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ABSTRACTThe paradox between massive rural revitalisation and cultural preservation calls for new sustainable development strategies. How to strike a balance between them has become a research hotspot especially for traditional villages in developing countries. Through data collection, field research and analogy, this article explores the spatial prototypes of mandala, Mount Meru and ‘zhai xin’ (central stronghold) concept, and points out the coupling relationship between spatial patterns and Tibetan settlements and its influences on settlement layout, architectural forms, and local customs, which includes the cultural philosophy of spatial patterns, the structure of human settlements, village layout, road network planning, and core node building complex. Taking Ge’en Tibetan Village revamping program in Aba Tibetan and Qiang County as an illustrative example, this design strategy will not only improve local residents’ living standards, but also helps to retain the cultural essence of traditional villages, thereby diving the development of tourism and other tertiary industries. Based on cultural heritage preservation, this work offers new perspectives on land use planning and architectural design for the revitalisation and sustainable development of traditional village in under-developed areas.

Smoke aerosols contain many harmful substances and have become one of the major sources of indoor pollutants. The smoke aerosols released into the environment are easy to react with various oxidants presented in the room, namely the aging process of the smoke aerosol, which can produce secondary organic aerosols. The chemical composition of smoke aerosol is an important factor affecting its indoor aging process, but so far, the change behavior of the composition and particle size distribution of smoke aerosol during the aging process remains to be studied, and the aging analysis of novel tobacco products has not been reported. Therefore, the present study applied a house-made vacuum ultraviolet photoionization time-of-flight mass spectrometer to detect the chemical composition of the smoke aerosols produced by combustion cigarette, and new tobacco products, e.g., heat-not-burn tobacco products and e-cigarette, achieving the in-situ online characterization of their gas-phase and particulate-phase components. The results showed that the chemical composition of the smoke aerosols generated by the three types of tobacco products is significantly different, among which the component content and species abundance of the combustion cigarette are much greater than those of the novel tobacco products, and the overall signal intensity of combustion cigarette in the gas-phase and the particulate-phase is about 100 times and 10 times that of novel tobacco products, respectively. In addition, it was found that the particulate-phase mass spectra of novel tobacco products contain a large amount of glycerol, which is almost absent in combustion cigarette, and its intensity is even more than nicotine. The detected aerosol components contain many unsaturated compounds, which are easily oxidized in the environment and generate new ultrafine particles (UFPs). The Teflon chamber was used to simulate the aging process of the above three smoke aerosols with different O3 concentrations. The results of scanning mobility particle sizer showed that UFPs are generated in all of them. Corresponding to the results of photoionization mass spectrometry, the aging processes of the three smoke aerosols are completely different, in which the formation conditions of UFPs are related to the content and composition distribution of precursors. Specifically, the indoor concentration of O3 (observed in this experiment was 40 μg/m3) can promote the formation of UFPs with a geometric mean diameter of about 26 nm in the combustion cigarette, while the UFPs formation of novel tobacco products requires a higher concentration of O3 or a longer aging time. This is mainly facts that the whole composition concentration of the novel tobacco products is much lower than that of combustion cigarette, and the particulate-phase components contain a lot of glycerol. Due to the strong viscosity and encapsulation, glycerol may exist in the particulate-phase of the smoke aerosol in the form of coating other substances, such as nicotine, and further prevents the aging process of other substances, resulting in differences in particle size distribution. This study explains the reason of difference in UFPs formation of various smoke aerosols during the aging process and provides a basis for tobacco exposure risk assessment, which is expected to reveal the aging mechanism of smoke aerosols in the future.

Abstract Background Severe pneumonia has consistently been associated with high mortality. We sought to identify risk factors for the mortality of severe pneumonia to assist in reducing mortality for medical treatment. Methods Electronic databases including PubMed, Web of Science, EMBASE, Cochrane Library, and Scopus were systematically searched till June 1, 2023. All human research were incorporated into the analysis, regardless of language, publication date, or geographical location. To pool the estimate, a mixed-effect model was used. The Newcastle–Ottawa Scale (NOS) was employed for assessing the quality of included studies that were included in the analysis. Results In total, 22 studies with a total of 3655 severe pneumonia patients and 1107 cases (30.29%) of death were included in the current meta-analysis. Significant associations were found between age [5.76 years, 95% confidence interval [CI] (3.43, 8.09), P

Abstract Background Computed tomography (CT) is widely in clinics and is affected by metal implants. Metal segmentation is crucial for metal artifact correction, and the common threshold method often fails to accurately segment metals. Purpose This study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom images of known size. Methods A retrospective study was conducted on 100 patients who received radiation therapy without metal artifacts, and simulated artifact data were generated using publicly available mask data. The study utilized 11,280 slices for training and verification, and 2,820 slices for testing. Metal mask segmentation was performed using DiffSeg, a diffusion model incorporating conditional dynamic coding and a global frequency parser (GFParser). Conditional dynamic coding fuses the current segmentation mask and prior images at multiple scales, while GFParser helps eliminate high-frequency noise in the mask. Clinical artifact images and phantom images are also used for model validation. Results Compared with the ground truth, the accuracy of DiffSeg for metal segmentation of simulated data was 97.89% and that of DSC was 95.45%. The mask shape obtained by threshold segmentation covered the ground truth and DSCs were 82.92% and 84.19% for threshold segmentation based on 2500 HU and 3000 HU. Evaluation metrics and visualization results show that DiffSeg performs better than other classical deep learning networks, especially for clinical CT, artifact data, and phantom data. Conclusion DiffSeg efficiently and robustly segments metal masks in artifact data with conditional dynamic coding and GFParser. Future work will involve embedding the metal segmentation model in metal artifact reduction to improve the reduction effect.

Abstract In this study, we propose a novel method for identifying lithology using an attention mechanism-enhanced graph convolutional neural network (AGCN). The aim of this method is to address the limitations of traditional approaches that evaluate unbalanced lithology by improving the identification of thin layers and small samples, while providing reliable data support for reservoir evaluation. To achieve this goal, we begin by using Principal Component Analysis (PCA) with maximum and minimum distance clustering (Max-min-distance) to correct the logging curves, which compensates for the low resolution of thin layers and enhances the accuracy of stratigraphic representation. Subsequently, we transform the logging data into graph-structured data by connecting distance similarity points and feature similarity points of the logging samples. We then use the graph convolutional network (GCN) to identify lithology, leveraging both labeled and unlabeled data to enhance the ability to identify lithology in small sample datasets. Additionally, our model incorporates a channel and spatial attention mechanism that assigns weights to the graph structure during lithology identification, improving the model’s capability to discern differences across samples. To evaluate the performance of our model, we constructed a lithology dataset comprising five wells and conducted experiments. The results indicate that our approach achieves a maximum accuracy of 97.67%, surpassing the performance of a singlestructure model in lithology identification. In conclusion, our proposed method provides a promising and effective approach for unbalanced lithology identification, significantly improving accuracy levels.

Abstract Among the causes of the annually traffic accidents, driving fatigue is the main culprit. In consequence, it is of great practical significance to carry out the research of driving fatigue detection and early warning system. However, there are still two problems in the latest methods of driving fatigue detection: one is that a single information cannot precisely reflect the actual state of the driver in different fatigue phases, another one is the detection effect is not very well or even difficult to detect under abnormal illumination. In this paper, the multi-task cascaded convolutional networks (MTCNN) and infrared-based remote photo-plethysmography (rPPG) theory are used to extract the driver’s facial and physiological information, and the multi-modal specific fatigue information is deeply excavated, and the multi-modal feature fusion model is constructed to comprehensively analyze the driver’s fatigue variation tendency. Aiming at the matter of low detection accuracy under abnormal illumination, the multi-modal features extracted from visible light images and infrared images are fused by multi-loss reconstruction (MLR) module, and the driving fatigue detection module is established which is based on Bi-LSTM model by utilizing fatigue timing. The experiments were validated under all-weather illumination scenarios and were carried out on the datasets NTHU-DDD, UTA-RLDDD and FAHD. The results show that the multi-modal driving fatigue detection model has better performance than the single-modal model, and the accuracy is improved by 8.1%. In the abnormal illumination such as strong and weak light, the accuracy of the method can reach 91.7% at the highest and 83.6% at the lowest. Meanwhile, in the normal illumination, it can reach 93.2%.

