bluuebunny/crossref_metadata_2025_split huggingface dataset:
Solvent-Free Catalytic Oxidation of Benzyl Alcohol over Au-Pd Bimetal Deposited on TiO2: Comparison of Rutile, Brookite, and Anatase
Authors: Li Xiaoliang , Feng Jiangjiang , Sun Jia , Wang Zhe , Zhao Wei 
Published: December 2019

AbstractTiO2 (P25)-supported Au-Pd bimetal nanoparticles displayed excellent performance in the solvent-free benzyl alcohol catalytic oxidation. However, little research attention has been paid to investigate the effects of TiO2 form on the catalytic activity of Au-Pd/TiO2. In the present research, rutile, brookite, and anatase TiO2 were successfully synthesized and subsequently applied as the carrier to load Au-Pd nanoparticles by the deposition-precipitation method. The experimental results indicated that the benzyl alcohol conversion employing the rutile TiO2-supported Au-Pd catalyst is higher than the conversion of anatase and brookite TiO2-loaded Au-Pd catalysts. However, the Au-Pd/TiO2-rutile displayed the lowest and highest selectivity toward benzaldehyde and toluene, respectively. ICP-AES, XRD, XPS, and TEM were conducted to characterize these catalysts. The corresponding experimental results revealed that the excellent performance of Au-Pd/TiO2-rutile catalyst was attributed to both the smaller Au-Pd nanoparticle size distribution and the higher concentrations of Oα and Pd2+ species on the catalyst surface. In the recycle experiments, the Au-Pd/TiO2-rutile catalyst displayed lower reaction stability compared with the Au-Pd/TiO2-anatase and Au-Pd/TiO2-brookite, which might be due to the coverage of larger amount of aldehyde products on the surface.
Current basis and future directions of zebrafish nutrigenomics
Authors: Williams Michael B. , Watts Stephen A. 
Published: December 2019

AbstractThis review investigates the current state of nutrigenomics in the zebrafish animal models. The zebrafish animal model has been used extensively in the study of disease onset and progression and associated molecular changes. In this review, we provide a synopsis of nutrigenomics using the zebrafish animal model. Obesity and dyslipidemia studies describe the genomics of dietary-induced obesity in relation to high-fat/high-calorie diets. Inflammation and cardiovascular studies describe dietary effects on the expression of acute inflammatory markers and resulting chronic inflammatory issues including atherosclerosis. We also evaluated the genomic response to bioactive dietary compounds associated with metabolic disorders. Carbohydrate metabolism and β-cell function studies describe the impacts of high-carbohydrate dietary challenges on nutritional programming. We also report tumorigenesis in relation to dietary carcinogen exposure studies that can result in permanent genomic changes. Vitamin and mineral deficiency studies demonstrate transgenerational genomic impacts of micronutrients in the diet and temporal expression changes. Circadian rhythm studies describe the relation between metabolism and natural temporal cycles of gene expression that impacts health. Bone formation studies describe the role of dietary composition that influences bone reabsorption regulation. Finally, this review provides future directions in the use of the zebrafish model for nutrigenomic and nutrigenetic research.
Manage with care: the frailty of self-connections in the European airport network
Authors: Cattaneo Mattia , Malighetti Paolo , Paleari Stefano , Redondi Renato 
Published: December 2019

AbstractThis study evaluates the attractiveness of self-hubbing in terms of the (a) symmetry of itineraries and the consequences for passengers in the case of missed flights. We compute the most attractive European origin-destination (O-D) pairs through self-connection and evaluate their robustness by estimating the expected delays relative to connecting times and the travel options available when a connection is missed.Results show that the potential of self-connecting markets is reduced when accounting for asymmetrical travel options and the consequences for travelers in the case of missed flights. In terms of frequencies, self-connecting passengers are, on average, found to have fewer alternatives to complete a given O-D pair than in the case of alliance-based connections (− 33%). Our findings moderate the confidence of past evidence on self-hubbing in light of the concrete reliability of self-connections for passengers. The itinerary choice made by passengers inevitably depends on the evaluation of travel quality attributes related to the (a) symmetry of the itineraries and the costs incurred through missed connections.
ki67 nuclei detection and ki67-index estimation: a novel automatic approach based on human vision modeling
Authors: Barricelli Barbara Rita , Casiraghi Elena , Gliozzo Jessica , Huber Veronica , Leone Biagio Eugenio , Rizzi Alessandro , Vergani Barbara 
Published: December 2019

Abstract
Background
The protein ki67 (pki67) is a marker of tumor aggressiveness, and its expression has been proven to be useful in the prognostic and predictive evaluation of several types of tumors. To numerically quantify the pki67 presence in cancerous tissue areas, pathologists generally analyze histochemical images to count the number of tumor nuclei marked for pki67. This allows estimating the ki67-index, that is the percentage of tumor nuclei positive for pki67 over all the tumor nuclei. Given the high image resolution and dimensions, its estimation by expert clinicians is particularly laborious and time consuming. Though automatic cell counting techniques have been presented so far, the problem is still open.

