bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diffuse unilateral subacute neuroretinitis: review article
Authors: Mazzeo Thiago José Muniz Machado , dos Santos Motta Mario Martins , Curi André Luiz Land 
Published: December 2019

AbstractDiffuse unilateral subacute neuroretinitis (DUSN) is an ocular infectious disease that can lead to severe visual impairment and blindness. It usually occurs in healthy young individuals and depending on the stage of the disease, it may present as vitritis, multifocal gray-white lesions in the outer retina, and derangement of the retinal pigment epithelium, narrowing of the retinal vessels and optic atrophy. Parasites of different sizes and species have been proposed as the etiologic agent of DUSN, including Ancylostoma caninum, Toxocara canis, and others. Thus, it is hypothesized that different infectious worms may be considered as the likely cause of both an autoimmune and toxic form of nematode retinopathy. Because serologic testing is variable, the definitive diagnosis is made when clinical characteristics of DUSN are found in conjunction with an intraocular worm. Ancillary tests can assist in the differential diagnosis when the nematode cannot be visualized, such as fluorescein and indocyanine green angiography, electrophysiological tests, visual field studies, and more recently, optical coherence tomography angiography. Cases in which the worm can be identified, it is defined as confirmed DUSN, and eyes with the typical clinical features but without identification of the worm should be classified as presumed DUSN. In confirmed DUSN, the classic treatment is directly photocoagulation of the worm; however, it can only be visualized in 30% (to 40%) of cases. Treatment of presumed DUSN cases with high-dose oral albendazole has shown encouraging results. However, perhaps due to the disease’s rarity or its underdiagnosis, there are no studies comparing current treatment modalities in both presumed and confirmed DUSN. Due to the possibility of this disease being, in part, autoimmune nematode retinopathy, corticosteroids associated with both albendazole or laser therapy, could be in any way beneficial. Thus, further comparative studies are necessary to elucidate the best treatment for this potentially blinding disease.
Population structure and genetic history of Tibetan Terriers
Authors: Janeš Mateja , Zorc Minja , Cubric-Curik Vlatka , Curik Ino , Dovc Peter 
Published: December 2019

AbstractBackgroundTibetan Terrier is a popular medium-sized companion dog breed. According to the history of the breed, the western population of Tibetan Terriers includes two lineages, Lamleh and Luneville. These two lineages derive from a small number of founder animals from the native Tibetan Terrier population, which were brought to Europe in the 1920s. For almost a century, the western population of Tibetan Terriers and the native population in Tibet were reproductively isolated. In this study, we analysed the structure of the western population of Tibetan Terriers, the original native population from Tibet and of different crosses between these two populations. We also examined the genetic relationships of Tibetan Terriers with other dog breeds, especially terriers and some Asian breeds, and the within-breed structure of both Tibetan Terrier populations.ResultsOur analyses were based on high-density single nucleotide polymorphism (SNP) array (Illumina HD Canine 170 K) and microsatellite (18 loci) genotypes of 64 Tibetan Terriers belonging to different populations and lineages. For the comparative analysis, we used 348 publicly available SNP array genotypes of dogs from other breeds. We found that the western population of Tibetan Terriers and the native Tibetan Terriers clustered together with other Asian dog breeds, whereas all other terrier breeds were grouped into a separate group. We were also able to differentiate the western Tibetan Terrier lineages (Lamleh and Luneville) from the native Tibetan Terrier population.ConclusionsOur results reveal the relationships between the western and native populations of Tibetan Terriers and support the hypothesis that Tibetan Terrier belongs to the group of ancient dog breeds of Asian origin, which are close to the ancestors of the modern dog that were involved in the early domestication process. Thus, we were able to reject the initial hypothesis that Tibetan Terriers belong to the group of terrier breeds. The existence of this native population of Tibetan Terriers at its original location represents an exceptional and valuable genetic resource.
IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning
Authors: Yan Cheng , Duan Guihua , Wu Fang-Xiang , Wang Jianxin 
Published: December 2019

AbstractBackgroundViral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited.ResultIn this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF).ConlusionThe experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.
Model-based cell clustering and population tracking for time-series flow cytometry data
Authors: Minoura Kodai , Abe Ko , Maeda Yuka , Nishikawa Hiroyoshi , Shimamura Teppei 
Published: December 2019

Abstract
Background
Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on “manual gating” by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for time-series flow cytometry data. Time-series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static time-point analysis. Therefore, there is a great need for tools to systematically analyze time-series flow cytometry data.

Results
We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for time-series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current time dependent on that at a previous timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior.

