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This systematic review presents the first synthesis of the scientific literature on estimating school and teacher/class effects on student academic performance using random-effects (RE) models with three or more levels. The review delves into the theoretical framework underpinning the estimation of educational effects, the associated statistical methods, and the empirical findings from multilevel analyses to date. It includes 94 analyses in 32 papers from diverse geographical regions, focusing on both primary and secondary education levels. The findings reveal a broad spectrum of Variance Partition Coefficients (VPCs) from RE models, highlighting the variability in the impact of school and teacher/class levels on student outcomes. The review emphasizes the necessity for comprehensive and diverse datasets, particularly the inclusion of detailed teacher-level data in African studies and the utilization of robust official educational databases in Chinese research. It also acknowledges the contributions of Latin American countries in advancing multilevel modeling and the holistic approach to educational analysis demonstrated in Cyprus. The conclusions underscore the importance of contextualizing educational policies and practices based on the multifaceted influences on student performance. The review calls for future research to expand data collection efforts, adopt standardized reporting practices, and explore advanced statistical techniques to address methodological challenges and approximate causal relationships more accurately.

Abstract Vaccines offer prophylactic treatments against atherosclerosis by eliciting effector T cell and antibody responses, which require effective delivery of antigen and adjuvant to activate dendritic cells (DC). Here we show that individual conjugation of antigen p210 and adjuvant CpG oligodeoxynucleotides onto superparamagnetic iron oxide nanoparticles formulates a nanovaccine cocktail that activates DCs for antigen cross-presentation and induction of co-stimulatory signals, cytokines and CD8+ effector/effector memory T cell responses. This nanovaccine modulates the DCs in the draining lymph nodes, activates both CD4+ and CD8+ T cells, elicits memory responses, and induces both anti-p210 IgM and IgG antibodies to suppress atherosclerosis. Lastly, three intradermal vaccinations of this nanovaccine mitigate the atherosclerosis development in the ApoE−/− mice. Our nanovaccine design and preclinical data thus presents a potential candidate for prophylactic treatment for atherosclerosis.

Abstract Swine mycoplasma pneumonia caused by Mycoplasma hyopneumoniae is the most prevalent and frequently occurring chronic respiratory disease in pigs worldwide. Pig breeds in China are more susceptible to Mycoplasma hyopneumoniae, and understanding the reasons for their susceptibility is crucial for the prevention and control of swine Mycoplasma pneumonia, as well as for promoting the healthy development of the swine industry. Mycoplasma hyopneumoniae has limited biosynthesis and metabolism, and has harsh requirements for nutrients, thus it is necessary to add approximately 15%-20% porcine serum in medium during in vitro culture, and porcine serum plays an irreplaceable role in the growth and reproduction of Mycoplasma hyopneumoniae. In this study, growth titres of different Mycoplasma hyopneumoniae strains cultured in the serum medium from different Chinese pig breeds and introduced pig breeds were compared. Non-targeted metabolomics analysis of representative introduced and Chinese pig breeds, Large white pig and Bama miniature pig, respectively, screened of key serum metabolites. The results showed that, expression level of L-Asparagine in Bama miniature pigs was 15–18 times higher than in Large white pigs. Additionally, the contents of inositol and Arachidonic acid in Bama miniature pigs were significantly upregulated as well. A certain concentration of L-Asparagine significantly increased the growth titer, metabolic capacity and pathogenicity of Mycoplasma hyopneumoniae. In conclusion, Bama miniature pig and Large white pig are representative pig breeds that are susceptible and non-susceptible to Mycoplasma hyopneumoniae respectively. L-Asparagine is the essential factor and biomarker for the susceptibility of Chinese pigs to Mycoplasma hyopneumoniae. Graphical Abstract

The corrosion inhibition behavior of Na2CrO4 on 316L in high-temperature concentrated NaOH solution was investigated. The results indicated that Na2CrO4 effectively inhibited the corrosion of 316L. The addition of Na2CrO4 induced the variation of film composition from Ni-rich to Cr-rich. TEM results showed that in the absence of Na2CrO4, the inner and outer films exhibited lattice fringe structures. In the presence of Na2CrO4, the inner and outer layers of the passive films exhibited amorphous structures. CrO42− exhibited a mixed mode of adsorption involving chemisorption and physisorption. Chemisorption promoted the formation of Cr-rich protective passive films. Physisorption was beneficial for the self-healing of localized defects in passive films. Besides, the presence of Na2CrO4 induced the competitive adsorption of CrO42− with OH−, which reduced the corrosion susceptibility of 316L.

