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Pulsed secondary electron multipacting (SEM) cathodes with channel-type structures have been developed. The electron emission performance of these cathodes was investigated using theoretical and particle-in-cell simulation methods. The results revealed that the electrical conductivity of the channel wall material is crucial to the performance of the cathodes. Materials with low conductivity cause the SEM process in the multipacting channel to stop quickly due to the positive charges deposited on the channel wall. These positive space charges, generated by the SEM process, create a space-charge field that reduces the impact energy of electrons on the channel wall, thereby decreasing the secondary electron emission yield. Consequently, materials with high electrical conductivity and high secondary electron emission yield, such as SnO 2 , are advantageous for the SEM process, leading to stable current output from the cathodes with high current density. For a SnO 2 cathode with three multipacting channels, an output current density of 242 A/cm 2 was achieved. [ABSTRACT FROM AUTHOR]

Rapid advancements in electronic devices yield an urgent demand for high‐performance electronic packaging materials with high thermal conductivity, low thermal expansion, and great mechanical properties. However, it is a great challenge for current design philosophies to fulfill all the requirements simultaneously. Here, an effective strategy is proposed for significantly promoting the thermal conductivity and machinability of negative thermal expansion alloy (Zr,Nb)Fe2 through eutectic precipitation of copper networks. The eutectic dual‐phase alloy exhibits an isotropic chips‐matched thermal expansion coefficient and a thermal conductivity enhancement exceeding 200% compared with (Zr,Nb)Fe2, along with an ultimate compressive strength of 550 MPa. The addition of copper reorganizes the composition of (Zr,Nb)Fe2, which smooths the magnetic transition and shifts it toward higher temperature, resulting in linear low thermal expansion in a wide temperature range. The highly fine eutectic copper lamellae construct high thermal conductivity networks within (Zr,Nb)Fe2, serving as highways for heat transfer electrons and phonons. The in situ forming of eutectic copper lamellae also facilitates the mechanical properties by enhancing interfacial bonding and bearing additional stress after yielding of (Zr,Nb)Fe2. This work provides a novel strategy for promoting thermal conductivity and mechanical properties of negative thermal expansion alloys via eutectic precipitation of copper networks. [ABSTRACT FROM AUTHOR]

Supersulfated cements (SSCs), as potential green alternatives to Portland cements, are in demand for developing new kinds of precursors instead of blast-furnace slag. A mixture of kaolin, quartz and sodium hydroxide was calcined to prepare SSC by grinding clinkers with lime and gypsum. Preparation factors were investigated and multiple characterisation methods were adopted to explore the calcination and hydration mechanisms. The sodium hydroxide additive was found to reduce the activation temperature of the kaolin and a moderate addition of sodium hydroxide improved the compressive strength of SSC made from calcined kaolin. Quartz added into the kaolin calcined with a moderate amount of sodium hydroxide also promoted the compressive strength. Comprehensive characterisations indicated that the alkali-added calcination of the kaolin generated pozzolanic zeolite and sodium aluminosilicates along with inert nepheline at a calcination temperature ≤950°C. The hydration of SSC pastes tended to produce C(N)–A–S–H/N(C)–A–S–H gels and crystalline ettringite. During sodium hydroxide-added calcination, quartz added into the kaolin hindered the generation of inert nepheline and produced cementitious N–S gels. The research facilitates the utilisation of the massive discharged coal gangues rich in kaolinite and quartz by synthesising SSCs economically. [ABSTRACT FROM AUTHOR]

Lewisite is a representative chemical warfare agent that contains arsenic. Thermal decomposition is an essential method for Lewisite destruction, and appropriate destruction conditions can enhance the environmental friendliness of this method by minimizing the hazards of destruction products. In this study, the variation of Lewisite during pyrolysis and incineration was calculated, with particular emphasis on the evolution behaviors of arsenic and chloride with temperature, pressure, and molar ratio. The main products identified during pyrolysis were HCl(g), C, As, and AsCl3(g). Under anoxic conditions, AsCl3(g) and As were the dominant species. In oxygen‐enriched conditions, arsenic existed primarily as AsCl3(g), As2O5, As2O4, and AsOCl. Below 600 °C, arsenic predominantly existed in high valence forms such as As2O5. Above 600 °C, the binding capacity of chlorine and arsenic was stronger than that of oxygen, resulting in an AsCl3(g) molar fraction exceeding 75 % at 1200 °C. The primary chlorides in oxygen‐enriched conditions were Cl2(g), HCl(g), AsCl3(g), and Cl(g). Lower incineration temperatures (around 500 °C), sufficient oxygen conditions (molar ratio above 4 : 1), and positive pressures were conducive to reducing the toxicity of destruction products. The results are in good agreement with the experimental data. [ABSTRACT FROM AUTHOR]

This work introduces a completely rewritten version of the program RMCProfile (version 7), big‐box, reverse Monte Carlo modelling software for analysis of total scattering data. The major new feature of RMCProfile7 is the ability to refine multiple phases simultaneously, which is relevant for many current research areas such as energy materials, catalysis and engineering. Other new features include improved support for molecular potentials and rigid‐body refinements, as well as multiple different data sets. An empirical resolution correction and calculation of the pair distribution function as a back‐Fourier transform are now also available. RMCProfile7 is freely available for download at https://rmcprofile.ornl.gov/. [ABSTRACT FROM AUTHOR]

This study considers critical data reduction steps and data analysis approaches required to determine explicitly the atomic arrangements in nanoparticles from time‐of‐flight neutron total scattering. A practical procedure is described for removing parasitic backgrounds caused by the incoherent scattering of hydrogen inevitably present in most nanoparticle samples and normalizing the recovered coherent scattering intensities onto an absolute scale. A model‐free analysis is presented of a pair‐distribution function derived from total scattering that can be used to determine thermal expansion coefficients and particle sizes directly from experimental data without knowledge of the material's structure. Finally, atomistic whole‐nanoparticle refinements of yttrium‐doped ceria nanoparticles from neutron total‐scattering data are demonstrated using the reverse Monte Carlo method implemented in the RMCProfile software. These results reveal a strong dependence of the cation–oxygen and oxygen–oxygen distances on the coordination numbers, which leads to gradients of these distances near the particle surface. The details are dependent on the surface coverage by ligands and adsorbates and on the structure of grain boundaries in nanocrystalline agglomerates. The refined models confirm the expectations of more substantial disorder at particle surfaces, with a distorted surface layer extending over several coordination shells. The results highlight the feasibility of whole‐nanoparticle refinements from neutron data, calling for further development of data reduction and analysis procedures. [ABSTRACT FROM AUTHOR]

The shale-oil reservoir emerges as a significant unconventional energy source, commonly predicted by anisotropic seismic inversion. Considering the intricate nature of shale-oil reservoirs, it becomes imperative to consider uncertainties during anisotropic inversion. An effective approach to address this involves stochastic inversion, specifically the anisotropic Bayesian linearized inversion (ABLI), which characterizes statistical and spatial correlations of subsurface parameters through a crucial multivariate correlation matrix constructed through geostatistics. However, an inevitable challenge in stochastic inversion arises from interference during the calibration of statistical and spatial correlations of subsurface parameters. This challenge becomes particularly pronounced in anisotropic inversion, heightened by the multitude of involved model parameters. Existing decorrelation approaches primarily address statistical correlation, neglecting the impact of spatial correlation. To tackle this issue, a novel multi-parameter decoupling strategy is proposed, formulating decoupling-ABLI (D-ABLI). D-ABLI introduces an advanced decorrelation approach, and uses principal component analysis (PCA) to simultaneously eliminate impact of statistical and spatial correlations on ABLI. The decoupling enhances the inversion accuracy of model parameters in ABLI, particularly for density and anisotropic parameters. The theoretical underpinnings of the decoupling strategy are demonstrated to be reasonable, and the effectiveness of D-ABLI is proved through a theoretical data test and a field data test regarding shale-oil reservoirs. The D-ABLI results offer the capability to estimate fracture density accurately and unveil the distribution of shale oil. [ABSTRACT FROM AUTHOR]

