bluuebunny/crossref_metadata_2025_split huggingface dataset:
Swimming training and Plantago psyllium ameliorate cognitive impairment and glucose tolerance in streptozotocin–nicotinamide-induced type 2 diabetic rats
Authors: Parsa Hesam , Moradi-Khaligh Zahra , Rajabi Sara , Ranjbar Kamal , Komaki Alireza 
Published: November 2021

AbstractBrain malfunction is common in diabetic patients. On the other hand, a growing body of research points to the beneficial effect of medicinal plants and exercise training on insulin sensitivity and brain function. Therefore, the aim of the present study was to investigate the effect of co-administration of swimming training and Plantago psyllium (mixed with standard pelleted food at a weight ratio of 5%) on learning and memory impairment and glucose tolerance in type 2 diabetic rats. For this purpose, 10 healthy and 40 rats with type 2 diabetes were randomly allocated to five groups: healthy sedentary control group (Con), sedentary diabetic group (D), diabetic group subjected to swimming training (D + Tr), diabetic group receiving P. psyllium (D + Ps), and diabetic group subjected to swimming training and receiving P. psyllium (D + Ps + Tr). Diabetes was induced by a single intraperitoneal injection of nicotinamide (120 mg/kg) and streptozotocin (65 mg/kg) separately with 15 min intervals. Experimental groups were treated with swimming training and P. psyllium independently and simultaneously for 12 weeks. Lipid profile and food intake were measured and also, glucose tolerance was evaluated by glucose area under the curve (AUCg) using an oral glucose tolerance test. Passive avoidance learning (PAL) and memory were evaluated by shuttle box test and cognitive memory was assessed by novel object recognition (NOR) and elevated plus-maze (EPM) tests. Diabetic rats exhibited a significant increase in food intake, lipid profile, and AUCg compared to healthy rats. Step-through latency in the PAL acquisition trial (STL-a) and retention test (STL-r) were significantly lower in diabetic rats than in the control group. In the diabetic group without treatment, time spent in the dark compartment increased compared to the control group in the shuttle box test. Discrimination index and distance traveled reduced in diabetic rats. On the other hand, swimming training and P. psyllium alleviated food intake, lipid profile, and glucose tolerance in diabetic rats. Also, the STL-a, STL-r, discrimination index, and distance travelled in the D + Ps + Tr group were significantly more than the diabetic group. Results showed that 12 weeks of swimming training and receiving P. psyllium improved memory deficit in streptozotocin–nicotinamide-induced type 2 diabetic rats possibly through hypolipidemic and hypoglycemic effects. These results suggest that the administration of swimming training and P. psyllium simultaneously might be an effective intervention for the treatment of diabetes-induced behavioral deficits.
A combined miR-34a and arsenic trioxide nanodrug delivery system for synergistic inhibition of HCC progression after microwave ablation
Authors: Hu Jian , Pei Wenceng , Jiang Zhenyou , Li Zihuang 
Published: November 2021

Abstract
Background
Microwave ablation (MWA) has become an alternative treatment for unresectable hepatocellular carcinoma (HCC), but it does not eliminate the risk of recurrence and metastasis after treatment. Recent studies have demonstrated that miR-34a presents decreased gene expression in residual tumours after ablation therapy and can increase the therapeutic effect of arsenic trioxide against HCC, which brings new opportunities for HCC treatment.

Methods
A pH-sensitive charge inversion material was used to construct a nanotargeted delivery system based on the synergistic effects of miR-34a and As2O3. We established in vitro and in vivo models of HCC microwave ablation and performed in-depth research on the dual-drug system to inhibit the rapid progression and induce pyroptosis in HCC cells after microwave ablation.

Results
The antitumour effects were enhanced with the dual-drug nanoparticles relative to the single-drug formulations, and the therapeutic efficacy of the nanoparticles was more significant in a weakly acidic environment. The dual-drug nanoparticles increased the N-terminal portion of GSDME and decreased the expression of Cyt-c and c-met.

