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The dynamics of RNAs are related intimately to their functions. Molecular flexibility, as a starting point for understanding their dynamics, has been utilized to predict many characteristics associated with their functions. Since the experimental measurement methods are time-consuming and labor-intensive, it is urgently needed to develop reliable theoretical methods to predict RNA flexibility. In this work, we develop an effective machine learning method, RNAfcg, to predict RNA flexibility, where the Random Forest (RF) is trained by features including the topological centralities, flexibility–rigidity index, and global characteristics first introduced by us, as well as some traditional sequence and structural features. The analyses show that the three types of features introduced first have significant contributions to RNA flexibility prediction, among which the topological type contributes the most, which indicates the importance of structural topology in determining RNA flexibility. The performance comparison indicates that RNAfcg outperforms the state-of-the-art machine learning methods and the commonly used Gaussian Network Model (GNM) models, achieving a much higher Pearson correlation coefficient (PCC) of 0.6619 on the test data set. This work is helpful for understanding RNA dynamics and can be used to predict RNA function information. The source code is available at https://github.com/ChunhuaLab/RNAfcg/.

This study mainly investigates the adaptive leader-following consensus tracking control problem for a class of nonlinear multiagent systems (MASs) subjected to unknown control directions, external disturbances, and sensor deception attacks. To start with, an equivalent MAS with known control directions is obtained by introducing a linear state transformation. For the purpose of estimating the unavailable system states caused by malicious attacks, a quantization-based fuzzy state observer is designed, and the fuzzy-logic system (FLS) is utilized to approximate nonlinear functions. Moreover, a dynamic uniform quantizer with scaling function is established to reduce information transmission. With the help of coordinate transformation and available compromised states, a novel compensation mechanism is designed to offset the influence of filter errors while avoiding the problem of “explosion of complexity” in the backstepping design process. In addition, the Nussbaum-type function is considered to eliminate the design obstacle of unknown control gains resulting from the attacks. Under the constructed consensus protocol, it is proved theoretically that the consensus tracking error converges to an adjustable small neighborhood of the origin, and all signals in the closed-loop system are bounded. Finally, the feasibility of the provided secure control scheme is verified through two simulation examples.

Dynamic cerebral perfusion CT (PCT) is an effective imaging technique for the clinical diagnosis and therapy guidance of many kinds of cerebrovascular diseases (CVDs), but the large radiation dose imposed on a patient during repeated CT scans greatly limits its clinical applications. Achieving low-dose PCT imaging with the help of advanced and satisfactory imaging methods is needed. A kinetic-induced voxel-clustering filter (KVCF) is proposed in this work to help process noisy and distorted PCT images acquired from the low-dose CT scan protocols. In this new method, the intrinsic kinetic information of objective PCT images is extracted and effectively utilized to construct an image filter for every PCT frame. The new method is validated using both the simulated and clinical low-dose PCT data, and the peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) are applied for quantitative evaluations of both the dynamic images and the calculated hemodynamic parametric maps. Compared to several existing methods, the proposed KVCF method produces the best qualitative and quantitative imaging effects. With satisfactory performance and high interpretability, KVCF is proven to be effective and implementable in the clinical low-dose PCT imaging tasks.

The mechanochromic materials can respond to environmental changes by altering the optical properties of the system, which has garnered significant attention in recently. The utilization of these materials for the purpose of sensing tensile and compressive stress has been extensively documented. However, the mechanochromic polymer that can effectively discriminate hardness of inorganic particles is still rarely reported. In this article, a force‐sensitive organic polysiloxane was developed by introducing triethenyl‐substituted rhodamine derivatives crosslinker into the polymethylhydrosiloxane network through hydrosilylation addition reaction. The results demonstrated that the grinding of the crosslinked polysiloxane effectively facilitated the transmission of external force to the polymeric network, leading to an open ring isomerization of rhodamine accompanied by significant alterations in visible and fluorescent color. In addition, we found that the optical properties of the system showed distinct variations when the inorganic particles with different Moh's hardness were co‐ground with the polysiloxane. High Moh's hardness particles, such as Al2O3, could effectively trigger the mechanochromic behavior of the polymer, while Na2SO4particles with low hardness failed to induce the corresponding change. Therefore, this organic polysiloxane could be used as a potential fluorescent indicator for hardness recognition.

The heat transfer efficiency of a thermal energy storage unit (TESU) can be improved by the addition of novel longitudinal fins. A series of TESUs are analyzed using the finite volume method (FVM) to determine the effect of fin angle on the heat transfer performance. As the fin angle increases, the TES rate first increases, then decreases, reaching a maximum rate at 60°, where the melting time is less by 30.9%, 28.58%, 21.99%, 9.02%, and 18.1% than at 0°, 15°, 30°, 45°, and 80°, respectively. In addition, it is found that the melting time of the phase change material is significantly greater at the bottom of the TESU. The time percentage of this stage decreases as the fin angle increases through these percentages by 7%, 14%, 23%, 33%, and 20%, respectively. Further, the response surface methodology (RSM) is applied to optimize the longitudinal fin by minimizing the total melting time. The analysis concludes that a fin angle of 58.68° reduces the complete melting time of the stearic acid by 44% below the time at 0°. These findings fill a gap in knowledge of the effect on melting performance of the design angle of longitudinal fins and provide a reference for the design of horizontally placed longitudinal finned thermal energy storage units.

