一款紧致化妆品的功效评价方法研究

通过对以棕榈酰五肽-4为主要功能成分化妆品的功效进行评价,研究建立人体紧致功效评价方法体系。筛选31名20~50岁女性受试者,分别在使用产品0周、4周和8周,通过客观测试数据、图像方法及受试者自我评估方法对皮肤各项指标进行评估,采集受试者脸颊、眼角部位的皮肤弹性、皮肤真皮密度等参数,以及TPEF图像及SHG图像,并进行比较分析。结果显示,8周后皮肤弹性、真皮密度(P<0.001)及真-表皮链接指数(DEJI)(P<0.01)有显著变化,与受试者自我评分结果一致,表明方法之间具有相关性。该研究方法可为建立化妆品紧致功效评价方法体系提供技术支持。

多光子成像技术及其在化妆品评估中的应用

多光子成像技术是一种观察人体皮肤组织结构的光学成像方法,其原理是利用不同波长的近红外激光使皮肤组织中特定的内源性或外源性荧光物质发出不同的荧光信号,从而观测、区分皮肤不同层的特定组织结构。近年来,该方法开始逐步应用于化妆品的评估中,如评估化妆品紧致抗皱、祛斑美白、透皮吸收的效果等。多光子成像技术能够通过监测皮肤形态或代谢的变化评估化妆品紧致抗皱的作用、定量黑色素分布和含量评估化妆品祛斑美白成分的功效性或判断特定皮肤的类型、可视化化妆品成分透皮吸收的过程评估其功效性或安全性。该方法获得的客观、可视化图像可以为化妆品评估提供定量的数据结果,在皮肤成像领域有着广阔的应用前景,本文综述了这种在体、无创、精准的三维成像方法在化妆品评估中的应用和进展。

A scoping review of methodologies for applying artificial intelligence to physical activity interventions

Purpose This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence(AI)applications in physical activity(PA)interventions;introduce them to prevalent machine learning(ML),deep learning(DL),and reinforcement learning(RL)algorithms;and encourage the adoption of AI methodologies.Methods A scoping review was performed in PubMed,Web of Science,Cochrane Library,and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes.AI methodologies were summarized and categorized to identify synergies,patterns,and trends informing future research.Additionally,a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application.Results The review included 24 studies that met the predetermined eligibility criteria.AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes.Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data.Comparisons of different AI models yielded mixed results,likely due to model performance being highly dependent on the dataset and task.An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed,addressing complex human–machine communication,behavior modification,and decision-making tasks.Six key areas for future AI adoption in PA interventions emerged:personalized PA interventions,real-time monitoring and adaptation,integration of multimodal data sources,evaluation of intervention effectiveness,expanding access to PA interventions,and predicting and preventing injuries.Conclusion The scoping review highlights the potential of AI methodologies for advancing PA interventions.As the field progresses,staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall well-being.

Bio-cementation for tidal erosion resistance improvement of foreshore slopes based on microbially induced magnesium and calcium precipitation

In most coastal and estuarine areas,tides easily cause surface erosion and even slope failure,resulting in severe land losses,deterioration of coastal infrastructure,and increased floods.The bio-cementation technique has been previously demonstrated to effectively improve the erosion resistance of slopes.Seawater contains magnesium ions(Mg^(2+))with a higher concentration than calcium ions(Ca^(2+));therefore,Mg^(2+)and Ca^(2+)were used together for bio-cementation in this study at various Mg^(2+)/Ca^(2+)ratios as the microbially induced magnesium and calcium precipitation(MIMCP)treatment.Slope angles,surface strengths,precipitation contents,major phases,and microscopic characteristics of precipitation were used to evaluate the treatment effects.Results showed that the MIMCP treatment markedly enhanced the erosion resistance of slopes.Decreased Mg^(2+)/Ca^(2+)ratios resulted in a smaller change in angles and fewer soil losses,especially the Mg^(2+)concentration below 0.2 M.The decreased Mg^(2+)/Ca^(2+)ratio achieved increased precipitation contents,which contributed to better erosion resistance and higher surface strengths.Additionally,the production of aragonite would benefit from elevated Mg^(2+)concentrations and a higher Ca^(2+)concentration led to more nesquehonite in magnesium precipitation crystals.The slopes with an initial angle of 53°had worse erosion resistance than the slopes with an initial angle of 35°,but the Mg^(2+)/Ca^(2+)ratios of 0.2:0.8,0.1:0.9,and 0:1.0 were effective for both slope stabilization and erosion mitigation to a great extent.The results are of great significance for the application of MIMCP to improve erosion resistance of foreshore slopes and the MIMCP technique has promising application potential in marine engineering.

