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Several natural and human factors are responsible for the defacement of the external walls and tiles of buildings, and the related deterioration can be a public safety hazard. Therefore, active building maintenance and repair processes are essential for ensuring building sustainability. However, conventional inspection methods are time-, cost-, and labor-intensive processes. Therefore, herein, this study proposes a convolutional neural network (CNN) model for image-based automated detection and localization of key building defects (efflorescence, spalling, cracking, and defacement). Based on a pretrained CNN VGG-16 classifier, this model applies class activation mapping for object localization. After identifying its limitations in real-life applications, this study determined the model’s robustness and ability to accurately detect and localize defects in the external wall tiles of buildings. For real-time detection and localization, this study applied this model by using mobile devices and drones. The results show that the application of deep learning with UAV can effectively detect various kinds of external wall defects and improve the detection efficiency.

Electric valves have significant importance in industrial applications, especially in nuclear power plants. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. However, it is difficult to inspect each valve in conventional maintenance. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. Thus, there exists a genuine demand for remote sensing of a valve condition through nonintrusive methods as well as prediction of its remaining useful life (RUL). In this paper, typical aging modes have been summarized. The data for sensing valve conditions were gathered during aging experiments through acoustic emission sensors. During data processing, convolution kernel integrated with LSTM is utilized for feature extraction. Subsequently, LSTM which has an excellent ability in sequential analysis is used for predicting RUL. Experiments show that the proposed method could predict RUL more accurately compared to other typical machine learning and deep learning methods. This will further enhance maintenance efficiency of any plant.

Obstructive sleep apnea (OSA) is caused by repeated blockage of the upper respiratory airways during sleep. The traditional evaluation methods for OSA severity are yet limited. This study aimed to screen gene signatures to effectively evaluate OSA severity. Expression profiles of peripheral blood mononuclear cells in the different severities of OSA patients were accessed from Gene Expression Omnibus (GEO) database. A total of 446 differentially expressed genes (DEGs) were screened among the varying severities of OSA samples by analysis of variance (ANOVA) test. A total of 1,152 DEGs were screened between the pre- and post-treatment OSA samples by using t test. Overlap of the two groups of DEGs was selected (88 DEGs) for Metascape enrichment analysis. Afterwards, Mfuzz package was used to perform soft clustering analysis on these 88 genes, by which 6 clusters were obtained. It was observed that the gene expression condition of the cluster 3 was positively associated with OSA severity degree; also, the gene expression condition in cluster 4 was negatively correlated with OSA severity. A total of 10 gene markers related to OSA progression were selected from cluster 3 and cluster 4. Their expression levels and correlation were analyzed. The marker genes in cluster 3 and cluster 4 were examined, finding that most genes were significantly correlated with apnea hypopnea index (AHI). An accurate and objective assessment of the severity of OSA is of great significance for formulating follow-up treatment strategies for patients with OSA. In this paper, a set of marker genes that can detect the severity of OSA were screened by bioinformatics methods, which could be jointly used with the traditional OSA diagnostic index to achieve a more reliable OSA severity evaluation. [ABSTRACT FROM AUTHOR]

Aims: To qualitatively explore potential experience among frontline nurses who had been fighting against the COVID‐19 infection since the outbreak. Background: Disasters are often sudden and uncertain. Since the COVID‐19 outbreak in Wuhan city, local frontline nurses had been responsible for treatment of COVID‐19 for several months. Qualitative study was required to assess complex multi‐component psychological experiences among frontline nurses. Methods: Twenty local frontline nurses were recruited from a designated hospital of COVID‐19 treatment. We conducted semi‐structured interview using phenomenological method. Descriptive phenomenological method was applied for thematic analysis. Results: Twenty female frontline nurses (aged 24 to 43 years old) were interviewed. Two broader themes, negative and positive, were identified. Negative experience included refusal and helpless (refusal to work at frontline, shortage of confidence in working and helpless), fear and anxiety, excessive miss, and other health issues. Positive experience included improved interpersonal relationship, sublimation of personal faith and strength, changes in understanding meaning of life and new possibility. Conclusion: Both positive and negative psychological response were observed, which can provide evidence based clues for making essential strategies and policy. Implications for Nursing Management: Understand subjective experience of frontline nurses can establish evidence for development of effective psychological intervention. Nursing administrator should consider the nurses' psychological experience comprehensively to promote psychological growth and lower post‐traumatic psychological burden. [ABSTRACT FROM AUTHOR]