An enzyme catalyzed strategy for the synthesis of a chiral hydrazine from 3-cyclopentyl-3-oxopropanenitrile 5and hydrazine hydrate 2is presented. An imine reductase (IRED) from Streptosporangium roseumwas identified to catalyze the reaction between 3-cyclopentyl-3-oxopropanenitrile 5and hydrazine hydrate 2to produce trace amounts of (R)-3-cyclopentyl-3-hydrazineylpropanenitrile 4. We employed a 2-fold approach to optimize the catalytic performance of this enzyme. First, a transition state analogue (TSA) model was constructed to illuminate the enzyme–substrate interactions. Subsequently, the Enzyme_design and Funclib methods were utilized to predict mutants for experimental evaluation. Through three rounds of site-directed mutagenesis, site saturation mutagenesis, and combinatorial mutagenesis, we obtained mutant M6 with a yield of 98% and an enantiomeric excess (ee) of 99%. This study presents an effective method for constructing a hydrazine derivative via IRED-catalyzed reductive amination of ketone and hydrazine. Furthermore, it provides a general approach for constructing suitable enzymes, starting from nonreactive enzymes and gradually enhancing their catalytic activity through active site modifications.

Sleep is a natural and essential physiological need for individuals. Our study aimed to research the associations between accumulated social risks and sleep disorders.

Polyamide (PA)-based nanofiltration (NF) membranes have demonstrated extensive applications for a sustainable water–energy–environment nexus. A rational control of interfacial polymerization (IP) is highly efficacious to enhance NF separation performance yet remains a technical challenge. Herein, we proposed a regulation strategy of constructing amphiphilic molybdenum disulfide/cetyltrimethylammonium bromide interlayer atop the Kevlar hydrogel substrate. The amphiphilic nanosheet interlayered NF membrane exhibited a crumpled PA surface with an elevated cross-linking degree of 76.9%, leading to an excellent water permeance (16.8 L m–2h–1bar–1) and an impressive Na2SO4rejection (99.1%). Meanwhile, the selectivity coefficient of Na2SO4/NaCl of the optimized TFC membrane reached 91, surpassing those of the recently reported NF membranes. Moreover, the optimized membrane exhibited a desirable rejection of over 90% against Mn2+and Cu2+in actual textile wastewater. Importantly, the underlying NF membrane formation mechanism was elucidated via both experiments and molecular simulations. The synchronous control of mass and heat transfer of IP process offers a new methodology for the state-of-the-art membrane fabrication, which opens more avenues in softening of brackish water and purification of industrial wastewater containing heavy metal ions.

The effects of 5 lactic acid bacteria (LAB) fermentation on the pasting properties of glutinous rice flour were compared, and suitable fermentation strains were selected based on the changes of viscosity, setback value, and breakdown value to prepare LAB compound starter cultures. The results revealed that Latilactobacillus sakeiHSD004 and Lacticaseibacillus rhamnosusHSD005 had apparent advantages in increasing the viscosity and reducing the setback and breakdown values of glutinous rice flour. In particular, the compound starter created using the two abovementioned LAB in the ratio of 3:1 had better performance than that using a single LAB in improving the pasting properties and increasing the water and oil absorption capacity of glutinous rice flour. Moreover, the gelatinization enthalpy of the fermented samples increased significantly. For frozen glutinous rice dough stored for 28 days, the viscoelasticity of frozen dough prepared by compound starter was better than that of control dough, and the freezable water content was lower than that of control dough. These results indicate that compound LAB fermentation is a promising technology in the glutinous rice-based food processing industry, which has significance for its application.

This paper is devoted to studying the stability of transonic shock solutions to the Euler-Poisson system in a one-dimensional nozzle of finite length. The background charge in the Poisson equation is a piecewise constant function. The structural stability of the steady transonic shock solution is obtained by the monotonicity argument. Furthermore, this transonic shock is proved to be dynamically and exponentially stable with respect to small perturbations of the initial data. One of the crucial ingredients of the analysis is to establish the global well-posedness of a free boundary problem for a quasilinear second order equation with nonlinear boundary conditions.

The BCN-heterocyclic B–N chain compounds, the sodium and potassium salts of 3and 4anions (Na3, Na4, and K4), were synthesized by reactions of ethane 1,2-diamineborane (BH3NH2CH2CH2NH2BH3, 1) and propane 1,2-diamineborane (BH3NH2CH2CH2CH2NH2BH3, 2) with MH (M = Na and K). Then, the neutral B–N chain compounds 5and 6were prepared with dehydrogenation of [NH4]3and [NH4]4, formed by metathesis reactions of Na3and Na4with NH4Cl or NH4SCN, respectively. Compounds 7and 8, analog 5, were also prepared using pyridine and 4-methoxypyridine instead of NH3in 5. These synthesized compounds were characterized spectroscopically, and the singe-crystal structures of the Na3·18-crown-6 and K4·18-crown-6 adducts were determined. Furthermore, the reactions of Na3and Na4with cationic B–N chain compounds, [NH3BH2NH3]Cl and [NH3BH2NH2BH2NH3]Cl, could not form longer BCN-heterocyclic B–N chain. The solubility of metal hydrides, the ability for proton abstracting, the basicity of Lewis bases, and the chelate effect may influence these reactions even though the reaction mechanism is not fully understood.