Based on broadband regional seismic data generated by six North Korean underground nuclear tests, recorded by networks in Northeast China and adjacent areas, this paper developed a set of seismological methods to monitor North Korean underground nuclear tests, including magnitude measurement and yield estimation, high-precision relocation, and discrimination between atomic explosions and natural earthquakes. Since the North Korean nuclear test site (NKTS) is uncalibrated, it is crucial to determine the appropriate empirical magnitude-yield equation for the NKTS. This paper gathered ground truth data from chemical explosions used for deep seismic sounding, including their locations, yields, and charge depths, and determined a magnitude-yield relation suitable for the NKTS. A high-precision relative relocation method was applied to the six nuclear explosions at NKTS. To further alleviate the tradeoff between the detonate time and the source depth, according to the fact that the Pn and Pg waves have different incident angles, Pn and Pg waveform correlations are jointly used to increase the accuracy of relative source depth calculation. By analyzing the P/S type spectral amplitude ratios, Pn/Lg, Pg/Lg, and Pn/Sn, for 6 nuclear tests, 4 natural earthquakes, and 3 chemical explosions, this paper found that, at above 2 Hz, the network averaged spectral amplitude ratios could reliably separate the North Korean nuclear tests from the natural earthquakes. The spectral amplitude ratios of 3 chemical explosions are located between those of the nuclear explosions and the natural earthquakes.

Abstract The process e + e − → K S 0 K S 0 ψ 3686 $$ {e}^{+}{e}^{-}\to {K}_S^0{K}_S^0\psi (3686) $$ is studied by analyzing e + e − collision data samples collected at eight center-of-mass energies ranging from 4.682 to 4.951 GeV with the BESIII detector operating at the BEPCII collider, corresponding to an integrated luminosity of 4.1 fb −1. Observation of the e + e − → K S 0 K S 0 ψ 3686 $$ {e}^{+}{e}^{-}\to {K}_S^0{K}_S^0\psi (3686) $$ process is found for the first time with a statistical significance of 6.3σ, and the cross sections at each center-of-mass energy are measured. The ratio of cross sections of e + e − → K S 0 K S 0 ψ 3686 $$ {e}^{+}{e}^{-}\to {K}_S^0{K}_S^0\psi (3686) $$ relative to e + e − → K + K − ψ(3686) is determined to be σ e + e − → K S 0 K S 0 ψ 3686 σ e + e − → K + K − ψ 3686 = 0.45 ± 0.25 $$ \frac{\sigma \left({e}^{+}{e}^{-}\to {K}_S^0{K}_S^0\psi (3686)\right)}{\sigma \left({e}^{+}{e}^{-}\to {K}^{+}{K}^{-}\psi (3686)\right)}=0.45\pm 0.25 $$ , which is consistent with the prediction based on isospin symmetry. The uncertainty includes both statistical and systematic contributions. Additionally, the K S 0 ψ 3686 $$ {K}_S^0\psi (3686) $$ invariant mass distribution is found to be consistent with three-body phase space. The significance of a contribution beyond three-body phase space is only 0.8σ.

Abstract In order to predict the hysteresis characteristics of nanocrystalline alloy materials at different frequencies, a data-driven hysteresis prediction model based on the encoder–decoder architecture, which combines long short-term memory network and feedforward neural network, is proposed in this paper. The data-driven based magnetic hysteresis prediction model can take advantage of the powerful nonlinear learning ability of artificial neural network to train and learn its magnetic hysteresis characteristics of nanocrystalline alloy materials at different frequencies. Firstly, based on the encoder–decoder architecture, a hysteresis prediction model is constructed by combining long short-term memory network and feedforward neural network. Subsequently, in order to obtain the training set and validation set used for the data-driven based hysteresis prediction model, the Jiles–Atherton (J–A) hysteresis model is identified based on the B–H measurement data of a small number of nanocrystalline alloy materials at different frequencies for expediency since it is quite cumbersome and time-consuming to get these B–H data by measurement. Finally, the validity and accuracy of the data-driven based hysteresis prediction model are proved by the validation set. The maximum error is about 10.29%. The results show that the hysteresis model of neural network is able to predict hysteresis characteristics with considering the effect of frequency, which provides a new way for the simulation of hysteresis characteristics.

