基于HMO方法、FMO理论和DFT计算理解Diels-Alder反应的立体专一性、区域选择性和立体选择性

Diels-Alder(D-A)反应是基础有机化学的重要教学内容,也是结构化学中运用物质结构基本原理予以分析处理的典型化学反应。本文基于休克尔分子轨道(HMO)方法、前线分子轨道(FMO)理论和量子化学计算研究了1-甲氧基-1,3-丁二烯和丙烯醛的Diels-Alder反应,给出了理解反应立体专一性、区域选择性和立体选择性的定量信息和直观物理图像。研究结果有助于加深学生对D-A反应特点和规律的认识、培养学生对计算化学的兴趣、提高学生分析问题和解决问题的能力。

Path Exploration and Countermeasure Innovation for Developing Circular Agriculture in Southeast China

This paper firstly elaborated current situation of agricultural resources and ecological environment protection of China,and pointed out that circular agriculture is an inevitable choice for sustainable development of agriculture and effective approach for low-carbon economic development. Then,it analyzed mode and benefit of typical circular agriculture,such as ecological orchard in southeast hills,sightseeing ecological agricultural garden,and crop straw use. Finally,it came up with countermeasures for further developing circular agriculture,including integrating building system and establishing suitable mode,formulating production standard and ensuring food safety,strengthening technological innovation,and supporting sustainable development.

Soil Organic Carbon Sequestration Potential in Nectarine Orchards under Different Reclamation Systems

The Red Soil Hilly Region in South China, where there is a high capacity of carbon(C), and the land use and vegetation cover change greatly, is an important ecological area in the world, and has an important impact on the global carbon cycle and the seasonal fluctuation of atmospheric CO_2. To better evaluate the effects of reclamation systems in orchards converted from grasslands on soil carbon sequestration, we investigated soil organic carbon(SOC) content and stable C isotope(δ^(13)C)composition in three nectarine orchards at Yuchi Experimental Station in South China. Compared with the sloping clean tillage orchard and terraced clean tillage orchard, SOC content in the terraced orchard with grass cover was increased by 14.90% to 38.49%, and 7.40% to 15.33%, respectively. During the 14 years after orchard establishment, the soil organic matter sources influenced both δ^(13)C distribution with depth and carbon replacement. SOC turnover of the upper soil layer in the terraced orchard with grass cover(a mean 63.05% of replacement in the 20 cm after 14 years) was 1.59 and 1.41 times larger than that of the sloping clean tillage orchard and terraced clean tillage orchard under subtropical conditions, respectively. The equilibrium value of soil organic carbon in the three treatments ranged from 16.067 to 25.608 g/kg under the experimental conditions. The equilibrium value of soil organic carbon in the surface layer under grass cover was 54.801 t/hm^2, and the carbon sequestration potential was 24.695 1 t/hm^2.

Adopting A.pintoi as Live Mulch for Peach Orchards in China:Benefits of Soil and Water Conservation and Comprehensive Eco-service

Soil erosion occurred in orchards has often attracted extensive attentions from the society with environmental considerations,as orchard is one of major methods of agricultural production in China.In the hilly red soil region of China,many orchards are established on slope lands with a lack of grass covers,leading to severe soil losses.In order to mitigate this common environmental problem and evaluate the efficiency of erosion-control approaches,four treatments were set in field plots in this study,including terraced peach orchard with Arachis pintoi cv.Amarillo as mulch and Paspalum natatu and Digitariasmutsii as hedgerows(TTM),terraced peach orchard without conservation measures(TTW),sloping peach orchard with A.pintoi as mulch and P.natatu and Digitariasmutsii as hedgerows(STM),and sloping peach orchard without conservation measures(STW).The surface runoff,sediment yields and the contents of soil nutrients and organic carbon were monitored in the four treatments and the comprehensive eco-service benefits were further evaluated.The results indicate that available phosphorus(AP),available potassium(AK),total nitrogen(TN) and organic matter(OM) in the soils of the TTM treatments and STM were significantly higher than those of the treatments TTW and STW,suggesting positive effects of the vegetation covers on the soil nutrients.Mean annual surface runoff and coefficient ranged from 0.86 to 34.79 m^3,and 0.007 to 0.282,respectively;the treatment TTM exhibited the best water conservation benefits and the treatment STW was the worst.Soil erosion modulus of the plots were 0-28.76t/hm^2 per year in average,and the treatments TTM and STM reduced significantly soil loss in comparison of the treatments TTW and STW;(d) total organic carbon in the vegetation covers ranged from 130.23 to 195.93 kg per year,and that for TTM and STM treatment significantly higher than TTW and STW treatment;comprehensive eco-service values of the orchards were evaluated considering all the factors including water conservation,soil fertility conservation,CO_2 fixation and O_2 supply,ranging from 563.35 $/y to 765.51 $/y.As expected,the treatments TTM and STM had significantly greater eco-service values than the treatments TTW and STW.In summary,we concluded that terraced orchard with A.pintoi as live mulch plus Paspalum natatu and Digitariasmutsii as hedgerows is a highly sustainable land use practice for the slope lands in red soil hilly region of China.