Revealing the local structure information of relaxor ferroelectrics is necessary for a clear understanding of their structure–property relationships, especially the determination of the size of polar nanoregions (PNRs), which is still a long-standing challenge. In this work, the local structure of the pseudo-cubic solid solutions 0.60Bi(Mg1/2Ti1/2)O3–0.40PbTiO3 and 0.65Bi(Mg1/2Ti1/2)O3–0.35PbTiO3, which exhibit re-entrant relaxor behavior, has been determined using the statistical model and reverse Monte Carlo (RMC) modelling of total scattering data. The pair distribution function revealed significant deviation between the local and long-range structures with each of the cations exhibiting unique polyhedral configurations, which required the use of a phase coexistence model to characterize the local structure. The lone-pair bearing Bi and Pb cations exhibited the greatest amount of displacement and disordering. An effective method was proposed to determine the size and orientation of PNRs (∼2 nm) based on the correlation angle between displaced A-site pairs. The size of these regions below freezing temperature is in agreement with the result of the statistical model. This method is suitable for relaxor systems, which lack long-range ferroelectric order, providing an excellent characterization of PNRs and an understanding of the physical properties of relaxor ferroelectrics. [ABSTRACT FROM AUTHOR]

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA–disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA–disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA–disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA–disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA–disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA. [ABSTRACT FROM AUTHOR]

N4-acetylcytidine (ac4C) is a modification found in ribonucleic acid (RNA) related to diseases. Expensive and labor-intensive methods hindered the exploration of ac4C mechanisms and the development of specific anti-ac4C drugs. Therefore, an advanced prediction model for ac4C in RNA is urgently needed. Despite the construction of various prediction models, several limitations exist: (1) insufficient resolution at base level for ac4C sites; (2) lack of information on species other than Homo sapiens ; (3) lack of information on RNA other than mRNA; and (4) lack of interpretation for each prediction. In light of these limitations, we have reconstructed the previous benchmark dataset and introduced a new dataset including balanced RNA sequences from multiple species and RNA types, while also providing base-level resolution for ac4C sites. Additionally, we have proposed a novel transformer-based architecture and pipeline for predicting ac4C sites, allowing for highly accurate predictions, visually interpretable results and no restrictions on the length of input RNA sequences. Statistically, our work has improved the accuracy of predicting specific ac4C sites in multiple species from less than 40% to around 85%, achieving a high AUC > 0.9. These results significantly surpass the performance of all existing models. [ABSTRACT FROM AUTHOR]

Field emission (FE) necessitates cathode materials with low work function and high thermal and electrical conductivity and stability. To meet these requirements, we developed FE cathodes based on high-quality wrinkled multilayer graphene (MLG) prepared using the bubble-assisted chemical vapor deposition (B-CVD) method and investigated their emission characteristics. The result showed that MLG cathodes prepared using the spin-coating method exhibited a high field emission current density (~7.9 mA/cm2), indicating the excellent intrinsic emission performance of the MLG. However, the weak adhesion between the MLG and the substrate led to the poor stability of the cathode. Screen printing was employed to prepare the cathode to improve stability, and the influence of a silver buffer layer was explored on the cathode's performance. The results demonstrated that these cathodes exhibited better emission stability, and the silver buffer layer further enhanced the comprehensive field emission performance. The optimized cathode possesses low turn-on field strength (~1.5 V/μm), low threshold field strength (~2.65 V/μm), high current density (~10.5 mA/cm2), and good emission uniformity. Moreover, the cathode also exhibits excellent emission stability, with a current fluctuation of only 6.28% during a 4-h test at 1530 V. [ABSTRACT FROM AUTHOR]

The external insulation strength of the power equipment sleeve in the substation and converter station will be reduced with the increase in altitude and atmosphere pollution. The weakness of external insulation seriously threatens the safe operation of electrical equipment. At present, there are few studies and conclusions that focus on the flashover characteristics of power equipment bushing in high-altitude polluted areas. Therefore, this paper takes the 220 kV voltage level bushing as the research object and studies the Alternating Current (AC) pollution flashover characteristics and lightning and switching impulse flashover characteristics at low pressure. The insulation configuration at high-altitude polluted areas is analyzed. Finally, the insulation configuration scheme, which is suitable for high-altitude polluted areas, is proposed. The results show that the AC pollution flashover voltages of the bushing decrease with the decrease in the atmospheric pressure and the increase in the salt density as a power exponent function. The impulse flashover voltages also decrease with the decrease in the atmospheric pressure as a power exponent function. The atmospheric pressure/pollution impact characteristic index is related to the type of voltage, the value of the atmospheric pressure, and the value of the salt density. The test bushing does not meet the insulation configuration requirements in some high-altitude polluted areas. Through the analysis and calculation of the test results, the bushing insulation configuration scheme in high-altitude polluted areas is proposed. The research results can provide a reference for the external insulation design of bushings in substations and converter stations in high-altitude polluted areas. [ABSTRACT FROM AUTHOR]

Traditional methods for estimating output property of the photovoltaic (PV) modules are strongly influenced by the selection of reference condition and transforming equations, which determine the calculated physical parameters under real operating conditions. The differences in the carrier transport properties of PV cells under varying operating conditions, such as the number and velocity of minority carriers at the junction edge and their recombination speed, lead to large deviations in the estimation of the output characteristics, especially under low irradiance conditions. To enhance the accuracy of performance estimation, we propose an improved method that is independent of reference condition. This method eliminates the impact of reference conditions and improves the transformation equations under all irradiance levels. Transformation equations of single diode model are established in different irradiance intervals based on the dependence of physical parameter on irradiance and temperature. Especially in the low irradiance range, all effects of irradiance and temperature are considered for each physical parameter in improved transformation equations. To optimize the unknown parameters in the transformation equations, the artificial hummingbird algorithm is used to fit experimental I–V data. The experimental results of six different types PV modules under a wide range of operating conditions are used to verify the effectiveness of the proposed method. The proposed method offers immediate benefits, including independence from reference condition and a more precise relationship between physical parameters and environmental factors in the estimation of PV output properties. Comparing the results to the traditional method by Laudani, the proposed method demonstrates superior capability in estimating I–V characteristics and accurately identifies the maximum power point under various operating conditions, which is of significant value for engineering applications. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop a prediction model for esophageal fistula (EF) in esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT), by integrating multi-omics features from multiple volumes of interest (VOIs). Methods: We retrospectively analyzed pretreatment planning computed tomographic (CT) images, three-dimensional dose distributions, and clinical factors of 287 EC patients. Nine groups of features from different combination of omics [Radiomics (R), Dosiomics (D), and RD (the combination of R and D)], and VOIs [esophagus (ESO), gross tumor volume (GTV), and EG (the combination of ESO and GTV)] were extracted and separately selected by unsupervised (analysis of variance (ANOVA) and Pearson correlation test) and supervised (Student T test) approaches. The final model performance was evaluated using five metrics: average area under the receiver-operator-characteristics curve (AUC), accuracy, precision, recall, and F1 score. Results: For multi-omics using RD features, the model performance in EG model shows: AUC, 0.817 ± 0.031; 95% CI 0.805, 0.825; p < 0.001, which is better than single VOI (ESO or GTV). Conclusion: Integrating multi-omics features from multi-VOIs enables better prediction of EF in EC patients treated with IMRT. The incorporation of dosiomics features can enhance the model performance of the prediction. [ABSTRACT FROM AUTHOR]

