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In bone fracture healing, new tissue gradually forms, ossifies, and eventually remodels itself to restore mechanical stiffness and strength across injury site. Mechanical strain at the fracture site has been implicated in controlling the process of healing and numerical mechanoregulation models with strain-based fuzzy logic rules have been applied to simulate bone healing for simple fracture geometries. However, many of these simplified models cannot capture in vivo observations such as delays in healing with torsional instability or differences in healing rate between different fracture types. Accordingly, the purpose of this work was to apply a fuzzy logic mechanoregulation fracture healing simulation technique to 3D models representing a range of clinically inspired fracture geometries with intramedullary nail fixation and multiaxial loading conditions. The models predicted that the rate of healing depends on the geometry of the fracture and that all fracture types experience a small healing delay with torsional instability. The results also indicated that when realistic torsional loading and fixator mechanics are included, previously published strain-based rules for tissue destruction lead to simulated nonunions that would not be expected in vivo. This suggested that fracture healing may be more robust to distortional strain than has been previously reported and that fuzzy logic models may require parameter tuning to correctly capture clinically relevant healing. The strengths of this study are that it includes fracture morphology effects, realistic implant mechanics, and an exploratory adaptation of the upper distortional strain threshold. These findings may help future researchers extend these methods into clinical fracture healing prediction.

What are the main findings? We proposed PanFusion-Net, a cross-modal fusion framework that combines multi-scale remote sensing structures with fine-grained street-view information and employs dual multi-linear pooling to strengthen high-order interactions and deep semantic fusion across heterogeneous modalities. The proposed method achieved a consistently superior performance on the WuhanUIS dataset we constructed, as well as on the ChinaUIS and S2UV datasets. What are the implications of the main findings? This work provides urban planners with automated and highly accurate tools for identifying informal settlements. The proposed approach establishes a new technical paradigm for cross-modal geospatial analyses and can be extended to a broader range of monitoring applications. Urban informal settlements (UISs), characterized by self-organized housing, a high population density, inadequate infrastructure, and insecure land tenure, constitute a critical, yet underexplored, aspect of contemporary urbanization. They necessitate scholarly scrutiny to tackle pressing challenges pertaining to equity, sustainability, and urban governance. The automated, accurate, and rapid extraction of UISs is of paramount importance for sustainable urban development. Despite its significance, this process encounters substantial obstacles. Firstly, from a remote sensing standpoint, informal settlements are typically characterized by a low elevation and a high density, giving rise to intricate spatial relationships. Secondly, the remote sensing observational features of these areas are often indistinct due to variations in shooting angles and imaging environments. Prior studies in remote sensing and geospatial data analysis have often overlooked the cross-modal interactions of features, as well as the progressive information encoded in the intrinsic hierarchies of each modality. We introduced a spatial network to solve this problem by combining panoramic and coarse-to-fine asymptotic perspectives, using remote sensing images and urban street view images to support a hierarchical analysis through fusion. Specifically, we utilized a multi-linear pooling technique and then established coarse-to-fine-grained and panoramic viewpoint details within an integrated structure, known as the panoramic fusion network (PanFusion-Net). Comprehensive testing was performed on a self-constructed WuhanUIS dataset as well as two open-source datasets, ChinaUIS and S2UV. The experimental results confirmed that the performance of the introduced PanFusion-Net exceeded all comparative models across all of the above datasets.

