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Abstract Bladder cancer (BCa), as the most common malignant tumor of the urinary system, has received significant attention in research on the clinical application of artificial intelligence algorithms. Nevertheless, it has been observed that certain investigations use data from various medical facilities to train models for BCa, which may pose a privacy risk. Given this concern, protecting patient privacy during machine learning algorithm training is a crucial aspect that requires substantial attention. One emerging machine learning paradigm that addresses this concern is federated learning (FL). FL enables multiple entities to collaboratively build machine learning models while preserving data privacy and security. In this study, we present a multicenter BCa magnetic resonance imaging (MRI) dataset. The dataset comprises 275 three-dimensional bladder T2-weighted MRI scans collected from four medical centers, and each scan includes diagnostic pathological labels for muscle invasion and pixel-level annotations of tumor contours. Four FL methods are used to assess the baseline of the dataset for both the task of diagnosing muscle-invasive bladder cancer and automatic bladder tumor lesion segmentation.

Abstract The traditional multi-line laser 3D reconstruction is based on binocular epipolar constraints and the equation of the laser optical plane. Firstly, matching points are identified based on the epipolar constraints. Then, the correct matching points are selected based on the optical plane equation of the multi-line laser. Finally, 3D reconstruction is performed using the selected matching points. In actual production processes involving multi-line lasers, noticeable distortion occurs due to the projection of Diffractive Optical Elements (DOE) at a large angle. This results in curved laser light planes and curved reflections on the measured objects. Under such circumstances, efficiently screening matching points using the traditional method based on the laser plane equation becomes challenging. Additionally, inherent noise in multi-line laser systems introduces errors in extracted laser center coordinates, making it impossible to directly obtain high-precision 3D data. To address these issues, this paper proposes a method that utilizes spatial quadric surfaces and geometric estimation for completing multi-line laser 3D reconstructions. By analyzing the distortion principle of DOE and the positional relationship after optical diffraction, the multi-line laser manifests as a quadric surface on the optical output plane. Consequently, by calibrating the equations of the quadric surface and applying binocular polar constraints, suitable matching points for the multi-line laser can be chosen. Once an accurate matching point is identified, a minimum geometric distance estimation can be established based on the distance constraint between the point and its corresponding epipolar line. This distance represents the separation between the left and right camera’s laser center points and their respective epipolar lines. Utilizing this estimate of the minimum geometric distance, a refined calculation can be performed to better satisfy epipolar constraints and obtain new matching points. Ultimately, utilizing these new matching points enables the completion of 3D reconstruction for multi-line lasers. In comparison with methods relying on spatial plane and epipolar constraints, our algorithm exhibits a superior degree of matchability and accuracy.According to multiple groups of test data, the average error before optimization is 0.3126 mm, and can be increased to 0.0336 mm after optimization.

Abstract Developing broad-spectrum influenza vaccines is crucial for influenza control and potential pandemic preparedness. Here, we reported a novel vaccine design utilizing circular RNA (circRNA) as a delivery platform for multi-subtype neuraminidases (NA) (influenza A N1, N2, and influenza B Victoria lineage NA) immunogens. Individual NA circRNA lipid nanoparticles (LNP) elicited robust NA-specific antibody responses with neuraminidase inhibition activity (NAI), preventing the virus from egressing and infecting neighboring cells. Additionally, the administration of circRNA LNP induced cellular immunity in mice. To achieve a universal influenza vaccine, we combined all three subtypes of NA circRNA-LNPs to generate a trivalent circRNA vaccine. The trivalent vaccine elicited a balanced antibody response against all three NA subtypes and a Th1-biased immune response in mice. Moreover, it protected mice against the lethal challenge of matched and mismatched H1N1, H3N2, and influenza B viruses, encompassing circulating and ancestral influenza virus strains. This study highlights the potential of delivering multiple NA antigens through circRNA-LNPs as a promising strategy for effectively developing a universal influenza vaccine against diverse influenza viruses.

China has abundant resources of tar-rich coal, and the in-situ pyrolysis extraction of oil and gas can not only achieve efficient, clean, and low-carbon utilization of tar-rich coal, but also alleviate the external dependence of oil and gas, which is one of the important development directions of tar-rich coal exploitation and utilization in the future. In this paper, the thermal and physical properties and distribution characteristics of tar-rich coal, the technological characteristics of surface pyrolysis and in-situ pyrolysis are described briefly. It focuses on the research status and progress of in-situ pyrolysis of tar-rich coal (including well layout, heating method, integration of in-situ gasification and pyrolysis, waste heat utilization, reservoir fracturing, and underground sealing technology). Also, the future development direction of tar-rich coal in-situ pyrolysis technology is prospected. The findings are summarized as follows: ① the cascade utilization is an important way for the surface pyrolysis of tar-rich coal. Well drilling in-situ pyrolysis can achieve underground oil recovery, but it is necessary to solve the insulation problem of long-distance transportation pipelines. Drilling in-situ pyrolysis is carried out through high-temperature medium or electric heating, and can use distributed energy from the mining area to heat the heat carrier fluid. ② It is recommended to use a composite heating technology with convection heating as the main method and others as auxiliary heating methods, which can use the residual heat from in-situ pyrolysis to preheat the heat carrier fluid, and the heat from the local oxidation reaction of coal seam can also be used in the in-situ pyrolysis process of oil-rich coal to reduce the heat injection energy consumption of heat carrier fluid. ③ The integrated technology of in-situ gasification and pyrolysis can effectively improve the utilization rate of thermal energy, but the gasification cavity needs to be filled. ④ In order to enhance the heat conduction efficiency of coal seam and reduce the migration difficulty of oil and gas products in coal seam, the porosity change law of reservoir fracturing and in-situ pyrolysis of oil-rich coal is comprehensively considered to control the fracture formation path and form the fracture network. ⑤ Based on the characteristics of the pyrolysis process of tar-rich coal, selecting suitable underground system closure technology can promote efficient energy utilization and ecological environment protection during the in-situ pyrolysis process of tar-rich coal, among them, the CO2 gas drive water sealing technology can not only realize the closure of the underground system, but also carry out oil and gas displacement and realize the geological storage of CO2.

Abstract Objective To explore the effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction (ASiR-V) strength level on the detection and characterization of pulmonary nodules by an artificial intelligence (AI) software in ultra-low-dose chest CT (ULDCT). Materials and methods An anthropomorphic thorax phantom containing 12 spherical simulated nodules (Diameter: 12 mm, 10 mm, 8 mm, 5 mm; CT value: -800HU, -630HU, 100HU) was scanned with three ULDCT protocols: Dose-1 (70kVp:0.11mSv, 100kVp:0.10mSv), Dose-2 (70kVp:0.34mSv, 100kVp:0.32mSv), Dose-3 (70kVp:0.53mSv, 100kVp:0.51mSv). All scanning protocols were repeated five times. CT images were reconstructed using four different strength levels of ASiR-V (0%=FBP, 30%, 50%, 70%ASiR-V) with a slice thickness of 1.25 mm. The characteristics of the physical nodules were used as reference standards. All images were analyzed using a commercially available AI software to identify nodules for calculating nodule detection rate (DR) and to obtain their long diameter and short diameter, which were used to calculate the deformation coefficient (DC) and size measurement deviation percentage (SP) of nodules. DR, DC and SP of different imaging groups were statistically compared. Results Image noise decreased with the increase of ASiR-V strength level, and the 70 kV images had lower noise under the same strength level (mean-value 70 kV: 40.14 ± 7.05 (dose 1), 27.55 ± 7.38 (dose 2), 23.88 ± 6.98 (dose 3); 100 kV: 42.36 ± 7.62 (dose 1); 30.78 ± 6.87 (dose 2); 26.49 ± 6.61 (dose 3)). Under the same dose level, there were no differences in DR between 70 kV and 100 kV (dose 1: 58.76% vs. 58.33%; dose 2: 73.33% vs. 70.83%; dose 3: 75.42% vs. 75.42%, all p > 0.05). The DR of GGNs increased significantly at dose 2 and higher (70 kV: 38.12% (dose 1), 60.63% (dose 2), 64.38% (dose 3); 100 kV: 37.50% (dose 1), 59.38% (dose 2), 66.25% (dose 3)). In general, the use of ASiR-V at higher strength levels (> 50%) and 100 kV provided better (lower) DC and SP. Conclusion Detection rates are similar between 70 kV and 100 kV scans. The 70 kV images have better noise performance under the same ASiR-V level, while images of 100 kV and higher ASiR-V levels are better in preserving the nodule morphology (lower DC and SP); the dose levels above 0.33mSv provide high sensitivity for nodules detection, especially the simulated ground glass nodules.

