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Uncertainty exists about whether lowering systolic blood pressure to less than 120 mm Hg is superior to that of less than 140 mm Hg, particularly in patients with diabetes and patients with previous stroke.

The solid-liquid equilibria of the quinary system LiCl–NaCl–CaCl2–H3BO3–H2O with sodium chlorine saturated at 323.15 K were studied by the isothermal dissolution equilibrium method. Based on the measured experimental solubility data, the dry-salt phase diagram, water content diagram, and sodium chloride content phase diagrams were plotted. The dry-salt phase diagram in this system consists of one invariant point saturated with LiCl·H2O, CaCl2·2H2O, and H3BO3, three univariant curves, and three crystallization regions saturated with NaCl corresponding to CaCl2·2H2O, LiCl·H2O, and H3BO3. No solid solution was found in the system. The areas of crystallization zones increase according to the above order. The Jänecke index of H2O and NaCl and the refractive index change regularly with the Jänecke index of LiCl. The calculated refractive indices agree well with the experimental data using the semiempirical equation. These results on phase diagrams of the quinary system can supply theoretical references for lithium salts and boric acid separation from calcium chloride-type brine.

Postoperative adhesion (PA) is a severe complication of abdominal surgery caused by the inability of clinical physical barriers to cope with diverse pathological factors in the process of PA formation. Herein, we described a multifunctional hydrogel composed of bioactive nanoparticles (BNs) and dual-responsive hydrogel to serve as a combination of physical and pharmacological therapy for preventing PA. Specifically, BNs with pro-inflammatory cell-targeted aggregation were designed by integrating hyaluronic acid onto the polydopamine (PDA)-coated hollow ZrO2nanoparticles loaded with antimicrobial peptides and platelet lysates that can eliminate bacterial infection and promote tissue repair. PDA can remove the excessive reactive oxygen species (ROS) and thus suppress the oxidative stress damage and accompanying inflammation in the presence of high ROS. The dynamically cross-linked host hydrogel presents injectable yet microenvironment-responsive properties, which enables complete coverage of the uneven tissue and instantly forms a physical barrier to effectively isolate injured tissues and neighboring organs, and synchronously acts as a niche to deliver the BNs in a controlled way. The hydrogel demonstrates a remarkable antiadhesion effect in a rat cecum-abdominal wall adhesion model. Together, this “all-in-one” composite hydrogel strategy capable of a physical barrier capability and pharmacological effects represents a promising clinical solution to prevent PA.

Titanium alloys, especially α+β type ones, are usually subjected to diverse heat treatments for tailoring their microstructures and mechanical properties. In this study, we investigated the microstructure, microtexture, and mechanical properties of a novel α+β type Ti–5Al–3Mo–3Cr–1Zr-0.15Si alloy with annealing treatments including single annealing, duplex annealing, and β annealing. It was found that the sizes of β grains, α colonies and secondary α, as well as the volume fraction of primary α, are closely dependent on the annealing temperatures. Additionally, the crystallographic orientations of the α and β phases change with specific annealing treatments while consistently adhering to the Burgers orientation relationship. Despite the variations in microtexture intensity with different treatments, the initial microtexture components are retained due to the texture memory effect. The microstructure and mechanical property characterizations revealed that the secondary α phase exert more contribution to the strengthening effect than the primary α phase, while continuous and straight grain boundary α phase undermines the ductility. Duplex annealing, which produces fine β grains and fine secondary α phase, achieves a good balance between the strength and ductility of the alloy.

The detection of anomalies in multisource time-series data is crucial for the efficient operation of the Internet of Things (IoT) devices and large-scale cross-domain systems. Recent methods have primarily focused on using deep learning to model time series directly. However, these approaches often overlook intricate inherent patterns within the data. We have introduced time-series decomposition into anomaly detection, allowing algorithms to explicitly uncover underlying periodicities. Additionally, to address the lead-lag effects among multiple sensors, we integrated the dynamic time warping (DTW) algorithm into the construction of graph neural networks. This integration enables the efficient and accurate construction of adjacency matrices based on sensor similarity. Furthermore, by leveraging graph networks for spatiotemporal modeling of time series and employing a joint loss function, our approach achieves greater accuracy and stability in anomaly detection performance. Comparative analysis with multiple datasets and algorithms demonstrates the consistent superiority and stability of our method.

The fragile ecological balance of lakeside plains on plateau makes groundwater hydrochemical composition highly susceptible to human disturbances. This research investigates a typical plain on Yunnan Plateau to quantitatively identify the potential sources of groundwater pollution in lakeside plains on plateau by combining receptor models with risk assessment models. Receptor models show there are three potential pollution sources for groundwater including water-rock interactions, agricultural pollution, and domestic sewage/manure discharge. Groundwater hydrochemical composition is predominantly shaped by natural water-rock interactions, accounting for an average of 50.12 % of the influence. This is followed by agricultural non-point source pollution, which contributes 10.79 %, and the discharge of domestic sewage and manure, which accounts for 10.61 %. Groundwater in the current plain could pose a minor non-carcinogenic risk for long-term drinking, with the total hazard index (THI) for adults varying between 0.015 and 1.089, and for children between 0.024 and 1.737. Notably, 4.35 % of groundwater samples have a THI exceeding the safe threshold of 1. NO3-(72 %) is the primary contributor to non-carcinogenic risks, accounting for 72 %, trailed by Mn (13 %), NH4+(9 %), Fe (6 %) and NO2-(1 %). The non-carcinogenic risks are predominantly attributed to agricultural non-point source pollution (71.6 %) and the discharge of domestic sewage and manure (26.9 %). A conceptual model has been developed to encapsulate the dynamics of groundwater quality formation in lakeside plains on plateau. This research will aid residents in safeguarding against groundwater contamination and enhancing agricultural practices in present plain and similar lakeside plain worldwide.

The removal of dichloromethane (DCM) from gas streams by a novel gas discharge reactor triggered by solution droplets was experimentally investigated. The influences of operation parameters such as input energy, gas flow rate, oxygen concentration, droplet dropping frequency, and the solution composition of droplets on the effectiveness of reactor operation and DCM removal efficiency were examined. Experimental results showed that DCM in the gas stream can be effectively degraded, and the removal of DCM decreased with the increase of the initial DCM concentration in the gas stream. With the increase of droplet dropping frequency, the removal of DCM experiences a process from increase to decrease, and the optimal droplet dropping frequency is around 0.5–1 drop·s−1. It was also found that the oxygen concentration in the range of 5–10 % in the gas stream is better for DCM decomposition, and the use of alkaline solution as droplets can effectively improve the removal of DCM. The final gas phase products of DCM degradation were mainly CO2and O3with the presence of O2in the gas stream, and the liquid phase products were chloride (Cl-) and hypochlorite (ClO-) ions.