Conclusions
Dual-drug nanoparticles may improve the therapeutic efficacy of HCC treatment after insufficient ablation through Cyt-c and GSDME-N and decrease the expression levels of c-met. These nanoparticles are expected to provide new treatment methods for residual HCC after MWA, prolong the survival of patients and improve their quality of life.
Dissecting genetic trends to understand breeding practices in livestock: a maternal pig line example
Authors: Abdollahi-Arpanahi Rostam , Lourenco Daniela , Legarra Andres , Misztal Ignacy 
Published: November 2021

Abstract
Background
Understanding whether genomic selection has been effective in livestock and when the results of genomic selection became visible are essential questions which we have addressed in this paper. Three criteria were used to identify practices of breeding programs over time: (1) the point of divergence of estimated genetic trends based on pedigree-based best linear unbiased prediction (BLUP) versus single-step genomic BLUP (ssGBLUP), (2) the point of divergence of realized Mendelian sampling (RMS) trends based on BLUP and ssGBLUP, and (3) the partition of genetic trends into that contributed by genotyped and non-genotyped individuals and by males and females.

Methods
We used data on 282,035 animals from a commercial maternal line of pigs, of which 32,856 were genotyped for 36,612 single nucleotide polymorphisms (SNPs) after quality control. Phenotypic data included 228,427, 101,225, and 11,444 records for birth weight, average daily gain in the nursery, and feed intake, respectively. Breeding values were predicted in a multiple-trait framework using BLUP and ssGBLUP.

Results
The points of divergence of the genetic and RMS trends estimated by BLUP and ssGBLUP indicated that genomic selection effectively started in 2019. Partitioning the overall genetic trends into that for genotyped and non-genotyped individuals revealed that the contribution of genotyped animals to the overall genetic trend increased rapidly from ~ 74% in 2016 to 90% in 2019. The contribution of the female pathway to the genetic trend also increased since genomic selection was implemented in this pig population, which reflects the changes in the genotyping strategy in recent years.

Conclusions
Our results show that an assessment of breeding program practices can be done based on the point of divergence of genetic and RMS trends between BLUP and ssGBLUP and based on the partitioning of the genetic trend into contributions from different selection pathways. However, it should be noted that genetic trends can diverge before the onset of genomic selection if superior animals are genotyped retroactively. For the pig population example, the results showed that genomic selection was effective in this population.
Efficient iterative Hi-C scaffolder based on N-best neighbors
Authors: Guan Dengfeng , McCarthy Shane A. , Ning Zemin , Wang Guohua , Wang Yadong , Durbin Richard 
Published: November 2021

Abstract
Background
Efficient and effective genome scaffolding tools are still in high demand for generating reference-quality assemblies. While long read data itself is unlikely to create a chromosome-scale assembly for most eukaryotic species, the inexpensive Hi-C sequencing technology, capable of capturing the chromosomal profile of a genome, is now widely used to complete the task. However, the existing Hi-C based scaffolding tools either require a priori chromosome number as input, or lack the ability to build highly continuous scaffolds.

Results
We design and develop a novel Hi-C based scaffolding tool, pin_hic, which takes advantage of contact information from Hi-C reads to construct a scaffolding graph iteratively based on N-best neighbors of contigs. Subsequent to scaffolding, it identifies potential misjoins and breaks them to keep the scaffolding accuracy. Through our tests on three long read based de novo assemblies from three different species, we demonstrate that pin_hic is more efficient than current standard state-of-art tools, and it can generate much more continuous scaffolds, while achieving a higher or comparable accuracy.

