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Abstract Osteotomy can correct kyphosis, restore the spinal sequence, and restore the healthy appearance of a patient. However, the aorta is stretched during pedicle subtraction osteotomy (PSO), and some surgeons are concerned about aortic injury. We used finite element analysis to construct an aortic model to simulate hemodynamic changes during osteotomy. 16 patients with ankylosing spondylitis kyphosis who had undergone a two-level osteotomy at the L1 and L3 levels was included in this study. Aortic computed tomography angiography (CTA) was performed, and a 3D image model was constructed. The length, transverse diameter, and curvature of the aorta were used to evaluate morphological changes. Finite element analysis was used to analyze the changes in aortic fluid dynamics. Blood pressure, wall shear stress, and blood flow velocity were compared pre- and postoperatively. The overall length of the aorta before surgery was 424.3 ± 42.9 mm, and the overall length of the aorta after surgery was 436.2 ± 54.8 mm. The aortic curvature decreased from 0.27 ± 0.13 to 0.17 ± 0.09. The mean transverse diameter of the aorta did not change (19.3 ± 6.6 vs. 19.2 ± 7.4 mm, P > 0.05). The blood flow velocity (2.8 ± 1.1 vs. 1.5 ± 0.8 m/s, P

Abstract Wastewater produced in beryllium mining seriously affects ecological balance and causes great environmental pressure. We designed a novel porous lotus leaf biochar modified with PO4 3−/NH4 + multifunctional groups (MLLB) and used it for beryllium(Be) removal from beryllium mining wastewater. Kinetic and thermodynamic experiments showed that the adsorption capacity (Qe) of Be with MLLB from the simulated beryllium mining wastewater could reach 40.38 g kg−1 (35 °C, pH = 5.5), and the adsorption process was spontaneous and endothermic. The dispersion coefficient Kd of Be with MLLB was 2.6 × 104 mL g−1, which proved that MLLB had strong selective adsorption capacity for Be. Phosphoric acid, ammonia, and hydroxyl groups on the MLLB surface would complex with Be to form Be(OH)2 and Be(NH4)PO4 complexation products, which implied that surface complexation and precipitation reactions might co-existed in the adsorption process. The above results showed that MLLB could effectively adsorb Be and prevent beryllium exposure in a beryllium mining process. Graphical Abstract

Abstract The miniaturization of antennas is crucial as it improves the integration of wireless communication system. In order to achieve miniaturization of antennas, a performance-constrained multi-objective optimization method (PCMOM) considering the size and return loss is proposed. In the PCMOM method, an optimization strategy based on a multi-port network model is introduced, enabling the formation of antennas with various structures. Furthermore, we integrate the constraint of return loss performance into the non-dominated sorting genetic algorithm II (NSGA-II), eliminating solutions that do not meet performance requirements. Three pixel antennas are designed using the PCMOM method and two of them are fabricated. Experimental results demonstrate that the proposed PCMOM method can effectively address the complex trade-off issues in antenna miniaturization design.

Abstract Lithium salt-doped spiro-OMeTAD is widely used as a hole-transport layer (HTL) for high-efficiency n-i-p perovskite solar cells (PSCs), but unfortunately facing awkward instability for commercialization arising from the intrinsic Li+ migration and hygroscopicity. We herein demonstrate a superoxide radicals (•O2 −) derived HTL of metal-free spiro-OMeTAD with remarkable capability of avoiding the conventional tedious oxidation treatment in air for highly stable PSCs. Present work explores the employing of variant-valence Eu(TFSI)2 salts that could generate •O2 − for facile and adequate pre-oxidation of spiro-OMeTAD, resulting in the HTL with dramatically increased conductivity and work function. Comparing to devices adopting HTL with LiTFSI doping, the •O2 −-derived spiro-OMeTAD increases the PSCs efficiency up to 25.45% and 20.76% for 0.05 cm2 active area and 6 × 6 cm2 module, respectively. State-of-art PSCs employing such metal-free HTLs are also demonstrated to show much-improved environmental stability even under harsh conditions, e.g., maintaining over 90% of their initial efficiency after 1000 h of operation at the maximum power point and after 80 light-thermal cycles under simulated low earth orbit conditions, respectively, indicating the potentials of developing metal-free spiro-OMeTAD for low-cost and shortened processing of perovskite photovoltaics.

The conventional terahertz radar suffers from limited operation range for long-distance, noncooperative target detection due to the low transmitter power and atmospheric attenuation effect, both of which pose a hindrance in meeting the requirements of warning detection applications. To improve the radar detection capability, this paper studies an ultrasensitive target detection method based on single-photon detectors to replace traditional radar receivers. The method is expected to considerably expand the operation range of terahertz radars. First, the statistical law of the number of echo photons of a terahertz single-photon radar system is analyzed, and the echo characteristics of the target are expounded from a microscopic perspective. Furthermore, a terahertz single-photon target detection model, incorporating the characteristics of a quantum capacitor detector, is established. In addition, the mathematical expression of the target detection performance is derived, and the performance is evaluated via simulations. Further, a target detection performance curve is obtained. Finally, a time-resolved terahertz photon-counting mechanism experiment is performed, wherein we realize high-precision ranging by counting echo pulses. This work can provide support for the research and development of ultrasensitive target detection technologies and single-photon radar systems in the terahertz band.

Background Personalized medicine has gained more attention for cancer precision treatment due to patient genetic heterogeneity in recent years. However, predicting the efficacy of antitumor drugs in advance remains a significant challenge to achieve this task. Objective This study aims to predict the efficacy of antitumor drugs in individual cancer patients based on clinical data. Methods This paper proposes to predict personalized antitumor drug efficacy based on clinical data. Specifically, we encode the clinical text of cancer patients as a probability distribution vector in hidden topics space using the Latent Dirichlet Allocation (LDA) model, named LDA representation. Then, a neural network is designed, and the LDA representation is input into the neural network to predict drug response in cancer patients treated with platinum drugs. To evaluate the effectiveness of the proposed method, we gathered and organized clinical records of lung and bowel cancer patients who underwent platinum-based treatment. The prediction performance is assessed using the following metrics: Precision, Recall, F1-score, Accuracy, and Area Under the ROC Curve (AUC). Results The study analyzed a dataset of 958 patients with non-small cell cancer treated with antitumor drugs. The proposed method achieved a stratified 5-fold cross-validation average Precision of 0.81, Recall of 0.89, F1-score of 0.85, Accuracy of 0.77, and AUC of 0.81 for cisplatin efficacy prediction on the data, which most are better than those of previous methods. Of these, the AUC value is at least 4% higher than those of the previous. At the same time, the superior result over the previous method persisted on an independent dataset of 266 bowel cancer patients, showing the generalizability of the proposed method. These results demonstrate the potential value of precise tumor treatment in clinical practice. Conclusions Combining LDA and neural networks can help predict the efficacy of antitumor drugs based on clinical text. Our approach outperforms previous methods in predicting drug clinical efficacy.

