珍珠灵芝复配物美白功效与机理初步研究

为研究珍珠灵芝提取物复配在美白类化妆品中开发的优势,采用体外酪氨酸酶活性和小鼠B16黑色素瘤细胞模型实验,分别对黑灵芝提取物、珍珠提取物及其复配物的美白功效进行评价,测定三者对体外酪氨酸酶活性、B16细胞相对黑色素含量、酪氨酸酶(TYR)和酪氨酸酶相关蛋白-1(TRP1)mRNA表达水平的影响。结果表明,1%黑灵芝提取物、2%珍珠提取物及二者组成的复配物均可显著抑制酪氨酸酶活性(P<0.05)、降低细胞内黑色素含量(P<0.05),其中复配物组对酪氨酸酶活性、细胞内黑色素生成抑制率分别达32.87%、68.27%,均优于单用组(P<0.05);此外,复配物组可显著下调TRP1 mRNA表达水平(P<0.05),而单用组无显著下调TRP1 mRNA表达水平的效果,提示复配物组较单用组有更好地抑制真黑色素生成的功效,具有开发成为化妆品功效添加剂加入美白化妆品的潜力。

Multi-year assessment of paralytic shellfish toxins in hard clam species along the coastline of Jiangsu Province, China

Paralytic shellfish toxins(PSTs) are notorious neurotoxins that threaten public health and food safety worldwide.Although PST monitoring programs have recently been established throughout China, the profiles and variation of PSTs in important commercial clams(e.g., Mactra veneriformis, Ruditapes philippinarum, and Meretrix meretrix) along the Jiangsu Province coastline remain largely unexplored. In this study, a validated hydrophilic interaction liquid chromatography–tandem mass spectrometry(HILIC-MS/MS) method was used to examine PST profiles and levels in 540 clam samples from natural production areas along Jiangsu Province coastline during2014–2016. Although the PST levels(≤6.38 μg saxitotoxin equivalents(eq)/kg) were consistently below European Union regulatory limits(≤800 μg saxitotoxin eq/kg) during this time period, saxitotoxin, decarbamoylsaxitotoxin,and gonyautoxins 1 and 4 were detected, and nearly 40% of the samples were saxitotoxin-positive. The PST levels also varied significantly by seasons, with peak values observed in May during 2014–2016. This is the first systematic report of PSTs in clams from Jiangsu Province, and additional research and protective measures are needed to ensure the safety of clams harvested in this area.

Early fire detection technology based on improved transformers in aircraft cargo compartments

The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety.The current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared light.This often results in a high false alarm rate in complex air transportation envi-ronments.The traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire information.This paper proposes a multi-technology collaborative fire detection method based on an improved transformers model.Dual-wavelength optical sensors,flue gas analyzers,and other equipment are used to carry out multi-technology collaborative detection methods and characterize various feature dimensions of fire to improve detection accuracy.The improved Transformer model which integrates the self-attention mechanism and position encoding mechanism is applied to the problem of long-time series modeling of fire information from a global perspective,which effectively solves the problem of gradient disappearance and gradient explosion in traditional RNN(recurrent neural network)and CNN(convolutional neural network).Two different multi-head self-attention mechanisms are used to classify and model multivariate fire information,respectively,which solves the problem of confusing time series modeling and classification modeling in dealing with multivariate classification tasks by a single attention mechanism.Finally,the output results of the two models are fused through the gate mechanism.The research results show that,compared with the traditional single-feature detection technology,the multi-technology collaborative fire detection method can better capture fire information.Compared with the traditional deep learning model,the multivariate fire pre-diction model constructed by the improved Transformer can better detect fires,and the accuracy rate is 0.995.

