A dual-RPA based lateral flow strip for sensitive,on-site detection of CP4-EPSPS and Cry1Ab/Ac genes in genetically modified crops

Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient.In this study,a one-tube dual recombinase polymerase amplification(RPA)reaction system for CP4-EPSPS and Cry1Ab/Ac was proposed and combined with a lateral flow immunochromatographic assay,named“Dual-RPA-LFD”,to visualize the dual detection of genetically modified(GM)crops.In which,the herbicide tolerance gene CP4-EPSPS and the insect resistance gene Cry1Ab/Ac were selected as targets taking into account the current status of the most widespread application of insect resistance and herbicide tolerance traits and their stacked traits.Gradient diluted plasmids,transgenic standards,and actual samples were used as templates to conduct sensitivity,specificity,and practicality assays,respectively.The constructed method achieved the visual detection of plasmid at levels as low as 100 copies,demonstrating its high sensitivity.In addition,good applicability to transgenic samples was observed,with no cross-interference between two test lines and no influence from other genes.In conclusion,this strategy achieved the expected purpose of simultaneous detection of the two popular targets in GM crops within 20 min at 37°C in a rapid,equipmentfree field manner,providing a new alternative for rapid screening for transgenic assays in the field.

Role of macroscopic on-site evaluation of endoscopic ultrasoundguided fine-needle aspiration/biopsy:Results of a multicentric prospective study

BACKGROUND The concept of macroscopic on-site evaluation(MOSE)was introduced in 2015 when the endoscopist observed better diagnostic yield when the macroscopically visible core on MOSE was superior to 4 mm.Recent studies suggest that MOSE by the endoscopist may be an excellent alternative to rapid on-site evaluation,and some classi-fications have been published.Few studies have assessed the adequacy of histologic cores in MOSE during endoscopic ultrasound-guided fine-needle aspiration/biopsy(EUS-FNA/FNB).AIM To evaluate the performance of MOSE during EUS-FNA/FNB.METHODS This multicentric prospective study was conducted in 16 centers in 3 countries(Egypt,Iraq,and Morocco)and included 1108 patients with pancreatic,biliary,or gastrointestinal pathology who were referred for EUS examination.We prospectively analyzed the MOSE in 1008 patients with available histopathological reports according to 2 classifications to determine the adequacy of the histological core samples.Data management and analysis were performed using a Statistical Package for Social Sciences(SPSS)version 27.RESULTS A total of 1074 solid lesions were biopsied in 1008 patients with available cytopathological reports.Mean age was 59 years,and 509 patients(50.5%)were male.The mean lesion size was 38 mm.The most frequently utilized needles were FNB-Franseen(74.5%)and 22 G(93.4%),with a median of 2 passes.According to 2 classifications,618 non-bloody cores(61.3%)and 964 good samples(95.6%)were adequate for histological evaluation.The overall diagnostic yield of cytopathology was 95.5%.The cytological examination confirmed the diagnosis of malignancy in 861 patients(85.4%),while 45 samples(4.5%)were inconclusive.Post-procedural adverse events occurred in 33 patients(3.3%).Statistical analysis showed a difference between needle types(P=0.035)with a high sensitivity of FNB(97%).The analysis of the relationship between the MOSE-score and the final diagnosis showed a significant difference between the different scores of the MOSE(P<0.001).CONCLUSION MOSE is a simple method that allows endoscopists to increase needle passes to improve sample quality.There is significantly higher FNB sensitivity and cytopathology diagnostic yield with good MOSE cores.

Science and reflections: With some thoughts to young applied scientists and engineers

I provide some science and reflections from my experiences working in geophysics,along with connections to computational and data sciences,including recent developments in machine learning.I highlight several individuals and groups who have influenced me,both through direct collaborations as well as from ideas and insights that I have learned from.While my reflections are rooted in geophysics,they should also be relevant to other computational scientific and engineering fields.I also provide some thoughts for young,applied scientists and engineers.

Analysis on On-Site Asphalt Pavement Quality Inspection Technology in Highway Engineering

The acceleration of urbanization has promoted the increase in the number of urban highway projects,resulting in an increase of emphasis on the quality of urban highway projects.The quality and safety of highway construction directly affects the driving comfort,safety,and service life after it is put into operation.Among them,asphalt pavement is an important structure in highway construction,and it is also a key link that directly affects the construction quality.In order to ensure the quality of construction,it is necessary to strengthen on-site testing during the construction process to discover problems in time,and to ensure that the construction is up to standard.This paper mainly presents an analysis on the necessity and application of on-site quality inspection technology for asphalt pavement construction of highway engineering,and outlines measures to improve highway engineering construction testing technology.