Abstract The type-1 ryanodine receptor (RyR1) is an intracellular calcium release channel for skeletal muscle excitation-contraction coupling. Previous structural studies showed that the RyR1 activity is modulated by the exogenous regulators including caffeine, ryanodine, PCB-95 and diamide. An additional transmembrane helix, located adjacent to S1 and S4, has been observed in some structures, although its function remains unclear. Here, we report that using a mild purification procedure, this helix is co-purified with RyR1 and is designated as S0. When RyR1 is coupled with S0, it can be activated by Ca2+ to an open state; however when decoupled from S0, it remains in primed state. S0 regulates the channel conformation by directly affecting the TM domain via the pVSD-S0-S4/S5 linker coupling, which facilitates the dilation of S6. Our results demonstrate that S0 is an essential component of RyR1 and plays a key role in the physiological regulation of RyR1 channel gating.

Abstract Early-onset lung cancer in young adults represents a less studied clinical entity with increasing incidence which still affects a large number of cancer patients. Here we perform a comprehensive analysis of early-onset lung cancer for the clinicopathological features, genomic alterations, gene expression, and immune landscape by establishing a cohort enrolling 421 non-small cell lung cancer (NSCLC) patients from ten medical centers in China. Comparative analysis reveals a distinct genomic alteration between younger and elder patients with NSCLC, with ERBB2 mutations and ALK-rearrangement strikingly more frequent in younger group. Transcriptome profiling indicates altered cellular metabolism and immune-related genes in tumors from younger patients. Immunological analysis reveals a decreased infiltration of immune cells (notably T cells) in tumors from younger patients. Cellular and mechanistic studies show that the prevalent ERBB2 mutants in cancer from younger patients can indeed drive tumorigenesis by elevating AKT signaling. Importantly, meta-analysis of clinical trials and our clinical practice further validate the promise of HER2-targeted therapy to treat early-onset NSCLC in East Asian patients. Our comprehensive and integrative analysis not only reveal multiple unrecognized characteristics of early-onset lung cancer, but also inform actionable therapeutics to manage this type of cancer.

Abstract Quantum combs play a vital role in characterizing and transforming quantum processes, with wide-ranging applications in quantum information processing. However, obtaining the explicit quantum circuit for the desired quantum comb remains a challenging problem. We propose PQComb, a novel framework that employs parameterized quantum circuits (PQCs) or quantum neural networks to harness the full potential of quantum combs for diverse quantum process transformation tasks. This method is well-suited for near-term quantum devices and can be applied to various tasks in quantum machine learning. As a notable application, we present two streamlined protocols for the time-reversal simulation of unknown qubit unitary evolutions, reducing the ancilla qubit overhead from six to three compared to the previous best-known method. We also extend PQComb to solve the problems of qutrit unitary transformation and channel discrimination. Furthermore, we demonstrate the hardware efficiency and robustness of our qubit unitary inversion protocol under realistic noise simulations of IBM-Q superconducting quantum hardware, yielding a significant improvement in average similarity over the previous protocol under practical regimes. PQComb’s versatility and potential for broader applications in quantum machine learning pave the way for more efficient and practical solutions to complex quantum tasks.

