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The strength of jointed rock is a fundamental factor in the slope stability of rock mass. This research investigates the effect of infill thickness on the strength of jointed rock specimens. Unlike previous studies involving artificial rock-like materials and saw-tooth surfaces, this work has been conducted on two natural types of sandstone with various rock surfaces. Natural low-plasticity clay of different thicknesses (1 mm to 3 mm) was used as the infill material. A series of shear box tests with a range of initial normal stresses from 0.5 MPa to 1.5 MPa were performed to obtain high-quality data regarding the shear strength of natural rock and to provide insights into the effect of infill and rock surface roughness on shear strength. The obtained results were also used to improve the current methods of rock strength predictions, which were initially designed to estimate the strength of artificial rock-like material. Based on the obtained laboratory data and the strength estimation using different methods, a newly proposed procedure was proved to provide more accurate estimations of the shear strength of jointed rock.

The metallurgical changes and micro defects after welding can influence the mechanical property, corrosion resistance and dimensional stability of the stainless steel wielding joints. It is necessary to explore an effective post-welded method to improve the service performance for stainless steel welding joints. Furthermore, the essential mechanism between microstructure changes and macro-property variation during this process needs deep reveal. Therefore, this paper aims at investigating the effects of different post-weld treatments including naturally aging, artificial aging and cryogenic treatment on the residual stress and mechanical properties of 304 stainless steel weldments. The microstructure evolution was also studied to reveal and establish the microstructure-performance relationship. The results showed that the distribution of residual stress was most uniform with lowest magnitude (decreased by up to 49%) after cryogenic treatment. At room temperature, weldments subjected to the three post treatments showed similar mechanical properties. At cryogenic temperature, cryogenic treatment exhibited an outstanding role on maintaining the mechanical properties of weldments. By contrast, the mechanical properties were obviously reduced by artificial aging. The transformation of δ-ferrite and the carbide precipitation in grain boundaries are the main reasons for the decrease of mechanical properties after artificially aging. Welding process was able to cause the transfer of the alloying elements and decrease the stability of austenite. Consequently, martensitic transformation can be induced during cryogenic treatment. The new formed martensite resulted in the refined microstructure and high mechanical properties of weldments, and also the release of residual stress after welding.

The Ni60/WC/La2O3 composite coatings with 0∼2 wt% La2O3 content were prepared on Cr12MoV substrates by laser cladding. The microstructure and tribological properties of the coatings were studied in detail. The results revealed that WC particles were partially dissolved to form new carbides with lower hardness. The dissolution mechanisms were consisted of dissolution-diffusion, diffusion-nucleation, and penetration-dissolution-nucleation. The wear mechanism of C0∼C3 coatings with La2O3 contents of no more than 1.5% was dominated by oxidative wear, while that of C4 coating with a La2O3 content of 2% was dominated by abrasive wear. The best performance of the coatings was obtained at a La2O3 content of 1.5 wt%. The hardness reached 1024.3 HV0.2, which was 3.64 times higher than that of the substrate. And the wear loss was 2.5 mg, which was 88.8% lower than that of the substrate.

Abstract Background The application of artificial intelligence (AI) in the ultrasound (US) diagnosis of breast cancer (BCa) is increasingly prevalent. However, the impact of US-probe frequencies on the diagnostic efficacy of AI models has not been clearly established. Objectives To explore the impact of using US-video of variable frequencies on the diagnostic efficacy of AI in breast US screening. Methods This study utilized different frequency US-probes (L14: frequency range: 3.0-14.0 MHz, central frequency 9 MHz, L9: frequency range: 2.5-9.0 MHz, central frequency 6.5 MHz and L13: frequency range: 3.6-13.5 MHz, central frequency 8 MHz, L7: frequency range: 3-7 MHz, central frequency 4.0 MHz, linear arrays) to collect breast-video and applied an entropy-based deep learning approach for evaluation. We analyzed the average two-dimensional image entropy (2-DIE) of these videos and the performance of AI models in processing videos from these different frequencies to assess how probe frequency affects AI diagnostic performance. Results The study found that in testing set 1, L9 was higher than L14 in average 2-DIE; in testing set 2, L13 was higher in average 2-DIE than L7. The diagnostic efficacy of US-data, utilized in AI model analysis, varied across different frequencies (AUC: L9 > L14: 0.849 vs. 0.784; L13 > L7: 0.920 vs. 0.887). Conclusion This study indicate that US-data acquired using probes with varying frequencies exhibit diverse average 2-DIE values, and datasets characterized by higher average 2-DIE demonstrate enhanced diagnostic outcomes in AI-driven BCa diagnosis. Unlike other studies, our research emphasizes the importance of US-probe frequency selection on AI model diagnostic performance, rather than focusing solely on the AI algorithms themselves. These insights offer a new perspective for early BCa screening and diagnosis and are of significant for future choices of US equipment and optimization of AI algorithms.

Abstract Purpose Prolonged exposure to plasma free fatty acids (FFAs) leads to impaired glucose tolerance (IGT) which can progress to type 2 diabetes (T2D) in the absence of timely and effective interventions. High-fat diet (HFD) leads to chronic inflammation and oxidative stress, impairing pancreatic beta cell (PBC) function. While Didymin, a flavonoid glycoside derived from citrus fruits, has beneficial effects on inflammation dysfunction, its specific role in HFD-induced IGT remains yet to be elucidated. Hence, this study aims to investigate the protective effects of Didymin on PBCs. Methods HFD-induced IGT mice and INS-1 cells were used to explore the effect and mechanism of Didymin in alleviating IGT. Serum glucose and insulin levels were measured during the glucose tolerance and insulin tolerance tests to evaluate PBC function and insulin resistance. Next, RNA-seq analysis was performed to identify the pathways potentially influenced by Didymin in PBCs. Furthermore, we validated the effects of Didymin both in vitro and in vivo. Mitochondrial electron transport inhibitor (Rotenone) was used to further confirm that Didymin exerts its ameliorative effect by enhancing mitochondria function. Results Didymin reduces postprandial glycemia and enhances 30-minute postprandial insulin levels in IGT mice. Moreover, Didymin was found to enhance mitochondria biogenesis and function, regulate insulin secretion, and alleviate inflammation and apoptosis. However, these effects were abrogated with the treatment of Rotenone, indicating that Didymin exerts its ameliorative effect by enhancing mitochondria function. Conclusions Didymin exhibits therapeutic potential in the treatment of HFD-induced IGT. This beneficial effect is attributed to the amelioration of PBC dysfunction through improved mitochondrial function.