Water Flow Characteristics and Related Effects in PEMFC

Water management in proton exchange membrane fuel cells(PEMFC)is a topic of great importance for the optimization of these systems.Effective proton conductivity calls for moderate moisture content in the membrane,while uneven water distribution can lead to instability of the whole flow field,thereby decreasing the performance of the fuel cell.In the present study,a simplified two-tier hybrid structure is used to investigate the impact of the dynamic behavior of liquid water on the current density of the PEMFC.Simulation results show that water droplets attached to wall sides tend to increase current density.Visualization experiments confirm the existence of liquid droplets and the enhancement of current density,while indicating that the best performance and stability of fuel cell are attained for a cathode air flow rate of 300 ml/min.

Applications for wavelength division multiplexers based on topological photonic crystals

Topological edge states have an important role in optical modulation with potential applications in wavelength division multiplexers(WDMs).In this paper,2D photonic crystals(PCs)with different rotation angles are combined to generate topological edge states.We reveal the relationship between the edge states and the rotation parameters of PCs,and further propose a WDM to realize the application of adjustable beams.Our findings successfully reveal the channel selectivity for optical transmission and provide a flexible way to promote the development of topological photonic devices.

基于转录组测序的姬松茸镉胁迫下内参基因筛选

【目的】筛选不同实验条件下,尤其是镉胁迫下的姬松茸内参基因,为研究姬松茸镉富集相关基因功能研究奠定基础。【方法】根据不同浓度Cd(0、2、5 mg/L)胁迫下菌丝中转录组表达量数据,筛选出18个候选基因,设计了30对引物;根据引物扩增的特异性、扩增效率初步筛选出来自不同基因的5对引物;运用qRT-PCR检测了5个基因在不同浓度Cd胁迫下的菌丝样品、原基、菌柄和菌盖中的Ct值,利用geNorm、NormFinder和BestKeeper对这些基因的表达稳定性进行评价。【结果】SGT2和STK是菌丝阶段Cd胁迫下最稳定的2个候选内参基因,GAPDH是不同组织中最稳定的内参基因,SGT2、STK和GAPDH是所有实验条件下最稳定的3个内参基因。【结论】在姬松茸镉胁迫条件下,本研究筛选出的SGT2和STK比常用的内参基因表现出更强的稳定性。

LabVIEW-based impedance biosensing system for detection of avian influenza virus

In order to detect the multiple avian influenza viruses(AIVs)rapidly,specifically and sensitively,a LabVIEW and microelectrode array-based impedance biosensor was developed and demonstrated.A laptop with LabVIEW software was used to generate excitation signals at different frequencies with an audio card and measure the impedance of target viruses through a data acquisition card.The audio card of the laptop was used as a function generator,while a data acquisition card was used for data communication.A virtual instrument was programmed with LabVIEW to provide a platform for impedance measurement,data processing,and control.Six interdigitated microelectrodes were placed at the bottom of six wells on a microplate to form six sensors for different AIVs and controls.Then,AIV specific ligands were immobilized on the microelectrode surface to capture target viruses.To enhance the sensitivity,AIV specific aptamers conjugated gold nanoparticles and thiocyanuric acid were employed to form a network structure and used as an amplifier.Results of the measured impedance were compared with a commercial IM6 impedance analyzer,and the error was less than 5%.The developed biosensor was portable with the sensitivity and specificity for applications to on-site or in-field rapid screening of avian influenza viruses.