Abundant lacustrine shale oil resources have been discovered in the Eocene saline lake system, Yingxiongling Sag, Qaidam Basin. Compared with the Cretaceous Qingshankou Formation in the Songliao Basin and Triassic Chang 7 Member in the Ordos Basin, the lithofacies of Yingxiongling shale are more complex, featuring thinner single-layer and faster vertical variations. How to establish a reasonable lithofacies classification and evaluate the effectiveness of different lithofacies is the key to unlocking the giant resources of Yingxiongling shale oil. Based on the total organic carbon analysis, rock pyrolysis analysis, fluorescence thin section observation, oil saturation analysis, helium porosity measurement, high pressure mercury intrusion by scanning electron microscopy, focused ion beam-scanning electron microscopy, and nitrogen adsorption analysis, the organic geochemistry, reservoir property, and oil-bearing property were comprehensively analyzed. This paper proposed a lithofacies classification scheme of Yingxiongling shale oil and systematically evaluated the quality of representative lithofacies. The key findings were as follows: (1) Eight types of lithofacies were identified based on the rock structure and mineral composition, namely, thin-bedded/laminated dolomitic limestone, thin-bedded/laminated limy dolostone, thin-bedded/laminated mixed rock, thin-bedded sandstone, and laminated shale. (2) The types and combinations of lamination varied greatly among different lithofacies. Dolomite primarily formed through calcite shrinkage during the dolomitization process, resulting in the formation of abundant intercrystalline pores. (3) More organic matter was observed in the calcite laminae, which was deposited as a result of small calcite particle flocculation. (4) The combination of laminated dolomitic limestone and thin-bedded limy dolostone formed a favorable source–reservoir assemblage, which was the pay zone for horizontal wells. These understandings could further be helpful in understanding shale oil enrichment in the Qaidam Basin and could provide scientific guidance and technical support for the exploration and development of global plateau shale oil.

Gasdermin D (GSDMD) is the executor of pyroptosis, which is important for host defence against pathogen infection. Following activation, caspase-mediated cleavage of GSDMD releases an amino-terminal fragment (GSDMD-NT), which oligomerizes and forms pores in the plasma membrane, leading to cell death and release of proinflammatory cytokines. The spatial and temporal regulation of this process in cells remains unclear. Here we identify GSDMD as a substrate for reversible S-palmitoylation on C192 during pyroptosis. The palmitoyl acyltransferase DHHC7 palmitoylates GSDMD to direct its cleavage by caspases. Subsequently, palmitoylation of GSDMD-NT promotes its translocation to the plasma membrane, where APT2 depalmitoylates GSDMD-NT to unmask the C192 residue and promote GSDMD-NT oligomerization. Perturbation of either palmitoylation or depalmitoylation suppresses pyroptosis, leading to increased survival of mice with lipopolysaccharide-induced lethal septic shock and increased sensitivity to bacterial infection. Our findings reveal a model through which a palmitoylation–depalmitoylation relay spatiotemporally controls GSDMD activation during pyroptosis.

ABSTRACTBackgroundA bidirectional relationship between chronic pain (CP) and mental disorders has been reported, and coffee was believed to be associated with both. However, the association of coffee in this bidirectional relationship remains unclear. We aim to analyze the association of coffee consumption on the relationship of CP with depression and anxiety.MethodsA total of 376,813 participants from UK Biobank were included. We collected data on anxiety, depression and CP from objects of our study population. The association of coffee consumption on the relationship of CP with depression and anxiety was assessed through logistic/linear regression models. Moreover, seemingly unrelated estimation test (SUEST) was used to compare whether the coefficients differed in two different groups.ResultsWe observed significant associations of coffee consumption in the interaction of CP with depression and anxiety, such as the association of multisite chronic pain (MCP) on self-reported depression (βcoffee = 0.421, βnon-coffee = 0.488, PSUEST = 0.001), and the association of MCP on generalized anxiety disorder-7 (GAD-7) scores (βcoffee = 0.561, βnon-coffee = 0.678, PSUEST = 0.004) were significantly different between coffee drinking and non-coffee drinking groups. Furthermore, in analysis stratified by gender, we found headache (βmale = 0.392, βfemale = 0.214, PSUEST = 0.022) and hip pain (βmale = 0.480, βfemale = 0.191, PSUEST = 0.021) had significant associations with self-reported depression between males and females groups in coffee drinkers.ConclusionsOur results suggested that coffee consumption has a significant association on the relationship of CP with depression and anxiety.

Ore production is usually affected by multiple influencing inputs at open-pit mines. Nevertheless, the complex nonlinear relationships between these inputs and ore production remain unclear. This becomes even more challenging when training data (e.g. truck haulage information and weather conditions) are massive. In machine learning (ML) algorithms, deep neural network (DNN) is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers. This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data. Before the prediction models were built, principal component analysis (PCA) was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables. To verify the superiority of DNN, three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models. The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer. The DNN model outperformed the extensively applied benchmark models in predicting ore production. This can provide engineers and researchers with an accurate method to forecast ore production, which helps make sound budgetary decisions and mine planning at open-pit mines.

