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Abstract Understanding a person’s behavior from their 3D motion sequence is a fundamental problem in computer vision with many applications. An important component of this problem is 3D action localization, which involves recognizing what actions a person is performing, and when the actions occur in the sequence. To promote the progress of the 3D action localization community, we introduce a new, challenging, and more complex benchmark dataset, BABEL-TAL (BT), for 3D action localization. Important baselines and evaluating metrics, as well as human evaluations, are carefully established on this benchmark. We also propose a strong baseline model, i.e., Localizing Actions with Transformers (LocATe), that jointly localizes and recognizes actions in a 3D sequence. The proposed LocATe shows superior performance on BABEL-TAL as well as on the large-scale PKU-MMD dataset, achieving state-of-the-art performance by using only 10% of the labeled training data. Our research could advance the development of more accurate and efficient systems for human behavior analysis, with potential applications in areas such as human-computer interaction and healthcare.

Abstract This study evaluated the therapeutic efficacy and underlying mechanisms of crisaborole combined with vitamin D in the treatment of allergic contact dermatitis. While crisaborole, a phosphodiesterase 4 inhibitor, and vitamin D analogs are commonly used in the treatment of atopic dermatitis, their combined therapeutic potential in allergic contact dermatitis (ACD) remains unexplored. Given their anti-inflammatory properties, we hypothesized that the combination of crisaborole and vitamin D could offer superior efficacy in mitigating the symptoms and underlying mechanisms of allergic contact dermatitis. In vitro, HaCaT cells stimulated with tumor necrosis factor-α and interferon-γ were treated with a combination of crisaborole and vitamin D, followed by cytokine expression analysis. In vivo, male C57BL/6 mice were divided into five groups and treated accordingly: blank control, dinitrochlorobenzene-induced model, crisaborole alone, vitamin D alone, and a combination of crisaborole and vitamin D. On day 14, dorsal skin and ear thickness were measured, followed by comprehensive pathological evaluations. In vivo and in vitro experiments showed that the expression levels of inflammatory factors were significantly lower in the DNCB + VD + Cri group than in the DNCB group. Histological analyses revealed that, compared with the DNCB group, the combined treatment group significantly reduced epidermal hyperkeratosis, improved epidermal transdermal water loss, decreased dermatitis scores, and diminished mast cell infiltration. Moreover, it lowered the expression levels of IL-6, IL-4, TNF-α, iNOS, IL-17, CC chemokine ligand 2 (CCL2), and CC chemokine receptor 2 (CCR2). CCL2 recognizes CCR2 and stimulates inflammatory cells, enhancing the inflammatory response. Increased CCL2 expression correlates with heightened inflammation and dendritic cell infiltration in ACD, while downregulation of CCL2 attenuates inflammation. Thus, the combined use of crisaborole and vitamin D demonstrates superior therapeutic efficacy over monotherapy in a mouse model of ACD. The combination of vitamin D and crisaborole significantly reduces inflammation and epidermal hyperkeratosis in a mouse model of allergic contact dermatitis, demonstrating superior therapeutic efficacy compared to either treatment alone. This suggests that the combined therapy could be a promising approach for the prevention and treatment of allergic contact dermatitis.

Abstract This paper addresses the adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems, where the states and nonlinear functions are not feasible for the controller design. To address the issue of unmeasurable states, a state observer is developed, and fuzzy logic systems are utilized to approximate unknown nonlinear functions. Under the framework of fixed-time Lyapunov function theory and cooperative control, an adaptive fixed-time fuzzy containment control protocol is derived via the adaptive backstepping and adding one power integrator method. The derived fixed-time containment controller can confirm that the closed-loop systems are practical fixed-time stable, which implies that all signals in the systems are bounded and all follower agents can converge to the convex hull formed by the leader agents within fixed-time in the presence of unmeasurable states and unknown nonlinear functions . Simulation examples are conducted to test the validity of the present control algorithm.

Abstract Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder predominant in childhood. Despite existing treatments, the benefits are still limited. This study explored the effectiveness of mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) loaded with miR-137 in enhancing autism-like behaviors and mitigating neuroinflammation. Utilizing BTBR mice as an autism model, the study demonstrated that intranasal administration of MSC-miR137-EVs ameliorates autism-like behaviors and inhibits pro-inflammatory factors via the TLR4/NF-κB pathway. In vitro evaluation of LPS-activated BV2 cells revealed that MSC-miR137-EVs target the TLR4/NF-κB pathway through miR-137 inhibits proinflammatory M1 microglia. Moreover, bioinformatics analysis identified that MSC-EVs are rich in miR-146a-5p, which targets the TRAF6/NF-κB signaling pathway. In summary, the findings suggest that the integration of MSC-EVs with miR-137 may be a promising therapeutic strategy for ASD, which is worthy of clinical adoption.

Abstract Metabolism has recently emerged as a major target of genes implicated in the evolutionary expansion of human neocortex. One such gene is the human-specific gene ARHGAP11B. During human neocortex development, ARHGAP11B increases the abundance of basal radial glia, key progenitors for neocortex expansion, by stimulating glutaminolysis (glutamine-to-glutamate-to-alpha-ketoglutarate) in mitochondria. Here we show that the ape-specific protein GLUD2 (glutamate dehydrogenase 2), which also operates in mitochondria and converts glutamate-to-αKG, enhances ARHGAP11B’s ability to increase basal radial glia abundance. ARHGAP11B + GLUD2 double-transgenic bRG show increased production of aspartate, a metabolite essential for cell proliferation, from glutamate via alpha-ketoglutarate and the TCA cycle. Hence, during human evolution, a human-specific gene exploited the existence of another gene that emerged during ape evolution, to increase, via concerted changes in metabolism, progenitor abundance and neocortex size.

Summary: Background: Multiplexed immunofluorescence (mIF) staining, such as CODEX and MIBI, holds significant clinical value for various fields, such as disease diagnosis, biological research, and drug development. However, these techniques are often hindered by high time and cost requirements. Methods: Here we present a Multimodal-Attention-based virtual mIF Staining (MAS) system that utilises a deep learning model to extract potential antibody-related features from dual-modal non-antibody-stained fluorescence imaging, specifically autofluorescence (AF) and DAPI imaging. The MAS system simultaneously generates predictions of mIF with multiple survival-associated biomarkers in gastric cancer using self- and multi-attention learning mechanisms. Findings: Experimental results with 180 pathological slides from 94 patients with gastric cancer demonstrate the efficiency and consistent performance of the MAS system in both cancer and noncancer gastric tissues. Furthermore, we showcase the prognostic accuracy of the virtual mIF images of seven gastric cancer related biomarkers, including CD3, CD20, FOXP3, PD1, CD8, CD163, and PD-L1, which is comparable to those obtained from the standard mIF staining. Interpretation: The MAS system rapidly generates reliable multiplexed staining, greatly reducing the cost of mIF and improving clinical workflow. Funding: Stanford 2022 HAI Seed Grant; National Institutes of Health 1R01CA256890.