基于C#语言与ArcGIS Engine 的开采沉陷预计系统开发

为了预计煤层开采引发的地表移动变形,基于极坐标系的概率积分法,借助C#语言和ArcGIS Engine组件工具库设计开发了矿区开采沉陷预计系统。开采沉陷预计系统适用于不规则形状的开采工作面,能够对开采区域地表各点不同开采时间后的下沉、水平移动、水平变形、倾斜、曲率进行预计,并对预计结果进行后处理,绘制地表移动变形云图。以陕西省某矿308工作面开采为例,根据工作面的开采条件、地层岩性等特征选取概率积分参数并代入本系统进行地表移动变形预计,将结果与实测地表移动变形进行对比分析。结果表明:使用该系统预计的地表下沉值与实测下沉值的相对误差小于10%,且下沉盆地形态与实际基本吻合,该系统的预计结果较为可靠,对矿区的开采沉陷预计具有一定的适用性。

基于Google earth engine渭-库绿洲果园遥感提取

针对干旱区果园大面积遥感提取困难、识别精度低等问题,本研究基于GEE(Google earth engine)平台,综合应用Sentinel-1/Sentinel-2影像构建特征集。通过对比原始特征组合、Jeffries-Matusita(J-M)距离、属性重要度3种优化方式,结合随机森林(Random forest,RF)分类方法,对比得到最佳优化方式,探索果园最优分类特征集。结果表明:识别效果最好的方案为G17 JM,总体精度为91.25%,kappa系数为0.89,面积精度为82.55%。最优特征集为B8_asm、B8_ent、B8_idm、NDVI re3、B6、B7、a、e、b、EVI、B11、B8A、B8、VV。使用J-M距离进行特征集优化,有效降低数据量、提高计算效率,更有利于精确遥感识别果园种植面积。表明GEE快速、准确获取果园种植面积的可行性,为获取果园动态变化提供强有力的基础。

Active Machine Learning for Chemical Engineers:A Bright Future Lies Ahead!

By combining machine learning with the design of experiments,thereby achieving so-called active machine learning,more efficient and cheaper research can be conducted.Machine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical engineering.While active machine learning algorithms are maturing,their applications are falling behind.In this article,three types of challenges presented by active machine learning—namely,convincing the experimental researcher,the flexibility of data creation,and the robustness of active machine learning algorithms—are identified,and ways to overcome them are discussed.A bright future lies ahead for active machine learning in chemical engineering,thanks to increasing automation and more efficient algorithms that can drive novel discoveries.

Biomimetic natural biomaterials for tissue engineering and regenerative medicine:new biosynthesis methods,recent advances,and emerging applications

Biomimetic materials have emerged as attractive and competitive alternatives for tissue engineering(TE)and regenerative medicine.In contrast to conventional biomaterials or synthetic materials,biomimetic scaffolds based on natural biomaterial can offer cells a broad spectrum of biochemical and biophysical cues that mimic the in vivo extracellular matrix(ECM).Additionally,such materials have mechanical adaptability,micro-structure interconnectivity,and inherent bioactivity,making them ideal for the design of living implants for specific applications in TE and regenerative medicine.This paper provides an overview for recent progress of biomimetic natural biomaterials(BNBMs),including advances in their preparation,functionality,potential applications and future challenges.We highlight recent advances in the fabrication of BNBMs and outline general strategies for functionalizing and tailoring the BNBMs with various biological and physicochemical characteristics of native ECM.Moreover,we offer an overview of recent key advances in the functionalization and applications of versatile BNBMs for TE applications.Finally,we conclude by offering our perspective on open challenges and future developments in this rapidly-evolving field.

基于Unreal Engine的虚拟现实技术在元宇宙图书馆中的应用

以虚拟现实技术作为研究切入点,探索元宇宙图书馆建设的技术实现路径。通过文献调研对比分析现有的虚拟现实技术,寻找一种适合高校图书馆自主研发的技术实现方法,并通过具体案例验证该方法的现实可行性。研究发现基于Unreal Engine的虚拟现实技术具有低成本、低代码量、可扩展等优点,适合经费少、计算机人才短缺的高校图书馆。对高校图书馆推进元宇宙图书馆建设具有一定的参考价值。

Compositional and Hollow Engineering of Silicon Carbide/Carbon Microspheres as High-Performance Microwave Absorbing Materials with Good Environmental Tolerance