Heliangin acts as a covalent ligand of RPS2 that disrupts pre-rRNA metabolic processes in NPM1-mutated acute myeloid leukemia

Although NPM1 mutations are frequently found in acute myeloid leukemia patients,therapeutic strategies are scarce and unsuitable for those who cannot tolerate intensive chemotherapy.Here we demonstrated that heliangin,a natural sesquiterpene lactone,exerts favorable therapeutic responses in NPM1 mutant acute myeloid leukemia cells,with no apparent toxicity to normal hematogenous cells,by inhibiting their proliferation,inducing apoptosis,causing cell cycle arrest,and promoting differentiation.In-depth studies on its mode of action using quantitative thiol reactivity platform screening and subsequent molecular biology validation showed that the ribosomal protein S2(RPS2)is the main target of heliangin in treating NPM1 mutant AML.Upon covalent binding to the C222 site of RPS2,the electrophilic moieties of heliangin disrupt pre-rRNA metabolic processes,leading to nucleolar stress,which in turn regulates the ribosomal proteins-MDM2-p53 pathway and stabilizes p53.Clinical data shows that the pre-rRNA metabolic pathway is dysregulated in acute myeloid leukemia patients with the NPM1 mutation,leading to a poor prognosis.We found that RPS2 plays a critical role in regulating this pathway and may be a novel treatment target.Our findings suggest a novel treatment strategy and lead compound for acute myeloid leukemia patients,especially those with NPM1 mutations.

Multiphysics numerical investigation of photothermal self-driving characteristics of SiO_(2)@Au Janus microparticles for drug delivery

The photothermal self-driving process of Janus microparticles has wide application prospects in the fields of biomedicine.Since silica and gold have good biocompatibility and high photothermal conversion efficiency,the SiO_(2)@Au Janus microparticles are widely used as drug carriers.Based on the multiphysics coupling method,the photothermal self-driving process of SiO_(2)@Au Janus microparticles was investi-gated,wherein the substrate was SiO_(2)particles and one side of the particles was coated with gold film.Under a continuous wave laser with irradiation of 20 W/cm^(2),the distance covered by the Janus particles was increased by increasing the thickness of the gold film and reducing the size of the SiO_(2)particles;the self-driving characteristics of the Janus particles were controlled substantially by increasing the intensity of the incident laser.Based on the simulation results,the thermophoretic motion and Brownian motion of particles can be measured by comparing the absolute values of the thermophoretic force impulse,Brownian force impulse,and drag force impulse.The Brownian force acting on Janus microparticles under low laser power cannot be ignored.Furthermore,the minimum laser power required for Janus particles to overcome Brownian motion was calculated.The results can effectively guide the design of Janus particles in biomedicine and systematically analyze the mechanism of particle thermophoretic motion during drug delivery.

Early smoke and flame detection based on transformer

Fire-detection technology plays a critical role in ensuring public safety and facilitating the development of smart cities.Early fire detection is imperative to mitigate potential hazards and minimize associated losses.However,existing vision-based fire-detection methods exhibit limited generalizability and fail to adequately consider the effect of fire object size on detection accuracy.To address this issue,in this study a decoder-free fully transformer-based(DFFT)detector is used to achieve early smoke and flame detection,improving the detection performance for fires of different sizes.This method effectively captures multi-level and multi-scale fire features with rich semantic information while using two powerful encoders to maintain the accuracy of the single-feature map prediction.First,data augmentation is performed to enhance the generalizability of the model.Second,the detection-oriented transformer(DOT)backbone network is treated as a single-layer fire-feature extractor to obtain fire-related features on four scales,which are then fed into an encoder-only single-layer dense prediction module.Finally,the prediction module aggregates the multi-scale fire features into a single feature map using a scale-aggregated encoder(SAE).The prediction module then aligns the classification and regression features using a task-aligned encoder(TAE)to ensure the semantic interaction of the classification and regression predictions.Experimental results on one private dataset and one public dataset demonstrate that the adopted DFFT possesses high detection accuracy and a strong generalizability for fires of different sizes,particularly early small fires.The DFFT achieved mean average precision(mAP)values of 87.40%and 81.12%for the two datasets,outperforming other baseline models.It exhibits a better detection performance on flame objects than on smoke objects because of the prominence of flame features.

基于多相催化体系构建的醇胺化合成N-烷基胺

N-烷基化胺类化合物由于其在染料、药物、农药、表面活性剂、橡胶助剂、功能材料等精细化学工业中的重要作用,它的合成吸引了化学家们广泛的研究兴趣。在众多合成方法中,胺醇烷基化是一种N-烷基胺高效、清洁的合成方法,其中水是唯一副产物。本文系统地介绍了Ni、Cu、Pd、Pt、Co、Mn、Fe、Au、Ru、Ag等多相催化体系应用于醇胺化反应合成N-烷基胺的研究进展,并指出醇胺化多相催化过程中所面临的问题和未来的发展方向。

