Developing an atmospheric aging evaluation model of acrylic coatings:A semi-supervised machine learning algorithm

To study the atmospheric aging of acrylic coatings,a two-year aging exposure experiment was conducted in 13 representative climatic environments in China.An atmospheric aging evaluation model of acrylic coatings was developed based on aging data including11 environmental factors from 567 cities.A hybrid method of random forest and Spearman correlation analysis was used to reduce the redundancy and multicollinearity of the data set by dimensionality reduction.A semi-supervised collaborative trained regression model was developed with the environmental factors as input and the low-frequency impedance modulus values of the electrochemical impedance spectra of acrylic coatings in 3.5wt%NaCl solution as output.The model improves accuracy compared to supervised learning algorithms model(support vector machines model).The model provides a new method for the rapid evaluation of the aging performance of acrylic coatings,and may also serve as a reference to evaluate the aging performance of other organic coatings.

Diagnostic performance of contrast-enhanced ultrasound and enhanced magnetic resonance for breast nodules

In the current study, we sought to evaluate the diagnostic efficacies of conventional ultrasound（US）, contrastenhanced US（CEUS）, combined US and CEUS and magnetic resonance imaging（MRI） in detecting focal solid breast lesions. Totally 117 patients with 120 BI-RADS category 4A-5 breast lesions were evaluated by conventional US and CEUS, and MRI, respectively. SonoVue was used as contrast agent in CEUS and injected as an intravenous bolus; nodule scan was performed 4 minutes after bolus injection. A specific sonographic quantification software was used to obtain color-coded maps of perfusion parameters for the investigated lesion, namely the time-intensity curve.The pattern of contrast enhancement and related indexes regarding the time-intensity curve were used to describe the lesions, comparatively with pathological results. Histopathologic examination revealed 46 benign and 74 malignant lesions. Sensitivity, specificity, and accuracy of US in detecting malignant breast lesions were 90.14%, 95.92%, and 92.52%, respectively. Meanwhile, CE-MRI showed sensitivity, specificity, and accuracy of 88.73%, 95.92%, and91.67%, respectively. The area under the ROC curve for combined US and CEUS in discriminating benign from malignant breast lesions was 0.936, while that of MRI was 0.923, with no significant difference between them, as well as among groups. The time-intensity curve of malignant hypervascular fibroadenoma and papillary lesions mostly showed a fast-in/fast-out pattern, with no good correlation between them（kappa 〈0.20）. In conclusion, the combined use of conventional US and CEUS displays good agreement with MRI in differentiating benign from malignant breast lesions.

An infrastructure with user-centered presentation data model for integrated management of materials data and services

With scientific research in materials science becoming more data intensive and collaborative after the announcement of the Materials Genome Initiative,the need for modern data infrastructures that facilitate the sharing of materials data and analysis tools is compelling in the materials community.In this paper,we describe the challenges of developing such infrastructure and introduce an emerging architecture with high usability.We call this architecture the Materials Genome Engineering Databases(MGED).MGED provides cloud-hosted services with features to simplify the process of collecting datasets from diverse data providers,unify data representation forms with user-centered presentation data model,and accelerate data discovery with advanced search capabilities.MGED also provides a standard service management framework to enable finding and sharing of tools for analyzing and processing data.We describe MGED’s design,current status,and how MGED supports integrated management of shared data and services.

Epidemiology of toxoplasmosis: role of the tick Haemaphysalis longicornis

Background:Toxoplasma gondii infection is mainly caused by ingestion of water or food that is contaminated with oocysts excreted by cats,or by eating raw meat containing T.gondii tissue cysts.However,oral transmission does not explain the common occurrence of toxoplasmosis in a variety of hosts,such as herbivorous animals,birds,and wild rodents.Little information exists on the maintenance of T.gondii parasites in nature and routes of transmission to domestic and wild animal hosts.Therefore,this study evaluated the role of Haemaphysalis longicornis ticks in the epidemiology of toxoplasmosis.Methods:The real-time polymerase chain reaction(qPCR)technique was used to detect the presence of T.gondii DNA in ticks collected from the field.To observe the amount of dynamic changes of T.gondii in the tick’s body and its infectivity,microinjection of green fluorescence parasites was performed.Under laboratory conditions,we evaluated if H.longicornis ticks were infected with T.gondii and their potential to transmit the infection to other hosts using traditional parasitological methods coupled with molecular detection techniques.Results:The infection rates of T.gondii parasites among field-collected adult and nymph H.longicornis ticks were 11.26%and 5.95%,respectively.T.gondii can survive and remain infective in a tick’s body for at least 15 days.We found that blood feeding of infected ticks did not transmit T.gondii to hosts,however,ingestion of infected ticks may be a transmission route between ticks and other common hosts.Conclusion:The T.gondii infection in ticks could serve as a reservoir for toxoplasmosis transmission.