Monocytes (Mos) are believed to play important roles during the generation of immune response. In our previous study, CVC1302, a complex of PRRs agonists, was demonstrated to recruit Mo into lymph nodes (LNs) in order to present antigen and secret chemokines (CXCL9 and CXCL10), which attracted antigen-specific CD4+ T cells. As it is known that Mos in mice are divided into two main Mo subsets (Ly6C+ Mo and Ly6C− Mo), we aimed to clarify the CVC1302-recruiting Mo subset and functions in the establishment of immunity. In this study, we found that CVC1302 attracted both Ly6C+ Mo and Ly6C− Mo into draining LNs, which infiltrated from different origins, injection muscles and high endothelial venule (HEV), respectively. We also found that the numbers of OVA+ Ly6C+ Mo in the draining LNs were significantly higher compared with OVA+ Ly6C− Mo. However, the levels of CXCL9 and CXCL10 produced by Ly6C− Mo were significantly higher than Ly6C+ Mo, which plays important roles in attracting antigen-specific CD4+ T cells. Under the analysis of their functions in initiating immune responses, we found that the ability of the Ly6C+ monocyte was mainly capturing and presenting antigens, otherwise; the ability of the Ly6C− monocyte was mainly secreting CXCL9 and CXCL10, which attracted antigen-specific CD4+ T cells through CXCR3. These results will provide new insights into the development of new immunopotentiators and vaccines. [ABSTRACT FROM AUTHOR]

Li‐rich cation‐disordered rock‐salt (DRX) materials have emerged as promising candidates for high‐capacity oxide cathodes. Their fluorinated variants have shown improved cycling stability with effectively suppressed oxygen loss. However, a comprehensive understanding of how fluorination impacts the multiscale structure and lithium transportation in DRX remains elusive in experiments. Herein, the neutron total scattering technique in conjunction with the advanced reverse Monte Carlo (RMC) fitting method is employed to characterize the intricate structure of Li1.16Ti0.37Ni0.37Nb0.1O2 (LTNNO) and the fluorinated Li1.2Ti0.35Ni0.35Nb0.1O1.8F0.2 (LTNNOF). Through rigorous statistical analysis, the multiscale structural evolution upon fluorination is quantified from atomic (≤5 Å) to long‐range scale (≈100 Å). The local Li‐rich environments around F induce a modest 2.4% increment in the number of fast Li 0TM (transition metal) channels. Crucially, at a broader scale, the proportion of 0TM channels participating in percolation increases significantly from 2.9% in LTNNO to 8.7% in LTNNOF. Fluorination improves the capacity release mainly through merging isolated fast Li channels into the percolation network. This work experimentally unravels the multiscale mechanism of fluorination‐induced performance improvement in DRX materials and highlights the necessity of adopting an advanced RMC fitting method to obtain a full view of the complex structural features in developing high‐capacity DRX cathodes. [ABSTRACT FROM AUTHOR]

Relaxor ferroelectrics are characterized by nanoscale structural heterogeneity and show superior electrical properties. However, the complicated local structures in lead-free counterparts are not well deciphered, impeding the understanding of the corresponding relationships between structure and properties. Herein, the temperature-dependent local structures of the unique tetragonal BaTiO3-(Bi0.5Li0.5)TiO3 relaxor, which presents an ultrahigh electrostrictive effect, were studied using neutron total scattering. Significant tetragonal distortion at the 1–10 Å local scale persisted between 100 and 500 K, despite the long-range structure having undergone a tetragonal to cubic phase transition. Interestingly, nanoscale Li–Bi clusters were indicated to present in the Ba matrix, with these clusters showing large polar displacements even at temperatures above Tm. A gradual disorder in the local polarization direction with increasing temperature was observed, accompanied by a nearly constant magnitude of the spontaneous polarization. This phenomenon was found to lead to a vanishing of macroscopic polarization during the diffuse phase transition. These findings have contributed to a better understanding of the role of local chemical heterogeneity in the relaxor behavior, and have provided a structural foundation for designing relaxors by utilizing short-range chemical ordering. [ABSTRACT FROM AUTHOR]

This study found a higher percentage of CD8+ T cells in piglets immunized with a CVC1302-adjuvanted inactivated foot-and-mouth disease virus (FMDV) vaccine. We wondered whether the CVC1302-adjuvanted inactivated FMDV vaccine promoted cellular immunity by promoting the antigen cross-presentation efficiency of ovalbumin (OVA) through dendritic cells (DCs), mainly via cytosolic pathways. This was demonstrated by the enhanced levels of lysosomal escape of OVA in the DCs loaded with OVA and CVC1302. The higher levels of ROS and significantly enhanced elevated lysosomal pH levels in the DCs facilitated the lysosomal escape of OVA. Significantly enhanced CTL activity levels was observed in the mice immunized with OVA-CVC1302. Overall, CVC1302 increased the cross-presentation of exogenous antigens and the cross-priming of CD8+ T cells by alkalizing the lysosomal pH and facilitating the lysosomal escape of antigens. These studies shed new light on the development of immunopotentiators to improve cellular immunity induced by vaccines. [ABSTRACT FROM AUTHOR]

Existing deep learning-based space target recognition methods rely on abundantly labeled samples and are not capable of recognizing samples from unseen classes without training. In this article, based on generalized zero-shot learning (GZSL), we propose a space target recognition framework to simultaneously recognize space targets from both seen and unseen classes. First, we defined semantic attributes to describe the characteristics of different categories of space targets. Second, we constructed a dual-branch neural network, termed the global-local visual feature embedding network (GLVFENet), which jointly learns global and local visual features to obtain discriminative feature representations, thereby achieving GZSL for space targets with higher accuracy. Specifically, the global visual feature embedding subnetwork (GVFE-Subnet) calculates the compatibility score by measuring the cosine similarity between the projection of global visual features in the semantic space and various semantic vectors, thereby obtaining global visual embeddings. The local visual feature embedding subnetwork (LVFE-Subnet) introduces soft space attention, and an encoder discovers the semantic-guided local regions in the image to then generate local visual embeddings. Finally, the visual embeddings from both branches were combined and matched with semantics. The calibrated stacking method is introduced to achieve GZSL recognition of space targets. Extensive experiments were conducted on an electromagnetic simulation dataset of nine categories of space targets, and the effectiveness of our GLVFENet is confirmed. [ABSTRACT FROM AUTHOR]

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA–disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA–disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA–disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA–disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA–disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA–disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases. [ABSTRACT FROM AUTHOR]

Currently, the Global Positioning System (GPS) is widely used, but its signal is attenuated by factors such as trees, walls, and ceilings, which severely degrade its positioning accuracy. To fill the gap, various indoor positioning techniques have attracted increasing attention in recent years. Visible light positioning (VLP) is a promising scheme for indoor positioning due to its high precision, high security, and low energy consumption; however, ubiquitous diffuse reflection affects the accuracy and robustness of VLP. During our testing, we found that diffuse reflection could increase the error in RSS values by 20~30%, severely affecting VLP accuracy; however, diffuse reflection is inevitable in real positioning environments. To solve this problem, this paper first establishes a wall diffuse reflection model and then implements a visible light positioning system based on an Internet of Things platform. Finally, this paper uses the system to verify the effectiveness of the diffuse reflection model. The experiments show that the proposed model effectively improves positioning accuracy by 36.7~61.3%. [ABSTRACT FROM AUTHOR]