Effect of Gastrodin on Early Brain Injury and Neurological Outcome After Subarachnoid Hemorrhage in Rats

Gastrodin is a phenolic glycoside that has been demonstrated to provide neuroprotection in preclinical models of central nervous system disease, but its effect in subarachnoid hemorrhage(SAH) remains unclear. In this study, we showed that intraperitoneal administration of gastrodin(100 mg/kg per day) significantly attenuated the SAH-induced neurological deficit, brain edema, and increased blood-brain barrier permeability in rats. Meanwhile, gastrodin treatment significantly reduced the SAHinduced elevation of glutamate concentration in the cerebrospinal fluid and the intracellular Ca2+ overload.Moreover, gastrodin suppressed the SAH-induced microglial activation, astrocyte activation, and neuronal apoptosis. Mechanistically, gastrodin significantly reduced the oxidative stress and inflammatory response, up-regulated the expression of nuclear factor erythroid 2–related factor2, heme oxygenase-1, phospho-Akt and B-cell lymphoma2, and down-regulated the expression of BCL2-associated X protein and cleaved caspase-3. Our results suggested that the administration of gastrodin provides neuroprotection against early brain injury after experimental SAH.

Bio-inspired Recyclable Carbon Interface for Solar Steam Generation

Solar power,as one of renewable energy,holds potential application for producing steam which relies on high-temperature liquid by traditional methods.Herein,steam was generated by a bio-inspired strategy derived from the plants transpiration using a Printed Recyclable Carbon Membrane(PRCM).The membrane structure facilitated the concentration of carbon particles for the photoreaction and the heat generation for water evaporation,thereby improving the photo-thermal conversion efficiency.The PRCM achieved the best steady evaporation efficiency of 51.9%,which was 5.6 times higher than the value for water and recycling tests were demonstrated.The carbon particles were separated from the water under the magnetism action,a convenient approach that avoided secondary pollution resulting from the disintegration of the PRCM.Rapid preparation,low cost,and reusability of the printed carbon membrane allow for photo-thermal applications such as solar steam generation and seawater desalination.

Organic Ligand-Free Hydroformylation with Rh Particles as Catalyst

Summary of main observation and conclusion An efficient and organic ligand-free heterogeneous catalytic system for hydroformylation of olefins is highly desirable for both academy and industry.In this study,simple Rh black was employed as a heterogeneous catalyst for hydroformylation of olefins in the absence of organic ligand.The Rh black catalyst showed good catalytic activity for a broad substrate scope including the aliphatic and aromatic olefins,affording the desired aldehydes in good yields.Taking the hydroformylation of ethylene as an example,86%yield of propanal and TOF of 200 h-1 were obtained,which was superior to the reported homogeneous catalytic systems.In addition,the catalyst could be reused five times without loss of activity under identical reaction conditions,and the Rh leaching was negligible after each cycle.

Public opinion analysis of novel coronavirus from online data

Novel coronavirus,now named COVID-19,has swept the world,which is regarded as‘public enemy number one’by WHO.In these months,the coronavirus has become a hot topic and led various public opinion.The traditional strategies for public opinion analyzing seldom take the entities and behaviors into consideration.Focusing on the high fluctuation of public opinion of novel coronavirus event,we propose a Key-Information-oriented Convolutional Neural Network(KIN-CNN)to analyze both relevant entities and behaviors in addition to public opinion trend on Chinese corpus.Firstly,we establish a knowledge set according to the characteristic of distribution in corpus of emotions,behaviors and entities.Secondly,we integrate the other prior knowledge to initialize the convolution kernel for better model performance.Thirdly,as COVID-19 event develops,the dominant public opinion trend is obtained by our approach.Furthermore,the relationship of dominant public opinion with entities and behaviors is established as well in this research.