Conclusions
Our results indicate that CYBERTRACK offers better understandings of time-dependent cell population dynamics to cytometry users by systematically analyzing time-series flow cytometry data.
SpliceFinder: ab initio prediction of splice sites using convolutional neural network
Authors: Wang Ruohan , Wang Zishuai , Wang Jianping , Li Shuaicheng 
Published: December 2019

Abstract
Background
Identifying splice sites is a necessary step to analyze the location and structure of genes. Two dinucleotides, GT and AG, are highly frequent on splice sites, and many other patterns are also on splice sites with important biological functions. Meanwhile, the dinucleotides occur frequently at the sequences without splice sites, which makes the prediction prone to generate false positives. Most existing tools select all the sequences with the two dimers and then focus on distinguishing the true splice sites from those pseudo ones. Such an approach will lead to a decrease in false positives; however, it will result in non-canonical splice sites missing.

Result
We have designed SpliceFinder based on convolutional neural network (CNN) to predict splice sites. To achieve the ab initio prediction, we used human genomic data to train our neural network. An iterative approach is adopted to reconstruct the dataset, which tackles the data unbalance problem and forces the model to learn more features of splice sites. The proposed CNN obtains the classification accuracy of 90.25%, which is 10% higher than the existing algorithms. The method outperforms other existing methods in terms of area under receiver operating characteristics (AUC), recall, precision, and F1 score. Furthermore, SpliceFinder can find the exact position of splice sites on long genomic sequences with a sliding window. Compared with other state-of-the-art splice site prediction tools, SpliceFinder generates results in about half lower false positive while keeping recall higher than 0.8. Also, SpliceFinder captures the non-canonical splice sites. In addition, SpliceFinder performs well on the genomic sequences of Drosophila melanogaster, Mus musculus, Rattus, and Danio rerio without retraining.

Conclusion
Based on CNN, we have proposed a new ab initio splice site prediction tool, SpliceFinder, which generates less false positives and can detect non-canonical splice sites. Additionally, SpliceFinder is transferable to other species without retraining. The source code and additional materials are available at https://gitlab.deepomics.org/wangruohan/SpliceFinder.
Improvement of the memory function of a mutual repression network in a stochastic environment by negative autoregulation
Authors: Hasan A. B. M. Shamim Ul , Kurata Hiroyuki , Pechmann Sebastian 
Published: December 2019

AbstractBackgroundCellular memory is a ubiquitous function of biological systems. By generating a sustained response to a transient inductive stimulus, often due to bistability, memory is central to the robust control of many important biological processes. However, our understanding of the origins of cellular memory remains incomplete. Stochastic fluctuations that are inherent to most biological systems have been shown to hamper memory function. Yet, how stochasticity changes the behavior of genetic circuits is generally not clear from a deterministic analysis of the network alone. Here, we apply deterministic rate equations, stochastic simulations, and theoretical analyses of Fokker-Planck equations to investigate how intrinsic noise affects the memory function in a mutual repression network.ResultsWe find that the addition of negative autoregulation improves the persistence of memory in a small gene regulatory network by reducing stochastic fluctuations. Our theoretical analyses reveal that this improved memory function stems from an increased stability of the steady states of the system. Moreover, we show how the tuning of critical network parameters can further enhance memory.ConclusionsOur work illuminates the power of stochastic and theoretical approaches to understanding biological circuits, and the importance of considering stochasticity when designing synthetic circuits with memory function.
A generative model for constructing nucleic acid sequences binding to a protein
Authors: Im Jinho , Park Byungkyu , Han Kyungsook 
Published: December 2019

AbstractBackgroundInteractions between protein and nucleic acid molecules are essential to a variety of cellular processes. A large amount of interaction data generated by high-throughput technologies have triggered the development of several computational methods either to predict binding sites in a sequence or to determine whether a pair of sequences interacts or not. Most of these methods treat the problem of the interaction of nucleic acids with proteins as a classification problem rather than a generation problem.ResultsWe developed a generative model for constructing single-stranded nucleic acids binding to a target protein using a long short-term memory (LSTM) neural network. Experimental results of the generative model are promising in the sense that DNA and RNA sequences generated by the model for several target proteins show high specificity and that motifs present in the generated sequences are similar to known protein-binding motifs.ConclusionsAlthough these are preliminary results of our ongoing research, our approach can be used to generate nucleic acid sequences binding to a target protein. In particular, it will help design efficient in vitro experiments by constructing an initial pool of potential aptamers that bind to a target protein with high affinity and specificity.
A ligation-based single-stranded library preparation method to analyze cell-free DNA and synthetic oligos
Authors: Troll Christopher J. , Kapp Joshua , Rao Varsha , Harkins Kelly M. , Cole Charles , Naughton Colin , Morgan Jessica M. , Shapiro Beth , Green Richard E. 
Published: December 2019

