1株进口胎牛血清源牛病毒性腹泻病毒的分离鉴定及小鼠致病性分析

【目的】对进口胎牛血清中的牛病毒性腹泻病毒(Bovine viral diarrhea virus,BVDV)进行分离鉴定和遗传进化分析。【方法】通过RT-PCR方法检测到实验室购买的某进口胎牛血清中存在BVDV核酸污染,将污染血清接种MDBK细胞,传代培养至第7代,对每一代细胞的培养物进行BVDV 5′-UTR扩增,PCR扩增产物连接pMD19-T载体并测序,对测序结果进行遗传进化分析;利用间接免疫荧光试验(IFA)进行分离株抗原检测和病毒滴度测定;将分离株接种BALB/c小鼠,采集眼眶血进行血常规检测和RT-PCR检测。【结果】该病毒分离株在培养MDBK细胞时未能引起细胞病变效应,细胞培养物RT-PCR扩增为阳性;基于5′-UTR序列的相似性分析表明,该分离株与BVDV UDEC-COY7-17(OL860952.1)毒株5′-UTR序列相似性为99.6%,同属BVDV-1e亚型;IFA检测到感染分离株的MDBK细胞细胞质中出现特异性绿色荧光信号,而对照组无信号;半数培养感染剂量(50%tissue culture infectivedose,TCID 50)为10-4.7/0.1 mL;接种BALB/c小鼠后第7天血常规检测其血液中白细胞(WBC)和淋巴细胞(LYM)数量极显著低于对照组(P<0.01)。提取血液总RNA进行5′-UTR RT-PCR检测,检测结果为阳性,扩增产物大小为298 bp,与预期相符。【结论】本研究在进口胎牛血清中分离得到1株非致细胞病变型BVDV-1e亚型毒株,证明进口胎牛血清中存在BVDV抗原污染,为进一步研究BVDV的相关分子机制提供试验材料。

Temporal pattern mining from user-generated content

Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content;however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. Dataset and codes are publicly available;the link is given in the dataset section.

Tailoring the optical and magnetic properties of La-BaM hexaferrites by Ni substitution

We investigate the impact of Ni insertion on the structural,optical,and magnetic properties of Ba_(0.8)La_(0.2)Fe_(12-x)Ni_(x)O_(19)hexaferrites(Ni substituted La-BaM hexaferrites).Samples were prepared using the conventional co-precipitation method and sintered at 1000℃for 4 hours to assist the crystallization process.An analysis of the structure of the samples was carried out using an x-ray diffraction(XRD)spectrometer.The M-type hexagonal structure of all the samples was confirmed using XRD spectra.The lattice parameters a and c were found to be in the ranges of 5.8925±0.001 nm–5.8952±0.001 nm and 23.2123±0.001 nm–23.2219±0.001 nm,respectively.The M-type hexagonal nature of the prepared samples was also indicated by the presence of corresponding FT-IR bands and Raman modes in the FT-IR and Raman spectra,respectively.EDX results confirmed the successful synthesis of the samples according to the required stoichiometric ratio.A UV-vis spectrometer was used to record the absorption spectra of the prepared samples in the wavelength range of 200 nm–1100 nm.The optical energy bandgap of the samples was found to be in the range of 1.21 eV–3.39 eV.The M–H loops of the samples were measured at room temperature at an applied magnetic field range of 0 kOe–60 kOe.A high saturation magnetization of 99.92 emu/g was recorded in the sample with x=0 at a microwave operating frequency of 22.2 GHz.This high value of saturation magnetization is due to the substitution of La3+ions at the spin-up(12k,2a,and 2b)sites.The Ni substitution is proven to be a potential candidate for the tuning of the optical and magnetic parameters of M-type hexaferrites.Therefore,we suggest that the prepared samples are suitable for use in magneto-optic applications.