Abstract Aim To evaluate the safety and efficacy of FreeFlow percutaneous atrial septal shunt device (manufactured by AOLIU Medical Technology Co., Ltd, Shanghai, China) in patients with refractory pulmonary arterial hypertension (PAH) for the first time. Methods The study enrolled adult patients diagnosed with refractory pulmonary arterial hypertension (PAH) at the Department of Cardiology, Zhongshan Hospital, Fudan University, between Oct 2021 and Oct 2023. The patients were treated with the FreeFlow percutaneous atrial septal shunt device and underwent follow-up immediately after operation, as well as before and after discharge (at 1, 3, 6, 12 months post-operation). The primary endpoints of the study included the rate of major cardiovascular and cerebrovascular adverse events (MACCEs), serious adverse events (SAEs), and serious device-related adverse events (SADEs) within 12 months of shunt implantation. Data analysis was conducted using SAS 9.3. Results A total of 12 patients were enrolled in the study and successfully completed the operation. 10 subjects had completed 12 months’ follow-up after operation, while two subjects had died. The incidence of MACCE was 0%, and the incidence of SAEs was 33%, which was unrelated to the treatment with this device. No systemic or instrumental embolizations occurred during the follow-up period. All ten subjects exhibited a stable right-to-left shunt after the operation (100% success rate). Seven patients’ New York Heart Association (NYHA) functional classification improved from grade III to grade II. The Short Form-36 (SF-36) score and the 6-minute walking distance (6MWD) at 12 months post-operation were significantly improved compared to baseline, with scores of 47.6 ± 19.5 versus 64.7 ± 24.6 (P = 0.029) and distances of 239.5 ± 137.8 m versus 401.7 ± 129.6 m (P = 0.045), respectively. Similarly, the levels of N-terminal pro-brain natriuretic peptide (NT-proBNP) and right heart diameter (RAD) also decreased significantly, from 3236 ± 1590.3 pg/mL to 1787.3 ± 703.7 pg/mL (P = 0.039) and from 59.1 ± 10.6 mm to 46.3 ± 7.5 mm (P = 0.046), respectively. Conclusions The results of this clinical study demonstrate that the product can attain the anticipated performance under typical conditions of use. The risks associated with the product are deemed acceptable when weighed against its potential benefits. All preclinical and clinical evaluations have furnished definitive and rational scientific evidence supporting the safety and efficacy of the percutaneous atrial septal shunt. Clinical trial number Not applicable.

Abstract We aim to assess the real-world accuracy (primary outcome), feasibility and acceptability (secondary outcomes) of an automated retinal photography and artificial intelligence (AI)-based cardiovascular disease (CVD) risk assessment system (rpCVD) in Australian primary care settings. Participants aged 45–70 years who had recently undergone all or part of a CVD risk assessment were recruited from two general practice clinics in Victoria, Australia. After consenting, participants underwent retinal imaging using an automated fundus camera, and an rpCVD risk score was generated by a deep learning algorithm. This score was compared against the World Health Organisation (WHO) CVD risk score, which incorporates age, sex, and other clinical risk factors. The predictive accuracy of the rpCVD and WHO CVD risk scores for 10-year incident CVD events was evaluated using data from the UK Biobank, with the accuracy of each system assessed through the area under the receiver operating characteristic curve (AUC). Participant satisfaction was assessed through a survey, and the imaging success rate was determined by the percentage of individuals with images of sufficient quality to produce an rpCVD risk score. Of the 361 participants, 339 received an rpCVD risk score, resulting in a 93.9% imaging success rate. The rpCVD risk scores showed a moderate correlation with the WHO CVD risk scores (Pearson correlation coefficient [PCC] = 0.526, 95% CI: 0.444–0.599). Despite this, the rpCVD system, which relies solely on retinal images, demonstrated a similar level of accuracy in predicting 10-year incident CVD (AUC = 0.672, 95% CI: 0.658-0.686) compared to the WHO CVD risk score (AUC = 0.693, 95% CI: 0.680-0.707). High satisfaction rates were reported, with 92.5% of participants and 87.5% of general practitioners (GPs) expressing satisfaction with the system. The automated rpCVD system, using only retinal photographs, demonstrated predictive accuracy comparable to the WHO CVD risk score, which incorporates multiple clinical factors including age, the most heavily weighted factor for CVD prediction. This underscores the potential of the rpCVD approach as a faster, easier, and non-invasive alternative for CVD risk assessment in primary care settings, avoiding the need for more complex clinical procedures.