Abstract In electronic warfare, radar signal deinterleaving is a critical task. While many researchers have applied deep learning and utilised known radar classes to construct interleaved pulse sequences training sets for deinterleaving models, these models face challenges in distinguishing between known and unknown radar classes in open‐set scenarios. To address this challenge, the authors propose a novel model, the Reconstruction Bidirectional Recurrent Neural Network (RBi‐RNN). RBi‐RNN utilises input reconstruction and employs a joint training strategy incorporating cross‐entropy loss, reconstruction loss, and centre loss. These strategies aim to maximise inter‐class latent representation distances while minimising intra‐class disparities. By incorporating an open‐set recognition method based on extreme value theory, RBi‐RNN adapts to open‐set scenarios. Simulation results demonstrate the superiority of RBi‐RNN over conventional models in both closed‐set and open‐set scenarios. In open‐set scenarios, it successfully discriminates between known and unknown radar signals within interleaved pulse sequences, deinterleaving known radar classes with high stability. The authors lay the foundation for future unsupervised deinterleaving methods designed specifically for unknown radar pulses.

In previous study we characterized the oncogenic role of long non-coding RNA MALAT1 in esophageal squamous cell carcinoma (ESCC), but the detailed mechanism remains obscure. Here we identified glyoxalase 1 (GLO1) as the most possible executor of MALAT1 by microarray screening. GLO1 is responsible for degradation of cytotoxic methylglyoxal (MGO), which is by-product of tumor glycolysis. Accumulated MGO may lead to glycation of DNA and protein, resulting in elevated advanced glycation end products (AGEs), while glyoxalase 1 detoxify MGO to alleviate its cytotoxic effect to tumor cells. GLO1 interfering led to accumulation of AGEs and following activation of DNA injury biomarkers, which lead to cell cycle arrest and growth inhibition. In silico analysis based on online database revealed abundant enrichment of histone acetylation marker H3K27ac in GLO1 promotor, and acetyltransferase inhibitor C646 declined GLO1 expression. Acetyltransferase KAT2B, which was also identified as a target of MALAT, mediated histone lysine acetylation of GLO1 promotor, which was confirmed by ChIP-qPCR experiment. Shared binding sites of miR-206 were found on MALAT1 and KAT2B mRNA. Dual-luciferase reporter assays confirmed interaction within MALAT1-miR-206-GLO1. Finally, we identified MALAT1 encapsuled by exosome from donor cells, and transferred malignant behaviors to recipient cells. The secreted exosomes may enter circulation, and serum MALAT1 level combined with traditional tumor markers showed potential power for ESCC diagnosis.

Background This study aims to investigate the errors in structure volume and shape caused by the replication, expansion, and merging operations of the Monaco system and analyze their influence on dosimetry evaluation. Methods A retrospective collection of 30 patients undergoing radiotherapy was utilized. Cylinders with radii of 5, 10, and 30 mm were delineated in computerized tomography (CT) images from 10 patients with thoracic and abdominal issues, and the Margins function in Monaco was used to expand the margins by 0, 3, 5, and 10 mm in 2D mode. In 10 patients with vertebral metastases, the Margins function was utilized to replicate and merge targets, and the Copy Structure function was employed to replicate targets. Cross-CT replication was performed for the targets of 10 patients with nasopharyngeal carcinoma. The deviation between the processed structure volume and the ideal value was compared. The difference in the maximum dose (Dmax) before and after lens replication was evaluated in 10 patients undergoing whole-brain radiotherapy. Results Monaco's Margins function increased the volume of the processed structure during the copying procedure. The margin error was equivalent to expanding the structure by 0.3–0.4 mm, and a margin error of 0.3–0.4 mm was produced in each expansion instance. The volume deviation for a cylinder with a radius of 5 mm was 12.99%. The Merge function of Margins copied substructures and merged them. The Copy Structure function did not alter the structure during copying, but the volume was reduced by less than 1% after copying across CT. Dmax after lens replication was higher than that before replication, with a median difference of 31.3 cGy for the left lenses. Conclusion Monaco's Margins function introduces errors in organ replication, expansion, and merging, resulting in incorrect dose assessment. Physicians should be mindful of the potential effects when utilizing them.

Abstract Objectives Accurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically. Methods A total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively. Results The proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89–9.33% and 14.75–16.67%, respectively, while 95HD and ASD decreased by 33.16–67.81% and 36.05–75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective. Conclusions Using the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

Abstract Background Extracorporeal membrane oxygenation (ECMO) is a cutting-edge life-support measure for patients with severe cardiac and pulmonary illnesses. Although there are several systematic reviews (SRs) about ECMO, it remains to be seen how quality they are and how efficacy and safe the information about ECMO they describe is in these SRs. Therefore, performing an overview of available SRs concerning ECMO is crucial. Methods We searched four electronic databases from inception to January 2023 to identify SRs with or without meta-analyses. The Assessment of Multiple Systematic Reviews 2 (AMSTAR-2) tool, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system were used to assess the methodological quality, and evidence quality for SRs, respectively. A bubble plot was used to visually display clinical topics, literature size, number of SRs, evidence quality, and an overall estimate of efficacy. Results A total of 17 SRs met eligibility criteria, which were combined into 9 different clinical topics. The methodological quality of the included SRs in this mapping was “Critically low” to “Moderate”. One of the SRs was high-quality evidence, three on moderate, three on low, and two on very low-quality evidence. The most prevalent study used to evaluate ECMO technology was observational or cohort study with frequently small sample sizes. ECMO has been proven beneficial for severe ARDS and ALI due to the H1N1 influenza infection. For ARDS, ALF or ACLF, and cardiac arrest were concluded to be probably beneficial. For dependent ARDS, ARF, ARF due to the H1N1 influenza pandemic, and cardiac arrest of cardiac origin came to an inconclusive conclusion. There was no evidence for a harmful association between ECMO and the range of clinical topics. Conclusions There is limited available evidence for ECMO that large sample, multi-center, and multinational RCTs are needed. Most clinical topics are reported as beneficial or probably beneficial of SRs for ECMO. Evidence mapping is a valuable and reliable methodology to identify and present the existing evidence about therapeutic interventions.

Abstract Background Sepsis-induced small-intestinal injury is associated with increased morbidity and mortality. Our previous study and other papers have shown that HIF-1α has a protective effect on intestinal mucosal injury in septic rats. The purpose of this study is to further verify the protective effect of HIF-1α on intestinal mucosa and its molecular mechanism in vitro experiments. Methods Caco-2 cells were selected and experiment was divided into 2 parts. Part I: HIF-1α activator and inhibitor were used to treat lipopolysacchrides (LPS)-stimulated Caco-2 cells respectively, to explore the effect of HIF-1α on LPS induced Caco-2 cell epithelial model; Part II: mTOR activator or inhibitor combined with or without HIF-1α activator, inhibitor to treat LPS-stimulated Caco-2 cells respectively, and then the molecular mechanism of HIF-1α reducing LPS induced Caco-2 cell epithelial model damage was detected. Results The results showed that HIF-1α activator decreased the permeability and up regulated tight junction (TJ) expression, while HIF-1α inhibitor had the opposite effect with the HIF-1α activator. mTOR activation increased, while mTOR inhibition decreased HIF-1α protein and expression of its downstream target molecules, which can be attenuated by HIF-1α activator or inhibitor. Conclusion This study once again confirmed that HIF-1α alleviates LPS-induced mucosal epithelial model damage through P70S6K signalling pathway. It is of great value to explore whether HIF-2α plays crucial roles in the regulation of mucosal epithelial model functions in the future.

