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To optimize the control of fan coil unit (FCU) systems under model-free conditions, researchers have integrated reinforcement learning (RL) into the control processes of system pumps and fans. However, traditional RL methods can lead to significant fluctuations in the flow of pumps and fans, posing a safety risk. To address this issue, we propose a novel FCU control method, Fluctuation Suppression–Deep Deterministic Policy Gradient (FS-DDPG). The key innovation lies in applying a constrained Markov decision process to model the FCU control problem, where a penalty term for process constraints is incorporated into the reward function, and constraint tightening is introduced to limit the action space. In addition, to validate the performance of the proposed method, we established a variable operating conditions FCU simulation platform based on the parameters of the actual FCU system and ten years of historical weather data. The platform's correctness and effectiveness were verified from three aspects: heat transfer, the air side and the water side, under different dry and wet operating conditions. The experimental results show that compared with DDPG, FS-DDPG avoids 98.20% of the pump flow and 95.82% of the fan flow fluctuations, ensuring the safety of the equipment. Compared with DDPG and RBC, FS-DDPG achieves 11.9% and 51.76% energy saving rates, respectively, and also shows better performance in terms of operational performance and satisfaction. In the future, we will further improve the scalability and apply the method to more complex FCU systems in variable environments. [ABSTRACT FROM AUTHOR]

As a critical component of the tumour immune microenvironment (TIME), the resident microbiota promotes tumorigenesis across a variety of cancer types. Here, we integrated multiple types of omics data, including microbiome, transcriptome, and metabolome data, to investigate the functional role of intratumoral bacteria in gastric cancer (GC). The microbiome was used to categorize GC samples into six subtypes, and patients with a high abundance of Streptococcus or Pseudomonas had a markedly worse prognosis. Further assays revealed that Streptococcus anginosus (SA) promoted tumour cell proliferation and metastasis while suppressing the differentiation and infiltration of CD8+ T cells. However, antibiotic treatment significantly suppressed tumorigenesis in SA+ mice in vivo. We further demonstrated that the SA arginine pathway increased the abundance of ornithine, which may be a major contributor to reshaping of the TIME. Our findings demonstrated that SA, a novel risk factor, plays significant roles in the initiation and progression of GC, suggesting that SA might be a promising target for the diagnosis and treatment of GC. [ABSTRACT FROM AUTHOR]

Background: The incidence and mortality rates of cancer are the highest globally. Developing novel methodologies that precisely, safely, and economically differentiate between benign and malignant lung conditions holds immense clinical importance. This research seeks to construct a predictive model utilizing a combination of diverse biomarkers to effectively discriminate between benign and malignant lung diseases. Methods: This retrospective study included patients admitted to the two general hospitals in Shanghai from 2014 to 2015. This study was developed using five tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), cytokeratin fragment 21-1 (CA211), squamous cell carcinoma antigen (SCC), and neuron specific enolase (NSE). The entire sample was divided into two groups according to the hospital: 1033 cases were included in the development cohort and 300 cases in the validation cohort. Logistic regression analysis was used for univariate analysis to explore individual correlations between each selected clinical variable and lung cancer diagnostic outcome. Diagnostic prediction models were constructed and validated based on independent prognostic factors identified using multifactorial analysis. A nomogram was created using these tumor markers (age and sex were additionally included) and validated using the concordance index and calibration curves. Clinical prediction models were evaluated using decision curve analysis. Results: Fully adjusted multivariate analysis showed that the risk of lung cancer was 2.38 times higher in men than in women. CEA positivity was associated with an 13.41-fold increased risk in lung cancer. The area under the curve (AUC) values for the development cohort and validation cohort models were 0.907 and 0.954, respectively. In the established nomogram, the AUC for the receiver operating characteristic curve was 0.907 (95% CI, 0.889–0.925). The validation model confirmed the strong discriminative power of the nomogram (AUC = 0.954). The described calibration curves demonstrated good fit predictions and observation probabilities. In addition, decision curve analysis concluded that the newly established nomogram has important implications for clinical decision making. Conclusions: Combined prediction models based on CEA, CA199, CA211, SCC, and NSE biomarkers could significantly the differentiation between benign and malignant lung diseases, thus facilitating better clinical decision making. [ABSTRACT FROM AUTHOR]

Riboflavin-5-phosphate (riboflavin) is the most commonly used photosensitizer in corneal crosslinking (CXL); while its efficient delivery into the stroma through the corneal epithelial barrier is challenging. In this paper, we presented novel responsive porous microneedles with ocular microinjection capability to deliver riboflavin controllably inside the cornea to facilitate CXL. The microneedle patch was composed of Poly (N-isopropyl acrylamide) (PNIPAM), graphene oxide (GO), and riboflavin-loaded gelatin. After penetrating the cornea by the stiff and porous gelatin needle tip, the photothermal-responsive characteristic of the PNIPAM/GO hydrogel middle layer could realize the contraction of the gel under the stimulation of near-infrared light, which subsequently could control the release of riboflavin from the backing layer into the cornea stromal site both in vitro and in vivo. Based on the microneedles system, we have demonstrated that this microinjection technique exhibited superior riboflavin delivery capacity and treatment efficacy to the conventional epithelial-on protocol in a rabbit keratoconus model, with benefits including minimal invasiveness and precise administering. Thus, we believe the responsive porous microneedles with riboflavin ocular microinjection capability are promising for clinical corneal crosslinking without epithelial debridement. [ABSTRACT FROM AUTHOR]