Conclusions
Pin_hic is an efficient Hi-C based scaffolding tool, which can be useful for building chromosome-scale assemblies. As many sequencing projects have been launched in the recent years, we believe pin_hic has potential to be applied in these projects and makes a meaningful contribution.
EvalDNA: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality
Authors: MacDonald Madolyn L. , Lee Kelvin H. 
Published: November 2021

Abstract
Background
To select the most complete, continuous, and accurate assembly for an organism of interest, comprehensive quality assessment of assemblies is necessary. We present a novel tool, called Evaluation of De Novo Assemblies (EvalDNA), which uses supervised machine learning for the quality scoring of genome assemblies and does not require an existing reference genome for accuracy assessment.

Results
EvalDNA calculates a list of quality metrics from an assembled sequence and applies a model created from supervised machine learning methods to integrate various metrics into a comprehensive quality score. A well-tested, accurate model for scoring mammalian genome sequences is provided as part of EvalDNA. This random forest regression model evaluates an assembled sequence based on continuity, completeness, and accuracy, and was able to explain 86% of the variation in reference-based quality scores within the testing data. EvalDNA was applied to human chromosome 14 assemblies from the GAGE study to rank genome assemblers and to compare EvalDNA to two other quality evaluation tools. In addition, EvalDNA was used to evaluate several genome assemblies of the Chinese hamster genome to help establish a better reference genome for the biopharmaceutical manufacturing community. EvalDNA was also used to assess more recent human assemblies from the QUAST-LG study completed in 2018, and its ability to score bacterial genomes was examined through application on bacterial assemblies from the GAGE-B study.

Conclusions
EvalDNA enables scientists to easily identify the best available genome assembly for their organism of interest without requiring a reference assembly. EvalDNA sets itself apart from other quality assessment tools by producing a quality score that enables direct comparison among assemblies from different species.
A multitask transfer learning framework for the prediction of virus-human protein–protein interactions
Authors: Dong Thi Ngan , Brogden Graham , Gerold Gisa , Khosla Megha 
Published: November 2021

AbstractBackgroundViral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein–protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses.ResultsWe developed amultitask transfer learningapproach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome to counter the problem of small training datasets. Instead of using hand-crafted protein features, we utilize statistically rich protein representations learned by a deep language modeling approach from a massive source of protein sequences. Additionally, we employ an additional objective which aims to maximize the probability of observing human protein–protein interactions. This additional task objective acts as a regularizer and also allows to incorporate domain knowledge to inform the virus-human protein–protein interaction prediction model.ConclusionsOur approach achieved competitive results on 13 benchmark datasets and the case study for theSARS-CoV-2virus receptor. Experimental results show that our proposed model works effectively for both virus-human and bacteria-human protein–protein interaction prediction tasks. We share our code for reproducibility and future research athttps://git.l3s.uni-hannover.de/dong/multitask-transfer.
Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations
Authors: Zhou Feng , Yin Meng-Meng , Jiao Cui-Na , Cui Zhen , Zhao Jing-Xiu , Liu Jin-Xing 
Published: November 2021

Abstract
Background
With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA–disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs.

Results
By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments.

Conclusions
Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.
The effects of habitat fragmentation on the genetic structure of wild boar (Sus scrofa) population in Lithuania
Authors: Griciuvienė Loreta , Janeliūnas Žygimantas , Jurgelevičius Vaclovas , Paulauskas Algimantas 
Published: November 2021

Abstract
Background
Wild boar (Sus scrofa) is a widely distributed ungulate whose success can be attributed to a variety of ecological features. The genetic variation and population structure of Lithuania’s wild boar population have not yet been thoroughly studied. The purposes of this study were to investigate the genetic diversity of S. scrofa and assess the effects of habitat fragmentation on the population structure of wild boar in Lithuania. A total of 96 S. scrofa individuals collected from different regions of Lithuania were genotyped using fifteen microsatellite loci.

Results
The microsatellite analysis of the wild boars indicated high levels of genetic diversity within the population. Microsatellite markers showed evidence of a single panmictic wild boar population in Lithuania according to STRUCTURE’s highest average likelihood, which was K = 1. This was supported by pairwise Fst values and AMOVA, which indicated no differentiation between the four sampling areas. The results of the Mantel test revealed a weak isolation by distance and geographic diversity gradients that persisted between locations. Motorway fencing and heavy traffic were not an effective barrier to wild boar movement.