CUB-domain containing protein 1 (CDCP1) is a transmembrane protein acting as an effector of SRC family kinases, which play an oncogenic role in multiple human cancers. However, its clinical and immune correlations in breast cancer (BrCa) have not been explored. To define the expression, prognostic value, and potential molecular role of CDCP1 in BrCa, multiple public datasets, and an in-house cohort were used. Compared with paratumor tissue, CDCP1 was remarkably upregulated in the tumor tissues at both mRNA and protein levels. In the in-house cohort, CDCP1 protein expression was related to several clinicopathological parameters, including age, ER status, PR status, molecular type, and survival status. Kaplan–Meier analysis and Cox regression analysis exhibited that CDCP1 was an important prognostic biomarker in BrCa. In addition, enrichment analysis uncovered that CDCP1 was not only involved in multiple oncogenic pathways, but correlated with overexpression of immune checkpoints. Overall, we reported that increased expression of CDCP1 is a favorable prognostic factor in patients with BrCa. In addition, the correlations between CDCP1 and immune checkpoints provide a novel insight into the adjuvant treatment for immune checkpoint blockade via targeting CDCP1. [ABSTRACT FROM AUTHOR]

Activation of NOD-like receptor (NLR) signaling pathway can promote downstream cytokine and proinflammatory cytokines release, and inflammation induced by excess nutrients leads to renal metabolic injury. How the NLRs influence metabolic progress and then lead to the renal injury remains poorly investigated. Compared with rodents, minipigs are more similar to humans and are more ideal animal models for human disease research. In this study, we established a diabetic minipig model through a high-sugar and high-fat diet combined with streptozotocin (STZ) injection. Blood biological markers and renal pathological markers, expression of NLRP subfamily members (NLRP1 and NLRP3) and their downstream cytokines (precursors of IL-1β and IL-18 and mature forms of IL-1β and IL-18), expression of NLRC subfamily members (NLRC1, NLRC2, and NLRC5) and their downstream nuclear factor-κB (NF-κB) signaling pathway molecules (IKKβ, IκBα, and NF-κB p65), and inflammatory cytokines (TNF-α and interleukin-6 (IL-6)) were systematically evaluated. The expression of NLRP3 and its downstream cytokine signaling molecules, the precursors of IL-1β and IL-18, and the mature forms of IL-1β and IL-18 was significantly upregulated. The expression levels of NLRC1, NLRC2, and NLRC5 and activation of the downstream NF-κB pathway molecules phospho-IKKβ, phospho-IκBα, NF-κB p65, and phospho-NF-κB p65 were significantly increased. The TNF-α and IL-6 levels were significantly increased in diabetic pig kidneys. The TGF-β/Smad signaling molecules, TGF-β and P-SMAD2/3, were also increased. These results suggested that the metabolic inflammation activated by NLRs might play an important role in diabetic renal injuries. [ABSTRACT FROM AUTHOR]

With the rapid development of connected vehicles, people can get a better driving experience. However, the interconnection with the external network may bring growing accidents caused by cybersecurity vulnerabilities. As a result, automakers are paying more attention to cybersecurity and spending more cost on developing cybersecurity defense mechanisms. Threat analysis and risk assessment (TARA) is an efficient method to ensure the defense effect and greatly save costs in the early stage of vehicle development. It analyzes the threat of vehicle systems and determines the hierarchical defense and corresponding mitigations according to the potential threat to the system. This paper gives an overview of threat analysis and risk assessment in the automotive field. First, a novel classification of different TARA methods has been proposed. The existing methods have been analyzed and compared. Then, we have found some commonly used tools applied to TARA and compared their performance. After that, a concept named attack-defense mapping is proposed to figure out how to map the already found threats and vulnerabilities of the system to the appropriate mitigations. At last, the future development directions of TARA in the automotive domain have been discussed. [ABSTRACT FROM AUTHOR]

Several natural and human factors are responsible for the defacement of the external walls and tiles of buildings, and the related deterioration can be a public safety hazard. Therefore, active building maintenance and repair processes are essential for ensuring building sustainability. However, conventional inspection methods are time-, cost-, and labor-intensive processes. Therefore, herein, this study proposes a convolutional neural network (CNN) model for image-based automated detection and localization of key building defects (efflorescence, spalling, cracking, and defacement). Based on a pretrained CNN VGG-16 classifier, this model applies class activation mapping for object localization. After identifying its limitations in real-life applications, this study determined the model's robustness and ability to accurately detect and localize defects in the external wall tiles of buildings. For real-time detection and localization, this study applied this model by using mobile devices and drones. The results show that the application of deep learning with UAV can effectively detect various kinds of external wall defects and improve the detection efficiency. [ABSTRACT FROM AUTHOR]