Abstract Background The postharvest rot of kiwifruit is one of the most devastating diseases affecting kiwifruit quality worldwide. However, the genomic basis and pathogenicity mechanisms of kiwifruit rot pathogens are lacking. Here we report the first whole genome sequence of Pestalotiopsis microspora, one of the main pathogens causing postharvest kiwifruit rot in China. The genome of strain KFRD-2 was sequenced, de novo assembled, and analyzed. Results The genome of KFRD-2 was estimated to be approximately 50.31 Mb in size, with an overall GC content of 50.25%. Among 14,711 predicted genes, 14,423 (98.04%) exhibited significant matches to genes in the NCBI nr database. A phylogenetic analysis of 26 known pathogenic fungi, including P. microspora KFRD-2, based on conserved orthologous genes, revealed that KFRD-2’s closest evolutionary relationships were to Neopestalotiopsis spp. Among KFRD-2’s coding genes, 870 putative CAZy genes spanned six classes of CAZys, which play roles in degrading plant cell walls. Out of the 25 other plant pathogenic fungi, P. microspora possessed a greater number of CAZy genes than 22 and was especially enriched in GH and AA genes. A total of 845 transcription factors and 86 secondary metabolism gene clusters were predicted, representing various types. Furthermore, 28 effectors and 109 virulence-enhanced factors were identified using the PHI (pathogen host-interacting) database. Conclusion This complete genome sequence analysis of the kiwifruit postharvest rot pathogen P. microspora enriches our understanding its disease pathogenesis and virulence. This study establishes a theoretical foundation for future investigations into the pathogenic mechanisms of P. microspora and the development of enhanced strategies for the efficient management of kiwifruit postharvest rots.

Abstract Processing spatiotemporal data sources with both high spatial dimension and rich temporal information is a ubiquitous need in machine intelligence. Recurrent neural networks in the machine learning domain and bio-inspired spiking neural networks in the neuromorphic computing domain are two promising candidate models for dealing with spatiotemporal data via extrinsic dynamics and intrinsic dynamics, respectively. Nevertheless, these networks have disparate modeling paradigms, which leads to different performance results, making it hard for them to cover diverse data sources and performance requirements in practice. Constructing a unified modeling framework that can effectively and adaptively process variable spatiotemporal data in different situations remains quite challenging. In this work, we propose hybrid spatiotemporal neural networks created by combining the recurrent neural networks and spiking neural networks under a unified surrogate gradient learning framework and a Hessian-aware neuron selection method. By flexibly tuning the ratio between two types of neurons, the hybrid model demonstrates better adaptive ability in balancing different performance metrics, including accuracy, robustness, and efficiency on several typical benchmarks, and generally outperforms conventional single-paradigm recurrent neural networks and spiking neural networks. Furthermore, we evidence the great potential of the proposed network with a robotic task in varying environments. With our proof of concept, the proposed hybrid model provides a generic modeling route to process spatiotemporal data sources in the open world.

Abstract The synthesis of multi-carbon (C2+) fuels via electrocatalytic reduction of CO, H2O using renewable electricity, represents a significant stride in sustainable energy storage and carbon recycling. The foremost challenge in this field is the production of extended-chain carbon compounds (Cn, n ≥ 3), wherein elevated *CO coverage (θco) and its subsequent multiple-step coupling are both critical. Notwithstanding, there exists a “seesaw” dynamic between intensifying *CO adsorption to augment θco and surmounting the C-C coupling barrier, which have not been simultaneously realized within a singular catalyst yet. Here, we introduce a facilely synthesized lattice-strain-stabilized nitrogen-doped Cu (LSN-Cu) with abundant defect sites and robust nitrogen integration. The low-coordination sites enhance θco and concurrently, the compressive strain substantially fortifies nitrogen dopants on the catalyst surface, promoting C-C coupling activity. The n-propanol formation on the LSN-Cu electrode exhibits a 54% faradaic efficiency and a 29% half-cell energy efficiency. Moreover, within a membrane electrode assembly setup, a stable n-propanol electrosynthesis over 180 h at a total current density of 300 mA cm−2 is obtained.

Abstract Introduction The estimated dose of radiation to immune cells (EDRIC) has been shown to correlate with the overall survival (OS) of patients who receive definitive thoracic radiotherapy. However, the planning target volume (PTV) may be a confounding factor. We assessed the prognostic value of EDRIC for non-small cell lung cancer (NSCLC) in patients who underwent postoperative radiotherapy (PORT) with homogeneous PTV. Methods Patients with NSCLC who underwent PORT between 2004 and 2019 were included. EDRIC was computed as a function of the number of radiation fractions and mean doses to the lungs, heart, and remaining body. The correlations between EDRIC and OS, disease-free survival (DFS), locoregional-free survival (LRFS), and distant metastasis-free survival (DMFS) were analyzed using univariate and multivariate Cox models. Kaplan–Meier analysis was performed to assess the survival difference between low- and high-EDRIC groups. Results In total, 345 patients were analyzed. The mean EDRIC was 6.26 Gy. Multivariate analysis showed that higher EDRIC was associated with worse outcomes in terms of OS (hazard ratio [HR] 1.207, P = .007), DFS (HR 1.129, P = .015), LRFS (HR 1.211, P = .002), and DMFS (HR 1.131, P = .057). In the low- and high-EDRIC groups, the 3-year OS was 81.2% and 74.0%, DFS 39.8% and 35.0%, LRFS 70.4% and 60.5%, and DMFS 73.9% and 63.1%, respectively. Conclusions EDRIC is an independent prognostic factor for survival in patients with NSCLC undergoing PORT. Higher doses of radiation to the immune system are associated with tumor progression and poor survival. Organs at risk for the immune system should be considered during radiotherapy planning.

Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy. However, the hot deformation of Mg alloy is highly sensitive to factors such as temperature, strain rate, and strain, leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy. To overcome the shortcomings of the conventional Arrhenius-type (AT) model, this study developed machine learning-based Arrhenius-type (ML-AT) models by combining the genetic algorithm (GA), particle swarm optimization (PSO), and artificial neural network (ANN). Results indicated that when describing the flow behavior of the AQ80 alloy, the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models. Moreover, an activation energy-processing (AEP) map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model. Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods, ultimately contributing to the precise determination of the optimum processing window. These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation.