Abstract Background Metabolic dysfunction-associated fatty liver disease (MAFLD) is one of the most prevalent metabolic syndromes worldwide. However, no approved pharmacological treatments are available for MAFLD. Chenpi, one kind of dried peel of citrus fruits, has traditionally been utilized as a medicinal herb for liver diseases. Didymin is a newly identified oral bioactive dietary flavonoid glycoside derived from Chenpi. In this study, we investigated the therapeutic potential of Didymin as an anti-MAFLD drug and elucidated its underlying mechanisms. Methods High-fat diet (HFD)-induced MAFLD mice and alpha mouse liver 12 (AML12) cells were utilized to evaluate the effects and mechanisms of Didymin in the treatment of MAFLD. Liver weight, serum biochemical parameters, and liver morphology were examined to demonstrate the therapeutic efficacy of Didymin in MAFLD treatment. RNA-seq analysis was performed to identify potential pathways that could be affected by Didymin. The impact of Didymin on Sirt1 was corroborated through western blot, molecular docking analysis, microscale thermophoresis (MST), and deacetylase activity assay. Then, a Sirt1 inhibitor (EX-527) was utilized to confirm that Didymin alleviates MAFLD via Sirt1. Western blot and additional assays were used to investigate the underlying mechanisms. Results Our results suggested that Didymin may possess therapeutic potential against MAFLD in vitro and in vivo. By promoting Sirt1 expression as well as directly binding to and activating Sirt1, Didymin triggers downstream pathways that enhance mitochondrial biogenesis and function while reducing apoptosis and enhancing lipophagy. Conclusions These suggest that Didymin could be a promising medication for MAFLD treatment. Furthermore, its therapeutic effects are mediated by Sirt1.

Summary: We evaluated the diagnostic performance of a multimodal deep-learning (DL) model for ovarian mass differential diagnosis. This single-center retrospective study included 1,054 ultrasound (US)-detected ovarian tumors (699 benign and 355 malignant). Patients were randomly divided into training (n = 675), validation (n = 169), and testing (n = 210) sets. The model was developed using ResNet-50. Three DL-based models were proposed for benign-malignant classification of these lesions: single-modality model that only utilized US images; dual-modality model that used US images and menopausal status as inputs; and multi-modality model that integrated US images, menopausal status, and serum indicators. After 5-fold cross-validation, 210 lesions were tested. We evaluated the three models using the area under the curve (AUC), accuracy, sensitivity, and specificity. The multimodal model outperformed the single- and dual-modality models with 93.80% accuracy and 0.983 AUC. The Multimodal ResNet-50 DL model outperformed the single- and dual-modality models in identifying benign and malignant ovarian tumors.

Abstract Background This retrospective study aims to validate the effectiveness of artificial intelligence (AI) to detect and classify non-mass breast lesions (NMLs) on ultrasound (US) images. Methods A total of 228 patients with NMLs and 596 volunteers without breast lesions on US images were enrolled in the study from January 2020 to December 2022. The pathological results served as the gold standard for NMLs. Two AI models were developed to accurately detect and classify NMLs on US images, including DenseNet121_448 and MobileNet_448. To evaluate and compare the diagnostic performance of AI models, the area under the curve (AUC), accuracy, specificity and sensitivity was employed. Results A total of 228 NMLs patients confirmed by postoperative pathology with 870 US images and 596 volunteers with 1003 US images were enrolled. In the detection experiment, the MobileNet_448 achieved the good performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.999 (95%CI: 0.997-1.000),96.5%,96.9% and 96.1%, respectively. It was no statistically significant compared to DenseNet121_448. In the classification experiment, the MobileNet_448 model achieved the highest diagnostic performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.837 (95%CI: 0.990-1.000), 70.5%, 80.3% and 74.6%, respectively. Conclusions This study suggests that the AI models, particularly MobileNet_448, can effectively detect and classify NMLs in US images. This technique has the potential to improve early diagnostic accuracy for NMLs.

Abstract The single-scattering albedo (SSA) of atmospheric aerosols is a key parameter that controls aerosol radiative effects. The variation of SSA is thought to be mainly regulated by aerosol absorption in the Himalayas and South Asia, but observations contradict this idea. In situ field campaigns conducted over two Himalayan sites revealed that SSA was strongly dependent on scattering but weakly correlated with absorption. Observational results combined with the Mie theory further illustrated that SSA was primarily modulated by size distribution rather than absorption. Aerosol Robotic Network (AERONET) data showed similar impacts of size distribution on SSA and that aerosol radiative forcing efficiencies were significantly dependent on SSA. Aerosol size distribution therefore considerably affects radiative forcing by modulating aerosol SSA over the Himalayas. This study highlighted the influence of aerosol size distribution on radiative forcing over the Himalayas, which has important implications for understanding aerosol radiative effects globally.

Summary: Skeletal muscle function declines in the aging process or disease; however, until now, skeletal muscle has remained one of the organs most undertreated with medication. In this study, naringenin (NAR) was found to build muscle endurance in wild-type mice of different ages by increasing oxidative myofiber numbers and aerobic metabolism, and it ameliorates muscle dysfunction in mdx mice. The transcription factor Sp1 was identified as a direct target of NAR and was shown to mediate the function of NAR on muscle. Moreover, the binding site of NAR on Sp1 was further validated as GLN-110. NAR enhances the binding of Sp1 to the CCCTGCCCTC sequence of the Esrrg promoter by promoting Sp1 phosphorylation, thus upregulating Esrrg expression. The identification of the Sp1-ERRγ transcriptional axis is of great significance in basic muscle research, and this function of NAR has potential implications for the improvement of muscle function and the prevention of muscle atrophy.

Abstract Background Diabetes and obesity are associated with muscle atrophy that reduces life quality and lacks effective treatment. Mesenchymal stromal cell (MSC)‐based therapy can ameliorate high fat‐diet (HFD) and immobilization (IM)‐induced muscle atrophy in mice. However, the effect of MSCs on muscle atrophy in type 2 diabetes mellitus (T2DM) and the potential mechanism is unclear. Here, we evaluated the efficacy and explored molecular mechanisms of human umbilical cord MSCs (hucMSCs) and hucMSC‐derived exosomes (MSC‐EXO) on diabetes‐ and obesity‐induced muscle atrophy. Methods Diabetic db/db mice, mice fed with high‐fat diet (HFD), mice with hindlimb immobilization (IM), and C2C12 myotubes were used to explore the effect of hucMSCs or MSC‐EXO in alleviating muscle atrophy. Grip strength test and treadmill running were used to measure skeletal muscle strength and performance. Body composition, muscle weight, and muscle fibre cross‐sectional area (CSA) was used to evaluate muscle mass. RNA‐seq analysis of tibialis anterior (TA) muscle and Western blot analysis of muscle atrophy signalling, including MuRF1 and Atrogin 1, were performed to investigate the underlying mechanisms. Results hucMSCs increased grip strength (P = 0.0256 in db/db mice, P = 0.012 in HFD mice, P = 0.0097 in IM mice), running endurance (P = 0.0154 in HFD mice, P = 0.0006 in IM mice), and muscle mass (P = 0.0004 in db/db mice, P = 0.0076 in HFD mice, P = 0.0144 in IM mice) in all models tested, with elevated CSA of muscle fibres (P