As the weak link in electro-optical imaging systems, photodetectors have always faced the threat of laser damage. In this paper, we experimentally investigated the damage mechanism of the photodetector induced by the out-of-band laser. The damage thresholds of the mid-infrared pulsed laser for Charge Coupled Device (CCD) and HgCdTe detectors were determined through damage experiments. The analysis of the damage phenomena and data for both CCD and HgCdTe detectors clearly demonstrated that out-of-band mid-infrared pulsed lasers could entirely incapacitate CCD and HgCdTe detectors. Our analysis of the damage process and data revealed that the primary mechanism of damage to CCD and HgCdTe detectors by mid-infrared pulsed lasers was primarily thermal. This study serves as a reference for further research on the mid-infrared pulsed laser damage mechanisms of CCD and HgCdTe detectors, as well as for laser protection and performance optimization in imaging systems. [ABSTRACT FROM AUTHOR]

Infrared (IR) camouflage conceals objects by reducing their temperatures or thermal emittances within the mid-wave IR (3–5 μm) and long-wave IR (8–14 μm) atmospheric transparent windows. Concerning textiles for IR camouflage on human bodies, it is crucial to consider not only low thermal emittances but also factors such as thermal comfort, resistance to abrasion and washing, breathability, and even blending with the intricate IR background. In this study, we develop a spectrally selective emittance textile (SET) using a roll-to-roll (R2R) magnetron sputtering deposition process. The unique properties of the SET arise from asymmetric Fabry–Pérot (F–P) cavity and surface impedance matching, resulting in low emittances of 0.39 and 0.44 in the IR detection wavelength range of 3–5 and 8–14 μm, respectively, and a high emittance of 0.79 in the undetectable range of 5–8 μm, enhancing thermal comfort. To improve the SET's resistance to abrasion and washing while maintaining breathability, we apply a highly IR-transparent polymer, styrene–ethylene–butylene–styrene (SEBS), to form a honeycomb pattern using a 3D-printed mask, producing a durable spectrally selective emittance textile (D-SET). The SEBS overcoating not only protects the IR camouflage textiles but also offers a versatile means of further engineering thermal emittance, realizing a spatially segmented emittance pattern with various IR colors, and ultimately achieving the IR-colorful spectrally selective emittance textile (IRC-SET). This work presents an approach aimed at achieving IR camouflage in textiles with both spectrally selective and spatially segmented emittances using scalable manufacturing techniques, which shows considerable potential for advancing the production and application of IR camouflage textiles in real-world scenarios. [ABSTRACT FROM AUTHOR]

Partitioning experiments and the chemistry of iron meteorites indicate that the light element nitrogen could be sequestered into the metallic core of rocky planets during core–mantle differentiation. The thermal conductivity and the mineralogy of the Fe–N system under core conditions could therefore influence the planetary cooling, core crystallization, and evolution of the intrinsic magnetic field of rocky planets. Limited experiments have been conducted to study the thermal properties and phase relations of Fe–N components under planetary core conditions, such as those found in the Moon, Mercury, and Ganymede. In this study, we report results from high-pressure experiments involving electrical resistivity measurements of Fe–N phases at a pressure of 5 GPa and temperatures up to 1400 K. Four Fe–N compositions, including Fe–10%N, Fe–6.4%N, Fe–2%N, and Fe–1%N (by weight percent), were prepared and subjected to recovery experiments at 5 GPa and 1273 K. These experiments show that Fe–10%N and Fe–6.4%N form a single hexagonal close-packed phase (ɛ-nitrides), while Fe–2%N and Fe–1%N exhibit a face-centered cubic structure (γ-Fe). In separate experiments, the resistivity data were collected during the cooling after compressing the starting materials to 5 GPa and heating to ~1400 K. The resistivity of all compositions, similar to the pure γ-Fe, exhibits weak temperature dependence. We found that N has a strong effect on the resistivity of metallic Fe under rocky planetary core conditions compared to other potential light elements such as Si. The temperature-dependence of the resistivity also revealed high-pressure phase transition points in the Fe–N system. A congruent reaction, ε ⇌ γ', occurs at ~673 K in Fe–6.4%N, which is ~280 K lower than that at ambient pressure. Furthermore, the resistivity data provided constraints on the high-pressure phase boundary of the polymorphic transition, γ ⇌ α, and an eutectoid equilibrium of γ' ⇌ α + ε. The data, along with the recently reported phase equilibrium experiments at high pressures, enable construction of a phase diagram of the Fe–N binary system at 5 GPa. [ABSTRACT FROM AUTHOR]

A silicone carrying combined functionalities of different mechanisms and physiochemical properties is synthesized as a strategy for faster and broader bacterial eliminations. Briefly, ring‐opening alcoholysis of succinic anhydride with 2‐dimethylaminoethanol and subsequent esterification of the alcoholysis product with 5‐(4‐hydroxyphenyl)imidazolidine‐2,4‐dione produced a structure with one tertiary amine and two hydantoinyl NH bonds. The tertiary amine was quaternized with 3‐chloro‐1‐propanol whose OH was then alcoholyzed with SiH of poly(methylhydrosiloxane). The polymeric product was interpenetrated into cotton at 30 MPa and 50°C in supercritical CO2 and treated with tert‐butyl hypochlorite to chlorinate its hydantoinyl NH bonds, forming a 78 nm biocidal layer of silicone with quaternized hydantoin‐based N‐chloramines. Antibacterial tests showed that the quaternary ammonium salt and two hydantoin‐based N‐chloramines act synergistically due to their complementary features, killing both Staphylococcus aureus and Escherichia coli much more efficiently than each functionality. More importantly, the ratio of N‐chloramine to quaternary ammonium salt of 2:1 herein also provided higher efficacy than a reported counterpart with a 1:1 ratio. Biocidability of the modified cotton had promising stability and rechargeability, which indicates that supercritical interpenetration produced a durable interfacial layer without the necessity of chemical bonding. [ABSTRACT FROM AUTHOR]