Verliesinterventies voor gezinnen zijn al veelbelovend gebleken om bij kinderen met een overleden ouder problemen te voorkomen en hun veerkracht te vergroten. Onderzoeksbevindingen over het ontstaan van een breed scala van psychische en lichamelijke gezondheidsproblemen bij kinderen na het overlijden van een ouder ondersteunen het idee dat er behoefte is aan een transdiagnostische benadering, die de aanpassing van gezinnen na het overlijden van een ouder bevordert en die zorgt voor vermindering van de meerdere problematische uitkomsten bij deze gezinnen. We bespreken zelfcompassie als een veelbelovend raamwerk voor een dergelijke transdiagnostische benadering. We betogen dat de drie elementen van zelfcompassie – mindfulness (vs. over-identificatie), zelfvriendelijkheid (vs. zelfveroordeling) en het besef mens onder de mensen te zijn (vs. isolement) – bevorderlijk kunnen zijn voor verliesgerichte coping, herstelgerichte coping en het proces van het afwisselen tussen die twee. Daarmee wordt de basis gelegd voor individuele en gezinsprocessen die de aanpassing na een verlies ondersteunen. Om uit te leggen op wat voor manier zelfcompassie de aanpassingsuitkomsten in gezinnen met een overleden ouder bevordert, bespreken we de bestaande literatuur, met een focus op emotieregulatie van ouders en effectief opvoeden, en doen we een voorstel voor een theoretisch model met toetsbare hypothesen als uitgangspunt voor meer onderzoek op dit terrein. In dit model is ouderlijke zelfcompassie een bron van veerkracht die bijdraagt aan meerdere adaptieve uitkomsten voor ouders zelf en voor hun kind, door de positieve invloed van zelfcompassie op de emotieregulatie van en effectief opvoeden door ouders. Ter illustratie van de bruikbaarheid van dit model bespreken we een preventieprogramma waarin gezinnen een verliesinterventie krijgen met zelfcompassietraining als een van de componenten. Ook bespreken we richtingen voor toekomstig onderzoek.

Reactive oxygen species (ROS) production in O2-perturbed subsurface environments has been increasingly documented in recent years. However, the constraining conditions under which abiotic and/or biotic mechanisms predominate for ROS production remain ambiguous. Here, we demonstrate that the ROS production mechanism, biotic and abiotic, is determined by sediment redox properties and sediment compositions. Upon the oxygenation of 10 field sediments, the cumulative H2O2concentrations reached up to 554 μmol/kg within 2 h. The autoclaving sterilization experiments showed that H2O2could be produced by both biotic and abiotic processes depending on the redox conditions. However, only the abiotic process could produce significant levels of •OH, and the production yield was closely related to the sediment components, particularly sediment Fe(II) and organic matter. Fe(II) bound with organic matter led to high yields of H2O2and •OH production. Sediment oxygenation contributed to the appearance of H2O2in groundwater, with the abiotic mechanism producing higher instantaneous H2O2concentrations than the biotic mechanism. These findings reveal that the redox conditions, compositions, and texture of sediments collectively control abiotic and biotic mechanisms for ROS production, which assists the identification of ROS production hotspots and the understanding of ROS distribution and utilization in the subsurface.

It is a challenging problem to accurately detect overlapping defects. This article proposes a system that can obtain optical and electromagnetic signals synchronously. It contains an industrial camera, a chromatic confocal displacement sensor (CCDS) and an eddy current testing (ECT) probe with high-resolution array tunneling magnetoresistance sensors. A multimodal fusion algorithm is proposed to combine the advantages of the optical testing and ECT. The fusion of the visual image and CCDS measurement results in morphology of the surface defects. To recognize the overlapping buried defect, a finite-element method model is developed to estimate the surface defect signal using parameters from the optical measurements, and then the surface defect signal is subtracted from the experimental image, based on which the overlapping defects are precisely detected. This method can be widely used in industry to comprehensively assess the health and integrity of structures.

ABSTRACT In view of the fact that the internal pore structure of electrolytic manganese residue based permeable brick (EMRB) is as difficult to observe as 'black box', which is not [...]

Photothermal membrane distillation (PMD) has emerged as a promising and sustainable approach for seawater desalination and wastewater purification. However, the wide application of the technique is severely impeded by low freshwater production and membrane fouling/wetting issues. Herein, we developed an advanced hydrogel-engineered membrane with simultaneously enhanced photothermal conversion capacity and desired fouling and wetting resistance for PMD. By the synergies of photothermal Ti3C2TxMXene nanosheets and the tannic acid–Fe3+network in the hydrogel, the membrane was endowed with excellent surface self-heating ability, yielding the highest freshwater production rate (1.71 kg m–2h–1) and photothermal efficiency among the fabricated hydrogel composite membranes under 1 sun irradiation. Meanwhile, the PMD membrane could robustly resist oil-induced fouling and surfactant-induced wetting, significantly extending the membrane lifespan in treating contaminated saline water. Furthermore, when desalinating real seawater, the membrane exhibited superior durability with a stable vapor flux and excellent ion rejection (e.g., 99.24% for boron) for 100 h.