Total anthocyanin (TA) and moisture content (MC) are critical indexes of processed purple sweet potatoes (PSPs). The current study examined the feasibility of hyperspectral imaging to investigate how MC and TA contents of PSPs change during processing under two different drying methods namely convective hot‐air drying (CHD) and microwave drying (MD). Models based on spectral data and the integration of spectral and one or three gray‐level co‐occurrence matrix features were established with partial least square regression. The best prediction outcome based on Spectra + HOMOGENEITY + CONTRAST + ENTROPY combinations were (Rp2 = 0.862, 0.847; RMSEP = 0.079, 0.303), respectively, for MC and TA for the CHD samples. Similarly, for the MD samples, the best prediction outcomes were (Rp2 = 0.867, 0.859; RMSEP = 0.088, 0.241), respectively, for MC and TA. Image algorithms were also developed to generate distribution of MC and TA in some representative samples. Practical applications: Anthocyanins have been reported as having the potential to lower blood pressure, improve eyesight, reduce cancer cell multiplication, impede tumor formation, and prevent diabetes. Studies have, however, shown that cooking modes including boiling, baking, and steaming decreased the contents of anthocyanins. This study assessed the impact of convective hot‐air and microwave drying (MD) process of purple sweet potato (PSP) on total anthocyanin (TA) content as a function of moisture loss. Hyperspectral Imaging (HSI) was useful in estimating not only the contents of TA and moisture, but also in generating a visual map of their distribution pattern in the processed PSPs. The TA content of PSPs processed by MD was higher than that by convective hot‐air drying, but the distribution uniformity of TA in microwave dried PSPs was worse than that in convective hot‐air dried samples. Finally, to guarantee consistency in drying processes of food products, the HSI could be used to obtain visual images of their chemical parameters. [ABSTRACT FROM AUTHOR]

Dentin formation was dependent on osteo-/odontogenic differentiation of dental pulp stem cells (DPSCs). It was observed in previous studies that antibiotic treatment in a clinical and animal model resulted in impaired mineralization of dental tissues. We previously reported that microbiota maintained the function of bone marrow mesenchymal stem cells, while whether microbiota dysbiosis caused by antibiotic treatment contributed to DPSCs dysfunction and impaired dentin formation is still not known. In this study, we aimed to clarify the role of microbiota or its metabolic products on dental mineralization and the function of DPSCs. Mice were treated with antibiotics to disrupt microbiota; then, the growth rate and histological characteristics of incisors as well as the biological characteristics of DPSCs in vitro were compared with specific pathogen-free (SPF) mice. In antibiotic-treated mice (AbT), we found a diminished quantity of microbiota and reduced growth rate of mechanical injured incisor, as well as decreased colony-forming rate and impaired ability of osteo-/odontogenic differentiation of DPSCs, in comparison to SPF mice. Colonization of AbT mice with SPF mice replanted the microbiota by cohousing (conventionalized (ConvD)) and normalized the growth rate of injured incisors and colony-forming and osteo-/odontogenic differentiation ability of DPSCs. Giving short-chain fatty acids (SCFAs) by oral gavage after antibiotic treatment also rescued the growth rate of incisors and the differentiation ability of DPSCs and enhanced proliferation ability of DPSCs. Collectively, gut microbiota could make contribution to maintain continuous growth of injured rodent incisor and differentiation capacity of DPSCs; SCFAs might play a crucial role in this process. [ABSTRACT FROM AUTHOR]

Electric valves have significant importance in industrial applications, especially in nuclear power plants. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. However, it is difficult to inspect each valve in conventional maintenance. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. Thus, there exists a genuine demand for remote sensing of a valve condition through nonintrusive methods as well as prediction of its remaining useful life (RUL). In this paper, typical aging modes have been summarized. The data for sensing valve conditions were gathered during aging experiments through acoustic emission sensors. During data processing, convolution kernel integrated with LSTM is utilized for feature extraction. Subsequently, LSTM which has an excellent ability in sequential analysis is used for predicting RUL. Experiments show that the proposed method could predict RUL more accurately compared to other typical machine learning and deep learning methods. This will further enhance maintenance efficiency of any plant. [ABSTRACT FROM AUTHOR]