Cell metabolism is a key regulator of human neocortex development and evolution. Several lines of evidence indicate that alterations in neural stem/progenitor cell (NPC) metabolism lead to abnormal brain development, particularly brain size-associated neurodevelopmental disorders, such as microcephaly. Abnormal NPC metabolism causes impaired cell proliferation and thus insufficient expansion of NPCs for neurogenesis. Therefore, the production of neurons, which is a major determinant of brain size, is decreased and the size of the brain, especially the size of the neocortex, is significantly reduced. This review discusses recent progress understanding NPC metabolism, focusing in particular on glucose metabolism, fatty acid metabolism and amino acid metabolism (e.g., glutaminolysis and serine metabolism). We provide an overview of the contributions of these metabolic pathways to brain development and evolution, as well as to the etiology of neurodevelopmental disorders. Furthermore, we discuss the advantages and disadvantages of various experimental models to study cell metabolism in the developing brain.

Proton exchange membrane (PEM) based electrochemical systems have the capability to operate in fuel cell (PEMFC) and water electrolyser (PEMWE) modes, enabling efficient hydrogen energy utilisation and green hydrogen production. In addition to the essential cell stacks, the system of PEMFC or PEMWE consists of four sub-systems for managing gas supply, power, thermal, and water, respectively. Due to the system's complexity, even a small fluctuation in a certain sub-system can result in an unexpected response, leading to a reduced performance and stability. To improve the system's robustness and responsiveness, considerable efforts have been dedicated to developing advanced control strategies. This paper comprehensively reviews various control strategies proposed in literature, revealing that traditional control methods are widely employed in PEMFC and PEMWE due to their simplicity, yet they suffer from limitations in accuracy. Conversely, advanced control methods offer high accuracy but are hindered by poor dynamic performance. This paper highlights the recent advancements in control strategies incorporating machine learning algorithms. Additionally, the paper provides a perspective on the future development of control strategies, suggesting that hybrid control methods should be used for future research to leverage the strength of both sides. Notably, it emphasises the role of artificial intelligence (AI) in advancing control strategies, demonstrating its significant potential in facilitating the transition from automation to autonomy.

Abstract Malignancy grading of prostate cancer (PCa) is fundamental for risk stratification, patient counseling, and treatment decision-making. Deep learning has shown potential to improve the expert consensus for tumor grading, which relies on the Gleason score/grade grouping. However, the core problem of interobserver variability for the Gleason grading system remains unresolved. We developed a novel grading system for PCa and utilized artificial intelligence (AI) and multi-institutional international datasets from 2647 PCa patients treated with radical prostatectomy with a long follow-up of ≥10 years for biochemical recurrence and cancer-specific death. Through survival analyses, we evaluated the novel grading system and showed that AI could develop a tumor grading system with four risk groups independent from and superior to the current five grade groups. Moreover, AI could develop a scoring system that reflects the risk of castration resistant PCa in men who have experienced biochemical recurrence. Thus, AI has the potential to develop an effective grading system for PCa interpretable by human experts.

Abstract Non-small cell lung cancer (NSCLC) shows high drug resistance and leads to low survival due to the high level of mutated Tumor Protein p53 (TP53). Cisplatin is a first-line treatment option for NSCLC, and the p53 mutation is a major factor in chemoresistance. We demonstrate that cisplatin chemotherapy increases the risk of TP53 mutations, further contributing to cisplatin resistance. Encouragingly, we find that the combination of cisplatin and fluvastatin can alleviate this problem. Therefore, we synthesize Fluplatin, a prodrug consisting of cisplatin and fluvastatin. Then, Fluplatin self-assembles and is further encapsulated with poly-(ethylene glycol)–phosphoethanolamine (PEG–PE), we obtain Fluplatin@PEG–PE nanoparticles (FP NPs). FP NPs can degrade mutant p53 (mutp53) and efficiently trigger endoplasmic reticulum stress (ERS). In this study, we show that FP NPs relieve the inhibition of cisplatin chemotherapy caused by mutp53, exhibiting highly effective tumor suppression and improving the poor NSCLC prognosis.

The Adam algorithm is a common choice for optimizing neural network models. However, its application often brings challenges, such as susceptibility to local optima, overfitting and convergence problems caused by unstable learning rate behavior. In this article, we introduce an enhanced Adam optimization algorithm that integrates Warmup and cosine annealing techniques to alleviate these challenges. By integrating preheating technology into traditional Adam algorithms, we systematically improved the learning rate during the initial training phase, effectively avoiding instability issues. In addition, we adopt a dynamic cosine annealing strategy to adaptively adjust the learning rate, improve local optimization problems and enhance the model's generalization ability. To validate the effectiveness of our proposed method, extensive experiments were conducted on various standard datasets and compared with traditional Adam and other optimization methods. Multiple comparative experiments were conducted using multiple optimization algorithms and the improved algorithm proposed in this paper on multiple datasets. On the MNIST, CIFAR10 and CIFAR100 datasets, the improved algorithm proposed in this paper achieved accuracies of 98.87%, 87.67% and 58.88%, respectively, with significant improvements compared to other algorithms. The experimental results clearly indicate that our joint enhancement of the Adam algorithm has resulted in significant improvements in model convergence speed and generalization performance. These promising results emphasize the potential of our enhanced Adam algorithm in a wide range of deep learning tasks.

With the development of e-commerce logistics in China, online shopping has become increasingly popular among consumers, and the fresh e-commerce industry has shifted to a pre-positioning warehouse distribution model closer to consumers. This study starts from consumer demands and considers the spatial distribution characteristics of consumers. Through cluster analysis, demand points with high consumer density are identified, and potential locations are determined through two rounds of clustering. Drawing insights from the warehousing and distribution models of Freshippo and MissFresh in Wuhan and Nanjing, four classification algorithms are compared to determine the optimal pre-positioning warehouse distribution model for the enterprise in this study. A multi-objective optimization model is established, aiming to minimize enterprise costs and maximize customer satisfaction, and the optimal site selection plan is obtained. By determining the number and locations of pre-positioning warehouses through the optimal solution, the enterprise can achieve maximum investment returns in the spatial layout of these warehouses.

Abstract Pancreatic fibrosis (PF) is primarily characterized by aberrant production and degradation modes of extracellular matrix (ECM) components, resulting from the activation of pancreatic stellate cells (PSCs) and the pathological cross‐linking of ECM mediated by lysyl oxidase (LOX) family members. The excessively deposited ECM increases matrix stiffness, and the over‐accumulated reactive oxygen species (ROS) induces oxidative stress, which further stimulates the continuous activation of PSCs and advancing PF; challenging the strategy toward normalizing ECM homeostasis for the regression of PF. Herein, ROS‐responsive and Vitamin A (VA) decorated micelles (named LR‐SSVA) to reverse the imbalanced ECM homeostasis for ameliorating PF are designed and synthesized. Specifically, LR‐SSVA selectively targets PSCs via VA, thereby effectively delivering siLOXL1 and resveratrol (RES) into the pancreas. The ROS‐responsive released RES inhibits the overproduction of ECM by eliminating ROS and inactivating PSCs, meanwhile, the decreased expression of LOXL1 ameliorates the cross‐linked collagen for easier degradation by collagenase which jointly normalizes ECM homeostasis and alleviates PF. This research shows that LR‐SSVA is a safe and efficient ROS‐response and PSC‐targeted drug‐delivery system for ECM normalization, which will propose an innovative and ideal platform for the reversal of PF.

