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Representative large models and their related applications, such as Bidirectional encoder representations from transformers (BERT), Generative pretrained transformer (GPT), Segment anything model (SAM), ChatGPT, DALL-E, Wenxin, and Pangu, have made astounding strides and exerted considerable influence across various fields domestically and abroad. They constantly attract the attention and follow-up of diverse societal sectors, including enterprises, universities, and research institutions. Large model applications have been successfully applied in scenarios such as biology, medicine, law, and social governance. Designing, modifying, and constructing domain-specific large models are crucial for truly harnessing their application value. Therefore, this paper provides inspiration for the application of large models in materials science. First, it provides an overview of large models, introducing their basic concepts, developmental process, technical classification, and features. Second, from the perspectives of the general domain and specific large models, this paper summarizes the applications of large models and analyzes the application scenarios and functions of various types of large models. Subsequently, considering the specific needs and current state of research in the field of materials science, this paper reviews the application of large language models, large visual models, and large multimodal models. It integrates engineering strategies and zero-shot knowledge transfer learning from specific tasks in natural language processing and computer vision and referencing typical application cases, clarifying current research paradigms and limiting factors for applying large models to materials science. To verify the effectiveness and potential of the visual large model, basal experiments of image segmentation and key structure extraction are performed on the microscopic image data of four types of materials using improved SAM, including Ni-superalloy, superalloy, polycrystalline pure iron grain, and Inconel 939. The experimental results reveal that the zero-shot segmentation capability of SAM has enormous potential for accurate and efficient representation of material microstructures. With the help of tailored prompt engineering, precise masks of the precipitated phase, grain boundaries, and cracks can be outputted without any label. Finally, this paper proposes future research opportunities for technologies and methods related to large models in materials science. This paper assesses the feasibility and technical challenges for the development and tuning of unimodal to comprehensive multimodal large models. With continuous innovations and collaborations, the horizon for large models in materials science seems boundlessly promising. The integration of these models can produce a new era of advanced research, leading to advancements that were previously considered unattainable. The symbiosis between materials science and large models can pave the way for unforeseen discoveries, enriching our scientific prowess.

Abstract Heat shock protein family B [small] member 6 (HSPB6), widely found in various muscles, has been recently identified as a tumor suppressor gene. However, its role in prostate cancer remains unexplored. Herein, we investigated the expression of HSPB6 in prostate cancer and its association with prognosis. Our findings revealed that HSPB6 downregulation in prostate cancer correlated with a poor prognosis. Moreover, we discovered that HSPB6 can be phosphorylated and activated by 8-Br-cGMP, leading to apoptosis in prostate cancer cells by activating Cofilin. Additionally, we demonstrated that knocking down E2F1 by quinidine administration enhances the transcriptional level of HSPB6. Furthermore, we evaluated the combination of quinidine and 8-Br-cGMP as a potential therapeutic strategy for prostate cancer. Our results revealed that the combined treatment was more effective than either treatment alone in inhibiting the growth of prostate cancer through the HSPB6 pathway, both in vitro and in vivo. Overall, our study provides compelling evidence that HSPB6 suppresses malignant behavior in prostate cancer by inducing apoptosis. The combination of quinidine and 8-Br-cGMP emerges as a promising approach for the treatment of prostate cancer.

Abstract Background Application of accumulated experience and management measures in the prevention and control of coronavirus disease 2019 (COVID-19) has generally depended on the subjective judgment of epidemic intensity, with the quality of prevention and control management being uneven. The present study was designed to develop a novel risk management system for COVID-19 infection in outpatients, with the ability to provide accurate and hierarchical control based on estimated risk of infection. Methods Infection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results. Results The ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results. Conclusion These innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections.