AbstractBackgroundCell-free DNA (cfDNA), present in circulating blood plasma, contains information about prenatal health, organ transplant reception, and cancer presence and progression. Originally developed for the genomic analysis of highly degraded ancient DNA, single-stranded DNA (ssDNA) library preparation methods are gaining popularity in the field of cfDNA analysis due to their efficiency and ability to convert short, fragmented DNA into sequencing libraries without altering DNA ends. However, current ssDNA methods are costly and time-consuming.ResultsHere we present an efficient ligation-based single-stranded library preparation method that is engineered to produce complex libraries in under 2.5 h from as little as 1 nanogram of input DNA without alteration to the native ends of template molecules. Our method, called Single Reaction Single-stranded LibrarY or SRSLY, ligates uniquely designed Next-Generation Sequencing (NGS) adapters in a one-step combined phosphorylation/ligation reaction that foregoes end-polishing. Using synthetic DNA oligos and cfDNA, we demonstrate the efficiency and utility of this approach and compare with existing double-stranded and single-stranded approaches for library generation. Finally, we demonstrate that cfDNA NGS data generated from SRSLY can be used to analyze DNA fragmentation patterns to deduce nucleosome positioning and transcription factor binding.ConclusionsSRSLY is a versatile tool for converting short and fragmented DNA molecules, like cfDNA fragments, into sequencing libraries while retaining native lengths and ends.
Putative biomarkers for predicting tumor sample purity based on gene expression data
Authors: Li Yuanyuan , Umbach David M. , Bingham Adrienna , Li Qi-Jing , Zhuang Yuan , Li Leping 
Published: December 2019

Abstract
Background
Tumor purity is the percent of cancer cells present in a sample of tumor tissue. The non-cancerous cells (immune cells, fibroblasts, etc.) have an important role in tumor biology. The ability to determine tumor purity is important to understand the roles of cancerous and non-cancerous cells in a tumor.

Methods
We applied a supervised machine learning method, XGBoost, to data from 33 TCGA tumor types to predict tumor purity using RNA-seq gene expression data.

Results
Across the 33 tumor types, the median correlation between observed and predicted tumor-purity ranged from 0.75 to 0.87 with small root mean square errors, suggesting that tumor purity can be accurately predicted υσινγ expression data. We further confirmed that expression levels of a ten-gene set (CSF2RB, RHOH, C1S, CCDC69, CCL22, CYTIP, POU2AF1, FGR, CCL21, and IL7R) were predictive of tumor purity regardless of tumor type. We tested whether our set of ten genes could accurately predict tumor purity of a TCGA-independent data set. We showed that expression levels from our set of ten genes were highly correlated (ρ = 0.88) with the actual observed tumor purity.

Conclusions
Our analyses suggested that the ten-gene set may serve as a biomarker for tumor purity prediction using gene expression data.
Phenotypic and molecular characterization of antimicrobial resistance in Trueperella pyogenes strains isolated from bovine mastitis and metritis
Authors: Rezanejad Mobin , Karimi Sepideh , Momtaz Hassan 
Published: December 2019

Abstract
Background
Trueperella pyogenes is one of the most clinically imperative bacteria responsible for severe cases of mastitis and metritis, particularly in postpartum dairy cows. The bacterium has emergence of antibiotic resistance and virulence characters. The existing research was done to apprise the phenotypic and genotypic evaluation of antibiotic resistance and characterization of virulence factors in the T. pyogenes bacteria of bovine mastitis and metritis in postpartum cows.

Methods
Two-hundred and twenty-six bovine mastitic milk and 172 uterine swabs were collected and transferred to laboratory. Samples were cultured and T. pyogenes isolates were subjected to disk diffusion and DNA extraction. Distribution of virulence and antibiotic resistance genes was studied by PCR.

Results
Thirty-two out of 226 (14.15%) mastitic milk and forty-one out of 172 (23.83%) uterine swab samples were positive for T. pyogenes. Isolates of mastitic milk harbored the highest prevalence of resistance toward gentamicin (100%), penicillin (100%), ampicillin (90.62%), amoxicillin (87.50%) and trimethoprim-sulfamethoxazole (87.50%), while those of metritis harbored the highest prevalence of resistance toward ampicillin (100%), amoxicillin (100%), gentamicin (97.56%), penicillin (97.56%) and cefalexin (97.56%). AacC, aadA1, aadA2 and tetW were the most generally perceived antibiotic resistance genes. All bacteria harbored plo (100%) and fimA (100%) virulence factors. NanP, nanH, fimC and fimE were also the most generally perceived virulence factors.