As the interface between seawater and the seabed, superficial sediments on the seabed are an important part of the marine acoustic field environment and are indispensable for marine resource investigations. Studying sediments several meters to hundreds of meters below the seafloor is highly valuable and important. This study processes and analyses the water depth, topography and bottom data and obtains the shallow bottom profile and topographic map of the northern continental slope of the South China Sea (SCS). The study analyzes the influence of physical parameter (including density, porosity, and grain size) on the acoustic velocity in sediments. Single-parameter and dual-parameter models are established to further examine this influence. The results show that porosity and density have greater influences on the acoustic velocity of sediments than does grain size. Finally, the acoustic properties of several typical stations with water depths are tested to analyze the variations in the acoustic properties of the shallow sediments in the northern SCS. The results show that the influence of each parameter on the prediction of the acoustic velocity of the sediment is in the following order: porosity>density>grain size. This study analyses and reveals the reason why the seafloor sediments in the local area cause the acoustic properties to change greatly. It may be caused by changes in the sediment type, lithology along with the depth. And the other reason is the development of interlayer in the land slope of the northern SCS.

Dust accumulation on the surface of solar photovoltaic panels diminishes their power generation efficiency, leading to reduced energy generation. Regular monitoring and cleaning of solar photovoltaic panels is essential. Thus, developing optimal procedures for their upkeep is crucial for improving component efficiency, reducing maintenance costs, and conserving resources. This study introduces an improved Adam optimization algorithm designed specifically for detecting dust on the surface of solar photovoltaic panels. Although the traditional Adam algorithm is the preferred choice for optimizing neural network models, it occasionally encounters problems such as local optima, overfitting, and not convergence due to inconsistent learning rates during the optimization process. To mitigate these issues, the improved algorithm incorporates Warmup technology and cosine annealing strategies with traditional Adam algorithm, that allows for a gradual increase in the learning rate, ensuring stability in the preliminary phases of training. Concurrently, the improved algorithm employs a cosine annealing strategy to dynamically tweak the learning rate. This not only counters the local optimization issues to some degree but also bolsters the generalization ability of the model. When applied on the dust detection on the surface of solar photovoltaic panels, this improved algorithm exhibited superior convergence and training accuracy on the surface dust detection dataset of solar photovoltaic panels in comparison to the standard Adam method. Remarkably, it displayed noteworthy improvements within three distinct neural network frameworks: ResNet-18, VGG-16, and MobileNetV2, thereby attesting to the effectiveness of the novel algorithm. These findings hold significant promise and potential applications in the field of surface dust detection of solar photovoltaic panels. These research results will create economic benefits for enterprises and individuals, and are an important strategic development direction for the country.

PurposeCreate a comprehensive automated solution for pediatric and adult VMAT-CSI including contouring, planning, and plan check to reduce planning time and improve plan quality.MethodsSeventy-seven previously treated CSI patients (age, 2-67 years) were used for creation of an auto-contouring model to segment 25 organs at risk (OARs). The auto-contoured OARs were evaluated using the Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and a qualitative ranking by one physician and one physicist (scale: 1-acceptable, 2-minor edits, 3-major edits). The auto-planning script was developed using the Varian Eclipse Scripting API and tested with 20 patients previously treated with either low-dose VMAT-CSI (12 Gy) or high-dose VMAT-CSI (36 Gy + 18 Gy boost). Clinically relevant metrics, planning time, and blinded physician review were used to evaluate significance of differences between the auto and manual plans. Finally, the plan preparation for treatment and plan check processes were automated to improve efficiency and safety of VMAT-CSI.ResultsThe auto-contours achieved an average DSC of 0.71 ± 0.15, HD95 of 4.81 ± 4.68, and reviewers’ ranking of 1.22 ± 0.39, indicating close to “acceptable-as-is” contours. Compared to the manual CSI plans, the auto-plans for both dose regimens achieved statistically significant reductions in body V50% and Dmean for parotids, submandibular, and thyroid glands. The variance in the dosimetric parameters decreased for the auto-plans as compared to the manual plans indicating better plan consistency. From the blinded review, the auto-plans were marked as equivalent or superior to the manual-plans 88.3% of the time. The required time for the auto-contouring and planning was consistently between 1-2 hours compared to an estimated 5-6 hours for manual contouring and planning.ConclusionsReductions in contouring and planning time without sacrificing plan quality were obtained using the developed auto-planning process. The auto-planning scripts and documentation will be made freely available to other institutions and clinics.

Abstract Deep neural networks (DNNs) extract thousands to millions of task-specific features during model training for inference and decision-making. While visualizing these features is critical for comprehending the learning process and improving the performance of the DNNs, existing visualization techniques work only for classification tasks. For regressions, the feature points lie on a high dimensional continuum having an inherently complex shape, making a meaningful visualization of the features intractable. Given that the majority of deep learning applications are regression-oriented, developing a conceptual framework and computational method to reliably visualize the regression features is of great significance. Here, we introduce a manifold discovery and analysis (MDA) method for DNN feature visualization, which involves learning the manifold topology associated with the output and target labels of a DNN. MDA leverages the acquired topological information to preserve the local geometry of the feature space manifold and provides insightful visualizations of the DNN features, highlighting the appropriateness, generalizability, and adversarial robustness of a DNN. The performance and advantages of the MDA approach compared to the existing methods are demonstrated in different deep learning applications.

Abstract Background Alzheimer’s disease (AD) and Parkinson’s disease (PD), two common irreversible neurodegenerative diseases, share similar early stage syndromes, such as olfaction dysfunction. Yet, the potential comorbidity mechanism of AD and PD was not fully elucidated. Methods The gene expression profiles of GSE5281 and GSE8397 were downloaded from the Gene Expression Omnibus (GEO) database. We utilized a series of bioinformatics analyses to screen the overlapped differentially expressed genes (DEGs). The hub genes were further identified by the plugin CytoHubba of Cytoscape and validated in the hippocampus (HIP) samples of APP/PS-1 transgenic mice and the substantial nigra (SN) samples of A53T transgenic mice by real-time quantitative polymerase chain reaction (RT-qPCR). Meanwhile, the expression of the target genes in the olfactory epithelium/bulb was detected by RT-qPCR. Finally, molecular docking was used to screen potential compounds for the target gene. Results One hundred seventy-four overlapped DEGs were identified in AD and PD. Five of the top ten enrichment pathways mainly focused on the synapse. Five hub genes were identified and further validated. As a common factor in AD and PD, the changes of synaptosomal-associated protein 25 (SNAP25) mRNA in olfactory epithelium/bulb were significantly decreased and had a strong association with those in the HIP and SN samples. Pazopanib was the optimal compound targeting SNAP25, with a binding energy of − 9.2 kcal/mol. Conclusions Our results provided a theoretical basis for understanding the comorbidity mechanism of AD and PD and highlighted that SNAP25 in the olfactory epithelium may serve as a potential target for early detection and intervention in both AD and PD.