This article studies the problem of collision/obstacle avoidance in the distributed cooperative output regulation of nonlinear multiagent systems (MASs). First, a nonlinear distributed command governor equipped by two dynamical barrier functions is constructed to generate safe command signals. Then, a filtering-based distributed command tracking control scheme is proposed. It is shown that the MAS adaptively reconfigures its formation shape in a distributed way when entering into the barrier function's coverage to avoid collisions, and quickly recovers the desired formation shape along with the reference trajectory after being away from the barrier function's coverage. Simulation results are given to illustrate the effectiveness of the proposed scheme.

In this paper, we present a microwave photonic (MWP) sensor whose operational range and sensing accuracy are enhanced through the utilization of a recurrent neural network (RNN). The MWP sensor utilizes an optical microresonator as the sensor probe and converts the optical resonance responses near the optical carrier frequency into variations in RF transmission with high interrogation resolution. To overcome bandwidth limitations and achieve wide-range operation, a tunable laser is employed to perform the high-resolution interrogation across multiple optical carrier frequencies during each measurement cycle. Subsequently, a RNN, leveraging long-range dependencies and shared parameters, is integrated to process the concatenated interrogation outputs after dimensionality reduction, compensating for output wavelength discrepancies of the tunable laser and enabling accurate wide-range sensing. The proposed approach is experimentally validated using a microring resonator to measure fructose solution concentrations while contending with laser frequency deviation and thermal interference. The operational range of the system is extended three times to 114 GHz, facilitating the measurement of solution concentrations ranging from 49.91% to 30.43% under a temperature variation of 0.61 °C and a laser frequency deviation of ±2 GHz. The established RNN model demonstrates a root-mean-square error of 0.11%, showcasing 1.60-fold, 2.77-fold, 1.10-fold, and 3.45-fold improvements in accuracy over models based on convolutional neural networks, multilayer perceptrons, sparse vision transformer, and linear fitting, respectively.

With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/.

The purpose of this study is to investigate the influence of different magnetic resonance (MR) sequences on the accuracy of generating computed tomography (sCT) images for nasopharyngeal carcinoma based on CycleGAN. In this study, 143 patients’ head and neck MR sequence (T1, T2, T1C, and T1DIXONC) and CT imaging data were acquired. The generator and discriminator of CycleGAN are improved to achieve the purpose of balance confrontation, and a cyclic consistent structure control domain is proposed in terms of loss function. Four different single-sequence MR images and one multi-sequence MR image were used to evaluate the accuracy of sCT. During the model testing phase, five testing scenarios were employed to further assess the mean absolute error, peak signal-to-noise ratio, structural similarity index, and root mean square error between the actual CT images and the sCT images generated by different models. T1 sequence-based sCT achieved better results in single-sequence MR-based sCT. Multi-sequence MR-based sCT achieved better results with T1 sequence-based sCT in terms of evaluation metrics. For metrological evaluation, the global gamma passage rate of sCT based on sequence MR was greater than 95% at 3%/3 mm, except for sCT based on T2 sequence MR. We developed a CycleGAN method to synthesize CT using different MR sequences, this method shows encouraging potential for dosimetric evaluation.

Telomere dysfunction is intricately linked to the aging process and stands out as a prominent cancer hallmark. Here we demonstrate that telomerase activity is differentially regulated in cancer and normal cells depending on the expression status of fructose-1,6-bisphosphatase 1 (FBP1). In FBP1-expressing cells, FBP1 directly interacts with and dephosphorylates telomerase reverse transcriptase (TERT) at Ser227. Dephosphorylated TERT fails to translocate into the nucleus, leading to the inhibition of telomerase activity, reduction in telomere lengths, enhanced senescence and suppressed tumor cell proliferation and growth in mice. Lipid nanoparticle-mediated delivery of FBP1mRNA inhibits liver tumor growth. Additionally, FBP1 expression levels inversely correlate with TERT pSer227 levels in renal and hepatocellular carcinoma specimens and with poor prognosis of the patients. These findings demonstrate that FBP1 governs cell immortality through its protein phosphatase activity and uncover a unique telomerase regulation in tumor cells attributed to the downregulation or deficiency of FBP1 expression.

The identification and characterization of immune cell subpopulations are critical to reveal cell development throughout life and immune responses to environmental factors. Next-generation sequencing technologies have dramatically advanced single-cell genomics and transcriptomics for immune cell classification. However, gene expression is often not correlated with protein expression, and immunotyping is mostly accepted in protein format. Current single-cell proteomic technologies are either limited in multiplex capacity or not sensitive enough to detect the critical functional proteins. Herein, we present a single-cell cyclic multiplex in situtagging (CycMIST) technology to simultaneously measure >400 proteins, a scale of >10 times than similar technologies. Such an ultrahigh multiplexity is achieved by reiterative staining of the single cells coupled with a MIST array for detection. This technology has been thoroughly validated through comparison with flow cytometry and fluorescence immunostaining techniques. Both peripheral blood mononuclear cells (PBMCs) and T cells are analyzed by the CycMIST technology, and almost the entire spectrum of cluster of differentiation (CD) surface markers has been measured. The landscape of fluctuation of CD protein expression in single cells has been uncovered by our technology. Further study found T cell activation signatures and protein–protein networks. This study represents the highest multiplexity of single immune cell marker measurement targeting functional proteins. With additional information from intracellular proteins of the same single cells, our technology can potentially facilitate mechanistic studies of immune responses under various disease conditions.

2-D carbon-based materials are well-known for their broadband absorption properties for efficient solar energy conversion. However, their high reflectivity poses a challenge for achieving efficient omnidirectional light absorption. Inspired by the multilevel structures of the flower, a Graphene Microflower (GM) material with gradient refractive index surface was fabricated on polymer substrates using the UV-intense laser-induced phase explosion technique under the synergistic design of the photothermal Marangoni effect and the fluid instability principle. The refractive index gradient reduces light reflection and absorbs at least 96% of light at incident angles of 0–60° across the entire solar wavelength range (200–2500 nm). Over 90% absorption even at 75° angle of incidence. The light absorption is enhanced by the multiple interferometric phase cancelation and localized surface plasmon resonance, resulting in a steady-state temperature 60 °C higher than ambient conditions under one solar irradiation. The max rate of temperature rise can reach up to 62 °C s–1. The device is then integrated at the hot end of the temperature difference generator at high altitude to ensure continuous and efficient power generation, producing a steady-state power of 196 mW.