Large language models are enabling rapid progress in robotic verbal communication, but nonverbal communication is not keeping pace. Physical humanoid robots struggle to express and communicate using facial movement, relying primarily on voice. The challenge is twofold: First, the actuation of an expressively versatile robotic face is mechanically challenging. A second challenge is knowing what expression to generate so that the robot appears natural, timely, and genuine. Here, we propose that both barriers can be alleviated by training a robot to anticipate future facial expressions and execute them simultaneously with a human. Whereas delayed facial mimicry looks disingenuous, facial coexpression feels more genuine because it requires correct inference of the human's emotional state for timely execution. We found that a robot can learn to predict a forthcoming smile about 839 milliseconds before the human smiles and, using a learned inverse kinematic facial self-model, coexpress the smile simultaneously with the human. We demonstrated this ability using a robot face comprising 26 degrees of freedom. We believe that the ability to coexpress simultaneous facial expressions could improve human-robot interaction. Editor's summary: Humanoid robots are capable of mimicking human expressions by perceiving human emotions and responding after the human has finished their expression. However, a delayed smile can feel artificial and disingenuous compared with a smile occurring simultaneously with a companion's smile. Hu et al. trained their anthropomorphic facial robot named Emo to display an anticipatory expression to match its human companion. Emo is equipped with 26 motors and flexible silicone skin to provide precise control over its facial expressions. The robot was trained with a video dataset of humans making expressions. By observing subtle changes in a human face, the robot could predict an approaching smile 839 milliseconds before the human smiled and adjust its face to smile simultaneously. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

To investigate the damage threshold and mechanism of a mid-infrared HgCdTe focal plane array (FPA) detector, relevant experimental and theoretical studies were conducted. The line damage threshold of a HgCdTe FPA detector may be within the range of 0.59 Jcm−2 to 0.71 Jcm−2. The full frame damage threshold of the detector may be in the range of 0.86 Jcm−2 to 1.17 Jcm−2. Experimental results showed that when the energy density reaches 1.17 Jcm−2, the detector exhibits irreversible full frame damage and is completely unable to image. Based on the finite element method, a three-dimensional model of HgCdTe FPAs detector was established to study the heat transfer mechanism, internal stress, and damage sequence. When HgCdTe melts, we think that the detector is damaged. Under these conditions, the theoretical damage threshold calculated using the detector model is 0.55 Jcm−2. The difference between theoretical and experimental values was analyzed. The relationship between damage threshold and pulse width was also studied. It was found that when the pulse width is less than 1000 ns, the damage threshold characterized by peak power density is inversely proportional to pulse width. This relationship can help us predict the experimental damage threshold of an FPA detector. This model is reasonable and convenient for studying the damage of FPA detectors with a mid-infrared pulse laser. The research content in this article has important reference significance for the damage and protection of HgCdTe FPA detectors. [ABSTRACT FROM AUTHOR]

Industrialization, urbanization, population expansion, and changes in lifestyles within the Group of Seven (G7) have raised the danger of global warming since CO2 emissions directly impact the quantity of power that can be produced from diverse sources. However, the intrinsic energy needs and CO2 emissions found in renewable energy, especially solar cells and associated equipment, which have been extensively embraced in low-income nations, are seldom, if ever, considered by decision-makers. We propose converting a conventional neural network into a quantum photonic system. First, the classical neurons are made reversible by adding extra bits. After that, unitarity and quantum reversibility are added to the list. This work provides a unique approach to lowering carbon emissions based on environmentally friendly renewable solar cells and environmental thermal image analysis using machine learning architectures. The ambient thermal picture collected from both developed and developing countries was processed using convolutional adversarial Gaussian markov neural networks. The usage of eco-renewable solar cells has led to a reduction in carbon emissions in both industrialized and developing countries. The results of the experiments are broken down into many categories, including prediction accuracy, energy consumption, resilience, execution time, and mean average precision. [ABSTRACT FROM AUTHOR]

Aims Blood eosinophil count and eosinophil cationic protein (ECP) concentration are risk factors of cardiovascular diseases. This study tested whether and how eosinophils and ECP contribute to vascular calcification and atherogenesis. Methods and results Immunostaining revealed eosinophil accumulation in human and mouse atherosclerotic lesions. Eosinophil deficiency in ΔdblGATA mice slowed atherogenesis with increased lesion smooth muscle cell (SMC) content and reduced calcification. This protection in ΔdblGATA mice was muted when mice received donor eosinophils from wild-type (WT), Il4−/− , and Il13−/− mice or mouse eosinophil-associated-ribonuclease-1 (mEar1), a murine homologue of ECP. Eosinophils or mEar1 but not interleukin (IL) 4 or IL13 increased the calcification of SMC from WT mice but not those from Runt-related transcription factor-2 (Runx2) knockout mice. Immunoblot analyses showed that eosinophils and mEar1 activated Smad-1/5/8 but did not affect Smad-2/3 activation or expression of bone morphogenetic protein receptors (BMPR-1A/1B/2) or transforming growth factor (TGF)-β receptors (TGFBR1/2) in SMC from WT and Runx2 knockout mice. Immunoprecipitation showed that mEar1 formed immune complexes with BMPR-1A/1B but not TGFBR1/2. Immunofluorescence double-staining, ligand binding, and Scatchard plot analysis demonstrated that mEar1 bound to BMPR-1A and BMPR-1B with similar affinity. Likewise, human ECP and eosinophil-derived neurotoxin (EDN) also bound to BMPR-1A/1B on human vascular SMC and promoted SMC osteogenic differentiation. In a cohort of 5864 men from the Danish Cardiovascular Screening trial and its subpopulation of 394 participants, blood eosinophil counts and ECP levels correlated with the calcification scores of different arterial segments from coronary arteries to iliac arteries. Conclusion Eosinophils release cationic proteins that can promote SMC calcification and atherogenesis using the BMPR-1A/1B-Smad-1/5/8-Runx2 signalling pathway. [ABSTRACT FROM AUTHOR]