Abstract Background This study aimed to evaluate the efficiency of hippocampal avoidance whole-brain radiotherapy with a simultaneous integrated boost (HA-WBRT-SIB) treating brain metastases (BM) and utility of the Hopkins Verbal Learning Test-Revised (HVLT-R) (Chinese version) in Chinese lung cancer patients. Methods Lung cancer patients with BM undergone HA-WBRT-SIB at our center were enrolled. Brain magnetic resonance imaging, The HVLT total learning score, and side effects were evaluated before radiotherapy and 1, 3, 6, and 12 months after radiotherapy. This study analyzed the overall survival rate, progression-free survival rate, and changes in HVLT-R immediate recall scores. Results Forty patients were enrolled between Jan 2016 and Jan 2020. The median follow-up time was 14.2 months. The median survival, progression-free survival, and intracranial progression-free survival of all patients were 14.8 months, 6.7 months and 14.8 months, respectively. Multivariate analysis indicated that male sex and newly diagnosed stage IV disease were associated with poor overall survival and progression-free survival, respectively. HVLT-R scores at baseline and 1, 3, and 6 months after radiotherapy were 21.94 ± 2.99, 20.88 ± 3.12, 20.03 ± 3.14, and 19.78 ± 2.98, respectively. The HVLT-R scores at 6 months after radiotherapy decreased by approximately 9.8% compared with those at baseline. No grade 3 toxicities occurred in the entire cohort. Conclusions HA-WBRT-SIB is of efficiency and cognitive-conserving in treating Chinese lung cancer BM. Trial registration This study was retrospectively registered on ClinicalTrials.gov in 24th Feb, 2024. The ClinicalTrials.gov ID is NCT06289023.

Abstract Background Metabolic dysfunction-associated fatty liver disease (MAFLD) is a growing threat leading to substantial disease burden globally. Poor sleep and physical inactivity are common in modern societies and independently associated with MAFLD, however, their joint effects on MAFLD remains unclear. Methods This population-based cross-sectional study was conducted in Xinjiang Uygur Autonomous Region, China, between July 2019 and September 2021. Self-reported sleep behaviors and physical activity (PA) were assessed using validated questionnaires. The primary outcome was radiological diagnosis of MAFLD. Results Of the 10 089 participants aged 47.0 (9.1) years (51.6% men), 3854 (38.2%) individuals had MAFLD. Poor sleep quality and physical inactivity were independently and jointly associated with an increased prevalence of MAFLD, independent of traditional risk factors (P

Air staged combustion reduces NOx emissions, but increases the H2S concentration in primary combustion zone. The excessive H2S concentration near the furnace wall is an important factor that aggravates the high-temperature corrosion of the water-cooled wall. With large-scale renewable power integrated into the grid, the demand for flexible peak-shaving operation of traditional thermal power units has increased. The H2S concentration distribution near the furnace wall under different boiler loads deserves attention. Hence, the influence of different operating parameters of tangentially coal-fired boiler on H2S concentration distribution near the furnace wall was investigated by orthogonal test. A supercritical 600 MW tangentially coal-fired boiler was selected to establish a numerical model. The L16(45) orthogonal numerical conditions was designed to cover four boiler loads, including 100% BMCR, 75% THA, 50% THA and 35% BMCR. A user-defined SOx generation model was employed to calculate the H2S concentration distribution inside the furnace. The release of fuel sulfur and mutual transformation of sulfur components were considered. At the same time, the model included a multiple surface reaction model for describing the heterogeneous reaction between coke and O2/CO2/H2O, and the ratio of the gasification rate to the consumption rate for char particle was calculated. The results show that the high H2S concentration areas are mainly located below the bottom burner that on operation and between the top burner and SOFA nozzle. The main reason for the high H2S concentration in the latter area is that the tangential circle of flue gas increases gradually along the furnace height. The orthogonal analysis indicates that the average H2S concentration in the key area of the furnace wall under 35% BMCR load is 364 μL/L, which is significantly lower than that under the other loads. In addition, the impact of operating parameters on the H2S concentration in key areas follows the order of boiler load > primary air rate > excess air coefficient of main combustion zone > imaginary tangent circle diameter > vertical swing angle of burner.

Abstract Solanum pimpinellifolium, the closest wild relative of the domesticated tomato, has high potential for use in breeding programs aimed at developing multi-pathogen resistance and quality improvement. We generated a chromosome-level genome assembly of S. pimpinellifolium LA1589, with a size of 833 Mb and a contig N50 of 31 Mb. We anchored 98.80% of the contigs into 12 pseudo-chromosomes, and identified 74.47% of the sequences as repetitive sequences. The genome evaluation revealed BUSCO and LAI score of 98.3% and 14.49, respectively, indicating high quality of this assembly. A total of 41,449 protein-coding genes were predicted in the genome, of which 89.17% were functionally annotated. This high-quality genome assembly serves as a valuable resource for accelerating the biological discovery and molecular breeding of this important horticultural crop.

Objective In the treatment of lumbar degenerative spondylolisthesis (LDS) with Posterior lumbar interbody fusion (PLIF) surgery, interbody fusion implants play a key role in supporting the vertebral body and facilitating fusion. The objective of this study was to assess the impact of implantation depth on sagittal parameters and functional outcomes in patients undergoing PLIF surgery. Methods This study reviewed 128 patients with L4‐L5 LDS between January 2016 and August 2019. All patients underwent an open PLIF surgery that included intravertebral decompression, implantation of pedicle screws and cage. We grouped according to the position of the center of the cage relative to the L5 vertebral endplate. Patients with the center of the cage located at the anterior 1/2 of the upper end plate of the L5 vertebral body were divided into Anterior group, and located at the posterior 1/2 of the upper end plate of the L5 vertebral body were divided into Posterior group. The lumbar lordosis (LL), segmental lordosis (SL), sacral slope (SS), pelvic incidence (PI), pelvic tilt (PT) and slope degree (SD) was measured for radiographic outcomes. We used the visual analog scale (VAS) and the oswestry disability index (ODI) score to assess functional outcomes. Paired t‐test was used to compare imaging and bedside data before and after surgery between the two groups, and independent sample t‐test, χ2 test and Fisher exact test were used to compare the data between the two groups. Result The mean follow‐up of Anterior group was 44.13 ± 9.23 months, and Posterior group was 45.62 ± 10.29 months (P > 0.05). The LL, SL, PT, SS, SD and PI‐LL after operation showed great improvements, relative to the corresponding preoperative values in both groups (P

Abstract By mimicking the neurons and synapses of the human brain and employing spiking neural networks on neuromorphic chips, neuromorphic computing offers a promising energy-efficient machine intelligence. How to borrow high-level brain dynamic mechanisms to help neuromorphic computing achieve energy advantages is a fundamental issue. This work presents an application-oriented algorithm-software-hardware co-designed neuromorphic system for this issue. First, we design and fabricate an asynchronous chip called “Speck”, a sensing-computing neuromorphic system on chip. With the low processor resting power of 0.42mW, Speck can satisfy the hardware requirements of dynamic computing: no-input consumes no energy. Second, we uncover the “dynamic imbalance” in spiking neural networks and develop an attention-based framework for achieving the algorithmic requirements of dynamic computing: varied inputs consume energy with large variance. Together, we demonstrate a neuromorphic system with real-time power as low as 0.70mW. This work exhibits the promising potentials of neuromorphic computing with its asynchronous event-driven, sparse, and dynamic nature.