The mechanisms by which Internet use influences rural women's health are explored in this paper using a mixed-methods approach. Findings indicate that internet use has a significant and positive effect on the health of rural women and hold up to endogenous and robustness testing. Additionally, the strength of the effects of internet use on self-rated health, depression, and cognition level decreases with age. Information accessibility and social trust as the important path mechanisms for Internet use to promote self-rated health and cognition level. We also investigated the impact of COVID-19 outbreak on this mechanism to further recognize the critical role of digital technology in global public health emergency preparedness and response efforts. These findings provide some insights for policy formulation by Governments and practical action by organizations to better improve women's health in rural areas and reduce rural health disparities.

ILD is a common manifestation in pSS and is associated with an increased risk of death. APCA are strongly expressed by hyperplastic alveolar epithelial cells in the fibrotic lung and are associated with an accelerated decline in lung function in IPF. In the present study, we aimed to evaluate the clinical utility of APCA in ILD patients with pSS.

In the late stage of exploiting heavy oil reservoirs with edge-bottom water, we are faced with problems such as high crude oil viscosity, channeling of bottom water, and rapid rise in water cut. To clarify the synergistic effect of N2, CO2, N2 foam, and viscosity reducer in water control and oil increase, and solve the problem of edge-bottom water rushing into production wells, one-dimensional sand-pack huff and puff experiments were carried out. First, the N2 foam huff and puff experiment evaluated the effect of N2 foam on controlling bottom water. Then the viscosity reducer assisted CO2 huff and puff experiment analyzed the synergistic viscosity reduction ability between the two to mobilize the remaining heavy oil. Next, the N2 foam-viscosity reducer-CO2 huff and puff experiment clarified the synergistic effect between the three. Finally, the N2 foam-viscosity reducer-CO2-N2 huff and puff experiment solved the problem of insufficient reservoir energy based on synergistic precipitation and oil increase. Experimental results show that N2 foam-viscosity reducer-CO2-N2 huff and puff is the optimal water control and oil increase solution. This solution can significantly reduce water production and increase oil production. In the one-dimensional sand-pack experiment, this technical solution reduced the water content by 68% and increased the recovery rate by 16.09%. The research results provide a reference for the development of exploitation technology schemes for similar edge-bottom water heavy oil reservoirs after entering high water cut.

In order to solve the problems of slow mining speed, high noise and poor data correlation in the existing online education big data mining methods, an online education big data mining method based on association rules is designed. Firstly, the recursive distance of the big data centre is determined, and the online education big data is extracted according to the calculation of fuzzy membership. Secondly, the covariance matrix is used to remove the noise in online education big data and reduce the dimension. Finally, the confidence and support of online education big data association rules are calculated, the association strength between online education big data in the set is determined, and the data mining is completed. The experimental results show that the mining speed of this method is significantly improved, the longest time is no more than 4 s, and the data mining is highly correlated.

Low-count positron emission tomography (PET) imaging is an effective way to reduce the radiation risk of PET at the cost of a low-signal-to-noise ratio. Our study aims to denoise low-count PET images in an unsupervised mode since the mainstream methods rely on paired data, which is not always feasible in clinical practice. We adopt the diffusion probabilistic model in consideration of its strong generation ability. Our model consists of two stages. In the training stage, we learn a score function network via evidence lower-bound (ELBO) optimization. In the sampling stage, the trained score function and low-count image are employed to generate the corresponding high-count image under two handcrafted conditions. One is based on restoration in latent space, and the other is based on noise insertion in latent space. Thus, our model is named the bidirectional condition diffusion probabilistic model (BC-DPM). The Zubal phantom and real patient whole-body data are utilized to evaluate our model. The experiments show that our model achieves better performance in both qualitative and quantitative respects compared to several traditional and recently proposed learning-based methods.

The short frames of low-count positron emission tomography (PET) images generally cause high levels of statistical noise. Thus, improving the quality of low-count images by using image postprocessing algorithms to achieve better clinical diagnoses has attracted widespread attention in the medical imaging community. Most existing deep learning-based low-count PET image enhancement methods have achieved satisfying results, however, few of them focus on denoising low-count PET images with the magnetic resonance (MR) image modality as guidance. The prior context features contained in MR images can provide abundant and complementary information for single low-count PET image denoising, especially in ultralow-count (2.5%) cases. To this end, we propose a novel two-stream dual PET/MR cross-modal interactive fusion network with an optical flow pre-alignment module, namely, OIF-Net. Specifically, the learnable optical flow registration module enables the spatial manipulation of MR imaging inputs within the network without any extra training supervision. Registered MR images fundamentally solve the problem of feature misalignment in the multimodal fusion stage, which greatly benefits the subsequent denoising process. In addition, we design a spatial-channel feature enhancement module (SC-FEM) that considers the interactive impacts of multiple modalities and provides additional information flexibility in both the spatial and channel dimensions. Furthermore, instead of simply concatenating two extracted features from these two modalities as an intermediate fusion method, the proposed cross-modal feature fusion module (CM-FFM) adopts cross-attention at multiple feature levels and greatly improves the two modalities’ feature fusion procedure. Extensive experimental assessments conducted on real clinical datasets, as well as an independent clinical testing dataset, demonstrate that the proposed OIF-Net outperforms the state-of-the-art methods.