Results
In this paper we present a novel automatic approach for the estimations of the ki67-index. The method starts by exploiting the STRESS algorithm to produce a color enhanced image where all pixels belonging to nuclei are easily identified by thresholding, and then separated into positive (i.e. pixels belonging to nuclei marked for pki67) and negative by a binary classification tree. Next, positive and negative nuclei pixels are processed separately by two multiscale procedures identifying isolated nuclei and separating adjoining nuclei. The multiscale procedures exploit two Bayesian classification trees to recognize positive and negative nuclei-shaped regions.

Conclusions
The evaluation of the computed results, both through experts’ visual assessments and through the comparison of the computed indexes with those of experts, proved that the prototype is promising, so that experts believe in its potential as a tool to be exploited in the clinical practice as a valid aid for clinicians estimating the ki67-index. The MATLAB source code is open source for research purposes.
Rearrangement analysis of multiple bacterial genomes
Authors: Noureen Mehwish , Tada Ipputa , Kawashima Takeshi , Arita Masanori 
Published: December 2019

Abstract
Background
Genomes are subjected to rearrangements that change the orientation and ordering of genes during evolution. The most common rearrangements that occur in uni-chromosomal genomes are inversions (or reversals) to adapt to the changing environment. Since genome rearrangements are rarer than point mutations, gene order with sequence data can facilitate more robust phylogenetic reconstruction. Helicobacter pylori is a good model because of its unique evolution in niche environment.

Results
We have developed a method to identify genome rearrangements by comparing almost-conserved genes among closely related strains. Orthologous gene clusters, rather than the gene sequences, are used to align the gene order so that comparison of large number of genomes becomes easier. Comparison of 72 Helicobacter pylori strains revealed shared as well as strain-specific reversals, some of which were found in different geographical locations.

Conclusion
Degree of genome rearrangements increases with time. Therefore, gene orders can be used to study the evolutionary relationship among species and strains. Multiple genome comparison helps to identify the strain-specific as well as shared reversals. Identification of the time course of rearrangements can provide insights into evolutionary events.
Identification of infectious disease-associated host genes using machine learning techniques
Authors: Barman Ranjan Kumar , Mukhopadhyay Anirban , Maulik Ujjwal , Das Santasabuj 
Published: December 2019

Abstract
Background
With the global spread of multidrug resistance in pathogenic microbes, infectious diseases emerge as a key public health concern of the recent time. Identification of host genes associated with infectious diseases will improve our understanding about the mechanisms behind their development and help to identify novel therapeutic targets.

Results
We developed a machine learning techniques-based classification approach to identify infectious disease-associated host genes by integrating sequence and protein interaction network features. Among different methods, Deep Neural Networks (DNN) model with 16 selected features for pseudo-amino acid composition (PAAC) and network properties achieved the highest accuracy of 86.33% with sensitivity of 85.61% and specificity of 86.57%. The DNN classifier also attained an accuracy of 83.33% on a blind dataset and a sensitivity of 83.1% on an independent dataset. Furthermore, to predict unknown infectious disease-associated host genes, we applied the proposed DNN model to all reviewed proteins from the database. Seventy-six out of 100 highly-predicted infectious disease-associated genes from our study were also found in experimentally-verified human-pathogen protein-protein interactions (PPIs). Finally, we validated the highly-predicted infectious disease-associated genes by disease and gene ontology enrichment analysis and found that many of them are shared by one or more of the other diseases, such as cancer, metabolic and immune related diseases.

Conclusions
To the best of our knowledge, this is the first computational method to identify infectious disease-associated host genes. The proposed method will help large-scale prediction of host genes associated with infectious-diseases. However, our results indicated that for small datasets, advanced DNN-based method does not offer significant advantage over the simpler supervised machine learning techniques, such as Support Vector Machine (SVM) or Random Forest (RF) for the prediction of infectious disease-associated host genes. Significant overlap of infectious disease with cancer and metabolic disease on disease and gene ontology enrichment analysis suggests that these diseases perturb the functions of the same cellular signaling pathways and may be treated by drugs that tend to reverse these perturbations. Moreover, identification of novel candidate genes associated with infectious diseases would help us to explain disease pathogenesis further and develop novel therapeutics.
Biomedical named entity recognition using deep neural networks with contextual information
Authors: Cho Hyejin , Lee Hyunju 
Published: December 2019

