorods with different concentration of sulfur vacancies (SVs) were controllably synthesized via a facile solvothermal strategy. Benefited from the high specific surface area, proper band gap, good reduction capacity and abundant SVs, the Bi 2 S 3 nanorods exhibit remarkable photocatalytic nitrogen fixation performance. The ammonia yield of the sulfur-vacancy-rich Bi 2 S 3 -3 is around 51.04 μmol g−1 h−1 under full solar irradiation. Moreover, Bi 2 S 3 -3 also responds sensitively to near-infrared (NIR) light, and the nitrogen reduction rate is 33.37 μmol g−1 h−1. The superior photocatalytic performance of Bi 2 S 3 is mainly attributed to SVs, which can absorb nitrogen and afford plentiful active sites to activate molecular nitrogen. Moreover, SVs also can trap the photoinduced electrons and contribute to the separation of the interfacial charge. This work provides a promising and sustainable defect-rich photocatalyst for the fixation of atmospheric nitrogen using solar energy. [ABSTRACT FROM AUTHOR]

• A network-based approach is developed to model the NaTech-related domino effect in process clusters. • Expected escalation vectors are used to characterize the pairwise connection strength. • Two constraints, escalation and probability thresholds, are imposed to reduce the computational complexity of traversing potential propagation pathways. • Improved community detection algorithm can discover process clusters in which dominoes effects occur intensively. • NaTech-related domino effect network structure is sensitive to the natural hazard intensity. Extreme disasters and their resulting cascading accidents are always being a considerable threat to process cluster safety. However, many current assessments of the domino effects within process clusters do not consider the uncertainty of the primary accidents triggered by natural hazards. Additionally, high-efficiency methods to model the complex dependencies of large-scale process clusters are still lacking. Hence, this paper expands a hazard scenario module at the front end of the assessment framework of a domino effect to consider the response of installations to hazard loads. Simultaneously, a network-based approach is developed to model the NaTech-related domino effect. The constructed network imposes two constraints, escalation and probability thresholds, to reduce the computational complexity of traversing potential propagation pathways. As a result, the proposed method can be applied to large-scale process clusters. The safety analysis of an oil storage base subject to hurricanes and concurrent flooding shows that the proposed method can clarify the role of each unit in a local and cross-community domino effect at the node and community levels, respectively. The results inform the implementation of emergency responses to accidents. Furthermore, sources of uncertainty indicate that the network structure of the NaTech-related domino effect is sensitive to hazard intensity. [Display omitted] [ABSTRACT FROM AUTHOR]

Hurricane-induced compound events (HICEs), such as coastal surges and winds, usually exhibit a high degree of nonlinear dependence, and thus, a single disaster modeling method cannot effectively evaluate and design the corresponding engineering applications. Therefore, this research aims at developing a statistical model suitable for HICEs to analyze and design multivariate hazard scenarios. Simultaneously, a risk-driven weighting function is constructed, considering the likelihood of event occurrence and response of the targets, to identify the riskiest design event in the critical event set. We apply the proposed model to an industrial area on the Galveston coast; use numerically synthesized HICEs to explore the dependence of the flood height, wind speed, and current velocity; and discuss the effects of different weighting rules on the design events. The modeling results show that three marginal variables are significantly correlated with one another, and the correlation between the flood height and wind speed in extreme events is enhanced. Additionally, on the same set of critical events, the riskiest event is typically not the most likely event, and the difference between them decreases as the return period increases. Moreover, the risk-driven weighting function provides a reliable scheme for disaster prevention design events of special petrochemical facilities. • A statistical model for hurricane-induced compound events (HICEs) is developed to analyze multivariate hazard scenarios. • A risk-driven weighting function is constructed to identify the riskiest design event in the critical event set. • The correlation between the flood height and wind speed in extreme events is enhanced. • The riskiest event is typically not the most likely event, and the difference decreases as the return period increases. • Risk-driven weighting functions provide a reliable scheme for disaster prevention design events. [ABSTRACT FROM AUTHOR]