Highlights Developing a universal strategy of the p–n homojunction engineering that could significantly boost electron mobility of electron transport layer (ETL) by two orders of magnitude. Proposing a new mechanism based on p–n homojunction to explain inhibited carrier loss at buried interface. Setting a new performance benchmark as high as 25.50% for planar perovskite solar cells employing TiO2 as ETLs.

Abstract Uterine myomas are the most common pelvic tumors in women, which can lead to abnormal uterine bleeding, abdominal pain, pelvic compression symptoms, infertility, or adverse pregnancy. In this article, we provide a dataset named uterine myoma MRI dataset (UMD), which can be used for clinical research on uterine myoma imaging. The UMD is the largest publicly available uterine MRI dataset to date including 300 cases of uterine myoma T2-weighted imaging (T2WI) sagittal patient images and their corresponding annotation files. The UMD covers 9 types of uterine myomas classified by the International Federation of Obstetrics and Gynecology (FIGO), which were annotated and reviewed by 11 experienced doctors to ensure the authority of the annotated data. The UMD is helpful for uterine myomas classification and uterine 3D reconstruction tasks, which has important implications for clinical research on uterine myomas.

Abstract Exploiting high‐performance yet low‐cost hard carbon anodes is crucial to advancing the state‐of‐the‐art sodium‐ion batteries. However, the achievement of superior initial Coulombic efficiency (ICE) and high Na‐storage capacity via low‐temperature carbonization remains challenging due to the presence of tremendous defects with few closed pores. Here, a facile hybrid carbon framework design is proposed from the polystyrene precursor bearing distinct molecular bridges at a low pyrolysis temperature of 800°C via in situ fusion and embedding strategy. This is realized by integrating triazine‐ and carbonyl‐crosslinked polystyrene nanospheres during carbonization. The triazine crosslinking allows in situ fusion of spheres into layered carbon with low defects and abundant closed pores, which serves as a matrix for embedding the well‐retained carbon spheres with nanopores/defects derived from carbonyl crosslinking. Therefore, the hybrid hard carbon with intimate interface showcases synergistic Na ions storage behavior, showing an ICE of 70.2%, a high capacity of 279.3 mAh g−1, and long‐term 500 cycles, superior to carbons from the respective precursor and other reported carbons fabricated under the low carbonization temperature. The present protocol opens new avenues toward low‐cost hard carbon anode materials for high‐performance sodium‐ion batteries.

The fluid‐transportation functions based on the volume‐regulating behavior of the chamber‐like organs inspire the development of artificial organs. Due to the intrinsic compliance of soft robotics for biomimicking purposes and the volume‐regulation capability of the 3D origami patterns, the soft robots with origami patterns show promising potential in such research. However, the folding deformation of the origami facets cannot be straightforwardly implemented as the actuation or the body movement, and the predetermined movements of the pattern limit the appropriate functions for specific applications. In this work, an origami‐patterned rigidification (OPR) method is proposed for applying rigid origami mechanisms (herein, the cuboctahedron origami ball) to the chamber‐like structure of soft robots. The motion of the soft robot is programmed by purposefully rigidified the soft chamber following the pattern. The resultant OPR structures are granted with functions corresponding to the predetermined motion of the pattern, and the expanded movements through the bifurcation brought by the soft–rigid characteristics. The concept, design, and fabrication of the OPR robot are presented. By analyzing the deformation of the soft creases, the kinematic models of the predetermined and expanded degrees of freedom are presented and verified by experiments. The extended functions of two OPR robots are demonstrated.

Terahertz (THz) radar is well-suited for dynamic mobile target surveillance due to its high frame rates, minimal delay, and superior resolution. However, the defocusing problem caused by the motion of targets in the scene and the nonideal motion of the airborne platform severely affect synthetic aperture radar (SAR) imaging, particularly in the THz wave band. To address this issue, this article proposes a high-resolution imaging algorithm for THz-SAR ground-moving targets using the equalized alternating direction method of multipliers (ADMM). First, the range-Doppler algorithm is employed to generate a coarse image and extract the moving target region as the region of interest. Then, the range profile can be obtained by applying the inverse, which serves as input for the ADMM solving algorithm. Finally, the 2-D image of the target is reconstructed iteratively. The algorithm fully exploits the low-rank and sparse characteristics of moving targets in the SAR image to separate them from the background. It updates the azimuthal matched filter iteratively by minimizing image entropy and designs the equalization factor to retain target details while maintaining image sparsity. This approach significantly improves focusing accuracy compared with conventional algorithms while maintaining high imaging speed without estimating Doppler parameters.

Abstract An innovative strategy for synthesizing novel dual-reaction-center (DRC) catalysts from chicken manure (CM) biochar to purify wastewater that contained emerging contaminants (ECs) is proposed to synchronously address the release of ECs and improper disposal of livestock manure. A series of characterization techniques reveal the formation of a special C-O-Ca bond bridge (cation-π) structure on resourcelized CM nanosheets (RCM NSs). RCM NSs exhibit distinct selectivity and anti-interference capability for various ECs removal in complex matrices, and the water purification system remains stable after 1735 hours (equivalent to 3470 cycles) of operation. Density Functional Tomography (DFT) calculations reveal that trace of peroxymonosulfate as an inducer initiates the continuous donation of electrons from electron-rich ECs and the C-O-Ca bond bridges provide a favorable pathway for electron transfer, which facilitates the electron capture effect of dissolved oxygen in the system. This study provides a novel strategy to convert livestock manure into DRC-catalysts for developing energy-saving and high-efficiency environmental remediation technologies. Graphical Abstract