Microwave absorbing materials(MAMs)characterized by high absorption efficiency and good environmental tolerance are highly desirable in practical applications.Both silicon carbide and carbon are considered as stable MAMs under some rigorous conditions,while their composites still fail to produce satisfactory microwave absorption performance regardless of the improvements as compared with the individuals.Herein,we have successfully implemented compositional and structural engineering to fabricate hollow Si C/C microspheres with controllable composition.The simultaneous modulation on dielectric properties and impedance matching can be easily achieved as the change in the composition of these composites.The formation of hollow structure not only favors lightweight feature,but also generates considerable contribution to microwave attenuation capacity.With the synergistic effect of composition and structure,the optimized SiC/C composite exhibits excellent performance,whose the strongest reflection loss intensity and broadest effective absorption reach-60.8 dB and 5.1 GHz,respectively,and its microwave absorption properties are actually superior to those of most SiC/C composites in previous studies.In addition,the stability tests of microwave absorption capacity after exposure to harsh conditions and Radar Cross Section simulation data demonstrate that hollow SiC/C microspheres from compositional and structural optimization have a bright prospect in practical applications.

基于ArcGIS Engine的风暴潮淹没适应气候变化策略辅助决策系统开发

本研究构建了基于ArcGIS Engine的风暴潮淹没适应气候变化策略辅助决策系统,将风暴潮淹没适应气候变化策略研究过程流程化和简单化,并提供7个面向用户开放的决策端口,辅助决策者做出科学的风暴潮淹没适应气候变化策略选择。以青岛市风暴潮淹没为例的系统应用,进一步验证了该系统能够切实提高研究过程的分析效率,为中国沿海地区的适应风暴潮灾害提供技术支持。

基于Google Earth Engine的八门湾红树林年际变化监测

该文基于Google Earth Engine(GEE)云平台,选择Landsat系列卫星数据,采用支持向量机(support vector machines,SVM)分类方法对八门湾地区进行地物类型分类,并基于分类结果监测该地区红树林年际变化。结果表明:红树林与陆地树木之间除红外波段外反射光谱曲线极其相似,利用红外波段特征指数并结合地形数据可进行有效区分,分类结果总体精度达到0.91;研究区红树林存在先减少后增加的变化趋势,在2009—2013年减少,2014—2016年基本保持不变,2017—2021年缓慢增加,红树林增加和坑塘减少时期是在“南红北柳”政策颁布之后,说明退塘还林政策成效显著;红树林主要是和坑塘相互变化转移,说明毁林造塘和退塘还林是影响该地区红树林变化的重要因素。红树林年际变化监测结果可以精细化分析红树林演变过程,并能精确量化红树林与其他土地类型的转化过程,从而在经济、政策上分析红树林演变因素,更有效地保护红树林。

Interface Engineering of Titanium Nitride Nanotube Composites for Excellent Microwave Absorption at Elevated Temperature

Currently,the microwave absorbers usually suffer dreadful electromagnetic wave absorption(EMWA)performance damping at elevated temperature due to impedance mismatching induced by increased conduction loss.Consequently,the development of high-performance EMWA materials with good impedance matching and strong loss ability in wide temperature spectrum has emerged as a top priority.Herein,due to the high melting point,good electrical conductivity,excellent environmental stability,EM coupling effect,and abundant interfaces of titanium nitride(TiN)nanotubes,they were designed based on the controlling kinetic diffusion procedure and Ostwald ripening process.Benefiting from boosted heterogeneous interfaces between TiN nanotubes and polydimethylsiloxane(PDMS),enhanced polarization loss relaxations were created,which could not only improve the depletion efficiency of EMWA,but also contribute to the optimized impedance matching at elevated temperature.Therefore,the TiN nanotubes/PDMS composite showed excellent EMWA performances at varied temperature(298-573 K),while achieved an effective absorption bandwidth(EAB)value of 3.23 GHz and a minimum reflection loss(RLmin)value of−44.15 dB at 423 K.This study not only clarifies the relationship between dielectric loss capacity(conduction loss and polarization loss)and temperature,but also breaks new ground for EM absorbers in wide temperature spectrum based on interface engineering.