Abstract Background Periprosthetic joint infection (PJI) is a severe complication that can occur after total joint arthroplasty (TJA). The timely and accurate diagnosis of PJI is the key to treatment. This study investigated the diagnostic value of platelet to lymphocyte ratio (PLR), platelet count to mean platelet volume ratio (PVR), neutrophil to lymphocyte ratio (NLR) and monocyte to lymphocyte ratio (MLR) in PJI after total knee arthroplasty (TKA) and total hip arthroplasty (THA). Methods We performed a retrospective analysis of the patients who underwent revision hip or knee arthroplasty at our Institute between June 2015 and June 2020. Of the 187 patients reviewed, 168 were included in the study. According to the diagnostic criteria of the Musculoskeletal Infection Society (MSIS), 58 patients were in the PJI group, and 110 patients were in the aseptic loosening (AL) group. We recorded and compared the preoperative peripheral blood white blood cell (WBC) count, platelet count (PLT), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), PLR, PVR, NLR, and MLR in both groups. The diagnostic performance of the WBC, PLT, PLR, PVR, NLR, and MLR individually and in combination with the ESR and CRP for PJI diagnosis was evaluated by receiver operating characteristic (ROC) curves, and the sensitivity, specificity, positive predictive value, and negative predictive value were calculated. Results Compared to those in the AL group, the mean WBC, PLT, ESR, CRP, PLR, PVR, NLR, and MLR in the peripheral blood of the PJI group were significantly greater (P

Abstract Background The value of postoperative radiotherapy (PORT) for patients with non-small cell lung cancer (NSCLC) remains controversial. A subset of patients may benefit from PORT. We aimed to identify patients with NSCLC who could benefit from PORT. Methods Patients from cohorts 1 and 2 with pathological Tany N2 M0 NSCLC were included, as well as patients with non-metastatic NSCLC from cohorts 3 to 6. The radiomic prognostic index (RPI) was developed using radiomic texture features extracted from the primary lung nodule in preoperative chest CT scans in cohort 1 and validated in other cohorts. We employed a least absolute shrinkage and selection operator-Cox regularisation model for data dimension reduction, feature selection, and the construction of the RPI. We created a lymph-radiomic prognostic index (LRPI) by combining RPI and positive lymph node number (PLN). We compared the outcomes of patients who received PORT against those who did not in the subgroups determined by the LRPI. Results In total, 228, 1003, 144, 422, 19, and 21 patients were eligible in cohorts 1–6. RPI predicted overall survival (OS) in all six cohorts: cohort 1 (HR = 2.31, 95% CI: 1.18–4.52), cohort 2 (HR = 1.64, 95% CI: 1.26–2.14), cohort 3 (HR = 2.53, 95% CI: 1.45–4.3), cohort 4 (HR = 1.24, 95% CI: 1.01–1.52), cohort 5 (HR = 2.56, 95% CI: 0.73–9.02), cohort 6 (HR = 2.30, 95% CI: 0.53–10.03). LRPI predicted OS (C-index: 0.68, 95% CI: 0.60–0.75) better than the pT stage (C-index: 0.57, 95% CI: 0.50–0.63), pT + PLN (C-index: 0.58, 95% CI: 0.46–0.70), and RPI (C-index: 0.65, 95% CI: 0.54–0.75). The LRPI was used to categorize individuals into three risk groups; patients in the moderate-risk group benefited from PORT (HR = 0.60, 95% CI: 0.40–0.91; p = 0.02), while patients in the low-risk and high-risk groups did not. Conclusions We developed preoperative CT-based radiomic and lymph-radiomic prognostic indexes capable of predicting OS and the benefits of PORT for patients with NSCLC.

Background/Aims: Cancer stem cells (CSCs) are believed to drive tumor development and metastasis. Activin and hepatocyte growth factor (HGF) are important cytokines with the ability to induce cancer stemness. However, the effect of activin and HGF combination treatment on CSCs is still unclear.Methods: In this study, we sequentially treated colorectal cancer cells with activin and HGF and examined CSC marker expression, self-renewal, tumorigenesis, and metastasis. The roles of forkhead box M1 (FOXM1) and sex-determining region Y-box 2 (SOX2), two stemness-related transcription factors, in activin/HGF-induced aggressive phenotype were explored.Results: Activin and HGF treatment increased the expression of CSC markers and enhanced sphere formation in colorectal cancer cells. The tumorigenic and metastatic capacities of colorectal cancer cells were enhanced upon activin and HGF treatment. Activin and HGF treatment preferentially promoted stemness and metastasis of CD133+ subpopulations sorted from colorectal cancer cells. FOXM1 was upregulated by activin and HGF treatment, and the knockdown of FOXM1 blocked activin/HGF-induced stemness, tumorigenesis, and metastasis of colorectal cancer cells. Similarly, SOX2 was silencing impaired sphere formation of activin/HGF-treated colorectal cancers. Overexpression of SOX2 rescued the stem cell-like phenotype in FOXM1-depleted colorectal cancer cells with activin and HGF treatment. Additionally, the inhibition of FOXM1 via thiostrepton suppressed activin/HGF-induced stemness, tumorigenesis and metastasis.Conclusions: Sequential treatment with activin and HGF promotes colorectal cancer stemness and metastasis through activation of the FOXM1/SOX2 signaling. FOXM1 could be a potential target for the treatment of colorectal cancer metastasis.

Phenolation is one of the effective strategies to synthesize lignin-based polyphenols, improve lignin's properties, and extend its value-added applications in biological, medicinal and cosmetic fields. Herein, by taking the structural feature advantage of lignin, an effective and green strategy was developed to molecularly engineer lignin into a robust lignin-3-(2-hydroxyphenyl)propionate ester (LPPE) derivative via a transesterification reaction between 3,4-dihydrocoumarin (DHC) and the aliphatic hydroxyls in lignin under organocatalysis. The strategy is optimized and the novel derivative was systematically characterized by 1H, 13C and 31P nuclear magnetic resonance (NMR) and Fourier transform infrared (FT-IR) spectroscopy. The findings indicated that the successful introduction of 3-(2-hydroxyphenyl)propionate groups using a OH groups/DHC/organic base molar ratio of 1꞉1꞉0.3 at 120 °C for 6 h increased the content of phenolic hydroxyl groups from 1.793 1 to 3.017 9 mmol/g, and the LPPE exhibited excellent ultraviolet-absorbing and antioxidant performance with up to 90% free radical scavenging activity within 20 min using 5 mg/mL of LPPE. In addition, good biocompatibility and a high Sun protection factor (SPF) value of 40.9 were achieved at 5% (w) dosage of LPPE in the cream, indicating its significant application potential in sunscreen.

Leveraging the unique attributes of functional soft materials to generate force and deformation, significant advancements in soft actuators are driving the evolution of smart robotics. Liquid metals (LMs), known for their high deformability and tunable morphology, demonstrate remarkable actuating capabilities through controllable surface tension. Inspired by the predation method of chameleons, this work introduces a bioinspired LM actuator (BLMA) by modulating the morphology of LM. This BLMA enables high‐strain (up to 170%) actuation by precisely directing LM droplets toward an electrode. Various parameters affecting the BLMA's actuating performance are explored. Notably, the application of a reductive voltage induces rapid solidification of supercooled LM, facilitating phase transition at room temperature. The solidified LM enhances its holding force of BLMA by over 1000 times. To underscore the superior capabilities of the BLMA, diverse applications, such as a complex two‐dimensional plane actuator, a stepper motor with adjustable step intervals, a phase transition‐controlled relay, and a laser code lock actuation gate set, are presented. It is anticipated that the exceptional characteristics of the BLMA will propel advancements in the realms of soft robotics and mechatronics.