Abstract Background Implementation of high-risk human papillomavirus (hrHPV) screening has greatly reduced the incidence and mortality of cervical cancer. However, a triage strategy that is effective, noninvasive, and independent from the subjective interpretation of pathologists is urgently required to decrease unnecessary colposcopy referrals in hrHPV-positive women. Methods A total of 3251 hrHPV-positive women aged 30–82 years (median = 41 years) from International Peace Maternity and Child Health Hospital were included in the training set (n = 2116) and the validation set (n = 1135) to establish Cervical cancer Methylation (CerMe) detection. The performance of CerMe as a triage for hrHPV-positive women was evaluated. Results CerMe detection efficiently distinguished cervical intraepithelial neoplasia grade 2 or worse (CIN2 +) from cervical intraepithelial neoplasia grade 1 or normal (CIN1 −) women with excellent sensitivity of 82.4% (95% CI = 72.6 ~ 89.8%) and specificity of 91.1% (95% CI = 89.2 ~ 92.7%). Importantly, CerMe showed improved specificity (92.1% vs. 74.9%) in other 12 hrHPV type-positive women as well as superior sensitivity (80.8% vs. 61.5%) and specificity (88.9% vs. 75.3%) in HPV16/18 type-positive women compared with cytology testing. CerMe performed well in the triage of hrHPV-positive women with ASC-US (sensitivity = 74.4%, specificity = 87.5%) or LSIL cytology (sensitivity = 84.4%, specificity = 83.9%). Conclusions PCDHGB7 hypermethylation-based CerMe detection can be used as a triage strategy for hrHPV-positive women to reduce unnecessary over-referrals. Trial registration ChiCTR2100048972. Registered on 19 July 2021.

This study aims to investigate the patterns of oil and gas dispersal within the fault-caprock configurations in an oil-gas-rich basin. Using the results of the mechanism and spatial distribution research of fault-caprock oil and gas migration and accumulation configuration patterns, a predictive method has been developed for determining the distribution of fault-caprock fluid migration and accumulation configuration patterns. This method involves superpositing source rock faults, mudstone and fault rock seals within fault-caprock configurations. The application of this method is demonstrated through the prediction of the distribution of fault-caprock fluid migration and accumulation configuration patterns within the lower sub-member of the 1st member of the Shahejie Formation (E3s1L) in the Qikou Sag, Bohai Bay Basin. The Dazhangtuo fault and regional mudstone caprocks within the middle sub-member of the 1st member of the Shahejie Formation (E3s1M) are involved. The results of this study demonstrate that the fault-caprock sealing of petroleum and natural gas by mudstone and fault-related lithological seals in the configuration pattern of the Dazhangtuo Fault and regional mudstone caprocks in E3s1M is primarily distributed in the western part of the basin, with localized occurrences in the eastern areas. This configuration pattern is optimal for the accumulation of petroleum and natural gas reservoirs in the E3s1L. The fault-caprock configuration pattern that seals oil and gas by mudstone is primarily located in the western portion, making it relatively favorable for the accumulation of oil and gas reservoirs in the E3s1L. However, the fault-caprock configuration pattern of sealing oil and gas by fault-related lithological is constrained to two specific localized areas in the eastern portion, which is not conducive to the occurrence of oil-gas convergence in the E3s1L. The location of detected oil and gas within the E3s1L at the present Dazhangtuo fault is consistent with the predicted pattern of oil and gas accumulation and their distribution locations using this method. This demonstrates the viability of forecasting using this approach to predict the location of fault-caprock migration and the accumulation oil and gas configuration pattern.

Abstract Background To investigate the relationship between body weight and Axial length in guinea pigs. Methods Forty pigmented guinea pigs were randomly divided into two groups, namely control group and negative lens-induced myopization (LIM) group. After measuring the baseline axial length and body weight (BW), guinea pigs of LIM group received bilateral negative lens-induced myopization using − 10.0 diopters lenses. One week later, the lenses were removed and biometric and ophthalmoscopic examinations were repeated. Results Two groups of guinea pigs showed no statistical difference in initial body weight and eye axis length. Compared to the control group, the lens-induced group had a lower weight (P = 0.02) and a longer axial length (P 0.05). Conclusion In guinea pigs of the same age, axial length was not correlated with body weight. Also, baseline body weight had no impact on natural axial length growth or lens-induced myopia. Lens-induction caused a significant reduction in body weight gain.

There are several unresolved issues in the field of ship instance segmentation in synthetic aperture radar (SAR) images. First, in inshore dense ship area, the problems of missed detections and mask overlap frequently occur. Second, in inshore scenes, false alarms occur due to strong clutter interference. In order to address these issues, we propose a novel ship instance segmentation network based on dynamic key points information enhancement. In the detection branch of the network, a dynamic key points module is designed to incorporate the target's geometric information into the parameters of the dynamic mask head using an implicit encoding technique. In addition, we introduce a dynamic key points encoding branch, which encodes the target's strong scattering regions as dynamic key points. It strengthens the network's ability to learn the correspondence between local regions with strong scattering and overall ship targets, effectively mitigating mask overlap issues. Moreover, it enhances the discriminative ability of network between ship targets and clutter interference, leading to a reduction in false alarm rates. To further enhance the dynamic key points information, an instancewise attention map module is designed, which decodes the key points during the mask prediction period, generating instancewise attention maps based on 2-D Gaussian distribution. This module further enhances the sensibility of network to specific instances. Simulation experiments conducted on the Polygon Segmentation SAR Ship Detection Dataset and High-Resolution SAR Images Dataset demonstrate the superiority of our proposed method over other state-of-the-art methods in inshore and offshore scenes.