Virome diversity of ticks feeding on domestic mammals in China

Ticks are considered the second most common pathogen vectors transmitting a broad range of vital human and veterinary viruses.From 2017 to 2018,640 ticks were collected in eight different provinces in central and western China.Six species were detected,including H.longicornis,De.everestianus,Rh.microplus,Rh.turanicus,Rh.sanguineous,and Hy.asiaticum.Sixty-four viral metagenomic libraries were constructed on the MiSeq Illumina platform,resulting in 13.44 G(5.88×10^(7))of 250-bp-end reads,in which 2,437,941 are viral reads.We found 27 nearly complete genome sequences,including 16 genome sequences encoding entire protein-coding regions(lack of 30 or 50 end non-coding regions)and complete viral genomes,distributed in the arboviral family(Chuviridae,Rhabdoviridae,Nairoviridae,Phenuiviridae,Flaviviridae,Iflaviridae)as well as Parvoviridae and Polyomaviridae that cause disease in mammals and even humans.In addition,13 virus sequences found in Chuviridae,Nairoviridae,Flaviviridae,Iflaviridae,Hepeviridae,Parvoviridae,and Polyomaviridae were identified as belonging to a new virus species in the identified viral genera.Besides,an epidemiological survey shows a high prevalence(9.38%and 15.63%)of two viruses(Ovine Copiparvovirus and Bovine parvovirus 2)in the tick cohort.

Comparison of Viromes in Ticks from Different Domestic Animals in China

Ticks are involved in the transmission of various arboviruses and some tick-borne viruses pose significant threats to the health of humans or livestock.This study aimed to investigate the geographical distribution of tick species and tickassociated viruses in central and eastern China.Total 573 ticks from domestic animals including dogs,sheep and cattle were collected in 2017.Two genera of ticks were identified including Rhipicephalus and Haemaphysalis.Sequencing was performed on Miseq Illumina platform to characterize the tick viromes from the four different sampling locations.Following trimming,13,640 reads were obtained and annotated to 19 virus families.From these sequences,above 37.74%of the viral reads were related to the RNA viruses.Virome comparison study revealed that the tick viral diversity was considerably different in the two identified tick genera.The viral diversity of R.microplus was significantly different from that of other Rhipicephalus species.On the other hand,substantial overlap in viral species was observed between the same genera.In addition,we found no evidence that the natural host played a major role in shaping virus diversity based on the comparison of their viromes.Rather,the geographic location seems to significantly influence the viral families.Phylogenetic study indicated that the novel negative-sense RNA viruses identified in this study was closely related to Bole tick virus 1 and 3 viruses.In conclusion,the present study provides a baseline for comparing viruses detected in ticks,according to species,natural hosts,and geographic locations.

Computational methods for identifying enhancer-promoter interactions

Background:As parts of the cis-regulatory mechanism of the human genome,interactions between distal enhancers and proximal promoters play a crucial role.Enhancers,promoters,and enhancer-promoter interactions(EPIs)can be detected using many sequencing technologies and computation models.However,a systematic review that summarizes these EPI identification methods and that can help researchers apply and optimize them is still needed.Results:In this review,we first emphasize the role of EPIs in regulating gene expression and describe a generic framework for predicting enhancer-promoter interaction.Next,we review prediction methods for enhancers,promoters,loops,and enhancer-promoter interactions using different data features that have emerged since 2010,and we summarize the websites available for obtaining enhancers,promoters,and enhancer-promoter interaction datasets.Finally,we review the application of the methods for identifying EPIs in diseases such as cancer.Conclusions:The advance of computer technology has allowed traditional machine learning,and deep learning methods to be used to predict enhancer,promoter,and EPIs from genetic,genomic,and epigenomic features.In the past decade,models based on deep learning,especially transfer learning,have been proposed for directly predicting enhancer-promoter interactions from DNA sequences,and these models can reduce the parameter training time required of bioinformatics researchers.We believe this review can provide detailed research frameworks for researchers who are beginning to study enhancers,promoters,and their interactions.