IntroductionThe frequent occurrence of mutations in the SARS-CoV-2 Spike (S) protein, with up to dozens of mutations, poses a severe threat to the current efficacy of authorized COVID-19 vaccines. Membrane (M) protein, which is the most abundant viral structural protein, exhibits a high level of amino acid sequence conservation. M protein ectodomain could be recognized by specific antibodies; however, the extent to which it is immunogenic and provides protection remains unclear.MethodsWe designed and synthesized multiple peptides derived from coronavirus M protein ectodomains, and determined the secondary structure of specific peptides using circular dichroism (CD) spectroscopy. Enzyme-linked immunosorbent assay (ELISA) was utilized to detect IgG responses against the synthesized peptides in clinical samples. To evaluate the immunogenicity of peptide vaccines, BALB/c mice were intraperitoneally immunized with peptide-keyhole limpet hemocyanin (KLH) conjugates adjuvanted with incomplete Freund’s adjuvant (IFA). The humoral and T-cell immune responses induced by peptide-KLH conjugates were assessed using ELISA and ELISpot assays, respectively. The efficacy of the S2M2-30-KLH vaccine against SARS-CoV-2 variants was evaluated in vivo using the K18-hACE2 transgenic mouse model. The inhibitory effect of mouse immune serum on SARS-CoV-2 virus replication in vitro was evaluated using microneutralization assays. The subcellular localization of the M protein was evaluated using an immunofluorescent staining method, and the Fc-mediated antibody-dependent cellular cytotoxicity (ADCC) activity of the S2M2-30-specific monoclonal antibody (mAb) was measured using an ADCC reporter assay.ResultsSeroconversion rates for ectodomain-specific IgG were observed to be high in both SARS-CoV-2 convalescent patients and individuals immunized with inactivated vaccines. To assess the protective efficacy of the M protein ectodomain-based vaccine, we initially identified a highly immunogenic peptide derived from this ectodomain, named S2M2-30. The mouse serum specific to S2M2-30 showed inhibitory effects on the replication of SARS-CoV-2 variants in vitro. Immunizations of K18-hACE2-transgenic mice with the S2M2-30-keyhole limpet hemocyanin (KLH) vaccine significantly reduced the lung viral load caused by B.1.1.7/Alpha (UK) infection. Further mechanism investigations reveal that serum neutralizing activity, specific T-cell response and Fc-mediated antibody-dependent cellular cytotoxicity (ADCC) correlate with the specific immuno-protection conferred by S2M2-30.DiscussionThe findings of this study suggest that the antibody responses against M protein ectodomain in the population most likely exert a beneficial effect on preventing various SARS-CoV-2 infections.

Abstract Background Consolidative thoracic radiotherapy (cTRT) has previously shown benefit to patients with extensive stage small cell lung cancer (ES‐SCLC) who respond to chemotherapy. However, the optimum dose of cTRT is unknown. The purpose of this randomized trial is to compare the efficacy of 45 Gy in 15 fractions with 30 Gy in 10 fractions cTRT in ES‐SCLC. Methods This phase III, multicenter, randomized trial is designed to evaluate the safety and efficacy of different cTRT dose in ES‐SCLC. Eligible patients with pathologically confirmed ES‐SCLC who responded to 4–6 cycles of etoposide plus cisplatin (EP) or carboplatin (EC) chemotherapy were randomized 1:1 to receive either 30 Gy in 10 fractions (standard dose) or 45 Gy in 15 fractions (high dose) cTRT. The primary endpoint is 2‐year overall survival (OS). Secondary endpoints include 2‐year progression‐free survival (PFS), 2‐year local control (LC) and treatment related toxicity as measured by adverse events according to the Common Terminology Criteria for Adverse Events (version 4.0). Discussion The present study is the first randomized phase III trial designed to evaluate the efficacy of higher versus lower dose cTRT in ES‐SCLC, providing evidence for future clinical practice in prolonging survival of patients with ES‐SCLC.

Abstract Check dams on the Chinese Loess Plateau (CLP) have captured billions of tons of eroded sediment, substantially reducing sediment load in the Yellow River. However, uncertainties persist regarding the precise sediment capture and the role of these dams in Yellow River flow and sediment dynamics due to the lack of available spatial distribution datasets. We produced the first vectorized dataset of silted land formed by check dams on the CLP, combining high-resolution and easily accessible Google Earth images with object-based classification methods. The accuracy of the dataset was verified by 1947 collected test samples, and the producer’s accuracy and user’s accuracy of the dam lands were 88.9% and 99.5%, respectively. Our dataset not only provides fundamental information for accurately assessing the ecosystem service functions of check dams, but also helps to interpret current changes in sediment delivery of the Yellow River and plan future soil and water conservation projects.

The special geography of the plateau area leads to a series of problems in boiler operation. In this study, the absorption coefficients and total emissivity of flue gas were determined under different air pressures during air combustion using the Line-By-Line (LBL) method based on the HITEMP2010 database (High-temperature molecular spectroscopic database). The effects of pressure, temperature, and molar fraction (H2O and CO2) on the radiative properties of flue gas were analyzed. An improved Weighted-Sum-of-Gray-Gases (WSGG) correlation, which relates the absorption coefficients to temperature and total pressure, was proposed. The results show that the reduced total pressure diminishes the total emissivity of flue gas. The maximum differences in total emissivity along the path lengths for the four working conditions with a pressure drop from 0.101 325 to 0.061 655 are 0.093 4, 0.084 5, 0.091 1, and 0.084 3, respectively. For a larger molar fraction, the effect of pressure on the total emissivity is greater for shorter path lengths but not for longer ones. Similarly, the higher temperature would reduce the total emissivity of flue gas. The maximum differences in total emissivity along the path lengths for the four working conditions with a temperature increase from 1 000 K to 2 500 K are 0.273 6, 0.270 5, 0.251 5, and 0.250 5, respectively. For a larger molar fraction, temperature has a greater effect on the total emissivity for shorter path lengths but not for longer ones. Furthermore, increasing the molar fraction enhances the total emissivity of flue gas. The maximum differences in total emissivity along the path lengths for the four working conditions with a molar fraction increase from 1 to 2 are 0.088 1, 0.100 4, 0.088 9, and 0.100 6, respectively. For a higher temperature or lower pressure, the effect of molar fraction on the total emissivity is smaller for shorter path lengths but greater for longer ones. The maximum relative error of the improved WSGG model for the total emissivity of flue gas under different working conditions is 3.67%. It is a significant reduction in the error compared to that of the existing WSGG model. Therefore, the improved WSGG model is more accurate for air combustion atmosphere and sub-atmospheric pressure.

Abstract Background Bioconversion of plant biomass into biofuels and bio-products produces large amounts of lignin. The aromatic biopolymers need to be degraded before being converted into value-added bio-products. Microbes can be environment-friendly and efficiently degrade lignin. Compared to fungi, bacteria have some advantages in lignin degradation, including broad tolerance to pH, temperature, and oxygen and the toolkit for genetic manipulation. Results Our previous study isolated a novel ligninolytic bacterial strain Erwinia billingiae QL-Z3. Under optimized conditions, its rate of lignin degradation was 25.24% at 1.5 g/L lignin as the sole carbon source. Whole genome sequencing revealed 4556 genes in the genome of QL-Z3. Among 4428 protein-coding genes are 139 CAZyme genes, including 54 glycoside hydrolase (GH) and 16 auxiliary activity (AA) genes. In addition, 74 genes encoding extracellular enzymes are potentially involved in lignin degradation. Real-time PCR quantification demonstrated that the expression of potential ligninolytic genes were significantly induced by lignin. 8 knock-out mutants and complementary strains were constructed. Disruption of the gene for ELAC_205 (laccase) as well as EDYP_48 (Dyp-type peroxidase), ESOD_1236 (superoxide dismutase), EDIO_858 (dioxygenase), EMON_3330 (monooxygenase), or EMCAT_3587 (manganese catalase) significantly reduced the lignin-degrading activity of QL-Z3 by 47–69%. Heterologously expressed and purified enzymes further confirmed their role in lignin degradation. Fourier transform infrared spectroscopy (FTIR) results indicated that the lignin structure was damaged, the benzene ring structure and groups of macromolecules were opened, and the chemical bond was broken under the action of six enzymes encoded by genes. The abundant enzymatic metabolic products by EDYP_48, ELAC_205 and ESOD_1236 were systematically analyzed via liquid chromatography–mass spectrometry (LC–MS) analysis, and then provide a speculative pathway for lignin biodegradation. Finally, The activities of ligninolytic enzymes from fermentation supernatant, namely, LiP, MnP and Lac were 367.50 U/L, 839.50 U/L, and 219.00 U/L by orthogonal optimization. Conclusions Our findings provide that QL-Z3 and its enzymes have the potential for industrial application and hold great promise for the bioconversion of lignin into bioproducts in lignin valorization.

