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We developed an Al/O/H ReaxFF force field to explore chemical reactions on α-Al2O3surfaces in H2O/H2gas-phase environments. This force field generates surface energy profiles of A-, C-, R-, and M-planes with various terminations (Al- or O-) and predicts the thermodynamic and kinetic behaviors of hydrolysis on Al-terminated α-Al2O3(0001), consistent with quantum chemical studies. Molecular dynamics (MD) simulations of H2O/α-Al2O3(0001) reveal that water autocatalysis plays a significant role in accelerating H2O dissociations on Al-terminated α-Al2O3(0001). Compared with the 50% Al-terminated surface, the 100% Al-terminated surface becomes more easily hydroxylated at temperatures as low as 350 K, relying more on an OxHyclustering mechanism than complete H2O dissociations, and desorbs significantly more H2O molecules once heated up to 500 K or higher. But heating cannot eliminate surface hydroxyls for either case, and achieving a Gibbsite-like surface by H2O exposure is unlikely. H2O dissociations on α-Al2O3(0001) terminated with randomly distributed surface Al species deviate from 1–2 and 1–4 pathways due to irregular vacancy defects, and a random surface appears to be more reactive to H2O than the ordered one with the same surface Al coverage. Simulations of H2/α-Al2O3suggest that the combination of a dense surface O coverage and a low thermodynamic surface stability leads to elevated H2dissociation kinetics. To accelerate the surface O removals of 100% O-terminated α-Al2O3(0001) in H2gas exposure, we reduced the H–H σ bond energy parameter, equivalent to lowering the H2dissociation barrier by ∼ 19.4 kcal/mol during the simulation. After ∼ 1.5 ns, the surface termination became comparable to the 100% Al-terminated one but retained a small quantity of hydroxyls. This force field reveals how the α-Al2O3crystallographic plane and the surface termination influence the dissociation behaviors of H2O/H2gas molecules and lays the foundation for future force field developments targeted at thin film epitaxy on sapphire.

Polyethylene terephthalate (PET) is one of the most widely produced man-made polymers and is a significant contributor to microplastics pollution. The environmental and human health impacts of microplastics pollution have motivated a concerted effort to develop microbe- and enzyme-based strategies to degrade PET and similar plastics. A PETase derived from the bacteria Ideonella sakaiensiswas previously shown to enzymatically degrade PET, triggering multidisciplinary efforts to improve the robustness and activity of this and other PETases. However, because these enzymes only erode the surface of the insoluble PET substrate, it is difficult to measure standard kinetic parameters, such as kon, koff, and kcat, complicating interpretation of the activity of mutants using traditional enzyme kinetics frameworks. To address this challenge, we developed a single-molecule microscopy assay that quantifies the landing rate and binding duration of quantum dot-labeled PETase enzymes interacting with a surface-immobilized PET film. Wild-type PETase binding durations were well fit by a biexponential with a fast population having a 2.7 s time constant, interpreted as active binding events, and a slow population interpreted as nonspecific binding interactions that last tens of seconds. A previously described hyperactive mutant, S238F/W159H had both a faster apparent on-rate and a slower off-rate than wild-type PETase, potentially explaining its enhanced activity. Because this single-molecule approach provides a more detailed mechanistic picture of PETase enzymatic activity than standard bulk assays, it should aid future efforts to engineer more robust and active PETases to combat global microplastics pollution.

Molecular docking remains an indispensable tool in computational biology and structure-based drug discovery. However, the correct prediction of binding poses remains a major challenge for molecular docking, especially for target proteins where a substrate binding induces significant reorganization of the active site. Here, we introduce an Induced Fit Docking (IFD) approach named AA/UA/CG-SA-IFD, which combines a hybrid All-Atom/United-Atom/Coarse-Grained model with Simulated Annealing. In this approach, the core region is represented by the All-Atom(AA) model, while the protein environment beyond the core region and the solvent are treated with either the United-Atom (UA) or the Coarse-Grained (CG) model. By combining the Elastic Network Model (ENM) for the CG region, the hybrid model ensures a reasonable description of ligand binding and the environmental effects of the protein, facilitating highly efficient and reliable sampling of ligand binding through Simulated Annealing (SA) at a high temperature. Upon validation with two testing sets, the AA/UA/CG-SA-IFD approach demonstrates remarkable accuracy and efficiency in induced fit docking, even for challenging cases where the docked poses significantly deviate from crystal structures.

ABSTRACTBackgroundThis study aimed to adopt the conventional signal detection methods to explore a new way of risk identification and to mine important drug risks from the perspective of big data based on Zhenjiang Adverse Event Reporting System (ZAERS).Research design and methodsData were extracted from ZAERS database between 2012 and 2022. The risks of all the reported drug event combinations were identified at the preferred term level and the standardized MedDRA query level using disproportionality analysis. Then, we conducted signal assessment according to the descriptions of drug labels.ResultsIn total 41,473 ADE were reported and there were 12 risky signals. Signal assessment indicates the suspected causal associations in clindamycin-taste and smell disorders, valsartan-hepatic enzyme increased and valsartan-edema peripheral; the specific manifestations of allergic reactions triggered by clindamycin, cefotaxime, cefazodime, ShexiangZhuanggu plaster, ShexiangZhuifeng plaster, and Yanhuning need to be refined in drug labels. In addition, the drug labels of NiuHuangShangQing tablet/capsule, Fuyanxiao capsule, and BiYanLing tablet should be improved.ConclusionsIn this study, we attempted a new way to find potential drug risks using small spontaneous reporting data. Our findings also suggested the need for more precise identification of allergic risks and the improvement of traditional Chinese medicine labels.

