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Abstract In the era of 5G, billions of terminal devices achieve global interconnection and intercommunication, which leads to the generation of massive data. However, the existing cloud‐based data‐sharing mechanism faces challenges such as sensitive information leakage and data islands, which makes it difficult to achieve secure sharing across domains. In this paper, the authors propose a fine‐grained data‐sharing scheme based on blockchain and ciphertext policy attribute‐based encryption, and design a verifiable outsourced computation method to reduce the computational pressure of end users. Second, the authors comprehensively consider the user's identity privacy and transaction privacy, and propose a multi‐level privacy protection method based on ring signature and garbled bloom filter, which enhance the user's data privacy and availability, and prevent the traceability of requests. Finally, the authors design a set of interconnected smart contracts, and verify that their scheme can achieve secure and efficient data sharing through security analysis and performance testing.

Effective construction project planning relies on addressing constraints related to materials, labor, equipment, and others. Planning meetings are typical venues for stakeholders to identify, communicate, and remove constraints. However, a critical gap exists in lacking an automated approach to identify, classify, analyze, and track constraint discussions during onsite planning meetings. Therefore, this research aims to 1. develop a natural language processing model to classify constraints in meeting discussions; 2. uncover the discussion patterns of managers and foremen regarding various constraints; and 3. extract the root causes for constraints, evaluate their impacts, and prepare managers to develop practical solutions for constraint removal. This research collected meeting transcripts from 94 onsite planning meetings of a building project, spanning 263,836 words. Next, this research leveraged a general pretrained transformer (GPT) to segment discussion dialogs into topics. A Bidirectional Encoder Representations from Transformers (BERT)-based model was developed to categorize constraint types for each topic. The constraint patterns among meeting attendees were assessed. Furthermore, a GPT-based tool was devised to track root causes, impacts, and solutions for various constraints. Test results revealed an 8.8% improvement in constraint classification accuracy compared with the traditional classification model. An occupational characteristic in constraint discussion was observed in that the management team tended to balance their focus on various constraints, while foremen concentrated on more practical issues. This research contributes to the body of knowledge by leveraging language models to analyze construction planning meetings. The findings facilitate project managers in establishing constraint logs for diagnosing and prognosticating planning issues.

Agricultural food commodities are highly susceptible to contamination by fungi and mycotoxins, which cause great economic losses and threaten public health. New technologies such as gamma ray irradiation, ultraviolet radiation, electron beam irradiation, microwave irradiation, pulsed light, pulsed electric fields, plasma, ozone, etc. can solve the problem of fungal and mycotoxin contamination which cannot be effectively solved by traditional food processing methods. This paper summarizes recent advancements in emerging food decontamination technologies used to control various fungi and their associated toxin contamination in food. It discusses the problems and challenges faced by the various methods currently used to control mycotoxins, looks forward to the new trends in the development of mycotoxin degradation methods in the future food industry, and proposes new research directions.

BackgroundObservational studies have hinted at a correlation between the gut microbiota and spinal pain (SP). However, the impact of the gut microbiota on SP remains inconclusive.MethodsIn this study, we employed a two-sample Mendelian randomization (MR) analysis to explore the causal relationship between the gut microbiota and SP, encompassing neck pain (NP), thoracic spine pain (TSP), low back pain (LBP), and back pain (BP). The compiled gut microbiota data originated from a genome-wide association study (GWAS) conducted by the MiBioGen consortium (n = 18,340). Summary data for NP were sourced from the UK Biobank, TSP from the FinnGen Biobank, and LBP from both the UK Biobank and FinnGen Biobank. Summary data for BP were obtained from the UK Biobank. The primary analytical approach for assessing causal relationships was the Inverse Variance Weighted (IVW) method, supplemented by various sensitivity analyses to ensure result robustness.ResultsThe IVW analysis unveiled 37 bacterial genera with a potential causal relationship to SP. After Benjamini-Hochberg corrected test, four bacterial genera emerged with a strong causal relationship to SP. Specifically, Oxalobacter (OR: 1.143, 95% CI 1.061–1.232, P = 0.0004) and Tyzzerella 3 (OR: 1.145, 95% CI 1.059–1.238, P = 0.0007) were identified as risk factors for LBP, while Ruminococcaceae UCG011 (OR: 0.859, 95% CI 0.791–0.932, P = 0.0003) was marked as a protective factor for LBP, and Olsenella (OR: 0.893, 95% CI 0.839–0.951, P = 0.0004) was recognized as a protective factor for low back pain or/and sciatica. No significant heterogeneity or horizontal pleiotropy was observed through alternative testing methods.ConclusionThis study establishes a causal relationship between the gut microbiota and SP, shedding light on the “gut-spine” axis. These findings offer novel perspectives for understanding the etiology of SP and provide a theoretical foundation for potential interventions targeting the gut microbiota to prevent and treat SP.