The electrochemical reduction of CO2(CO2RR) to generate high-value fuels presents a promising strategy for addressing the global warming crisis. Cobalt phthalocyanine (CoPc) is a promising CO2electrocatalyst in which the local electronic environment of the Co–N site plays a key role in regulating the catalytic performance. In this work, CoPc was grown in situon carbon nanotubes (CNT) viaa hydrothermal reaction, resulting in the synthesis of the molecularly dispersed CoPc-CNT catalyst. X-ray absorption fine structure (XAFS) analysis confirmed the presence of Co–N6ligands with atomically dispersed cobalt in the CoPc-CNT catalyst, which served as active sites for CO2conversion to CO. The coupling of CoPc and CNT resulted in a reduction of Co–N bond lengths, enhancing the Co–N binding energy and thereby improving catalytic stability. The CO Faradaic efficiency of the CoPc-CNT catalyst, exhibiting excellent stability, reached 99.66% (flow cell) at −1.6 V (vsAg/AgCl), significantly surpassing that of the CoPc catalyst.

Cascade conversion of chitin into soluble and functional chitooligosaccharides has gained great attention. However, the biotransformation route is still limited to the low catalytic performances of chitin deacetylases (CDAs) and complicated procedures. In this study, a CDA from Arthrobactersp. Jub115 (ArCDA) was identified and characterized, which showed a higher catalytic stability than the reported CDAs, with residual activity of 80.49%, 71.12%, and 56.09% after incubation at 30, 35, and 40 °C for 24 h, respectively. Additionally, ArCDA was identified to have a broad substrate spectrum toward β-chitin and N-acetyl chitooligosaccharides. Moreover, an engineered chitin-degrading bacteria (CDB) with cell-surface-displayed deacetylase ArCDA and chitinase SaChiB was constructed to simplify catalysis procedures, facilitating the chitobiose production of 294.30 ± 16.43 mg/L in 10 h. This study not only identified a CDA with the desirable catalytic performance but also provided a strategy for constructing CDB, facilitating the high-value utilization of chitin.

Herein, a palladium-catalyzed bicycloaromatization of o-(alkynyl)styrenes with alkynes is reported. In this protocol, the 6-endo-digcyclization of o-(alkynyl)styrenes is followed by deprotonation to complete the first cycloaromatization, and then, a regioselective alkyne insertion/C–H activation occurs to achieve the second cycloaromatization, resulting in atom- and step-economical syntheses of polysubstituted chrysenes. Notably, the products can be further used to construct π-extended arenes using the Scholl reaction.

Future wearable devices for health monitoring require sensitive and efficient monitoring systems. Here, we propose an optical fiber wearable sensor based on the Vernier effect and structural design sensitization. The cascaded dual micro-/nano-fiber configuration forms the Vernier effect. The pre-bending structure not only increases sensitivity but also improves the tensile performance of the sensor. The wearable sensor we proposed has a significant temperature sensitivity of 6.54 nm/°C, which is several times or even tens of times higher than the sensitivity of conventional sensors. The ultrahigh sensitivity, mechanical robustness, and excellent flexibility and biocompatibility make this sensor system an advanced wearable high-precision temperature monitoring medical device. This wearable sensor overcomes the limitations of commonly reported flexible devices based on electrical, optical, and materials. It has shown advantages in detecting the average skin temperature and local temperature of the human body.

This article proposed an active clamp flyback (ACF) converter with a single transformer with multiple output windings as a power processing unit to provide energy for the ion thruster. The ACF converter has the advantages of the simple circuit structure and suitability for a wide range of input and output voltages. Through circuit mathematical model analysis, the parameters of the ACF converter are designed. In order to meet the thrust accuracy regulation, a high-precision control circuit is designed with a field programmable gate array and 24-bit external analog-to-digital converter as the controller, and its operating analysis shows that its voltage accuracy meets the requirements. A 10 W/20 kHz with output voltage varying between 400 and 2000 V prototype is built to verify the effectiveness. The ion thruster experimental test shows that the proposed ACF converter meets the thrust requirements and voltage resolution adjustment. The maximum efficiency can reach 87.7% at normal input voltage.

The dielectric characteristics of aqueous insulating oil has an important impact on the safe operation of transformers. In this article, the integration method is used to modify the Maxwell-Wanger nonuniform dielectric model to make it suitable for aqueous insulating oil, and the reliability of the new model is verified. The experimental results show that when the water content is 20 ppm, the maximum error of the real part of the complex dielectric constant of the insulating oil does not exceed 0.0541%, and the maximum error of the imaginary part does not exceed 1.076%. Finally, based on this model, the dielectric spectrum of the aqueous insulating oil is obtained, the variation law of the real part and the imaginary part of the aqueous insulating oil under different conditions is revealed, and the internal reasons for the dielectric properties of the aqueous insulating oil with temperature, frequency, and moisture content are analyzed, which provides an important reference data for evaluation and online monitoring of insulation performance of the aqueous insulating oil.

Federated Learning (FL) facilitates collaborative machine learning by training models on local datasets, and subsequently aggregating these local models at a central server. However, the frequent exchange of model parameters between clients and the central server can result in significant communication overhead during the FL training process. To solve this problem, this letter proposes a novel FL framework, the Model Aggregation with Layer Divergence Feedback mechanism (FedLDF). Specifically, we calculate model divergence between the local model and the global model from the previous round. Then through model layer divergence feedback, the distinct layers of each client are uploaded and the amount of data transferred is reduced effectively. Moreover, the theoretical analysis reveals that the access ratio of clients has a positive correlation with model performance. Simulation results show that our algorithm uploads local models with reduced communication overhead while upholding a superior global model performance.

Bacterial trans-acyltransferase polyketide synthases (trans-AT PKSs) are among the most complex enzymes, which are responsible for generating a wide range of natural products, identified as trans-AT polyketides. These polyketides have received significant attention in drug development due to their structural diversity and potent bioactivities. With approximately 300 synthesized molecules discovered so far, trans-AT PKSs are found widespread in bacteria. Their biosynthesis pathways exhibit considerable genetic diversity, leading to the emergence of numerous enzymes with novel mechanisms, serving as a valuable resource for genetic engineering aimed at modifying small molecules’ structures and creating new engineered enzymes. Despite the systematic discussions on trans-AT polyketides and their biosynthesis in earlier studies, the continuous advancements in tools, methods, compound identification, and biosynthetic pathways require a fresh update on accumulated knowledge. This review seeks to provide a comprehensive discussion for the 27 types of trans-AT polyketides discovered within the last seven years, detailing their sources, structures, biological activities, and biosynthetic pathways. By reviewing this new knowledge, a more profound understanding of the trans-AT polyketide family can be achieved.