In this letter, a free-space method complex permittivity test system using a Gaussian beam model in 110–170 GHz is proposed. In a free-space method test system, accurate extraction of transmission and reflection coefficients is the key to obtaining the complex permittivity of materials. The traditional free-space method approximates the focused Gaussian beam as the plane-wave to invert the complex permittivity. This plane-wave assumption will introduce a test error. In this study, the transmission and reflection coefficients of the Gaussian beam model were derived based on the plane-wave spectrum technology (PWST) and plane-wave scattering matrix (PWSM) theory. Based on this, a material complex permittivity test system and a beam waist radius test system were built at 110–170 GHz. The test results based on the Gaussian beam model and plane-wave model were compared. Meanwhile, the results were compared with the test results of the quasi-optical cavity method. The results prove that the complex permittivity inversion using the Gaussian beam model has higher accuracy. It provides a more accurate theoretical model for the measurement of complex permittivity of materials by the free-space method in the terahertz range.

A three-dimensional sea urchin core–shell (Ag/CAU-17@MoS2) S-scheme heterojunction was prepared viaa two-step hydrothermal and photodeposition route. CAU-17 features a unique three-dimensional open frame structure. With the addition of MoS2, the band gap of CAU-17 is narrowed, giving adequate surface active sites. The photocatalytic hydrogen production of Ag/CAU-17@MoS2is enhanced to 326.5 μmol h−1g−1, which is 12 times higher than that of CAU-17. Tetracycline photocatalytically degraded with an efficiency of 91.6% when exposed to visible light. Interestingly, the obvious photothermal effect of Ag/CAU-17@MoS2greatly promotes the photocatalytic performance. In addition, transient photoluminescence tests confirm that the Ag/CAU-17@MoS2heterojunction has a longer charge lifetime. At the same time, in situX-ray photoelectron spectroscopy is carried out to verify the transfer mechanism of the photogenerated charge following the S-scheme heterojunction. The novel approach and design concept presented herein offer a viable approach for the development of high-efficiency S-scheme heterojunction photocatalysts.

This paper presents data driven and expert knowledge method for diagnosing rotating machinery fault. The belief rule-based (BRB) inference method is used to model the complex nonlinear relationship between the abnormal vibration features of rotating machinery and its fault type. If the data of all features are used for diagnosis, then computation burden will be too large to realise real-time diagnosis. First, the inputs of BRB model are reduced and weighted through neural network based on data driven algorithm. The outputs of BRB model are fault types of rotating machinery. The belief rules activated by the inputs are combined by the evidential reasoning (ER) algorithm so as to obtain the fused belief structure about the fault, and then, the accurate diagnose result can be calculated from the fused result. The diagnosis results cannot only judge the fault type, but also give the probability of potential fault. The model parameters are open and interpretable. Finally, in the experiment of fault diagnosis of motor rotor, the effectiveness of the proposed method is illustrated.

In the planning phase of radiation therapy, positron emission tomography (PET) images are frequently integrated with computed tomography (CT) and MRI to accurately delineate the target region for treatment. However, obtaining additional CT or magnetic resonance (MR) images solely for localization purposes proves to be financially burdensome, time-intensive, and may increase patient radiation exposure. To alleviate these issues, a deep learning model with dynamic modality translation capabilities is introduced. This approach is achieved through the incorporation of adaptive modality translation layers within the decoder module. The adaptive modality translation layer effectively governs modality transformation by reshaping the data distribution of features extracted by the encoder using switch codes. The model’s performance is assessed on images with reference images using evaluation metrics, such as peak signal-to-noise ratio, structural similarity index measure, and normalized mean square error. For results without reference images, subjective assessments are provided by six nuclear medicine physicians based on clinical interpretations. The proposed model demonstrates impressive performance in transforming nonattenuation corrected PET images into user-specified modalities (attenuation corrected PET, MR, or CT), effectively streamlining the acquisition of supplemental modality images in radiation therapy scenarios.

Large-scale genetic mutant libraries are powerful approaches to interrogating genotype–phenotype correlations and identifying genes responsible for certain environmental stimuli, both of which are the central goal of life science study. We produced the first large-scale CRISPR-Cas9-induced library in a nonmodel multicellular organism, Bombyx mori. We developed a piggyBac-delivered binary genome editing strategy, which can simultaneously meet the requirements of mixed microinjection, efficient multipurpose genetic operation, and preservation of growth-defect lines. We constructed a single-guide RNA (sgRNA) plasmid library containing 92,917 sgRNAs targeting promoters and exons of 14,645 protein-coding genes, established 1726 transgenic sgRNA lines following microinjection of 66,650 embryos, and generated 300 mutant lines with diverse phenotypic changes. Phenomic characterization of mutant lines identified a large set of genes responsible for visual phenotypic or economically valuable trait changes. Next, we performed pooled context-specific positive screens for tolerance to environmental pollutant cadmium exposure, and identified KWMTBOMO12902 as a strong candidate gene for breeding applications in sericulture industry. Collectively, our results provide a novel and versatile approach for functional B. morigenomics, as well as a powerful resource for identifying the potential of key candidate genes for improving various economic traits. This study also shows the effectiveness, practicality, and convenience of large-scale mutant libraries in other nonmodel organisms.