Background. Stroke can lead to disruption of the whole-brain network in patients. Acupuncture can modulate the functional network on a large-scale level in healthy individuals. However, whether and how acupuncture can make a potential impact on the disrupted whole-brain network after ischemic stroke remains elusive. Methods. 26 stroke patients with a right hemispheric subcortical infarct were recruited. We gathered the functional magnetic resonance imaging (fMRI) from patients with stroke and healthy controls in the resting state and after acupuncture intervention, to investigate the instant alterations of the large-scale functional networks. The graph theory analysis was applied using the GRETNA and SPM12 software to construct the whole-brain network and yield the small-world parameters and network efficiency. Results. Compared with the healthy subjects, the stroke patients had a decreased normalized small-worldness (σ), global efficiency ( E g ), and the mean local efficiency ( E loc ) of the whole-brain network in the resting state. There was a correlation between the duration after stroke onset and E loc . Acupuncture improved the patients' clustering coefficient ( C p ) and E loc but did not make a significant impact on the σ and E g . The postacupuncture variables of the whole-brain network had no association with the time of onset. Conclusion. The poststroke whole-brain network tended to a random network with reduced network efficiency. Acupuncture was able to modulate the disrupted patterns of the whole-brain network following the subcortical ischemic stroke. Our findings shed light on the potential mechanisms of the functional reorganization on poststroke brain networks involving acupuncture intervention from a large-scale perspective. [ABSTRACT FROM AUTHOR]

A coupled‐line band‐pass filter (BPF) with T‐shaped stub structure is presented. Five transmission poles within the passband and eight deep transmission zeros (TZs) from 0 to 2f0 (f0 denotes filter's center frequency) are realized through input impedance calculations. With the simple T‐shaped structure, the positions of six TZs can be appropriately adjusted to achieve high frequency selectivity and stopband rejection. For demonstration, a BPF prototype centered at 2.05 GHz is designed and fabricated, whose measured rejection levels are of over 45.5 dB at lower stopband and better than 19.5 dB at upper stopband. The simulation and measurement results are in good agreement, which validates the design idea. [ABSTRACT FROM AUTHOR]

The human gut microbiota is affected by genetic and environmental factors. It remains unclear how host genetic and environmental factors affect the composition and function of gut microbiota in populations living at high altitudes. We used a metagenome-wide analysis to investigate the gut microbiota composition in 15 native Tibetans and 12 Hans living on the Tibetan Plateau. The composition of gut microbiota differed significantly between these two groups (P < 0.05). The Planctomycetes was the most abundant phyla both in native Tibetans and in Hans. Furthermore, the most relatively abundant phyla for native Tibetans were Bacteroidetes (15.66%), Firmicutes (11.10%), Proteobacteria (1.32%), Actinobacteria (1.10%), and Tenericutes (0.35%), while the most relatively abundant phyla for Hans were Bacteroidetes (16.28%), Firmicutes (8.41%), Proteobacteria (2.93%), Actinobacteria (0.49%), and Cyanobacteria (0.21%). The abundance of the majority of genera was significantly higher in Tibetans than in Hans (P < 0.01). The number of microbial genes was 4.9 times higher in Tibetans than in Hans. The metabolic pathways and clusters of orthologous groups differed significantly between the two populations (P < 0.05). The abundance of carbohydrate-active enzyme modules and antibiotic resistance genes was significantly lower in Tibetans compared to Hans (P < 0.05). Our results suggest that different genetic factors (race) and environmental factors (diets and consumption of antibiotics) may play important roles in shaping the composition and function of gut microbiota in populations living at high altitudes. [ABSTRACT FROM AUTHOR]