Abstract The response rate of cancer immune checkpoint inhibitors (ICI) varies among patients, making it challenging to pre-determine whether a particular patient will respond to immunotherapy. While gene mutation is critical to the treatment outcome, a framework capable of explicitly incorporating biology knowledge has yet to be established. Here we aim to propose and validate a mutation-based deep learning model for survival analysis on 1571 patients treated with ICI. Our model achieves an average concordance index of 0.59 ± 0.13 across nine types of cancer, compared to the gold standard Cox-PH model (0.52 ± 0.10). The “black box” nature of deep learning is a major concern in healthcare field. This model’s interpretability, which results from incorporating the gene pathways and protein interaction (i.e., biology-aware) rather than relying on a ‘black box’ approach, helps patient stratification and provides insight into novel gene biomarkers, advancing our understanding of ICI treatment.

Abstract Background Treatment of distal tibial fractures is a challenge due to their specific anatomical location. However, there is no appropriate mouse model to simulate a clinical distal tibial fracture for basic research. The aim of this investigation was to evaluate the feasibility of simulating a clinical fracture of the distal tibia of mice and to investigate the effect of ovariectomy (OVX)-induced osteoporosis on fracture healing in this model. Methods Sixty female 8-week-old C57BL/6 mice were randomly divided into two groups, either sham or OVX. A semi-fixation distal tibia fracture was established in the right tibia after 8 weeks of OVX. The right tibias were collected at 7, 14, 21, and 28 days post fracture. Results In the semi-fixation distal tibia fracture model, the posterior callus in the sham group showed excessive bone resorption and lower bone mass phenotype compared with the anterior site; a similar trend was not found in the OVX group. At 28 days post fracture, the posterior callus was more mineralized than the anterior callus in the OVX group. Although the fracture healing of the sham group showed a special phenotype in this mode, the progress and quality of fracture healing were still better than those of the OVX group. Conclusion A semi-fixed distal tibial closed fracture mouse model was successfully established. In this model, excess bone resorption of the posterior callus impaired normal fracture healing, but not in OVX-induced osteoporotic bone. Although the stress shielding effect was not observed in the OVX group, impaired bone healing caused by OVX was still present. Our results suggest that this fracture model may have potential for studies on distal tibial fractures and stress shielding.

In this study, a gastrointestinal image classification method based on the improved Adam algorithm is proposed. Gastrointestinal image classification is of great significance in the field of medical image analysis, but it presents numerous challenges, including slow convergence, susceptibility to local minima, and the complexity and imbalance of medical image data. Although the Adam algorithm is widely used in stochastic gradient descent, it tends to suffer from overfitting and gradient explosion issues when dealing with complex data. To address these problems, this paper proposes an improved Adam algorithm, AdamW_AGC, which combines the weight decay and Adaptive Gradient Clipping (AGC) strategies. Weight decay is a common regularization technique used to prevent machine learning models from overfitting. Adaptive gradient clipping avoids the gradient explosion problem by restricting the gradient to a suitable range and helps accelerate the convergence of the optimization process. In order to verify the effectiveness of the proposed algorithm, we conducted experiments on the HyperKvasir dataset and validation experiments on the MNIST and CIFAR10 standard datasets. Experimental results on the HyperKvasir dataset demonstrate that the improved algorithm achieved a classification accuracy of 75.8%, compared to 74.2% for the traditional Adam algorithm, representing an improvement of 1.6%. Furthermore, validation experiments on the MNIST and CIFAR10 datasets resulted in classification accuracies of 98.69% and 71.7%, respectively. These results indicate that the AdamW_AGC algorithm has advantages in handling complex, high-dimensional medical image classification tasks, effectively improving both classification accuracy and training stability. This study provides new ideas and expansions for future optimizer research.

Due to the complexity and illegibility of medical images, it brings inconvenience and difficulty to the diagnosis of medical personnel. To address these issues, an optimization algorithm called GSL(Gradient sine linear) based on Adam algorithm improvement is proposed in this paper, which introduces gradient pruning strategy, periodic adjustment of learning rate, and linear interpolation strategy. The gradient trimming technique can scale the gradient to prevent gradient explosion, while the periodic adjustment of the learning rate and linear interpolation strategy adjusts the learning rate according to the characteristics of the sinusoidal function, accelerating the convergence while reducing the drastic parameter fluctuations, improving the efficiency and stability of training. The experimental results show that compared to the classic Adam algorithm, this algorithm can demonstrate better classification accuracy, the GSL algorithm achieves an accuracy of 78% and 75.2% on the MobileNetV2 network and ShuffleNetV2 network under the Gastroenterology dataset; and on the MobileNetV2 network and ShuffleNetV2 network under the Glaucoma dataset, an accuracy of 84.72% and 83.12%. The GSL optimizer achieved significant performance improvement on various neural network structures and datasets, proving its effectiveness and practicality in the field of deep learning, and also providing new ideas and methods for solving the difficulties in medical image recognition.

Fungi play a pivotal role in fermentation processes, influencing the breakdown and transformation of metabolites. However, studies focusing on the effects of fungal–metabolite correlations on leaf fermentation quality enhancement are limited. This study investigated specific metabolites and fungi associated with high- and low-quality fermented plant leaves. Their changes were monitored over fermentation periods of 0, 8, 16, and 24 days. The results indicated that organoheterocyclic compounds, lipids, lipid-like molecules, organic nitrogen compounds, phenylpropanoids, and polyketides were predominant in high-quality samples. The fungi Saccharomyces (14.8%) and Thermoascus (4.6%) were predominantly found in these samples. These markers exhibited significant changes during the 24-day fermentation period. The critical influence of fungal community equilibrium was demonstrated by interspecies interactions (e.g., between Saccharomyces and Eurotium). A co-occurrence network analysis identified Saccharomyces as the primary contributor to high-quality samples. These markers collectively enhance the quality and sensory characteristics of the final product.