Azithromycin eye drops with a bioadhesive ocular drug‐delivery system can offer a simplified dosing regimen. In this study, we compared the pharmacokinetic properties and assessed the bioequivalence of a newly developed generic azithromycin eye drop with a branded formulation. This open‐label, single‐dose, randomized, crossover, sparse‐sampling ocular bioequivalence study was conducted on 48 healthy Chinese volunteers. Tear samples were collected for up to 36 hours, and each participant was randomly allocated to one of the prespecified sampling times. Tear drug concentrations were determined using a validated liquid chromatography‐tandem mass spectrometry method. The pharmacokinetic parameters were calculated via noncompartmental analysis. A nonparametric bootstrap method was used to obtain 90% confidence intervals (CIs) for the ratios of the test and reference drugs. Tolerability was evaluated for adverse events (AEs). After bootstrapping (1000 iterations), the 90%CIs for the log‐transformed ratios of Cmax, AUC0‐t, and AUC0–∞were within the acceptable bioequivalence range (80%–125%). No moderate‐to‐severe AEs were reported for either formulation. Bioequivalence was demonstrated between the two formulations. The sparse‐sampling design with the bootstrapping technique is promising for bioequivalence studies of topical ophthalmic drugs. [ABSTRACT FROM AUTHOR]

Background: Myopia has emerged as a major public health concern globally, which is tightly associated with scleral extracellular matrix (ECM) remodeling and choroidal vasculopathy. Choroidal vasculopathy has gradually been recognized as a critical trigger of myopic pathology. However, the precise mechanism controlling choroidal vasculopathy remains unclear. Transfer RNA-derived fragments (tRFs) are known as a novel class of small non-coding RNAs that plays important roles in several biological and pathological processes. In this study, we investigated the role of tRF-22-8BWS72092 (tRF-22) in choroidal vasculopathy and myopia progression. Methods: The tRF-22 expression pattern under myopia-related stresses was detected by qRT-PCR. MTT assays, EdU incorporation assays, Transwell migration assays, and Matrigel assays were conducted to detect the role of tRF-22 in choroidal endothelial cell function in vitro. Isolectin B4 staining and choroidal sprouting assay ex vivo were conducted to detect the role of tRF-22 in choroidal vascular dysfunction in vivo. Immunofluorescent staining, western blot assays and ocular biometric parameters measurement were performed to examine whether altering tRF-22 expression in choroid affects scleral hypoxia and ECM remodeling and myopia progression in vivo. Bioinformatics analysis and luciferase activity assays were conducted to identify the downstream targets of tRF-22. RNA-sequencing combined with m6A-qPCR assays were used to identify the m6A modified targets of METTL3. Gain-of-function and Loss-of-function analysis were performed to reveal the mechanism of tRF-22/METTL3-mediated choroidal vascular dysfunction. Results: The results revealed that tRF-22 expression was significantly down-regulated in myopic choroid. tRF-22 overexpression alleviated choroidal vasculopathy and retarded the progression of myopia in vivo. tRF-22 regulated choroidal endothelial cell viability, proliferation, migration, and tube formation ability in vitro. Mechanistically, tRF-22 interacted with METTL3 and blocked m6A methylation of Axin1 and Arid1b mRNA transcripts, which led to increased expression of Axin1 and Arid1b. Conclusions: Our study reveals that the intervention of choroidal vasculopathy via tRF-22-METTL3- Axin1/Arid1b axis is a promising strategy for the treatment of patients with myopic pathology. [ABSTRACT FROM AUTHOR]

Phase engineering is an emerging strategy for tuning the electronic states and catalytic functions of nanomaterials. Great interest has recently been captured by phase-engineered photocatalysts, including the unconventional phase, amorphous phase, and heterophase. Phase engineering of photocatalytic materials (including semiconductors and cocatalysts) can effectively affect the light absorption range, charge separation efficiency, or surface redox reactivity, resulting in different catalytic behavior. The applications for phase-engineered photocatalysts are widely reported, for example, hydrogen evolution, oxygen evolution, CO2 reduction, and organic pollutant removal. This review will firstly provide a critical insight into the classification of phase engineering for photocatalysis. Then, the state-of-the-art development of phase engineering toward photocatalytic reactions will be presented, focusing on the synthesis and characterization methodologies for unique phase structure and the correlation between phase structure and photocatalytic performance. Finally, personal understanding of the current opportunities and challenges of phase engineering for photocatalysis will also be provided. [ABSTRACT FROM AUTHOR]