Coastal bathymetry inversion using SAR-based altimetric gravity data:A case study over the South Sandwich Island

The global bathymetry models are usually of low accuracy over the coastline of polar areas due to the harsh climatic environment and the complex topography.Satellite altimetric gravity data can be a supplement and plays a key role in bathymetry modeling over these regions.The Synthetic Aperture Radar(SAR)altimeters in the missions like CryoSat-2 and Sentinel-3A/3B can relieve waveform contamination that existed in conventional altimeters and provide data with improved accuracy and spatial resolution.In this study,we investigate the potential application of SAR altimetric gravity data in enhancing coastal bathymetry,where the effects on local bathymetry modeling introduced from SAR altimetry data are quantified and evaluated.Furthermore,we study the effects on bathymetry modeling by using different scale factor calculation approaches,where a partition-wise scheme is implemented.The numerical experiment over the South Sandwich Islands near Antarctica suggests that using SARbased altimetric gravity data improves local coastal bathymetry modeling,compared with the model calculated without SAR altimetry data by a magnitude of 3:55 m within 10 km of offshore areas.Moreover,by using the partition-wise scheme for scale factor calculation,the quality of the coastal bathymetry model is improved by 7.34 m compared with the result derived from the traditional method.These results indicate the superiority of using SAR altimetry data in coastal bathymetry inversion.

Spatiotemporal dynamics and geo-environmental factors influencing mangrove gross primary productivity during 2000–2020 in Gaoqiao Mangrove Reserve, China

Background:Mangrove forests are a significant contributor to the global carbon cycle,and the accurate estimation of their gross primary productivity(GPP)is essential for understanding the carbon budget within blue carbon ecosystems.Little attention has been given to the investigation of spatiotemporal patterns and ecological variations within mangrove ecosystems,as well as the quantitative analysis of the influence of geo-environmental factors on time-series estimations of mangrove GPP.Methods:This study explored the spatiotemporal dynamics of mangrove GPP from 2000 to 2020 in Gaoqiao Mangrove Reserve,China.A leaf area index(LAI)-based light-use efficiency(LUE)model was combined with Landsat data on Google Earth Engine(GEE)to reveal the variations in mangrove GPP using the Mann-Kendall(MK)test and Theil-Sen median trend.Moreover,the spatiotemporal patterns and ecological variations in mangrove ecosystems across regions were explored using four landscape indicators.Furthermore,the effects of six geo-environmental factors(species distribution,offshore distance,elevation,slope,planar curvature and profile curvature)on GPP were investigated using Geodetector and multi-scale geo-weighted regression(MGWR).Results:The results showed that the mangrove forest in the study area experienced an area loss from 766.26 ha in 2000 to 718.29 ha in 2020,mainly due to the conversion to farming,terrestrial forest and aquaculture zones.Landscape patterns indicated high levels of vegetation aggregation near water bodies and aquaculture zones,and low levels of aggregation but high species diversity and distribution density near building zone.The mean value of mangrove GPP continuously increased from 6.35 g C⋅m^(-2)⋅d^(-1) in 2000 to 8.33 g C⋅m^(-2)⋅d^(-1) in 2020,with 23.21%of areas showing a highly and significantly increasing trend(trend value>0.50).The Geodetector and MGWR analyses showed that species distribution,offshore distance and elevation contributed most to the GPP variations.Conclusions:These results provide guidelines for selecting GPP products,and the combination of Geodetector and MGWR based on multiple geo-environmental factors could quantitatively capture the mode,direction,pathway and intensity of the influencing factors on mangrove GPP variation.The findings provide a foundation for understanding the spatiotemporal dynamics of mangrove GPP at the landscape or regional scale.

Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings

Purpose:Media exaggerations of health research may confuse readers’understanding,erode public trust in science and medicine,and cause disease mismanagement.This study built artificial intelligence(AI)models to automatically identify and correct news headlines exaggerating obesity-related research findings.Design/methodology/approach:We searched popular digital media outlets to collect 523 headlines exaggerating obesity-related research findings.The reasons for exaggerations include:inferring causality from observational studies,inferring human outcomes from animal research,inferring distant/end outcomes(e.g.,obesity)from immediate/intermediate outcomes(e.g.,calorie intake),and generalizing findings to the population from a subgroup or convenience sample.Each headline was paired with the title and abstract of the peer-reviewed journal publication covered by the news article.We drafted an exaggeration-free counterpart for each original headline and fined-tuned a BERT model to differentiate between them.We further fine-tuned three generative language models-BART,PEGASUS,and T5 to autogenerate exaggeration-free headlines based on a journal publication’s title and abstract.Model performance was evaluated using the ROUGE metrics by comparing model-generated headlines with journal publication titles.Findings:The fine-tuned BERT model achieved 92.5%accuracy in differentiating between exaggeration-free and original headlines.Baseline ROUGE scores averaged 0.311 for ROUGE-1,0.113 for ROUGE-2,0.253 for ROUGE-L,and 0.253 ROUGE-Lsum.PEGASUS,T5,and BART all outperformed the baseline.The best-performing BART model attained 0.447 for ROUGE-1,0.221 for ROUGE-2,0.402 for ROUGE-L,and 0.402 for ROUGE-Lsum.Originality/value:This study demonstrated the feasibility of leveraging AI to automatically identify and correct news headlines exaggerating obesity-related research findings.

Prospective observational study on the prognosis of ureteral lesions caused by impacted stones via dual-energy spectral computed tomography

Objective Ureteral lesions caused by impacted ureteral stones are likely to result in postoperative ureteral stricture.On this basis,the study aimed to investigate if dual-energy spectral computed tomography can predict ureteral hardening caused by impacted stones and to explore the relationship between different types of ureteral lesions and the risk of ureteral stricture.Methods This prospective study collected data of 93 patients with impacted stones from hospital automation system during January 2018 to October 2019.They underwent an abdominal scan on a dual-energy spectral computed tomography.During surgery,the operator used ureteroscopy to identify ureteral lesions,which were classified into four categories:edema,polyps,pallor,and hardening.Seven months later,90 patients were reviewed for the degree of hydronephrosis.Results Endoscopic observations revealed 38(41%)cases of ureteral edema,20(22%)cases of polyps,13(14%)cases of pallor,and 22(24%)cases of hardening.There were significant differences in hydronephrosis,the period of impaction,the calcium concentration of the ureter,and the slope of the spectral Hounsfield unit curve between the four groups.After that,we evaluated the factors associated with ureteral hardening and found that the calcium concentration of the ureter and hydronephrosis remained independent predictors of ureteral hardening.Receiver operating characteristic curve analysis showed that 5.3 mg/cm^(3)calcium concentration of the ureter is an optimal cut-off value to predict ureteral hardening.The result of follow-up showed that 80 patients had complete remission of hydronephrosis,with a complete remission rate of 61.9%(13/21)in the hardening group and 97.1%(67/69)in the non-hardening group(p<0.001).Conclusion Calcium concentration of the ureter is an independent predictor of ureteral hardening.Patients with ureteral hardening have more severe hydronephrosis after ureteroscopic lithotripsy.When the calcium concentration of the ureter is less than 5.3 mg/cm^(3),ureteral lesions should be actively treated.

PtZn nanoparticles supported on porous nitrogen-doped carbon nanofibers as highly stable electrocatalysts for oxygen reduction reaction

The oxygen reduction reaction(ORR)electrocatalytic activity of Pt-based catalysts can be significantly improved by supporting Pt and its alloy nanoparticles(NPs)on a porous carbon support with large surface area.However,such catalysts are often obtained by constructing porous carbon support followed by depositing Pt and its alloy NPs inside the pores,in which the migration and agglomeration of Pt NPs are inevitable under harsh operating conditions owing to the relatively weak interaction between NPs and carbon support.Here we develop a facile electrospinning strategy to in-situ prepare small-sized PtZn NPs supported on porous nitrogen-doped carbon nanofibers.Electrochemical results demonstrate that the as-prepared PtZn alloy catalyst exhibits excellent initial ORR activity with a half-wave potential(E_(1/2))of 0.911 V versus reversible hydrogen electrode(vs.RHE)and enhanced durability with only decreasing 11 mV after 30,000 potential cycles,compared to a more significant drop of 24 mV in E_(1/2)of Pt/C catalysts(after 10,000 potential cycling).Such a desirable performance is ascribed to the created triple-phase reaction boundary assisted by the evaporation of Zn and strengthened interaction between nanoparticles and the carbon support,inhibiting the migration and aggregation of NPs during the ORR.