Abstract
Background
In biomedical text mining, named entity recognition (NER) is an important task used to extract information from biomedical articles. Previously proposed methods for NER are dictionary- or rule-based methods and machine learning approaches. However, these traditional approaches are heavily reliant on large-scale dictionaries, target-specific rules, or well-constructed corpora. These methods to NER have been superseded by the deep learning-based approach that is independent of hand-crafted features. However, although such methods of NER employ additional conditional random fields (CRF) to capture important correlations between neighboring labels, they often do not incorporate all the contextual information from text into the deep learning layers.

Results
We propose herein an NER system for biomedical entities by incorporating n-grams with bi-directional long short-term memory (BiLSTM) and CRF; this system is referred to as a contextual long short-term memory networks with CRF (CLSTM). We assess the CLSTM model on three corpora: the disease corpus of the National Center for Biotechnology Information (NCBI), the BioCreative II Gene Mention corpus (GM), and the BioCreative V Chemical Disease Relation corpus (CDR). Our framework was compared with several deep learning approaches, such as BiLSTM, BiLSTM with CRF, GRAM-CNN, and BERT. On the NCBI corpus, our model recorded an F-score of 85.68% for the NER of diseases, showing an improvement of 1.50% over previous methods. Moreover, although BERT used transfer learning by incorporating more than 2.5 billion words, our system showed similar performance with BERT with an F-scores of 81.44% for gene NER on the GM corpus and a outperformed F-score of 86.44% for the NER of chemicals and diseases on the CDR corpus. We conclude that our method significantly improves performance on biomedical NER tasks.

Conclusion
The proposed approach is robust in recognizing biological entities in text.
Population genetic structure and habitat connectivity for jaguar (Panthera onca) conservation in Central Belize
Authors: Menchaca Angelica , Rossi Natalia A. , Froidevaux Jeremy , Dias-Freedman Isabela , Caragiulo Anthony , Wultsch Claudia , Harmsen Bart , Foster Rebecca , de la Torre J. Antonio , Medellin Rodrigo A. , Rabinowitz Salisa , Amato George 
Published: December 2019

AbstractBackgroundConnectivity among jaguar (Panthera onca) populations will ensure natural gene flow and the long-term survival of the species throughout its range. Jaguar conservation efforts have focused primarily on connecting suitable habitat in a broad-scale. Accelerated habitat reduction, human-wildlife conflict, limited funding, and the complexity of jaguar behaviour have proven challenging to maintain connectivity between populations effectively. Here, we used non-invasive genetic sampling and individual-based conservation genetic analyses to assess genetic diversity and levels of genetic connectivity between individuals in the Cockscomb Basin Wildlife Sanctuary and the Maya Forest Corridor. We used expert knowledge and scientific literature to develop models of landscape permeability based on circuit theory with fine-scale landscape features as ecosystem types, distance to human settlements and roads to predict the most probable jaguar movement across central Belize.ResultsWe used 12 highly polymorphic microsatellite loci to identify 50 individual jaguars. We detected high levels of genetic diversity across loci (HE = 0.61, HO = 0.55, and NA = 9.33). Using Bayesian clustering and multivariate models to assess gene flow and genetic structure, we identified one single group of jaguars (K = 1). We identified critical areas for jaguar movement that fall outside the boundaries of current protected areas in central Belize. We detected two main areas of high landscape permeability in a stretch of approximately 18 km between Sittee River Forest Reserve and Manatee Forest Reserve that may increase functional connectivity and facilitate jaguar dispersal from and to Cockscomb Basin Wildlife Sanctuary. Our analysis provides important insights on fine-scale genetic and landscape connectivity of jaguars in central Belize, an area of conservation concern.ConclusionsThe results of our study demonstrate high levels of relatively recent gene flow for jaguars between two study sites in central Belize. Our landscape analysis detected corridors of expected jaguar movement between the Cockscomb Basin Wildlife Sanctuary and the Maya Forest Corridor. We highlight the importance of maintaining already established corridors and consolidating new areas that further promote jaguar movement across suitable habitat beyond the boundaries of currently protected areas. Continued conservation efforts within identified corridors will further maintain and increase genetic connectivity in central Belize.
Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter
Authors: Chen Zhan-Heng , You Zhu-Hong , Li Li-Ping , Wang Yan-Bin , Qiu Yu , Hu Peng-Wei 
Published: December 2019

Abstract
Background
Identification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical proteins can interact with each other and they are the specific type of PPIs. More and more researchers draw attention to the SIPs detection, and several prediction model have been proposed, but there are still some problems. Hence, there is an urgent need to explore a efficient computational model for SIPs prediction.