Recently, a new family of anti‐perovskite Li2TMSO was discovered as promising cathode materials for Li‐ion batteries (LIBs) with superiorities in high specific capacity, low cost, and environmental friendliness. However, the applications of these anti‐perovskite materials meet severe challenges in the cyclability and rate performance. Herein, a cation‐disordered anti‐perovskite type solid solution Li2Fe1−xMnxSO (LFMSO, x = 0, 0.2, 0.5) with excellent electrochemical performance is reported. On the basis of comprehensive structural characterizations, the role of the cation disordering in LFMSO is clarified. In comparison with Li2FeSO (LFSO), the reduced Li‐ion diffusion barrier and the increased Li‐rich octahedral configurations in LFMSO with higher configurational entropy imply the facilitated long‐range Li‐ion diffusion and the suppressed phase transition, which favor the high‐rate capability and cycling stability. In addition, the large lattice distortion and Coulombic interaction between the anions and cations lead to the breathing of the unit cell during charge/discharge. The variation of the unit cell volume decreases to 2.5% upon Li‐ion delithiation. A superstructure is observed in LFMSO for the first time. These findings help to pave the way for the research and development of novel cathode materials for the next generation LIBs. [ABSTRACT FROM AUTHOR]

Biomedical electronic signals play an important role in clinical diagnosis. EEG as one kind of biomedical electronic signals has been widely used for an epilepsy diagnosis. EEG data are strongly characterized by the inner cluster style. The classic clustering-based detection techniques such as FCM, K-means, and AP cannot effectively partition the manifold data without considering the inner cluster style. Therefore, in this paper, we propose a novel stylistic data-driven possibilistic fuzzy clustering technique (SD-PFC). SD-PFC has its merits in two aspects: (1) a stylistic standardization matrix is used to represent the stylistic information of samples contained in the inner clusters. (2) The distance matrix is re-constructed by samples which are transformed by style normalization matrices. Extensive experiments on artificial datasets and real-life datasets show the effectiveness of the novel clustering technique. [ABSTRACT FROM AUTHOR]

The Ruddlesden–Popper (RP) layered perovskite structure is of great interest due to its inherent tunability, and the emergence and growth of the compositionally complex oxide (CCO) concept endows the RP family with further possibilities. Here, a comprehensive assessment of thermodynamic stabilization, local order/disorder, and lattice distortion was performed in the first two reported examples of lanthanum-deficient Lan+1BnO3n+1 (n = 1, B = Mg, Co, Ni, Cu, Zn) obtained via various processing conditions. Chemical short-range order (CSRO) at the B-site and the controllable excess interstitial oxygen (δ) in RP-CCOs are uncovered by neutron pair distribution function analysis. Reverse Monte Carlo analysis of the data, Metropolis Monte Carlo simulations, and extended x-ray absorption fine structure analysis implies a modest degree of magnetic element segregation on the local scale. Further, ab initio molecular dynamics simulations results obtained from special quasirandom structure disagree with experimentally observed CSRO but confirm Jahn–Teller distortion of CuO6 octahedra. These findings highlight potential opportunities to control local order/disorder and excess interstitial oxygen in layered RP-CCOs and demonstrate a high degree of freedom for tailoring application-specific properties. They also suggest a need for expansion of theoretical and data modeling approaches in order to meet the innate challenges of CCO and related high-entropy phases. [ABSTRACT FROM AUTHOR]

Simple Summary: The acute oral mucositis (AOM) is a prevalent side effect of radiation therapy for nasopharyngeal carcinoma (NPC). Severe AOM could impair the survival and quality of life for NPC patients. Accurate method to predict the incidence of severe AOM can aid clinicians in adjusting the treatment plan to improve the outcomes for NPC patients. We integrated multi-modalities, multi-omics and multi-regions data with two methods, integrate the original data or combine data after feature selection. The performance of models using each data integration method with different modalities, types of data and VOIs were analyzed. We developed a best-performing model with mean AUC at 0.81 ± 0.10 to predict the incidence of severe AOM for NPC patients following radiation therapy. (1) Background: Acute oral mucositis is the most common side effect for nasopharyngeal carcinoma patients receiving radiotherapy. Improper or delayed intervention to severe AOM could degrade the quality of life or survival for NPC patients. An effective prediction method for severe AOM is needed for the individualized management of NPC patients in the era of personalized medicine. (2) Methods: A total of 242 biopsy-proven NPC patients were retrospectively recruited in this study. Radiomics features were extracted from contrast-enhanced CT (CECT), contrast-enhanced T1-weighted (cT1WI), and T2-weighted (T2WI) images in the primary tumor and tumor-related area. Dosiomics features were extracted from 2D or 3D dose-volume histograms (DVH). Multiple models were established with single and integrated data. The dataset was randomized into training and test sets at a ratio of 7:3 with 10-fold cross-validation. (3) Results: The best-performing model using Gaussian Naive Bayes (GNB) (mean validation AUC = 0.81 ± 0.10) was established with integrated radiomics and dosiomics data. The GNB radiomics and dosiomics models yielded mean validation AUC of 0.6 ± 0.20 and 0.69 ± 0.14, respectively. (4) Conclusions: Integrating radiomics and dosiomics data from the primary tumor area could generate the best-performing model for severe AOM prediction. [ABSTRACT FROM AUTHOR]

Highly dynamic induction motor drives require converter-driven low-inertia induction machines, which are continuously operated with high torque dynamics to accelerate and brake linear or rotating masses in a highly dynamic manner. However, every rapid change in the torque requires a correspondingly rapid change in the rotor current, which leads to the excitation of the transient skin effect in the massive rotor bars of squirrel cage motors. The additional eddy current losses resulting from the transient skin effect can cause overheating problems, especially in the case of deep rotor bars with fast load cycles. This paper is intended to show the reader how the additional rotor losses caused by the transient skin effect can be reduced through the design optimization procedure. At the same time, the other operating characteristics of the induction motor drive are not impaired. In addition, the moment of inertia of the drive motor can also be reduced as another optimization target by the multi-objective optimization process. As the underlying optimization algorithm, the differential evolution and the particle swarm optimization are implemented and compared with each other in order to verify the correctness of the optimization results. During the whole optimization work, great importance is attached to the interdisciplinary calculation method so that the interaction between the electromagnetic, thermal, fluid mechanical and control engineering processes can be taken into account through the coupled calculation. In the end, the theoretical and simulative findings are verified with two test benches. [ABSTRACT FROM AUTHOR]