Obesity and binge drinking often coexist and work synergistically to promote steatohepatitis; however, the underlying mechanisms remain obscure. In this mini-review, we briefly summarize clinical evidence of the synergistical effect of obesity and heavy drinking on steatohepatitis and discuss the underlying mechanisms obtained from the study of several mouse models. High-fat diet (HFD) feeding and binge ethanol synergistically induced steatohepatitis and fibrosis in mice with significant intrahepatic neutrophil infiltration; such HFD-plus-ethanol treatment markedly up-regulated the hepatic expression of many chemokines with the highest fold (approximately 30-fold) induction of chemokine (C-X-C motif) ligand 1 (Cxcl1), which contributes to hepatic neutrophil infiltration and liver injury. Furthermore, HFD feeding activated peroxisome proliferator-activated receptor gamma that subsequently inhibited CXCL1 upregulation in hepatocytes, thereby forming a negative feedback loop to prevent neutrophil overaction; whereas binge ethanol blocked this loop and then exacerbated CXCL1 elevation, neutrophil infiltration, and liver injury. Interestingly, inflamed mouse hepatocytes attracted neutrophils less effectively than inflamed human hepatocytes due to the lower induction of CXCL1 and the lack of the interleukin (IL)-8 gene in the mouse genome, which may be one of the reasons for difficulty in development of mouse models of alcoholic steatohepatitis and nonalcoholic steatohepatitis (NASH). Hepatic overexpression of Cxcl1 and/or IL-8 promoted steatosis-to-NASH progression in HFD-fed mice by inducing neutrophil infiltration, oxidative stress, hepatocyte death, fibrosis, and p38 mitogen-activated protein kinase activation. Collectively, obesity and binge drinking synergistically promote steatohepatitis via the induction of CXCL1 and subsequent hepatic neutrophil infiltration.

A smarter city should be a safer city. Nighttime safety in metropolitan areas has long been a global concern, particularly for large cities with diverse demographics and intricate urban forms, whose citizens are often threatened by higher street-level crime rates. However, due to the lack of night-time urban appearance data, prior studies based on street view imagery (SVI) rarely addressed the perceived night-time safety issue, which can generate important implications for crime prevention. This study hypothesizes that night-time SVI can be effectively generated from widely existing daytime SVIs using generative AI (GenAI). To test the hypothesis, this study first collects pairwise day-and-night SVIs across four cities diverged in urban landscapes to construct a comprehensive day-and-night SVI dataset. It then trains and validates a day-to-night (D2N) model with fine-tuned brightness adjustment, effectively transforming daytime SVIs to nighttime ones for distinct urban forms tailored for urban scene perception studies. Our findings indicate that: (1) the performance of D2N transformation varies significantly by urban-scape variations related to urban density; (2) the proportion of building and sky views are important determinants of transformation accuracy; (3) within prevailed models, CycleGAN maintains the consistency of D2N scene conversion, but requires abundant data. Pix2Pix achieves considerable accuracy when pairwise day–and–night-night SVIs are available and are sensitive to data quality. StableDiffusion yields high-quality images with expensive training costs. Therefore, CycleGAN is most effective in balancing the accuracy, data requirement, and cost. This study contributes to urban scene studies by constructing a first-of-its-kind D2N dataset consisting of pairwise day-and-night SVIs across various urban forms. The D2N generator will provide a cornerstone for future urban studies that heavily utilize SVIs to audit urban environments.

Cell division cycle 25 (CDC25) phosphatases serve as crucial regulators of cell cycle phase transitions and essential components of the checkpoint machinery involved in DNA damage response. Emerging evidence indicates the oncogenic potential of CDC25 family members across various cancers. However, comprehensive insights into the expression pattern and function of the CDC25 family in diverse cancers remain unexplored. In our study, we investigated CDC25 family using multiple databases, including gene expression levels, molecular signatures, diagnosis value, and prognostic value in pan-cancer. Furthermore, we focused on melanoma and systematically explored CDC25A expression and its clinical correlations. As a result, the expression of CDC25 family members is significantly abnormal in most cancers, correlating with poorer prognosis. CDC25 family members are differently regulated by DNA methylation and genetic alterations across various cancers. In addition, CDC25 family plays a critical role in the malignant progression of melanoma. Functional investigation reveals that CDC25A inhibition suppresses the proliferation of melanoma cells and sensitizes melanoma cells to chemotherapy and NK cell therapy. In conclusion, our study suggests that CDC25 family may serve as a significant biomarker for diagnosis and prognosis across multiple cancers, with CDC25A as a promising therapeutic target for melanoma.

This study investigated alterations in the gut microbiota signature and microbial metabolites in children with metabolic dysfunction-associated steatotic liver disease (MASLD). We found that an increased abundance of Ruminococcus torques was associated with increased levels of deoxycholic acid and the progression of MASLD, suggesting that R. torques may serve as a novel clinical target in pediatric MASLD.