Cheesy material on macroscopic on-site evaluation after endoscopic ultrasound-guided fine-needle biopsy:Don't miss the tuberculosis

Endoscopic ultrasound-guided fine-needle biopsy(EUS-FNB)is an excellent investigation to diagnose pancreatic lesions and has shown high accuracy for its use in pathologic diagnosis.Recently,macroscopic on-site evaluation(MOSE)performed by an endoscopist was introduced as an alternative to rapid on-site cytologic evaluation to increase the diagnostic yield of EUS-FNB.The MOSE of the biopsy can estimate the adequacy of the sample directly by the macroscopic evaluation of the core tissue obtained from EUS-FNB.Isolated pancreatic tuberculosis is extremely rare and difficult to diagnose because of its non-specific signs and symptoms.Therefore,this challenging diagnosis is based on endoscopy,imaging,and the bacteriological and histological examination of tissue biopsies.This uncommon presentation of tuberculosis can be revealed as pancreatic mass mimicking cancer.EUS-FNB can be very useful in providing a valuable histopathological diagnosis.A calcified lesion with a cheesy core in MOSE must be suggestive of tuberculosis,leading to the request of the GeneXpert,which can detect Mycobacterium tuberculosis deoxyribonucleic acid and resistance to rifampicin.A decent diagnostic strategy is crucial to prevent unnecessary surgical resection and to supply conservative management with antitubercular therapy.

Ionization Engineering of Hydrogels Enables Highly Efficient Salt‑Impeded Solar Evaporation and Night‑Time Electricity Harvesting

Interfacial solar evaporation holds immense potential for brine desalination with low carbon footprints and high energy utilization.Hydrogels,as a tunable material platform from the molecular level to the macroscopic scale,have been considered the most promising candidate for solar evaporation.However,the simultaneous achievement of high evaporation efficiency and satisfactory tolerance to salt ions in brine remains a challenging scientific bottleneck,restricting the widespread application.Herein,we report ionization engineering,which endows polymer chains of hydrogels with electronegativity for impeding salt ions and activating water molecules,fundamentally overcoming the hydrogel salt-impeded challenge and dramatically expediting water evaporating in brine.The sodium dodecyl benzene sulfonate-modified carbon black is chosen as the solar absorbers.The hydrogel reaches a ground-breaking evaporation rate of 2.9 kg m−2 h−1 in 20 wt%brine with 95.6%efficiency under one sun irradiation,surpassing most of the reported literature.More notably,such a hydrogel-based evaporator enables extracting clean water from oversaturated salt solutions and maintains durability under different high-strength deformation or a 15-day continuous operation.Meantime,on the basis of the cation selectivity induced by the electronegativity,we first propose an all-day system that evaporates during the day and generates salinity-gradient electricity using waste-evaporated brine at night,anticipating pioneer a new opportunity for all-day resource-generating systems in fields of freshwater and electricity.

基于Google Earth Engine遥感大数据云平台的盐城盐沼植被精细分类研究

江苏省自1982年开始引种互花米草以来,滨海盐沼植被景观格局发生了巨大变化,芦苇和互花米草面积不断增长,挤占了碱蓬生态位,破坏了生态系统平衡。滨海盐沼湿地是三大蓝碳生态系统之一,具有高固碳和缓解温室效应的生态功能。利用遥感云平台手段监测滨海盐沼植被时空动态变化规律,可为滨海盐沼湿地生态系统保护以及修复提供科学支撑。本文选取植被类型丰富的江苏沿海地区为研究区,基于Google Earth Engine(GEE)平台,选取2000-2020年的Landsat 5、Landsat 7、Landsat 8以及Sentinel-2影像,提出了一种湿地植被提取方法。首先,构建盐城盐沼湿地地表覆盖分类体系,选取样本点,计算特征指数,进行地物分类;然后,利用时间谐波分析法对盐沼植被归一化植被指数(NDVI)变化特征进行分析,确定三类盐沼植被的物候差异;最后,基于物候特征,利用随机森林(Random Forest,RF)分类法提取盐城滨海湿地盐沼植被地表覆盖信息。结果表明:(1)相较传统监测方法,本文所构建的滨海盐沼植被精细化提取方法能够对滨海盐沼植被进行有效细分和监测;(2)2000-2020年,芦苇和互花米草面积呈扩张趋势,碱蓬面积不断缩减;(3)盐城三种典型盐沼植被NDVI值呈现不同变化特征;(4)降水对盐沼植被生长的影响较为显著,气温对盐沼植被生长的直接影响较小。研究结果可为盐城盐沼湿地自然遗产保护以及湿地生态可持续发展提供科学支撑。

Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural habitat.FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards.The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after feeding.To assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark functions.These functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test suite.FLO’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative analysis.The simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search process.This balanced approach enables FLO to outperform several competing algorithms in numerous test cases.Additionally,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization challenges.Overall,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

Trace Cobalt Doping and Defect Engineering of High Surface Area α-Ni(OH)_(2) for Electrocatalytic Urea Oxidation