Conclusions
All bacteria harbored plo and fimA virulence factors which showed that they can use as specific genetic markers with their important roles in pathogenicity of T. pyogenes bacteria. Phenotypic pattern of antibiotic resistance was confirmed by genotypic characterization of antibiotic resistance genes.
Genome-wide analysis of polygalacturonase gene family from pear genome and identification of the member involved in pear softening
Authors: Zhang Suling , Ma Min , Zhang Huping , Zhang Shaoling , Qian Ming , Zhang Zhen , Luo Weiqi , Fan Jinbu , Liu Zhiqiang , Wang Libin 
Published: December 2019

Abstract
Background
Polygalacturonase (PG), as an important hydrolase participating in the degradation of pectin, plays an important role in softening process of fruit. However, information on PG gene family in pear genome and the specific member involved in fruit softening is still rudimentary.

Results
In this study, a total of 61 PG genes, which could be divided into six subclasses, were identified from the pear genome with diverse chromosome locations, gene structures, motifs and cis-acting elements. Most PbrPGs were derived from WGD/segmental duplication blocks, and purifying selection was the main driving force for their expansion. The expression profiles of PbrPGs in pear were tissue/development-stage/cultivar-dependent. During ‘Housui’ pear storage, associated with the reduction of firmness was the accumulation of PG activity. Totally, 28 PbrPGs were expressed during fruit storage, which could be classified into five categories based on different expression patterns; most demonstrated an increased trend. Of these, PbrPG6 were proposed to account for pear softening in combination of the phylogenetic and correlation analysis among firmness, PG activity and PbrPGs. By constructing the silencing vector, a higher firmness was observed in PbrPG6-silenced fruit when compared with that of the control (empty vector). In a further study, we found that the expression of PbrPG6 was regulated by postharvest 1-MCP/ethrel treatment, and several PbrERFs might function in this process.

Conclusions
We identified 61 PbrPG genes from pear genome; of these, PbrPG6 was involved in fruit softening process; furthermore, the expression of PbrPG6 might be under the control of PbrERF. This study provides a foundation for future work aimed at elucidating the molecular mechanism underlying pear softening.
Sex differences in 10-year ischemic cardiovascular disease risk prediction in Chinese patients with prediabetes and type 2 diabetes
Authors: Yang Lihong , Fish Anne F. , Zhu Yuanyuan , Yuan Xiaodan , Li Jianing , Wang Xiaoyun , Yuan Li , Jia Zhumin , Liu Chao , Xu Yunchen , Lou Qingqing 
Published: December 2019

Abstract
Background
Cardiovascular disease has become a serious public health problem in recent years in China. The aim of the study was to examine sex differences in cardiovascular risk factors and 10-year ischemic cardiovascular disease (ICVD) risk in Chinese patients with prediabetes (PreDM) and type 2 diabetes mellitus (T2DM).

Methods
This was a multi-site retrospective case-control study conducted from April–November 2016 using an electronic medical record database, involving 217 PreDM and 900 T2DM patients admitted to endocrinology units in four hospitals in China. CVD risk was estimated using the Chinese 10-year ICVD risk model. The differences in 10-year absolute ICVD risk according to PreDM, T2DM < 1 year, T2DM 1–5 years or T2DM ≥5 years and sex were analyzed using ANOVA.

Results
When compared to PreDM females, males with PreDM had significantly higher 10-year ICVD risk In contrast, the opposite pattern of 10-year ICVD risk was observed in T2DM; males had significantly lower 10-year ICVD risk. Moreover, compared to T2DM females, males with T2DM had a lower proportion s with moderate or greater ICVD risk (p < 0.001). When compared to PreDM males, males with T2DM < 1 year, and with T2DM 1–5 years had no difference in 10-year ICVD risk, but had higher ICVD risk with T2DM ≥5 years (p < 0.05). Compared to PreDM females, females with T2DM in all subgroups had higher ICVD risk (p < 0.05). Among those with T2DM, hypertension rates of awareness, treatment and control were 78.60%, 65.38% and 31.10%, respectively; hyperlipidemia rates of awareness, treatment and control were lower (29.15%, 8.30% and 3.47%, respectively). Females with T2DM had higher prevalence, awareness and treatment of hypertension and hyperlipidemia than males with T2DM (p < 0.001).

Conclusions
There is a greater need for cardiovascular risk reduction programs for females with T2DM at diagnosis. Given the low numbers for awareness, treatment and control of hypertension and hyperlipidemia in both males and females, significant resources focused on them must be expended, specifically improving regular assessment of blood pressure and blood lipids. Strengthening the management of chronic diseases through adherence to evidence-based guidelines to enhance clinical treatment may reduce 10-year ICVD in patients with T2DM in China.