The pathogens are reduced by volatile organic compounds (VOCs) generated by yeasts play an important role in controlling postharvest diseases. The yeast HXMG-1, which works effectively against the grey mould pathogen of grapes (Botrytis cinerea), was evaluated for its potential to generate volatile organic compounds as one of its modes of action. A double Petri dish assay was used to evaluate the effect of VOCs produced by HXGM-1 on mycelial and spore development of the target pathogens. Compared to the control, the VOCs produced by yeast HXMG-1 significantly reduced the growth of mycelium and spore germination of Botrytis cinerea. Specifically, the mycelial growth of Botrytis cinereawas completely restricted and the rate of spore germination of Botrytis cinereawas only 20.11% at a concentration of 1 × 109CFU/mL. It was also found that the VOCs could significantly inhibit mycelial growth with an inhibition of 82.46% at a concentration of 1 × 108CFU/mL. The VOCs caused the mycelium to grow curved, resulting in larger mycelial tips, fewer nuclei, and shorter mycelial septum spacing. In vivo tests, noninjure or injure grapes were artificially inoculated with the pathogen hyphal disc followed by biofumigation with VOCs produced by yeast HXMG-1, and the treatments (Wp2 and Wp3) significantly controlled pathogenic infection, confirming the results of in vitro tests. By molecular biological identification based on comparative sequence analysis of the 18S rDNA gene, the HXMG-1 strain was identified as Hanseniaspora uvarum.Through the creation of a phylogenetic tree, HXMG-1 was recognised as a member of the Ascomycota, Hemiascomycota, Yeasts, and Hanseniasporasp. families. In conclusion, the yeast strain HXMG-1 created VOCs that significantly inhibited the development of Botrytis cinereaon grapes and is expected to be further developed and utilised. This study lays the foundation for the use of Hanseniasporasp. for biological control of postharvest disease.

The attenuated total reflection Fourier transform infrared spectroscopy attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FT-IR) detection was used to investigate the mechanisms of cold atmospheric plasma (CAP) treatment in wound healing. The peaks of ester carbonyl and $\alpha $ -helix in proteins, serving as the spectral fingerprints in the original infrared spectra and their second derivative spectra, of the wound samples were analyzed. The experimental results showed that the CAP treatment resulted in the reduction of the ester carbonyl contents, and the increase of the contents of $\alpha $ -helix in the proteins. This indicates that the CAP treatment accelerated the lipid metabolism to provide required energy for the protein production, which was also supported by the fact that the fibrin deposition in the wounds was more obvious in the plasma group than that in the control group.

Aim: Few previous studies have investigated the impact of multiple types of electronic devices on health status, and the moderating effects of gender, age, and BMI. Our aim is to examine the relationships between the use of four types of electronics and three health status indicators in a middle-aged and elderly population, and how these relationships varied by gender, age, and BMI. Subject and methods: Using data from 376,806 participants aged 40–69 years in the UK Biobank, we conducted a multivariate linear regression to estimate the association between electronic device use and health status. Electronics use was categorized as TV watching, computer use, computer gaming, and mobile phone use, and health status included self-rated health (SRH), multisite chronic pain (MCP), and total physical activity (TPA). Interaction terms were utilized to assess whether the above associations were modified by BMI, gender, and age. Further stratified analysis was performed to explore the role of gender, age, and BMI. Results: Higher levels of TV watching (BSRH= 0.056, BMCP= 0.044, BTPA= −1.795), computer use (BSRH= 0.007, BTPA= −3.469), and computer gaming (BSRH= 0.055, BMCP= 0.058, BTPA= −6.076) were consistently associated with poorer health status (all P< 0.05). Contrastingly, earlier exposure to mobile phones (BSRH= −0.048, BTPA= 0.933, BMCP= 0.056) was inconsistent with health (all P< 0.05). Additionally, BMI (Bcomputer use-SRH= 0.0026, Bphone-SRH= 0.0049, BTV-MCP= 0.0031, and BTV-TPA= −0.0584) exacerbated the negative effects of electronics use, and male (Bphone-SRH= −0.0414, Bphone-MCP= −0.0537, Bphone-TPA= 2.8873) were healthier with earlier exposure to mobile phones (all P< 0.05). Conclusion: Our findings suggest that the adverse health effects associated with watching TV, computer use, and computer gaming were consistent and were moderated by BMI, gender, and age, which advances a comprehensive understanding of the association between multiple types of electronic devices and health status, and provides new perspectives for future research.

Texturing of sealing faces is an effective approach to reduce wear failure and improve the working efficiency and service life of mechanical seal, particularly under insufficient lubrication. Herein, a silicon carbide surface with a novel squamous texture is fabricated using laser. A reciprocating pin-on-disk wear test is conducted on silicon carbide and graphite surfaces to investigate the tribological properties of squamous texture arrangements and parameters under mixed lubrication. Results demonstrate that the symmetrical and evenly distributed texture arrangement displays remarkable load-carrying capacity. Appropriate adjustment of texture parameters can achieve a considerably low oil contact angle. The squamous texture with a major to minor axis ratio of 1.5 has the excellent tribological properties, reducing the average coefficient of friction by 26.7 %. Moreover, the squamous texture facilitates the formation of a graphite transfer film and traps debris, which helps avoid particle and adhesive wear, resulting in a low wear rate. However, high pressure nullifies the hydrodynamic effect and accelerates the shearing of graphite asperities, thereby increasing friction and wear. This study provides a novel strategy for enhancing the tribological behavior of sealing surfaces.