Results
In this study, we developed an effective model to predict SIPs, called RP-FIRF, which merges the Random Projection (RP) classifier and Finite Impulse Response Filter (FIRF) together. More specifically, each protein sequence was firstly transformed into the Position Specific Scoring Matrix (PSSM) by exploiting Position Specific Iterated BLAST (PSI-BLAST). Then, to effectively extract the discriminary SIPs feature to improve the performance of SIPs prediction, a FIRF method was used on PSSM. The R’classifier was proposed to execute the classification and predict novel SIPs. We evaluated the performance of the proposed RP-FIRF model and compared it with the state-of-the-art support vector machine (SVM) on human and yeast datasets, respectively. The proposed model can achieve high average accuracies of 97.89 and 97.35% using five-fold cross-validation. To further evaluate the high performance of the proposed method, we also compared it with other six exiting methods, the experimental results demonstrated that the capacity of our model surpass that of the other previous approaches.

Conclusion
Experimental results show that self-interacting proteins are accurately well-predicted by the proposed model on human and yeast datasets, respectively. It fully show that the proposed model can predict the SIPs effectively and sufficiently. Thus, RP-FIRF model is an automatic decision support method which should provide useful insights into the recognition of SIPs.
A novel algorithm for alignment of multiple PPI networks based on simulated annealing
Authors: Hu Jialu , He Junhao , Li Jing , Gao Yiqun , Zheng Yan , Shang Xuequn 
Published: December 2019

AbstractProteins play essential roles in almost all life processes. The prediction of protein function is of significance for the understanding of molecular function and evolution. Network alignment provides a fast and effective framework to automatically identify functionally conserved proteins in a systematic way. However, due to the fast growing genomic data, interactions and annotation data, there is an increasing demand for more accurate and efficient tools to deal with multiple PPI networks. Here, we present a novel global alignment algorithm NetCoffee2 based on graph feature vectors to discover functionally conserved proteins and predict function for unknown proteins. To test the algorithm performance, NetCoffee2 and three other notable algorithms were applied on eight real biological datasets. Functional analyses were performed to evaluate the biological quality of these alignments. Results show that NetCoffee2 is superior to existing algorithms IsoRankN, NetCoffee and multiMAGNA++ in terms of both coverage and consistency. The binary and source code are freely available under the GNU GPL v3 license at https://github.com/screamer/NetCoffee2.
Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information
Authors: Li Yongjun , Klápště Jaroslav , Telfer Emily , Wilcox Phillip , Graham Natalie , Macdonald Lucy , Dungey Heidi S. 
Published: December 2019

AbstractBackgroundNon-key traits (NKTs) in radiata pine (Pinus radiataD. Don) refer to traits other than growth, wood density and stiffness, but still of interest to breeders. Branch-cluster frequency, stem straightness, external resin bleeding and internal checking are examples of such traits and are targeted for improvement in radiata pine research programmes. Genomic selection can be conducted before the performance of selection candidates is available so that generation intervals can be reduced. Radiata pine is a species with a long generation interval, which if reduced could significantly increase genetic gain per unit of time. The aim of this study was to evaluate the accuracy and predictive ability of genomic selection and its efficiency over traditional forward selection in radiata pine for the following NKTs: branch-cluster frequency, stem straightness, internal checking, and external resin bleeding.ResultsNine hundred and eighty-eight individuals were genotyped using exome capture genotyping by sequencing (GBS) and 67,168 single nucleotide polymorphisms (SNPs) used to develop genomic estimated breeding values (GEBVs) with genomic best linear unbiased prediction (GBLUP). The documented pedigree was corrected using a subset of 704 SNPs. The percentage of trio parentage confirmed was about 49% and about 50% of parents were re-assigned. The accuracy of GEBVs was 0.55–0.75 when using the documented pedigree and 0.61–0.80 when using the SNP-corrected pedigree. A higher percentage of additive genetic variance was explained and a higher predictive ability was observed when using the SNP-corrected pedigree than using the documented pedigree. With the documented pedigree, genomic selection was similar to traditional forward selection when assuming a generation interval of 17 years, but worse than traditional forward selection when assuming a generation interval of 14 years. After the pedigree was corrected, genomic selection led to 37–115% and 13–77% additional genetic gain over traditional forward selection when generation intervals of 17 years and 14 years were assumed, respectively.ConclusionIt was concluded that genomic selection with a pedigree corrected by SNP information was an efficient way of improving non-key traits in radiata pine breeding.