The tunneling process in water-rich silty fine sand stratum often faces challenges such as arch collapse due to the instability of the initial support arch foot. The present study focuses on the Taoshuping 3# inclined shaft section, modifies the two-side heading method (THM), and introduces the double-heading at bottom method (DBM). Field monitoring and numerical simulations are employed to investigate the formation pressure, deformation evolution, and the advantages of the construction scheme using the loosened zone and stress distribution features. The obtained results show that DBM exhibits a maximu m settlement during arch excavation, constituting approximately 36% of the total settlement, with a total value of 222.21 mm. Furthermore, the plastic zone induced by DBM ranges from 1.23 to 2 times the tunnel diameter, with vertical and horizontal surrounding soil pressures of 140.72 kPa and 46.25 kPa, respectively. DBM is markedly superior to THM. This approach reduces the formation of wedge-shaped shear bodies caused by excessive stress at the sidewalls in THM excavation. Even with tunnel excavation support, the surrounding soil maintains an arch effect, validated through calculations using Protodyakonovco theory and Terzaghi theory, verifying the efficiency of the support structure design.

New particle formation (NPF) in fire smoke is thought to be unlikely due to large condensation and coagulation sinks that scavenge molecular clusters. We analyze aircraft measurements over the Amazon and find that fires significantly enhance NPF and ultrafine particle (UFP < 50 nm diameter) numbers compared to background conditions, contrary to previous understanding. We identify that the nucleation of dimethylamine with sulfuric acid, which is aided by the formation of extremely low volatility organics in biomass-burning smoke, can overcome the large condensation and coagulation sinks and explain aircraft observations. We show that freshly formed clusters rapidly grow to UFP sizes through biomass-burning secondary organic aerosol formation, leading to a 10-fold increase in UFP number concentrations. We find a contrasting effect of UFPs on deep convective clouds compared to the larger particles from primary emissions for the case investigated here. UFPs intensify the deep convective clouds and precipitation due to increased condensational heating, while larger particles delay and reduce precipitation.

Two studies were conducted to investigate the relationship between Early Life Adversity and Malevolent Creativity as well as its underlying mechanism. Study 1 established a moderated mediation model to explore the mediating role of Belief in a Just World (BJW) and the moderating role of Dark Triad. A total of 353 college students (Mage= 21.25, SDage= 2.21, 58.9% women) were recruited to fill out questionnaires. The results showed that Early Life Adversity positively predicted Malevolent Creativity behavior and indirectly predicted Malevolent Creativity behavior through BJW. Dark Triad reduced the effects of Early Life Adversity on BJW and intensified the effects of Early Life Adversity on Malevolent Creativity behavior. Study 2 explored the effect of threatening BJW on Malevolent Creativity potential. A total of 180 participants (Mage= 20.53, SDage= 1.7, 64.4% women) were investigated with questionnaires and a Malevolent Creativity Problem‐Solving Task. Study 2 validated study 1 and illustrated that the Malevolent Creativity potential (including fluency, originality, and malevolence) was enhanced when Belief in a Just World was threatened. However, greater fluency was only observed in men. As a whole, these finding emphasizes the important linking role of BJW between Early Life Adversity and Malevolent Creativity, and Dark Triad and gender influence the expression of Malevolent Creativity as well.

Zinc-ion hybrid supercapacitors (ZIHCs) hold great promise in the realm of renewable energy storage. However, their development is severely hampered by the unsatisfactory practical capacity and poor stability. Herein, we report an effective strategy to improve the electrochemical performance by constructing hollow bimetallic nitride TiVN (H-TiVN) using a sacrificial template method. The optimized H-TiVN//Zn shows a high capacitance of 183.7 F g–1at 0.2 A g–1along with excellent stability (an 84.7% capacitance retention after 20,000 cycles at 5 A g–1). More importantly, the energy density can reach 57.42 Wh kg–1, accompanied by a power density of 249.35 W kg–1. Our work not only establishes H-TiVN as a high-performance electrode material for ZIHCs but also provides a general strategy for improving the electrochemical performance of nanomaterial electrodes.

Pulmonary hypertension (PH) is an intractable, severe, and progressive cardiopulmonary disease. Recent findings suggest that human umbilical cord mesenchymal stromal cells (HUCMSCs) and HUCMSC-derived exosomes (HUCMSC-Exos) possess potential therapeutic value for PH. However, whether they have beneficial effects on hypoxic pulmonary hypertension (HPH) is unclear. Exos are released into the extracellular environment by the fusion of intracellular multivesicular bodies with the cell membrane, and they play an important role in cellular communication. Exos ameliorate immune inflammation levels, alter macrophage phenotypes, regulate mitochondrial metabolic function, and inhibit pulmonary vascular remodeling, thereby improving PH. Macrophages are important sources of cytokines and other transmitters and can promote the release of cytokines, vasoactive molecules, and reactive oxygen species, all of which are associated with pulmonary vascular remodeling. Therefore, the aim of this study was to investigate whether HUCMSC-Exos could improve the lung inflammatory microenvironment and inhibit pulmonary vascular remodeling by targeting macrophages and identifying the underlying mechanisms. The results showed that HUCMSC-Exos promoted M2 macrophage polarization, decreased pro-inflammatory factors, increased IL-10 levels, and inhibited IL-33/ST2 axis expression, thereby inhibiting hypoxia-induced proliferation of pulmonary artery smooth muscle cells and ameliorating HPH.Graphical Abstract

Selective laser melting (SLM) known as a rapid manufacturing technology can fabricate complex and dense metallic components of 18Ni300 with excellent properties. The performance of 18Ni300 can be improved by intermetallics using heat treatment, not including anti-corrosion property. In this study, the effects of heat and coating treatments on microstructure and properties of 18Ni300 fabricated by SLM were investigated. The characterization results indicated that grain coarsening, dislocation reduction, and texture strengthening occurred after the optimal heat treatment. The sequential processing of 820 °C for 2 h and 480 °C for 3 h can maximize the microhardness (increased by 74 %) and ultimate tensile strength (increased by 79 %), accompanying cost of Charpy impact (reduced by 43 %) and elongation (decreased by 32 %). However, excessively high or low aging temperature and time can lead to insufficient performance improvement because of austenite reversal. In addition, the great interface bonding is observed after the laser cladding coating treatment, and the wear resistance maintains well while improving the anti-corrosion property significantly with low quality loss.