The flexible self-supporting PAN/BiOBr-Cl composite microfibers exhibit superior and sustainable photocatalytic activity for nitrogen fixation and dye degradation. [Display omitted] • PAN/BiOBr-Cl microfibers were fabricated by a solvothermal route on PAN support. • PAN/BiOBr-Cl exhibits prominent flexibility and self-supporting property. • PAN/BiOBr-Cl has remarkable photocatalytic N 2 fixation and degradation performance. Self-supporting chlorine (Cl) doped bismuth oxybromide (BiOBr)/polyacrylonitrile (PAN) microfiber photocatalysts was successfully constructed via a simple solvothermal method by utilizing PAN nanofibers as the flexible carrier. Benefited from the high specific surface area, proper band gap, superior light absorption and reduction capacity, the Cl doped BiOBr/PAN microfibers have remarkable photocatalytic performance. The ammonia yield of the PAN/BiOBr-5 %Cl is 234.4 μmol g−1h−1 and the degradation rate of RhB is 91% within 240 min. Meanwhile, the microfiber photocatalyst proves superior stability and reusability though cyclic experiments. More importantly, compared to the traditional suspended nanoparticles, Cl doped BiOBr/PAN microfiber photocatalyst exhibits prominent flexibility and self-supporting property and can be easily separated and recovered from the service environment. This work affords a significant view into the design of excellent recoverable composite fiber materials for photocatalytic nitrogen fixation and pollutant degradation. [ABSTRACT FROM AUTHOR]

• A hybrid Bayesian network-based landslide risk assessment model is developed. • HBN-LRA model decomposes the landslide risk into three submodules. • Landslide risk is sensitive to geological and geomorphic conditions. • The proactive remedial module can assess and design slope drainage systems. Industrialization exposes more petrochemical facilities in the slope-industrial interfaces (SIIs) to the impact range of landslides, demanding practical assessment approaches for addressing associated damage and its potential consequences. However, accurately predicting the expansion and upgrade of landslide hazards in industrial plants is challenging, involving a series of cascading-event trigger-response analyses. Accordingly, this paper develops a hybrid Bayesian network-based landslide risk assessment (HBN-LRA) model to evaluate the landslide risk on storage tanks in SIIs. This model decomposes the landslide risk into three submodules: slope stability, failure of targets, and proactive remedial measures and updates. It transmits the landslide risk through conditional dependence between subsystems. The results of applying the HBN-LRA model toward quantifying the landslide risk in a typical SII area indicate that the distance from the slope in the system risk factors directly determines storage tank damage. Landslide risk is sensitive to geological and geomorphic conditions, such as soil depth, cohesion, and unit weight; their relative importance all exceed 0.15. Tests on the case slope demonstrate that reducing the drainage paving distance from 10 to 8 m can improve slope stability by 50%. This result highlights the potential of the proactive remedial module in evaluating and designing slope drainage systems. [ABSTRACT FROM AUTHOR]

• A risk-based method for evacuation planning in chemical accidents is presented. • Cumulative individual risk is used as the evaluation index for evacuation strategy. • Multi-hazard characteristics and domino effects are considered in this method. • The method can decide whether to evacuate or shelter-in-place after an accident. • This method can be applied in the development of emergency response strategies. With the agglomeration and complication of the chemical industry, the multi-hazard and dynamic characteristics have become new challenges for chemical safety management and loss prevention research. As an important topic of safety research in chemical industries, the evacuation strategy also needs to be improved according to the new characteristics of chemical accidents. Here we propose a method for evacuation strategy development in chemical accidents based on dynamic and multi-hazard risk assessment. This method adopts the cumulative individual risk as the evaluation indicator for the optimal evacuation strategy. This method can dynamically consider the multi-hazard characteristics and domino effects of chemical accidents during the evacuation. A multi-hazard chemical accident that happened in Xiangshui, China in 2019 is taken as a case study to show the effects of risk assessment results on evacuation strategy development. The case shows that a reasonable time period for shelter-in-place after the occurrence of the primary accident can reduce the cumulative individual risk in the evacuation process. It is expected that this paper can provide guidance and reference for loss prevention in chemical industries, and can also provide useful tools for the management of emergency response. [ABSTRACT FROM AUTHOR]