Reinforcement learning has a demand for massive data in complex problems, which makes it infeasible to be applied to real cases where sampling is difficult. The key to coping with these few-shot problems is knowledge generalization, and related algorithms are often called few-shot reinforcement learning (FS-RL). However, there lacks a formal definition and comprehensive analyses of few-shot scenarios and FS-RL algorithms. Therefore, after giving a uniform definition, we categorize few-shot scenarios into two types. The first type pursues more professional performance, while the other one pursues more general performance. In the process of knowledge transfer, few-shot scenarios usually have an obvious tendency to some type of knowledge. Based on this, we divide FS-RL algorithms into two types: the direct transfer case and the indirect transfer case. Thereafter, existing algorithms are discussed under this classification. Finally, we discuss future directions of FS-RL from the aspect of both theory and application. [ABSTRACT FROM AUTHOR]

The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but the structures of the clusters can be computationally expensive to predict. In this work, we present the largest database of cluster structures and properties determined using ab-initio methods to date. We report the methodologies used to discover low-energy clusters as well as the energies, relaxed structures, and physical properties (such as relative stability, HOMO-LUMO gap among others) for 63,015 clusters across 55 elements. We have identified clusters for 593 out of 1595 cluster systems (element-size pairs) explored by literature that have energies lower than those reported in literature by at least 1 meV/atom. We have also identified clusters for 1320 systems for which we were unable to find previous low-energy structures in the literature. Patterns in the data reveal insights into the chemical and structural relationships among the elements at the nanoscale. We describe how the database can be accessed for future studies and the development of nanocluster-based technologies. [ABSTRACT FROM AUTHOR]

Nowadays, millions of patients suffer from physical disabilities, including lower-limb disabilities. Researchers have adopted a variety of physical therapies based on the lower-limb exoskeleton, in which it is difficult to adjust equipment parameters in a timely fashion. Therefore, intelligent control methods, for example, deep reinforcement learning (DRL), have been used to control the medical equipment used in human gait adjustment. In this study, based on the key-value attention mechanism, we reconstructed the agent's observations by capturing the self-dependent feature information for decision-making in regard to each state sampled from the replay buffer. Moreover, based on Softmax Deep Double Deterministic policy gradients (SD3), a novel DRL-based framework, key-value attention-based SD3 (AT_SD3), has been proposed for gait adjustment. We demonstrated the effectiveness of our proposed framework in gait adjustment by comparing different gait trajectories, including the desired trajectory and the adjusted trajectory. The results showed that the simulated trajectories were closer to the desired trajectory, both in their shapes and values. Furthermore, by comparing the results of our experiments with those of other state-of-the-art methods, the results proved that our proposed framework exhibited better performance. [ABSTRACT FROM AUTHOR]

Background: Mounting evidence has revealed the dynamic variations in the cellular status and phenotype of the smooth muscle cell (SMC) are vital for shaping the atherosclerotic plaque microenvironment and ultimately mapping onto heterogeneous clinical outcomes in coronary artery disease. Currently, the underlying clinical significance of SMC evolutions remains unexplored in atherosclerosis. Methods: The dissociated cells from diseased segments within the right coronary artery of four cardiac transplant recipients and 1070 bulk samples with atherosclerosis from six bulk cohorts were retrieved. Following the SMC fate trajectory reconstruction, the MOVICS algorithm integrating the nearest template prediction was used to develop a stable and robust molecular classification. Subsequently, multi-dimensional potential biological implications, molecular features, and cell landscape heterogeneity among distinct clusters were decoded. Results: We proposed an SMC cell fate decision signature (SCFDS)-based atherosclerosis stratification system and identified three SCFDS subtypes (C1–C3) with distinguishing features: (i) C1 (DNA-damage repair type), elevated base excision repair (BER), DNA replication, as well as oxidative phosphorylation status. (ii) C2 (immune-activated type), stronger immune activation, hyper-inflammatory state, the complex as well as varied lesion microenvironment, advanced stage, the most severe degree of coronary stenosis severity. (iii) C3 (stromal-rich type), abundant fibrous content, stronger ECM metabolism, immune-suppressed microenvironment. Conclusions: This study uncovered atherosclerosis complex cellular heterogeneity and a differentiated hierarchy of cell populations underlying SMC. The novel high-resolution stratification system could improve clinical outcomes and facilitate individualized management. [ABSTRACT FROM AUTHOR]

Currently, reinforcement learning (RL) has shown great potential in energy saving in HVAC systems. However, in most cases, RL takes a relatively long period to explore the environment before obtaining an excellent control policy, which may lead to an increase in cost. To reduce the unnecessary waste caused by RL methods in exploration, we extended the deep forest-based deep Q-network (DF-DQN) from the prediction problem to the control problem, optimizing the running frequency of the cooling water pump and cooling tower in the cooling water system. In DF-DQN, it uses the historical data or expert experience as a priori knowledge to train a deep forest (DF) classifier, and then combines the output of DQN to attain the control frequency, where DF can map the original action space of DQN to a smaller one, so DF-DQN converges faster and has a better energy-saving effect than DQN in the early stage. In order to verify the performance of DF-DQN, we constructed a cooling water system model based on historical data. The experimental results show that DF-DQN can realize energy savings from the first year, while DQN realized savings from the third year. DF-DQN's energy-saving effect is much better than DQN in the early stage, and it also has a good performance in the latter stage. In 20 years, DF-DQN can improve the energy-saving effect by 11.035% on average every year, DQN can improve by 7.972%, and the model-based control method can improve by 13.755%. Compared with traditional RL methods, DF-DQN can avoid unnecessary waste caused by exploration in the early stage and has a good performance in general, which indicates that DF-DQN is more suitable for engineering practice. [ABSTRACT FROM AUTHOR]