Abstract Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted “ComBat” to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package “Seurat” in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan–Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.

High-pixel-number synchrotron radiation computed tomography (CT) has the advantages of high sensitivity, high resolution, and a large field of view. It has been widely used in biomedicine, cultural heritage research, non-destructive testing, and other fields. The Nyquist sampling theorem states that when the detector’s pixels per row are increased, it requires more CT projections, resulting in a lengthened CT scan time and increased radiation damage. Sparse-view CT can significantly reduce radiation damage and improve the projection data acquisition speed. However, there is insufficient sparse projection data, and the slices reconstructed show aliasing artifacts. Currently, aliasing artifact correction processes more medical CT images, and the number of pixels of such images is small (mainly 512×512 pixels). This paper presents an aliasing artifact correction algorithm based on deep learning for synchrotron radiation CT with a high pixel number (1728×1728 pixels). This method crops high-pixel-number CT images with aliasing artifacts into patches with overlapping features. During the network training process, a convolutional neural network is utilized to enhance the details of the patches, after which the patches are reintegrated into a new CT slice. Subsequently, the network parameters are updated to optimize the new CT slice that closely approximates the full-view slice. To align with practical application requirements, the neural network is trained using only three samples to optimize network parameters and applied successfully to untrained samples for aliasing artifact correction. Comparative analysis with typical deep learning aliasing artifact correction algorithms demonstrates the superior ability of our method to correct aliasing artifacts while preserving image details more effectively. Furthermore, the effect of aliasing artifact correction at varying levels of projection sparsity is investigated, revealing a positive correlation between image quality after deep learning processing and the number of projections. However, the trade-off between rapid experimentation and artifact correction remains a critical consideration.

Abstract Objective The aim of the present study was to explore the effect of cytoplasmic transduction peptide (CTP)-phosphatase and tensin homolog (PTEN) on the proliferation, cell cycle, apoptosis, migration and invasion of bladder cancer cells and the underlying molecular mechanism. Methods A eukaryotic expression vector, pTT5-CTP-PTEN, was constructed. The constructed vector was transfected into HEK 293-6E cells to express a fusion protein, CTP-PTEN. The fusion protein was purified. 5637 bladder cancer cells were cocultured with purified CTP-PTEN fusion protein. Target gene expression, protein expression, cell proliferation, cell cycle, apoptosis, cell invasion and cell migration were examined by reverse transcription polymerase chain reaction (RT-PCR), western blot, MTT assay, flow cytometry, Transwell assay, and cell scratch assay, respectively. Results Both PTEN and CTP-PTEN fusion protein inhibited the proliferation, cell cycle, invasion and migration of bladder cancer cells and promoted the apoptosis of bladder cancer cells. The effect of CTP-PTEN was more significant. Conclusions The fused expression of CTP and PTEN significantly increased the penetrability of the tumor suppressor gene PTEN into cancer cells. The CTP-PTEN fusion protein exhibited a significant carcinostatic effect on 5637 bladder cancer cells.

In synchrotron radiation X-ray imaging, the imaging field of view and spatial resolution are mutually restricted, which makes it impossible to have both a large field of view and high resolution when carrying out experiments. Constructing an oversampled image through the micro-scanning method and using the deconvolution algorithm to eliminate the point spread function introduced by pixel overlap can increase the resolution under a fixed imaging field of view, thereby improving the ratio of the field of view to the spatial resolution. In this paper, numerical simulation and synchrotron radiation experiments are carried out with a different number of micro-scanning steps. In numerical simulation experiments only affected by the image pixel size, as the number of micro-scanning steps increases, the ability of the oversampled image with deconvolution to improve the resolution is stronger. The achievable resolution of the oversampled image with deconvolution is basically the same as that of the sample image. In the synchrotron radiation experiments, the resolution of the oversampled image with deconvolution in the 2 × 2 mode is significantly improved. However, as the number of micro-scanning steps increases, the resolution improvement is limited, or even no longer improved. Finally, by analyzing the results of numerical simulation and synchrotron radiation experiments, three factors (four other factors affecting the resolution besides the camera resolution, translational accuracy of micro-scanning, and the signal-to-noise ratio of projections) affecting the micro-scanning method are proposed and verified by experiments.