Owing to the intrinsically sluggish kinetics of urea oxidation reaction(UOR)involving a six-electron transfer process,developing efficient UOR electrocatalyst is a great challenge remained to be overwhelmed.Herein,by taking advantage of 2-Methylimidazole,of which is a kind of alkali in water and owns strong coordination ability to Co^(2+)in methanol,trace Co(1.0 mol%)addition was found to induce defect engineering onα-Ni(OH)_(2)in a dual-solvent system of water and methanol.Physical characterization results revealed that the synthesized electrocatalyst(WM-Ni_(0.99)Co_(0.01)(OH)_(2))was a kind of defective nanosheet with thickness around 5-6 nm,attributing to the synergistic effect of Co doping and defect engineering,its electron structure was finely altered,and its specific surface a rea was tremendously enlarged from 68 to 172.3 m^(2)g^(-1).With all these merits,its overpotential to drive 10 mA cm^(-2)was reduced by 110 mV.Besides,the interfacial behavior of UOR was also well deciphered by operando electrochemical impedance spectroscopy.

Biomaterials and tissue engineering in traumatic brain injury:novel perspectives on promoting neural regeneration

Traumatic brain injury is a serious medical condition that can be attributed to falls, motor vehicle accidents, sports injuries and acts of violence, causing a series of neural injuries and neuropsychiatric symptoms. However, limited accessibility to the injury sites, complicated histological and anatomical structure, intricate cellular and extracellular milieu, lack of regenerative capacity in the native cells, vast variety of damage routes, and the insufficient time available for treatment have restricted the widespread application of several therapeutic methods in cases of central nervous system injury. Tissue engineering and regenerative medicine have emerged as innovative approaches in the field of nerve regeneration. By combining biomaterials, stem cells, and growth factors, these approaches have provided a platform for developing effective treatments for neural injuries, which can offer the potential to restore neural function, improve patient outcomes, and reduce the need for drugs and invasive surgical procedures. Biomaterials have shown advantages in promoting neural development, inhibiting glial scar formation, and providing a suitable biomimetic neural microenvironment, which makes their application promising in the field of neural regeneration. For instance, bioactive scaffolds loaded with stem cells can provide a biocompatible and biodegradable milieu. Furthermore, stem cells-derived exosomes combine the advantages of stem cells, avoid the risk of immune rejection, cooperate with biomaterials to enhance their biological functions, and exert stable functions, thereby inducing angiogenesis and neural regeneration in patients with traumatic brain injury and promoting the recovery of brain function. Unfortunately, biomaterials have shown positive effects in the laboratory, but when similar materials are used in clinical studies of human central nervous system regeneration, their efficacy is unsatisfactory. Here, we review the characteristics and properties of various bioactive materials, followed by the introduction of applications based on biochemistry and cell molecules, and discuss the emerging role of biomaterials in promoting neural regeneration. Further, we summarize the adaptive biomaterials infused with exosomes produced from stem cells and stem cells themselves for the treatment of traumatic brain injury. Finally, we present the main limitations of biomaterials for the treatment of traumatic brain injury and offer insights into their future potential.

基于Google Earth Engine的前郭县春季农田覆膜提取

本文基于Google Earth Engine(GEE)云平台,综合考虑光学影像的波段反射率、光谱指数特征和雷达影像的极化、纹理特征,分别构建仅使用光学特征、仅使用雷达特征以及光学和雷达特征组合3种特征输入组合;根据精度确定最佳输入特征后,分别结合机器学习中的分类与回归树、支持向量机、最小距离分类法、梯度提升树和随机森林5种方法建立覆膜提取模型,依据结果精度评估不同方法的性能,并基于最优化模型提取出最终的覆膜农田面积。结果表明:①最佳输入特征为波段反射率特征+光谱指数特征+极化特征+纹理特征;②采用随机森林方法建立的模型精度最高,研究区I的总体精度达到了95.84%,Kappa系数为0.95,地物错分率为1.2%,明显优于其他4种方法(地物错分率较分类与回归树、支持向量机、最小距离和梯度提升树法降低0.8%、7.3%、38.0%和0.3%),研究区II的验证精度达到了87.84%,证明该模型在覆膜提取中可以取得更加准确的结果;③使用本文方法得到2022年研究区I覆膜农田面积为1302.48 km2,估算地膜使用量约为7585.62 t。本文综合考虑光学和雷达影像在地物识别中的特点建立模型,可以准确、高效的识别农田地膜,掌握地膜面积,对环境治理与防治具有重要意义。