In order to study the deformation and failure mechanism of ribbed steel rebar bolts anchorage structure, the axisymmetric numerical model of anchorage structure was established by ABAQUS software. Based on the cohesion model, the numerical test was carried out to obtain the distribution and evolution law of bolt deformation, load and anchorage body displacement field during the pull-out process of anchorage structure. The test results show that under the same pull-out displacement, the small rib spacing bolt can also bear higher pull-out load, which indicates that the small rib spacing bolt has stronger bearing capacity for the deformation of surrounding rock of roadway. In the pull-out process of bolt, with the increase of pull-out displacement, the rock mass failure and separation area spread to the deep rock mass in an inverted bowl shape. When the pull-out displacement increases to 9.06 mm, the displacement growth rate at the end of the anchorage section of the rock mass increases, and the maximum displacement of the rock mass is 4.79 mm, which appears at the end of the anchorage section, eventually leading to the failure of the anchorage section matrix. The axial force of the bolt is negatively correlated with the rib spacing of the bolt, and the axial force of the bolt increases with the decrease of the rib spacing.

Abstract Objective This study aimed to present a deep-learning network called contrastive learning-based cycle generative adversarial networks (CLCGAN) to mitigate streak artifacts and correct the CT value in four-dimensional cone beam computed tomography (4D-CBCT) for dose calculation in lung cancer patients. Methods 4D-CBCT and 4D computed tomography (CT) of 20 patients with locally advanced non-small cell lung cancer were used to paired train the deep-learning model. The lung tumors were located in the right upper lobe, right lower lobe, left upper lobe, and left lower lobe, or in the mediastinum. Additionally, five patients to create 4D synthetic computed tomography (sCT) for test. Using the 4D-CT as the ground truth, the quality of the 4D-sCT images was evaluated by quantitative and qualitative assessment methods. The correction of CT values was evaluated holistically and locally. To further validate the accuracy of the dose calculations, we compared the dose distributions and calculations of 4D-CBCT and 4D-sCT with those of 4D-CT. Results The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) of the 4D-sCT increased from 87% and 22.31 dB to 98% and 29.15 dB, respectively. Compared with cycle consistent generative adversarial networks, CLCGAN enhanced SSIM and PSNR by 1.1% (p

[Objective] The resource sharing among main substations in urban rail transit power supply system can lead to significant cost savings. In this context, it is essential to investigate reactive power compensation schemes for different phases of main substation resource sharing to ensure that the power factor at the PCC (point of common coupling) meets the standards. [Method] Taking the power supply system of a city's metro line as example, based on the train schedule and power supply system structure, the minimum reactive power compensation required at PCC before and after main substation resource sharing under daily power factor compliance is calculated. A reactive power compensation setting scheme considering main substation resource sharing is proposed, based on the minimum reactive power compensation that meets the compensation requirements before and after main substation resource sharing, with the goal to minimize SVG (static var generators) capacity, determining the installation capacity of SVG and reactor or capacitator. [Result & Conclusion] In the power supply system of the case study line, the actual configuration of the reactive power compensation devices for the 1# main substation main transformer power supply zone is as follows: reactor capacity is 6.0 Mvar, and SVG capacity is 1.0 Mvar. For the 2# main transformer power supply zone, the configuration is a reactor capacity of 3.0 Mvar and an SVG capacity of 0.5 Mvar. The proposed reactive power compensation scheme replaces a total capacity of 9.0 Mvar SVG devices with reactors, meeting the reactive power compensation requirements while reducing investment costs.

Objectives Analyzing the lower limb coronal morphological parameters in populations without knee osteoarthritis (KOA) holds significant value in predicting, diagnosing, and formulating surgical strategies for KOA. This study aimed to comprehensively analyze the variability in these parameters among Chinese non‐KOA populations, employing a substantial sample size. Methods A cross‐sectional retrospective analysis was performed on the Chinese non‐KOA populations (n = 407; 49.9% females). The study employed an in‐house developed artificial intelligence software to meticulously assess the coronal morphological parameters of all 814 lower limbs. The parameters evaluated included the hip‐knee‐ankle angle (HKAA), weight‐bearing line ratio (WBLR), joint line convergence angle (JLCA), mechanical lateral‐proximal‐femoral angle (mLPFA), mechanical lateral‐distal‐femoral angle (mLDFA), mechanical medial‐proximal‐tibial angle (mMPTA), and mechanical lateral‐distal‐tibial angle (mLDTA). Differences in these parameters were compared between left and right limbs, different genders, and different age groups (with 50 years as the cut‐off point). Results HKAA and JLCA exhibited left–right differences (left vs. right: 178.2° ± 3.0° vs. 178.6° ± 2.9° for HKAA, p = 0.001; and 1.8° ± 1.5° vs. 1.4° ± 1.6° for JLCA, p

Calculating the maximum transmission power of loosely coupled coils represents a crucial step in the design of inductively coupled power transfer (ICPT) systems. However, the majority of calculations for the maximum power transfer point (MPTP) are exclusively relevant to scenarios featuring pure sinusoidal excitation. This paper introduces a more universally applicable solution for determining the MPTP in loosely coupled systems subjected to arbitrary excitations. The approach takes into account the contributions of both the fundamental waveform and all harmonics to the transmission capacity, offering a precise method for investigating the maximum power capacity of ICPT systems under non-sinusoidal excitation. Through simulations and experiments, it is demonstrated that the proposed method yields improved agreement with various waveform excitations, including square/triangular waves, as well as actual chopping waves characterized by large-scale ringing and overshoot. The versatility of the method extends to various inductively coupled systems with generic excitations, encompassing non-resonant-type, direct chopper, or multi-level inverter-driven ICPT systems. Furthermore, the method ensures greater accuracy in instances of excitation distortion induced by nonlinear effects.

Background: Idiopathic pulmonary fibrosis (IPF) leads to excessive fibrous tissue in the lungs, increasing the risk of lung cancer (LC) due to heightened fibroblast activity. Advances in nucleotide point mutation studies offer insights into fibrosis-to-cancer transitions. Methods: A two-sample Mendelian randomization (TSMR) approach was used to explore the causal relationship between IPF and LC. A weighted gene co-expression network analysis (WGCNA) identified shared gene modules related to immunogenic cell death (ICD) from transcriptomic datasets. Machine learning selected key genes, and a multi-layer perceptron (MLP) model was developed for IPF prediction and diagnosis. SMR and PheWAS were used to assess the expression of key genes concerning IPF risk. The impact of core genes on immune cells in the IPF microenvironment was explored, and in vivo experiments were conducted to examine the progression from IPF to LC. Results: The TSMR approach indicated a genetic predisposition for IPF progressing to LC. The predictive model, which includes eight ICD key genes, demonstrated a strong predictive capability (AUC = 0.839). The SMR analysis revealed that the elevated expression of MS4A4A was associated with an increased risk of IPF (OR = 1.275, 95% CI: 1.029–1.579; p = 0.026). The PheWAS did not identify any significant traits linked to MS4A4A expression. The rs9265808 locus in MS4A4A was identified as a susceptibility site for the progression of IPF to LC, with mutations potentially reprogramming lung neutrophils and increasing the LC risk. In vivo studies suggested MS4A4A as a promising therapeutic target. Conclusions: A causal link between IPF and LC was established, an effective prediction model was developed, and MS4A4A was highlighted as a therapeutic target to prevent IPF from progressing to LC.