Objective. To observe whether there are sexual-related differences in response to mid- or low-premixed insulin in type 2 diabetic patients. Methods. This was an analysis of CGM data of a previous study. After screening, patients with longstanding T2D receive a 7-day continuous subcutaneous insulin infusion (CSII) therapy, and then subjects were randomly assigned 1 : 1 into two groups receiving Novo Mix 30 or Humalog Mix 50 regimen for a 2-day phage, followed by a 4-day cross-over period. A 4-day continuous glucose monitoring (CGM) was performed during the cross-over period. The primary endpoint was the differences in glycemic control between male and female patients receiving mid- or low-premixed insulin therapy. Results. A total of 102 patients (52 men and 50 women) completed the study. Our data showed that male patients had significant decrease in mean glucose levels monitored by CGM after three meals during Humalog Mix 50 treatment period compared to those received Novo Mix 30 regimen (0900: 11.0 ± 2.5 vs. 12.2 ± 2.8 , 1000: 9.9 ± 2.9 vs. 11.3 ± 3.1 , 1200: 8.0 ± 1.9 vs. 9.1 ± 2.5 , 1400: 9.2 ± 2.3 vs. 10.3 ± 2.5 , and 2000: 7.3 ± 2.1 vs. 8.2 ± 2.4 mmol / L , p < 0.05 , respectively). In addition, male patients receiving Novo Mix 30 experienced a significantly increased hypoglycemic duration compared to those of receiving Humalog Mix 50 (0 (0, 4.8) vs. 0 (0, 0), p < 0.05). Conclusion. Our data indicate that male patients with T2D receiving mid-premixed insulin analogue regimen may have a potential benefit of improvement in glycemic control compared to female patients. This trial is registered with ClinicalTrials.gov ChiCTR-IPR-15007340. [ABSTRACT FROM AUTHOR]

While key negotiation schemes, such as those based on Diffie–Hellman, have been the subject of ongoing research, designing an efficient and security scheme remains challenging. In this paper, we propose a novel key negotiation scheme based on blockchain, which can be deployed in blockchain-enabled contexts such as data sharing or facilitating electric transactions between vehicles (e.g., unmanned vehicles). We propose three candidates for flexible selection, namely, key exchanges via transaction currency values through value channels (such as the amount in transactions), automated key exchanges through static scripts,and dynamic scripts, which can not only guarantee key availability with timeliness but also defend against MITM (man-in-the-middle) attacks, packet-dropping attacks, and decryption failure attacks. [ABSTRACT FROM AUTHOR]

The wave-based method (WBM) is a feasible method which investigates the free vibration characteristics of orthotropic cylindrical shells under general boundary conditions. Based on Reissner–Naghid's shell theory, the governing motion equation is established, and the displacement variables are transformed into wave functions formed to satisfy the governing equations. On the basis of the kinematic relationship between the force resultant and displacement vector, the overall matrix of the shell is established. Comparison studies of this paper with the solutions in the literatures were carried out to validate the accuracy of the present method. Furthermore, by analyzing some numerical examples, the free vibration characteristics of orthogonal anisotropic cylindrical shells under classical boundary conditions, elastic boundary conditions, and their combinations are studied. Also, the effects of the material parameter and geometric constant on the natural frequencies for the orthotropic circular cylindrical shell under general boundary conditions are discussed. The conclusions obtained can be used as data reference for future calculation methods. [ABSTRACT FROM AUTHOR]

This paper describes a unified solution to investigate free vibration solutions of functionally graded (FG) spherical shell with general boundary restraints. The analytical model is established based on the first-order shear deformation theory, and the material varies uniformly along the thickness of FG spherical shell which is divided into several sections along the meridian direction. The displacement functions along circumferential and axial direction are, respectively, composed by Fourier series and Jacobi polynomial regardless of boundary restraints. The boundary restraints of FG spherical shell can be easily simulated according to penalty method of spring stiffness technique, and the vibration solutions are obtained by Rayleigh–Ritz method. To verify the reliability and accuracy of the present solutions, the convergence and numerical verification have been conducted about different boundary parameters, Jacobi parameter, etc. The results obtained by the present method closely agree with those obtained from the published literatures, experiments, and finite element method (FEM). The impacts of geometric dimensions and boundary conditions on the vibration characteristics of FG spherical shell structure are also presented. [ABSTRACT FROM AUTHOR]

A microwave scattering model is proposed for the ground surface covered with multilayers vegetation. The vegetation leaves are simulated with the discrete elliptical particles in layers, which are randomly distributed above the randomly rough surface. The finite element method is applied to solve the scattering magnetic field equation based on the Dirichlet boundary, and the relationship between the radar cross section and bidirectional reflectance distribution function is deduced with the coherent scattering field. The internal mechanism of microwave scattering of multilayers vegetation is explored with the numerical results, and the relationship of vegetation growing parameter with scattering characteristic is established. [ABSTRACT FROM AUTHOR]