Microplastics (MPs) and pesticides are two categories contaminants with proposed negative impacts to aqueous ecosystems, and adsorption of pesticides on MPs may result in their long-range transport and compound combination effects. Florpyrauxifen-benzyl, a novel pyridine-2-carboxylate auxin herbicide has been widely used to control weeds in paddy field, but the insights of which are extremely limited. Therefore, adsorption and desorption behaviors of florpyrauxifen-benzyl on polyvinyl chloride (PVC), polyethylene (PE) and disposable face masks (DFMs) in five water environment were investigated. The impacts of various environmental factors on adsorption capacity were evaluated, as well as adsorption mechanisms. The results revealed significant variations in adsorption capacity of florpyrauxifen-benzyl on three MPs, with approximately order of DFMs > PE > PVC. The discrepancy can be attributed to differences in structural and physicochemical properties, as evidenced by various characterization analysis. The kinetics and isotherm of florpyrauxifen-benzyl on three MPs were suitable for different models, wherein physical force predominantly governed adsorption process. Thermodynamic analysis revealed that both high and low temperatures weakened PE and DFMs adsorption, whereas temperature exhibited negligible impact on PVC adsorption. The adsorption capacity was significantly influenced by most environmental factors, particularly pH, cations and coexisting herbicide. This study provides valuable insights into the fate of florpyrauxifen-benzyl in presence of MPs, suggesting that PVC, PE and DFMs can serve as carriers of florpyrauxifen-benzyl in aquatic environment.

Abstract In advanced liver fibrosis (LF), macrophages maintain the inflammatory environment in the liver and accelerate LF deterioration by secreting proinflammatory cytokines. However, there is still no effective strategy to regulate macrophages because of the difficulty and complexity of macrophage inflammatory phenotypic modulation and the insufficient therapeutic efficacy caused by the extracellular matrix (ECM) barrier. Here, AC73 and siUSP1 dual drug‐loaded lipid nanoparticle is designed to carry milk fat globule epidermal growth factor 8 (MFG‐E8) (named MUA/Y) to effectively inhibit macrophage proinflammatory signals and degrade the ECM barrier. MFG‐E8 is released in response to the high reactive oxygen species (ROS) environment in LF, transforming macrophages from a proinflammatory (M1) to an anti‐inflammatory (M2) phenotype and inducing macrophages to phagocytose collagen. Collagen ablation increases AC73 and siUSP1 accumulation in hepatic stellate cells (HSCs) and inhibits HSCs overactivation. Interestingly, complete resolution of liver inflammation, significant collagen degradation, and HSCs deactivation are observed in methionine choline deficiency (MCD) and CCl4 models after tail vein injection of MUA/Y. Overall, this work reveals a macrophage‐focused regulatory treatment strategy to eliminate LF progression at the source, providing a new perspective for the clinical treatment of advanced LF.

Background. The purpose of this study was to analyze SUMO activating enzyme subunit 1 (SAE1) expression in breast cancer (BC). Through bioinformatics analysis and in vitro experiments, the biological function and possibly associated signal pathways of SAE1 in BC were further analyzed. Methods. Bioinformatics analysis was applied to analyze SAE1 expression in BC and normal breast tissues, its relationship with clinicopathologic characteristics and prognosis in BC patients, and data from the Cancer Genome Atlas database and Gene Expression Omnibus dataset. We performed immunohistochemistry to analyze SAE1 expression in BC tissues and para-cancer tissues in 79 breast cancer patients. BC cell proliferation was detected with the Cell Counting Kit-8 and by the colony formation assay. Cell cycle progression was analyzed by flow cytometry, and the expression of cell cycle-related proteins (E2F1, cyclin D3, and cyclin-dependent kinase 2) was determined by western blots in SAE1 small interfering RNA (siRNA) transfected cells. The GSE1456 dataset was used to analyze possible signal pathways associated with SAE1 by gene set enrichment analysis (GSEA), and the expression of PI3K/AKT/mTOR pathway-related proteins (such as p-PI3K, p-AKT, and mTOR) in SAE1-siRNA cells was detected by western blots. Results. The bioinformatics and immunohistochemical results showed that SAE1 mRNA and protein expression in BC tissues were significantly higher than those in normal tissues. The SAE1 overexpression was significantly associated with the tumor size, tumor-node-metastasis stage, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and whether or not it was a triple-negative BC. Patients with SAE1 overexpression had a worse overall survival (OS), recurrence-free survival (RFS), and distant metastasis-free survival compared with lower expression patients. Multivariate Cox regression analysis showed that SAE1 may be an independent prognostic factor for OS of BC patients. The proliferation and cell cycle process of BC cells were inhibited by SAE1-siRNA in vitro. The result of GSEA showed that SAE1 was significantly associated with 12 gene sets, including unfolded protein reaction, DNA repair, oxidative phosphorylation, and cell cycle, among others. Additionally, two signal pathways, mTORC1 and PI3K/Akt/mTOR, were significantly correlated with SAE1 overexpression. Western blots confirmed that the expression of PI3K/Akt/mTOR pathway-related proteins (p-PI3K, p-AKT, and mTOR) in BC cells was decreased after knocking down SAE1. Conclusion. SAE1 was highly expressed in BC. Its overexpression was associated with poor BC prognosis. Additionally, it was an independent prognostic factor for BC patients. We demonstrated that in vitro SAE1 knockdown effectively inhibited BC proliferation and its cell cycle process. Furthermore, the biological function of SAE1 may be associated with the PI3K/Akt/mTOR pathway. SAE1 will be a potential target for BC treatment.

Artificial intelligence (AI) is developing rapidly and has found widespread applications in medicine, especially radiotherapy. This paper provides a brief overview of AI applications in radiotherapy, and highlights the research directions of AI that can potentially make significant impacts and relevant ongoing research works in these directions. Challenging issues related to the clinical applications of AI, such as robustness and interpretability of AI models, are also discussed. The future research directions of AI in the field of medical physics and radiotherapy are highlighted.

Abstract Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatment strategies. A significant hurdle for clinical translation of current data-driven deep learning models is lack of interpretability, often attributable to a disconnect from the underlying pathobiology. Here, we present a biology-guided deep learning approach that enables simultaneous prediction of the tumor immune and stromal microenvironment status as well as treatment outcomes from medical images. We validate the model for predicting prognosis of gastric cancer and the benefit from adjuvant chemotherapy in a multi-center international study. Further, the model predicts response to immune checkpoint inhibitors and complements clinically approved biomarkers. Importantly, our model identifies a subset of mismatch repair-deficient tumors that are non-responsive to immunotherapy and may inform the selection of patients for combination treatments.

Current stomach disease detection and diagnosis is challenged by data complexity and high dimensionality and requires effective deep learning algorithms to improve diagnostic accuracy. To address this challenge, in this paper, an improved strategy based on the Adam algorithm is proposed, which aims to alleviate the influence of local optimal solutions, overfitting, and slow convergence rates by controlling the restart strategy and the gradient norm joint clipping technique. This improved algorithm is abbreviated as the CG-Adam algorithm. The control restart strategy performs a restart operation by periodically checking the number of steps and once the number of steps reaches a preset restart period. After the restart is completed, the algorithm will restart the optimization process. It helps the algorithm avoid falling into the local optimum and maintain convergence stability. Meanwhile, gradient norm joint clipping combines both gradient clipping and norm clipping techniques, which can avoid gradient explosion and gradient vanishing problems and help accelerate the convergence of the optimization process by restricting the gradient and norm to a suitable range. In order to verify the effectiveness of the CG-Adam algorithm, experimental validation is carried out on the MNIST, CIFAR10, and Stomach datasets and compared with the Adam algorithm as well as the current popular optimization algorithms. The experimental results demonstrate that the improved algorithm proposed in this paper achieves an accuracy of 98.59%, 70.7%, and 73.2% on the MNIST, CIFAR10, and Stomach datasets, respectively, surpassing the Adam algorithm. The experimental results not only prove the significant effect of the CG-Adam algorithm in accelerating the model convergence and improving generalization performance but also demonstrate its wide potential and practical application value in the field of medical image recognition.