Abstract Background Abnormal glucose metabolism is one of the determinants of maintaining malignant characteristics of cancer. Targeting cancer metabolism is regarded as a new strategy for cancer treatment. Our previous studies have found that TOP1MT is a crucial gene that inhibits glycolysis and cell metastasis of gastric cancer (GC) cells, but the mechanism of its regulation of glycolysis remains unclear. Methods Transcriptome sequencing data, clinic-pathologic features of GC from a variety of public databases, and WGCNA were used to identify novel targets of TOP1MT. Immunohistochemical results of 250 patients with GC were used to analyze the relative expression relationship between TOP1MT and PDK4. The function of TOP1MT was investigated by migration assays and sea-horse analysis in vitro. Results We discovered a mitochondrial topoisomerase I, TOP1MT, which correlated with a higher risk of metastasis. Functional experiments revealed that TOP1MT deficiency promotes cell migration and glycolysis through increasing PDK4 expression. Additionally, the stimulating effect of TOP1MT on glycolysis may be effectively reversed by PDK4 inhibitor M77976. Conclusions In brief, our work demonstrated the critical function of TOP1MT in the regulation of glycolysis by PDK4 in gastric cancer. Inhibiting glycolysis and limiting tumor metastasis in GC may be accomplished by suppressing PDK4.

In this article, to address the long-distance imaging problems, a novel forward-looking 3-D imaging method based on terahertz single-photon radar is presented. The imaging method is based on single-input and single-output architecture system and adopts a novel data-driven approach named scaling training. In the scaling training, by scaling the parameters of the long-distance scene to be imaged, a parameter scaling system is constructed, and then it is utilized to collect the training data to train the artificial neural network (ANN) model. Once the training is completed, 3-D image of the long-distance scene can be retrieved using the ANN model from solely the 1-D photon-counting histogram echo recorded by terahertz single-photon radar. On the basis, to further improve the imaging quality, the depth correction method is implemented. Finally, numerical simulations are carried out to demonstrate the feasibility of the proposed method.

α‐Fe2O3 with suitable band structure, good chemical stability, and easy preparation, is a potential photoanode material. However, the key to enhance the performance of α‐Fe2O3 photoanode is to improve the transport characteristics of bulk carriers. It is expected to form a Schottky barrier to improve the carrier separation efficiency by embedding metal nanoparticles into the matrix, but the process is still challenging. Herein, a strategy of forming the Schottky barrier is shown to improve bulk carrier transport dynamics by embedding laser‐generated Pt nanocrystals in α‐Fe2O3 photoanode, which achieves photocurrent densities of up to 1.16 mA cm−2 at 1.23 VRHE (from original 0.21 mA cm−2). This work provides another way to promote the carrier transfer and separation of α‐Fe2O3, which is of great significance to improve the photoelectrochemical water splitting performance.

The substitution of ethyl acetate for ammonia in NH3-SCR provides a novel strategy for the simultaneous removal of VOCs and NO. In this study, three distinct types of biochar were fabricated through pyrolysis at 700 °C. MnOx and TiO2 were sequentially loaded onto these biochar substrates via a hydrothermal process, yielding a family of biochar-based catalysts with optimized dosages. Upon exposure to xenon lamp irradiation at 240 °C, the biochar catalyst designated as 700-12-3GN, derived from Ginkgo shells, demonstrated the highest catalytic activity when contrasted with its counterparts prepared from moso bamboo and loofah. The conversion efficiencies for NO and ethyl acetate (EA) peaked at 73.66% and 62.09%, respectively, at a catalyst loading of 300 mg. The characterization results indicate that the 700-12-3GN catalyst exhibits superior activity, which can be attributed to the higher concentration of Mn4+ and Ti4+ species, along with its superior redox properties and suitable elemental distribution. Notably, the 700-12-3GN catalyst has the smallest specific surface area but the largest pore volume and average BJH pore size, indicating that the specific surface area is not the predominant factor affecting catalyst performance. Instead, pore volume and average BJH pore diameter appear to be the more influential parameters. This research provides a reference and prospect for the resource utilization of biochar and the development of photothermal co-catalytic ethyl acetate and NO at low cost.

As industrialization and urbanization accelerate, the quality of the water environment has been deteriorating, and pollution from novel pollutants (ECs), such as microplastics, pharmaceutical residues, and endocrine disruptors, has become increasingly prominent. Although the concentration of these new pollutants in the environment is very low, they pose a long-term cumulative threat to human health and ecosystem security because of their persistent and difficult-to-degrade properties. This paper reviews the treatment technologies for novel pollutants such as microplastics, pharmaceutical residues, and endocrine disruptors, including physical (e.g., sand filtration, adsorption, membrane separation), chemical (e.g., flocculation, advanced oxidation, photocatalysis), and biological (e.g., microbial degradation) methods. The various technologies’ advantages, disadvantages, and application statuses are analyzed, and future research directions and challenges are presented.

Herein, a novel and effective method for detecting radar targets with a low signal-to-clutter ratio is proposed based on the information geometry theory. In the proposed method, the target detection problem is converted to distinguishing the target from a clutter background on a manifold. However, this is challenging when dealing with small and weak targets embedded in a complex and strong clutter background, which limits the detection performance. Therefore, to address this issue, an orthogonal projection based subband information geometry detection method is proposed. In this method, the received radar signal undergoes subband decomposition by a designed filter bank, and the robust estimation of clutter signal subspace in each subband is implemented on the matrix manifold. Subsequently, the suppression of the strong clutter is achieved through orthogonal projection based on the manifold, thereby improving the discrimination between the target and the clutter. Finally, the effectiveness of the proposed method is evaluated using simulated and real sea clutter data. The experimental results confirm that the proposed method effectively suppresses strong clutter and exhibits excellent detection performance.

When underwater acoustic signals propagate to the water surface, the acoustic impedance difference between water and air leads to transverse microamplitude waves on the water surface. These waves carry vibration frequencies containing relevant information about the sound source. Radar systems detect the slight displacement of the target through the phase difference between the target echoes; hence, radar systems can be used to detect small displacement changes on the water surface, thereby obtaining the water-surface vibration signal and subsequently inverting the underwater sound source information. In this study, we first analyzed the attenuation characteristics of underwater sound propagation and the physical model of water-surface vibration. Building upon the radar echo model for detecting acoustic water-surface vibration, we proposed a wavelet-Kalman filter signal detection method through theoretical analysis. Lastly, experiments were conducted in the large-scale comprehensive anechoic pool and the Yellow Sea water using terahertz radar for acoustic water-surface micromotion signal detection. The results demonstrate the capability of the terahertz radar to successfully detect acoustic water-surface microvibrations. The proposed method effectively filters water-surface interference and radar phase noise and extracts vibration signals. For the first time, submicron vibration signals were detected under a secondary sea state, providing a foundation for water-space transmedia information transmission and underwater vehicle detection.

