bluuebunny/crossref_metadata_2025_split huggingface dataset:
Verification of Convection-Allowing WRF Model Forecasts of the Planetary Boundary Layer Using Sounding Observations
Authors: Coniglio Michael C. , Correia James , Marsh Patrick T. , Kong Fanyou 
Published: January 2013

Abstract
This study evaluates forecasts of thermodynamic variables from five convection-allowing configurations of the Weather Research and Forecasting Model (WRF) with the Advanced Research core (WRF-ARW). The forecasts vary only in their planetary boundary layer (PBL) scheme, including three “local” schemes [Mellor–Yamada–Janjić (MYJ), quasi-normal scale elimination (QNSE), and Mellor–Yamada–Nakanishi–Niino (MYNN)] and two schemes that include “nonlocal” mixing [the asymmetric cloud model version 2 (ACM2) and the Yonei University (YSU) scheme]. The forecasts are compared to springtime radiosonde observations upstream from deep convection to gain a better understanding of the thermodynamic characteristics of these PBL schemes in this regime. The morning PBLs are all too cool and dry despite having little bias in PBL depth (except for YSU). In the evening, the local schemes produce shallower PBLs that are often too shallow and too moist compared to nonlocal schemes. However, MYNN is nearly unbiased in PBL depth, moisture, and potential temperature, which is comparable to the background North American Mesoscale model (NAM) forecasts. This result gives confidence in the use of the MYNN scheme in convection-allowing configurations of WRF-ARW to alleviate the typical cool, moist bias of the MYJ scheme in convective boundary layers upstream from convection. The morning cool and dry biases lead to an underprediction of mixed-layer CAPE (MLCAPE) and an overprediction of mixed-layer convective inhibition (MLCIN) at that time in all schemes. MLCAPE and MLCIN forecasts improve in the evening, with MYJ, QNSE, and MYNN having small mean errors, but ACM2 and YSU having a somewhat low bias. Strong observed capping inversions tend to be associated with an underprediction of MLCIN in the evening, as the model profiles are too smooth. MLCAPE tends to be overpredicted (underpredicted) by MYJ and QNSE (MYNN, ACM2, and YSU) when the observed MLCAPE is relatively small (large).
Prediction of B-cell epitopes using evolutionary information and propensity scales
Authors: Lin Scott Yi-Heng , Cheng Cheng-Wei , Su Emily Chia-Yu 
Published: January 2013

Abstract

Background
Development of computational tools that can accurately predict presence and location of B-cell epitopes on pathogenic proteins has a valuable application to the field of vaccinology. Because of the highly variable yet enigmatic nature of B-cell epitopes, their prediction presents a great challenge to computational immunologists.

Methods
We propose a method, BEEPro (B-cell e pitope prediction by e volutionary information and pro pensity scales), which adapts a linear averaging scheme on 16 properties using a support vector machine model to predict both linear and conformational B-cell epitopes. These 16 properties include position specific scoring matrix (PSSM), an amino acid ratio scale, and a set of 14 physicochemical scales obtained via a feature selection process. Finally, a three-way data split procedure is used during the validation process to prevent over-estimation of prediction performance and avoid bias in our experiment results.

Results
In our experiment, first we use a non-redundant linear B-cell epitope dataset curated by Sollner et al. for feature selection and parameter optimization. Evaluated by a three-way data split procedure, BEEPro achieves significant improvement with the area under the receiver operating curve (AUC) = 0.9987, accuracy = 99.29%, mathew's correlation coefficient (MCC) = 0.9281, sensitivity = 0.9604, specificity = 0.9946, positive predictive value (PPV) = 0.9042 for the Sollner dataset. In addition, the same parameters are used to evaluate performance on other independent linear B-cell epitope test datasets, BEEPro attains an AUC which ranges from 0.9874 to 0.9950 and an accuracy which ranges from 93.73% to 97.31%. Moreover, five-fold cross-validation on one benchmark conformational B-cell epitope dataset yields an accuracy of 92.14% and AUC of 0.9066.

Conclusions
Compared with other current models, our method achieves a significant improvement with respect to AUC, accuracy, MCC, sensitivity, specificity, and PPV. Thus, we have shown that an appropriate combination of evolutionary information and propensity scales with a support vector machine model can significantly enhance the prediction performance of both linear and conformational B-cell epitopes.
Prediction of peptides binding to MHC class I and II alleles by temporal motif mining
Authors: Meydan Cem , Otu Hasan H , Sezerman Osman Uğur 
Published: January 2013

AbstractBackgroundMHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently.ResultsThe prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II.ConclusionsTemporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths.
Identifying cross-category relations in gene ontology and constructing genome-specific term association networks
Authors: Peng Jiajie , Chen Jin , Wang Yadong 
Published: January 2013

Abstract

Background
Gene Ontology (GO) has been widely used in biological databases, annotation projects, and computational analyses. Although the three GO categories are structured as independent ontologies, the biological relationships across the categories are not negligible for biological reasoning and knowledge integration. However, the existing cross-category ontology term similarity measures are either developed by utilizing the GO data only or based on manually curated term name similarities, ignoring the fact that GO is evolving quickly and the gene annotations are far from complete.