Abstract Data security is a key feature of future communications networks. Physical layer introduces rich physical mechanisms to increase the complexity of deciphering and provides extensive protection, but faces challenges in compatibility with commercial systems. Quantum noise stream cipher (QNSC) has been proposed as a promising solution to overcome this problem by fusing the stream cryptography regime with the quantum noise masking physical mechanism. However, it has limitations in terms of digital to analog conversion and clock data synchronization of ultra-high-order ciphertext as well as flexible control of masking noise. Here we report a 147.9-Gbps device-compatible quadrature amplitude modulation (QAM) QNSC secure scheme over 75-km fiber. Thanks to delta-sigma modulator, the transmission of 220 × 220-order QAM-QNSC signal are established through the low-order digital signal. We develop a theoretical model for flexibly regulating the transmission rate and security performance. Broadband optical chaos introduces true randomness and acts on the masking noise.

Abstract Objective This study aims to investigate the prevalence of dyslipidemia and assess the joint association of physical activity (PA) and diet quality on dyslipidemia risk in urban areas of Xinjiang. Methods Conducted from July 2019 to September 2021 in Xinjiang, China, this cross-sectional study involved 11,855 participants (mean age 47.1 ± 9.4 years, 53.1% male). Standard methods were used to measure plasma cholesterol levels, and validated questionnaires were employed to evaluate dietary habits and PA. The definition of dyslipidemia is based on 2023 Chinese guidelines for lipid management. PA was divided into guideline-recommended moderate-to-vigorous physical activity (MVPA) and non-MVPA, following World Health Organization guidelines. The Food Frequency Questionnaire was used to obtain the intake frequency of each dietary term. Each item was scored based on consumption frequency and divided into three groups (good, intermediate, and poor) based on total dietary score. Multivariate logistic regression analysis was performed to identify dyslipidemia risk factors, as well as the joint association of PA and diet quality. Results Dyslipidemia prevalence among urban adults in Xinjiang was 39.3%, with notable sex disparities (52.6% in males vs. 24.3% in females, P

Electrospun nanofibers have been widely employed in bone tissue engineering for their ability to mimic the micro to nanometer scale network of the native bone extracellular matrix. However, the dense fibrous structure and limited mechanical support of these nanofibers pose challenges for the treatment of critical size bone defects. In this study, we propose a facile approach for creating a three-dimensional scaffold using interconnected electrospun nanofibers containing melatonin (Scaffold@MT). The hypothesis posited that the sponge-like Scaffold@MT could potentially enhance bone regeneration and angiogenesis by modulating mitochondrial energy metabolism. Melatonin-loaded gelatin and poly-lactic-acid nanofibers were fabricated using electrospinning, then fragmented into shorter fibers. The sponge-like Scaffold@MT was created through a process involving homogenization, low-temperature lyophilization, and chemical cross-linking, while maintaining the microstructure of the continuous nanofibers. The incorporation of short nanofibers led to a low release of melatonin and increased Young's modulus of the scaffold. Scaffold@MT demonstrated positive biocompatibility by promoting a 14.2 % increase in cell proliferation. In comparison to the control group, Scaffold@MT significantly enhanced matrix mineralization by 3.2-fold and upregulated the gene expression of osteoblast-specific markers, thereby facilitating osteogenic differentiation of bone marrow mesenchymal stem cells (BMMSCs). Significantly, Scaffold@MT led to a marked enhancement in the mitochondrial energy function of BMMSCs, evidenced by elevated adenosine triphosphate (ATP) production, mitochondrial membrane potential, and protein expression of respiratory chain factors. Furthermore, Scaffold@MT promoted the migration of human umbilical vein endothelial cells (HUVECs) and increased tube formation by 1.3 times compared to the control group, accompanied by an increase in vascular endothelial growth factor (VEGFA) expression. The results of in vivo experiments indicate that the implantation of Scaffold@MT significantly improved vascularized bone regeneration in a distal femur defect in rats. Micro-computed tomography analysis conducted 8 weeks post-surgery revealed that Scaffold@MT led to optimal development of new bone microarchitecture. Histological and immunohistochemical analyses demonstrated that Scaffold@MT facilitated bone matrix deposition and new blood vessel formation at the defect site. Overall, the utilization of melatonin-loaded nanofiber sponges exhibits significant promise as a scaffold that promotes bone growth and angiogenesis, making it a viable option for the repair of critical-sized bone defects.

Allogeneic hematopoietic stem cell transplantation (aHSCT) is utilized as a potential curative treatment for various hematologic malignancies. However, graft-versus-host disease (GVHD) post-aHSCT is a severe complication that significantly impacts patients' quality of life and overall survival, becoming a major cause of non-relapse mortality. In recent years, the association between epigenetics and GVHD has garnered increasing attention. Epigenetics focuses on studying mechanisms that affect gene expression without altering DNA sequences, primarily including DNA methylation, histone modifications, non-coding RNAs (ncRNAs) regulation, and RNA modifications. This review summarizes the role of epigenetic regulation in the pathogenesis of GVHD, with a focus on DNA methylation, histone modifications, ncRNA, RNA modifications and their involvement and applications in the occurrence and development of GVHD. It also highlights advancements in relevant diagnostic markers and drugs, aiming to provide new insights for the clinical diagnosis and treatment of GVHD.

This paper investigates robust set stability for the switched Boolean networks (SBNs) with arbitrary switching signal affected by one-bit function perturbation (OBFP). Firstly, the dynamics of these networks are converted into algebraic forms utilizing semi-tensor product (STP) method. Secondly, OBFP impact on the set stability of SBNs is divided into four cases. Then, by constructing a state set and defining an index vector, several necessary and sufficient conditions are provided to detect whether an SBN with arbitrary switching signal is still stable to the given set after OBFP. Finally, a biological example is proposed to demonstrate the effectiveness of the obtained theoretical results.

Abstract For 800 Gbps/λ and beyond optical transmission systems, frequency-dependent impairments (FDI) degrade coherent optical transceiver (CO-TRx) performance severely. Calibration and compensation of such FDI in factories includes amplitude/phase frequency response (AFR/PFR), skew, and ripple for both transmitters(Tx) and receivers (Rx). However, due to the polarization rotation and phase rotation effects in optical link, the separation and extraction of FDI from different polarization or I/Q tributaries is challenging. Here we report a FDI calibration method based on orthogonal separation scheme and frequency domain analysis. The proposal can simultaneously characterize and separate the Tx/Rx sides FDI including AFR, PFR, and time skew of four tributaries. Finally, the effectiveness is demonstrated by transmitting a 96 GBuad Nyquist-16QAM signal on a 64 GBaud-class CO-TRx.

Abstract It is widely believed the brain-inspired spiking neural networks have the capability of processing temporal information owing to their dynamic attributes. However, how to understand what kind of mechanisms contributing to the learning ability and exploit the rich dynamic properties of spiking neural networks to satisfactorily solve complex temporal computing tasks in practice still remains to be explored. In this article, we identify the importance of capturing the multi-timescale components, based on which a multi-compartment spiking neural model with temporal dendritic heterogeneity, is proposed. The model enables multi-timescale dynamics by automatically learning heterogeneous timing factors on different dendritic branches. Two breakthroughs are made through extensive experiments: the working mechanism of the proposed model is revealed via an elaborated temporal spiking XOR problem to analyze the temporal feature integration at different levels; comprehensive performance benefits of the model over ordinary spiking neural networks are achieved on several temporal computing benchmarks for speech recognition, visual recognition, electroencephalogram signal recognition, and robot place recognition, which shows the best-reported accuracy and model compactness, promising robustness and generalization, and high execution efficiency on neuromorphic hardware. This work moves neuromorphic computing a significant step toward real-world applications by appropriately exploiting biological observations.