Voltage source converters (VSCs) are being widely applied in power systems due to the development of renewable energies. The simulation scale of the network is limited by the nonlinearity of VSCs and variable nodal admittance matrix (NAM) due to the switching events. In this letter, a general linearized modeling method with fixed NAM is proposed to realize the efficient modeling of VSCs for real-time simulation. By establishing the state space equations based on the port characteristics of VSCs together with the switching states, a companion circuit with ac and dc sides decoupled is derived by the modified Euler method. Moreover, the proposed model can eliminate the delay between the companion circuit and the global solution of the system, which can effectively improve the simulation accuracy. The performance of the model is validated by real-time simulation and hardware-in-the-loop tests for both pulses-enabled and pulses-blocked scenarios.

In this study, a series of Mn-Ce/Al2O3catalysts was prepared by different methods of deposition-precipitation (MnCeAl-DP), impregnation (MnCeAl-IM) and citric acid (MnCeAl-CA), and the distinct effect of preparation methods on NOxremoval performance at low temperature was explored. Results show that MnCeAl-DP exhibits not only the best activity but also the highest resistance against SO2/H2O. With the assistance of comprehensive characterizations from scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET), X-ray diffraction (XRD), H2-temperature programmed reduction (H2-TPR), NH3-temperature programmed desorption (NH3-TPD), and X-ray photoelectron spectroscopy (XPS), it is revealed that the MnCeAl-DP sample owns admired features of large surface area and pore volume, enriched Mn4+and chemisorbed oxygen species originating from enhanced interaction between MnOxand CeO2, as well as improved adsorption capacity to NH3and NO. All these factors contribute to activity enhancement. Further in-situDRIFTS studies reveal that the improvement of NH3-SCR performance over MnCeAl-DP is related to the formation of abundant nitrate species, which is beneficial to the “NH4NO3” reaction pathway and thus enhances low-temperature activity.

The Tibetan Plateau (TP) region, also known as the “Asian water tower”, provides a vital water resource for downstream regions. Previous studies of water cycle changes over the TP have been conducted with climate models of coarse resolution in which deep convection must be parameterized. In this study, we present results from a first set of high-resolution climate change simulations that permit convection at approximately 3.3-km grid spacing, with a focus on the TP, using the Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Two 12-year simulations were performed, consisting of a retrospective simulation (2008–20) with initial and boundary conditions from ERA5 reanalysis and a pseudo-global warming projection driven by modified reanalysis-derived initial and boundary conditions by adding the monthly CMIP6 ensemble-mean climate change under the SSP5-8.5 scenario. The retrospective simulation shows overall good performance in capturing the seasonal precipitation and surface air temperature. Over the central and eastern TP, the average biases in precipitation (temperature) are less than −0.34 mm d−1(−1.1°C) throughout the year. The simulated biases over the TP are height-dependent. Cold (wet) biases are found in summer (winter) above 5500 m. The future climate simulation suggests that the TP will be wetter and warmer under the SSP5-8.5 scenario. The general features of projected changes in ICON are comparable to the CMIP6 ensemble projection, but the added value from kilometer-scale modeling is evident in both precipitation and temperature projections over complex topographic regions. These ICON-downscaled climate change simulations provide a high-resolution dataset to the community for the study of regional climate changes and impacts over the TP.

As individuals age, there is a gradual decline in cardiopulmonary function, often accompanied by cardiac pump dysfunction leading to increased pulmonary vascular resistance (PVR). Our study aims to investigate the changes in cardiac and pulmonary vascular function associated with aging. Additionally, we aim to explore the impact of phosphodiesterase 9A (PDE9A) inhibition, which has shown promise in treating cardiometabolic diseases, on addressing left ventricle (LV) dysfunction and elevated PVR in aging individuals. Young (3 months old) and aged (32 months old) male C57BL/6 mice were used. Aged mice were treated with the selective PDE9A inhibitor PF04447943 (1 mg/kg/day) through intraperitoneal injections for 10 days. LV function was evaluated using cardiac ultrasound, and PVR was assessed in isolated, ventilated lungs perfused under a constant flow condition. Additionally, changes in PVR were measured in response to perfusion of the endothelium-dependent agonist bradykinin or to nitric oxide (NO) donor sodium nitroprusside (SNP). PDE9A protein expression was measured by Western blots. Our results demonstrate the development of LV diastolic dysfunction and increased PVR in aged mice. The aged mice exhibited diminished decreases in PVR in response to both bradykinin and SNP compared to the young mice. Moreover, the lungs of aged mice showed an increase in PDE9A protein expression. Treatment of aged mice with PF04447943 had no significant effect on LV systolic or diastolic function. However, PF04447943 treatment normalized PVR and SNP-induced responses, though it did not affect the bradykinin response. These data demonstrate a development of LV diastolic dysfunction and increase in PVR in aged mice. We propose that inhibitors of PDE9A could represent a novel therapeutic approach to specifically prevent aging-related pulmonary dysfunction.

Flexible intelligent sensing is a burgeoning field of study that covers various disciplines, including but not restricted to chemistry, physics, electronics and biology. However, the widespread use of flexible sensors remains challenging because of certain constraints, such as limited stretchability, poor biocompatibility, low responsivity, and the complexity of multifunctional integration. Conductive hydrogels with remarkable material properties are presently in the spotlight of flexible sensing. In the pursuit of high-performance and “green” conductive hydrogel-based sensors, cellulose is a promising candidate owing to its renewability, low cost, appealing mechanical properties, easy modification and other functional characteristics. Herein, cutting-edge progress in the fabrication of conductive cellulose hydrogels (CCHs) using cellulose and cellulose derivatives in terms of structural features, preparation approaches, functional properties, applications, and prospects for sensors is comprehensively summarized. The correlation between CCHs performances, reinforcement strategies and sensor properties is highlighted to gain insight into the process of developing smart sensors by utilizing CCHs. Besides, the state-of-the-art advances of CCHs toward emerging wearable sensors, including strain/pressure sensors, temperature sensors, humidity sensors, and biosensors, are systematically discussed. Finally, potential challenges and future outlooks of such attractive CCH-based flexible sensors are presented, providing valuable information for the development of next-generation cellulose-based electronic devices.