(TiC + TiBx)/Ti composite coatings were prepared on a Ti6Al4V substrate by laser cladding to improve its microhardness and water erosion resistance. The 1 wt% Gr + 49 wt% Ni60 + 50 wt% Ti6Al4V composite powders were used as the precursor. The effects of laser energy density and powder deposition density on the geometric morphology, phase composition, microstructure, microhardness and water erosion resistance characteristics of the coatings were comprehensively studied through numerical simulation and experimental characterization. The results showed that the geometric morphology of the coating was determined by the laser energy density and powder deposition density; however, the microstructure of the coating was not completely understood. A high scanning speed was more conducive to grain refinement than a low scanning speed. In addition, the earlier precipitated hard phases provided heterogeneous nucleation sites for other grains and promoted their dispersion. Due to the dispersion strengthening and grain refinement effects of the in situ synthesized hard phases, the microhardness and water erosion resistance characteristics of the coatings significantly improved. With a laser energy density of 64 J mm−2 and a powder deposition density of 5 × 10−3 g mm−2, the microhardness of the coating reached 919.9 HV0.3, which was 2.8 times greater than that of the Ti6Al4V substrate. The water erosion resistance of the coating was the best.

Early cancer diagnosis increases therapy efficiency and saves huge medical costs. Traditional blood-based cancer markers and endoscopy procedures demonstrate limited capability in the diagnosis. Reliable, non-invasive, and cost-effective methods are in high demand across the world. Worm-based diagnosis, utilizing the chemosensory neuronal system of C. elegans, emerges as a non-invasive approach for early cancer diagnosis with high sensitivity. It facilitates effectiveness in large-scale cancer screening for the foreseeable future. Here, we review the progress of a unique route of early cancer diagnosis based on the chemosensory neuronal system of C. elegans. We first introduce the basic procedures of the chemotaxis assay of C. elegans: synchronization, behavior assay, immobilization, and counting. Then, we review the progress of each procedure and the various cancer types for which this method has achieved early diagnosis. For each procedure, we list examples of microfluidics technologies that have improved the automation, throughput, and efficiency of each step or module. Finally, we envision that microfluidics technologies combined with the chemotaxis assay of C. elegans can lead to an automated, cost-effective, non-invasive early cancer screening technology, with the development of more mature microfluidic modules as well as systematic integration of functional modules.

Purpose: The objective of this research was to investigate the efficacy of various parameter combinations of Convolutional Neural Networks (CNNs) models, namely MobileNet and DenseNet121, and different input image resolutions (REZs) ranging from 64×64 to 512×512 pixels, for diagnosing breast cancer. Materials and methods: During the period of June 2015 to November 2020, two hospitals were involved in the collection of two-dimensional ultrasound breast images for this retrospective multicenter study. The diagnostic performance of the computer models MobileNet and DenseNet 121 was compared at different resolutions. Results: The results showed that MobileNet had the best breast cancer diagnosis performance at 320×320pixel REZ and DenseNet121 had the best breast cancer diagnosis performance at 448×448pixel REZ. Conclusion: Our study reveals a significant correlation between image resolution and breast cancer diagnosis accuracy. Through the comparison of MobileNet and DenseNet121, it is highlighted that lightweight neural networks (LW-CNNs) can achieve model performance similar to or even slightly better than large neural networks models (HW-CNNs) in ultrasound images, and LW-CNNs' prediction time per image is lower.

To promote the real-time dispatching of a power grid and balanced decision-making of power producers, accuracy and real-time forecasting are two main problems that need to be solved in ultra-short-term photovoltaic (PV) forecasting. Focusing on the problems of slow model training speed and low forecasting accuracy due to the redundancy of training data samples and insufficient long periodic capture of data in complex weather, this paper proposes a two-stage method for ultra-short-term PV power forecasting based on data-driven. In the meteorological analysis stage, the generation power samples similar to the forecast day were extracted by inputting daily meteorological features and using maximal information coefficient (MIC) weighted grey correlation degree to form the corresponding forecast data set. In the power forecasting stage, the temporal convolutional used network (TCN) extracts local features to maintain the sequence of extracted features. Then the bidirectional gating unit (BiGRU) combined with the Skip connection strategy was used to fully learn the long and the short time sequence of photovoltaic sequences, and the attention mechanism was used to pay adaptive attention to the more important historical states. This study experimented on the measured data from a photovoltaic power station in Southeastern China. The experimental results show that this method is effective in photovoltaic power short-term forecasts. In addition, compared with the latest model, this method has smaller forecasting errors and higher robustness in the time scales of 15 minutes, 30 minutes, 45 minutes, and 60 minutes. Specifically, indicator R2 increased by an average of 5.4%, while indicators RMSE and MAE decreased by 8.6% and 7.3%, respectively.

Objectives Anterior cervical discectomy and fusion (ACDF) with zero‐profile interbody has a lower incidence of complications in treating cervical spondylotic myelopathy (CSM). However, postoperative axial neck pain is still commonly occurred, and the factors affecting which is not known. Here, we retrospectively analyze the risk factors for postoperative axial pain after performing ACDF with zero‐profile implant in single‐level CSM. Methods Patients who suffered from single‐level CSM and who received ACDF with zero‐profile implant between 2018 January to 2020 December were reviewed. Of 180 single‐level CSM patients, 144 patients who passed the inclusion criteria were enrolled. Patients were divided into two groups according to the severity of postoperative axial pain as measured by postoperative neck visual analogue scale (nVAS). Clinical parameters including age, sex, smoking history, symptom duration, body mass index (BMI), the Japanese Orthopaedic Association (JOA) scores, as well as radiological parameters were obtained pre‐ and post‐operatively, and the data were compared between two groups. Pearson's chi‐square tests and Mann–Whitney U tests were implemented to identify statistically significant differences between subgroups for categorical and continuous data, respectively; otherwise, the data were tested with Student's t‐test. Risk factors were identified using logistic regression. Results Of the patients (97.8%) achieved satisfied neurological recovery, and 88.2% of the patients achieved fusion at 1‐year follow‐up. 33% of the patients (48 patients out of 144) had sustained postoperative axial pain after the surgery. Comparison of different severity groups exhibited no significant differences in terms of the possible risk factors (P > 0.05) except for pre‐ and post‐operative C2–C7 Cobb angles (6.33 ± 6.53 vs. 11.88 ± 7.41, P