Conclusions
There was limited evidence of population genetic structure among the wild boar, supporting the presence of a single population across the study area and indicating that there may be no barriers hindering wild boar dispersal across the landscape. The widespread wild boar population in Lithuania, the high level of genetic variation observed within subpopulations, and the low level of variation identified between subpopulations suggest migration and gene flow between locations. The results of this study should provide valuable information in future for understanding and comparing the detailed structure of wild boar population in Lithuania following the outbreak of African swine fever.
Baiting out a full length sequence from unmapped RNA-seq data
Authors: Li Dongwei , Huang Qitong , Huang Lei , Wen Jikai , Luo Jing , Li Qing , Peng Yanling , Zhang Yubo 
Published: November 2021

Abstract
Background
As a powerful tool, RNA-Seq has been widely used in various studies. Usually, unmapped RNA-seq reads have been considered as useless and been trashed or ignored.

Results
We develop a strategy to mining the full length sequence by unmapped reads combining with specific reverse transcription primers design and high throughput sequencing. In this study, we salvage 36 unmapped reads from standard RNA-Seq data and randomly select one 149 bp read as a model. Specific reverse transcription primers are designed to amplify its both ends, followed by next generation sequencing. Then we design a statistical model based on power law distribution to estimate its integrality and significance. Further, we validate it by Sanger sequencing. The result shows that the full length is 1556 bp, with insertion mutations in microsatellite structure.

Conclusion
We believe this method would be a useful strategy to extract the sequences information from the unmapped RNA-seq data. Further, it is an alternative way to get the full length sequence of unknown cDNA.
Characterisation of novel endogenous geminiviral elements in macadamia
Authors: Zakeel Mohamed C. M. , Geering Andrew D. W. , Thomas John E. , Akinsanmi Olufemi A. 
Published: November 2021

Abstract
Background
The presence of geminivirus sequences in a preliminary analysis of sRNA sequences from the leaves of macadamia trees with abnormal vertical growth (AVG) syndrome was investigated.

Results
A locus of endogenous geminiviral elements (EGE) in the macadamia genome was analysed, and the sequences revealed a high level of deletions and/or partial integrations, thus rendering the EGE transcriptionally inactive. The replication defective EGE in the macadamia genome indicates its inability to be the source of new viral infections and thus cause AVG or any other disease in macadamia. The EGE sequences were detected in two edible Macadamia species that constitute commercial cultivars and the wild germplasm of edible and inedible species of Macadamia. This strongly suggests that the integration preceded speciation of the genus Macadamia. A draft genome of a locus of EGE in Macadamia was developed. The findings of this study provide evidence to suggest the endogenization of the geminiviral sequences in the macadamia genome and the ancestral relationship of EGE with Macadamia in the Proteaceae family. Random mutations accumulating in the EGE inform that the sequence is evolving.

Conclusions
The EGE in Macadamia is inactive and thus not a direct cause of any diseases or syndromes including AVG in macadamia. The insertion of the EGE in the macadamia genome preceded speciation of the genus Macadamia.
Alteration of the gut microbiota in rhesus monkey with spontaneous osteoarthritis
Authors: Yan Yaping , Yi Xiaoyan , Duan Yanchao , Jiang Bin , Huang Tianzhuang , Inglis Briauna Marie , Zheng Bingrong , Si Wei 
Published: November 2021

Abstract
Background
The spontaneous osteoarthritis (OA) in rhesus macaque is similar to OA in human, which maintains an upright body posture and shows very similar biomechanical properties of bones to humans. At present, there is no good treatment for OA. This study aims to explore relationship between OA and intestinal microbiota, and provide a reference for the treatment of clinical OA.