Aims/BackgroundObesity and depression frequently co-occur, and the relationship between them is bidirectional. Being obese or overweight increases the risk of depression, and conversely, depression increases the risk of obesity or overweight. Emerging clinical research has shown that probiotics may be effective in treating obesity and associated depression. Modulating gut microbiota with probiotics may improve obesity-related depression, but current evidence is inconsistent.MethodsWe systematically searched PubMed, EMBASE, Web of Science, and Cochrane Library for randomized controlled trials examining probiotics for depression in obese adults. Mean difference (MD) in depression scores between probiotic and placebo groups was pooled across studies using random-effects models. Heterogeneity was assessed using I2to explain heterogeneity in meta-regression analysis. To detect the publication bias of the included studies, a funnel plot, and Begg and Egger tests, were used. Possible heterogeneity moderators were detected by subgroup and sensitivity analyses, Galbraith plot, and graphic display of study heterogeneity (GOSH) analysis. Leave-one-out and Influence analyses were performed to assess sensitivity.ResultsFive trials (n = 488) were included. Overall, probiotics did not significantly improve depressive symptoms versus placebo (MD = 0.08, 95% confidence interval [CI] = −0.63 to 0.79, p= 0.82). Considerable heterogeneity among studies was observed (I2= 63%). In subgroup analyses, probiotics significantly reduced depression as measured by the Edinburgh Postnatal Depression Scale (MD = −0.60, 95% CI = −1.17 to –0.03, p= 0.04) with no heterogeneity (I2= 0%). Probiotics also decreased depressive symptoms after 12 weeks of supplementation (MD = −0.78, 95% CI = −1.58 to –0.01, p= 0.05) versus placebo. No publication bias was found using Begg and Egger tests. The GOSH diagnostics revealed three outliers, among the clusters identified by K-means, DBSCAN (Density-based spatial clustering of applications with noise algorithm), and GMM (Gaussian Mixture Model) analyses.ConclusionOverall, probiotics did not improve depressive symptoms in obesity. However, beneficial effects were observed with the Edinburgh Postnatal Depression Scale and short-term use of probiotic. Additional rigorous randomized controlled trials are warranted to elucidate the therapeutic potential of probiotics for obesity-related depression.

Micronuclear batteries harness energy from the radioactive decay of radioisotopes to generate electricity on a small scale, typically in the nanowatt or microwatt range1,2. Contrary to chemical batteries, the longevity of a micronuclear battery is tied to the half-life of the used radioisotope, enabling operational lifetimes that can span several decades3. Furthermore, the radioactive decay remains unaffected by environmental factors such as temperature, pressure and magnetic fields, making the micronuclear battery an enduring and reliable power source in scenarios in which conventional batteries prove impractical or challenging to replace4. Common radioisotopes of americium (241Am and 243Am) are α-decay emitters with half-lives longer than hundreds of years. Severe self-adsorption in traditional architectures of micronuclear batteries impedes high-efficiency α-decay energy conversion, making the development of α-radioisotope micronuclear batteries challenging5,6. Here we propose a micronuclear battery architecture that includes a coalescent energy transducer by incorporating 243Am into a luminescent lanthanide coordination polymer. This couples radioisotopes with energy transducers at the molecular level, resulting in an 8,000-fold enhancement in energy conversion efficiency from α decay energy to sustained autoluminescence compared with that of conventional architectures. When implemented in conjunction with a photovoltaic cell that translates autoluminescence into electricity, a new type of radiophotovoltaic micronuclear battery with a total power conversion efficiency of 0.889% and a power per activity of 139 microwatts per curie (μW Ci−1) is obtained.

A versatile agent with the desired catalytic activity for disinfection and pigment removal, good affinity with bacteria, and excellent biocompatibility is essential for both eradicating the oral bacterial biofilm and tooth whitening in the oral cavity. Herein, we proposed a strategy to construct a “cofactor corona” equipped a gold nanozyme by using antimicrobial peptides (AMPs) as promising green reductants and “cofactor” due to the specific physical and chemical properties to reduce HAuCl4into gold corona (Au@AMP) by a simple and eco-friendly redox way. Compared to gold nanoparticles, the as-prepared peptide corona equipped nanozyme via “cofactor” mimicking contributes to enhancing the POD-like catalytic activity by donating electrons from peptides to gold. Meanwhile, it could be used in targeting bacteria by peptide corona and killing S. mutans, disrupting the S. mutansbiofilm efficiently by locally generated ROS from a regulated POD-like catalytic reaction with a low amount of H2O2under NIR irradiation to maintain the optimized temperature. Moreover, it can display excellent capability of tooth whitening in cases of both extrinsic tooth staining and intrinsic tooth staining without enamel damage. The “cofactor corona” equipped gold nanozyme is biocompatible, shedding light on the development of functional and biosafe enzyme mimics and exhibiting potential in diverse biomedical applications.

Petal-like heterojunction materials ZnCo2O4/CoMoO4with abundant oxygen vacancies are prepared on nickel foam (NF) using modified ionic hybrid thermal calcination technology. Nanoscale ion intermixing between Zn and Mo ions induces oxygen vacancies in the annealing process, thus creating additional electrochemical active sites and enhancing the electrical conductivity. The ZnCo2O4/CoMoO4conductive network skeleton forms the primary transport pathway for electrons, while the internal electric field of the heterojunction serves as the secondary pathway. ZnCo2O4/CoMoO4exhibits excellent rate performance and high capacity attributable to its unique double electron transport mode and the effect of oxygen vacancies. The initial discharge capacity at a current of 0.1 A g–1is approximately 1774 mAh g–1, and the reversible capacity remains at 1100 mAh g–1after 200 cycles. After a high current of 1 A g–1, the reversible capacity is observed to remain at approximately 1240 mAh g–1. The electronic structure, crystal structure, and work function of the heterojunction interface model are then analyzed by density functional theory (DFT). The analysis results indicate that the charge at the ZnCo2O4/CoMoO4interface is unevenly distributed, which leads to an enhanced degree of electrochemical reaction. The presence of an internal electric field improves the transport efficiency of the carriers. Experimental and theoretical calculations demonstrate that the ZnCo2O4/CoMoO4anode material designed in this work provides a reference for fabricating transition metal oxide-based lithium-ion batteries.