In-line X-ray phase contrast imaging, which is simple to experiment with, provides significantly higher sensitivity, compared to conventional X-ray absorption imaging. The inversion of the relationship between recorded Fresnel diffraction intensity and the phase shift induced by the object is called phase retrieval. The transport of intensity equation (TIE), a simple method of phase retrieval, which is solved by the fast Fourier transform algorithm proposed by Paganin et al., has been widely adopted. However, the existing method suffers from excessive suppression of high-frequency information, resulting in loss of image details after phase retrieval, or insufficient detail contrast, leading to blurry images. Here, we present a straightforward extension of the two-distance FFT-TIE method by modifying the Fourier filter through the use of a five-point approximation to calculate the inverse Laplacian in a discrete manner. Additionally, we utilize a combination of continuous Fourier transform and a four-point approximation to compute the gradient operator. The method is evaluated by simulating samples with a shape similar to the resolution test map and by using a photograph of a dog for further evaluation. The algorithm that incorporates the modified gradient operator and the algorithm that solely utilizes the continuous Fourier transform for gradient computation were compared with the results obtained using the two-distance FFT-TIE method. The comparisons were conducted using the results obtained from two distances from the sample to the detector. The results show that this method improves the contrast of spatial details and reduces the suppression of high spatial frequencies compared to the two-distance FFT-TIE method. Furthermore, in the low-frequency domain, our algorithm does not lose much information compared to the original method, yielding consistent results. Furthermore, we conducted our experiments using carbon rods. The results show that both our method and the FFT-TIE method exhibit low-frequency distortion due to the requirement of close proximity between the absorption maps and the detector. However, upon closer inspection, our proposed method demonstrates superior accuracy in reproducing the finer details of the carbon rod fibers.

PurposeDigital imaging technology and a real-time fluorescent quantitative polymerase chain reaction (RQ-PCR) were used to determine the changes in dental plaque caused by different toothbrushing tools.MethodsA total of 120 subjects were selected and divided into four groups: a manual toothbrush group, a manual toothbrush combined with an oral irrigator group, an electric toothbrush combined with an oral irrigator group, and an electric toothbrush group. We compared the changes in plaque count, plaque area, and colony colonization of the four groups after different cleaning tools had been used for a period of time.ResultsDental plaque count and plaque area decreased in all four groups. The decreases in plaque count and Streptococcus mutans in the electric toothbrush combined with an oral irrigator group were significantly higher than those in other groups.ConclusionElectric toothbrush combined with an oral irrigator shows a good result for plaque removal effect. Digital image analysis combined with biological methods can be used to evaluate dental plaque.

Background: To explore the role and clinical significance of serum adiponectin and leptin levels in patients with psoriasis accompanied by atherosclerosis. Methods: Eighty patients diagnosed with psoriasis in our dermatology department and 40 healthy people in our physical examination centre were included as the study group and control group, respectively. All the included patients underwent fasting blood and serum tests. Levels of adiponectin, leptin, and the blood lipid content; colour Doppler ultrasonography of both common carotid arteries, internal carotid and external carotid arteries; and intimal-medial thickness (IMT) and carotid plaque were evaluated. Results: In the study group, the leptin level increased, and the serum adiponectin level decreased; these levels were statistically significantly different compared with those in the control group (t = 6.774, P < 0.001 and t = –3.511, P < 0.05, respectively). IMT was negatively correlated with adiponectin levels (r = –0.378, P < 0.001) and positively correlated with leptin levels (r = 0.581, P < 0.001). Conclusions: The imbalanced expression of serum and adiponectin levels will aggravate psoriasis and promote the occurrence of atherosclerosis. Serum levels can be used to assess the disease severity, detect vascular lesions early, and prevent the development of psoriasis to cardiovascular disease.