Three-dimensional point clouds, as an advanced imaging technique, enable researchers to capture plant traits more precisely and comprehensively. The task of plant segmentation is crucial in plant phenotyping, yet current methods face limitations in computational cost, accuracy, and high-throughput capabilities. Consequently, many researchers have adopted 3D point cloud technology for organ-level segmentation, extending beyond manual and 2D visual measurement methods. However, analyzing plant phenotypic traits using 3D point cloud technology is influenced by various factors such as data acquisition environment, sensors, research subjects, and model selection. Although the existing literature has summarized the application of this technology in plant phenotyping, there has been a lack of in-depth comparison and analysis at the algorithm model level. This paper evaluates the segmentation performance of various deep learning models on point clouds collected or generated under different scenarios. These methods include outdoor real planting scenarios and indoor controlled environments, employing both active and passive acquisition methods. Nine classical point cloud segmentation models were comprehensively evaluated: PointNet, PointNet++, PointMLP, DGCNN, PointCNN, PAConv, CurveNet, Point Transformer (PT), and Stratified Transformer (ST). The results indicate that ST achieved optimal performance across almost all environments and sensors, albeit at a significant computational cost. The transformer architecture for points has demonstrated considerable advantages over traditional feature extractors by accommodating features over longer ranges. Additionally, PAConv constructs weight matrices in a data-driven manner, enabling better adaptation to various scales of plant organs. Finally, a thorough analysis and discussion of the models were conducted from multiple perspectives, including model construction, data collection environments, and platforms.

The study aimed to investigate the clinical significance of the interaction between hypoxia and the immune system in esophageal squamous cell carcinoma (ESCC) microenvironment. A comprehensive evaluation of 13 hypoxia phenotype-related genes (HPRs) was conducted using data from TCGA-ESCC and two GEO cohorts. Three distinct HPRclusters were identified, and the HPRscore was established as an independent prognostic factor (p = 0.001), with higher scores indicating poorer prognosis. The HPRscore was validated in various immunotherapy cohorts, demonstrating its efficacy in evaluating immunotherapy and chemotherapy outcomes. Additionally, phenome-wide association study (PheWAS) analysis showed that PKP1 had no significant correlation with other traits at the gene level. PKP1 was identified as a potential prognostic marker for ESCC, with upregulated expression observed in ESCC patients. In vitro experiments showed that the knockdown of PKP1 inhibited ESCC cell proliferation and migration. These findings suggest that the novel HPRscore and PKP1 may serve as prognostic tools and therapeutic targets for ESCC patients.

Abstract To analyze the deformation behavior of short glass fiber-reinforced poly-ether-ether-ketone (SGFR-PEEK) under various conditions through numerical simulations, it is crucial to construct a constitutive model that can describe its stress–strain behavior over a wide range of strain rates and temperatures. In this study, quasi-static compression tests were conducted on SGFR-PEEK composites with varying mass fractions, and dynamic tests were performed using a Split Hopkinson Pressure Bar to acquire the material's compressive stress–strain response under quasi-static and dynamic conditions. The results indicate that, under compression, the yield stress of SGFR-PEEK composites increases with an augmentation in glass fiber content, rises with increasing strain rate, and decreases with elevated temperature. Based on experimental findings, a modified Johnson–Cook constitutive model was established to characterize the mechanical performance of SGFR-PEEK. In comparison to the traditional Johnson–Cook intrinsic structure model, the modified model takes into account the glass fiber mass fraction as comprehensively as possible and better predicts the material's flow behavior at high strain rates. Finally, this modified constitutive model was implemented in the ABAQUS software using the user-defined subroutine VUMAT to simulate the compression behavior of SGFR-PEEK composites under different loading conditions, and the model was validated. This research provides valuable insights for the practical application of SGFR-PEEK composites in engineering.

A study on the film characteristics of a Cr2O3-forming nickel-based superalloy is conducted. A triplex structure is observed, in which Ta–Ti layer is developed between the external Cr2O3 and inner Al2O3 layer. When the exposure time increased, Ti atoms are divorced from the Ta–Ti layer, filling the voids inside the Cr2O3 layer, which improve the density of the external Cr2O3 layer and retard the inward penetration of the nitrogen. As a result, the degree of internal nitrides is reduced. Consequently, the synergy effect of Ta and Ti in the Cr2O3 forming alloy is beneficial to improve the oxidation resistance.

Few-shot image classification aims to improve the performance of traditional image classification when faced with limited data. Its main challenge lies in effectively utilizing sparse sample label data to accurately predict the true feature distribution. Recent approaches have employed data augmentation techniques like random Mask or mixture interpolation to enhance the diversity and generalization of labeled samples. However, these methods still encounter several issues: (1) random Mask can lead to complete blockage or exposure of foreground, causing loss of crucial sample information; and (2) uniform data distribution after mixture interpolation makes it difficult for the model to differentiate between different categories and effectively distinguish their boundaries. To address these challenges, this paper introduces a novel data augmentation method based on saliency mask blending. Firstly, it selectively preserves key image features through adaptive selection and retention using visual feature occlusion fusion and confidence clipping strategies. Secondly, a visual feature saliency fusion approach is employed to calculate the importance of various image regions, guiding the blending process to produce more diverse and enriched images with clearer category boundaries. The proposed method achieves outstanding performance on multiple standard few-shot image classification datasets (miniImageNet, tieredImageNet, Few-shot FC100, and CUB), surpassing state-of-the-art methods by approximately 0.2–1%.

Abstract The rotor of the axial split‐phase bearingless switched reluctance motor (ASPBSRM) is structured with convex poles. The magnetic flux between the stator and the rotor varies with the rotor position, resulting in time‐dependent displacement stiffness and current stiffness coefficients of the suspension force. This scenario leads to a time‐varying characteristic of the suspension system. This study analyzed the electromagnetic characteristics of the suspension force in the ASPBSRM to characterize the time‐varying suspension system accurately. The study elucidated the hinge relationship between the magnetic flux in the suspension system and the rotor position. Moreover, a mathematical model for the suspension force considering the magnetic flux multitooth hinge was constructed by leveraging the advantages of the Maxwell stress method for the localized modelling and adopting the concept of the main edge magnetic path parallel analysis. Finally, the effectiveness of the model was verified through finite element analysis.

The coal and gas outburst and rockburst coupling disaster is becoming increasingly serious due to deep mining. To clarify the mechanism inducing the outburst–rockburst coupling disaster, a true triaxial single-sided unloading mechanical test was conducted with the aid of a true triaxial solid–thermal–gas coupling test device, an industrial computed tomography (CT) system, and an acoustic emission system. Through this test, the mechanical characteristics, meso crushing characteristics, and acoustic characteristics in the disaster formation process were obtained. Additionally, the outburst–rockburst coupling instability disaster law was verified by numerical simulation. The results demonstrated that the stress unloading degree of the coal body was negatively correlated with the initial gas pressure in the outburst–rockburst coupling disaster. The time domain parameter count and energy of acoustic emission exhibited a “bimodal” distribution pattern in the instability stage. The rockburst would occur when the peak value was in a “low-count and high-energy” state, while coal and gas outburst would occur when the peak value was in a “high-count and low-energy” state. The meso slice revealed that gas degradation promoted the development of microcracks in the coal body, and the penetration of cracks resulted in the main cracks of structural instability during rockburst. The coal and gas outburst was mainly attributed to the “cross” shear failure pattern of the coal body. These research findings may lay a foundation for the effective prevention and control of outburst–rockburst coupling disasters.

Multiple natural frequencies may be encountered when analyzing the essential natural vibration of a symmetric mechanical system or sub-structure system or a system with special parameters. The transfer matrix method (TMM) is a useful tool for analyzing the natural vibration characteristics of mechanical or structural systems. It derives a nonlinear eigen-problem (NEP) in general, even a transcendental eigen-problem. This investigation addresses the NEP in TMM and proposes a novel method, called the determinant-differentiation-based method, for calculating multiple natural frequencies and determining their multiplicities. Firstly, the characteristic determinant is differentiated with respect to frequency, transforming the even multiple natural frequencies into the odd multiple zeros of the differentiation of the characteristic determinant. The odd multiple zeros of the first derivative of the characteristic determinant and the odd multiple natural frequencies can be obtained using the bisection method. Among the odd multiple zeros, the even multiple natural frequencies are picked out by the proposed judgment criteria. Then, the natural frequency multiplicities are determined by the higher-order derivatives of the characteristic determinant. Finally, several numerical simulations including the multiple natural frequencies show that the proposed method can effectively calculate the multiple natural frequencies and determine their multiplicities.