Abstract The role of epithelial membrane protein 1 (EMP1) in tumor microenvironment (TME) remodeling has not yet been elucidated. In addition, the biological function of EMP1 in triple-negative breast cancer (TNBC) is largely unclear. In this study, we examined the infiltration landscape of cell types in the TME of breast cancer, and found that EMP1 expression was positively correlated with stromal and microenvironmental scores. Infiltration analysis and immunohistochemical (IHC) staining of serial sections confirmed the critical role of EMP1 in cancer-associated fibroblast (CAF) infiltration. Cell co-culture assays, xenograft tumor experiments, loss-of-function, gain-of-function, RNA sequencing studies, and rescue assays were performed to confirm the role of EMP1 in CAF infiltration in vitro and in vivo. These findings revealed that EMP1 depletion in TNBC cells resulted in considerable inhibition of CAF infiltration in vivo and in vitro. Mechanistically, EMP1 knockdown induced a substantial decrease in IL6 secretion from TNBC through the NF-κB signaling pathway, hindering CAF proliferation and subsequently inhibiting TNBC progression and metastasis. These cumulative results indicate that EMP1 functions as an oncogene in TNBC by mediating the cell communication of TNBC and CAFs. Targeted inhibition of EMP1 by suppressing CAF infiltration is a promising strategy for TNBC treatment.

In the present study, several hybrids and coupled forms of machine learning algorithms were developed to provide accurate elastic modulus of recycled aggregate concrete’s (ERAC) estimation, called multilayer perceptron neural networks (MLPNN). For this gain, a comprehensive dataset was collected from literature containing 400 rows of samples. Networks with different hidden layer numbers were used to develop MLPNN (from one layer to three layers). The accuracy of MLPNN deeply related to neuron numbers of each hidden layer, wherein the present paper, three optimization algorithms; the arithmetic optimization algorithm (AOA), equilibrium optimizer (EO), and flow direction algorithm (FDA); are merged with MLPNN to obtain the ideal value of neuron numbers. Considering all models, all developed models have the acceptable ability for predicting the ERAC by the coefficient of determination of at least 0.9306 for the learning stage and 0.9411 for the examining stage. All in all, the MLPNN model with two numbers of hidden layers with a structure of 17-14-1 optimized with AOA can be proposed as the most appropriate model. The created MLPNN models may optimize material selection and quality control in building processes, reducing dependence on natural aggregates.

Abstract Background The principal objective of this research is to develop highly specific polymorphic molecular markers, with the aim of addressing the paucity of simple sequence repeat (SSR) markers in Pinus koraiensis. The objective of this initiative is to facilitate the efficient management of genetic resources within Pinus koraiensis, enable precise pedigree identification and establish a foundational framework for subsequent whole-genome sequencing and assembly strategies. To achieve this objective, a simplified genome sequencing approach was employed, utiliZing RAD-Seq technology on a sample of 100 clones of Pinus koraiensis sourced from 6 seed orchards. The SSR sequences present within the contig were identified via TBtools-II software, which was also employed to generate a comprehensive summary and analysis of the sequence characteristics. On the Basis of these insights, primers were designed and subjected to meticulous screening using bioinformatics methodologies, and their efficacy was subsequently verified. The SSR markers were subsequently employed to examine the genetic diversity and structure of the plus tree population. The genetic data were then integrated with multiyear cone production records from the population of plus trees, thus facilitating the construction of a production population. Results A total of 80,539 SSR sites were identified among the 5,840,917 reads that were subjected to analysis. Notably, that the majority of these SSRs were dinucleotide to trinucleotide repeats, constituting 93.838% of the total. As the number of repeats increased, a gradual decline in the number of SSRs was observed. The most prevalent repetitive motif within the SSR loci was (AT/TA)n, representing 30% of all loci. A total of 1,933 SSR sites were selected for primer design, resulting in the successful formulation of 1,162 primer pairs, representing a success rate of 60.114%. Among these, 205 SSR primer pairs exhibited incomplete coverage of the SSR positions, whereas 79 primer pairs demonstrated specificity, as confirmed by e-PCR. Further analysis revealed 27 primer pairs suitable for amplification of the target fragments, nine of which exhibited polymorphisms. The mean number of alleles per primer (N A ) was calculated to be 9.333, whereas the mean number of effective alleles (N E ) was 4.032. The mean polymorphic information content (PIC) was 0.530. The Shannon index (I) for the plus tree population was 1.159, with observed heterozygosity (H O ), expected heterozygosity (H E ), and unbiased expected heterozygosity (µH E ) values of 0.583, 0.530, and 0.533, respectively, and a fixation index (F) of −0.079. No statistically significant genetic differentiation was observed among the subgroups within the population, and the level of genetic diversity was comparable among subgroups delineated by different criteria. Significant differences in cone production were observed between the plus trees (P