The optimization algorithm plays a crucial role in image recognition by neural networks. However, it is challenging to accelerate the model’s convergence and maintain high precision. As a commonly used stochastic gradient descent optimization algorithm, the momentum method requires many epochs to find the optimal parameters during model training. The velocity of its gradient descent depends solely on the historical gradients and is not subject to random fluctuations. To address this issue, an optimization algorithm to enhance the gradient descent velocity, i.e., the momentum reacceleration gradient descent (MRGD), is proposed. The algorithm utilizes the point division of the current momentum and the gradient relationship, multiplying it with the gradient. It can adjust the update rate and step size of the parameters based on the gradient descent state, so as to achieve faster convergence and higher precision in training the deep learning model. The effectiveness of this method is further proven by applying the reacceleration mechanism to the Adam optimizer, resulting in the MRGDAdam algorithm. We verify both algorithms using multiple image classification datasets, and the experimental results show that the proposed optimization algorithm enables the model to achieve higher recognition accuracy over a small number of training epochs, as well as speeding up model implementation. This study provides new ideas and expansions for future optimizer research.

The problem of large stockpiles of red mud needs to be solved, and the use of red mud to prepare inorganic fibers is a new way of applying red mud on a large scale. The role of CaO/Al2O3 in the melting point and melt structure of red mud was investigated by molecular dynamics simulations and thermodynamic calculations. Liquid phase line temperatures for different CaO/Al2O3 systems were calculated using the Factsage program. The radial distribution function and the type of oxygen bonding were used to characterize the effect of different CaO/Al2O3 on the structure of the red mud melt. The melting point of MgAl2O4 is lower than that of CaTiO3 due to the fact that the type of oxygen bonding in MgAl2O4 is predominantly bridging oxygen bonds. When the red mud system has a low SiO2 content and CaO/Al2O3 is between 0.3 and 3.9, the melting point temperature increases significantly, which is not conducive to the fibrillation of the red mud melt.

This paper introduces an enhanced variant of the Adam optimizer—the BGE-Adam optimization algorithm—that integrates three innovative technologies to augment the adaptability, convergence, and robustness of the original algorithm under various training conditions. Firstly, the BGE-Adam algorithm incorporates a dynamic β parameter adjustment mechanism that utilizes the rate of gradient variations to dynamically adjust the exponential decay rates of the first and second moment estimates (β1 and β2), the adjustment of β1 and β2 is symmetrical, which means that the rules that the algorithm considers when adjusting β1 and β2 are the same. This design helps to maintain the consistency and balance of the algorithm, allowing the optimization algorithm to adaptively capture the trending movements of gradients. Secondly, it estimates the direction of future gradients by a simple gradient prediction model, combining historic gradient information with the current gradient. Lastly, entropy weighting is integrated into the gradient update step. This strategy enhances the model’s exploratory nature by introducing a certain amount of noise, thereby improving its adaptability to complex loss surfaces. Experimental results on classical datasets, MNIST and CIFAR10, and gastrointestinal disease medical datasets demonstrate that the BGE-Adam algorithm has improved convergence and generalization capabilities. In particular, on the specific medical image gastrointestinal disease test dataset, the BGE-Adam optimization algorithm achieved an accuracy of 69.36%, a significant improvement over the 67.66% accuracy attained using the standard Adam algorithm; on the CIFAR10 test dataset, the accuracy of the BGE-Adam algorithm reached 71.4%, which is higher than the 70.65% accuracy of the Adam optimization algorithm; and on the MNIST dataset, the BGE-Adam algorithm’s accuracy was 99.34%, surpassing the Adam optimization algorithm’s accuracy of 99.23%. The BGE-Adam optimization algorithm exhibits better convergence and robustness. This research not only demonstrates the effectiveness of the combination of these three technologies but also provides new perspectives for the future development of deep learning optimization algorithms.

The Adam algorithm is a widely used optimizer for neural network training due to efficient convergence speed. The algorithm is prone to unstable learning rate and performance degradation on some models. To solve these problems, in this paper, an improved algorithm named Linear Curvature Momentum Adam (LCMAdam) is proposed, which introduces curvature-controlled gradient and linear interpolation strategies. The curvature-controlled gradient can make the gradient update smoother, and the linear interpolation technique can adaptively adjust the size of the learning rate according to the characteristics of the curve during the training process so that it can find the exact value faster, which improves the efficiency and robustness of training. The experimental results show that the LCMAdam algorithm achieves 98.49% accuracy on the MNIST dataset, 75.20% on the CIFAR10 dataset, and 76.80% on the Stomach dataset, which is more difficult to recognize medical images. The LCMAdam optimizer achieves significant performance gains on a variety of neural network structures and tasks, proving its effectiveness and utility in the field of deep learning.

Abstract Brain subcortical structures are paramount in many cognitive functions and their aberrations during infancy are predisposed to various neurodevelopmental and neuropsychiatric disorders, making it highly essential to characterize the early subcortical normative growth patterns. This study investigates the volumetric development and surface area expansion of six subcortical structures and their associations with Mullen scales of early learning by leveraging 513 high-resolution longitudinal MRI scans within the first two postnatal years. Results show that (1) each subcortical structure (except for the amygdala with an approximately linear increase) undergoes rapid nonlinear volumetric growth after birth, which slows down at a structure-specific age with bilaterally similar developmental patterns; (2) Subcortical local area expansion reveals structure-specific and spatiotemporally heterogeneous patterns; (3) Positive associations between thalamus and both receptive and expressive languages and between caudate and putamen and fine motor are revealed. This study advances our understanding of the dynamic early subcortical developmental patterns.

Abstract Osteoporosis, a chronic complication of diabetes mellitus, is characterized by a reduction in bone mass, destruction of bone microarchitecture, decreased bone strength, and increased bone fragility. Because of its insidious onset, osteoporosis renders patients highly susceptible to pathological fractures, leading to increased disability and mortality rates. However, the specific pathogenesis of osteoporosis induced by chronic hyperglycemia has not yet been fully elucidated. But it is currently known that the disruption of Wnt signaling triggered by chronic hyperglycemia is involved in the pathogenesis of diabetic osteoporosis. There are two main types of Wnt signaling pathways, the canonical Wnt signaling pathway (β-catenin-dependent) and the non-canonical Wnt signaling pathway (non-β-catenin-dependent), both of which play an important role in regulating the balance between bone formation and bone resorption. Therefore, this review systematically describes the effects of abnormal Wnt pathway signaling on bone homeostasis under hyperglycemia, hoping to reveal the relationship between Wnt signaling and diabetic osteoporosis to further improve understanding of this disease.

Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems.

Abstract Circulating tumor cells (CTCs) and tumor‐derived exosomes (TDEs) play an irreplaceable role in the metastatic cascade and preventing them from reaching distant organs via blood circulation helps to reduce the probability of cancer recurrence and metastasis. However, technologies that can simultaneously prevent CTCs and TDEs from reaching distant organs have not been thoroughly developed until now. Here, inspired by hemoperfusion, a pro‐metastatic derivative eliminator (PMDE) is developed for the removal of both CTCs and TDEs from the peripheral blood, which also inhibits their biodistribution in distant organs. This device is designed with a dual antibody‐modified immunosorbent filled into a capture column that draws peripheral blood out of the body to flow through the column to specifically capture CTCs and TDEs, followed by retransfusing the purified blood into the body. The PMDE can efficiently remove CTCs and TDEs from the peripheral blood and has excellent biocompatibility. Interestingly, the PMDE device can significantly inhibit the biodistribution of CTCs and TDEs in the lung and liver by scavenging them. This work provides a new perspective on anti‐metastatic therapy and has broad prospects in clinical applications to prevent metastasis and recurrence.

As a carbon capture and utilization (CCU) technology, gas diffusion electrode (GDE) based electrochemical CO2 reduction reaction (eCO2RR) can convert CO2 to valuable products, such as formate and CO. However, the electrode parameters and operational conditions need to be studied and optimised to enhance the performance and reduce the net cost of the eCO2RR process before its industrial application. In this work, a machine learning algorithm, i.e., extended adaptive hybrid functions (E-AHF) is combined with a multi-physics model for the data-driven three-objective optimisation and techno-economic analysis of the GDE-based eCO2RR process. The effects of eight design variables on the product yield (PY), CO2 conversion (CR) and specific electrical energy consumption (SEEC) of the process are analysed. The results show that the R2 of the E-AHF model for the prediction of PY, CR and SEEC are all higher than 0.96, indicating the high accuracy of the developed machine learning algorithm for the prediction of the eCO2RR process. The process performance experiences a notable improvement after optimisation and is affected by a combination of eight variables, amongst which the electrolyte concentration having the most significant impact on PY and CR. The optimal trade-off single-pass PY, CR and SEEC are 3.25×10−9 kg s−1, 0.663% and 9.95 kWh kg−1 based on flow channels with 1 cm in length, respectively. The SEEC is reduced by nearly half and PY and CR are improved more than two times after optimisation. The production cost of the GDE-based eCO2RR process was approximately $378 t−1product (CO and formate), much lower than that of traditional CO2 utilisation factories ($835 t−1product). The electricity cost accounted for more than 80% of the total cost, amounting to $318 t−1, indicating that cheaper and cleaner electricity sources would further reduce the production cost of the process, which is the key to the economics of this technology.

The increasing demand for net zero solutions has prompted the exploration of electrochemical CO2 reduction reaction (eCO2RR) systems powered by renewable energy sources. Here, we present a comprehensive AI-enabled framework for the adaptive optimisation of the dynamic eCO2RR processes in response to the intermittent renewable energy supply. The framework includes (1). a Bi-LSTM (bidirectional long-short-term memory) to predict the meteorological data for renewable energy input; (2). a deep learning surrogate model to predict the eCO2RR process performance; and (3). a NSGA-II algorithm for multi-objective optimisation, targeting the trade-off of the single-pass Faraday efficiency (FE), product yield (PY) and conversion. The framework seamlessly integrates the three different AI modules, enabling adaptive optimisation of the eCO2RR system composed of electrolyser stacks and renewable energy sources, and providing insights into system's performance and feasibility under real-world conditions.

The development of artificial intelligence (AI) greatly boosts scientific and engineering innovation. As one of the promising candidates for transiting the carbon intensive economy to zero emission future, proton exchange membrane (PEM) fuel cells has aroused extensive attentions. The gas diffusion layer (GDL) strongly affects the water and heat management during PEM fuel cells operation, therefore multi-variable optimization, including thickness, porosity, conductivity, channel/rib widths and compression ratio, is essential for the improved cell performance. However, traditional experiment-based optimization is time consuming and economically unaffordable. To break down the obstacles to rapidly optimize GDLs, physics-based simulation and machine-learning-based surrogate modelling are integrated to build a sophisticated M5 model, in which multi-physics and multi-phase flow simulation, machine-learning-based surrogate modelling, multi-variable and multi-objects optimization are included. Two machine learning methodologies, namely response surface methodology (RSM) and artificial neural network (ANN) are compared. The M5 model is proved to be effective and efficient for GDL optimization. After optimization, the current density and standard deviation of oxygen distribution at 0.4 V are improved by 20.8% and 74.6%, respectively. Pareto front is obtained to trade off the cell performance and homogeneity of oxygen distribution, e.g., 20.5% higher current density is achieved when sacrificing the standard deviation of oxygen distribution by 26.0%.

Abstract Background Breast cancer (BC) is a common malignant tumor in women worldwide. Circular RNA (circRNA) has been proven to play a critical role in BC progression. However, the exact biological functions and underlying mechanisms of circRNAs in BC remain largely unknown. Methods Here, we first screened for differentially expressed circRNAs in 4 pairs of BC tissues and adjacent non-tumor tissues using a circRNA microarray. Functionally, gain- and loss-of-function experiments in vitro and in vivo showed that circDNAJC11 promoted BC cell proliferation, migration, invasion, and tumor growth. Mechanistically, RNA pull-down, mass spectrum, RNA immunoprecipitation, fluorescence in situ hybridization assays, and rescue experiments were executed. Results We found that circDNAJC11 was significantly upregulated in triple-negative breast cancer tissues and cells. Clinical data revealed that the high expression of circDNAJC11 was closely correlated with a poor prognosis of BC patients and could be an independent risk factor for BC prognosis. Functionally, gain- and loss-of-function experiments in vitro and in vivo showed that circDNAJC11 promoted BC cell proliferation, migration, invasion, and tumor growth. Mechanistically, RNA pull-down, mass spectrum, RNA immunoprecipitation, fluorescence in situ hybridization assays, and rescue experiments were executed. We demonstrated that circDNAJC11 combined with TAF15 to promote BC progression via stabilizing MAPK6 mRNA and activating the MAPK signaling pathway. Conclusions The circDNAJC11/TAF15/MAPK6 axis played a crucial role in the progression and development of BC, suggesting that circDNAJC11 might be a novel biomarker and therapeutical target for BC.

Existing genomic data analysis methods tend to not take full advantage of underlying biological characteristics. Here, the authors leverage the inherent interactions of scRNA-seq data and develop a cartography strategy to contrive the data into a spatially configured genomap for accurate deep pattern discovery.