Abstract Background Biopsies obtained from primary oesophageal squamous cell carcinoma (ESCC) guide diagnosis and treatment. However, spatial intra‐tumoral heterogeneity (ITH) influences biopsy‐derived information and patient responsiveness to therapy. Here, we aimed to elucidate the spatial ITH of ESCC and matched lymph node metastasis (LNmet). Methods Primary tumour superficial (PTsup), deep (PTdeep) and LNmet subregions of patients with locally advanced resectable ESCC were evaluated using whole‐exome sequencing (WES), whole‐transcriptome sequencing and spatially resolved digital spatial profiling (DSP). To validate the findings, immunohistochemistry was conducted and a single‐cell transcriptomic dataset was analysed. Results WES revealed 15.72%, 5.02% and 32.00% unique mutations in PTsup, PTdeep and LNmet, respectively. Copy number alterations and phylogenetic trees showed spatial ITH among subregions both within and among patients. Driver mutations had a mixed intra‐tumoral clonal status among subregions. Transcriptome data showed distinct differentially expressed genes among subregions. LNmet exhibited elevated expression of immunomodulatory genes and enriched immune cells, particularly when compared with PTsup (all P

Existing clinical treatments for tendinopathy mainly focus on reducing pain, whereas inhibiting or reversing disease progression remains challenging. Local therapeutic drugs, such as glucocorticoids, cause adverse effects on the metabolism of tendon tissues and injection-related complications. Therefore, new administration modalities for tendinopathy need to be developed. In this study, we designed a hydrogel-based microneedle (MN) system for the long-term transdermal delivery of our novel biological cell-free fat extract (CEFFE) to treat tendinopathies. We found that CEFFE-loaded MNs (CEFFE-MNs) had good biosafety and inhibited lipopolysaccharide (LPS)-induced apoptosis and matrix degradation in Achilles tendon cells of rats. The Achilles tendons of rats returned to their maximum mechanical strength after applying CEFFE-MNs. The administration of CEFFE-MNs had better anti-apoptosis and tendon repair-promoting effects than CEFEF injections in vivo. Transcriptome sequencing indicated that the anti-apoptosis effect of CEFFE-MNs was highly related to tumor necrosis factor (TNF) signaling. CEFFE-MNs inhibited the expression of TNF, TNF receptor 1, and downstream nuclear factor-kappa B signaling. Additionally, CEFFE-MNs rescued LPS-induced mitochondrial dynamics in tendon cells via the TNF-Drp1 axis. Our study reports a novel CEFFE-MN system that exhibits long-term anti-inflammatory and anti-apoptotic effects, suggesting it as a new treatment route for tendinopathy with broad clinical translation prospects.

Background and Objective: Severe pneumonia is a critical respiratory disease with high mortality. There is insufficient evidence on the efficacy and safety of traditional Chinese medicine (TCM) adjuvant therapy for severe pneumonia. This study aims to identify, describe, assess, and summarize the currently available high-quality design evidence on TCM adjuvant therapy for severe pneumonia to identify evidence gaps using the evidence mapping approach.Methods: Systematic searches were performed on English and Chinese online databases (PubMed, EMBASE, Cochrane Library, Web of Science, CNKI, WanFang Data, CQVIP, and SinoMed) to identify papers from inception until August 2023 for inclusion into the review. Randomized controlled trials (RCTs), systematic reviews (SRs), and meta-analyses concerning TCM adjuvant therapy for severe pneumonia or its complications in adults were included. The risk of bias in RCTs was evaluated by using the Cochrane Handbook ROB tool. The Assessment of Multiple Systematic Reviews 2 (AMSTAR-2), the Risk of Bias in Systematic Review (ROBIS) tool, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system were used to assess the methodological quality, risk of bias, and evidence quality of SRs or meta-analyses, respectively. Then, a bubble plot was designed to visually display information in four dimensions.Results: A total of 354 RCTs and 17 SRs or meta-analyses met the inclusion criteria. The published RCTs had several flaws, such as unreasonable design, limited sample size, insufficient attention to non-drug therapy studies and syndrome differentiation, improper selection or use of outcome indicators, and failure to provide high-quality evidence. Sixteen SRs or meta-analyses of methodological quality scored “Critically Low” confidence. Twelve SRs or meta-analyses were rated as “High Risk.” Most outcomes were rated as “Low” evidence quality. We found that TCM combined with conventional treatment could improve the clinical total effective rate and the TCM syndromes efficacy. The combined approach could also shorten mechanical ventilation time, infection control time, and length of hospital and ICU stay; significantly reduce temperature, respiratory rate, heart rate, white blood cell counts, levels of C-reactive protein, procalcitonin, blood inflammatory factors, bacteriological response, and D-dimer; decrease CPIS, APACHE II score, and PSI score; improve pulmonary imaging features, arterial blood gas indicators (including arterial oxygen pressure, arterial oxygen saturation, and oxygen index), and lung function (including forced vital capacity and forced expiratory volume in the first second) for severe pneumonia compared with conventional treatment only (p < 0.05). There was no significant difference in adverse reactions and incidence of adverse events (p > 0.05). In addition, compared with conventional treatment only, most SRs or meta-analyses concluded that TCM combined with conventional treatment was “Beneficial” or “Probably beneficial.”Conclusion: TCM combined with conventional treatment had advantages in efficacy, clinical signs, laboratory results, and life quality outcomes of severe pneumonia, with no difference in safety outcomes compared with conventional treatment only. QingJin Huatan decoction is the most promising target, and Xuanbai Chengqi decoction has a “Probably beneficial” conclusion. XueBiJing injection and TanReQing injection are two commonly used Chinese herbal injections for treating severe pneumonia, and both are “Probably beneficial.” However, there was a need for multicenter RCTs with large sample sizes and high methodological quality in the future. In addition, the methodological design and quality of SRs or meta-analyses should be improved to form high-quality, evidence-based medical evidence and provide evidence for the effectiveness and safety of TCM adjuvant therapy for severe pneumonia.

Objective To explore the effect of leptin on inflammation in psoriasis-like skin induced by imiquimod (IMQ). Methods Eight leptin gene knockout (ob/ob) obese mice and eight normal control (wt) mice were randomly divided into four groups (n=4 each), i.e. wt-IMQ, wt-vaseline, ob/ob-IMQ, ob/ob-vaseline. Mice were treated with either imiquimod (62.5 mg/back, 10 mg/ear) or Vaseline (same dose as imiquimod) for 6 consecutive days. The severity of skin lesions was evaluated with TSS score, and HE staining was used to assess inflammatory cells in the skin. Flow cytometry was used to detect the inflammatory cells in the dermis (dendritic cells, macrophages, neutrophils, etc.) and the epidermis (Langerhans cells, gdT cells), while the expression levels of IL-6, IL-17, TNF-α, IL-23 and other inflammatory factors were detected by qRT-PCR. Results ob/ob-IMQ mice displayed milder clinical symptoms, lesser increases in ear thickness, and lower TSS scores than wt-IMQ mice (all P0.05). Expression levels of inflammatory cytokines (IL-6 and IL-17) in ob/ ob-IMQ mice were lower than that in wt-IMQ mice. Conclusions Leptin may aggravate psoriasis in individuals accompanied with obesity. Leptin deficiency counteracts IMQ-induced psoriasis-like cutaneous inflammation.

Although traditional convolutional neural networks (CNN) have been significantly improved for target detection, they cannot be completely applied to objects with occlusions in commodity detection. Therefore, we propose a target detection method based on an improved YOLOv5 model and an improved attention mechanism algorithm is proposed to solve the commodity occlusion problem. This method improves the traditional YOLO deep convolution network, features a more detailed BiFPN layer, and performs lightweight two-way feature fusion, where the multidimensional features of the commodities are convolved and fused, thus improving the overall detection speed and accuracy of the YOLO-R algorithm. Feature entropy is introduced to the attention channel to restrict the threshold value and obtain the global information of the occlusion target. The global information obtained is fused with a bidirectional feature pyramid layer to enhance the robustness of the features. This method could accurately and quickly detect the occluded commodities and the detection accuracy has been greatly improved. Experiments show that the improved YOLO-R model can improve the accuracy and speed of commodity detection, and can achieve good results in objective evaluation. The average accuracy of commodity detection on the self-made product dataset is up to 97.80%, and the detection rate is 22.72F/s. Therefore, the method in this paper has high detection accuracy and fast detection speed.