The article titled "Retraction Note: Comparative analysis optical communication based renewable solar cell and quantum network for the reduction of carbon emission" published in Optical & Quantum Electronics has been retracted by the Publisher due to concerns raised during an investigation. The retraction was based on issues such as the article being out of scope for the journal, inconsistencies in the peer-review process, and inappropriate references. The authors did not respond to the Publisher's inquiries regarding the retraction. [Extracted from the article]

The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but predicting the stable structures of clusters can be computationally expensive. In this work, we present a procedure for rapidly predicting low-energy structures of nanoclusters by combining a genetic algorithm with interatomic potentials actively learned on-the-fly. Applying this approach to aluminum clusters with 21 to 55 atoms, we have identified structures with lower energy than any reported in the literature for 25 out of the 35 sizes. Our benchmarks indicate that the active learning procedure accelerated the average search speed by about an order of magnitude relative to genetic algorithm searches using only density functional calculations. This work demonstrates a feasible way to systematically discover stable structures for large nanoclusters and provides insights into the transferability of machine-learned interatomic potentials for nanoclusters. [ABSTRACT FROM AUTHOR]

The study used linearly and circularly polarized femtosecond pulsed lasers to irradiate a Ta2O5/SiO2 film. Firstly, the damage thresholds of the film for linearly and circularly polarized femtosecond pulsed lasers were measured in 1-on-1 mode. The results showed that the damage threshold (1.70 J/cm2) under a circularly polarized laser was higher than that (1.68 J/cm2) under a linearly polarized laser. For femtosecond lasers, the multi-photon ionization cross-section under circular polarization was lower than that under linear polarization. The lower ionization rate under circular polarization led to a higher damage threshold compared to the case under linear polarization. Secondly, the damage morphology of the film irradiated by linearly and circularly polarized femtosecond lasers was observed by microscope. The damage caused by linearly polarized laser was more evident than that caused by the circularly polarized laser. Finally, the damage thresholds induced by linearly and circularly polarized femtosecond pulsed lasers were measured in S-on-1 (S = 2, 5, and 10) mode. For the same S value (2, 5, or 10), the damage threshold under the circularly polarized laser was higher than that under the linearly polarized laser. The damage thresholds under two polarized laser pulses decreased with an increase in the number of laser shots, indicating that repeated laser pulses had a cumulative effect on the damage of the film. [ABSTRACT FROM AUTHOR]

Visual query of spatiotemporal data is becoming an increasingly important function in visual analytics applications. Various works have been presented for querying large spatiotemporal data in real time. However, the real-time query of spatiotemporal data distribution is still an open challenge. As spatiotemporal data become larger, methods of aggregation, storage and querying become critical. We propose a new visual query system that creates a low-memory storage component and provides real-time visual interactions of spatiotemporal data. We first present a peak-based kernel density estimation method to produce the data distribution for the spatiotemporal data. Then a novel density dictionary learning approach is proposed to compress temporal density maps and accelerate the query calculation. Moreover, various intuitive query interactions are presented to interactively gain patterns. The experimental results obtained on three datasets demonstrate that the presented system offers an effective query for visual analytics of spatiotemporal data. [ABSTRACT FROM AUTHOR]

Circulating exosomes delivering microRNAs are involved in the occurrence and development of cardiovascular diseases. How are the circulating exosomes involved in the repair of endothelial injury in acute myocardial infarction (AMI) convalescence (3‐7 days) was still not clear. In this study, circulating exosomes from AMI patients (AMI‐Exo) and healthy controls (Normal‐Exo) were extracted. In vitro and in vivo, our study showed that circulating exosomes protected endothelial cells (HUVECs) from oxidative stress damage; meanwhile, Normal‐Exo showed better protective effects. Through the application of related inhibitors, we found that circulating exosomes shuttled between HUVECs via dynamin. Microarry analysis and qRT‐PCR of circulating exosomes showed higher expression of miR‐193a‐5p in Normal‐Exo. Our study showed that miR‐193a‐5p was the key factor on protecting endothelial cells in vitro and in vivo. Bioinformatics analyses found that activin A receptor type I (ACVR1) was the potential downstream target of miR‐193a‐5p, which was confirmed by ACVR1 expression and dual‐luciferase report. Inhibitor of ACVR1 showed similar protective effects as miR‐193a‐5p. While overexpression of ACVR1 could attenuate protective effects of miR‐193a‐5p. To sum up, these findings suggest that circulating exosomes could shuttle between cells through dynamin and deliver miR‐193a‐5p to protect endothelial cells from oxidative stress damage via ACVR1. [ABSTRACT FROM AUTHOR]

Clinical studies reveal changes in blood eosinophil counts and eosinophil cationic proteins that may serve as risk factors for human coronary heart diseases. Here we report an increase of blood or heart eosinophil counts in humans and mice after myocardial infarction (MI), mostly in the infarct region. Genetic or inducible depletion of eosinophils exacerbates cardiac dysfunction, cell death, and fibrosis post-MI, with concurrent acute increase of heart and chronic increase of splenic neutrophils and monocytes. Mechanistic studies reveal roles of eosinophil IL4 and cationic protein mEar1 in blocking H2O2- and hypoxia-induced mouse and human cardiomyocyte death, TGF-β-induced cardiac fibroblast Smad2/3 activation, and TNF-α-induced neutrophil adhesion on the heart endothelial cell monolayer. In vitro-cultured eosinophils from WT mice or recombinant mEar1 protein, but not eosinophils from IL4-deficient mice, effectively correct exacerbated cardiac dysfunctions in eosinophil-deficient ∆dblGATA mice. This study establishes a cardioprotective role of eosinophils in post-MI hearts. Blood eosinophil (EOS) counts may serve as risk factors for human coronary heart diseases. Here the authors show that increased circulating and myocardial EOS after myocardial infarction play a cardioprotective role by reducing cardiomyocyte death, cardiac fibroblast activation and fibrosis, and endothelium activation-mediated inflammatory cell accumulation. [ABSTRACT FROM AUTHOR]