Two-dimensional (2D) transition metal carbides (Ti3C2TxMXene) have demonstrated substantial application potential across various fields, owing to their excellent metallic conductivity and solution processability. However, the rapid oxidation of Ti3C2Txin aqueous environments, leading to a loss of stability within mere days, poses a significant obstacle for its practical applications. Herein, we introduce an antioxidant strategy that combines free radical scavenging with surface passivation, culminating in the design and synthesis of imidazolium-based ionic liquids (ILs) incorporating siloxane groups. By deploying a straightforward hydrolysis-addition reaction, we successfully fabricated IL-modified Ti3C2Txmaterials (Ti3C2Tx-IL). The Ti3C2Tx-IL not only displayed exceptional conductivity exceeding 3.85 × 104S/m and hydrophilic contact angles below 45° but also showcased its superior chemical stability and antioxidation mechanisms through various analyses, including visual color change experiments, spectroscopic and energy spectrum characterization, free radical scavenging tests, and density-functional-theory-based molecular simulations. Furthermore, when utilized as a conductive filler in the fabrication of a poly(vinyl alcohol)/nanocellulose fiber (PVA/CNF) composite hydrogel (PCMIL), the resultant sensors exhibited remarkable mechanical performance with up to 535% strain, 1.59 MPa strength, 4.35 MJ/m3toughness, and a conductivity of 3.40 mS/cm, as well as a high sensitivity gauge factor of 3.3. Importantly, even after 45 days of storage, the PCMIL retained most of its functionalities, demonstrating superior performance in human-machine interaction applications compared to hydrogels made from unmodified Ti3C2Tx. This research establishes a robust antioxidant protection strategy for Ti3C2Tx, offering substantial technical reinforcement for its prospective applications in the realm of flexible electronics and sensing technologies.

Identifying causative toxicants in mixtures is critical, but this task is challenging when mixtures contain multiple chemical classes. Effect-based methods are used to complement chemical analyses to identify toxicants, yet conventional bioassays typically rely on an apical and/or single endpoint, providing limited diagnostic potential to guide chemical prioritization. We proposed an event-driven taxonomy framework for mixture risk assessment that relied on high-throughput screening bioassays and toxicant identification integrated by deep learning. In this work, the framework was evaluated using chemical mixtures in sediments eliciting aryl-hydrocarbon receptor activation and oxidative stress response. Mixture prediction using target analysis explained <10% of observed sediment bioactivity. To identify additional contaminants, two deep learning models were developed to predict fingerprints of a pool of bioactive substances (event driver fingerprint, EDFP) and convert these candidates to MS-readable information (event driver ion, EDION) for nontarget analysis. Two libraries with 121 and 118 fingerprints were established, and 247 bioactive compounds were identified at confidence level 2 or 3 in sediment extract using GC-qToF-MS. Among them, 12 toxicants were analytically confirmed using reference standards. Collectively, we present a “bioactivity-signature-toxicant” strategy to deconvolute mixtures and to connect patchy data sets and guide nontarget analysis for diverse chemicals that elicit the same bioactivity.

This article studies the problem of obstacle avoidance in the distributed optimal coordination (DOC) for a class of uncertain multirobot systems. Due to the existence of obstacle regions, the considered optimization problem is intrinsically nonconvex, which will result in the generation of some unexpected equilibriums (local minima). The existing results lack systematic obstacle-avoidance trajectory planning approaches such that the robots potentially fall in the unexpected equilibriums and cannot reach the global optimal solution. To address it, a safe reference trajectory planning approach is first designed by online projecting the unsafe part of the existing distributed optimization trajectory into the peripheral boundary of the obstacle region. On this basis, a distributed backstepping tracking control scheme is proposed based on a novel multiplicity-integral-type barrier Lyapunov function. It is proved that all the robot systems can keep away from the unexpected equilibriums and asymptotically reach the global optimal position while avoiding collisions with moving obstacles.

Paper-based electronics offer a simple and cost-effective means to fabricate reconfigurable devices. In response to the problem of fixed shape and single function of most antennas, which limits their applications, a reconfigurable paper-based metamaterial antenna with 2D and 3D forms is presented for tunable operating frequency. The proposed antenna consists of two square split resonant rings fed by a coplanar waveguide. The working frequency of the 2D antenna is adjusted by the length, width, and opening size of the internal open resonant ring. While the folding angle of the antenna turns from 0° to 90°, the operating frequency of the paper-based metamaterial antenna changes from 2.759 to 4.223 GHz. In terms of 3D form, an additional resonant peak is generated by bending the paper-based metamaterial antenna, thus further realizing dual-band antenna design. After a simple process flow, a series of proposed antennas are fabricated and evaluated. The simulated and measured results both demonstrate that the proposed antenna has a good performance in turning the working band. The environment-friendly nature and pliability of paper, as well as simple fabrication procedures, make paper-based metamaterial promising candidates for future green electronics.

To achieve a synergistic solution for both sustainable waste management and permanent CO2sequestration, CO2mineralization via fly ash particles is an option. Based on computational fluid dynamics, two specialized reactors for fly ash mineralization were designed. The reactor designs were strategically tailored to optimize the interactions between fly ash particles and flue gas within the reactor chamber while concurrently facilitating efficient post-reaction-phase separation. The impinging-type inlet configuration dramatically enhanced the interfacial interaction between the fly ash particles and the gaseous mixture, predominantly composed of CO2and steam. This design modality lengthens the particle residency and reaction times, substantially augmenting the mineralization efficiency. A rigorous investigation of three operational parameters, that is, flue gas velocity, carrier gas velocity, and particle velocity, revealed their influential roles in gas-particle contact kinetics. Through a computational investigation, it can be ascertained that the optimal velocity regime for the flue gas was between 20 and 25 m⋅s−1. Concurrently, the carrier gas velocity should be confined to the range of 9–15 m⋅s−1. Operating within these finely tuned parameters engenders a marked enhancement in reactor performance, thereby providing a robust theoretical basis for operational efficacy. Overall, a judicious reactor design was integrated with data-driven parameter optimization.