Abstract Background Ankylosing spondylitis (AS) is a chronic inflammatory autoimmune disease, and the diagnosis and treatment of AS have been limited because its pathogenesis is still unclear. Pyroptosis is a proinflammatory type of cell death that plays an important role in the immune system. However, the relationship between pyroptosis genes and AS has never been elucidated. Methods GSE73754, GSE25101, and GSE221786 datasets were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed pyroptosis-related genes (DE-PRGs) were identified by R software. Machine learning and PPI networks were used to screen key genes to construct a diagnostic model of AS. AS patients were clustered into different pyroptosis subtypes according to DE-PRGs using consensus cluster analysis and validated using principal component analysis (PCA). WGCNA was used for screening hub gene modules between two subtypes. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for enrichment analysis to elucidate underlying mechanisms. The ESTIMATE and CIBERSORT algorithms were used to reveal immune signatures. The connectivity map (CMAP) database was used to predict potential drugs for the treatment of AS. Molecular docking was used to calculate the binding affinity between potential drugs and the hub gene. Results Sixteen DE-PRGs were detected in AS compared to healthy controls, and some of these genes showed a significant correlation with immune cells such as neutrophils, CD8 + T cells, and resting NK cells. Enrichment analysis showed that DE-PRGs were mainly related to pyroptosis, IL-1β, and TNF signaling pathways. The key genes (TNF, NLRC4, and GZMB) screened by machine learning and the protein–protein interaction (PPI) network were used to establish the diagnostic model of AS. ROC analysis showed that the diagnostic model had good diagnostic properties in GSE73754 (AUC: 0.881), GSE25101 (AUC: 0.797), and GSE221786 (AUC: 0.713). Using 16 DE-PRGs, AS patients were divided into C1 and C2 subtypes, and these two subtypes showed significant differences in immune infiltration. A key gene module was identified from the two subtypes using WGCNA, and enrichment analysis suggested that the module was mainly related to immune function. Three potential drugs, including ascorbic acid, RO 90–7501, and celastrol, were selected based on CMAP analysis. Cytoscape showed GZMB as the highest-scoring hub gene. Finally, molecular docking results showed that GZMB and ascorbic acid formed three hydrogen bonds, including ARG-41, LYS-40, and HIS-57 (affinity: -5.3 kcal/mol). GZMB and RO-90–7501 formed one hydrogen bond, including CYS-136 (affinity: -8.8 kcal/mol). GZMB and celastrol formed three hydrogen bonds, including TYR-94, HIS-57, and LYS-40 (affinity: -9.4 kcal/mol). Conclusions Our research systematically analyzed the relationship between pyroptosis and AS. Pyroptosis may play an essential role in the immune microenvironment of AS. Our findings will contribute to a further understanding of the pathogenesis of AS.

Highlights The design, preparation and application of poly(ethylene oxide) (PEO)/ceramic composite solid electrolytes (CSEs) are summarized from “ceramic in polymer” and “polymer in ceramic”. The summary and outlook on existing challenges and future research directions of PEO/ceramic CSEs for lithium metal batteries are proposed.

Wide-bandgap perovskite solar cells (WBG PSCs) have garnered significant research attention for their potential in tandem solar cells. However, they face challenges such as high open-circuit voltage losses and severe phase instability. These issues are primarily owing to the formation of defects, ion migration, and energy level mismatches at the interface of WBG perovskite devices. Meanwhile, inverted PSCs demonstrate superior stability potential and compatibility with tandem devices, making them the most promising application for WBG perovskite materials. Consequently, interface modulation for such devices has become imperative. In this review, from the perspective of applicability in tandem devices, we first provided a concise overview of WBG perovskite research and its efficiency progress in inverted devices. We further discussed interface carrier dynamics and the potential impact of interfaces on such device performance. Afterward, we presented a comprehensive summary of interface engineering in inverted WBG perovskite (1.60 eV–1.80 eV) solar cells. The research particularly explored both the upper and buried interfaces of WBG absorbers in the inverted PSCs, thoroughly investigating interface design strategies and outlining promising research directions. Finally, this review provides insight into the future development of interface engineering for high-performance and large-area WBG PSCs.

Abstract Naïve T cells become effector T cells following stimulation by antigen-loaded dendritic cells (DCs) and sequential cytokine activation. We aimed to develop procedures to efficiently activate T cells with tumor-associated antigens (TAAs) to glioblastoma (GBM) stem cells. To remove antigen presentation outside of the immunosuppressive tumor milieu, three different glioma stem cell (GSC) specific antigen sources to load DCs were compared in their ability to stimulate lymphocytes. An activated T cell (ATC) protocol including cytokine activation and expansion in culture to target GSCs was generated and optimized for a planned phase I clinical trial. We compared three different antigen-loading methods on DCs to effectively activate T cells, which were GBM patient-derived GSC-lysate, acid-eluate of GSCs and synthetic peptides derived from proteins expressed in GSCs. DCs derived from HLA-A2 positive blood sample were loaded with TAAs. Autologous T cells were activated by co-culturing with loaded DCs. Efficiency and cytotoxicity of ATCs were evaluated by targeting TAA-pulsed DCs or T2 cells, GSCs, or autologous PHA-blasts. Characteristics of ATCs were evaluated by Flow Cytometry and ELISpot assay, which showed increased number of ATCs secreting IFN-γ targeting GSCs as compared with non-activated T cells and unloaded target cells. Neither GSC-lysate nor acid-eluate loading showed enhancement in response of ATCs but the synthetic peptide pool showed significantly increased IFN-γ secretion and increased cytotoxicity towards target cells. These results demonstrate that ATCs activated using a TAA synthetic peptide pool efficiently enhance cytotoxicity specifically to target cells including GSC.