The effects of post-weld aging and cryogenic treatment on self-fusion welded austenitic stainless steel thick plates were investigated in the present work. The results showed that fusion zone microstructure consisted of austenite matrix and vermicular ferrite. Aging treatment promoted the decomposition of δ-ferrite into σ-ferrites as well as the precipitation of carbides. Isothermal martensitic transformation in the Tungsten Inert Gas Welding (TIG) specimen was induced by cryogenic treatment, and stacking faults were increased. The fusion zone microstructure of Electron Beam Welding (EBW) and Laser Welding (LW) was finer than that of TIG, with acicular ferrite distributed in the austenite matrix. A large number of twins were generated in the austenite matrix after LW. Cryogenic treatment produced a large number of sub-grains in LW specimens, which was due to the entanglement and accumulation of dislocations in the vicinity of ferrite. Post-weld aging and cryogenic treatment have no influence on the strength of weldments with different welding methods while cryogenic treatment could improve the impact toughness of EBW and LW weldments by the extent of 7.4% and 8.8%, respectively. The aging treatment reduced the impact toughness by 49%, 33% and 15.5%, as well as the uniform elongation by 44%, 39% and 17% for TIG, EBW and LW, respectively. Aging treatment reduced the surface residual stress of TIG weldment by 58.8% in Y direction and 61.2% in X direction. Cryogenic treatment at could also release the surface residual stress of TIG weldment by 36.8% in X direction and 16.3% in Y direction.

We investigate in-band pumping of a Tm,Ho,Lu:CaGdAlO4 laser using a Raman-shifted Er-fiber laser (1678 nm), in the continuous-wave (CW) and mode-locked (ML) regimes. A maximum output power of 524 mW is obtained in the CW regime with a 5% output coupler at an absorbed pump power of 2.04 W, corresponding to a slope efficiency of 27.9%. A maximum CW wavelength tuning range of 160 nm at the zero level, from 1984 to 2144 nm, is obtained with a 0.2% output coupler. In the ML regime, pumping with 5.5 W (unpolarized), the average output power (0.2% output coupler) reaches 148 mW at a repetition rate of ~96 MHz. The output spectrum is centered at 2071.5 nm with a FWHM of 21.5 nm (σ-polarization). The pulse duration amounts to 218 fs (time-bandwidth product equal to 0.327).

Abstract PTH-related peptide (PTHrP) improves the bone marrow micro-environment to activate the bone-remodelling, but the coordinated regulation of PTHrP and transforming growth factor-β (TGFβ) signalling in TMJ-OA remains incompletely understood. We used disordered occlusion to establish model animals that recapitulate the ordinary clinical aetiology of TMJ-OA. Immunohistochemical and histological analyses revealed condylar fibrocartilage degeneration in model animals following disordered occlusion. TMJ-OA model animals administered intermittent PTHrP (iPTH) exhibited significantly decreased condylar cartilage degeneration. Micro-CT, histomorphometry, and Western Blot analyses disclosed that iPTH promoted subchondral bone formation in the TMJ-OA model animals. In addition, iPTH increased the number of osterix (OSX)-positive cells and osteocalcin (OCN)-positive cells in the subchondral bone marrow cavity. However, the number of osteoclasts was also increased by iPTH, indicating that subchondral bone volume increase was mainly due to the iPTH-mediated increase in the bone-formation ability of condylar subchondral bone. In vitro, PTHrP treatment increased condylar subchondral bone marrow-derived mesenchymal stem cell (SMSC) osteoblastic differentiation potential and upregulated the gene and protein expression of key regulators of osteogenesis. Furthermore, we found that PTHrP-PTH1R signalling inhibits TGFβ signalling during osteoblastic differentiation. Collectively, these data suggested that iPTH improves OA lesions by enhancing osteoblastic differentiation in subchondral bone and suppressing aberrant active TGFβ signalling. These findings indicated that PTHrP, which targets the TGFβ signalling pathway, may be an effective biological reagent to prevent and treat TMJ-OA in the clinic.

Abstract Maxillofacial bone defects are commonly seen in clinical practice. A clearer understanding of the regulatory network directing maxillofacial bone formation will promote the development of novel therapeutic approaches for bone regeneration. The fibroblast growth factor (FGF) signalling pathway is critical for the development of maxillofacial bone. Klotho, a type I transmembrane protein, is an important components of FGF receptor complexes. Recent studies have reported the presence of Klotho expression in bone. However, the role of Klotho in cranioskeletal development and repair remains unknown. Here, we use a genetic strategy to report that deletion of Klotho in Osx-positive mesenchymal progenitors leads to a significant reduction in osteogenesis under physiological and pathological conditions. Klotho-deficient mensenchymal progenitors also suppress osteoclastogenesis in vitro and in vivo. Under conditions of inflammation and trauma-induced bone loss, we find that Klotho exerts an inhibitory function on inflammation-induced TNFR signaling by attenuating Rankl expression. More importantly, we show for the first time that Klotho is present in human alveolar bone, with a distinct expression pattern under both normal and pathological conditions. In summary, our results identify the mechanism whereby Klotho expressed in Osx+-mensenchymal progenitors controls osteoblast differentiation and osteoclastogenesis during mandibular alveolar bone formation and repair. Klotho-mediated signaling is an important component of alveolar bone remodeling and regeneration. It may also be a target for future therapeutics.

In this study, nitrogen-doped carbon dots (N-CDs) with excellent peroxidase-like activity were prepared using locust powder as the carbon source by a self-exothermic reaction. The obtained N-CDs could catalyze the oxidation of the chromogenic substrate 3,3′,5,5′-tetramethylbenzidine (TMB) in the presence of H2O2 to generate a blue oxidized product (TMBox) with a maximum absorption peak at 654 nm. The catalytic reaction conditions were optimized; furthermore, steady-state kinetic analysis indicated that N-CDs exhibited high affinity toward both TMB and H2O2, and the Michaelis-Menten constant (km) values were 0.115 mM (TMB) and 0.764 mM (H2O2). A smartphone-based colorimetric method was developed for quantitative detection. The 1/L values (L stands for lightness in HSL color space) of the TMBox solution were recorded via an iPhone application Color Analyzer. Since H2O2 is the by-product of glucose (Glu) oxidation in the presence of glucose oxidase (GOx), a simple, sensitive, and selective smartphone-based colorimetric method was developed for the determination of Glu, and the detection limit was 1.09 μM. The smartphone-based method was successfully applied to determine Glu in different food samples with recoveries in the range of 88.5–109.0 %.