Results
In this paper we introduce a new cross-category similarity measurement called CroGO by incorporating genome-specific gene co-function network data. The performance study showed that our measurement outperforms the existing algorithms. We also generated genome-specific term association networks for yeast and human. An enrichment based test showed our networks are better than those generated by the other measures.

Conclusions
The genome-specific term association networks constructed using CroGO provided a platform to enable a more consistent use of GO. In the networks, the frequently occurred MF-centered hub indicates that a molecular function may be shared by different genes in multiple biological processes, or a set of genes with the same functions may participate in distinct biological processes. And common subgraphs in multiple organisms also revealed conserved GO term relationships. Software and data are available online at http://www.msu.edu/~jinchen/CroGO.
Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints
Authors: Ren Shaogang , Zeng Bo , Qian Xiaoning 
Published: January 2013

Abstract

Background
Optimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for genome-scale dynamic analysis at steady states. Based on FBA, many current optimization methods for targeted bio-productions have been developed under the maximum cell growth assumption. The optimization problem to derive gene knockout strategies recently has been formulated as a bi-level programming problem in OptKnock for maximum targeted bio-productions with maximum growth rates. However, it has been shown that knockout mutants in fact reach the steady states with the minimization of metabolic adjustment (MOMA) from the corresponding wild-type strains instead of having maximal growth rates after genetic or metabolic intervention. In this work, we propose a new bi-level computational framework--MOMAKnock--which can derive robust knockout strategies under the MOMA flux distribution approximation.

Methods
In this new bi-level optimization framework, we aim to maximize the production of targeted chemicals by identifying candidate knockout genes or reactions under phenotypic constraints approximated by the MOMA assumption. Hence, the targeted chemical production is the primary objective of MOMAKnock while the MOMA assumption is formulated as the inner problem of constraining the knockout metabolic flux to be as close as possible to the steady-state phenotypes of wide-type strains. As this new inner problem becomes a quadratic programming problem, a novel adaptive piecewise linearization algorithm is developed in this paper to obtain the exact optimal solution to this new bi-level integer quadratic programming problem for MOMAKnock.

Results
Our new MOMAKnock model and the adaptive piecewise linearization solution algorithm are tested with a small E. coli core metabolic network and a large-scale iAF1260 E. coli metabolic network. The derived knockout strategies are compared with those from OptKnock. Our preliminary experimental results show that MOMAKnock can provide improved targeted productions with more robust knockout strategies.
Molecular docking analysis of 2009-H1N1 and 2004-H5N1 influenza virus HLA-B*4405-restricted HA epitope candidates: implications for TCR cross-recognition and vaccine development
Authors: Su Chinh TT , Schönbach Christian , Kwoh Chee-Keong 
Published: January 2013

Abstract

Background
The pandemic 2009-H1N1 influenza virus circulated in the human population and caused thousands deaths worldwide. Studies on pandemic influenza vaccines have shown that T cell recognition to conserved epitopes and cross-reactive T cell responses are important when new strains emerge, especially in the absence of antibody cross-reactivity. In this work, using HLA-B*4405 and DM1-TCR structure model, we systematically generated high confidence conserved 2009-H1N1 T cell epitope candidates and investigated their potential cross-reactivity against H5N1 avian flu virus.

Results
Molecular docking analysis of differential DM1-TCR recognition of the 2009-H1N1 epitope candidates yielded a mosaic epitope (KEKMNTEFW) and potential H5N1 HA cross-reactive epitopes that could be applied as multivalent peptide towards influenza A vaccine development. Structural models of TCR cross-recognition between 2009-H1N1 and 2004-H5N1 revealed steric and topological effects of TCR contact residue mutations on TCR binding affinity.

Conclusions
The results are novel with regard to HA epitopes and useful for developing possible vaccination strategies against the rapidly changing influenza viruses. Yet, the challenge of identifying epitope candidates that result in heterologous T cell immunity under natural influenza infection conditions can only be overcome if more structural data on the TCR repertoire become available.
Improved moderation for gene-wise variance estimation in RNA-Seq via the exploitation of external information
Authors: Patrick Ellis , Buckley Michael , Lin David Ming , Yang Yee Hwa 
Published: January 2013

Abstract

Background
The cost of RNA-Seq has been decreasing over the last few years. Despite this, experiments with four or less biological replicates are still quite common. Estimating the variances of gene expression estimates becomes both a challenging and interesting problem in these situations of low replication. However, with the wealth of microarray and other publicly available gene expression data readily accessible on public repositories, these sources of information can be leveraged to make improvements in variance estimation.