Abstract Background Exosomal long non‐coding RNA (lncRNA) has been shown to be potential biomarker for cancer diagnosis and follow up. However, little is known about its application in esophageal squamous cell carcinoma (ESCC) detection. Here, we sought to develop a novel diagnostic model based on serum exosomal lncRNAs to improve ESCC screening efficiency. Methods A multiphase, case‐control study was conducted among 140 ESCC patients and 140 healthy controls. Microarray screening was performed to acquire differentially expressed exosomal lncRNAs in the discovery phase. The diagnostic model Index I was constructed based on a panel of three lncRNAs using logistic regression in the training phase, and were confirmed in a subsequent validation phase. A receiver operating characteristic (ROC) curve was generated to calculate the diagnostic value. The effects of the selected lncRNAs level on ESCC mortality were evaluated using a Cox hazard regression model and Kaplan‐Meier curve analysis, and the expression level with clinicopathological features was also calculated. Finally, we explored the oncogenic potential of candidate lncRNA RASSF8‐AS1 in vitro and by target mRNA sequencing. Results Index I was able to discriminate ESCC patients from healthy controls, and showed superiority to classic tumor biomarkers. Moreover, serum levels of the exosomal lncRNAs correlated with clinicopathological features and prognosis. The in vitro assays showed that RASSF8‐AS1 played an oncogenic role in ESCC. Target mRNA scanning results suggested involvement of RASSF8‐AS1 in tumor immunity and metabolism. Conclusion The newly identified serum exosomal lncRNAs could be used as new biomarkers for ESCC, and showed oncogenic potential in ESCC.

The patella's inferior pole transmits force generated by contraction of the quadriceps muscle to the tibial tuberosity through the attached patellar ligament, thus completing knee extension. Therefore, fractures of the patella's inferior pole disrupt the coherence of mechanical transmission in the lower extremities. There appears to be no consensus among trauma centers regarding the treatment of infrapatellar pole fractures, primarily because there is no consistent design or application of internal fixation for this type of fracture. We designed a new internal implant similar to the smile necklace based on our previous study. This smile‐necklace plate (SNP) has the advantage of both plate fixation and tension‐band wiring fixation, permitting early rehabilitation, especially in osteoporotic comminuted infrapatellar pole fractures. Finite element analysis helped verify the biomechanical advantages of the SNP in comparison with existing studies. Hence, this novel implant is a promising treatment option for inferior pole patellar fractures.

Background: Stable fixation is crucial in fracture treatment. Currently, optimal fracture fixation devices with osteoinductivity, mechanical compatibility, and corrosion resistance are urgently needed for clinical practice. Niobium (Nb), whose mechanical properties are similar to those of bone tissue, has excellent biocompatibility and corrosion resistance, so it has the potential to be the most appropriate fixation material for internal fracture treatment. However, not much attention has been paid to the use of Nb in the area of clinical implants. Yet its role and mechanism of promoting fracture healing remain unclear. Hence, this study aims at elucidating on the effectiveness of Nb by systematically evaluating its osteogenic performance via in vivo and ex vivo tests. Methods: Systematic in vivo and in vitro experiments were conducted to evaluate the osteogenic properties of Nb. In vitro experiments, the biocompatibility and osteopromoting activity of Nb were assessed. And the osteoinductive activity of Nb was assessed by alizarin red, ALP staining and PCR test. In vivo experiments, the effectiveness and biosafety of Nb in promoting fracture healing were evaluated using a rat femoral fracture model. Through the analysis of gene sequencing results of bone scab tissues, the upregulation of PI3K-Akt pathway expression was detected and it was verified by histochemical staining and WB experiments. Results: Experiments in this study had proved that Nb had excellent in-vitro cell adhesion and proliferation-promoting effects without cytotoxicity. In addition, ALP activity, alizarin red staining and semi-quantitative analysis in the Nb group had indicated its profound impact on enhancing osteogenic differentiation of MC3T3-E1 cells. We also found that the use of Nb implants can accelerate fracture healing compared to that with Ti6Al4V using an animal model of femur fracture in rats, and the biosafety of Nb was confirmed in vivo via histological evaluation. Furthermore, we found that the osteogenic effects of Nb were achieved through activation of the PIK/Akt3 signalling pathway. Conclusion: As is shown in the present research, Nb possessed excellent biosafety in clinical implants and accelerated fracture healing by activating the PI3K-Akt signalling pathway, which had good prospects for clinical translation, and it can replace titanium alloy as a material for new functional implants.

To solve the low energy utilization and high pollutant emission caused by the incompatibility between the combustion flame shape and the heating surface structure of the stove chamber, the concept of flame adaptability was proposed based on the adaptability theory of synergetics. A flame adaptability evaluation model was established to quantitatively evaluate the adaptability of flames and an integrated Chinese cooking stove. Six suitability evaluation indexes were determined, including temperature uniformity, reflux area, pollutant emission, flow resistance, heat transfer intensity, and heat transfer power consumption. And the flame adaptability of the integrated Chinese cooking stove under different working conditions was calculated and analyzed. The study shows that after adding an adaptive structure with the appropriate structural parameters, the experimentally measured flame length and the NOx emission had been reduced by about 48.6% and 80%, respectively, and the adaptive degree of the flame and the stove chamber is the highest at 0.841 with proper structure parameter, while it was 0.406 before adaptability improvement, which shows that it is feasible to applying the flame adaptability method to evaluate the fit state of flame and combustion space, and more importantly, it provides a viable way for efficient energy utilization.

In recent years, the development of image super-resolution (SR) has explored the capabilities of convolutional neural networks (CNNs). The current research tends to use deeper CNNs to improve performance. However, blindly increasing the depth of the network does not effectively enhance its performance. Moreover, as the network depth increases, more issues arise during the training process, requiring additional training techniques. In this paper, we propose a lightweight image super-resolution reconstruction algorithm (SISR-RFDM) based on the residual feature distillation mechanism (RFDM). Building upon residual blocks, we introduce spatial attention (SA) modules to provide more informative cues for recovering high-frequency details such as image edges and textures. Additionally, the output of each residual block is utilized as hierarchical features for global feature fusion (GFF), enhancing inter-layer information flow and feature reuse. Finally, all these features are fed into the reconstruction module to restore high-quality images. Experimental results demonstrate that our proposed algorithm outperforms other comparative algorithms in terms of both subjective visual effects and objective evaluation quality. The peak signal-to-noise ratio (PSNR) is improved by 0.23 dB, and the structural similarity index (SSIM) reaches 0.9607.

The effects of water content and water activity on the lipid stability of air-dried hairtail (Trichiurus haumela) were investigated during chilled storage. Air-dried hairtail samples with high and low water contents were comparatively analyzed over 8 days of storage at 4 °C. The results indicated that the decreases in water activity and increases in the NaCl content significantly inhibited lipid oxidation in the air-dried hairtail samples. The peroxidation value (PV), conjugated diene value (CD), thiobarbituric acid reactive substance (TBARS) value, and p-anisidine value (p-AnV) of the air-dried hairtail significantly increased with the extension of storage time. The low water content significantly inhibited the activity of neutral and alkaline lipase, in addition to lipoxygenase, and retarded the rapid increases in the non-esterified fatty acid (NEFA) content in the hairtail samples. The correlation analysis results showed that the TBARS, p-AnV, and lipase activity were positively correlated in the air-dried hairtail samples, and the lower water content significantly inhibited the progress of lipid oxidation. This study offers a theoretical framework for the industrial processing and storage of air-dried hairtail products.