LSGAN-AT:enhancing malware detector robustness against adversarial examples

Adversarial Malware Example(AME)-based adversarial training can effectively enhance the robustness of Machine Learning(ML)-based malware detectors against AME.AME quality is a key factor to the robustness enhancement.Generative Adversarial Network(GAN)is a kind of AME generation method,but the existing GAN-based AME generation methods have the issues of inadequate optimization,mode collapse and training instability.In this paper,we propose a novel approach(denote as LSGAN-AT)to enhance ML-based malware detector robustness against Adversarial Examples,which includes LSGAN module and AT module.LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square(LS)loss to optimize boundary samples.AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector(RMD).Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack.The results also verify the performance of the generated RMD in the recognition rate of AME.

LSGAN‑AT:enhancing malware detector robustness against adversarial examples

Adversarial Malware Example(AME)-based adversarial training can effectively enhance the robustness of Machine Learning(ML)-based malware detectors against AME.AME quality is a key factor to the robustness enhancement.Generative Adversarial Network(GAN)is a kind of AME generation method,but the existing GAN-based AME generation methods have the issues of inadequate optimization,mode collapse and training instability.In this paper,we propose a novel approach(denote as LSGAN-AT)to enhance ML-based malware detector robustness against Adversarial Examples,which includes LSGAN module and AT module.LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square(LS)loss to optimize boundary samples.AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector(RMD).Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack.The results also verify the performance of the generated RMD in the recognition rate of AME.

On the combination of data augmentation method and gated convolution model for building effective and robust intrusion detection

Deep learning(DL)has exhibited its exceptional performance in fields like intrusion detection.Various augmentation methods have been proposed to improve data quality and eventually to enhance the performance of DL models.However,the classic augmentation methods cannot be applied to those DL models which exploit the system-call sequences to detect intrusion.Previously,the seq2seq model has been explored to augment system-call sequences.Following this work,we propose a gated convolutional neural network(GCNN)model to thoroughly extract the potential information of augmented sequences.Also,in order to enhance themodel’s robustness,we adopt adversarial training to reduce the impact of adversarial examples on the model.Adversarial examples used in adversarial training are generated by the proposed adversarial sequence generation algorithm.The experimental results on different verified models show that GCNN model can better obtain the potential information of the augmented data and achieve the best performance.Furthermore,GCNN with adversarial training can enhance robustness significantly.

On the combination of data augmentation method and gated convolution model for building effective and robust intrusion detection

Deep learning(DL)has exhibited its exceptional performance in fields like intrusion detection.Various augmentation methods have been proposed to improve data quality and eventually to enhance the performance of DL models.However,the classic augmentation methods cannot be applied to those DL models which exploit the system-call sequences to detect intrusion.Previously,the seq2seq model has been explored to augment system-call sequences.Following this work,we propose a gated convolutional neural network(GCNN)model to thoroughly extract the potential information of augmented sequences.Also,in order to enhance themodel’s robustness,we adopt adversarial training to reduce the impact of adversarial examples on the model.Adversarial examples used in adversarial training are generated by the proposed adversarial sequence generation algorithm.The experimental results on different verified models show that GCNN model can better obtain the potential information of the augmented data and achieve the best performance.Furthermore,GCNN with adversarial training can enhance robustness significantly.

DLX3(Q178R)mutation delays osteogenic differentiation via H19/miR-29c-3p/KDM5B axis in TDO-iPSCs-derived MSCs

Tricho-dento-osseous(TDO)syndrome is a rare autosomal dominant disease resulting from distal-less homeobox 3(DLX3)mutation.1,2 Accumulative bone density in alveolar bone is a clinically favorable phenotype for TDO patients.However,the limited number of bone marrow mesenchymal stem cells(BMSCs)in TDO patients restricts their application.