Hall conductance of a non-Hermitian two-band system with k-dependent decay rates

Two-band model works well for Hall effect in topological insulators. It turns out to be non-Hermitian when the system is subjected to environments, and its topology characterized by Chern numbers has received extensive studies in the past decades. However, how a non-Hermitian system responses to an electric field and what is the connection of the response to the Chern number defined via the non-Hermitian Hamiltonian remains barely explored. In this paper, focusing on a k-dependent decay rate, we address this issue by studying the response of such a non-Hermitian Chern insulator to an external electric field. To this aim, we first derive an effective non-Hermitian Hamiltonian to describe the system and give a specific form of k-dependent decay rate. Then we calculate the response of the non-Hermitian system to a constant electric field.We observe that the environment leads the Hall conductance to be a weighted integration of curvature of the ground band and hence the conductance is no longer quantized in general. And the environment induces a delay in the response of the system to the electric field. A discussion on the validity of the non-Hermitian model compared with the master equation description is also presented.

Regulation of RNA methylation and immune infiltration patterns by m5C regulators in head and neck squamous cell carcinoma

5-Methylcytosine(m5C)methylation contributes to the development and progression of various malignant tumors.This study aimed to explore the potential role of m5C methylation regulators(m5CMRs)in head and neck squamous cell carcinoma(HNSCC).Methods:The transcription data of HNSCC samples were obtained from The Cancer Genome Atlas(TCGA)and the Gene Expression Omnibus(GEO)databases.Subsequently,the m5C patterns in HNSCC were evaluated based on 14 m5CMRs.Then,the m5Cscore was developed to quantify m5C patterns by using principal component analysis(PCA)algorithms.Two single-cell RNA sequencing datasets and various methods were employed to assess the prognostic value and sensitivity to immunotherapy.Finally,key prognostic m5CMRs were identified using univariate COX regression analysis,and their clinical significance was validated based on the Human Protein Atlas(HPA)database and by using immunohistochemistry.Results:Two distinct m5C clusters were identified.m5C cluster A is characterized by an immune-activated microenvironment and is associated with a favorable prognosis.Notable differences were observed in prognosis,immune infiltration,and immunotherapy response between the high-and low-m5Cscore groups.Patients in the high-m5Cscore group exhibited high TMB,which is correlated with poor prognosis.The m5Cscore of epithelial cells in HNSCC was higher than that in other cells.Key prognostic m5CMRs,including NSUN2,DNMT3B,ALKBH1,and Y-Box Binding Protein 1(YBX1),were associated with poor prognosis.Conclusion:Our research indicates that in head and neck squamous cell carcinoma,the m5C modification profoundly affects the TME’s diversity and complexity,influencing prognosis and the success of immunotherapy.Targeting m5C regulatory elements may be a new method for enhancing the efficacy of immunotherapy in HNSCC.

热消融治疗肺部亚实性结节专家共识(2021年版)

局部热消融技术在肺部结节治疗领域正处在起步与发展阶段,为了肺结节热消融治疗的临床实践和规范发展,由"中国医师协会肿瘤消融治疗技术专家组""中国医师协会介入医师分会肿瘤消融专业委员会""中国抗癌协会肿瘤消融治疗专业委员会""中国临床肿瘤学会消融专家委员会"组织多学科国内有关专家,讨论制定了"热消融治疗肺部亚实性结节专家共识(2021年版)"。主要内容包括:(1)肺部亚实性结节的临床评估;(2)热消融治疗肺部亚实性结节技术操作规程、适应证、禁忌证、疗效评价和相关并发症;(3)存在的问题和未来发展方向。

ASPM在肺腺癌中的表达及其与肺腺癌的进展和预后关系的研究

背景与目的已有的研究表明人类异常纺锤体样小头畸形相关蛋白(abnormal spindle-likemicrocephalyassociated protein,ASPM)是一个与肿瘤发生发展相关的蛋白,ASPM的突变会导致常染色体隐性原发性小头畸形。本研究主要是探讨肺腺癌组织中的表达情况与肺癌进展和预后的关系。方法收集90例肺腺癌组织标本和对应的90例肺良性病变组织标本,应用免疫组织化学技术检测其中90对组织中ASPM的表达情况,应用实时荧光定量核酸扩增法和蛋白印迹法的方法检测其中12对组织中ASPM的表达情况。结果肺良性病变组织标本中ASPM表达为阴性,肺腺癌组织中ASPM的表达为阳性结果且表达水平均显著高于肺良性病变肺组织(P<0.05)。伴有淋巴结进展的肺腺癌中ASPM的表达水平与不伴有淋巴结进展的肺癌没有差异,无统计学意义(P>0.05)。肿瘤大小≥4 cm的ASPM表达水平均显著高于肿瘤大小<4 cm(P<0.05)。分层分析结果表明T分期与ASPM表达层度相关(P<0.05)。肺腺癌组织中ASPM的高表达水平与不良预后呈正相关(P<0.05)。结论肺腺癌中ASPM的表达水平明显升高,且与肺腺癌的进展有密切关系。肺腺癌组织中ASPM的表达水平与不良预后呈正相关(P<0.05)。检测肺腺癌中ASPM的表达水平有助于提早的预测肺腺癌的预后。