Auxin Response Factors (ARFs) mediate auxin signaling and govern diverse biological processes. However, a comprehensive analysis of the ARF gene family and identification of their key regulatory functions have not been conducted in Melastoma dodecandrum, leading to a weak understanding of further use and development for this functional shrub. In this study, we successfully identified a total of 27 members of the ARF gene family in M. dodecandrum and classified them into Class I–III. Class II–III showed more significant gene duplication than Class I, especially for MedARF16s. According to the prediction of cis-regulatory elements, the AP2/ERF, BHLH, and bZIP transcription factor families may serve as regulatory factors controlling the transcriptional pre-initiation expression of MedARF. Analysis of miRNA editing sites reveals that miR160 may play a regulatory role in the post-transcriptional expression of MeARF. Expression profiles revealed that more than half of the MedARFs exhibited high expression levels in the stem compared to other organs. While there are some specific genes expressed only in flowers, it is noteworthy that MedARF16s, MedARF7A, and MedARF9B, which are highly expressed in stems, also demonstrate high expressions in other organs of M. dodecandrum. Further hormone treatment experiments revealed that these MedARFs were sensitive to auxin changes, with MedARF6C and MedARF7A showing significant and rapid changes in expression upon increasing exogenous auxin. In brief, our findings suggest a crucial role in regulating plant growth and development in M. dodecandrum by responding to changes in auxin. These results can provide a theoretical basis for future molecular breeding in Myrtaceae.

Heat shock factors (HSFs) are the key regulators of heat stress responses and play pivotal roles in tissue development and the temperature-induced regulation of secondary metabolites. In order to elucidate the roles of HSFs in Cymbidium ensifolium, we conducted a genome-wide identification of CeHSF genes and predicted their functions based on their structural features and splicing patterns. Our results revealed 22 HSF family members, with each gene containing more than one intron. According to phylogenetic analysis, 59.1% of HSFs were grouped into the A subfamily, while subfamily HSFC contained only two HSFs. And the HSF gene families were differentiated evolutionarily between plant species. Two tandem repeats were found on Chr02, and two segmental duplication pairs were observed on Chr12, Chr17, and Chr19; this provided evidence for whole-genome duplication (WGD) events in C. ensifolium. The core region of the promoter in most CeHSF genes contained cis-acting elements such as AP2/ERF and bHLH, which were associated with plant growth, development, and stress responses. Except for CeHSF11, 14, and 19, each of the remaining CeHSFs contained at least one miRNA binding site. This included binding sites for miR156, miR393, and miR319, which were responsive to temperature and other stresses. The HSF gene family exhibited significant tissue specificity in both vegetative and floral organs of C. ensifolium. CeHSF13 and CeHSF15 showed relatively significant expression in flowers compared to other genes. During flower development, CeHSF15 exhibited markedly elevated expression in the early stages of flower opening, implicating critical regulatory functions in organ development and floral scent-related regulations. During the poikilothermic treatment, CeHSF14 was upregulated over 200-fold after 6 h of heat treatment. CeHSF13 and CeHSF14 showed the highest expression at 6 h of low temperature, while the expression of CeHSF15 and CeHSF21 continuously decreased at a low temperature. The expression patterns of CeHSFs further confirmed their role in responding to temperature stress. Our study may help reveal the important roles of HSFs in plant development and metabolic regulation and show insight for the further molecular design breeding of C. ensifolium.

Existing vision-based fatigue detection methods commonly utilize RGB cameras to extract facial and physiological features for monitoring driver fatigue. These features often include single indicators such as eyelid movement, yawning frequency, and heart rate. However, the accuracy of RGB cameras can be affected by factors like varying lighting conditions and motion. To address these challenges, we propose a non-invasive method for multi-modal fusion fatigue detection called RPPMT-CNN-BiLSTM. This method incorporates a feature extraction enhancement module based on the improved Pan–Tompkins algorithm and 1D-MTCNN. This enhances the accuracy of heart rate signal extraction and eyelid features. Furthermore, we use one-dimensional neural networks to construct two models based on heart rate and PERCLOS values, forming a fatigue detection model. To enhance the robustness and accuracy of fatigue detection, the trained model data results are input into the BiLSTM network. This generates a time-fitting relationship between the data extracted from the CNN, allowing for effective dynamic modeling and achieving multi-modal fusion fatigue detection. Numerous experiments validate the effectiveness of the proposed method, achieving an accuracy of 98.2% on the self-made MDAD (Multi-Modal Driver Alertness Dataset). This underscores the feasibility of the algorithm. In comparison with traditional methods, our approach demonstrates higher accuracy and positively contributes to maintaining traffic safety, thereby advancing the field of smart transportation.

Dynamic visual vending machines are rapidly growing in popularity, offering convenience and speed to customers. However, there is a prevalent issue with consumers damaging goods and then returning them to the machine, severely affecting business interests. This paper addresses the issue from the standpoint of defect detection. Although existing industrial defect detection algorithms, such as PatchCore, perform well, they face challenges, including handling goods in various orientations, detection speeds that do not meet real-time monitoring requirements, and complex backgrounds that hinder detection accuracy. These challenges hinder their application in dynamic vending environments. It is crucial to note that efficient visual features play a vital role in memory banks, yet current memory repositories for industrial inspection algorithms do not adequately address the problem of location-specific feature redundancy. To tackle these issues, this paper introduces a novel defect detection algorithm for goods using adaptive subsampling and partitioned memory banks. Firstly, Grad-CAM is utilized to extract deep features, which, in combination with shallow features, mitigate the impact of complex backgrounds on detection accuracy. Next, graph convolutional networks extract rotationally invariant features. The adaptive subsampling partitioned memory bank is then employed to store features of non-defective goods, which reduces memory consumption and enhances training speed. Experimental results on the MVTec AD dataset demonstrate that the proposed algorithm achieves a marked improvement in detection speed while maintaining accuracy that is comparable to state-of-the-art models.

Though conserved in higher plants, the WOX transcription factors play crucial roles in plant growth and development of Melastoma dodecandrum Lour., which shows pioneer position in land ecosystem formation and produces nutritional fruits. Identifying the WOX family genes in M. dodecandrum is imperative for elucidating its growth and development mechanisms. However, the WOX genes in M. dodecandrum have not yet been characterized. In this study, by identification 22 WOX genes in M. dodecandrum based on current genome data, we classified family genes into three clades and nine types with homeodomains. We highlighted gene duplications of MedWOX4, which offered evidences of whole-genome duplication events. Promoter analysis illustrated that cis-regulatory elements related to light and stress responses and plant growth were enriched. Expression pattern and RT-qPCR results demonstrated that the majority of WOX genes exhibited expression in the stem. MedWOX13s displayed highest expression across various tissues. MedWOX4s displayed a specific expression in the stem. Collectively, our study provided foundations for elucidating WOX gene functions and further molecular design breeding in M. dodecandrum.