Perovskite nanocrystals (PNs) have received much attention as luminescence materials in the field of electrochemiluminescence (ECL). However, as one key factor for determining the optoelectronic properties of the surface state of PNs, the surface passivation layer of PNs has enormous difficulty in simultaneously meeting the requirements of high ECL efficiency, conductivity, and stability. Herein, an effective surface modification strategy with cyanuric acid (CA) is used to solve such issue. As confirmed, the CA molecules are chemically anchored onto the surface of PNs via the Lewis interaction between π electrons of the triazine ring and the empty orbit of Pb2+. Benefiting from the above interaction, the electrochemical impedance of PNs is decreased greatly without the loss of light-emitting efficiency. Moreover, the stability of PNs under O2exposure is improved by almost sixfold. These improvements are confirmed to be beneficial for enhancing the ECL behaviors of PNs under electrochemical operation. Upon cathode ECL driving conditions in aqueous media, the ECL intensity and efficiency of PNs are increased to 200 and 170%, respectively. This work provides a new modification strategy to holistically improve the ECL performance of PNs, which is instructive to exploring robust perovskite nanomaterials for electrochemical applications.

Purpose: Non-invasive fetal electrocardiography (fECG) has a promising application prospect in offering crucial information for assessing early diagnosis and intervention of fetal distress and morbidity during pregnancy. Nevertheless, the detection and extraction of fetal ECG signals are still challenging since fetal ECG signals are exceedingly weak, and liability is affected by maternal ECG and other noises. Methods: In this study, a comprehensive framework is developed for fECG signal extraction and fetal QRS complex location. A negative entropy-based blind source separation (BSS) method combined with a template subtraction (TS) method is exploited to obtain fECG signals from abdominal ECG (aECG) recordings. It effectively combines the arithmetic characteristics of fixed-point iteration and the effectiveness of template filtering, making the algorithm simple and fast to obtain clearer fetal ECG signals. Additionally, the combination of filter transformation and adaptive threshold algorithm is adopted for fetal QRS wave location. The filtering operation makes the fECG signal into single peaks. The design of low threshold and high threshold ensures that R waves can be located and detected more accurately. Results: The performance results in terms of diagnostic sensitivity (Se), positive predictive value (PPV), accuracy (ACC), and harmonic mean (F1) scores are 96.12%, 96.20%, 92.60%, and 95.94% for the PCDB database, respectively, and 99.78%, 99.10%, 98.88%, and 99.44% for the ADFECGDB database. In addition, the results in terms of Se, PPV, ACC, and F1 scores are 99.46%, 97.89%, 97.37%, and 98.67% for the AECGDB database, respectively. Conclusion: This study demonstrates that the proposed framework exhibits superior performance, which can improve the accuracy of fetal QRS complex detection.

Purpose: Non-invasive fetal electrocardiography (fECG) offers crucial information for assessing early diagnosis of fetal distress and morbidity. However, the non-invasive fECG signals probably contain various non-stationary noises, which may generate a bad influence on signal processing. Signal quality assessment plays a crucial role in accurate feature estimation for obtaining high-quality signals. Methods: This study develops a comprehensive framework for the assessment of signal quality for non-invasive fECG signals. Firstly, the ECG collection equipment is employed to gather abdominal ECG signal data from eight pregnant women in the hospital. Secondly, signal preprocessing is operated including signal segmentation and data normalization process. Subsequently, a total of thirty-seven signal quality indexes (SQIs) are extracted which consist of the amplitude-based SQI, R-wave-based SQI, statistical-based SQI, fractal dimension SQI, power spectrum distribution-based SQI, and entropy domain-based SQI. Then, in order to reduce the dimensionality of features and improve the experimental performance, information gain is carried out to identify the subset of the optimal features. At last, the classifier combines different feature numbers to classify the quality of the non-invasive fECG signal. Results: Ten classifiers are selected to perform a classification task between good-quality and bad-quality abdominal signals. The experimental results show that the combination of twenty-four effective features and random forest achieved the highest classification outcome, which in terms of the ACC, and F1 scores are 0.9508, and 0.9510, respectively. Conclusion: The experimental results indicate that our work can reliably assess the signal quality for non-invasive fECG signals and filter out good-quality signals. This proposed algorithm can help to improve the accuracy of fetal signal extraction and fetal heart rate estimation for further analysis, which is beneficial to promoting fetal health monitoring.

LiDAR sensors provide rich geometrical information for 3-D scene understanding, which has been widely used as a unique input for 3-D object detection. However, due to the intrinsic property, point clouds scanned by LiDAR are always sparse and incomplete, and objects are occluded to different extents, which will deteriorate the detection accuracy. The existing methods overlook occlusion or tackle occlusion implicitly. In this article, we emphasize the universality of occlusion in point clouds and propose a novel occlusion–attention strategy, which aims to increase model’s sensitivity to occlusion and maintain great performance in occlude scenes. The proposed method simulates different types and levels of occlusion and explores the relationship between the uncertainty caused by occlusion and the prediction distribution. The major changes include the following: 1) data augmentation specifically for occlusion scenes to force feature extractor into learning efficient features regardless of damage and 2) uncertainty estimation module to model prediction as a distribution instead of the deterministic label. We incorporate the proposed methods into various classical 3-D base detectors and demonstrate performance gain in the KITTI dataset, which proves the particularity of occlusion structure and the necessity of uncertainty estimation.