Purpose: The study aimed to predict acute radiation esophagitis (ARE) with grade ≥ 2 for patients with locally advanced lung cancer (LALC) treated with intensity-modulated radiation therapy (IMRT) using multi-omics features, including radiomics and dosiomics. Methods: 161 patients with stage IIIA−IIIB LALC who received chemoradiotherapy (CRT) or radiotherapy by IMRT with a prescribed dose from 45 to 70 Gy from 2015 to 2019 were enrolled retrospectively. All the toxicity gradings were given following the Common Terminology Criteria for Adverse Events V4.0. Multi-omics features, including radiomics, dosiomics (including dose−volume histogram dosimetric parameters), were extracted based on the planning CT image and three-dimensional dose distribution. All data were randomly divided into training cohorts (N = 107) and testing cohorts (N = 54). In the training cohorts, features with reliably high outcome relevance and low redundancy were selected under random patient subsampling. Four classification models (using clinical factors (CF) only, using radiomics features (RFs) only, dosiomics features (DFs) only, and the hybrid features (HFs) containing clinical factors, radiomics and dosiomics) were constructed employing the Ridge classifier using two-thirds of randomly selected patients as the training cohort. The remaining patient was treated as the testing cohort. A series of models were built with 30 times training–testing splits. Their performances were assessed using the area under the ROC curve (AUC) and accuracy. Results: Among all patients, 51 developed ARE grade ≥ 2, with an incidence of 31.7%. Next, 8990 radiomics and 213 dosiomics features were extracted, and 3, 6, 12, and 13 features remained after feature selection in the CF, DF, RF and DF models, respectively. The RF and HF models achieved similar classification performance, with the training and testing AUCs of 0.796 ± 0.023 (95% confidence interval (CI [0.79, 0.80])/0.744 ± 0.044 (95% CI [0.73, 0.76]) and 0.801 ± 0.022 (95% CI [0.79, 0.81]) (p = 0.74), respectively. The model performances using CF and DF features were poorer, with training and testing AUCs of 0.573 ± 0.026 (95% CI [0.56, 0.58])/ 0.509 ± 0.072 (95% CI [0.48, 0.53]) and 0.679 ± 0.027 (95% CI [0.67, 0.69])/0.604 ± 0.041 (95% CI [0.53, 0.63]) compared with the above two models (p < 0.001), respectively. Conclusions: In LALC patients treated with CRT IMRT, the ARE grade ≥ 2 can be predicted using the pretreatment radiotherapy image features. To predict ARE, the multi-omics features had similar predictability with radiomics features; however, the dosiomics features and clinical factors had a limited classification performance. [ABSTRACT FROM AUTHOR]

Background: Laennec's capsule is a fibrous membrane attached to the surface of the liver, which is independent of the hepatic veins. However, the presence of Laennec's capsule surrounding the peripheral hepatic veins is controversial. This study aims to describe the characteristic of Laennec's capsule around the hepatic veins at all levels. Methods: Seventy‐one hepatic surgical specimens were collected along the cross and longitudinal sections of the hepatic vein. Tissue sections of 3–4 mm were cut and stained with hematoxylin and eosin (H&E), resorcinol‐fuchsin (R&F), and Victoria blue (V&B). Elastic fibers were observed around the hepatic veins. They were measured using K‐Viewer software. Results: Morphologically, we observed a thin, dense fibrous layer (so‐called Laennec's capsule) around the hepatic veins at all levels, which was different from the thick elastic fibers of the hepatic vein wall. Therefore, there was a potential gap between Laennec's capsule and the hepatic veins. Laennec's capsule was visualized significantly better with R&F and V&B staining compared to H&E staining. The thickness of Laennec's capsule around the main, first, and secondary branches of the hepatic vein were 79.86 ± 24.20 μm, 48.41 ± 18.25 μm, and 23.56 ± 10.03 μm in the R&F staining, and 80.15 ± 21.85 μm, 49.46 ± 17.52 μm, and 25.05 ± 11.03 μm in the V&B staining, respectively. They were significantly different from each other (P <.001). Conclusion: The hepatic veins were surrounded by Laennec's capsule at all levels, including the peripheral hepatic veins. However, it is thinner along the vein branches. The gap between the Laennec's capsule and hepatic veins shows potential supplemental value for liver surgery. [ABSTRACT FROM AUTHOR]

The deformation force is an important factor affecting the forming accuracy of parts in the incremental forming process of sheet metal. This paper proposes an analytical calculation method of the deformation force based on pure shear deformation. After assuming and simplifying the factors affecting the deformation force, a graphical method is used to approximate the contact area between the forming tool and the sheet metal. A forming test is also designed. In addition, the deformation force is measured in the experiment, and its theoretical analysis value is compared with the actual measurement value of the forming test to validate the analytical method of deformation force calculation. The results show that the radial forming deviations are 28.5% and 22.5%, the axial deformation force deviations are 9.8% and 16.1%, and the forming force deviations are 6.3% and 10.3%, which demonstrates the effectiveness of using the analytical method to calculate the deformation force. [ABSTRACT FROM AUTHOR]

Relaxor ferroelectrics are known for outstanding piezoelectric properties, finding a broad range of applications in advanced electromechanical devices. Decoding the origins of the enhanced properties, however, have long been complicated by the heterogeneous local structures. Here, we employ the advanced big-box refinement method by fitting neutron-, X-ray-based total scattering, and X-ray absorption spectrum simultaneously, to extract local atomic polar displacements and construct 3D polar configurations in the classical relaxor ferroelectric Pb(Mg1/3Nb2/3)O3–PbTiO3. Our results demonstrate that prevailing order-disorder character accompanied by the continuous rotation of local polar displacements commands the composition-driven global structure evolution. The omnidirectional local polar disordering appears as an indication of macroscopic relaxor characteristics. Combined with phase-field simulations, it demonstrates that the competing local polar order-disorder between different states with balanced local polar length and direction randomness leads to a flattening free-energy profile over a wide polar length, thus giving rise to high piezoelectricity. Our work clarifies that the critical structural feature required for high piezoelectricity is the competition states of local polar rather than relaxor. Understanding high piezoelectricity in relaxors is challenging due to the heterogeneous structures. Here, the authors demonstrate that the competing local polar order-disorder state with balanced length and direction randomness is the key. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is an aggressive malignancy with high morbidity and mortality. Conversion therapy can improve surgical resection rate and prolong survival time for patients with advanced HCC. We show that combination therapy with lenvatinib and camrelizumab is a novel approach to downstage unresectable HCC. Case presentation: A 49-year-old man was diagnosed with massive HCC with hilar lymph node and lung metastases. Since radical resection was not feasible, lenvatinib and camrelizumab were administered as first-line therapy. After 10 cycles of camrelizumab and continuous oral administration of lenvatinib, the tumor exhibited striking shrinkage in volume indicating a partial radiological response, accompanied by a reduction in the alpha-fetoprotein levels, followed by salvage resection. Intriguingly, an improvement in predictive biomarkers, like lactate dehydrogenase (LDH) and neutrophil-to-lymphocyte ratio (NLR), was observed. Notably, the pathological examination found high levels of necrosis in the resected tumor, and flow cytometry analysis indicated a significant increase in the ratio of CD5+ and CD5− B lymphocytes in the peripheral blood. After the treatment, the overall survival period was over 24 months, and no recurrence was observed 17-month post-surgery. Conclusions: A combination of lenvatinib and camrelizumab may be a new conversion therapy for initially unresectable HCC to resectable HCC, thus contributing to improve the disease prognosis. In addition, the combination regimen could cause an activated immune response, and LDH, NLR, and CD5+ B-cell levels might be predictors for immunotherapy efficacy. [ABSTRACT FROM AUTHOR]

For practical motor imagery (MI) brain-computer interface (BCI) applications, generating a reliable model for a target subject with few MI trials is important since the data collection process is labour-intensive and expensive. In this paper, we address this issue by proposing a few-shot learning method called temporal episode relation learning (TERL). TERL models MI with only limited trials from the target subject by the ability to compare MI trials through episode-based training. It can be directly applied to a new user without being re-trained, which is vital to improve user experience and realize real-world MIBCI applications. We develop a new and effective approach where, unlike the original episode learning, the temporal pattern between trials in each episode is encoded during the learning to boost the classification performance. We also perform an online evaluation simulation, in addition to the offline analysis that the previous studies only conduct, to better understand the performance of different approaches in real-world scenario. Extensive experiments are completed on four publicly available MIBCI datasets to evaluate the proposed TERL. Results show that TERL outperforms baseline and recent state-of-the-art methods, demonstrating competitive performance for subject-specific MIBCI where few trials are available from a target subject and a considerable number of trials from other source subjects. [ABSTRACT FROM AUTHOR]