In order to realize waypoint-based automatic berthing alongside or astern, a generation algorithm of dynamic virtual ships is proposed to guide the underactuated surface vehicle (USV). In this paper, a nonlinear model predictive control (NMPC) problem is set up with multiple shooting method, which lifts the system and improves convergency. Measuring error and disturbances are reduced by moving horizon estimation (MHE) where the weights of sensors are auto-tuned according to their variation. Simulations of berthing alongside and astern of ship BAY CLASS illustrates the position error is smaller than 0.6 m and course error is smaller than 0.5°. Compared to adaptive sliding mode control, proposed method has advantage on state smoothness and the control performance meets the requirements of berthing accuracy.

In this paper, a new method is proposed and verified to eliminate the non-ideal resonance caused by DGS application in GCPW by adding via holes to reduce electromagnetic leakage. By adding a dense through-hole array around the dumbbell DGS can form an electrical wall to confine the electromagnetic to the central conductor. The test results show that the non-ideal resonance is eliminated after adding the through hole and a wide stopband from 16.1 GHz to 17.8 GHz with rejection larger than 20 dB. A double-dumbbell bandpass filter was successfully designed with this tech eliminating the non-ideal resonance at 8.053 GHz and 14.232 GHz. The results show that this structure can effectively eliminate the irrational resonance caused by electromagnetic leakage.

Abstract On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities, such as ambiguous boundary, variable thickness, nonuniform material properties. This work develops for the first time a method that uses ultrasound echo groups and artificial neural network (ANN) for reliable on-site real-time identification of material parameters. The use of echo groups allows the use of lower frequencies, and hence more accommodative to structural complexity. To train the ANNs, a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure. The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model. This results in a set of dataset for training the ANN model. Once the ANN is trained, the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN. Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method. The results show that the maximum identification error of numerical example is less than 2%, and the maximum identification error of experimental test is less than 7%. Compared with currently prevailing methods and equipment, the proposefy the density and thickness, in addition to the elastic constants. Moreover, the reliability and accuracy of inverse prediction is significantly improved. Thus, it has broad applications and enables real-time field measurements, which has not been fulfilled by any other available methods or equipment.

Abstract Background Previous studies have indicated a possible link between obesity and myopia, although the results have varied. The objective of this study was to investigate the correlation between a new measure of obesity, the weight-adjusted waist index (WWI), and myopia. Method This cross-sectional study included individuals between the ages of 12 and 25 who participated in a noncycloplegic vision examination as part of the National Health and Nutrition Examination Survey (NHANES) conducted from 1999 to 2008. WWI was calculated as waist circumference divided by the square root of body weight. Myopia was characterized by a spherical equivalent (SE) of ≤ − 0.5 diopters (D) and further categorized into mild (-3.00D

Semi-volatile organic compounds (SVOCs) are modern chemical substances that are present in large quantities in indoor environments. Understanding the emission of SVOCs from building materials is essential to identify the main sources of indoor SVOCs and to improve indoor air quality. In this study, a reference method employing custom-designed microchambers (630 mL) was optimized by improving the structure of the gas path and adding polytetrafluoroethylene inner coating to the chamber. After optimization, the recoveries of the microchamber method were significantly improved (75.4–96.7%), and the background in the microchamber was greatly reduced (SERa) of total SVOCs emitted from architectural coatings and PVC floorings were in the range of 4.09–1309 μg/m2/h) (median: 10.3 μg/m2/h) and 0.508–345 μg/m2/h (median: 11.9 μg/m2/h), respectively. Propanoic acid had the highest SERa (3143 μg/m2/h) in architectural coatings, while methylbenzene (345 μg/m2/h), 2-methylnaphthalene (65.2 μg/m2/h), and naphthalene (60.3 μg/m2/h) were main SVOCs emitted from PVC floorings. Meanwhile, the average second-stage (adsorbed phase) emission mass of the total SVOCs accounts for 66.3% and 47.3% in architectural coatings and PVC floorings, respectively, suggesting that the SVOCs emitted from building materials have a strong tendency to be absorbed on the surface of the room, e.g., the interior wall, the desk or even the skin.