Enhanced Best Fit Algorithm for Merging Small Files

In the Big Data era,numerous sources and environments generate massive amounts of data.This enormous amount of data necessitates specialized advanced tools and procedures that effectively evaluate the information and anticipate decisions for future changes.Hadoop is used to process this kind of data.It is known to handle vast volumes of data more efficiently than tiny amounts,which results in inefficiency in the framework.This study proposes a novel solution to the problem by applying the Enhanced Best Fit Merging algorithm(EBFM)that merges files depending on predefined parameters(type and size).Implementing this algorithm will ensure that the maximum amount of the block size and the generated file size will be in the same range.Its primary goal is to dynamically merge files with the stated criteria based on the file type to guarantee the efficacy and efficiency of the established system.This procedure takes place before the files are available for the Hadoop framework.Additionally,the files generated by the system are named with specific keywords to ensure there is no data loss(file overwrite).The proposed approach guarantees the generation of the fewest possible large files,which reduces the input/output memory burden and corresponds to the Hadoop framework’s effectiveness.The findings show that the proposed technique enhances the framework’s performance by approximately 64%while comparing all other potential performance-impairing variables.The proposed approach is implementable in any environment that uses the Hadoop framework,not limited to smart cities,real-time data analysis,etc.

Real-Time Spammers Detection Based on Metadata Features with Machine Learning

Spammer detection is to identify and block malicious activities performing users.Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social spaces.Previous research aimed to find spammers based on hybrid approaches of graph mining,posted content,and metadata,using small and manually labeled datasets.However,such hybrid approaches are unscalable,not robust,particular dataset dependent,and require numerous parameters,complex graphs,and natural language processing(NLP)resources to make decisions,which makes spammer detection impractical for real-time detection.For example,graph mining requires neighbors’information,posted content-based approaches require multiple tweets from user profiles,then NLP resources to make decisions that are not applicable in a real-time environment.To fill the gap,firstly,we propose a REal-time Metadata based Spammer detection(REMS)model based on only metadata features to identify spammers,which takes the least number of parameters and provides adequate results.REMS is a scalable and robust model that uses only 19 metadata features of Twitter users to induce 73.81%F1-Score classification accuracy using a balanced training dataset(50%spam and 50%genuine users).The 19 features are 8 original and 11 derived features from the original features of Twitter users,identified with extensive experiments and analysis.Secondly,we present the largest and most diverse dataset of published research,comprising 211 K spam users and 1 million genuine users.The diversity of the dataset can be measured as it comprises users who posted 2.1 million Tweets on seven topics(100 hashtags)from 6 different geographical locations.The REMS’s superior classification performance with multiple machine and deep learning methods indicates that only metadata features have the potential to identify spammers rather than focusing on volatile posted content and complex graph structures.Dataset and REMS’s codes are available on GitHub(www.github.com/mhadnanali/REMS).

Phase transformation from cubic ZnS to hexagonal ZnO by thermal annealing

We have investigated the mechanism of phase transformation from ZnS to hexagonal ZnO by high- temperature thermal annealing. The ZnS thin films were grown on Si （001） substrate by thermal evaporation system using ZnS powder as source material. The grown films were annealed at different temperatures and characterized by X-ray diffraction （XRD）, photoluminescence （PL）, four-point probe, scanning electron microscope （SEM） and energy dispersive X-ray diffraction （EDX）. The results demonstrated that as-deposited ZnS film has mixed phases but high-temperature annealing leads to transition from ZnS to ZnO. The observed result can be explained as a two- step process： （1） high-energy O atoms replaced S atoms in lattice during annealing process, and （2） S atoms diffused into substrate and/or diffused out of the sample. The dissociation energy of ZnS calculated from the Arrhenius plot of 1000/T versus log （resistivity） was found to be 3.1 eV. PL spectra of as-grown sample exhibits a characteristic green emission at 2.4 eV of ZnS but annealed samples consist of band-to-band and defect emission of ZnO at 3.29 eV and 2.5 eV respectively. SEM and EDX measurements were additionally performed to strengthen the argument.