Background: Acne vulgaris (AV) is a chronic inflammatory disease of the pilosebaceous unit. Many factors are involved in the occurrence of acne. It has been confirmed that some adipokines play an important role in the development of AV. Irisin is a novel adipokine, which is highly expressed in skeletal muscle, liver, and fat. It improves insulin resistance (IR) by inducing the browning of white adipose tissue, increasing heat production and energy expenditure. Objective: The purpose of this study was to investigate the role of serum irisin as an adipokine to explore its function in the pathogenesis of AV and its correlation with IR, and whether it can be used as a potential biomarker of insulin sensitivity. Although the hyperinsulinemic-euglycemic clamp remains the gold standard for accurate determination of IR, it cannot be performed routinely. Various alternative simpler measures have been used, the most common being homeostasis model assessment. However, these metrics are limited by their accuracy, cost, and blood collection requirements.[1] Therefore, an effective and feasible serum biomarker is an attractive and relatively straightforward method, which may provide clinicians with a more accurate and simple method for the prediction and diagnosis of IR. IR can often be detected before other symptoms appear, so establishing an early diagnosis method will allow for the appropriate treatment of patients before the disease develops. Patients and Methods: The study included 171 subjects; 115 patients with newly diagnosed AV and 56 apparently healthy subjects. The contents of irisin and interleukin-1 alpha in serum were determined by enzyme-linked immunosorbent assay. The IR index was calculated by the homeostasis model. Results: Serum irisin levels in AV patients and control group were (24.0 ± 11.3) and (104.3 ± 27.0) ng/dl, respectively, which were significantly lower than those in control group (P < 0.001). Serum irisin was negatively correlated with IR (r = −0.711, P 0.001). The sensitivity of irisin was 100.0%, the specificity was 92.8%, and the cutoff point was 53.32. The decrease of serum irisin level could predict the patients with IR in acne. Conclusion: Serum irisin levels in AV patients were significantly decreased. Serum irisin showed acceptable performance criteria in the diagnosis of AV with IR. Serum irisin seems to be a good diagnostic and prognostic marker for IR. Further multi-center studies are needed to confirm this link, which could pave the way for new treatment options.

Objective To evaluate the clinical reliability and validity of the Chinese version of the Patient Health Questionnaire-9 (C-PHQ-9) in patients with psoriasis.Design Cross-sectional study.Setting Tertiary care centre.Participants Patients with psoriasis who have not been diagnosed with depression (n=148; mean age 43.37±17.46 years; 31.19% female).Primary and secondary outcome measures The primary outcome measures considered in this study were the C-PHQ-9 and the Hamilton Depression Scale (HAMD). The American Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-V) was used as the gold standard for the diagnosis of depression. Cronbach’s α and test–retest reliability after 1 week were evaluated using reliability analysis, and criterion and structural validity were assessed using validity analysis. Receiver operating characteristic (ROC) analysis was performed to identify the best demarcation score and diagnostic accuracy.Results Compared with DSM-V (27.27%), both C-PHQ-9 (39.19%) and HAMD (31.01%) had higher rates for detecting depression. The mean completion time for C-PHQ-9 evaluation (2.02±0.84 min) was significantly less than that for HAMD (23.37±3.21 min, p

Full-matrix capture (FMC)-based ultrasonic imaging provides good sensitivity to small defects in non-destructive testing and has gradually become a mainstream research topic. Many corresponding algorithms have been developed, e.g., the total focusing method (TFM). However, the efficiency of the TFM is limited, especially in multi-layered structures. Although the appearance of wavenumber algorithms, such as extended phase-shift migration (EPSM) methods, has improved imaging efficiency, these methods cannot be applied to cases with oblique incidence. Therefore, a modified wavenumber method for full-matrix imaging of multi-layered structures with oblique array incidence is proposed. This method performs a coordinate rotation in the frequency domain to adapt it to the oblique incidence. It then utilizes wave-field extrapolation to migrate the transmitting and receiving wave field to each imaging line, and a correlation imaging condition is used to reconstruct a total focused image. The proposed method can deal with any incident angle without precision loss. Moreover, it inherits the computational efficiency advantages of the wavenumber algorithms. The simulation and experimental results show that the proposed method performs better in terms of accuracy and efficiency than the TFM. Specifically, it is nearly 60 times faster than the TFM when processing an FMC dataset with a size of 4096 × 64 × 64.

Uncertainty analysis plays a pivotal role in identifying the important parameters affecting building energy consumption and estimate their effects at the early design stages. In this work, we consider the adaptive Lasso for uncertainty analysis in building performance simulation. This procedure has several appealing features: (1) We can introduce a large number of possible physical and environmental parameters at the initial stage to obtain a more complete picture of the building energy consumption. (2) The procedure could automatically select parameters and estimate influences simultaneously and no prior knowledge is required. (3) Due to computational efficiency of the procedure, non-linear relationship between the building performance and the input parameters could be accommodated. (4) The proposed adaptive Lasso can use a small number of samples to achieve high modeling accuracy and further reduce the huge computational cost of running building energy simulation programs. Furthermore, we propose a stable algorithm to rank input parameters to better identify important input parameters that affect energy consumption. A case study shows the superior performance of the procedure compared with LS and OMP in terms of modeling accuracy and computational cost.