Abstract X-ray nondestructive testing technology is widely used for inspecting welds and identifying defects, and is crucial in the manufacturing industry. However, the diversity of welding defects and the imbalanced defect samples reduce defect classification model accuracy and can cause classifier overfitting. This paper proposes an improved DCGAN model for generating welding defect samples by integrating deep convolutional neural networks to enhance the training relationship between the generator and discriminator, thus increasing the number of training samples. We introduce a lightweight DG-MobileNet model to address low accuracy in welding defect identification and poor model convergence. Dilated convolution modules and Squeeze-and-Excitation self-attention mechanisms expand the convolutional receptive field and improve feature extraction capabilities. The fully connected layer is replaced by a global average pooling layer, reducing training parameters and mitigating overfitting. Additionally, combining DropBlock technology with Batch Normalization optimizes the feature extraction process, enhancing generalization ability. The experimental results demonstrate that the proposed model achieves a recognition accuracy of 98.78% and exhibits superior performance in terms of efficiency and lightweight design, highlighting its potential for deployment in real-world industrial applications.

Abstract Ischemia–reperfusion injury (IRI) is one of the leading causes of mortality and disability worldwide. Owing to its complex pathogenesis, there is still a lack of effective therapeutic targets in clinical practice, and exploring the mechanism and targets of IRI is still a major clinical challenge. This study aimed to explore the genetic alterations in leukocytes in peripheral blood after ischemia–reperfusion, aiming to discover new biomarkers and potential therapeutic targets. KAS-Seq (Kethoxal-assisted single-strand DNA sequencing) was used to obtain gene expression profiles of circulating leukocytes in a porcine ischemia–reperfusion model at 24, 48, and 72 h post-ischemia‒reperfusion. This method integrated genes that exhibited regular changes over time. In this study, we thoroughly analyzed the dynamic changes in gene expression post-IRI, revealing significant enrichment in key signaling pathways that regulate immune responses and T-cell activation over time. Our identification of the interleukin-7 receptor (IL7R) was particularly striking, as it plays a crucial molecular role in IRI. Additionally, using database mining technology, we confirmed the close relationship between IL7R and IRI, explored the interaction between interferon-γ (IFNG) and IL7R in T-cell activation, and clarified their joint influence on ischemia–reperfusion injury. Using KAS-Seq analysis of leukocytes from peripheral blood, we successfully delineated the temporal patterns of gene expression and changes in signal transduction pathways in porcine models of ischemia–reperfusion. Subsequent in-depth analysis identified IL7R as a potential novel therapeutic target for IRI. The pivotal role of this gene in modulating immune responses provides innovative avenues for the development of IRI treatments.

Abstract Programmable metasurfaces (PMs), also called reconfigurable intelligent surfaces (RISs), are planar structures capable of dynamically manipulating electromagnetic waves in real-time. Regarded as a key enabling technology for implementing smart wireless propagation environments, PMs/RISs also serve as an ideal supporting platform for integrated sensing and communication (ISAC). Here, we propose two ISAC schemes based on a special type of PMs/RISs: space-time-coding metasurfaces (STCMs). By leveraging space-time-coding strategies, STCMs simultaneously control the propagation at the fundamental (carrier) frequency for reliable wireless communication and generate spatially distributed harmonics for sensing. The proposed schemes seamlessly integrate both communication and sensing on a shared hardware platform, eliminating the need for additional sensors. For experimental validation, we implemented an ISAC system using a 2-bit STCM operating at microwave frequencies. Experimental results align with theoretical predictions, confirming the practical viability and effectiveness of the proposed ISAC schemes for applications in communication, imaging, radar, and sensing systems.