Building Information Modeling (BIM) is envisioned as an indispensable opportunity in the architecture, engineering, and construction (AEC) industries as a revolutionary technology and process. Smart construction relies on BIM for manipulating information flow, data flow, and management flow. Currently, BIM model has been explored mainly for information construction and utilization, but rare works pay efforts to information security, e.g., critical model audit and sensitive model exposure. Moreover, few BIM systems are proposed to chase after upcoming computing paradigms, such as mobile cloud computing, big data, blockchain, and Internet of Things. In this paper, we make the first attempt to propose a novel BIM system model called bcBIM to tackle information security in mobile cloud architectures. More specifically, bcBIM is proposed to facilitate BIM data audit for historical modifications by blockchain in mobile cloud with big data sharing. The proposed bcBIM model can guide the architecture design for further BIM information management system, especially for integrating BIM cloud as a service for further big data sharing. We propose a method of BIM data organization based on blockchains and discuss it based on private and public blockchain. It guarantees to trace, authenticate, and prevent tampering with BIM historical data. At the same time, it can generate a unified format to support future open sharing, data audit, and data provenance. [ABSTRACT FROM AUTHOR]

Pseudomonas sp. QTF5 was isolated from the continuous permafrost near the bitumen layers in the Qiangtang basin of Qinghai-Tibetan Plateau in China (5,111 m above sea level). It is psychrotolerant and highly and widely tolerant to heavy metals and has the ability to metabolize benzoic acid and salicylic acid. To gain insight into the genetic basis for its adaptation, we performed whole genome sequencing and analyzed the resistant genes and metabolic pathways. Based on 120 published and annotated genomes representing 31 species in the genus Pseudomonas, in silico genomic DNA-DNA hybridization (<54%) and average nucleotide identity calculation (<94%) revealed that QTF5 is closest to Pseudomonas lini and should be classified into a novel species. This study provides the genetic basis to identify the genes linked to its specific mechanisms for adaptation to extreme environment and application of this microorganism in environmental conservation. [ABSTRACT FROM AUTHOR]

Aim. To demonstrate the regulatory role of miRNA in colorectal carcinoma (CRC) and reveal the transcript markers that may be associated with CRC clinical outcomes. Method. Herein, we analyzed both mRNA and miRNA gene expression profiles of 255 CRC tumor samples from The Cancer Genome Atlas project to reveal the regulatory association between miRNA and mRNA. Also, the potential role of gene coexpression network in CRC has been explored. Results. The negative correlation between miR-200c and DCN (Decorin) was calculated in CRC, indicating that DCN could be a potential target of miR-200c. Clinical features indicated that colon polyp history and overall survival were significantly related to the expression level of miR-200c. Three coexpression networks have been constructed, and genes involved in the networks are related to cell cycle, NOTCH, and mTOR signaling pathways. Conclusion. Our result provides a new insight into cancer related mRNA coexpression network in CRC research. [ABSTRACT FROM AUTHOR]

This study aims to find the difference of genomewide DNA methylation in Schwann cells (SCs) before and after peripheral nerve system (PNS) injury by Methylated DNA Immunoprecipitation Sequencing (MeDIP-Seq) and seek meaningful differentially methylated genes related to repairment of injured PNS. SCs harvested from sciatic nerve were named as activated Schwann cells (ASCs), and the ones harvested from brachial plexus were named as normal Schwann cells (NSCs). Genomic DNA of ASCs and NSCs were isolated and MeDIP-Seq was conducted. Differentially methylated genes and regions were discovered and analyzed by bioinformatic methods. MeDIP-Seq analysis showed methylation differences were identified between ASCs and NSCs. The distribution of differentially methylated regions (DMRs) peaks in different components of genome was mainly located in distal intergenic regions. GO and KEGG analysis of these methylated genes were also conducted. The expression patterns of hypermethylated genes (Dgcr8, Zeb2, Dixdc1, Sox2, and Shh) and hypomethylated genes (Gpr126, Birc2) detected by qRT-PCR were opposite to the MeDIP analysis data with significance (p < 0.05), which proved MeDIP analysis data were real and believable. Our data serve as a basis for understanding the injury-induced epigenetic changes in SCs and the foundation for further studies on repair of PNS injury. [ABSTRACT FROM AUTHOR]