Bone healing has been traditionally described as a four-phase process: inflammatory response, soft callus formation, hard callus development, and remodeling. The remodeling phase has been largely neglected in most numerical mechanoregulation models of fracture repair in favor of capturing early healing using a pre-defined callus domain. However, in vivo evidence suggests that remodeling occurs concurrently with repair and causes changes in cortical bone adjacent to callus that are typically neglected in numerical models of bone healing. The objective of this study was to use image processing techniques to quantify this early-stage remodeling in ovine osteotomies. To accomplish this, we developed a numerical method for radiodensity profilometry with optimization-based curve fitting to mathematically model the bone density gradients in the radial direction across the cortical wall and callus. After assessing data from 26 sheep, we defined a dimensionless density fitting function that revealed significant remodeling occurring in the cortical wall adjacent to callus during early healing, a 23% average reduction in density compared to intact. This fitting function is robust for modeling radial density gradients in both intact bone and fracture repair scenarios and can capture a wide variety of the healing responses. The fitting function can also be scaled easily for comparison to numerical model predictions and may be useful for validating future mechanoregulatory models of coupled fracture repair and remodeling.

The electrochemical machining (ECM) is used to solve the difficult machining problem of metal screw pump stator inner hole; the mathematical model of electrolyte flow field in machining gap is established, and the distribution of electrolyte flow field in ECM machining gap of processing screw pump stator inner hole is simulated by using COMSOL simulation software. The simulation results show that, at the end of the machining gap, the electrolyte is not full, therefore setting up the added liquid hole on the cathode working zone by optimizing the cathode structure in the relevant machining gap zone to improve the distribution of flow field. Finally, the experiments are carried out which were conducted with different voltage and feed rate parameters; the test piece is sliced and measure it after the experiments, based on the measurement results to adjust the process parameters. The experiments result show that, when the processing voltage is 16 V and the feed rate is 6 mm/min, the distribution of electrolyte flow field in machining gap is more uniform, and the test piece size reached the product requirements. The simulation greatly shorten the cathode design cycle and optimize the structure of cathode by using COMSOL.

The accurate quantification of plant types can provide a scientific basis for crop variety improvement, whereas efficient automatic classification methods greatly enhance crop management and breeding efficiency. For leafy crops such as Chinese cabbage, differences in the plant type directly affect their growth and yield. However, in current agricultural production, the classification of Chinese cabbage plant types largely depends on manual observation and lacks scientific and unified standards. Therefore, it is crucial to develop a method that can quickly and accurately quantify and classify plant types. This study has proposed a method for the rapid and accurate quantification and classification of Chinese cabbage plant types based on point-cloud data processing and the deep learning algorithm PointNet++. First, we quantified the traits related to plant type based on the growth characteristics of Chinese cabbage. K-medoids clustering analysis was then used for the unsupervised classification of the data, and specific quantification of Chinese cabbage plant types was performed based on the classification results. Finally, we combined 1024 feature vectors with 10 custom dimensionless features and used the optimized PointNet++ model for supervised learning to achieve the automatic classification of Chinese cabbage plant types. The experimental results showed that this method had an accuracy of up to 92.4% in classifying the Chinese cabbage plant types, with an average recall of 92.5% and an average F1 score of 92.3%.

Palbociclib is a CDK4/6 inhibitor approved for the treatment of breast cancer by suppressing cell proliferation. However, monotherapy with palbociclib was discouraging in prostate cancer, calling for a mechanism-based effective therapy. In this study, we reported in prostate cancer that palbociclib is a potent sensitizer of ferroptosis, which is worked out by downregulating the expression of TRIB3, a gene highly expressed in prostate cancer. Specifically, TRIB3 knockdown augmented the response of prostate cancer cells to ferroptosis inducers, whereas, TRIB3 overexpression rescued prostate cancer cells from palbociclib-induced ferroptosis. Mechanistically, TRIB3 inhibition by palbociclib resulted in downregulation of SOX2, which subsequently led to compromised expression of SLC7A11, a cystine/glutamate antiporter that counteracts ferroptosis. Functionally, a combined treatment of palbociclib with ferroptosis inducer significantly suppressed prostate cancer growth in a xenograft tumor model. Together, these results uncover an essential role of TRIB3/SOX2/SLC7A11 axis in palbociclib-induced ferroptosis, suggesting palbociclib a promising targeted therapy in combine with ferroptosis induction for the treatment of prostate cancer.