基于放电过程的锂离子电池剩余寿命预测

针对锂离子电池剩余寿命预测时的容量难以测量和预测精度不高等问题,提出一种基于放电过程的锂离子电池剩余寿命预测方法。首先,提取锂离子电池放电过程中特定电压区间放电时间作为健康因子;接着,构建双向极限学习机的剩余寿命预测模型;最后,基于锂离子电池实验数据验证该预测方法的准确性。结果表明:该模型能准确地预测电池剩余寿命,且与普通的极限学习机模型和BP神经网络模型相比,预测误差更小,收敛速度更快。

A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm

Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.

Effect of Nitrogen Fertilizer on the Accumulation and Distribution of Dry Matter in Broomcorn Millet

[Objective] To understand the effect of nitrogen application on dry matter accumulation and allocation dynamics in broomcorn millet. [Method] The accumulation and distribution of dry matter were studied using cultivars Jin Shu 7 and Huang Mizi at different levels of nitrogen fertilizer at the jointing stage. [Result] The results showed that increasing N application led to the increase of green leaf area and the delay of leaf senescence, which was beneficial to the accumulation of dry matter.Appropriate nitrogen application(90 kg/hm2) could coordinate the translocation rate of dry matter among different plant parts, thereby enhancing the yield of broomcorn millet; among different organs, the contribution rate of stem to kernel was greater than that of leaf to kernel; there was obvious correlation between dry matter and yield. For Jin Shu 7, leaf area and dry weight of spike showed significant negative correlation with yield. [Conclusion] The formation of grain yield of broomcorn millet involved the accumulation and allocation of dry matter, the appropriate amount of nitrogen application(90 kg/hm2) could improve the rates of translocation and contribution of dry matter, thereby promoting the yield of broomcorn millet.

Bubble size fractal dimension,gas holdup,and mass transfer in a bubble column with dual internals

As the scale of residual oil treatment increases and cleaner production improves in China,slurry bubble column reactors face many challenges and opportunities for residual oil hydrogenation technology.The internals development is critical to adapt the long-term stable operation.In this paper,the volumetric mass transfer coefficient,gas holdup and bubble size in a gas-liquid up-flow column are studied with two kinds of internals.The gas holdup and volumetric mass transfer coefficient increase by 120% and 42% when the fractal dimension of bubbles increases from 0.56 to 2.56,respectively.The enhanced mass transfer processing may improve the coke suppression ability in the slurry reactor for residual oil treatment.The results can be useful for the exploration of reacting conditions,scale-up strategies,and oil adaptability.This work is valuable for the design of reactor systems and technological processes.

Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning

Dear editor,Deep reinforcement learning(DRL),combining the perception capability of deep learning(DL)and the decision-making capability of reinforcement learning(RL)[1],has been widely investigated for autonomous driving decision-making tasks.In this letter,Fund:supported in part by the National Natural Science Foundation of China(NSFC)(62173325);the Beijing Municipal Natural Science Foundation(L191002).

Comparative population genomics dissects the genetic basis of seven domestication traits in jujube

Jujube(Ziziphus jujuba Mill.)is an important perennial fruit tree with a range of interesting horticultural traits.It was domesticated from wild jujube(Ziziphus acidojujuba),but the genomic variation dynamics and genetic changes underlying its horticultural traits during domestication are poorly understood.Here,we report a comprehensive genome variation map based on the resequencing of 350 accessions,including wild,semi-wild and cultivated jujube plants,at a>15×depth.Using the combination of a genome-wide association study(GWAS)and selective sweep analysis,we identified several candidate genes potentially involved in regulating seven domestication traits in jujube.For fruit shape and kernel shape,we integrated the GWAS approach with transcriptome profiling data,expression analysis and the transgenic validation of a candidate gene to identify a causal gene,ZjFS3,which encodes an ethyleneresponsive transcription factor.Similarly,we identified a candidate gene for bearing-shoot length and the number of leaves per bearing shoot and two candidate genes for the seed-setting rate using GWAS.In the selective sweep analysis,we also discovered several putative genes for the presence of prickles on bearing shoots and the postharvest shelf life of fleshy fruits.This study outlines the genetic basis of jujube domestication and evolution and provides a rich genomic resource for mining other horticulturally important genes in jujube.