Simple Summary: The N staging system for Nasopharyngeal Carcinoma (NPC) is constantly improving for better survival risk stratification with accumulating clinical evidence. Discovering new prognostic factors often depends on clinical observations, which often lack comprehensiveness and precision. This study aimed to propose new quantitative spatial characterizations of LN tumor and demonstrate their feasibility of improving N stage. Independent anatomical prognostic factors were discovered and achieved superior risk stratification performance when combined with N stage. This quantitative approach could be applied to other cancer sites to discover new prognostic or predictive factors and ultimately benefit precision medicine. This study aims to investigate the feasibility of improving the prognosis stratification of the N staging system of Nasopharyngeal Carcinoma (NPC) from quantitative spatial characterizations of metastatic lymph node (LN) for NPC in a multi-institutional setting. A total of 194 and 284 NPC patients were included from two local hospitals as the discovery and validation cohort. Spatial relationships between LN and the surrounding organs were quantified by both distance and angle histograms, followed by principal component analysis. Independent prognostic factors were identified and combined with the N stage into a new prognostic index by univariate and multivariate Cox regressions on disease-free survival (DFS). The new three-class risk stratification based on the constructed prognostic index demonstrated superior cross-institutional performance in DFS. The hazard ratios of the high-risk to low-risk group were 9.07 (p < 0.001) and 4.02 (p < 0.001) on training and validation, respectively, compared with 5.19 (p < 0.001) and 1.82 (p = 0.171) of N3 to N1. Our spatial characterizations of lymph node tumor anatomy improved the existing N-stage in NPC prognosis. Our quantitative approach may facilitate the discovery of new anatomical characteristics to improve patient staging in other diseases. [ABSTRACT FROM AUTHOR]

The three fundamental origins of the Lorentz factor for neutron time‐of‐flight powder diffraction are revisited. A detailed derivation of the Lorentz factor is presented in the context of diffuse scattering modelling in reciprocal space when perfect periodicity is assumed, and the total scattering pattern is constructed in its discrete form – the factor in this case becomes 1/Q2 (or d2). Discussion is also presented with respect to practical data reduction where a vanadium measurement is usually taken as the normalization factor (to account for various factors such as detector efficiency), and it is shown that the existence of the Lorentz factor is independent of such a normalization process. [ABSTRACT FROM AUTHOR]

We present an exploration of a family of compositionally complex cubic spinel ferrites featuring combinations of Mg, Fe, Co, Ni, Cu, Mn, and Zn cations, systematically investigating the average and local atomic structures, chemical short-range order, magnetic spin configurations, and magnetic properties. All compositions result in ferrimagnetic average structures with extremely similar local bonding environments; however, the samples display varying degrees of cation inversion and, therefore, differing apparent bulk magnetization. Additionally, first-order reversal curve analysis of the magnetic reversal behavior indicates varying degrees of magnetic ordering and interactions, including potentially local frustration. Finally, reverse Monte Carlo modeling of the spin orientation demonstrates a relationship between the degree of cation inversion and the spin collinearity. Collectively, these observations correlate with differences in synthesis procedures. This work provides a framework for understanding magnetic behavior reported for "high-entropy spinels," revealing many are likely compositionally complex oxides with differing degrees of chemical short-range order—not meeting the community established criteria for high or medium entropy compounds. Moreover, this work highlights the importance of reporting complete sample processing histories and investigating local to long-range atomic arrangements when evaluating potential entropic mixing effects and assumed property correlations in high entropy materials. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed a significant global public health threat with an escalating number of new cases and death toll daily. The early detection of COVID-related CXR abnormality potentially allows the early isolation of suspected cases. Chest X-Ray (CXR) is a fast and highly accessible imaging modality. Recently, a number of CXR-based AI models have been developed for the automated detection of COVID-19. However, most existing models are difficult to interpret due to the use of incomprehensible deep features in their models. Confronted with this, we developed an interpretable TSK fuzzy system in this study for COVID-19 detection using radiomics features extracted from CXR images. There are two main contributions. (1) When TSK fuzzy systems are applied to classification tasks, the commonly used binary label matrix of training samples is transformed into a soft one in order to learn a more discriminant transformation matrix and hence improve classification accuracy. (2) Based on the assumption that the samples in the same class should be kept as close as possible when they are transformed into the label space, the compactness class graph is introduced to avoid overfitting caused by label matrix relaxation. Our proposed model for a multi-categorical classification task (COVID-19 vs. No-Findings vs. Pneumonia) was evaluated using 600 CXR images from publicly available datasets and compared against five state-of-the-art AI models in aspects of classification accuracy. Experimental findings showed that our model achieved classification accuracy of over 83%, which is better than the state-of-the-art models, while maintaining high interpretability. [ABSTRACT FROM AUTHOR]

Inverse synthetic aperture radar (ISAR) imaging for maneuvering targets suffers from a Doppler frequency time-varying problem, leading to the ISAR images blurred in the azimuth direction. Given that the traditional imaging methods have poor imaging performance or low efficiency, and the existing deep learning imaging methods cannot effectively reconstruct the deblurred ISAR images retaining rich details and textures, an unblurring ISAR imaging method based on an advanced Transformer structure for maneuvering targets is proposed. We first present a pseudo-measured data generation method based on the DeepLabv3+ network and Diamond-Square algorithm to acquire an ISAR dataset for training with good generalization to measured data. Next, with the locally-enhanced window Transformer block adopted to enhance the ability to capture local context as well as global dependencies, we construct a novel Uformer-based GAN (UFGAN) to restore the deblurred ISAR images with rich details and textures from blurred imaging results. The simulation and measured experiments show that the proposed method can achieve fast and high-quality imaging for maneuvering targets under the condition of a low signal-to-noise ratio (SNR) and sparse aperture. [ABSTRACT FROM AUTHOR]