Imaging-derived phenotypes (IDPs) have been increasingly used in population-based cohort studies in recent years. As widely reported, magnetic resonance imaging (MRI) is an important imaging modality for assessing the anatomical structure and function of the brain with high resolution and excellent soft-tissue contrast. The purpose of this article was to describe the imaging protocol of the brain MRI in the China Phenobank Project (CHPP). Each participant underwent a 30-min brain MRI scan as part of a 2-h whole-body imaging protocol in CHPP. The brain imaging sequences included T1-magnetization that prepared rapid gradient echo, T2fluid-attenuated inversion-recovery, magnetic resonance angiography, diffusion MRI, and resting-state functional MRI. The detailed descriptions of image acquisition, interpretation, and post-processing were provided in this article. The measured IDPs included volumes of brain subregions, cerebral vessel geometrical parameters, microstructural tracts, and function connectivity metrics.

Cell–cell adhesion is at the center of structure and dynamics of epithelial tissue. E-cadherin–catenin complexes mediate Ca2+-dependent trans-homodimerization and constitute the kernel of adherens junctions. Beyond the basic function of cell–cell adhesion, recent progress sheds light the dynamics and interwind interactions of individual E-cadherin–catenin complex with E-cadherin superclusters, contractile actomyosin and mechanics of the cortex and adhesion. The nanoscale architecture of E-cadherin complexes together with cis-interactions and interactions with cortical actomyosin adjust to junctional tension and mechano-transduction by reinforcement or weakening of specific features of the interactions. Although post-translational modifications such as phosphorylation and glycosylation have been implicated, their role for specific aspects of in E-cadherin function has remained unclear. Here, we provide an overview of the E-cadherin complex in epithelial cell and tissue morphogenesis focusing on nanoscale architectures by super-resolution approaches and post-translational modifications from recent, in particular in vivo, studies. Furthermore, we review the computational modelling in E-cadherin complexes and highlight how computational modelling has contributed to a deeper understanding of the E-cadherin complexes.

Interstitial lung disease (ILD) and pulmonary hypertension (PH) caused by connective tissue disease (CTD) are one of the main causes of morbidity and death in patients. Although the International Society for Heart & Lung Transplant suggested that ILD and PH related to CTD are rare indications for lung transplantation in 2006, many lung transplantation centers are concerned that the multisystem involvement of CTD will affect survival outcomes after lung transplantation, and CTD is regarded as a relative contraindication for lung transplantation. However, long‐term and short‐term survival after lung transplantation in CTD patients is similar compared with survival in common indications for lung transplantation such as idiopathic pulmonary fibrosis (IPF), and no higher incidence of complications after transplantation in many lung transplant centers. This suggests that lung transplantation may be beneficial in CTD patients with disease that progresses to end‐stage lung disease, and CTD should not be considered a contraindication for lung transplantation. In the future, more prospective studies are needed to analyze the risk factors of lung transplantation in CTD patients to improve survival rates and reduce the risk of complications. This narrative review summarizes the selection and evaluation of candidates for CTD before lung transplantation and describes the clinical outcomes in CTD after lung transplantation in large‐capacity lung transplantation center. The purpose of this review is to help rheumatologists decide when to refer patients with CTD‐related lung involvement to a lung transplantation center and the conditions to consider before transplantation and to provide confidence to lung transplant experts.

ABSTRACT Magnesium oxysulfate (MOS) cement is a type of green inorganic cementitious material, with the advantages of light weight, low thermal conductivity, binding ability in light-weight panels, high temperature resistance [...]

C33H23Br2N5O4, triclinic, P1̄ (no. 2), a= 10.649(3) Å, b= 11.244(3) Å, c= 14.301(4) Å, α= 68.254(7)°, β= 72.939(7)°, γ= 81.125(7)°, V= 1518.5(7) Å3, Z= 2, Rgt(F) = 0.0514, wRref(F2) = 0.1327, T= 273(2) K.

Reactive fillers consisting of reduced sulfur and iron species (SFe-ReFs) have received increasing attention in tertiary wastewater treatment for nitrate and phosphate coremoval. However, the existing SFe-ReFs suffer from either low performance (e.g., pyrrhotite and pyrite) or unsatisfactory use in terms of combustible risk and residual nonreactive impurities (e.g., sulfur mixing with natural iron ores). Here, we developed a new type of sulfur-siderite composite ReF (SSCReF) with a structure of natural siderite powders eventually embedded into sulfur. SSCReFs exhibited many excellent properties, including higher mechanical strengths and hardness and especially much poorer ignitability compared to pure sulfur. By using SSCReF to construct packed-bed reactors, the highest denitrification and dephosphorization rates reached 829.70 gN/m3/d (25 wt % siderite) and 36.70 gP/m3/d (75 wt % siderite), respectively. Dephosphorization was demonstrated to be dependent on sulfur-driven denitrification, in which the acid produced from the later process promoted Fe(II)dissolution, which then directly combined with phosphate to form vivianite or further converted into phosphate adsorbents (ferrihydrite, a green rust-like compound). Water flush was an effective way to finally wash out these surface deposited Fe–P compounds, as well as those nonreactive impurities (Si and Al-bearing compounds) detached from SSCReF. Such a highly efficient and safe SSCReF holds considerable application potential in secondary effluent polishing.