Rigidity and softness are essential for robot motion and manipulation in various complex scenarios. To integrate these two contrary features, several variable‐stiffness structures are investigated while requiring a longer processing time and more energy for switching from a stiffer to softer status, with only one status at a specific point in time. Inspired by the combined softness–rigidity hybrid property of desert iguanas’ skin, the concept of an anisotropic stiffness structure is developed, which simultaneously possesses rigidity and softness properties in different directions, leading to the development of novel soft robots. This anisotropic stiffness structure comprises a silicone–paper composite with multiple superimposed origami patterns and has high stiffness and softness properties. The anisotropic stiffness structure is then constructed by developing three novel soft robots: a crawling robot capable of extending and contracting with a payload of 117 times its own weight, a multifunctional prosthetic hand capable of grasping fragile items with its soft side and lifting and crushing items with its hard side, and a snake robot capable of traveling with the soft side and extruding out with over 75% of its body length with the stiffer side.

Metamaterial-based coded aperture imaging (MCAI) is a forward-looking radar imaging technique based on wavefront modulation. The scattering coefficients of the target can resolve as an ill-posed inverse problem. Data-based deep-learning methods provide an efficient, but expensive, way for target reconstruction. To address the difficulty in collecting paired training data, an untrained deep radar-echo-prior-based MCAI (DMCAI) optimization model is proposed. DMCAI combines the MCAI model with a modified U-Net for predicting radar echo. A joint loss function based on deep-radar echo prior and total variation is utilized to optimize network weights through back-propagation. A target reconstruction strategy by alternatively using the imaginary and real part of the radar echo signal (STAIR) is proposed to solve the DMCAI. It makes the target reconstruction task turn into an estimation from an input image by the U-Net. Then, the optimized weights serve as a parametrization that bridges the input image and the target. The simulation and experimental results demonstrate the effectiveness of the proposed approach under different SNRs or compression measurements.

The extraction of the micro-Doppler (m-D) feature based on time-frequency distribution (TFD) is of great significance for target detection and identification. To improve the feature extraction performance, numerous TFDs have been developed, with the majority falling under Cohen’s class. Nevertheless, these TFDs basically face a trade-off between artifact suppression and energy concentration. The main reason is that each Cohen’s class TFD is constructed by applying the two-dimensional Fourier transform to a kerneled ambiguity function directly, while existing kernels generally attenuate artifacts at the expense of losing valuable information. In this paper, a TFD reconstruction method employing an adaptive short-time kernel (ASTK) is developed in the framework of sparse representation (SR) theory to overcome this trade-off and enhance the m-D feature. Firstly, the task of the optimal kernel is explained from the viewpoint of the instantaneous auto-correlation function (IAF). Secondly, based on the quasi-linear frequency modulation feature of most m-D signals during short-time periods, the distribution rule of the short-time IAF (STIAF) in the ambiguity plane is concluded. Guided by this rule, an ASTK that can effectively remove unwanted artifacts with the least information loss is designed. Finally, an SR-based reconstruction procedure is conducted on the kerneled STIAF to generate an artifact-free TFD with high energy concentration, which can effectively enhance the m-D feature. Experiments using both simulated and real-world m-D signals demonstrate the effectiveness of the proposed method.

In the production process of zinc sulfate, the presence of Fe and Mn had an adverse effect on the crystallization process of zinc sulfate production and reduce the purity of MgSO4·7H2O. Traditional chemical treatment methods are costly and easy to introduce new impurity ions. To obtain high-quality zinc sulfate, it is essential to develop a new Fe2+ and Mn2+ removal process. In this experiment, micro-nano bubble and ozone oxidation combined technology was used to explore the influence of different gas sources, temperature, CaCO3 dosage method and dosage on the oxidation effect of Fe2+ and Mn2+, and to analyze the feasibility of the technology in a practical production application. Results showed that the removal rate of Fe2+ and Mn2+ was more than 99.5% with ozone as the gas source, which was much higher than that with air and oxygen as the gas source. The increase of temperature had an effect on the stability of micro and nano bubbles and the solubility of ozone, which was not conducive to the oxidation of Fe2+ and Mn2+. The treatment time of Mn2+ at the maximum temperature of 18℃ was shortened by 12.5% compared with that at 24℃. The addition of CaCO3 significantly improved the removal efficiency of Fe2+ and Mn2+ by micro-nano ozone. When the relative dosage of 0.44 g/L CaCO3 was added every 15 min, the adequate total amount of CaCO3 was 17.6 g, and the complete Removal of Fe2+ and Mn2+ took about 50 min, which shortened the treatment time by 33% compared with the treatment time without CaCO3. Effect of intermittent dosing was better than that of one-time dosing, and the amount of CaCO3 consumed was more petite.

Microchannels are the essential elements for the design of artificial devices but the fabrication of three dimensional (3D) microchannels with complex geometry and a high aspect ratio remains challenging. Here, the authors demonstrate a simple and solvent-free fabrication method capable of producing monolithic microchannels with complex 3D structures, long length, and small diameter.

Abstract Hollow carbon nanocages (HCNCs) consisting of sp2 carbon shells featured by a hollow interior cavity with defective microchannels (or customized mesopores) across the carbon shells, high specific surface area, and tunable electronic structure, are quilt different from the other nanocarbons such as carbon nanotubes and graphene. These structural and morphological characteristics make HCNCs a new platform for advanced electrochemical energy storage and conversion. This review focuses on the controllable preparation, structural regulation, and modification of HCNCs, as well as their electrochemical functions and applications as energy storage materials and electrocatalytic conversion materials. The metal single atoms‐functionalized structures and electrochemical properties of HCNCs are summarized systematically and deeply. The research challenges and trends are also envisaged for deepening and extending the study and application of this hollow carbon material. The development of multifunctional carbon‐based composite nanocages provides a new idea and method for improving the energy density, power density, and volume performance of electrochemical energy storage and conversion devices.

Abstract According to the characteristics of the manoeuvring targets and the superiorities of Terahertz Inverse Synthetic Aperture Radar (THz‐ISAR), a precise motion compensation method based on image sharpness maximization for manoeuvring targets in the terahertz radar was proposed in this letter. Computational efficiency and compensation accuracy are improved by modified Newton's method. Then a 330‐GHz imaging radar and an experiment on a corner reflector were introduced. The validity of the method was verified, by the experimental results, to be promising in THz‐ISAR imaging of manoeuvring targets.