For providing a better understanding of the quantitative relationship between typical wetland vegetation and their tidal creek habitats in the Yellow River Estuary, the tidal creeks and three wetland vegetation types of Spartina alterniflora, Phragmites australis and Suaeda salsa were selected to analyze their distribution characteristics and explore the correlation between their spatial distributions within the different areas in the study. Authors applied the methods of regional statistical analysis, proximity analysis and spatial autocorrelation analysis to quantifying the spatial distribution characteristics of vegetation and tidal creeks and their spatial interaction as well. The results showed that 86.87% of S. alterniflora was distributed in the salt-fresh water interaction zones along the Yellow River. S. alterniflora occupied more space around the tidal creeks. P. australis and S. salsa were only closely related to low-order tidal creeks, but the curvature of their surrounding tidal creeks was higher than that of S. alterniflora. The three types of vegetation in the Tidal Creek Buffer Zone (TCBZ) had spatial agglomeration in their habitats. Among of three vegetation types, S. alterniflora had the closest relationships with tidal creeks. This study has an important theoretical and practical significance for the management and restoration of related wetland ecosystems. [ABSTRACT FROM AUTHOR]

Water and sediment regulation scheme (WSRS) is an annual water conservancy project which aims to alleviate sand depositions and reduce flood risks in the Yellow River of China since 2002. There are few researches on the sand bar within Yellow River tail channel and its estuary from each WSRS effect, though these relative researches are important to be carried out for a better understanding of WSRS. By interpreting two GF-1 satellite images in the end of 2013 and the start of 2015 after carefully controlling some potential bias factors from the change of water level, precipitation or tidal, we compared and analyzed the change of sand bars by one WSRS implemented in 2014 in this paper. Our results indicated this WSRS changed obviously the area/shape of sand bars. It made the area of the river sand bar decreased totally by 5.55 km2, while the estuarine sand bar increased by 2.08 km2. And it achieved the highest scour efficiency (90.11%) in the straight channel, but it achieved the lowest scour efficiency (44.71%) in the more bends channel. From the prospect in the change of estuarine sand bar, the west side area increased, while the east side area shrank. There were some broken small sand bars on the north side and they were forming a large sand bar. [ABSTRACT FROM AUTHOR]

Aims Obesity is a risk factor of abdominal aortic aneurysm (AAA). Inflammatory cytokine interleukin-18 (IL18) has two receptors: IL18 receptor (IL18r) and Na-Cl co-transporter (NCC). In human and mouse AAA lesions, IL18 colocalizes to its receptors at regions rich in adipocytes, suggesting a role of adipocytes in promoting IL18 actions in AAA development. Methods and results We localized both IL18r and NCC in human and mouse AAA lesions. Murine AAA development required both receptors. In mouse AAA lesions, IL18 binding to these receptors increased at regions enriched in adipocytes or adjacent to perivascular adipose tissue. 3T3-L1 adipocytes enhanced IL18 binding to macrophages, aortic smooth muscle cells (SMCs), and endothelial cells by inducing the expression of both IL18 receptors on these cells. Adipocytes also enhanced IL18r and IL18 expression from T cells and macrophages, AAA-pertinent protease expression from macrophages, and SMC apoptosis. Perivascular implantation of adipose tissue from either diet-induced obese mice or lean mice but not that from leptin-deficient ob/ob mice exacerbated AAA development in recipient mice. Further experiments established an essential role of adipocyte leptin and fatty acid-binding protein 4 (FABP4) in promoting IL18 binding to macrophages and possibly other inflammatory and vascular cells by inducing their expression of IL18, IL18r, and NCC. Conclusion Interleukin-18 uses both IL18r and NCC to promote AAA formation. Lesion adipocyte and perivascular adipose tissue contribute to AAA pathogenesis by releasing leptin and FABP4 that induce IL18, IL18r, and NCC expression and promote IL18 actions. Open in new tab Download slide Open in new tab Download slide [ABSTRACT FROM AUTHOR]

In urban analysis, it is desirable to find regions where a primary socio‐economic activity dominates as a key endeavour. This can be accomplished by aggregating neighbouring locations where similar activities take place. However, people move and their activities change over time. Furthermore, the boundaries of regions are not stationary. Thus, it is challenging to update region divisions and track their evolution. Geo‐textual data embody geographical information and activity descriptions. We obtain changes in regional boundaries by iteratively applying a community detection process to a sequence of latent graphs that are constructed from geo‐textual data. Region characteristics are interpreted by topics learned by the latent Dirichlet allocation model. We also propose a matching algorithm to expose region transformations between different timestamps. Interesting patterns of evolution emerge after clustering the migration trajectories of region centroids. In our visual system, users can explore the evolution of regions through animations and linked snapshots. To facilitate visual comparisons, we represent regions by hexagonal tiling that better construct arbitrary regional shapes. The effectiveness of our method is evaluated on two case studies using real‐world datasets, and a user study shows that our visual analytics system is highly effective in performing studies on such regional maps. [ABSTRACT FROM AUTHOR]