In recent years, the field of bionics has attracted the attention of numerous scholars. Some models combined with biological vision have achieved excellent performance in computer vision and image processing tasks. In this paper, we propose a new bio-inspired lightweight contour detection network (BLCDNet) by combining parallel processing mechanisms of bio-visual information with convolutional neural networks. The backbone network of BLCDNet simulates the parallel pathways of ganglion cell–lateral geniculate nucleus and primary visual cortex (V1) area, realizing parallel processing and step-by-step extraction of input information, effectively extracting local features and detailed features in images, and thus improving the overall performance of the model. In addition, we design a depth feature extraction module combining depth separable convolution and residual connection in the decoding network to integrate the output of the backbone network, which further improves the performance of the model. We conducted a large number of experiments on BSDS500 and NYUD datasets, and the experimental results show that the BLCDNet proposed in this paper achieves the best performance compared with traditional methods and previous biologically inspired contour detection methods. In addition, BLCDNet still outperforms some VGG-based contour detection methods without pre-training and with fewer parameters, and it is competitive among all of them. The research in this paper also provides a new idea for the combination of biological vision and convolutional neural networks.

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.

In this brief, the resilient consensus problem for multi-agent systems (MASs) is addressed based on the dynamic event-triggered (DE) mechanism, when the network is subject to malicious attacks. A dynamic variable is introduced in the DE mechanism to adjust the threshold dynamically. Via the proposed dynamic event-triggered mean-subsequence-reduced (DE-MSR) algorithm, all cooperative agents are guaranteed to reach an agreement on the same consensus value, so that the MAS achieves resilient consensus despite the influence of some noncooperative agents in the network. Compared to existing event-based resilient algorithms, the proposed DE-MSR algorithm is superior in reducing communication overheads while maintaining a resilient consensus. Finally, a comparative case study is conducted to validate the theoretical findings.

In this brief, a novel inverse design model, constructed using a generative adversarial network (GAN), is proposed for a varactor-based microstrip tunable lowpass filter (TLPF). Transfer learning algorithm is utilized to simplify the training process of the constructed model. The planar resonant circuits of the TLPFs are represented as pixelated patterns and tunable characteristics are achieved by the tunable capacitance of the varactors. The GAN-based model incorporates multiple simulators that are constructed using convolutional neural networks (CNNs) to predict the outcomes under various tuning conditions. In this way, the relationship between these patterns and the corresponding EM behaviors is established under different tuning voltages applied to the varactors. To significantly reduce the training time and the number of samples required by the extra simulators, an effective solution based on transfer learning is proposed by expanding a single CNN of a well-trained simulator to a series of CNNs of similar simulators with a common architecture. Utilizing the inverse design model, four TLPFs with different customized tuning ranges within 5 to 10 GHz are designed with an efficient process. The training of the model requires approximately 201 min, while the inference time for each design is 12.5 min. The simulation results of the designed TLPFs are in good agreement with the customized ones. Circuit prototypes combining these TLPFs and two additional rejection cells were fabricated and the measurement results show that a 17 dB rejection level is achieved over a wide stopband up to 28 GHz.

Hierarchical frameworks—a special class of directed frameworks with a layer-by-layer architecture—can be an effective mechanism to coordinate robot swarms. Their effectiveness was recently demonstrated by the mergeable nervous systems paradigm (Mathews et al., 2017), in which a robot swarm can switch dynamically between distributed and centralized control depending on the task, using self-organized hierarchical frameworks. New theoretical foundations are required to use this paradigm for formation control of large swarms. In particular, the systematic and mathematically analyzable organization and reorganization of hierarchical frameworks in a robot swarm is still an open problem. Although methods for framework construction and formation maintenance via rigidity theory exist in the literature, they do not address cases of hierarchy in a robot swarm. In this article, we extend bearing rigidity to directed topologies and extend the Henneberg constructions to generate self-organized hierarchical frameworks with bearing rigidity. We investigate three-key self-reconfiguration problems: 1) framework merging; 2) robot departure; and 3) framework splitting. We also derive the mathematical conditions of these problems and then develop algorithms that preserve rigidity and hierarchy using only local information. Our approach can be used for formation control generally, as in principle it can be coupled with any control law that makes use of bearing rigidity. To demonstrate and validate our proposed hierarchical frameworks and methods, we apply them to four scenarios of reactive formation control using an example control law.

Fires, including wildfires, harm air quality and essential public services like transportation, communication, and utilities. These fires can also influence atmospheric conditions, including temperature and aerosols, potentially affecting severe convective storms. Here, we investigate the remote impacts of fires in the western United States (WUS) on the occurrence of large hail (size: ⩾ 2.54 cm) in the central US (CUS) over the 20-year period of 2001–20 using the machine learning (ML), Random Forest (RF), and Extreme Gradient Boosting (XGB) methods. The developed RF and XGB models demonstrate high accuracy (> 90%) and F1 scores of up to 0.78 in predicting large hail occurrences when WUS fires and CUS hailstorms coincide, particularly in four states (Wyoming, South Dakota, Nebraska, and Kansas). The key contributing variables identified from both ML models include the meteorological variables in the fire region (temperature and moisture), the westerly wind over the plume transport path, and the fire features (i.e., the maximum fire power and burned area). The results confirm a linkage between WUS fires and severe weather in the CUS, corroborating the findings of our previous modeling study conducted on case simulations with a detailed physics model.

This brief presents a prescribed-time bearing-based formation control scheme for underactuated autonomous surface vessels (ASVs) subject to external disturbances. Compared with the existing methods, our method enables the ASV swarm to maneuver with flexible size scaling and user-defined convergence time. Assuming that the underlying graph of ASV swarm satisfies the leader-first follower structure, a hierarchical formation protocol is designed. We firstly design a prescribed-time disturbance observer to estimate the external disturbance. On this basis, a bearing-based formation control law is integrated such that the vessels achieve the target formation within user-defined time. Rigorous theoretical stability analysis and comparative simulation results are presented to substantiate the effectiveness of the proposed method.