Results
We have proposed a novel approach called Tshrink+ for inferring differential gene expression through improved modelling of the gene-wise variances. Existing methods share information between genes of similar average expression by shrinking, or moderating, the gene-wise variances to a fitted common variance. We have been able to achieve improved estimation of the common variance by using gene-wise sample variances from external experiments, as well as gene length.

Conclusions
Using biological data we show that utilising additional external information can improve the modelling of the common variance and hence the calling of differentially expressed genes. These sources of additional information include gene length and gene-wise sample variances from other RNA-Seq and microarray datasets, of both related and seemingly unrelated tissue types. The results of this are promising, with our differential expression test, Tshrink+, performing favourably when compared to existing methods such as DESeq and edgeR when considering both gene ranking and sensitivity. These improved variance models could easily be implemented in both DESeq and edgeR and highlight the need for a database that offers a profile of gene variances over a range of tissue types and organisms.
A cross-sectional study of the prevalence and risk factors for hypertension in rural Nepali women
Authors: Khan Rumana J , Stewart Christine P , Christian Parul , Schulze Kerry J , Wu Lee , LeClerq Steven C , Khatry Subarna K , West Keith P 
Published: January 2013

Abstract

Background
The prevalence of hypertension is increasing in much of the South Asian region, including Nepal. This paper reports the prevalence and risk factors of hypertension and pre-hypertension among adult women in a rural community of Nepal.

Methods
Cross-sectional data on socioeconomic status (SES), lifestyle factors and blood pressure (BP) were collected from a cohort of 15,934 women in rural Nepal in 2006–08. Among a subsample (n = 1679), anthropometry and biomarkers of cardiovascular risk were measured.

Results
The mean age of women was 34.2 years (range 16.4-71.2 years). More than three percent (3.3%) had hypertension and 14.4% had pre-hypertension. In an adjusted analysis, lower SES, especially lower household farm assets and storage of food for long term consumption, was associated with increased odds of hypertension (OR = 1.14 for mid-level SES and OR = 1.40 for low SES; p for trend < 0.01). Smoking, alcohol use and not working outside the home were also associated with higher risk. In a subsample, both systolic BP (SBP) and diastolic BP (DBP) were positively associated with high triglycerides (SBP β = 4.1 mm Hg; DBP β =3.6 mm Hg), high HbA1c (SBP β = 14.0; DBP β = 9.2), raised fasting glucose (SBP β = 10.0; DBP β = 6.9), high BMI (SBP β = 6.7; DBP β = 5.1) and high waist circumference (SBP β = 6.2; DBP β = 5.3) after adjusting for potential confounders (p for all <0.01).

Conclusions
Although the prevalence of hypertension was low in this cohort, it was more prevalent among the poorer women and was strongly associated with other cardiovascular risks. These associations at a relatively young age may confer greater risk for cardiovascular disease among women in later life, indicating the need for interventions to reduce the progression from pre-hypertension to hypertension.
Micro-ultracapacitors with highly doped silicon nanowires electrodes
Authors: Thissandier Fleur , Pauc Nicolas , Brousse Thierry , Gentile Pascal , Sadki Saïd 
Published: January 2013

Abstract
Highly n-doped silicon nanowires (SiNWs) with several lengths have been deposited via chemical vapor deposition on silicon substrate. These nanostructured silicon substrates have been used as electrodes to build symmetrical micro-ultracapacitors. These devices show a quasi-ideal capacitive behavior in organic electrolyte (1 M NEt4BF4 in propylene carbonate). Their capacitance increases with the length of SiNWs on the electrode and has been improved up to 10 μFcm−2 by using 20 μm SiNWs, i.e., ≈10-fold bulk silicon capacitance. This device exhibits promising galvanostatic charge/discharge cycling stability with a maximum power density of 1.4 mW cm−2.
People, plants and health: a conceptual framework for assessing changes in medicinal plant consumption
Authors: Smith-Hall Carsten , Larsen Helle Overgaard , Pouliot Mariève 
Published: January 2013