UAVs have been widely used in deformation monitoring because of their high availability and flexibility. However, the quality of UAV images is affected by changing attitude and surveying environments, resulting in a low monitoring accuracy. Cross-shaped markers are used to improve the accuracy of UAV monitoring due to their distinct center contrast and absence of eccentricity. However, existing methods cannot rapidly and precisely detect these markers in UAV images. To address these problems, this paper proposes an adaptive Radon-transform-based marker detection and localization method for UAV displacement measurements, focusing on two critical detection parameters, namely, the radius of marker information acquisition and the edge width of the cross-shaped scoring template. The experimental results show that the marker detection rate is 97.2% under different combinations of flight altitudes, radius ratios of marker information acquisition, and marker sizes. Furthermore, the root mean square error of detection and localization is 0.57 pixels, significantly surpassing the performance and accuracy of other methods. We also derive the critical detection radius and appropriate parameter combinations for different heights to further improve the practicality of the method.

Background: Severe neurological condition like Alzheimer’s disease (AD) has a significantly negative impact on families and society, wherein there is no proven cure. As one of the principal active constituents of Achyranthes bidentata Blume, ecdysterone (ECR) has demonstrated antioxidant and cognitive dysfunction improvement effects. Nonetheless, the mechanism underlying the improvement of cognitive dysfunction by ECR remains unclear. This study sought to ascertain whether ECR may allebviate cognitive impairment by reducing oxidative stress via activation of the nuclear factor erythroid-2-related factor-2 (Nrf2) antioxidant system through Akt/GSK3β pathway. Methods: In terms of the experimental procedure, we determined the neuroprotective benefits of ECR in vivo via a cognitive impairment model of senescence-accelerated mouse prone 8 (SAMP8), we performed procedures such as behavioral testing, biochemical assaying, Nissl and TUNEL stainings, as well as flow cytometry, immunohistochemistry and western blotting. Furthermore, we investigated the underlying mechanistic action of ECR by activating PC12 cells with β-amyloid peptide fragment 25-35 (Aβ25-35). Results: In vivo studies showed that ECR effectively improved cognitive impairment in SAMP8 via enhancement of learning and memory capabilities, but decreased oxidative stress, apoptosis and neuronal damage in the hippocampus. During the in vitro study, we observed that ECR dose-dependently reduced the oxidative stress and apoptosis that were induced in PC12 cells by Aβ25-35. Additionally, the use of Akt inhibitors further established the potential of ECR to control Nrf2 through activation of the Akt/GSK3β pathway and protect the PC12 cells from Aβ25-35 induced damage. Conclusions: These findings offer proof that ECR reduces cognitive impairment by triggering the Nrf2 antioxidant system via the Akt/GSK3β pathway and offer fresh information on ECR’s potential as a promising therapeutic development candidate for AD.

The control of inclusion features, i.e., chemical composition, distribution, quantity, and size, is in high demand for stainless steels to achieve good surface quality, pitting corrosion resistance, and mechanical properties. In this paper, the effects of rare earth (RE) modification on the inclusions of martensitic stainless steel were explored. Martensitic stainless steels with various contents of Ce were prepared, and the inclusions in the cast billets were characterized by field emission scanning electron microscopy (FE-SEM) and an automatic analysis system for nonmetallic inclusions in steels (OTS). The effect of Ce on the modification of nonmetallic inclusions during solidification was explored with thermodynamic calculations. The results showed that the addition of Ce modified the oxygen and sulfur inclusions by transforming them from irregular elongated inclusions to nearly spherical RE composite inclusions. Compared with those in basic martensitic stainless steel, nine types of RE inclusions were found in the presence of Ce. As the Ce content increased from 0.048 wt% to 0.092 wt%, the CeAlO _3 content decreased, whereas the Ce _2 O _2 S content increased. Moreover, there were 80% fewer inclusions than in the basic martensitic stainless steel.

Summary: The E3 ubiquitin ligase Ube3a is biallelically expressed in neural progenitors and glial cells, suggesting that UBE3A gain-of-function mutations might cause neurodevelopmental disorders irrespective of parent of origin. Here, we engineered a mouse line that harbors an autism-linked UBE3AT485A (T503A in mouse) gain-of-function mutation and evaluated phenotypes in animals that inherited the mutant allele paternally, maternally, or from both parents. We find that paternally and maternally expressed UBE3AT503A results in elevated UBE3A activity in neural progenitors and glial cells. Expression of UBE3AT503A from the maternal allele, but not the paternal one, leads to a persistent elevation of UBE3A activity in neurons. Mutant mice display behavioral phenotypes that differ by parent of origin. Expression of UBE3AT503A, irrespective of its parent of origin, promotes transient embryonic expansion of Zcchc12 lineage interneurons. Phenotypes of Ube3aT503A mice are distinct from Angelman syndrome model mice. Our study has clinical implications for a growing number of disease-linked UBE3A gain-of-function mutations.

Abstract Background Long noncoding RNAs (lncRNAs) are implicated in the oncogenesis and metastasis of multiple human cancers. Nonetheless, the precise molecular mechanisms underlying the oncogenic role of lncRNA in esophageal squamous cell carcinoma (ESCC) remains to be clarified. Methods The expression of GK intronic transcript 1 (GK‐IT1) was analyzed using ESCC RNA‐seq data from The Cancer Genome Atlas database. Quantitative real‐time PCR was used to measure the expression of GK‐IT1 in ESCC clinical samples and cells. The correlation between GK‐IT1 expression and clinicopathological variables was examined using chi‐squared tests. Kaplan–Meier survival and Cox regression analyses were employed to generate the survival curve and assess the prognostic value of GK‐IT1. Functional experiments were utilized to explore the role of GK‐IT1 in promoting cell migration, invasion, proliferation, and suppressing apoptosis and autophagy in ESCC. To understand the mechanism, an RNA pulldown assay, RNA immunoprecipitation, agarose gel electrophoresis, immunofluorescence, and co‐immunoprecipitation assays were used. Results In this study we identified an unreported lncRNA, termed GK‐IT1 that was aberrantly overexpressed in ESCC tissues and cells. GK‐IT1 was closely associated with advanced clinical stage, and it was an independent prognostic indicator of ESCC. Functional assays verified that GK‐IT1 significantly promoted ESCC proliferation, invasion, and migration, and suppressed ESCC apoptosis and autophagy. Furthermore, tumorigenesis experiments in nude mice indicated that GK‐IT1 promoted ESCC tumor growth and metastasis. Mechanistically, GK‐IT1 competitively bound to mitogen‐activated protein kinase 1 (MAPK1) to prevent the interaction between dual specificity phosphatase 6 (DUSP6) and MAPK1, thereby controlling the phosphorylation of MAPK1 and promoting ESCC progression. Conclusion Our study revealed that GK‐IT1 competed with DUSP6 to attenuate the interaction between DUSP6 and MAPK1, leading to activation of the ERK/MAPK pathway, thereby promoting progression of ESCC. Our research indicated that GK‐IT1 served as a novel potential target for the diagnosis and treatment of ESCC.