Abstract Establishing covalent heterointerfaces with face-to-face contact is promising for advanced energy storage, while challenge remains on how to inhibit the anisotropic growth of nucleated crystals on the matrix. Herein, face-to-face covalent bridging in-between the 2D-nanosheets/graphene heterostructure is constructed by intentionally prebonding of laser-manufactured amorphous and metastable nanoparticles on graphene, where the amorphous nanoparticles were designed via the competitive oxidation of Sn-O and Sn-S bonds, and metastable feature was employed to facilitate the formation of the C-S-Sn covalent bonding in-between the heterostructure. The face-to-face bridging of ultrathin SnS2 nanosheets on graphene enables the heterostructure huge covalent coupling area and high loading and thus renders unimpeded electron/ion transfer pathways and indestructible electrode structure, and impressive reversible capacity and rate capability for sodium-ion batteries, which rank among the top in records of the SnS2-based anodes. Present work thus provides an alternative of constructing heterostructures with planar interfaces for electrochemical energy storage and even beyond.

YOLOv5 remains one of the most widely used real-time detection models due to its commendable performance in accuracy and generalization. However, compared to more recent detectors, it falls short in label assignment and leaves significant room for optimization. Particularly, recognizing targets with varying shapes and poses proves challenging, and training the detector to grasp such features requires expert verification or collective discussion during the dataset labeling process, especially in domain-specific contexts. While deformable convolutions offer a partial solution, their extensive usage can enhance detection capabilities but at the expense of increased computational effort. We introduce DP-YOLO, an enhanced target detector that efficiently integrates the YOLOv5s backbone network with deformable convolutions. Our approach optimizes the positive sample selection during label assignment, resulting in a more scientifically grounded process. Notably, experiments on the COCO benchmark validate the efficacy of DP-YOLO, which utilizes an image size of [640, 640], achieves a remarkable 41.2 AP, and runs at an impressive 69 fps on an RTX 3090. Comparatively, DP-YOLO outperforms YOLOv5s by 3.2 AP, with only a small increase in parameters and GFLOPSs. These results demonstrate the significant advancements made by our proposed method.

In recent years, terahertz (THz) radar has been widely researched for its high-resolution imaging. However, the traditional inverse synthetic aperture radar (ISAR) imaging algorithms in the microwave band perform unsatisfactorily in the THz band. Firstly, due to THz radar’s large bandwidth and short wavelength, the rotation of the target will result in serious space-varying(SV) range migration and space-varying phase error. Furthermore, it is challenging to accurately estimate the rotational velocity and compensate for phase errors in the presence of severe range migration effects. Therefore, in this paper, a high-precision THz-ISAR imaging algorithm is proposed. The algorithm includes the following step: First, the SV first-order range migration(FRM) is corrected using keystone transform (KT); then, the minimum entropy based on modified newton (ME-MN) is used to estimate the rotational velocity roughly, and the remaining SV second-order range migration(SRM) is corrected to obtain the range profile with the envelope alignment. Finally, the echo after the envelope alignment is processed for the second time based on ME-MN. The target rotation velocity is accurately estimated, and the phase error is compensated to obtain a well-focused imaging result. The validity of the proposed method is verified by numerical simulation and electromagnetic calculation data.

The Minimum Weighted Connected Vertex Cover problem (MWCVC) is to find a subset $F\subset V(G)$ with minimum weight in a node-weighted graph $G$ , such that when removing the set $F$ , the inducing graph of remaining vertices holds no edges, and the graph induced from set $F$ in $G$ is required to be connected. This problem comes from the classical combinatorial problem in graph theory, i.e., the Vertex Cover Problem. A large number of results on algorithms for the MWCVC problem have been reported. In this paper, we proposed two heuristic algorithms, denoted as VCC and LCVCC, to find a connected vertex cover set in a general weighted graph. The time complexity of both two algorithms are less than $O(n^{4})$ . We compare these two algorithms with two known heuristic algorithms GR and GD (proposed by Dagdeviren in 2021) on connected graphs, and draw a conclusion that both of VCC and LCVCC perform better than GR or GD. Relatively speaking, LCVCC is expected to have better performance in dense graphs than VCC.

Since its unique advantages in the tradeoff between data acquisition rate and hardware cost, the scanning 1-D multiple-input-multiple-output (MIMO) array setup has been widely used in nondestructive testing applications, especially for product quality control on the assembly line. In this article, two fast fully focused algorithms based on scanning 1-D MIMO array are developed for imaging half-space medium structures. Both of the algorithms are derived under the half-space Green's function, and are implemented through fast Fourier transform (FFT) and phase compensation. The only difference between these two algorithms is that the matrix multiplication and nonuniform FFT are, respectively, employed to reconstruct the range profile. Simulation and experimental results verify the effectiveness of the proposed algorithms in computation efficiency and image reconstruction quality.

Tin (II) sulfide (SnS) has been regarded as an attractive anode material for lithium-ion batteries (LIBs) owing to its high theoretical capacity. However, sulfide undergoes significant volume change during lithiation/delithiation, leading to rapid capacity degradation, which severely hinders its further practical application in lithium-ion batteries. Here, we report a simple and effective method for the synthesis of SnS@C/G composites, where SnS@C nanoparticles are strongly coupled onto the graphene oxide nanosheets through dopamine-derived carbon species. In such a designed architecture, the SnS@C/G composites show various advantages including buffering the volume expansion of Sn, suppressing the coarsening of Sn, and dissolving Li2S during the cyclic lithiation/delithiation process by graphene oxide and N-doped carbon. As a result, the SnS@C/G composite exhibits outstanding rate performance as an anode material for lithium-ion batteries with a capacity of up to 434 mAh g−1 at a current density of 5.0 A g−1 and excellent cycle stability with a capacity retention of 839 mAh g−1 at 1.0 A g−1 after 450 cycles.

The vortex ElectroMagnetic (EM) wave has a unique spiral phase wavefront. The rotational Doppler effect, induced by the lateral micromotion of a target is expected to provide a new mode of radar target detection. Under the illumination of vortex EM waves, the micromotion of a cone-shaped target periodically modulates the instantaneous frequency of the radar echo; this can effectively reflect the geometric characteristics and micromotion parameters of a cone-shaped target. This paper focuses on the estimation of cone-shaped target parameters under forward-looking radar conditions. First, based on the principle of vortex EM wave target rotational Doppler detection, a mathematical equation describing the vortex EM wave of a cone-shaped target echo is derived, and an echo rotational Doppler model is established. Second, a method for cone-shaped target parameter estimation under forward-looking conditions is proposed. Using the two-dimensional rotational Doppler information of the scattering points at the top and bottom of a cone-shaped target, the micromotion and geometric parameters of the target can be effectively estimated. The simulation results verify the effectiveness and robustness of the method proposed in this paper.