We developed a rotating scanning Hall probe apparatus as a non-destructive measurement method for measuring the self-generated magnetic field distribution around the superconducting cable. To validate the accuracy of our system, we performed measurements by energizing the tape of the outer conductor of the cable, one by one. We confirmed that the measured values are in good accordance with the calculated values. It was also confirmed that the measurement results of the magnetic field distribution when current was passed through two tapes simultaneously coincided well with the sum of the magnetic field distributions when current was passed one by one. In addition, we found that some adjacent tapes within the cable were in contact with each other. These results suggest that this method can also be used as a non-destructive testing method for cable quality control.

This letter proposes a novel wideband crossed-dipole antenna that utilizes liquid metal switches for polarization reconfiguration. The antenna consists of a wideband single-fed crossed bowtie dipole with each arm featuring a gradually varying width bent slot. A back cavity structure serves as a reflector to achieve directional radiation and circular polarization (CP) bandwidth enhancement. The proposed antenna uses movable liquid metal columns in microchannels as coupling switches to open or close the slots. By controlling the switches to close either the vertical or horizontal slots, the antenna achieves directional radiation of left-hand circular polarization or right-hand circular polarization, respectively. When all the slots are opened or closed simultaneously, the directional radiation of linear polarization (LP) is achieved. Experimental results indicate that the antenna has an impedance bandwidth (IBW) of 65.02% and an axial ratio bandwidth of 43.14% in the CP mode, with a maximum gain of 11.6 dBic. In the LP mode, the antenna exhibits an IBW of 42.5% and a maximum gain of 11.48 dBi. These results demonstrate the proposed antenna's versatility and potential for use in mobile communication terminals.

Vaccination is the best way to build an immune barrier to control a new infectious disease. Considering the limited production capacity at the initial stage of vaccine production, the problem of multistage vaccine production and allocation is studied under the policy of free treatment for infected patients by the government. Given the population and infection rates of different regions and the vaccine production capacity of each stage, an integer programming model is established to minimize the sum of production-and-vaccination cost and treatment cost of infected patients, which is solved by the GUROBI solver. The correctness of the model is verified by simulation, and the necessity of considering the treatment cost of infected patients in the objective function is further analyzed, which confirmed the correctness of the free treatment policy for patients in China. A heuristic algorithm is designed to solve the large-scale problem and numerical experiments are conducted to verify the efficiency of this algorithm. Furthermore, based on the sensitivity analysis results of parameters, the optimal vaccine production and allocation strategy are proposed, which provide the decision basis for the government departments to make reasonable vaccine production and vaccination plan.

CD8+invariant natural killer T (iNKT) cells are functionally different from other iNKT cells and are enriched in human but not in mouse. To date, their developmental pathway and molecular basis for fate decision remain unclear. Here, we report enrichment of CD8+iNKT cells in neonatal mice due to their more rapid maturation kinetics than CD8-iNKT cells. Along developmental trajectories, CD8+and CD8-iNKT cells separate at stage 0, following stage 0 double-positive iNKT cells, and differ in HIVEP3 expression. HIVEP3 is lowly expressed in stage 0 CD8+iNKT cells and negatively controls their development, whereas it is highly expressed in stage 0 CD8-iNKT cells and positively controls their development. Despite no effect on IFN-?, HIVEP3 inhibits granzyme B but promotes interleukin-4 production in CD8+iNKT cells. Together, we reveal that, as a negative regulator for CD8+iNKT fate decision, low expression of HIVEP3 in stage 0 CD8+iNKT cells favors their development and T helper 1–biased cytokine responses as well as high cytotoxicity.As a negative regulator for CD8+iNKT fate decision, a low HIVEP3 level in CD8+iNKT cells favors their development, Th1-biased responses, and high cytotoxicity.

Our previous study found that fucogalactan sulfate (FS) from Laminaria japonicaexhibited significant hypolipidemic effects. To further elucidate the mechanism, we first constructed a dyslipidemia mouse model with humanized gut microbiota and proved the main differential metabolic pathway involved bile acid metabolism. Then, we evaluated the beneficial effects of FS on dyslipidemia in this model mice, which revealed that oral FS administration reduced serum cholesterol levels and mitigated liver fat accumulation. Gut microbiota and microbiome analysis showed FS increased the abundance of Ruminococcaceae_NK4A214_group, GCA-900066755, and Eubacterium, which were positively associated with the fecal DCA, β-MCA, and HDCA. Further investigation demonstrated that FS inhibited the hepatic farnesoid X receptor (FXR), while activating the intestinal FXR-FGF19 pathway, leading to suppression of CYP7A1 and CYP8B1, as well as potentially reduced bile acid synthesis and lipid absorption. Overall, FS regulated lipid metabolism in diet-induced humanized dyslipidemia mice via the bile acid-mediated intestinal FXR-FGF19-CYP7A1/CYP8B1 pathway.

ABSTRACT Bridge structures are exposed to vehicle loading and aggressive environments, damage of concrete will inevitably occur. In this study, influences of crack, freeze-thaw (F-T) cycling and carbonation on mechanical [...]

Cathode electrochemiluminescence (ECL) of C3N4material has suffered from weak and unstable ECL emission for a long time, which greatly limits its practical application. Herein, a novel approach was developed to improve the ECL performance by regulating the crystallinity of the C3N4nanoflower for the first time. The high-crystalline C3N4nanoflower achieved a pretty strong ECL signal as well as excellent long-term stability compared to low-crystalline C3N4when K2S2O8was used as a co-reactant. Through the investigation, it is found that the enhanced ECL signal is attributed to the simultaneous inhibition of K2S2O8catalytic reduction and enhancement of C3N4reduction in the high-crystalline C3N4nanoflower, which can provide more opportunities for SO4•–to react with electro-reduced C3N4•–, and a new “activity passivation ECL mechanism” was proposed, while the improvement of the stability is mainly ascribed to the long-range ordered atomic arrangements caused by structure stability in the high-crystalline C3N4nanoflower. As a benefit from the excellent ECL emission and stability of high-crystalline C3N4, the C3N4nanoflower/K2S2O8system was employed as a Cu2+detection sensing platform, which exhibited high sensitivity, excellent stability, and good selectivity with a wide linear range from 6 nM to 10 μM and a low detection limit of 1.8 nM.