The measured diffusion coefficient and soil-water characteristic curve (SWCC) of remolded loess were used to modify the Mualem model for increasing its accuracy. The obtained results show that the goodness of fit between the Mualem model and the variable parameter-modified Mualem method comparing with the test results was not high. The saturation of 0.65 was introduced as the boundary to divide the curve of the measured diffusion coefficient into two segments. When the segmentation method combined with the variable parameter method was used to modify the Mualem model, the fitting correlation coefficient was increased to 0.921–0.998. The modified parameters Ko and L corresponding to remolded loess were calculated for different dry densities. Based on the exponential function between Ko and dry density and the linear relation between L and dry density, the segmentally modified Mualem model was established for remolded loess by considering variation in dry density. The results of the study can be used for directly determining the unsaturated infiltration coefficient and for indirectly determining the SWCC through diffusion coefficient. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) ranks first in morbidity and mortality among female malignant tumors worldwide. This study is aimed at clarifying clinical value of contrast-enhanced ultrasound (CEUS) in the diagnosis and differentiation of BC. A total of 108 BC patients admitted to our hospital from January 2019 to December 2021 were enrolled. All patients underwent conventional color Doppler ultrasound and CEUS imaging examination. All ultrasound images were analyzed by a senior (5+ years) sonographer. The lesion location, echo, size, and color Doppler flow imaging (CDFI) blood flow distribution of benign and malignant BC were assessed. The transverse and longitudinal diameters of malignant BC presented significant elevation compared with the control group (P < 0.05). CEUS is more reliable than conventional ultrasound in the differentiation of benign and malignant breast lesions, and CEUS has the best reliability. The comparison of CEUS observation indicators between benign and malignant groups demonstrated that CEUS enhancement patterns (time and intensity) and morphological features (lesion boundary, shape, range, homogeneity, and filling defect) presented statistical significance (P < 0.01). Irregular shape and range expansion were high-specificity indicators (all >90.00%); fast-forward, high enhancement, clear boundary, and range expansion were high-sensitivity (all >90.00%); and fast-forward, high enhancement, and clear boundary were low-specificity indicators (all <50.00%); moderate sensitivity is as follows: homogeneous enhancement and range expansion (all >80.00%). The area under curve of CEUS (0.735 ± 0.053) presented elevation relative to conventional ultrasound (0.901 ± 0.024), with statistical significance (Z 1 = 2.462 , P < 0.05). Relative to conventional ultrasound, the specificity and positive predictive value of CEUS presented elevation (P < 0.05). In conclusion, in the differentiation of benign and malignant breast lesions, CEUS has better diagnostic accuracy and reliability than conventional ultrasound. The diagnostic advantages of CEUS are to elevate the diagnostic specificity and positive predictive value and reduce the misdiagnosis rate. [ABSTRACT FROM AUTHOR]

In this study, impact of crystal size on salt (NaCl) uptake and water activity (aw) of dry‐cured pork was investigated by hyperspectral imaging. Three grades of NaCl labeled fine flake, medium flake, and large flakes were used during the 3 day salting period. Spectra and spatial information were extracted from three subsurfaces labeled surface‐1, surface‐2, and surface‐3 for each sample and prediction models were established based on partial least squares regression, synergy interval partial least squares, and competitive adaptive reweighted sampling partial least squares. The overall results indicated that processing with NaCl of different crystal sizes influenced NaCl uptake and aw of pork samples. Distribution maps based on spatial analysis further revealed that crystal size impacted NaCl uptake to a large extent. Thus, in the drive to process meat with lower NaCl content, crystal morphology may be considered in further studies in dry‐cured meat processing. Practical applications: Meat preservation with salt (NaCl) is a widely applied traditional technique of safe‐guiding meat from microbial invasion because of its effectiveness and the ease of accessing the commodity. Yet, several studies have associated noncommunicable diseases, including hypertension, cardiovascular disease, and stroke to the high intake of NaCl. As a result, several strategies are being developed for reducing NaCl contents in processed foods in a bid to avert these health‐related concerns. Some of these strategies involved studying the dissolution characteristics of salt crystals in saliva, brining with high‐powered ultrasound, among others. Dry‐cured pork is a worldwide delicacy. Thus, for the purposes of understanding how crystal size influence total NaCl levels in dry‐cured pork, this investigation was carried out. The outcome of this study may be considered in the formulation of future research needs in the drive to achieving sodium reduction in salted meat while maintaining quality and salt intensity. [ABSTRACT FROM AUTHOR]