The vortex ElectroMagnetic (EM) wave, whose phase wavefront is modulated by the Orbital Angular Momentum (OAM), has received immense attention, especially in the field of forward-looking radar imaging. Based on the fundamental principle and imaging method of the vortex EM radar, the azimuth resolution was studied in this paper. First, the circumstance of considering the Bessel amplitude term was analyzed, indicating that the azimuth resolution was determined by the effective scope of OAM modes. Then, an effective method for calculating the scope of OAM modes was proposed, including the expressions of azimuth resolution, spatial resolution, and super-resolution were characterized. Finally, the fundamental resolution performance with different influencing factors was analyzed via simulations. The analysis showed that changing the wavelength aperture ratio and imaging elevation could increase the effective scope of OAM modes, which improved the azimuth resolution. Through data fitting, the approximate expressions of the effective scope of OAM modes and the resolution of the super-real aperture radar with respect to the wavelength aperture ratio and imaging elevation were obtained separately, providing a reference for the parameters design and optimization of vortex EM wave radar.

Medical image segmentation can effectively identify lesions in medicine, but some small and rare lesions cannot be well identified. Existing studies do not take into account the uncertainty of the occurrence of diseased tissue, and the problem of long-tailed distribution of medical data. Meanwhile, the grayscale image obtained from Magnetic Resonance Imaging (MRI) detection has problems, such as the features being difficult to extract and invalid features being difficult to distinguish. In order to solve these problems, we propose a new weighted attention ResUNet (WA-ResUNet) and a class weight formula based on the number of images contained in the class, which improves the performance of the model in the low-frequency class and the overall effect of the model by improving the degree of attention paid to the valid features and invalid ones and rebalancing the learning efficiency among the classes. We evaluated our method on an uterine MRI dataset and compared it with the ResUNet. WA-ResUNet increased Intersection over Union (IoU) in the low-frequency class (Nabothian cysts) by 21.87%, and the overall mIoU increased by more than 6.5%.

Abstract Background Integrase inhibitors (INIs)-based antiretroviral therapies (ART) are more recommended than efavirenz (EFV)-based ART for people living with HIV/AIDS (PLWHA). Yet, the advantage of integrase inhibitors in treating TB/HIV coinfection is uncertain. Therefore, the objective of this systematic review is to evaluate the effects and safety of INIs- versus EFV-based ART in TB/HIV coinfection, and demonstrate the feasibility of the regimens. Methods Four electronic databases were systematically searched through September 2020. Fixed-effects models were used to calculate pooled effect size for all outcomes. The primary outcomes were virologic suppression and bacteriology suppression for INIs- versus EFV-based ART. Secondary outcomes included CD4+ cell counts change from baseline, adherence and safety. Results Three trials (including 672 TB/HIV patients) were eligible. ART combining INIs and EFV had similar effects for all outcomes, with none of the point estimates argued against the INIs-based ART on TB/HIV patients. Compared to EFV-based ART as the reference group, the RR was 0.94 (95% CI 0.85 to 1.05) for virologic suppression, 1.00 (95% CI 0.95 to 1.05) for bacteriology suppression, 0.98 (95% CI 0.95 to 1.01) for adherence. The mean difference in CD4+ cell counts increase between the two groups was 14.23 cells/μl (95% CI 0− 6.40 to 34.86). With regard to safety (adverse events, drug-related adverse events, discontinuation for drugs, grade 3–4 adverse events, IRIS (grade 3–4), and death), INIs-based regimen was broadly similar to EFV-based regimens. The analytical results in all sub-analyses of raltegravir- (RAL) and dolutegravir (DTG) -based ART were valid. Conclusion This meta-analysis demonstrates similar efficacy and safety of INIs-based ART compared with EFV-based ART. This finding supports INIs-based ART as a first-line treatment in TB/HIV patients. The conclusions presented here still await further validation owing to insufficient data.

Underwater video object detection is a challenging task due to the poor quality of underwater videos, including blurriness and low contrast. In recent years, Yolo series models have been widely applied to underwater video object detection. However, these models perform poorly for blurry and low-contrast underwater videos. Additionally, they fail to account for the contextual relationships between the frame-level results. To address these challenges, we propose a video object detection model named UWV-Yolox. First, the Contrast Limited Adaptive Histogram Equalization method is used to augment the underwater videos. Then, a new CSP_CA module is proposed by adding Coordinate Attention to the backbone of the model to augment the representations of objects of interest. Next, a new loss function is proposed, including regression and jitter loss. Finally, a frame-level optimization module is proposed to optimize the detection results by utilizing the relationship between neighboring frames in videos, improving the video detection performance. To evaluate the performance of our model, We construct experiments on the UVODD dataset built in the paper, and select mAP@0.5 as the evaluation metric. The mAP@0.5 of the UWV-Yolox model reaches 89.0%, which is 3.2% better than the original Yolox model. Furthermore, compared with other object detection models, the UWV-Yolox model has more stable predictions for objects, and our improvements can be flexibly applied to other models.

The oxidative dehydrogenation of alkanes is a prospective method for olefins production. CO2-assisted propane dehydrogenation over metal oxide catalysts provides an opportunity to increase propylene production with collateral CO2 utilization. We prepared the chromia catalysts on various mesoporous aluminosilicate supports, such as halloysite nanotubes, nanostructured core/shell composites of MCM-41/halloysite (halloysite nanotubes for the core; silica of MCM-41-type for the shell), and MCM-41@halloysite (silica of MCM-41-type for the core; halloysite nanotubes for the shell). The catalysts have been characterized by X-ray fluorescence analysis, low-temperature nitrogen adsorption, X-ray diffraction, temperature-programmed reduction, temperature-programmed desorption of ammonia, transmission electron microscopy with energy-dispersive X-ray spectroscopy, and thermogravimetric analysis. The catalysts’ performance in carbon-dioxide-assisted propane dehydrogenation has been estimated in a fixed-bed reactor at atmospheric pressure. The most stable catalyst is Cr/halloysite, having the lowest activity and the largest pore diameter. The catalyst, Cr/MCM-41/HNT, shows the best catalytic performance: having the highest conversion (19–88%), selectivity (83–30%), and space–time yield (4.3–7.1 mol C3H6/kg catalyst/h) at the temperature range of 550–700 °C. The highest space–time yield could be related to the uniform distribution of the chromia particles over the large surface area and narrow pore size distribution of 2–4 nm provided by the MCM-41-type silica and transport channels of 12–15 nm from the halloysite nanotubes.