At present, when performing material identification of metal wear debris, most sensors can only roughly distinguish between ferromagnetic and non-ferromagnetic metal wear debris, but fail to distinguish them deeply. To achieve fine detection of metal wear debris in the oil, this article designs an asymmetric micro-three-coil sensor, which generates an asymmetric magnetic field by using two excitation coils of different sizes. Different metal wear debris are affected differently in the magnetic field and the shape of the output signal is also different, therefore, the wear debris material can be distinguished by the shape of the signal of the induction electric potential output from the induction coil alone, and the wear debris size can be determined by the amplitude of the signal. Copper particles, aluminum particles, iron particles, and 304 stainless steel particles were selected as experimental objects, and the experimental results showed that the material differentiation of these four types of particles can be achieved by the asymmetric magnetic field of the sensor. The method provides a new research direction for the inductive method to distinguish non-ferrous metals, which is important for the fine detection of multi-parameter pollutants and the diagnosis and monitoring of mechanical system faults.

ABSTRACTFungi and bacteria often co-exist and physically or chemically interact with each other in their natural niches. This inter-kingdom species interaction is exemplified by the gram-positive bacterial pathogen Streptococcus mutansand opportunistic fungal pathogen Candida albicans, which co-exist in the human mouth. It has been demonstrated that the dynamic interaction between these two species plays a critical role in their virulence and biofilm development. In this study, we discovered that S. mutansrepresses filamentous development and virulence in C. albicansthrough secreting a secondary metabolite, mutanocyclin (a tetramic acid). Mutanocyclin functions by regulating the PKA catabolic subunit Tpk2 and its preferential binding target Sfl1. Inactivation of Tpk2 in C. albicansresults in an increased sensitivity to mutanocyclin, whereas overexpression of Tpk2 leads to an increased resistance. Dysfunction of SFL1and its downstream target genes overrides the hyphal growth defect caused by mutanocyclin. Further investigation demonstrates that three glycosylphosphatidylinositol (GPI)-anchored proteins (Spr1, Hyr4, and Iff8), associated with cell wall biogenesis and remodeling, and a set of filamentous regulators also contribute to the mutanocyclin response. We propose that both transcriptional regulation and cell wall composition contribute to mutanocyclin-mediated filamentous inhibition. This repressive effect of mutanocyclin could function as a natural regulator of filamentous development in C. albicans.

This paper is concerned with an event-triggered adaptive fault-tolerant problem for an uncertain non-affine system. The implicit function theorem and mean value theorem are utilized to transform a non-affine system into an affine one, and an extended state observer and a tracking differentiator are used to estimate unknown dynamics and the derivative of virtual control laws, respectively. Adaptive laws are designed for unknown faults, and an event-triggered control scheme with a time-varying threshold, based on a tracking error and adaptive parameters, is developed. The tracking error is steered to converge to a bounded set with the help of a predefined performance function, and its transient performance is improved despite of faults. The stability of the closed-loop system is analyzed by the theorem of the input-to-state practically stability, and the Zeno behavior is excluded. Finally, two examples are given to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper proposed a fully integrated MBAN (2360–2400 MHz) continuous phase modulated transmitter (TX) with tunable less than 0dBm output power for medical band. A duty-cycle tuning strategy was proposed for the power amplifier (PA) featuring adaptive optimized efficiency for different output powers. A fully on-chip transformer-based match network was proposed to suppress the 2nd harmonic using a series $LC$ resonator and to suppress the 3rd harmonic by introducing a transformer inter-winding capacitor feedback path. A fractional-N all-digital phase locked loop (ADPLL) with a transformer-based digitally controlled oscillator (DCO) is employed to reduce power consumption as well as improve modulation quality. The transmitter was fabricated in 40-nm CMOS technology, occupying an active area of 0.48mm2. Experimental results show a 26% drain efficiency with −10dBm PA output and 4dB tunable range. A 2mW total power consumption was measured with a TX efficiency of 5% and an energy efficiency of 2nJ/bit. The measured 2nd and 3rd harmonic distortion of the output were −44.3dBm and −57.2dBm, respectively, with on-chip matching network. The measured FSK error of CPM was 2.3% with an M of 2 and 1.57% with an M of 4.

The shift towards sustainable energy requires efficient electrochemical conversion technologies, emphasizing the crucial need for robust electrocatalyst design. Recent findings reveal that the efficiency of some electrocatalytic reactions is spin-dependent, with spin configuration dictating performance. Consequently, understanding the spin's role and controlling it in electrocatalysts is important. This review succinctly outlines recent investigations into spin-dependent electrocatalysis, stressing its importance in energy conversion. It begins with an introduction to spin-related features, discusses characterization techniques for identifying spin configurations, and explores strategies for fine-tuning them. At the end, the article provides insights into future research directions, aiming to reveal more unknown fundamentals of spin-dependent electrocatalysis and encourage further exploration in spin-related research and applications.This review presents the current understanding of spin-dependent electrocatalysis, highlighting key aspects such as spin features in electrocatalysts, techniques for spin characterization, strategies for spin manipulation, as well as the challenges and future perspectives in this emerging field.

Circular RNAs (circRNAs) have been demonstrated to play vital roles in cancer development and progression. However, studies on the association between circRNAs and prostate cancer (PCa) are still lacking. CircRNA sequencing of two pairs of PCa tissues and adjacent normal tissues was conducted in the present study, and qRT–PCR was performed to verify the results. Functional experiments were performed to investigate cellular functions after specific changes. Mass spectrometry analysis after RNA pull-down experiments and Co-IP assays were further conducted. Downstream target proteins were predicted via online databases and detected in vitro by Western blot analysis and in vivo by immunohistochemistry. Hsa_circ_0006156 (subsequently named circ_0006156) expresses at low levels in both PCa tissues and cells, and it significantly inhibits the migration and invasion of PCa cells. Circ_0006156 binds to and blocks the ubiquitination of S100A9. Moreover, functional assays revealed that circ_0006156 represses the malignant progression of PCa by binding to S100A9. Finally, in vivo experiments showed that circ_0006156 suppresses PCa migration and invasion by increasing S100A9, revealing circ_0006156 as a potential novel effective target for PCa treatment.