Oral inflammatory diseases such as apical periodontitis are common bacterial infectious diseases that may affect the periapical alveolar bone tissues. A protective process occurs simultaneously with the inflammatory tissue destruction, in which mesenchymal stem cells (MSCs) play a primary role. However, a systematic and precise description of the cellular and molecular composition of the microenvironment of bone affected by inflammation is lacking. In this study, we created a single-cell atlas of cell populations that compose alveolar bone in healthy and inflammatory disease states. We investigated changes in expression frequency and patterns related to apical periodontitis, as well as the interactions between MSCs and immunocytes. Our results highlight an enhanced self-supporting network and osteogenic potential within MSCs during apical periodontitis-associated inflammation. MSCs not only differentiated toward osteoblast lineage cells but also expressed higher levels of osteogenic-related markers, including Sparc and Col1a1. This was confirmed by lineage tracing in transgenic mouse models and human samples from oral inflammatory-related alveolar bone lesions. In summary, the current study provides an in-depth description of the microenvironment of MSCs and immunocytes in both healthy and disease states. We also identified key apical periodontitis-associated MSC subclusters and their biomarkers, which could further our understanding of the protective process and the underlying mechanisms of oral inflammatory-related bone disease. Taken together, these results enhance our understanding of heterogeneity and cellular interactions of alveolar bone cells under pathogenic and inflammatory conditions. We provide these data as a tool for investigators not only to better appreciate the repertoire of progenitors that are stress responsive but importantly to help design new therapeutic targets to restore bone lesions caused by apical periodontitis and other inflammatory-related bone diseases.

BackgroundWith the increasing interest of academics in the application of artificial intelligence to sepsis, thousands of papers on this field had been published in the past few decades. It is difficult for researchers to understand the themes and latest research frontiers in this field from a multi-dimensional perspective. Consequently, the purpose of this study is to analyze the relevant literature in the application of artificial intelligence to sepsis through bibliometrics software, so as to better understand the development status, study the core hotspots and future development trends of this field.MethodsWe collected relevant publications in the application of artificial intelligence to sepsis from the Web of Science Core Collection in 2000 to 2021. The type of publication was limited to articles and reviews, and language was limited to English. Research cooperation network, journals, cited references, keywords in this field were visually analyzed by using CiteSpace, VOSviewer, and COOC software.ResultsA total of 8,481 publications in the application of artificial intelligence to sepsis between 2000 and 2021 were included, involving 8,132 articles and 349 reviews. Over the past 22 years, the annual number of publications had gradually increased exponentially. The USA was the most productive country, followed by China. Harvard University, Schuetz, Philipp, and Intensive Care Medicine were the most productive institution, author, and journal, respectively. Vincent, Jl and Critical Care Medicine were the most cited author and cited journal, respectively. Several conclusions can be drawn from the analysis of the cited references, including the following: screening and identification of sepsis biomarkers, treatment and related complications of sepsis, and precise treatment of sepsis. Moreover, there were a spike in searches relating to machine learning, antibiotic resistance and accuracy based on burst detection analysis.ConclusionThis study conducted a comprehensive and objective analysis of the publications on the application of artificial intelligence in sepsis. It can be predicted that precise treatment of sepsis through machine learning technology is still research hotspot in this field.

Abstract Pulmonary artery sling (PAS) is a rare congenital vascular anomaly, and is usually diagnosed during the infantile or fetal period. Adult presentation of PAS is rare. We report a 55‐year‐old woman with left pulmonary artery sling and left lung bronchiectasis, performing as persistent shortness of breath, coronary computed tomography angiography (CTA) showed the aberrant left pulmonary artery emerging from the right pulmonary artery and crossing to the left between the trachea and oesophagus. We experienced a rare adult case with LPAS and left bronchiectasis, stressing the importance of the anatomic abnormalities in such cases.

Summary: The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings and compared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames, all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames.

In view of the urgent problem of oil pipeline corrosion in the petroleum industry, Co-based coating was prepared on 42CrMo pipeline steel through laser cladding technology to realize the surface strengthening. The objective of the paper is to investigate the effects of process parameters (Led = 64–100 J/mm2) on the geometric characteristics, microstructure and corrosion resistance of the coating. It can be seen from the results that the increase of Led led to the increase of the dilution ratio and the Marangoni convection effect. At the Led of 90 J/mm2, the grain size of the coating was refined, and the dendrite growth direction was clearly visible. In the electrochemical test, the corrosion resistance of the coating first increased and then decreased with the increase of Led. Using the appropriate process parameters, the corrosion current density of the coating was only 1.740 × 10−7 (A/cm2), and a dense passive film was formed on the Co-based coatings. The Led was closely related to the properties of the coating. While at the Led of 64 J/mm2, the passive film resistance reached 6.474e+003 Ω cm2, and many microcracks appeared on the surface of the coating after corrosion. However, at the Led of 90 J/mm2, the corrosion products on the coating surface were less with excellent corrosion resistance. Therefore, the process parameters of laser cladding showed great effect on the forming quality and corrosion resistance of the coating. The coating with excellent corrosion resistance could be obtained by the adjustment of the process parameters.

Vancomycin-associated acute kidney injury (AKI) continues to pose a major challenge to both patients and healthcare providers. The purpose of this study is to construct a machine learning framework for stratified predicting and interpreting vancomycin-associated AKI. Our study is a retrospective analysis of medical records of 724 patients who have received vancomycin therapy from 1 January 2015 through 30 September 2020. The basic clinical information, vancomycin dosage and days, comorbidities and medication, laboratory indicators of the patients were recorded. Machine learning algorithm of XGBoost was used to construct a series risk prediction model for vancomycin-associated AKI in different underlying diseases. The vast majority of sub-model performed best on the corresponding sub-dataset. Additionally, the aim of this study was to explain each model and to explore the influence of clinical variables on prediction. As the results of the analysis showed that in addition to the common indicators (serum creatinine and creatinine clearance rate), some other underappreciated indicators such as serum cystatin and cumulative days of vancomycin administration, weight and age, neutrophils and hemoglobin were the risk factors for cancer, diabetes mellitus, heptic insufficiency respectively. Stratified analysis of the comorbidities in patients with vancomycin-associated AKI further confirmed the necessity for different patient populations to be studied.