Evacuation path planning is an important research topic in emergency management of chemical accidents. Facing the challenges of the continuous increase in the frequency of multi-hazard chemical accidents including fires, explosions, and toxic releases, the requirement to improve the evacuation path planning method considering the dynamic and multi-hazard characteristics of the accidents has become increasingly urgent. In order to address the shortcomings of the static, single-hazard, and passive evacuation path planning methods, this paper proposes an evacuation path planning method for multi-hazard accidents in chemical industries based on advance and on-site risk perception. This method applies dynamic and comprehensive risk analysis as the equivalent distance criterion to improve the D* path planning algorithm. A chemical park in Guangzhou, China, is adopted as a case study to verify the effectiveness of the proposed method and quantitatively demonstrate the effect of the method in improving evacuation efficiency. This paper further discusses the flexibility of the proposed method in considering environmental factors, evacuation preferences, and emergency response requirements. The evacuation path planning method proposed in this paper is expected to contribute to the optimization of emergency management of multi-hazard accidents and provide guidance for the enhancement of safety and loss prevention in chemical industries. [ABSTRACT FROM AUTHOR]

[Display omitted] Breast cancer, as a malignant disease that seriously threatens women's health, urgently needs to be researched to develop effective and safe therapeutic drugs. Nanoparticle drug delivery systems (NDDS), provide a powerful means for drug targeting to the breast cancer, enhancing the bioavailability and reducing the adverse effects of anticancer drug. However, the breast cancer microenvironment together with heterogeneity of cancer, impedes the tumor targeting effect of NDDS. Breast cancer microenvironment, exerts endogenous stimuli, such as hypoxia, acidosis, and aberrant protease expression, shape a natural shelter for tumor growth, invasion and migration. On the basis of the ubiquitous of endogenous stimuli in the breast cancer microenvironment, researchers exploited them to design the stimuli-responsive NDDS, which response to endogenous stimulus, targeted release drug in breast cancer microenvironment. In this review, we highlighted the effect of the breast cancer microenvironment, summarized innovative NDDS responsive to the internal stimuli in the tumor microenvironment, including the material, the targeting groups, the loading drugs, targeting position and the function of stimuli-responsive nanoparticle drug delivery system. The limitations and potential applications of the stimuli-responsive nanoparticle drug delivery systems for breast cancer treatment were discussed to further the application. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the therapeutic efficacy of Emodin-loaded polymer lipid hybrid nanoparticles (E-PLNs) for breast cancer. The size, Zeta potential, surface morphology, encapsulation efficiency, stability, in vitro drug release of E-PLNs prepared by the nanoprecipitation method were characterized. The uptake, in-vitro cytotoxicities and apoptosis of free drug, E-PLNs were investigated against MCF-7 cells. The efficacy of E-PLNs in tumor bearing nude mice has also been studied.The average particle size of the experimentally prepared E-PLNs was (122.7±1.79) nm, and the encapsulation rate was 72.8%. Compared with free Emodin (EMO), E-PLNs showed greater toxicity to MCF-7 cells by promoting the uptake of EMO, and can promote the early apoptosis of MCF-7 cells. In addition to the morphological changes of apoptotic cells, the ratio of Bax/Bcl-2 was significantly increased, which indicated that E-PLNs can induce apoptosis in MCF-7 cells to achieve anticancer effect. Finally, E-PLNs significantly inhibited tumor growth by more than 60%, which may be related to its passive targeting effect on tumor site. Our results suggest that E-PLNs have shown good anti-breast cancer effect than free EMO. Moreover, the effect of E-PLNs on MCF-7 cells is mainly related to the induction of apoptosis. [Display omitted] [ABSTRACT FROM AUTHOR]