Liquid crystal (LC)-based sensors have been extensively applied in the detection of chemical and biological events. However, the calculation of the optical images of the LC-based sensors is usually time-consuming and also might bring some errors due to the use of different judgment criteria by different users. In the present study, an automated calculation method for LC sensing images based on deep learning is provided. A convolutional network is trained with the prepared LC sensing images and their corresponding segmentation annotations to predict the positive responses. The ratio is calculated from the area of positive response to the total area selected by our image processing method. The robustness of the proposed algorithm is validated on both the test set and the label-free Cd2+detection. The results show that the method based on deep learning can detect the positive response area in real time and the speed is much faster than the manual processing method. In addition, deep learning method can be directly applied to other label-free molecular detection assays.

Nanotechnology provides platforms to deliver medical agents to specific cells. However, the nanoparticle’s surface becomes covered with serum proteins in the blood after administration despite engineering efforts to protect it with targeting or blocking molecules. Here, we developed a strategy to identify the main interactions between nanoparticle-adsorbed proteins and a cell by integrating mass spectrometry with pooled genome screens and Search Tool for the Retrieval of Interacting Genes analysis. We found that the low-density lipoprotein (LDL) receptor was responsible for approximately 75% of serum-coated gold nanoparticle uptake in U-87 MG cells. Apolipoprotein B and complement C8 proteins on the nanoparticle mediated uptake through the LDL receptor. In vivo, nanoparticle accumulation correlated with LDL receptor expression in the organs of mice. A detailed understanding of how adsorbed serum proteins bind to cell receptors will lay the groundwork for controlling the delivery of nanoparticles at the molecular level to diseased tissues for therapeutic and diagnostic applications.

The Lennard–Jones (LJ) energy functions are commonly used to describe the nonbonded interactions in bulk coarse-grained (CG) models, which contribute significantly to the stabilization of a local binding configuration or a self-assembly system. In many cases, systematic development of the LJ interaction parameters in a CG model requires a comprehensive sampling of the objective molecules at the all-atom (AA) level, which is therefore extremely time-consuming for large systems. Inspired by the concept of electrostatic potential (ESP), we define the LJ static potential (LJSP), by which the embedding potential energy surface can be constructed analytically. A semianalytic approach, namely, the LJSP matching method, is developed here to derive the CG parameters by minimizing the LJSP difference between the AA and the CG models, which provides a universal way to derive the CG LJ parameters from the AA models without doing presampling. The LJSP matching method is successful not only in deriving the LJ interaction energy landscape in the CG models for proteins, lipids, and DNA but also in reproducing the critical properties such as intermediate structures and enthalpy contributions as exemplified in simulating the self-assembly process of the dipalmitoylphosphatidylcholine (DPPC) lipids.

Targeting ligands are conjugated onto nanoparticles to increase their selectivity for diseased cells. However, they become covered by serum proteins which prevent them from binding to target receptors. Here, we show that the nanoparticle protein corona achieved a maximum thickness in serum because the protein adsorption and desorption rates reached an equilibrium. Simulation experiments suggest that the number of molecular interactions between proteins decrease with distance from the nanoparticle surface until the forces are too weak to hold the proteins together. This results in an equilibration state between the proteins on the nanoparticle surface and in biological fluids. Conjugating targeting ligands to this equilibrated protein corona allowed the nanoparticles to bind to target cells in the presence of serum proteins. In contrast, conjugating targeting ligands directly to the nanoparticle surface resulted in a 55% reduction in binding to target cells in serum. We demonstrated this concept using two nanoparticle material types with different surface chemistries. We present a ligand-on-corona conjugation strategy that overcomes the negative impact of serum protein adsorption on nanoparticle cellular targeting.

The Intergovernmental Panel on Climate Change (IPCC) was established by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP) in 1988 to recognize the problem of potential [...]

Growing evidence supports the transcription of enhancer RNAs (eRNAs) and their important roles in gene regulation. However, their interactions with other biomolecules and their corresponding functionality remain poorly understood. In an attempt to facilitate mechanistic research, this study presents eRNA-IDO, the first integrative computational platform for the identification, interactome discovery, and functional annotation of human eRNAs. eRNA-IDO comprises two modules: eRNA-ID and eRNA-Anno. Functionally, eRNA-ID can identify eRNAs from de novoassembled transcriptomes. eRNA-ID includes eight kinds of enhancer makers, enabling users to customize enhancer regions flexibly and conveniently. In addition, eRNA-Anno provides cell-/tissue-specific functional annotation for both new and known eRNAs by analyzing the eRNA interactome from prebuilt or user-defined networks between eRNAs and protein-coding genes. The prebuilt networks include the Genotype-Tissue Expression (GTEx)-based co-expression networks in normal tissues, The Cancer Genome Atlas (TCGA)-based co-expression networks in cancer tissues, and omics-based eRNA-centric regulatory networks. eRNA-IDO can facilitate research on the biogenesis and functions of eRNAs. The eRNA-IDO server is freely available at http://bioinfo.szbl.ac.cn/eRNA_IDO/.