Background. Papillary thyroid microcarcinoma (PTMC) refers to papillary thyroid carcinoma (PTC) with a maximum diameter of 10 mm. Thermal ablation, including radiofrequency ablation (RFA), microwave ablation (MWA), and laser ablation (LA), has been applied in the treatment of benign thyroid nodules and captured extensive attention. At present, the application of thermal ablation in PTMC has been extensively reported, but outcomes such as volume reduction rate (VRR), complete remission rate (CRR), and adverse reaction rate (ARR) vary considerably. Therefore, this meta-analysis was performed to evaluate the safety and efficacy of different treatment methods of PTMC. Methods. We did a systematic review and network meta-analysis. We searched PubMed, EMBase, and Cochrane-Library from the date of inception to January 10, 2022, to retrieve the VRR, CRR, and ARR of MWA, RFA, LA and surgical treatment of PTMC, and a meta-analysis was performed using the R meta-package. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, and sensitivity analyses, cumulative meta-analyses, and publication bias were also performed. Relevant literature was retrieved with keywords; the eligible cohort studies were screened based on the established inclusion and exclusion criteria. Results. A total of 1515 patients were included in the 12-month follow-up. The overall VRR was 86.25% (95% CI: 77.89, 94.60), and the VRR was RFA > WMA > LA, but the differences were not significant. A total of 1483 patients were included in the last follow-up. The overall VRR was 99.41% (95% CI: 99.11, 99.72), and the VRR was RFA > WMA > LA, but the differences were not significant. A total of 1622 patients showed complete remission at the last follow-up, and the overall CRR was 0.63 (95% CI: 0.46, 0.79). The CRR was RFA > LA > WMA, but the differences were not significant. A total of 1883 patients had adverse reactions at the last follow-up, and the overall ARR was 0.06 (95% CI: 0.03, 0.08). The ARR at the last follow-up was RFA = Surg < LA < WMA. The ARR of the RFA and Surg subgroups was significantly lower than that of the WMA subgroup. Conclusions. Similar good efficacy and safety profiles were observed in WMA, RFA, LA, and surgical treatment in PTMC, among which RFA showed the best volume reduction, complete remission rate, and adverse reaction reduction. However, there is a slight bias in the limited literature included in this study, and we did not conduct or refer to mechanistic studies to confirm its specific mechanism of action. Clinicians are advised to use their discretion in the choice of treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: Acute radiation pneumonitis (ARP) is one of the common radiation toxicities in patients with non-small-cell lung cancer (NSCLC) treated by radiotherapy. The prediction for ARP can benefit clinical management and thereby improve the quality of life of patients. Markers derived from radiomics and dosiomics features can be adopted to predict ARP. Moreover, the features from lung subregions can improve the prediction for ARP compared to the whole lung region. The previous studies using lung subregion features for predicting ARP ignored the low-dose covered lung region. In this manuscript, we proposed an incremental-dose-interval-based lung subregion segmentation method by covering the low-dose lung region for ARP modeling and evaluated its effectiveness in improving ARP predictions using pre-treatment CT radiomics features and planning dose features for patients with NSCLC treated by radiotherapy. The performance among ARP models using features from our proposed lung subregion and the whole lung were compared for both multi-omics and single-omics. The results could provide insight into the subregion's ability to predict the ARP and could be useful in predicting the risk of radiation-induced toxicity. This manuscript and its results were neither published in any conferences nor journals. Purpose: To evaluate the effectiveness of features obtained from our proposed incremental-dose-interval-based lung subregion segmentation (IDLSS) for predicting grade ≥ 2 acute radiation pneumonitis (ARP) in lung cancer patients upon intensity-modulated radiotherapy (IMRT). (1) Materials and Methods: A total of 126 non-small-cell lung cancer patients treated with IMRT were retrospectively analyzed. Five lung subregions (SRs) were generated by the intersection of the whole lung (WL) and five sub-regions receiving incremental dose intervals. A total of 4610 radiomics features (RF) from pre-treatment planning computed tomographic (CT) and 213 dosiomics features (DF) were extracted. Six feature groups, including WL-RF, WL-DF, SR-RF, SR-DF, and the combined feature sets of WL-RDF and SR-RDF, were generated. Features were selected by using a variance threshold, followed by a Student t-test. Pearson's correlation test was applied to remove redundant features. Subsequently, Ridge regression was adopted to develop six models for ARP using the six feature groups. Thirty iterations of resampling were implemented to assess overall model performance by using the area under the Receiver-Operating-Characteristic curve (AUC), accuracy, precision, recall, and F1-score. (2) Results: The SR-RDF model achieved the best classification performance and provided significantly better predictability than the WL-RDF model in training cohort (Average AUC: 0.98 ± 0.01 vs. 0.90 ± 0.02, p < 0.001) and testing cohort (Average AUC: 0.88 ± 0.05 vs. 0.80 ± 0.04, p < 0.001). Similarly, predictability of the SR-DF model was significantly stronger than that of the WL-DF model in training cohort (Average AUC: 0.88 ± 0.03 vs. 0.70 ± 0.030, p < 0.001) and in testing cohort (Average AUC: 0.74 ± 0.08 vs. 0.65 ± 0.06, p < 0.001). By contrast, the SR-RF model significantly outperformed the WL-RF model only in the training set (Average AUC: 0.93 ± 0.02 vs. 0.85 ± 0.03, p < 0.001), but not in the testing set (Average AUC: 0.79 ± 0.05 vs. 0.77 ± 0.07, p = 0.13). (3) Conclusions: Our results demonstrated that the IDLSS method improved model performance for classifying ARP with grade ≥ 2 when using dosiomics or combined radiomics-dosiomics features. [ABSTRACT FROM AUTHOR]

Background. Papillary thyroid microcarcinoma (PTMC) refers to papillary thyroid carcinoma (PTC) with a maximum diameter of 10 mm. Thermal ablation, including radiofrequency ablation (RFA), microwave ablation (MWA), and laser ablation (LA), has been applied in the treatment of benign thyroid nodules and captured extensive attention. At present, the application of thermal ablation in PTMC has been extensively reported, but outcomes such as volume reduction rate (VRR), complete remission rate (CRR), and adverse reaction rate (ARR) vary considerably. Therefore, this meta-analysis was performed to evaluate the safety and efficacy of different treatment methods of PTMC. Methods. We did a systematic review and network meta-analysis. We searched PubMed, EMBase, and Cochrane-Library from the date of inception to January 10, 2022, to retrieve the VRR, CRR, and ARR of MWA, RFA, LA and surgical treatment of PTMC, and a meta-analysis was performed using the R meta-package. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, and sensitivity analyses, cumulative meta-analyses, and publication bias were also performed. Relevant literature was retrieved with keywords; the eligible cohort studies were screened based on the established inclusion and exclusion criteria. Results. A total of 1515 patients were included in the 12-month follow-up. The overall VRR was 86.25% (95% CI: 77.89, 94.60), and the VRR was RFA > WMA > LA, but the differences were not significant. A total of 1483 patients were included in the last follow-up. The overall VRR was 99.41% (95% CI: 99.11, 99.72), and the VRR was RFA > WMA > LA, but the differences were not significant. A total of 1622 patients showed complete remission at the last follow-up, and the overall CRR was 0.63 (95% CI: 0.46, 0.79). The CRR was RFA > LA > WMA, but the differences were not significant. A total of 1883 patients had adverse reactions at the last follow-up, and the overall ARR was 0.06 (95% CI: 0.03, 0.08). The ARR at the last follow-up was RFA = Surg < LA < WMA. The ARR of the RFA and Surg subgroups was significantly lower than that of the WMA subgroup. Conclusions. Similar good efficacy and safety profiles were observed in WMA, RFA, LA, and surgical treatment in PTMC, among which RFA showed the best volume reduction, complete remission rate, and adverse reaction reduction. However, there is a slight bias in the limited literature included in this study, and we did not conduct or refer to mechanistic studies to confirm its specific mechanism of action. Clinicians are advised to use their discretion in the choice of treatment. [ABSTRACT FROM AUTHOR]

In this paper, a direct immersion solid‐phase microextraction procedure for the simultaneous analyses of four primary riot control agents: 2‐chloroacetophenone, o‐chlorobenzylidene malonitrile, dibenz(b,f)‐1,4‐oxazepine, and oleoresin capsicum at μg/L concentration from environmental water was developed. Several parameters that influence the extraction effectiveness were investigated, including fiber type, extraction temperature, extraction time, starring rate, and salinity. Under the recommended conditions, the optimized method had reasonable linearity and accuracy. The average recovery of this method ranged from 84 to 108.1%. The limit of detection for all the analytes ranged from 0.2 to 3 μg/L and the limit of quantification ranged from 1 to 10 μg/L, respectively. A relative standard deviation from 3.0 to 4.3% can be achieved depending on the compounds. The procedure was applied to analyze all the four riot control agents simultaneously in several environmental samples. [ABSTRACT FROM AUTHOR]