• Application of unstructured geometry in simulating utility tunnel fire. • New model for heat flux affected by extension flame under curved ceiling. • Explaining competition mechanism among heat flux components from transverse view. To analyze transverse fire protection, the distribution law and evolution mechanism of transverse heat transfer during fire in utility tunnels is required urgently. In this study, the experiment and numerical simulation were employed to investigate the evolution of transverse heat transfer under the curved ceiling. After validation, the fire dynamics simulation models with unstructured geometry were established to predict the transverse heat flux. Through the experiment and simulation, the fire source and target location under the curved ceiling were considered, and the prediction models of heat flux provided by the flame body before the impingement point and the heat flux provided by the extension flame body and thermal environment were proposed. Based on the proposed models, the shift relationship (competition mechanism) between the components provided by flame body before the impingement point and the extension flame body (including the thermal environment) under the curved ceiling was illustrated. The transverse heat flux attenuation of each component in different fire scenarios with different fire sources and target point locations was compared to reveal the competition mechanism among the components in the heat flux. This work provides a reference for the transverse fire protection design and risk assessment in utility tunnels. [ABSTRACT FROM AUTHOR]

Highlights • Ultrahigh volume ultrafine BFS are prepared through wet-milling and dry-separation respectively. • The reaction degree of WS system develop at a faster rate owing to dissolution of ions. • Mixes containing WS can exhibit compressive strength of 43.9 ± 2.3 MPa after 3d of curing. Abstract The dosage of blast furnace slag (BFS) in cement is limited on account of relative low reaction rate and low early strength. Improving the early age activity of BFS is the fundamental solution for high volume BFS cementitious materials. Generally, mechanical activation is one of the main techniques to accelerate the early reaction of mineral admixtures. In this study, wet-milling and dry-separation are respectively used to prepare ultrafine BFS to improve its early reactivity. The early hydration characteristics, mechanical property, hydration products of ultrahigh volume BFS cementitious materials are analyzed to assess the efficiency of ultrafine BFS on the properties of BFS cement paste. The results show that the initial and final setting time both gradually decrease as the dry-separation BFS (GS) dosage increases, while the wet-milling BFS (WS) series present the opposite tendency. The electrical resistivity of WS specimens develops at a faster rate than that of GS specimens. Mixes containing WS, at a maximum dosage of 90%, have higher mechanical property than those with GS, resulting in an enhancement of strength. The hydration products mainly include calcium hydroxide, hydrotalcite, hydrotalcite-like phase, calcium carbonate, strätlingite, C-S-H, C-A-S-H, and akermanite. [ABSTRACT FROM AUTHOR]

Chronic inflammation and immune responses are two core element that characterize the tumor microenvironment. A large number of immune/inflammatory cells (including tumor associated macrophages, neutrophils and myeloid derived suppressor cells) as well as cytokines (such as IL-6, IL-10, TGF-β) are present in the tumor microenvironment, which results in both a chronic inflammatory state and immunosuppression. As a consequence tumor cell migration, invasion, metastasis and anticancer drug sensitivity are modulated. On the one hand, secreted cytokines change the function of cytotoxic T lymphocytes and antigen presenting cells, thereby inhibiting tumor specific immune responses and consequently inducing a special immunosuppressive microenvironment for tumor cells. On the other hand, tumor cells change the differentiation and function of immune/inflammatory cells in the tumor microenvironment especially via the NF-κB and STAT3 signaling pathways. This may promote proliferation of tumor cells. Here we review these double edged effects of immune/inflammatory cells and cytokines on tumor cells, and explored their interactions with inflammation, hypoxia, and immune responses in the tumor microenvironment. The tumor inflammatory or immunosuppressive reactions mediated by the high activity of NF-κB or STAT3 can occur alone or simultaneously, and there is a certain connection between them. Inhibiting the NF-κB or STAT3 signaling pathway is likely to curb the growth of tumor cells, reduce the secretion of pro-inflammatory factors, and enhance the anti-tumor immune response. [ABSTRACT FROM AUTHOR]