The efficient visualization of dynamic network structures has become a dominant problem in many big data applications, such as large network analytics, traffic management, resource allocation graphs, logistics, social networks, and large document repositories. In this paper, we present a large-graph visualization system called ModuleGraph. ModuleGraph is a scalable representation of graph structures by treating a graph as a set of modules. The main objectives are: (1) to detect graph patterns in the visualization of large-graph data, and (2) to emphasize the interconnecting structures to detect potential interactions between local modules. Our first contribution is a hybrid modularity measure. This measure partitions the cohesion of the graph at various levels of details. We aggregate clusters of nodes and edges into several modules to reduce the overlap between graph components on a 2D display. Our second contribution is a k-clustering method that can flexibly detect the local patterns or substructures in modules. Patterns of modules are preserved by the ModuleGraph system to avoid information loss, while sub-graphs are clustered as a single node. Our experiments show that this method can efficiently support large-scale social and spatial network visualization. Graphical Abstract: Graphical Abstract text [ABSTRACT FROM AUTHOR]

When deep reinforcement learning (DRL) methods are applied in energy consumption prediction, performance is usually improved at the cost of the increasing computation time. Specifically, the deep deterministic policy gradient (DDPG) method can achieve higher prediction accuracy than deep Q-network (DQN), but it requires more computing resources and computation time. In this paper, we proposed a deep-forest-based DQN (DF–DQN) method, which can obtain higher prediction accuracy than DDPG and take less computation time than DQN. Firstly, the original action space is replaced with the shrunken action space to efficiently find the optimal action. Secondly, deep forest (DF) is introduced to map the shrunken action space to a single sub-action space. This process can determine the specific meaning of each action in the shrunken action space to ensure the convergence of DF–DQN. Thirdly, state class probabilities obtained by DF are employed to construct new states by considering the probabilistic process of shrinking the original action space. The experimental results show that the DF–DQN method with 15 state classes outperforms other methods and takes less computation time than DRL methods. MAE, MAPE, and RMSE are decreased by 5.5%, 7.3%, and 8.9% respectively, and R 2 is increased by 0.3% compared to the DDPG method. [ABSTRACT FROM AUTHOR]

Laser-induced damage threshold (LIDT) is an essential factor in measuring the anti-laser damage of optical films. The damage threshold and morphology of the Ta2O5/SiO2 multilayer film prepared by electron beam evaporation were studied by femtosecond (50 fs) and picosecond (30 ps) laser irradiations. The results showed that the LIDT of the film was 1.7 J·cm−2 under the femtosecond laser. The damage morphology developed from surface damage to a clear layered structure, and the outline has become more transparent and regular with an increase in the laser fluence. Under the picosecond laser irradiation, the LIDT of the film was 2.0 J·cm−2. The damage morphology developed from small range to thin film layer separation, and the outline changed from blurry to clear with an increase in laser fluence. Therefore, the LIDT of the film decreased with a decrease in the laser pulse width. [ABSTRACT FROM AUTHOR]

District heating networks make up an important public energy service, in which leakage is the main problem affecting the safety of pipeline network operation. This paper proposes a Leakage Fault Detection (LFD) method based on the Linear Upper Confidence Bound (LinUCB) which is used for arm selection in the Contextual Bandit (CB) algorithm. With data collected from end-users' pressure and flow information in the simulation model, the LinUCB method is adopted to locate the leakage faults. Firstly, we use a hydraulic simulation model to simulate all failure conditions that can occur in the network, and these change rate vectors of observed data form a dataset. Secondly, the LinUCB method is used to train an agent for the arm selection, and the outcome of arm selection is the leaking pipe label. Thirdly, the experiment results show that this method can detect the leaking pipe accurately and effectively. Furthermore, it allows operators to evaluate the system performance, supports troubleshooting of decision mechanisms, and provides guidance in the arrangement of maintenance. [ABSTRACT FROM AUTHOR]

The pH in atherosclerotic lesions varies between individuals. IgE activates macrophage Na+-H+ exchanger (Nhe1) and induces extracellular acidification and cell apoptosis. Here, we show that the pH-sensitive pHrodo probe localizes the acidic regions in atherosclerotic lesions to macrophages, IgE, and cell apoptosis. In Apoe–/– mice, Nhe1-deficiency or anti-IgE antibody reduces atherosclerosis and blocks lesion acidification. Reduced atherosclerosis in Apoe–/– mice receiving bone marrow from Nhe1- or IgE receptor FcεR1-deficient mice, blunted foam cell formation and signaling in IgE-activated macrophages from Nhe1-deficient mice, immunocomplex formation of Nhe1 and FcεR1 in IgE-activated macrophages, and Nhe1-FcεR1 colocalization in atherosclerotic lesion macrophages support a role of IgE-mediated macrophage Nhe1 activation in atherosclerosis. Intravenous administration of a near-infrared fluorescent pH-sensitive probe LS662, followed by coregistered fluorescent molecular tomography-computed tomography imaging, identifies acidic regions in atherosclerotic lesions in live mice, ushering a non-invasive and radiation-free imaging approach to monitor atherosclerotic lesions in live subjects. Na+-H+ exchanger 1 (Nhe1) regulates extracellular pH by extruding protons in exchange for extracellular Na+. Here, Liu et al. show that Nhe1 promotes the development and acidification of atherosclerotic lesions and that pH-sensitive probes can be used to monitor plaque growth and acidification. [ABSTRACT FROM AUTHOR]