Highly sensitive color fine-tuning of diblock copolymeric nano-aggregates with tri-metallic cations,Eu(Ⅲ),Tb(Ⅲ),and Zn(Ⅱ),for flexible photoluminescence films(FPFs)

Luminescence materials have shown promise as display apparatus and lighting devices.The particularly interesting systems are photoluminescence materials that are capable of reversible colors emitting repeatedly on exposure to light.Here we report a series of color tunable flexible and transparent photoluminescence films consisting of multi-metals(Eu^(3+),Tb^(3+)and Zn^(3+))induced polymer aggregates(MIPAs)which are distributed uniformly in the polyacrylonitrile(PAN)films without agglomeration.MIPAs have a unique spherical structure due to the self-assembly of polystyrene-block-polyacrylic acid(PS-b-PAA)induced by metal ions.Notably,when applied in photoluminescence devices,these photoluminescence films exhibit not only red,green,blue colors(RGB)light,but also other tuned various color light covering the whole visible range upon excitation of 345 nm through adjusting the relative ratios of metal complexes.As the most important key point,non-conductive polymers can be used in photoluminescence devices as host medium,which is not realized in electroluminescent devices.Thus,the flexible photoluminescence films(FPFs)innovated herein exhibit the great potential to apply for flexible light-color and light-energy transformation devices.

Synthesis of size-controlled hollow Fe3O4 nanospheres and their growth mechanism

Size-controlled hollow Fe3O4 nanospheres were synthesized via a one-pot hydrothermal method as a function of reaction time and sodium citrate,polyacrylamide,and urea content.Multiple characterization techniques such as scanning and transmission electron microscopy and Raman spectroscopy were employed to investigate the crystal structure and morphology of the obtained nanospheres.The Fe3O4 nanosphere formation mechanism was elucidated from analyzing the characterization data.High levels of sodium citrate and longer reaction times were observed to increase the diameter of the nanospheres until hollow structures formed.Furthermore,polyacrylamide and urea promoted the formation of hollow structures.The hollow-structured Fe3O4 nanospheres exhibited high magnetization saturation values in the range of 48.8-58.7 emu/g.The facile synthesis method described herein,to generate size-controlled Fe3O4 nanospheres with tailored properties,demonstrates potential across a wide range of fields from drug-delivery and stealth devices,to environmental and energy applications.

A multi-scale agent-based model of infectious disease transmission to assess the impact of vaccination and non-pharmaceutical interventions:The COVID-19 case

Mathematical and computational models are useful tools for virtual policy experiments on infectious disease con-trol.Most models fail to provide flexible and rapid simulation of various epidemic scenarios for policy assessment.This paper establishes a multi-scale agent-based model to investigate the infectious disease propagation between cities and within a city using the knowledge from person-to-person transmission.In the model,the contact and infection of individuals at the micro scale where an agent represents a person provide insights for the interactions of agents at the meso scale where an agent refers to hundreds of individuals.Four cities with frequent population movements in China are taken as an example and actual data on traffic patterns and demographic parameters are adopted.The scenarios for dynamic propagation of infectious disease with no external measures are compared versus the scenarios with vaccination and non-pharmaceutical interventions.The model predicts that the peak of infections will decline by 67.37%with 80%vaccination rate,compared to a drop of 89.56%when isolation and quarantine measures are also in place.The results highlight the importance of controlling the source of infection by isolation and quarantine throughout the epidemic.We also study the effect when cities implement inconsis-tent public health interventions,which is common in practical situations.Based on our results,the model can be applied to COVID-19 and other infectious diseases according to the various needs of government agencies.

Entity and relation extraction with rule-guided dictionary as domain knowledge

Entity and relation extraction is an indispensable part of domain knowledge graph construction,which can serve relevant knowledge needs in a specific domain,such as providing support for product research,sales,risk control,and domain hotspot analysis.The existing entity and relation extraction methods that depend on pretrained models have shown promising performance on open datasets.However,the performance of these methods degrades when they face domain-specific datasets.Entity extraction models treat characters as basic semantic units while ignoring known character dependency in specific domains.Relation extraction is based on the hypothesis that the relations hidden in sentences are unified,thereby neglecting that relations may be diverse in different entity tuples.To address the problems above,this paper first introduced prior knowledge composed of domain dictionaries to enhance characters’dependence.Second,domain rules were built to eliminate noise in entity relations and promote potential entity relation extraction.Finally,experiments were designed to verify the effectiveness of our proposed methods.Experimental results on two domains,including laser industry and unmanned ship,showed the superiority of our methods.The F1 value on laser industry entity,unmanned ship entity,laser industry relation,and unmanned ship relation datasets is improved by+1%,+6%,+2%,and+1%,respectively.In addition,the extraction accuracy of entity relation triplet reaches 83%and 76%on laser industry entity pair and unmanned ship entity pair datasets,respectively.