Background: The associations between mood disorders (anxiety and depression) and mild cognitive impairment (MCI) or Alzheimer’s dementia (AD) remain unclear. Methods: Data from the Australian Imaging, Biomarker & Lifestyle (AIBL) study were subjected to logistic regression to determine both cross-sectional and longitudinal associations between anxiety/depression and MCI/AD. Effect modification by selected covariates was analysed using the likelihood ratio test. Results: Cross-sectional analysis was performed to explore the association between anxiety/depression and MCI/AD among 2,209 participants with a mean [SD] age of 72.3 [7.4] years, of whom 55.4% were female. After adjusting for confounding variables, we found a significant increase in the odds of AD among participants with two mood disorders (anxiety: OR 1.65 [95% CI 1.04–2.60]; depression: OR 1.73 [1.12–2.69]). Longitudinal analysis was conducted to explore the target associations among 1,379 participants with a mean age of 71.2 [6.6] years, of whom 56.3% were female. During a mean follow-up of 5.0 [4.2] years, 163 participants who developed MCI/AD (refer to as PRO) were identified. Only anxiety was associated with higher odds of PRO after adjusting for covariates (OR 1.56 [1.03–2.39]). However, after additional adjustment for depression, the association became insignificant. Additionally, age, sex, and marital status were identified as effect modifiers for the target associations. Conclusion: Our study provides supportive evidence that anxiety and depression impact on the evolution of MCI/AD, which provides valuable epidemiological insights that can inform clinical practice, guiding clinicians in offering targeted dementia prevention and surveillance programs to the at-risk populations.

Federated Learning (FL), a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks (VECN), allows a distributed approach to train datasets of nodes locally, e.g., for mobile vehicles, and exchanges model parameters to obtain an accurate model without raw data transmission. However, the existence of malicious vehicular nodes as well as the inherent heterogeneity of the vehicles hinders the attainment of accurate models. Moreover, the local model training and model parameter transmission during FL exert a notable energy burden on vehicles constrained in resources. In view of this, we investigate FL client selection and resource management problems in FL-enabled UAV-assisted Vehicular Networks (FLVN). We first devise a novel reputation-based client selection mechanism by integrating both data quality and computation capability metrics to enlist reliable high-performance vehicles. Further, to fortify the FL reliability, we adopt the consortium blockchain to oversee the reputation information, which boasts tamper-proof and interference-resistant qualities. Finally, we formulate the resource scheduling problem by jointly optimizing the computation capability, the transmission power, and the number of local training rounds, aiming to minimize the cost of clients while guaranteeing accuracy. To this end, we propose a reinforcement learning algorithm employing an asynchronous parallel network structure to achieve an optimized scheduling strategy. Simulation results show that our proposed client selection mechanism and scheduling algorithm can realize reliable FL with an accuracy of 0.96 and consistently outperform the baselines in terms of delay and energy consumption.

THE CASE A man, aged 21 years, presented with a 4-month history of progressive swelling of the right testicle. Ultrasound revealed a heterogenous solid mass in the right testicle suspicious [...]

A new class of Daidzein derivatives were developed, and their protective effects on neuronal cells and their mechanisms were examined. The protective effects of Daidzein derivatives against oxygen and glucose deprivation/reoxygenation (OGD/R) injury in HT22 cells were evaluated viaa Cell Counting Kit 8 (CCK-8) assay. Biomarkers associated with ferroptosis, including changes in reactive oxygen species (ROS), lipid peroxidation, ferrous ion (Fe2+), glutathione (GSH), superoxide dismutase (SOD) and malondialdehyde (MDA) levels, were detected viafluorescent probes and specific kits. In addition, the protein expression levels of glutathione peroxidase 4 (GPX4), recombinant solute carrier family 7, member 11 (SLC7A11 or xCT) and nuclear Factor 2 (Nrf2) were analyzed viaWestern blotting. The newly synthesized Daidzein derivative outperforms not only its parent compound, especially derivative 3, in improving the viability of OGD/R-treated HT22 cells but also edaravone, a positive control drug. This study further revealed the mechanism of action of derivative 3: reducing the level of ROS and lipid peroxidation induced by OGD/R in HT22 cells, restoring SOD and GSH activities, reducing MDA and Fe2+accumulation, and increasing the protein expression of GPX4, xCT and Nrf2. Derivative 3 has significant neuroprotective effects, and its mechanism may involve activating the Nrf2/xCT/GPX4 pathway and inhibiting neuronal ferroptosis. This study provides a new perspective for neuroprotection research and a direction for drug development.

Electric heating load (EHL) is a type of thermostatically controlled load whose power consumption mode can be flexibly adjusted, as long as the customers’ thermal comfort is ensured. The key to unlocking the regulatory potential of EHL lies in characterizing and harnessing its post-aggregation regulatory capacity, given its inherent dispersion and heterogeneity. Firstly, the second-order equivalent thermal parameter (ETP) model, grounded in the thermal equilibrium of the room, is employed to analyze the operational characteristics and to construct a model for aggregating load group. Secondly, leveraging the analysis of individual EHL regulation capacity, the temperature state characteristic quantity is introduced as a sorting index. Considering the dynamic properties of baseline power over time, a control strategy is proposed, based on state complementary characteristics among EHLs, for a more comprehensive and dynamic system management. Finally, the key indexes for reporting and implementation phases are identified to evaluate the regulation capacity of load group. The case study evidences that the newly introduced control strategy and evaluation method can provide more stable regulation power support for power grid dispatching, and more deeply harness the regulation capacity to maximize its potential value. In addition, this paper also discusses the influence of related factors such as the number, the rated power, the inside temperature control interval, the inside temperature setting value and the outside temperature on the regulation ability of the EHL group, offering a robust framework for the dispatching center to devise well-informed and rational regulation instructions.

Background: Ovarian endometriosis cysts (OEC) are common chronic diseases occurring in women of reproductive age. Hormone therapy, including the use of oral contraceptives (COCs) and progestins, is widely employed in the treatment of endometriosis and OEC.Objective: This study aims to compare therapeutic effects of long-term daily use of dienogest (DNG, 2 mg) versus drospirenone (DRSP, 3 mg)/ethinyl estradiol (EE, 20 μg) daily on ovarian endometrial cysts, particularly focusing on cysts suppression.Methods: A retrospective analysis was conducted on 84 patients diagnosed with OEC, with 42 receiving DNG and 42 treated with DRSP/EE. After 6 months, assessments were made regarding pain symptoms, size of OEC, tumor markers, and adverse reactions.Results: After 6 months of treatment, both DNG and DRSP/EE effectively alleviated pain in patients with ovarian endometriotic cysts. DNG demonstrated more significant improvements than DRSP/EE in reducing tumor size, mainly in terms of higher drug efficacy and greater percentage of tumor shrinkage. However, DRSP/EE significantly reduced the expression of the tumor markers CA125 and CA199 in our study. Patients taking DNG had a higher likelihood of experiencing side effects than those on DRSP/EE, mainly vaginal bleeding and irregular menstrual periods.Conclusions: Both DNG and DRSP/EE are effective in treating ovarian endometrial cysts. DNG appears to be superior in terms of tumor reduction, while DRSP/EE more significantly decreases the expression of the tumor markers and has fewer side effects.