Data center interconnections are cost-sensitive. We propose and demonstrate a cost-effective C-band intensity modulation direct detection (IM/DD) transmitter for extended range (ER) inter-datacenter interconnects utilizing only a 2-bit digital-to-analog converter (DAC). Tomlinson-Harashima precoding (THP) is used in transmitter DSP to compensate for dispersion-induced impairments. Due to the uniform distribution of signal levels after THP encoding, a high-resolution DAC is required to generate electrical signals. The hardware cost of the transmitter can be significantly reduced by compressing the physical number of bits (PNOB) of the DAC to 2 bits. We quantize the signal to 4 levels and utilize up-sampling and noise shaping to greatly reduce the in-band quantization noise. The in-band signal to noise ratio (SNR) can be increased to 23 dB. We experimentally demonstrate a cost-effective 40-Gbaud probabilistic shaping PAM-4 C-band IM/DD transmission over 50-km utilizing only a 2-bit DAC in the transmitter without any optical dispersion compensation. The performance of the proposed scheme is proximate to the common case of using an 8-bit DAC. It has a BER gap of about 2 × 10−3 only when the received optical power (ROP) is higher than −6 dBm.

The Mach-Zehnder modulator (MZM) is widely used in high-speed optical communication, and the stability of its bias point is the key to maintain the performance of the communication system. Numerous automatic bias control methods have been proposed to stabilize the bias point, which can be classified into power-based and dither-based methods. The power-based method features low-complexity and is considered of low-precision, thus it is usually applied to low-order modulation. In contrast, dither-based method features high-complexity and is considered of high-precision, therefore it is usually applied to high-order modulation. This paper explores the bias impact mechanism and analyzes the impact of the bias and dither signal. Traditional view is that the performance of dither-based outperforms power-based ones. However, the impact of dither is ignored. Here we compare the performance of the power-based and dither-based method under fair conditions. The results show that the power-based method has higher SNR and better performance taking into account the impact of dither, which indicates that practically it is more suitable for high-order modulation.

Dynamic recrystallization (DRX) and deformation twinning play a pivotal role in stress partitioning during hot deformation of magnesium alloy. However, the influence of twinning on stress is diverse, and the interaction between DRX and twinning is intricate. Existing constitutive models cannot simultaneously describe the relationship between DRX and twinning, as well as their effects on stress. In this study, the hot compression tests were conducted to study the flow behavior and microstructure evolution of AZ31. The obtained flow curves and microstructure revealed that AZ31 alloy undergoes simultaneously DRX softening and twinning hardening during hot deformation. Furthermore, texture evolution was significantly influenced by both DRX and twinning behaviors. As a result of DRX behavior, there was a decrease in twin volume fraction in the later stages of deformation. Consequently, a twin volume fraction model that incorporates the inhibitory effect of DRX was proposed. According to the experimental results of high temperatures and low strain rates, a constitutive model incorporating work hardening, dynamic recovery (DRV), and DRX was established. Additionally, the influence of twinning on flow stress was incorporated into the model, resulting in a constitutive model that considers both DRX and twinning simultaneously. Finally, the proposed model's reliability was validated through the wedging spinning process, successfully predicting both macroscopic deformation and microstructure evolution of the spinning parts.

Abstract Background More and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data. Results In this paper, we propose a new computational method based on global heterogeneous networks to predict the functions of lncRNAs, called DNGRGO. DNGRGO first calculates the similarities among proteins, miRNAs, and lncRNAs, and annotates the functions of lncRNAs according to its similar protein-coding genes, which have been labeled with gene ontology (GO). To evaluate the performance of DNGRGO, we manually annotated GO terms to lncRNAs and implemented our method on these data. Compared with the existing methods, the results of DNGRGO show superior predictive performance of maximum F-measure and coverage. Conclusions DNGRGO is able to annotate lncRNAs through capturing the low-dimensional features of the heterogeneous network. Moreover, the experimental results show that integrating miRNA data can help to improve the predictive performance of DNGRGO.

Pansharpening refers to the process of fusing multispectral (MS) images with panchromatic (PAN) images to obtain high-resolution multispectral (HRMS) images. However, due to the low correlation and similarity between MS and PAN images, as well as inaccuracies in spatial information injection, HRMS images often suffer from significant spectral and spatial distortions. To address these issues, a pansharpening method based on multimodal texture correction and adaptive edge detail fusion is proposed in this paper. To obtain a texture-corrected (TC) image that is highly correlated and similar to the MS image, the target-adaptive CNN-based pansharpening (A-PNN) method is introduced. By constructing a multimodal texture correction model, intensity, gradient, and A-PNN-based deep plug-and-play correction constraints are established between the TC and source images. Additionally, an adaptive degradation filter algorithm is proposed to ensure the accuracy of these constraints. Since the TC image obtained can effectively replace the PAN image and considering that the MS image contains valuable spatial information, an adaptive edge detail fusion algorithm is also proposed. This algorithm adaptively extracts detailed information from the TC and MS images to apply edge protection. Given the limited spatial information in the MS image, its spatial information is proportionally enhanced before the adaptive fusion. The fused spatial information is then injected into the upsampled multispectral (UPMS) image to produce the final HRMS image. Extensive experimental results demonstrated that compared with other methods, the proposed algorithm achieved superior results in terms of both subjective visual effects and objective evaluation metrics.

IntroductionChimerism is closely correlated with disease relapse after allogeneic hematopoietic stem cell transplantation (allo-HSCT). However, chimerism rate is dynamic changes, and the sensitivity of different chimerism requires further research.MethodsTo investigate the predictive value of distinct chimerism for relapse, we measured bone marrow (BM), peripheral blood (PB), and T-cell (isolated from BM) chimerism in 178 patients after allo-HSCT.ResultsReceiver operating characteristic (ROC) curve showed that T-cell chimerism was more suitable to predict relapse after allo-HSCT compared with PB and BM chimerism. The cutoff value of T-cell chimerism for predicting relapse was 99.45%. Leukemia and myelodysplastic syndrome (MDS) relapse patients’ T-cell chimerism was a gradual decline from 2 months to 9 months after allo-HSCT. Higher risk of relapse and death within 1 year after allo-HSCT. The T-cell chimerism rates in remission and relapse patients were 99.43% and 94.28% at 3 months after allo-HSCT (P = 0.009), 99.31% and 95.27% at 6 months after allo-HSCT (P = 0.013), and 99.26% and 91.32% at 9 months after allo-HSCT (P = 0.024), respectively. There was a significant difference (P = 0.036) for T-cell chimerism between early relapse (relapse within 9 months after allo-HSCT) and late relapse (relapse after 9 months after allo-HSCT) at 2 months after allo-HSCT. Every 1% increase in T-cell chimerism, the hazard ratio for disease relapse was 0.967 (95% CI: 0.948–0.987, P

The repair of critical-sized bone defects poses a significant challenge due to the absence of periosteum, which plays a crucial role in coordinating the processes of osteogenesis and vascularization during bone healing. Herein, we hypothesized that melatonin-encapsuled silk Fibronin electrospun nanofibers (SF@MT) could provide intrinsic induction of both osteogenesis and angiogenesis, thereby promoting vascularized bone regeneration. The sustained release of melatonin from the SF@MT nanofibers resulted in favorable biocompatibility and superior osteogenic induction of bone marrow mesenchymal stem cells (BMMSCs). Interestingly, melatonin promoted the migration and tube formation of human umbilical vein endothelial cells (HUVECs) in a BMMSC-dependent manner, potentially through the upregulation of vascular endothelial growth factor (VEGFA) expression in SF@MT-cultured BMMSCs. SF@MT nanofibers enhanced the BMMSC-mediated angiogenesis by activating the PI3K/Akt signaling pathway. In vivo experiments indicated that the implantation of SF@MT nanofibers into rat critical-sized calvarial defects significantly enhances the production of bone matrix and the development of new blood vessels, leading to an accelerated process of vascularized bone regeneration. Consequently, the utilization of melatonin-encapsulated silk Fibronin electrospun nanofibers shows great promise as a potential solution for artificial periosteum, with the potential to regulate the coupling of osteogenesis and angiogenesis in critical-sized bone defect repair.