Uterine myomas affect 70% of women of reproductive age, potentially impacting their fertility and health. Manual film reading is commonly used to identify uterine myomas, but it is time-consuming, laborious, and subjective. Clinical treatment requires the consideration of the positional relationship among the uterine wall, uterine cavity, and uterine myomas. However, due to their complex and variable shapes, the low contrast of adjacent tissues or organs, and indistinguishable edges, accurately identifying them in MRI is difficult. Our work addresses these challenges by proposing an instance segmentation network capable of automatically outputting the location, category, and masks of each organ and lesion. Specifically, we designed a new backbone that facilitates learning the shape features of object diversity, and filters out background noise interference. We optimized the anchor box generation strategy to provide better priors in order to enhance the process of bounding box prediction and regression. An adaptive iterative subdivision strategy ensures that the mask boundary details of objects are more realistic and accurate. We conducted extensive experiments to validate our network, which achieved better average precision (AP) results than those of state-of-the-art instance segmentation models. Compared to the baseline network, our model improved AP on the uterine wall, uterine cavity, and myomas by 8.8%, 8.4%, and 3.2%, respectively. Our work is the first to realize multiclass instance segmentation in uterine MRI, providing a convenient and objective reference for the clinical development of appropriate surgical plans, and has significant value in improving diagnostic efficiency and realizing the automatic auxiliary diagnosis of uterine myomas.

The complete mitochondrial genome of the Cyprinodontiformes fish Poropanchax normani was studied in this study. The size of the entire mitochondrial genome was 16,878 bp, and the total length of the encoding sequence was 11,435 bp, accounting for 67.75%, encoding 3800 amino acids in total. Genome base compositions were: A was 27.30%, C was 28.30%, G was 16.17%, and T was 28.23%. ND2 and CO1 started with GTG, and other protein-coding genes started with ATG. ND1, ND2, and ND3 ended with TAG, respectively, CO2 and ND4 ended with a single T, and other PCGS ended with TAA. The ribosomal RNA lengths of 12S and 16S were 950 bp and 1690 bp, respectively. The control area (D-loop) was 1194 bp in size and ranged from 15,685 to 16,878 bp. It showed negative GC skew value (–0.2728) and negative AT skewness (–0.0168). Phylogenetic analysis showed that P. normani was most closely related to Xenotoca eiseni. The complete sequence of the mitochondrial genome will provide a new perspective for classification and help to draw a more complete picture of species diversity within the Cyprinodontiformes.

The classification of Badidae family based on morphology has been revised several times, but data on complete mitogenome are scarce, the complete mitochondrial genome of the Badidae fish Dario dario was characterized for the first time in the present study. The whole mitogenome was 16,830 bp in size and consisted of 13 protein-coding genes, 22 tRNAs, two rRNAs genes, a control region and origin of light-strand replication. The proportion of coding sequences with a total length of 11,431 bp was 67.92%, which encoded 3800 amino acids. The genome composition was highly A + T biased (58.12%), and exhibited a negative AT-skew (–0.0045) and GC-skew (–0.2347). All protein-coding genes started with ATG except for GTG in CO1, while stopped with the standard TAN codons or a single T. The control region (D-loop) ranging from 15,658 bp to 16,830 bp was 1173 bp in size. Phylogenetic analysis showed that D. dario was most closely related to Badis badis. The complete mitochondrial genome sequence provided new insight into taxonomic classification, and a more complex picture of species diversity within the Anabantiformes.

Ultrablack materials have attracted wide attention due to their high light absorbance and efficient photothermal conversion used in the field of solar steam generation to alleviate the drinking water crisis. Herein, ultrablack and low‐cost poly(vinyl alcohol)‐graphite composite xerogels (PGCXs) that have vertically arranged pores and high thermal conductivities are fabricated. The PGCX‐50% shows high light absorbance (95.44%), good wettability, and a water evaporation rate of 1.24 kg m−2 h−1 under 1 sun illumination when coupled in a two dimensional (2D) evaporator. To further improve the solar steam generation performance, the PGCX‐50% is switched to a three dimensional (3D) evaporator by rotating it. Under 1 sun illumination, the 3D evaporator has a highest water evaporation rate of 3.80 kg m−2 h−1, which is 3.07 times that of the 2D evaporator, due to its larger evaporation area and higher surface area at subambient temperature. Finally, a heatsink‐like 3D evaporator is designed and a water evaporation rate of up to 9.54 kg m−2 h−1 is achieved when coupled with wind energy. The high water evaporation rates of the PGCX even in concentrated brines ensure its great potential for drinking water production and seawater desalination.

The oncogenic role of Ladinin-1 (LAD1), an anchoring filament protein, is largely unknown. In this study, we conducted a series of studies on the oncogenic role of LAD1 in lung adenocarcinoma (LUAD). Firstly, we analyzed the aberrant expression of LAD1 in LUAD and its correlation with patient survival, tumor immune infiltration, and the activation of cancer signaling pathways. Furthermore, the relationship between LAD1 expression and K-Ras and EGF signaling activation, tumor cell proliferation, migration, and colony formation was studied by gene knockout/knockout methods. We found that LAD1 was frequently overexpressed in LUAD, and high LAD1 expression predicts a poor prognosis. LAD1 exhibits promoter hypomethylation in LUAD, which may contribute to its mRNA upregulation. Single-sample gene set enrichment analysis (ssGSEA) showed that acquired immunity was negatively correlated with LAD1 expression, which was verified by the downregulated GO terms of “Immunoglobulin receptor binding” and “Immunoglobulin complex circulating” in the LAD1 high-expression group through Gene Set Variation Analysis (GSVA). Notably, the Ras-dependent signature was the most activated signaling in the LAD1 high-expression group, and the phosphorylation of downstream effectors, such as ERK and c-jun, was strongly inhibited by LAD1 deficiency. Moreover, we demonstrated that LAD1 depletion significantly inhibited the proliferation, migration, and cell-cycle progression of LUAD cells and promoted sensitivity to Gefitinib, K-Ras inhibitor, and paclitaxel treatments. We also confirmed that LAD1 deficiency remarkably retarded tumor growth in the xenograft model. Conclusively, LAD1 is a critical prognostic biomarker for LUAD and has potential as an intervention target.