Abstract Backgrounds High level of anion gap (AG) was associated with organic acidosis. This study aimed to explore the relationship between delta AG (ΔAG = AGmax − AGmin) during first 3 days after intensive care unit (ICU) admission and hospital mortality for patients admitted in the cardiothoracic surgery recovery unit (CSRU). Methods In this retrospective cohort study, we identified patients from the open access database called Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC III). A logistic regression model was established to predict hospital mortality by adjusting confounding factors using a stepwise backward elimination method. We conducted receiver operating characteristic (ROC) curves to compare the diagnostic performance of acid–base variables. Cox regression model and Kaplan Meier curve were applied to predict patients’ 90-day overall survival (OS). Results A total of 2,860 patients were identified. ΔAG was an independent predictive factor of hospital mortality (OR = 1.24 per 1 mEq/L increase, 95% CI: 1.11–1.39, p

Abstract Background & aims Sarcopenia is associated with poor clinical outcomes of patients who underwent esophagectomy. The current diagnostic criteria for sarcopenia are complex and laborious. We aimed to employ the simple and economic indicator sarcopenia index (SI = creatinine/cystatin C ×100) to screen for sarcopenia and to evaluate its prognostic value in patients with esophageal cancer (EC). Methods Older participants in the National health and nutrition examination survey (NHANES) database (1999–2002) were divided into three groups according to tertiles of the SI value to explore the feasibility of SI in the diagnosis of sarcopenia. Restricted cubic spline (RCS) was utilized to show the non-linear relationship between all-cause mortality and SI. Patients with EC admitted to Jinling Hospital were enrolled to validate the efficacy and prognostic value of SI. Cut-off values of SI were determined using receiver operating characteristic curves. Multivariable logistic analyses and Cox analyses were used to identify the independent factors of postoperative complications and long-term survival, respectively. Results A total of 989 participants were identified from the NHANES database. SI showed the diagnostic value of sarcopenia (tertile 1 vs. tertile 3: odds ratio [OR]=3.67, 95% confidence interval [CI]: 1.52–8.87, p=0.004; tertile 2 vs. tertile 3: OR=1.79, 95% CI: 0.75–4.28, p=0.191) adjusted for race, gender, and body mass index (BMI). Individuals with SI ≤ 68 had a poorer overall survival (OS) (hazard ratio [HR]=2.14, 95% CI: 1.71–2.68, p

Objective: The aim of this study is to generate virtual noncontrast (VNC) computed tomography (CT) from intravenous enhanced CT by using Unet convolutional neural network (CNN). The differences among enhanced, VNC, and noncontrast CT in proton dose calculation were compared. Methods: A total of 30 groups of CT images of patients who received enhanced and noncontrast CT were selected. Enhanced and noncontrast CT were registered. Among these patients, 20 groups of the CT images were chosen as the training set. Enhanced CT images were used as the input, and the corresponding noncontrast CT images were used as output to train the Unet neural network. The remaining 10 groups of CT images were chosen as the test set. VNC images were generated by the trained Unet neural network. The same proton radiotherapy plan for esophagus cancer was designed based on three images. Proton dose distributions in enhanced, VNC, and noncontrast CT were calculated. The relative dose differences in enhanced CT with VNC and noncontrast CT were analyzed. Results: The mean absolute error (MAE) of the CT values between enhanced and noncontrast CT was 32.3 ± 2.6 HU. The MAE of the CT values between VNC and noncontrast CT was 6.7 ± 1.3 HU. The mean values of the enhanced CT in the great vessel, heart, lung, liver, and spinal cord were significantly higher than those of noncontrast CT, he differences were 97, 83, 42, 40, and 10 HU, respectively. The mean values of the VNC CT showed no significant difference with noncontrast CT. The differences among enhanced, VNC, and noncontrast CT in terms of the average relative proton dose for clinical target volume (CTV), heart, great vessels, and lung were also investigated. The average relative proton doses of the enhanced CT for these organs were significantly lower than those of noncontrast CT. The largest difference was observed in the great vessel, while the differences in other organs were relatively small. The γ-passing rates of the enhanced and VNC CT were calculated by 2% dose difference and 2 mm distance to agreement. Results showed that the mean γ-passing rate of VNC CT was significantly higher than enhanced CT (p

Abstract Background In recent years, preoperative nutrition has received great attention, especially for patients who received surgical reconstruction of the digestive tract such as esophagectomy. Preoperative nutrition therapy was reported to accelerate the patient's postoperative recovery. In addition, immune suppression, nausea, and vomiting may lead to poor immune and nutritional status of patients with esophageal cancer who underwent neoadjuvant chemotherapy (NAC), which is not conducive to surgery. Therefore, preoperative nutritional treatment is necessary for patients with esophageal cancer who underwent NAC. Method Patients with esophageal cancer who received NAC at Nanjing Jinling Hospital from January 2018 to September 2020 were retrospectively identified. Patients were divided into enteral immunonutrition (EIN) group (those who received a conventional diet and immunonutrition supplement, Peptisorb, Nutricia, 500 mL/day * 7 via oral intake), and control group (those who only received a conventional diet were divided into). The primary outcomes were immune and nutritional indicators changes, including immunoglobulin M (IgM), immunoglobulin A (IgA), immunoglobulin G (IgG), and albumin (ALB), which were measured at preoperative day (PRD) 7, PRD-1, postoperative day (POD) 1 and POD-7. The secondary outcomes were postoperative complications, adverse reactions, and length of hospital stay. Results A total of 124 eligible patients were included in the study, with 21 patients in EIN group. After 1:2 matching, significant difference in baseline characteristics between the two groups was not observed (EIN: n = 21, Control group: n = 42). Compared with the control group, the IgA is significantly increased in the EIN group at POD-7 (p = 0.017). However, we observed that the IgM level in the control group was significantly higher than those in the EIN group at POD-7 (p = 0.007). The incidence of pneumonia and total complications in the EIN group were significantly lower than those in control group (p = 0.024, p = 0.028, respectively). There is no significant difference in ALB and adverse reactions between two groups (p = 0.303, p = 0.108, respectively). Conclusion Immunonutritional supplement after NAC is an effective strategy to improve the postoperative immune status of esophageal cancer patients and could reduce the incidence of infectious complication. More well-designed prospective studies are needed to verify and update our finding.

AP2/ERF transcription factors play crucial roles in various biological activities, including plant growth, development, and responses to biotic and abiotic stressors. However, limited research has been conducted on the AP2/ERF genes of Melastoma dodecandrum for breeding of this potential fruit crop. Leveraging the recently published whole genome sequence, we conducted a comprehensive assessment of this superfamily and explored the expression patterns of AP2/ERF genes at a genome-wide level. A significant number of genes, totaling 218, were discovered to possess the AP2 domain sequence and displayed notable structural variations among five subfamilies. An uneven distribution of these genes was observed on 12 pseudochromosomes as the result of gene expansion facilitated by segmental duplications. Analysis of cis-acting elements within promoter sites and 87.6% miRNA splicing genes predicted their involvement in multiple hormone responses and abiotic stresses through transcriptional and post-transcriptional regulations. Transcriptome analysis combined with qRT-PCR results indicated that certain candidate genes are involved in tissue formation and the response to developmental changes induced by IAA hormones. Overall, our study provides valuable insights into the evolution of ERF genes in angiosperms and lays a solid foundation for future breeding investigations aimed at improving fruit quality and enhancing adaptation to barren land environments.

Magnesium (Mg) alloys that have both antibacterial and osteogenic properties are suitable candidates for orthopedic implants. However, the fabrication of ideal Mg implants suitable for bone repair remains challenging because it requires implants with interconnected pore structures and personalized geometric shapes. In this study, we fabricated a porous 3D-printed Mg-Nd-Zn-Zr (denoted as JDBM) implant with suitable mechanical properties using selective laser melting technology. The 3D-printed JDBM implant exhibited cytocompatibility in MC3T3-E1 and RAW267.4 cells and excellent osteoinductivity in vitro. Furthermore, the implant demonstrated excellent antibacterial ratios of 90.0% and 92.1% for methicillin-resistant S. aureus (MRSA) and Escherichia coli, respectively. The 3D-printed JDBM implant prevented MRSA-induced implant-related infection in a rabbit model and showed good in vivo biocompatibility based on the results of histological evaluation, blood tests, and Mg2+ deposition detection. In addition, enhanced inflammatory response and TNF-α secretion were observed at the bone-implant interface of the 3D-printed JDBM implants during the early implantation stage. The high Mg2+ environment produced by the degradation of 3D-printed JDBM implants could promote M1 phenotype of macrophages (Tnf, iNOS, Ccl3, Ccl4, Ccl5, Cxcl10, and Cxcl2), and enhance the phagocytic ability of macrophages. The enhanced immunoregulatory effect generated by relatively fast Mg2+ release and implant degradation during the early implantation stage is a potential antibacterial mechanism of Mg-based implant. Our findings indicate that 3D-printed porous JDBM implants, having both antibacterial property and osteoinductivity, hold potential for future orthopedic applications.