ER5356, ER2319 and Zn wires were synchronously fed using a multi-wire gas metal arc directed energy deposition (DED) system. This allowed the successfully fabrication of Al-6.0Zn-2.5Mg-1.5Cu alloy. The microstructure and mechanical properties of the fabricated parts were studied. It is shown that the main precipitated phases are η and nanoscale η'. The upper and lower parts of the deposited parts are composed by coarse equiaxed grains and columnar dendrite structures, respectively. The average microhardness of the fabricated alloy was 118.5 HV. The ultimate tensile strength and elongations in the perpendicular to the building direction (BD) and parallel to the BD were 267 MPa and 2.7 %, 238 MPa and 2.3 %, respectively. The average tensile strength is higher than that of 7075-O alloy. The fracture mode of the samples composed brittle and ductile features, although the former dominated. This multi-wire arc DED approach provides a new choice for the in-situ synthesis of Al-Zn-Mg-Cu alloys.

CO2geological utilization and storage (CGUS) is a key technology to achieve carbon neutrality goals. To apply CGUS on a larger scale, the issue of steel corrosion during the process must be addressed to mitigate technological risks. This paper provides an overview of CO2-induced steel corrosion mechanisms and identifies factors that influence corrosion. The impact of CO2partial pressure, temperature, salinity, pH, impurities, and fluid flow on steel corrosion behavior are also discussed. With the presence of water, the corrosive effect of supercritical CO2on steel is stronger than that of dissolved CO2or gaseous CO2. As the temperature increases, the corrosion rate of steel first increases and then decreases. Increasing salinity and decreasing pH lead to an accelerated corrosion rate of steel. Corrosion inhibitors, coatings, and corrosion-resistant alloys are recommended protective measures against CO2-induced corrosion. Compared with coatings, corrosion inhibitors and corrosion-resistant alloys are more commonly used in CGUS projects. Future research directions include further exploration of the mechanisms underlying CO2-induced steel corrosion, clarifying the coupled effects of various environmental factors, and developing corrosion protection technologies under high-pressure and high-concentration CO2conditions.

Dental caries, the most prevalent chronic disease across all age groups, has a high prevalence, particularly among children. However, there is no specific and effective treatment for the prevention of caries in primary teeth (Pr.T.), which stems from a lack of knowledge regarding the basic nature of the tooth surface. Herein, we observed that the adhesion energies of the caries-related bacteria Streptococcus mutansand Streptococcus sanguinisto Pr.T were approximately 10 and 5.5 times higher than those to permanent teeth (Pe.T). A lower degree of mineralization and more hydrophilic characteristics of the Pr.T enamel account for this discrepancy. Accordingly, we proposed that the on-target modification of both hydroxyapatite and organic components on Pr.T by dual modification would render a sufficient hydration layer. This resulted in an approximately 11-time decrease in bacterial adhesion energy after treatment. In contrast, a single hydroxyapatite modification on Pe.T and young permanent teeth (Y.Pe.T) was sufficient to achieve a similar effect. Theoretical simulation further verified the rationality of the approach. Our findings may help understand the reason for Pr.T being caries-prone and provide references for treatment using resin restorations. This strategy offers valuable insights into daily oral hygiene and dental prophylactic treatment in children.

Moisture power generation (MPG) technology, producing clean and sustainable energy from a humid environment, has drawn significant attention and research efforts in recent years as a means of easing the energy crisis. Despite the rapid progress, MPG technology still faces numerous challenges with the most significant one being the low power-generating performance of individual MPG devices. In this review, we introduce the background and underlying principles of MPG technology while thoroughly explaining how the selection of suitable materials (carbons, polymers, inorganic salts, etc.) and the optimization of the device structure (pore structure, moisture gradient structure, functional group gradient structure, and electrode structure) can address the existing and anticipated challenges. Furthermore, this review highlights the major scientific and engineering hurdles on the way to advancing MPG technology and offers potential insights for the development of high-performance MPG systems.

Fiber-integrated micro/nanostructures play a crucial role in modern industry, mainly owing to their compact size, high sensitivity, and resistance to electromagnetic interference. However, the three-dimensional manufacturing of fiber-tip functional structures beyond organic polymers remains challenging. It is essential to construct fiber-integrated inorganic silica with designed functional nanostructures for microsystem applications. Here, we develop a strategy for the 3D nanolithography of fiber-integrated silica from hybrid organic–inorganic materials by ultrafast laser-induced multiphoton absorption. Without silica nanoparticles and polymer additives, the acrylate-functionalized precursors can be locally cross-linked through a nonlinear effect. Followed by annealing at low temperature, the as-printed micro/nanostructures are transformed to high-quality silica with sub-100 nm resolution. Silica microcantilever probes and microtoroid resonators are directly integrated onto the optical fiber, showing strong thermal stability and quality factors. This work provides a promising strategy for fabricating desired fiber-tip silica micro/nanostructures, which is helpful for the development of integrated functional device applications.

Penta-frequency signals of BDS-3 and Galileo have the potential to provide a greater number of signal combinations with varying characteristics, which can enable accurate ambiguity resolution within a range of tens to hundreds of kilometers. Nonetheless, this potential has yet to be fully realized. To address this issue, we propose a five-frequency carrier ambiguity resolution (FiCAR) method that utilizes ionosphere-reduced (IR) combinations of extra wide-lane (EWL), wide-lane (WL), and narrow-lane (NL) signals from BDS-3 and Galileo for stepwise ambiguity resolution (AR). First, we select the EWL, WL, and NL combined signals with low noise and small ionospheric scale factor characteristics and use Kalman filtering to estimate the tropospheric wet delay while disregarding the residual ionospheric errors. This method does not require the estimation of ionospheric delay, enabling high-precision real-time kinematic (RTK) positioning over baselines ranging from several hundred to thousands of kilometers. Second, based on the FiCAR method, we propose a modified FiCAR (MFiCAR) method in which the PAR strategy of NL is further optimized. To validate our approach, we conducted experiments on four baselines containing BDS-3 and Galileo penta-frequency signals, with baseline lengths ranging from 151 to 1946 kilometers. The experimental results demonstrate that the geometry-based (GB) ionosphere-reduced FiCAR and MFiCAR methods can instantly fix the EWL and WL signals, providing positioning services at the sub-meter level. Additionally, the MFiCAR method enhances the NL ambiguity fix rate on longer baselines, with fix rates of 99.17%, 98.82%, 95.89%, and 88.96% for the four baselines, respectively.