Results
We collected colonic contents of the 20 rhesus macaque (6–15 years old, female) for intestinal microbiota analysis by metagenomics sequencing, of which 10 were spontaneous OA monkeys and 10 were normal monkeys. Our results showed the diversity of gut microbiota in monkeys with OA was decreased compared to the normal monkeys (p = 0.16). Mollicutes, Tenericutes, Coprobacillus and Faecalitalea may be biomarkers for the monkeys of OA. Lactobacillus found significantly increased in OA monkeys. Prevotella and Ruminococcus were higher in the normal group than OA group. Zinc/manganese transport system permease protein (p = 0.0011) and Cyclopropane-fatty-acyl-phospholipid synthase (p = 0.0012) are a microbiota metabolic pathway related to cartilage production.

Conclusions
Our results indicate that the diversity and composition of intestinal microbiota in monkeys with OA are different compared to the normal monkeys. we have found microbes that may be a biomarker for the diagnosis of osteoarthritis. Functional analysis of the microbiota also predicts cartilage damage in the monkeys with osteoarthritis. Non-human primates are closely related to humans, so this study can provide a reference for the development of drugs for the treatment of OA.
ABCA1 and ABCG1 DNA methylation in epicardial adipose tissue of patients with coronary artery disease
Authors: Miroshnikova Valentina V. , Panteleeva Alexandra A. , Pobozheva Irina A. , Razgildina Natalia D. , Polyakova Ekaterina A. , Markov Anton V. , Belyaeva Olga D. , Berkovich Olga A. , Baranova Elena I. , Nazarenko Maria S. , Puzyrev Valery P. , Pchelina Sofya N. 
Published: November 2021

Abstract
Background
Recent studies have focused on the potential role of epicardial adipose tissue (EAT) in the development of coronary artery disease (CAD). ABCA1 and ABCG1 transporters regulate cell cholesterol content and reverse cholesterol transport. We aimed to determine whether DNA methylation and mRNA levels of the ABCA1 and ABCG1 genes in EAT and subcutaneous adipose tissue (SAT) were associated with CAD.

Methods
Paired EAT and SAT samples were collected from 82 patients undergoing elective cardiac surgery either for coronary artery bypass grafting (CAD group, N = 66) or valve surgery (NCAD group, N = 16). ABCA1 and ABCG1 mRNA levels in EAT and SAT samples were analyzed using real time polymerase chain reaction, ABCA1 protein levels in EAT samples were assessed by western blotting. ABCA1 and ABCG1 DNA methylation analysis was performed in 24 samples from the CAD group and 9 samples from the NCAD group via pyrosequencing.

Results
DNA methylation levels in the ABCA1 promoter and ABCG1 cg27243685 and cg06500161 CpG sites were higher in EAT samples from patients with CAD compared with NCAD (21.92% vs 10.81%, p = 0.003; 71.51% vs 68.42%, p = 0.024; 46.11% vs 37.79%, p = 0.016, respectively). In patients with CAD, ABCA1 and ABCG1 DNA methylation levels were higher in EAT than in SAT samples (p < 0.05). ABCA1 mRNA levels in EAT samples were reduced in the subgroup of patients with CAD and concomitant carotid artery disease or peripheral artery disease compared with the NCAD group (p = 0.024). ABCA1 protein levels in EAT samples tended to be lower in CAD patients than in the NCAD group (p = 0.053). DNA methylation levels at the ABCG1 cg27243685 site positively correlated with plasma triglyceride concentration (r = 0.510, p = 0.008), body mass index (r = 0.556, p = 0.013) and waist-to-hip ratio (r = 0.504, p = 0.012) in SAT samples.

Conclusion
CAD is associated with ABCA1 and ABCG1 DNA hypermethylation in EAT. CAD with concomitant carotid artery disease or peripheral artery disease is accompanied by decreased ABCA1 gene expression in EAT. DNA methylation levels at the ABCG1 cg27243685 locus in SAT are associated with hypertriglyceridemia and obesity.