Abstract Background Mesenchymal stem cells (MSCs) show promising therapeutic potential in treating type 2 diabetes mellitus (T2DM) in clinical studies. Accumulating evidence has suggested that the therapeutic effects of MSCs are not due to their direct differentiation into functional β-cells but are instead mediated by their paracrine functions. Among them, exosomes, nano-sized extracellular vesicles, are important substances that exert paracrine functions. However, the underlying mechanisms of exosomes in ameliorating T2DM remain largely unknown. Methods Bone marrow mesenchymal stem cell (bmMSC)-derived exosomes (bmMDEs) were administrated to T2DM rats and high-glucose-treated primary islets in order to detect their effects on β-cell dedifferentiation. Differential miRNAs were then screened via miRNA sequencing, and miR-146a was isolated after functional verification. TargetScan, reporter gene detection, insulin secretion assays, and qPCR validation were used to predict downstream target genes and involved signaling pathways of miR-146a. Results Our results showed that bmMDEs reversed diabetic β-cell dedifferentiation and improved β-cell insulin secretion both in vitro and in vivo. Results of miRNA sequencing in bmMDEs and subsequent functional screening demonstrated that miR-146a, a highly conserved miRNA, improved β-cell function. We further found that miR-146a directly targeted Numb, a membrane-bound protein involved in cell fate determination, leading to activation of β-catenin signaling in β-cells. Exosomes derived from miR-146a-knockdown bmMSCs lost the ability to improve β-cell function. Conclusions These findings demonstrate that bmMSC-derived exosomal miR-146a protects against diabetic β-cell dysfunction by acting on the NUMB/β-catenin signaling pathway, which may represent a novel therapeutic strategy for T2DM.

Summary: Diabetic kidney disease (DKD) is the leading cause of end-stage renal diseases. DKD does not have efficacious treatment. The cGAS-STING pathway is activated in podocytes at the early stage of kidney dysfunction, which is associated with the activation of STING downstream effectors TBK1 and NF-κB but not IRF3. Lipotoxicity induces mitochondrial damage and mtDNA leakage to the cytosol through Bcl-2 associated X protein (BAX) in podocytes. BAX-mediated mtDNA cytosolic leakage can activate the cGAS-STING pathway in the absence of lipotoxicity and is sufficient to cause podocyte injury. Depletion of cytosolic mtDNA, genetic STING knockdown, or pharmacological inhibition of STING or TBK1 alleviates podocyte injury and improves renal functions in cultured podocytes or mouse models of diabetes and obesity. These results suggest that the mtDNA-cGAS-STING pathway promotes podocyte injury and is a potential therapeutic target for DKD or other obesity-related kidney diseases.

Particle-in-Cell simulations and statistical analysis are carried out to study the dynamic evolution of a collisionless, magnetized plasma with co-existing whistler turbulence and electron temperature anisotropy as the initial condition, and the competing consequences of whistler turbulence cascade and whistler anisotropy instability growth. The results show that the operation of the whistler instability within whistler turbulence has almost no effects on the fluctuating magnetic field energy and intermittency generated by turbulence. However, it leads to a small reduction of the magnetic field wavevector anisotropy and a major reduction of the intermittency of electron temperature anisotropy. Hence, while the overall effect from whistler instability is minor as compared to that of whistler turbulence due to its much smaller field energy, the whistler instability may act as a regulation mechanism for kinetic-range turbulence through wave-particle interactions.

This paper proposes a new image hashing method, which uses histogram reconstruction to solve the problem of the histogram not being sensitive to the change of pixel position, while ensuring the robustness of the hashing algorithm against common content preservation attacks (such as blurring, noise addition and rotation). The proposed algorithm can resist arbitrary angles of rotation, possibly because the reconstructed histogram leverages the rotational symmetry and its own invariance to rotation operations. We measure the similarity between different images by calculating the Hamming distance of the hash vectors of different images. Our experiments show that the proposed method performs well in robustness and discrimination compared with other established algorithms. In addition, we conduct a receiver operating characteristic curve analysis to further verify the superior overall performance of our image hash method.

Pseudomonas aeruginosa is a ubiquitous opportunistic bacterial pathogen, which is a leading cause of nosocomial pneumonia. Early identification of the risk factors is urgently needed for severe infection patients with P. aeruginosa. However, no detailed relevant investigation based on machine learning has been reported, and little research has focused on exploring relationships between key risk clinical variables and clinical outcome of patients. In this study, we collected 571 severe infections with P. aeruginosa patients admitted to the Xijing Hospital of the Fourth Military Medical University from January 2010 to July 2021. Basic clinical information, clinical signs and symptoms, laboratory indicators, bacterial culture, and drug related were recorded. Machine learning algorithm of XGBoost was applied to build a model for predicting mortality risk of P. aeruginosa infection in severe patients. The performance of XGBoost model (AUROC = 0.94 ± 0.01, AUPRC = 0.94 ± 0.03) was greater than the performance of support vector machine (AUROC = 0.90 ± 0.03, AUPRC = 0.91 ± 0.02) and random forest (AUROC = 0.93 ± 0.03, AUPRC = 0.89 ± 0.04). This study also aimed to interpret the model and to explore the impact of clinical variables. The interpretation analysis highlighted the effects of age, high-alert drugs, and the number of drug varieties. Further stratification clarified the necessity of different treatment for severe infection for different populations.

PurposeThe purpose of this study was to explore the performance of different parameter combinations of deep learning (DL) models (Xception, DenseNet121, MobileNet, ResNet50 and EfficientNetB0) and input image resolutions (REZs) (224 × 224, 320 × 320 and 488 × 488 pixels) for breast cancer diagnosis.MethodsThis multicenter study retrospectively studied gray-scale ultrasound breast images enrolled from two Chinese hospitals. The data are divided into training, validation, internal testing and external testing set. Three-hundreds images were randomly selected for the physician-AI comparison. The Wilcoxon test was used to compare the diagnose error of physicians and models under P=0.05 and 0.10 significance level. The specificity, sensitivity, accuracy, area under the curve (AUC) were used as primary evaluation metrics.ResultsA total of 13,684 images of 3447 female patients are finally included. In external test the 224 and 320 REZ achieve the best performance in MobileNet and EfficientNetB0 respectively (AUC: 0.893 and 0.907). Meanwhile, 448 REZ achieve the best performance in Xception, DenseNet121 and ResNet50 (AUC: 0.900, 0.883 and 0.871 respectively). In physician-AI test set, the 320 REZ for EfficientNetB0 (AUC: 0.896, P < 0.1) is better than senior physicians. Besides, the 224 REZ for MobileNet (AUC: 0.878, P < 0.1), 448 REZ for Xception (AUC: 0.895, P < 0.1) are better than junior physicians. While the 448 REZ for DenseNet121 (AUC: 0.880, P < 0.05) and ResNet50 (AUC: 0.838, P < 0.05) are only better than entry physicians.ConclusionBased on the gray-scale ultrasound breast images, we obtained the best DL combination which was better than the physicians.