Radiomic model reliability is a central premise for its clinical translation. Presently, it is assessed using test–retest or external data, which, unfortunately, is often scarce in reality. Therefore, we aimed to develop a novel image perturbation-based method (IPBM) for the first of its kind toward building a reliable radiomic model. We first developed a radiomic prognostic model for head-and-neck cancer patients on a training (70%) and evaluated on a testing (30%) cohort using C-index. Subsequently, we applied the IPBM to CT images of both cohorts (Perturbed-Train and Perturbed-Test cohort) to generate 60 additional samples for both cohorts. Model reliability was assessed using intra-class correlation coefficient (ICC) to quantify consistency of the C-index among the 60 samples in the Perturbed-Train and Perturbed-Test cohorts. Besides, we re-trained the radiomic model using reliable RFs exclusively (ICC > 0.75) to validate the IPBM. Results showed moderate model reliability in Perturbed-Train (ICC: 0.565, 95%CI 0.518–0.615) and Perturbed-Test (ICC: 0.596, 95%CI 0.527–0.670) cohorts. An enhanced reliability of the re-trained model was observed in Perturbed-Train (ICC: 0.782, 95%CI 0.759–0.815) and Perturbed-Test (ICC: 0.825, 95%CI 0.782–0.867) cohorts, indicating validity of the IPBM. To conclude, we demonstrated capability of the IPBM toward building reliable radiomic models, providing community with a novel model reliability assessment strategy prior to prospective evaluation. [ABSTRACT FROM AUTHOR]

Pulmonary nodules are the early manifestation of lung cancer, which appear as circular shadow of no more than 3 cm on the computed tomography (CT) image. Accurate segmentation of the contours of pulmonary nodules can help doctors improve the efficiency of diagnosis. Deep learning has achieved great success in computer vision. In this study, we propose a novel network for pulmonary nodule segmentation from CT images based on U-NET. The proposed network has two merits: one is that it introduces dense connection to transfer and utilize features. Additionally, the problem of gradient disappearance can be avoided. The second is that it introduces a new loss function which is tolerance on the pixels near the borders of the nodule. Experimental results show that the proposed network at least achieves 1% improvement compared with other state-of-art networks in terms of different criteria. [ABSTRACT FROM AUTHOR]

In this study, a novel Takagi–Sugeno–Kang (TSK) fuzzy system termed as CLT–TSK in which the consequent of each fuzzy rule owns a common linear term is exploited to demonstrate its four distinctive merits. They are: (1) since much less parameters are involved, CLT–TSK has enhanced interpretability. (2) As an extensively used computational intelligence tool, a slightly changed function-link neural network (FLNN) is provably equivalent to CLT–TSK. As a result, FLNN is actually revisited with the first attempt from the philosophy of fuzzy models. (3) With a mild assumption that each component in Gaussian mixture model (GMM) equally contributes to the formulation of GMM in the sense of the effect of the intrinsic structure of training samples in each component on the corresponding label structure, CLT–TSK is theoretically proved to be equivalent to GMM, which actually helps us understand both CLT–TSK and FLNN from a new statistical perspective. (4) The output expression of CLT–TSK is exactly in accordance with the recently-drawn observation that a simple regression should be a basic yet very important component of final prediction models for various data modeling tasks. Owing to the equivalence among CLT–TSK, FLNN and GMM, their respective effective learning methods can be mutually employed from now on, and any new effort in training one model will actually provide a potential new learning method for another among these three models. In particular, with the help of our previous work about the least learning machine, we develop a fast-learning method for CLT–TSK in this study. Experimental results on different kinds of datasets demonstrate the promising classification and runtime performance of CLT–TSK. [ABSTRACT FROM AUTHOR]

In the late December of 2019, a novel coronavirus was discovered in Wuhan, China. In March 2020, WHO announced this epidemic had become a global pandemic and that the novel coronavirus may be mild to most people. However, some people may experience a severe illness that results in hospitalization or maybe death. COVID-19 classification remains challenging due to the ambiguity and similarity with other known respiratory diseases such as SARS, MERS, and other viral pneumonia. The typical symptoms of COVID-19 are fever, cough, chills, shortness of breath, loss of smell and taste, headache, sore throat, chest pains, confusion, and diarrhoea. This research paper suggests the concept of transfer learning using the deterministic algorithm in all binary classification models and evaluates the performance of various CNN architectures. The datasets of 746 CT images of COVID-19 and non-COVID-19 were divided for training, validation, and testing. Various augmentation techniques were applied to increase the number of datasets except for testing images. The images were then pretrained using CNN to obtain a binary class. ResNeXt101 and ResNet152 have the best F1 score of 0.978 and 0.938, whereas GoogleNet has an F1 score of 0.762. ResNeXt101 and ResNet152 have an accuracy of 97.81% and 93.80%. ResNeXt101, DenseNet201, and ResNet152 have 95.71%, 93.81%, and 90% sensitivity, whereas ResNeXt101, ResNet101, and ResNet152 have 100%, 99.58%, and 98.33 specificity, respectively. [ABSTRACT FROM AUTHOR]

Cyclic dimeric guanosine monophosphate (c-di-GMP) is a bacterial second messenger with immunomodulatory activities in mice, suggesting potential applications as a vaccine immunopotentiator or therapeutic agent. In this study, we evaluated the efficacy of c-di-GMP as an immunopotentiator for pseudorabies virus (PRV) inactivated vaccine in a murine model. We found that c-di-GMP improved the humoral and cellular immune responses induced by PRV inactivated vaccine and its effects on immunity reached the level comparable to that of a live attenuated vaccine. Furthermore, c-di-GMP enhanced the murine antibody response against the viral glycoprotein gB up to 120 days after immunization. The c-di-GMP–adjuvanted PRV inactivated vaccine induced long-term humoral immunity by promoting a potent T follicular helper cell response, which is known to directly control the magnitude of the germinal center B cell response. Furthermore, the c-di-GMP enhanced the response of bone marrow plasma cells and upregulated the expression of Bcl-2 and Mcl-1 , which have been identified as anti-apoptotic regulatory genes of germinal center and memory B cells. Our findings open a new avenue for improving the immune efficacy of PRV inactivated vaccines. [ABSTRACT FROM AUTHOR]

Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren–Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the role of different multi-organ omics-based prediction models for pre-treatment prediction of Adaptive Radiotherapy (ART) eligibility in patients with nasopharyngeal carcinoma (NPC). Methods and Materials: Pre-treatment contrast-enhanced computed tomographic and magnetic resonance images, radiotherapy dose and contour data of 135 NPC patients treated at Hong Kong Queen Elizabeth Hospital were retrospectively analyzed for extraction of multi-omics features, namely Radiomics (R), Morphology (M), Dosiomics (D), and Contouromics (C), from a total of eight organ structures. During model development, patient cohort was divided into a training set and a hold-out test set in a ratio of 7 to 3 via 20 iterations. Four single-omics models (R, M, D, C) and four multi-omics models (RD, RC, RM, RMDC) were developed on the training data using Ridge and Multi-Kernel Learning (MKL) algorithm, respectively, under 10-fold cross validation, and evaluated on hold-out test data using average area under the receiver-operator-characteristics curve (AUC). The best-performing single-omics model was first determined by comparing the AUC distribution across the 20 iterations among the four single-omics models using two-sided student t -test, which was then retrained using MKL algorithm for a fair comparison with the four multi-omics models. Results: The R model significantly outperformed all other three single-omics models (all p-value<0.0001), achieving an average AUC of 0.942 (95%CI: 0.938-0.946) and 0.918 (95%CI: 0.903-0.933) in training and hold-out test set, respectively. When trained with MKL, the R model (R_MKL) yielded an increased AUC of 0.984 (95%CI: 0.981-0.988) and 0.927 (95%CI: 0.905-0.948) in training and hold-out test set respectively, while demonstrating no significant difference as compared to all studied multi-omics models in the hold-out test sets. Intriguingly, Radiomic features accounted for the majority of the final selected features, ranging from 64% to 94%, in all the studied multi-omics models. Conclusions: Among all the studied models, the Radiomic model was found to play a dominant role for ART eligibility in NPC patients, and Radiomic features accounted for the largest proportion of features in all the multi-omics models. [ABSTRACT FROM AUTHOR]