The traditional multi-line laser calibration method relies primarily on calibrating the spatial plane equations of multiple laser lines and reconstructing the multi-line laser in three dimensions through the plane equation and camera parameters. In traditional methods, the multi-line laser is projected as a straight line by default, but in reality, the laser line projected by the multi-line laser is not a plane due to the Diffractive Optical Element (DOE). Instead, it is a surface with a certain curvature in space. During the production process, the multi-line laser experiences significant distortion because of the DOE projecting at a large angle. Consequently, if traditional methods are used, accurate calibration of the multi-line laser cannot be achieved. To address this issue, this paper introduces a new calibration algorithm for multi-line laser based on a spatial quadric equation. By calibrating the spatial quadric equation of the multi-line laser, the spatial characteristics of the laser can be accurately captured. The algorithm involves projecting a crossed 7-line laser onto a calibration plate and changing the position of the calibration plate while capturing images. This allows for obtaining the spatial three-dimensional coordinates of each laser line in the camera coordinate system. Next, spatial quadric equations are fitted for each laser line based on the three-dimensional coordinates. Finally, 3D reconstruction is performed using the quadratic surface parameters of the multi-line laser and camera parameters. The results from 3D reconstruction demonstrate that, compared to traditional laser calibration methods based on planes, the proposed algorithm achieves higher calibration accuracy. It also improves the number of successful binocular multi-line laser matches and overall accuracy.

The inerter was introduced as a mechanical counterpart to the electrical capacitor, completing the force-current analogy. This is a one-port, two-terminal device in which the equal and opposite forces exerted at its terminals are proportional to the relative acceleration between them. Within this relationship, the “inertance” is the coefficient of proportionality and carries the unit of mass. This implies that the inerter can exert an inertial force at its terminals, effectively representing a virtual mass. Due to these properties, inerters have gained popularity, finding applications as components of vibration control systems and energy harvesters. Derived from passive inerters, semi-active inerters are integrated with active control systems to regulate their inertance. Since their introduction, semi-active inerters have been pivotal in situations demanding active monitoring of natural frequency or control force, generally outperforming their passive counterparts. While numerous significant reviews on passive inerters and their applications have been published in respected journals, dedicated literature reviews on semi-active inerters remain scarce. This review seeks to bridge this gap, offering a comprehensive literature review on semi-active inerters and highlighting research challenges and opportunities. Given the novelty of semi-active inerters, they present a fascinating area of study.

Metallic plastic deformation involves complex microstructural changes and defect evolution, posing challenges in predicting and controlling the quality and performance of formed parts. Therefore, a pressing demand exists for a proficient online defect‐sensing system to monitor defects evolution continuously within components during plastic deformation in real time. This article proposes an intelligent online sensing approach for detecting defects in metallic plastic forming based on acoustic emission (AE) and machine learning. A comparative analysis is conducted on AE amplitude signals, stress–strain curves, and defect evolution during the tensile process of TA15 titanium alloy specimens under different stress states. It is found that the defect formation process can be divided into four stages based on the AE amplitude signals. A convolutional neural network model for intelligent defect sensing is established. It leverages transfer learning and is grounded in the relationship between AE signals and the evolution of internal defects. The prediction accuracy using different pretrained models is investigated and compared. It is discerned that utilizing GoogleNet as the pretrained model offers the swiftest training pace with a prediction accuracy of 97.57%. This approach enables intelligent online sensing of internal defect evolution in metal plastic deformation processes.

In this paper, the NWT600 airfoil with a thickness ratio of 60% is taken as the research object. The aerodynamic performance of the airfoil is analyzed by experiments and numerical simulations. The results simulated by various turbulence models used in the 2D steady-state RANS method are compared, including the Spalart–Allmaras model, k-ω SST model, k-ε realizable model, and Reynolds stress (linear pressure-strain) model. The influence of blunt trailing-edge thickness on aerodynamic characteristics is studied by adding thickness symmetrically. The results show that even under the low subsonic flow with a Mach number of 0.149, the airflow is prone to severe separation. The aerodynamic performance of the airfoil is very different from that of the conventional thin airfoil. Although the 2D steady-state RANS models overestimate the pressure on the surface of the airfoil in most cases, it is qualitatively acceptable to predict the pressure distribution of the very thick airfoil. Numerical results simulated by the Reynolds stress model are in the best agreement with the experimental data. It is also found that symmetrically thickening the trailing edge effectively improves the maximum lift coefficient and reduces the drag coefficient at a small angle of attack.

Abstract Background Radiotherapy‐induced esophagitis (RE) diminishes the quality of life and interrupts treatment in patients with non‐small cell lung cancer (NSCLC) undergoing postoperative radiotherapy. Dosimetric models showed limited capability in predicting RE. We aimed to develop dosiomic models to predict RE. Methods Models were trained with a real‐world cohort and validated with PORT‐C randomized controlled trial cohort. Patients with NSCLC undergoing resection followed by postoperative radiotherapy between 2004 and 2015 were enrolled. The endpoint was grade ≥2 RE. Esophageal three‐dimensional dose distribution features were extracted using handcrafted and convolutional neural network (CNN) methods, screened using an entropy‐based method, and selected using minimum redundancy and maximum relevance. Prediction models were built using logistic regression. The areas under the receiver operating characteristic curve (AUC) and precision‐recall curve were used to evaluate prediction model performance. A dosimetric model was built for comparison. Results A total of 190 and 103 patients were enrolled in the training and validation sets, respectively. Using handcrafted and CNN methods, 107 and 4096 features were derived, respectively. Three handcrafted, four CNN‐extracted and three dosimetric features were selected. AUCs of training and validation sets were 0.737 and 0.655 for the dosimetric features, 0.730 and 0.724 for handcrafted features, and 0.812 and 0.785 for CNN‐extracted features, respectively. Precision‐recall curves revealed that CNN‐extracted features outperformed dosimetric and handcrafted features. Conclusions Prediction models may identify patients at high risk of developing RE. Dosiomic models outperformed the dosimetric‐feature model in predicting RE. CNN‐extracted features were more predictive but less interpretable than handcrafted features.

Abstract Objective To explore the differential expression of placental specific gene 1 (PLAC1) and neurite guidance factor 1 (netrin-1) in colorectal cancer (CRC) liver metastasis and its predictive value. Methods Paraffin specimens of primary CRC were selected, including 60 simple colorectal cancer specimens and 47 liver metastasis specimens. At the same time, 40 cases of normal colorectal mucosa were taken as the control group. The expression of PLAC1 and Netrin-1 in tissue was detected by immunohistochemistry (IHC). The correlation between PLAC1 and Netrin-1 expression and clinicopathological characteristics of patients with CRC liver metastases was analyzed. Logistic analysis was adopted to analyze the influencing factors of liver metastasis in CRC. A prediction model was established and ROC curve was used to detect the discrimination of the prediction model. The clinical value of PLAC1 and netrin-1 in predicting liver metastasis of CRC was analyzed using ROC curve. The relationship between the expression of PLAC1 and netrin-1 and the prognosis of CRC patients with liver metastasis was analyzed using Kaplan Meier survival curve. Results The positive staining of PLAC1 and netrin-1 was mainly located in the cytoplasm by IHC detection. Positive expression of PLAC1 and netrin-1 in CRC tissues was markedly higher than that in normal colorectal mucosal epithelium (P 0.05). The metastasis group had much higher positive expression of netrin-1 than the non-metastasis group (P