BackgroundThe most-commonly used multi-slice Simpson's method employed with routine two-dimensional segmented cine images makes it difficult to evaluate left ventricular (LV) volume and function due to endocardial border blurring and beat-to-beat variation during atrial fibrillation (AF) status.ObjectivesTo assess the feasibility of compressed sensing real-time (CSRT) cine imaging combined with an area-length method for quantification of LV systolic function in normal sinus rhythm (NSR) and AF.MethodsThe CSRT cine sequence and routine segmented balanced Steady-State-Free-Precession cine sequence were performed in 71 patients with NSR (n = 36) or AF (n = 35). Image quality and edge sharpness for both sequences were assessed. The LV functional measurements in patients with NSR included end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), cardiac output (CO), cardiac index (CI), and LV mass (LVM); all were assessed using segmented cine with Simpson's rule in short axis (SegSA_Simpson, as a reference standard) and area-length (AL) method in the two chamber (Seg2CH_AL) or four chamber (Seg4CH_AL) and CSRT cine with AL method in the two chamber (CSRT2CH_AL) or four chamber (CSRT4CH_AL). Finally, the mean, maximum, and minimum values of each LV functional parameter [EDV/ESV/SV/EF/CO/CI/LVM/heart rate (HR)] from 4~5 consecutive heartbeats were measured using CSRT2CH_AL in patients with AF.ResultsIn patients with NSR, measurements of EDV (p > 0.05), ESV (p > 0.05), SV (p > 0.05), EF (p > 0.05), and LVM (p > 0.05) assessed with CSRT2CH_AL did not differ significantly from those obtained with SegSA_Simpson. In patients with AF, CSRT image quality score (p < 0.001) and edge sharpness (p < 0.001) both were significantly higher than those obtained from segmented cine. The CSRT2CH_AL provided significantly different results among mean, maximum, and minimum values of each LV parameter from 4~5 consecutive heartbeats (all p < 0.001) with strong inter- and intra-observer agreement in AF.ConclusionsThe CSRT cine sequence combined with two chamber area-length analysis accurately assessed LV systolic function in NSR. This approach is expected to permit the assessment of multiple parameters in consecutive heartbeats with good inter- and intra-observer reproducibility for beat-to-beat analysis of LV function in AF.

In this paper, a super-resolution (SR) method through an internal and external dictionary-based regularization was proposed for improving the resolution of single low-resolution (LR) image. Firstly, the input was divided into several similar groups, and then the principal components analysis (PCA) dictionary learning method was used to construct the internal dictionary of each corresponding group. Secondly, the high-frequency details of external high-resolution (HR) images was divided into groups with similar structures, and the PCA method was adopted to construct the external dictionary corresponding to each group. Next, the nonlocal regression model was used to design two complementary regularities to solve the problem of SR uncertainty. Finally, the SR reconstruction was achieved through an iterative optimization algorithm with gradient descent. The experimental results showed that the proposed method could improve the peak signal to noise ratio (PSNR) by 0.2 dB on average and the structural similarity (SSIM) by 0.01 on average, showing better visual quality than the other compared methods.

Despite achieving success in many domains, deep learning models remain mostly black boxes, especially in electroencephalogram (EEG)-related tasks. Meanwhile, understanding the reasons behind model predictions is quite crucial in assessing trust and performance promotion in EEG-related tasks. In this work, we explore the use of representative interpretable models to analyze the learning behavior of convolutional neural networks (CNN) in EEG-based emotion recognition. According to the interpretable analysis, we find that similar features captured by our model and state-of-the-art model are consistent with previous brain science findings. Next, we propose a new model by integrating brain science knowledge with the interpretability analysis results in the learning process. Our knowledge-integrated model achieves better recognition accuracy on standard EEG-based recognition datasets.

Bone cements such as polymethyl methacrylate and calcium phosphates have been widely used for the reconstruction of bone. Despite their remarkable clinical success, the low degradation rate of these materials hampers a broader clinical use. Matching the degradation rate of the materials with neo bone formation remains a challenge for bone-repairing materials. Moreover, questions such as the mechanism of degradation and how the composition of the materials contribute to the degradation property remain unanswered. Therefore, the review provides an overview of currently used biodegradable bone cements such as calcium phosphates (CaP), calcium sulfates and organic-inorganic composites. The possible degradation mechanism and clinical performance of the biodegradable cements are summarized. This paper reviews up-to-date research and applications of biodegradable cements, hoping to provide researchers in the field with inspirations and references.

In this report, red-emissive carbon dots (C-dots) were facilely prepared from o-phenylenediamine via microwave-assisted hydrothermal treatment. The C-dots demonstrated excitation wavelength-independent emission with maximums at 621 nm that could be effectively quenched by Ag+ via static quenching. This phenomenon was exploited to establish a sensitive fluorescence assay with a low detection limit (0.37 μM) and wide linear range (0–50 μM). In addition, this assay demonstrated excellent selectivity toward Ag+, free from the interference of 16 commonly seen metal ions. Most importantly, the assay demonstrated high reliability toward samples in deionized water, mineral water, lake water, and serum, which could indicate potential applications for Ag+ monitoring in complicated natural and biological environments.

When the extension state of the non-ellipsoidal extended target (NET) changes, the performance of traditional multiple target tracking algorithms based on the constant number of sub-objects will decrease. To solve this problem, this paper proposes a gamma Gaussian inverse Wishart probability hypothesis density filter for non-ellipsoidal extended targets with varying number of sub-objects, called VN-NET-GGIW-PHD filter. In the proposed filter, each NET is considered as a combination of multiple spatially close sub-objects, and the label management is introduced to realize the association between the NET and corresponding sub-objects. Then, by target spawning and combination, the number of sub-objects for approximating the extension state of each NET can be adjusted automatically. Furthermore, to obtain the partition of the measurement set, an approach based on the clustering by fast search and find of density peaks (CFSFDP) algorithm and expectation maximization (EM) algorithm is proposed. Simulation results show that the proposed filter can adaptively adjust the number of sub-objects and has better performance when the extension state of the NET changes.

This study aims to examine the influence of pre-existing discontinuities on the strengths of four natural rocks of different origins. A series of unconfined compression tests was performed on specimens of two types of sandstones, argillite and basalt that contain open and filled cracks. It was found that the presence of cracks tends to decrease the overall strength for all studied rocks; however, the magnitude of strength reduction is related to the property of rock. The larger strength decrease was observed for the relatively harder argillite and basalt, compared to the softer sandstone. It was also found that the infill material could increase the strength of rock specimens, while the obtained strength depended on the characteristics of the fill material.