AbstractBackgroundA large number of people in both developing and developed countries rely on medicinal plant products to maintain their health or treat illnesses. Available evidence suggests that medicinal plant consumption will remain stable or increase in the short to medium term. Knowledge on what factors determine medicinal plant consumption is, however, scattered across many disciplines, impeding, for example, systematic consideration of plant-based traditional medicine in national health care systems. The aim of the paper is to develop a conceptual framework for understanding medicinal plant consumption dynamics. Consumption is employed in the economic sense: use of medicinal plants by consumers or in the production of other goods.MethodsPubMed and Web of Knowledge (formerly Web of Science) were searched using a set of medicinal plant key terms (folk/peasant/rural/traditional/ethno/indigenous/CAM/herbal/botanical/phytotherapy); each search terms was combined with terms related to medicinal plant consumption dynamics (medicinal plants/health care/preference/trade/treatment seeking behavior/domestication/sustainability/conservation/urban/migration/climate change/policy/production systems). To eliminate studies not directly focused on medicinal plant consumption, searches were limited by a number of terms (chemistry/clinical/in vitro/antibacterial/dose/molecular/trial/efficacy/antimicrobial/alkaloid/bioactive/inhibit/antibody/purification/antioxidant/DNA/rat/aqueous). A total of 1940 references were identified; manual screening for relevance reduced this to 645 relevant documents. As the conceptual framework emerged inductively, additional targeted literature searches were undertaken on specific factors and link, bringing the final number of references to 737.ResultsThe paper first defines the four main groups of medicinal plant users (1. Hunter-gatherers, 2. Farmers and pastoralists, 3. Urban and peri-urban people, 4. Entrepreneurs) and the three main types of benefits (consumer, producer, society-wide) derived from medicinal plants usage. Then a single unified conceptual framework for understanding the factors influencing medicinal plant consumption in the economic sense is proposed; the framework distinguishes four spatial levels of analysis (international, national, local, household) and identifies and describes 15 factors and their relationships.ConclusionsThe framework provides a basis for increasing our conceptual understanding of medicinal plant consumption dynamics, allows a positioning of existing studies, and can serve to guide future research in the area. This would inform the formation of future health and natural resource management policies.
Fatty acid variability in three medicinal herbs of Panaxspecies
Authors: Zhang Xiao-Jing , Huang Li-Li , Cai Xiu-Jiang , Li Peng , Wang Yi-Tao , Wan Jian-Bo 
Published: January 2013

Abstract

Background
Fatty acid profiling has been widely used in the bacteria species identification, we hypothesized that fatty acid characteristics might discriminate the Panax herbs according to species. To test the hypothesis, fatty acids of Panax species, including Panax ginseng, Panax notoginseng and Panax quinquefolius, were characterized and compared using gas chromatography–mass spectrometry (GC-MS) followed by multivariate statistical analysis.

Results
The content of investigated 11 fatty acids, including myristic acid, pentadecanoic acid, palmitic acid, palmitoleic acid, heptadecanoic acid, stearic acid, oleic acid, linoleic acid, α-linolenic acid, arachidic acid and eicosadienoic acid, obviously varied among three species, suggesting each species has its own fatty acid pattern. Principal component analysis and hierarchical clustering analysis according to the absolute and relative contents of fatty acids, showed that 30 tested samples could be clearly differentiated according to the species.

Conclusions
These findings demonstrated that GC-MS-based fatty acid profiling coupled with multivariate statistical analysis provides reliable platform to classify these three Panax species, which is helpful for ensuring their safety and efficacy.
Expression of trypsin modulating oostatic factor (TMOF) in an entomopathogenic fungus increases its virulence towards Anopheles gambiae and reduces fecundity in the target mosquito
Authors: Kamareddine Layla , Fan Yanhua , Osta Mike A , Keyhani Nemat O 
Published: January 2013

Abstract

Background
Adult and larval mosquitoes regulate food digestion in their gut with trypsin modulating oostatic factor (TMOF), a decapeptide hormone synthesized by the ovaries and the neuroendocrine system. TMOF is currently being developed as a mosquitocide, however, delivery of the peptide to the mosquito remains a significant challenge. Entomopathogenic fungi offer a means for targeting mosquitoes with TMOF.

Findings
The efficacy of wild type and transgenic Beauveria bassiana strains expressing Aedes aegypti TMOF (Bb-Aa1) were evaluated against larvae and sugar- and blood-fed adult Anopheles gambiae mosquitoes using insect bioassays. Bb- Aa1 displayed increased virulence against larvae, and sugar and blood fed adult A. gambiae when compared to the wild type parent strain. Median lethal dose (LD50) values decreased by ~20% for larvae, and ~40% for both sugar and blood-fed mosquitoes using Bb-Aa1 relative to the wild type parent. Median lethal time (LT50) values were lower for blood-fed compared to sugar-fed mosquitoes in infections with both wild type and Bb-Aa1. However, infection using Bb-Aa1 resulted in 15% to 25% reduction in LT50 values for sugar- and blood fed mosquitoes, and ~27% for larvae, respectively, relative to the wild type parent. In addition, infection with Bb-Aa1 resulted in a dramatic reduction in fecundity of the target mosquitoes.

Conclusions

B. bassiana expressing Ae. aegypti TMOF exhibited increased virulence against A. gambiae compared to the wild type strain. These data expand the range and utility of entomopathogenic fungi expressing mosquito-specific molecules to improve their biological control activities against mosquito vectors of disease.