Since most Hepatocellular Carcinoma (HCC) typically arises as a consequence of long‐term liver damage, the hepatic molecular characteristics are closely related to the occurrence of HCC. Gaining comprehensive information about the location, morphology, and hepatic molecular alterations related to HCC is essential for accurate diagnosis. However, there is a dearth of technological advancements capable of concurrently providing precise HCC diagnosis and discerning the accompanying hepatic molecular alterations. In this study, an integrated information system is developed for the pathological‐level diagnosis of HCC and the revelation of critical molecular alterations in the liver. This system utilizes computed tomography/Surface‐enhanced Raman scattering combined with an artificial intelligence strategy to establish connections between the occurrence of HCC and alterations in hepatic biomolecules. Employing artificial intelligence techniques, the SERS spectra from both healthy and HCC groups are successfully classified into two distinct categories with a remarkable accuracy rate of 91.38%. Based on molecular profiling, it is identified that the nucleotide‐to‐lipid signal ratio holds significant potential as a reliable indicator for the occurrence of HCC, thereby serving as a promising tool for prevention and therapeutic surveillance. The development of an artificial intelligence‐driven platform aims to unveil critical hepatic molecular alterations in hepatocellular carcinoma development. Contrast‐enhanced computed tomography (CT) and spectral CT imaging facilitate the precise morphological visualization and localization of hepatocellular carcinoma. Concurrently, the analysis of Surface‐enhanced Raman scattering (SERS) spectra from hepatic molecules, coupled with artificial intelligence, unveils notable biomolecular alterations potentially linked to the occurrence of carcinoma.

Designing and synthesizing non-noble metal catalysts with efficient hydrogen evolution efficiency are of great significance for large-scale application. Here, a bimetallic compound-combined matrix strategy is developed for the synthesis of honeycomb NiCo-4-P nanosheets on nickel foam (NF). Due to the synergistic advantage of bimetallic atoms, improved conductivity and increased active sites were provided on the NF. The self-supporting NiCo-4-P/NF catalyst proved to be an excellent hydrogen evolution reaction (HER) catalyst in 1 M KOH with a low overpotential of 99 mV at a current density of 10 mA cm–2, a Tafel slope of 47 mV dec–1, and a good long-term stability for 12 h. In addition, the effect of bimetallic atom concentration on the hydrogen evolution performance of the catalyst has been discussed. This study provides an effective method for developing nonprecious metal HER electrocatalysts for industrial hydrogen production.

The polysaccharides of Salvia miltiorrhiza(SMP) were prepared through water-extraction and alcohol-precipitation method optimized by employing the response surface methodology. Furthermore, the structural characteristics and activities of SMP as well as its three derivatives (SMP-S, SMP-Fe and SMP-Se) were analyzed. SMP was composed of galacturonic acid, glucose, galactose and arabinose and the molecular weights were 3.79 × 106Da, 3.17 × 104Da and 693.68 Da. The sulfur content of SMP-S was 13.53%, the iron content of SMP-Fe was 26.12% and the content of selenium in SMP-Se was 0.984 mg/g. Their surface morphology, meso-structure and thermal stability were determined by scanning electron microscope, transmission electron microscope and thermogravimetric analyzer, respectively. Further, the antioxidant and immunomodulatory activities of SMPs were studied. The study found that three modified polysaccharides not only exhibited higher antioxidant activity, but also higher immunomodulatory activity compared to SMP. This lays a theoretical foundation for the development of Salvia miltiorrhizapolysaccharide-related foods.

ABSTRACTObstructive sleep apnea syndrome (OSAS) is a respiratory disorder and chronic intermittent hypoxia (CIH) is an important pathological characteristic of OSAS. Injuries on renal tubular epithelial cells were observed under the condition of CIH. Pyroptosis is a programmed mode of cell death following cell apoptosis and cell necrosis, which is mediated by NLRP3 signaling. The present study aims to investigate the effects of CIH on the pyroptosis of renal tubular epithelial cells and the underlying mechanism. Firstly, CIH was induced in two renal tubular epithelial cell lines, HK-2 cells and TCMK-1 cells. As the aggravation of hypoxia, an increasing trend of elevated apoptotic rate was observed in HK-2 cells and TCMK-1 cells, accompanied by the excessive release of ROS and LDH, and upregulation of NLRP3. Subsequently, the CIH model was established on rats. The pathological analysis results indicated that in CIH rats, the glomerular bottom membrane and mesangium were slightly thickened and edema was observed in the renal tubule epithelium. More serious injury was observed in the moderate intermittent hypoxia group. The expression level of IL-1β and IL-18 was promoted as the aggravation of hypoxia, accompanied by the elevated production of LDH and ROS. The expression level of cleaved Caspase-1, Caspase-1, GSDMD, TLR4, MyD88, NF-κB, p-NF-κB, and NLRP3 was found significantly upregulated as the aggravation of hypoxia. Lastly, the pathological changes in rats induced by CIH were dramatically abolished by MCC950, a specific inhibitor of NLRP3. Collectively, CIH triggered the pyroptosis of renal tubular epithelial cells by activating the NLRP3 inflammasome.

Lithium-ion batteries are yet to realize their full promise because of challenges in the design and construction of electrode architectures that allow for their entire interior volumes to be reversibly accessible for ion storage. Electrodes constructed from the same material and with the same specifications, which differ only in terms of dimensions and geometries of the constituent particles, can show surprising differences in polarization, stress accumulation and capacity fade. Here, using operando synchrotron X-ray diffraction and energy dispersive X-ray diffraction (EDXRD), we probe the mechanistic origins of the remarkable particle geometry-dependent modification of lithiation-induced phase transformations in V2O5as a model phase-transforming cathode. A pronounced modulation of phase coexistence regimes is observed as a function of particle geometry. Specifically, a metastable phase is stabilized for nanometre-sized spherical V2O5particles, to circumvent the formation of large misfit strains. Spatially resolved EDXRD measurements demonstrate that particle geometries strongly modify the tortuosity of the porous cathode architecture. Greater ion-transport limitations in electrode architectures comprising micrometre-sized platelets result in considerable lithiation heterogeneities across the thickness of the electrode. These insights establish particle geometry-dependent modification of metastable phase regimes and electrode tortuosity as key design principles for realizing the promise of intercalation cathodes.