Abstract Background Mesenchymal stem cell (MSC)-based therapy is currently considered to be an effective treatment strategy for diabetes and hepatic disorders, such as liver cirrhosis and non-alcoholic fatty liver disease. Exosomes are important mediators of cellular connections, and increasing evidence has suggested that exosomes derived from MSCs may be used as direct therapeutic agents; their mechanisms of action, however, remain largely unclear. Here, we evaluated the efficacy and molecular mechanisms of human umbilical cord MSC-derived exosomes (HucMDEs) on hepatic glucose and lipid metabolism in type 2 diabetes mellitus (T2DM). Methods HucMDEs were used to treat T2DM rats, as well as palmitic acid (PA)-treated L-O2 cells, in order to determine the effects of HucMDEs on hepatic glucose and lipid metabolism. To evaluate the changes in autophagy and potential signaling pathways, autophagy-related proteins (BECN1, microtubule-associated protein 1 light chain 3 beta [MAP 1LC3B]), autophagy-related genes (ATGs, ATG5, and ATG7), AMP-activated protein kinase (AMPK), and phosphorylated AMPK (p-AMPK) were assessed by Western blotting. Results HucMDEs promoted hepatic glycolysis, glycogen storage, and lipolysis, and reduced gluconeogenesis. Additionally, autophagy potentially contributed to the effects of HucMDE treatment. Transmission electron microscopy revealed an increased formation of autophagosomes in HucMDE-treated groups, and the autophagy marker proteins, BECN1 and MAP 1LC3B, were also increased. Moreover, autophagy inhibitor 3-methyladenine significantly reduced the effects of HucMDEs on glucose and lipid metabolism in T2DM rats. Based on its phosphorylation status, we found that the AMPK signaling pathway was activated and induced autophagy in T2DM rats and PA-treated L-O2 cells. Meanwhile, the transfection of AMPK siRNA or application of the AMPK inhibitor, Comp C, weakened the therapeutic effects of HucMDEs on glucose and lipid metabolism. Conclusions These findings demonstrate that HucMDEs improved hepatic glucose and lipid metabolism in T2DM rats by activating autophagy via the AMPK pathway, which provides novel evidence suggesting the potential for HucMDEs in clinically treating T2DM patients.

Considering the necessity of merging the physical constraints in joint space for redundant robot motion control in practice by regarding the kinematics of robots, a discrete perturbation-immunity neural network (DPINN) model with high robustness and predominant convergence is proposed in this article for managing the problem. It is worth emphasizing that this proposed neural network is developed to provide a solution algorithm to dispose of the practical applications in robot motion control that is investigated by reforming it into perturbed dynamic quadratic programming (QP) with equality and inequality constraints, which remedies the lack and drawbacks of existing methodologies. Moreover, theoretical verifications and results verify the convergence performance and noise suppression ability of the proposed neural network, which is further verified by simulation examples. In addition, the simulations on robot motion control embody its superiority and powerful versatility compared to the existing methods. In summary, the proposed neural network extends the sphere of application in perturbed dynamic QP with double-bound constraints and offers a feasible scheme capable of controlling robots in a neural-network thought.

Aluminum matrix composites have been widely used in aerospace and automotive fields due to their excellent physical properties. Cryogenic treatment was successfully adopted to improve the performance of aluminum alloy components, while its effect and mechanism on the aluminum matrix composite remained unclear. In this work, the effects of cryogenic treatment on the microstructure evolution and mechanical properties of 15%SiCp/2009 aluminum matrix composites were systematically investigated by means of Thermoelectric Power (TEP), Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The results showed that TEP measurement can be an effective method for evaluating the precipitation characteristics of 15%SiCp/2009 aluminum matrix composites during aging. The addition of cryogenic treatment after solution and before aging treatment promoted the precipitation from the beginning stage of aging. Furthermore, the aging time for the maximum precipitation of the θ″ phase was about 4 h advanced, as the conduction of cryogenic treatment accelerates the aging kinetics. This was attributed to the great difference in the linear expansion coefficient between the aluminum alloy matrix and SiC-reinforced particles, which could induce high internal stress in their boundaries for precipitation. Moreover, the lattice contraction of the aluminum alloy matrix during cryogenic treatment led to the increase in dislocation density and micro defects near the boundaries, thus providing more nucleation sites for precipitation during the aging treatment. After undergoing artificial aging treatment for 20 h, the increase in dispersive, distributed precipitates after cryogenic treatment improved the hardness and yield strength by 4% and 16 MPa, respectively.

Resulting from bacterial infection, apical periodontitis (AP) is a common inflammatory disease of the periapical region of the tooth. The regeneration of the destroyed periapical alveolar bone and the surrounding periodontium tissues has long been a difficult task in clinical practice. These lesions are closely related to pathogen invasion and an overreactive immune response. It is worth noting that the protective healing process occurs simultaneously, in which mesenchymal stem cells (MSCs) have a crucial function in mediating the immune system and promoting regeneration. Here, we review the recent studies related to AP, with a focus on the regulatory network of MSCs. We also discuss the potential therapeutic approaches of MSCs in inflammatory diseases to provide a basis for promoting tissue regeneration and modulating inflammation in AP. A deeper understanding of the protective action of MSCs and the regulatory networks will help to delineate the underlying mechanisms of AP and pave the way for stem-cell-based regenerative medicine in the future.

Parathyroid hormone (PTH) and parathyroid hormone-related protein (PTHrP) are two peptides that regulate mineral ion homeostasis, skeletal development, and bone turnover by activating parathyroid hormone 1 receptor (PTH1R). PTH1R signaling is of profound clinical interest for its potential to stimulate bone formation and regeneration. Recent pre-clinical animal studies and clinical trials have investigated the effects of PTH and PTHrP analogs in the orofacial region. Dental mesenchymal stem cells (MSCs) are targets of PTH1R signaling and have long been known as major factors in tissue repair and regeneration. Previous studies have begun to reveal important roles for PTH1R signaling in modulating the proliferation and differentiation of MSCs in the orofacial region. A better understanding of the molecular networks and underlying mechanisms for modulating MSCs in dental diseases will pave the way for the therapeutic applications of PTH and PTHrP in the future. Here we review recent studies involving dental MSCs, focusing on relationships with PTH1R. We also summarize recent basic and clinical observations of PTH and PTHrP treatment to help understand their use in MSCs-based dental and bone regeneration.

Large sparse linear algebraic systems can be found in a variety of scientific and engineering fields and many scientists strive to solve them in an efficient and robust manner. In this paper, we propose an interpretable neural solver, the Fourier neural solver (FNS), to address them. FNS is based on deep learning and a fast Fourier transform. Because the error between the iterative solution and the ground truth involves a wide range of frequency modes, the FNS combines a stationary iterative method and frequency space correction to eliminate different components of the error. Local Fourier analysis shows that the FNS can pick up on the error components in frequency space that are challenging to eliminate with stationary methods. Numerical experiments on the anisotropic diffusion equation, convection–diffusion equation, and Helmholtz equation show that the FNS is more efficient and more robust than the state-of-the-art neural solver.