Literature Datasets Research Articles

Inferring disease-associated long non-coding RNAs using genome-wide tissue expression profiles.

Long non-coding RNAs (lncRNAs) are important regulators in wide variety of biological processes, which are linked to many diseases. Compared to protein-coding genes (PCGs), the association between diseases and lncRNAs is still not well studied. Thus, inferring disease-associated lncRNAs on a genome-wide scale has become imperative. In this study, we propose a machine learning-based method, DislncRF, which infers disease-associated lncRNAs on a genome-wide scale based on tissue expression profiles. DislncRF uses random forest models trained on expression profiles of known disease-associated PCGs across human tissues to extract general patterns between expression profiles and diseases. These models are then applied to score associations between lncRNAs and diseases. DislncRF was benchmarked against a gold standard dataset and compared to other methods. The results show that DislncRF yields promising performance and outperforms the existing methods. The utility of DislncRF is further substantiated on two diseases in which we find that top scoring candidates are supported by literature or independent datasets. https://github.com/xypan1232/DislncRF. Supplementary data are available at Bioinformatics online.

A dataset of scientific literature on floods, 1990 – 2017

A dataset of scientific literature on floods, 1990 – 2017

A PageRank-based heuristic for the minimization of open stacks problem.

The minimization of open stacks problem (MOSP) aims to determine the ideal production sequence to optimize the occupation of physical space in manufacturing settings. Most of current methods for solving the MOSP were not designed to work with large instances, precluding their use in specific cases of similar modeling problems. We therefore propose a PageRank-based heuristic to solve large instances modeled in graphs. In computational experiments, both data from the literature and new datasets up to 25 times fold larger in input size than current datasets, totaling 1330 instances, were analyzed to compare the proposed heuristic with state-of-the-art methods. The results showed the competitiveness of the proposed heuristic in terms of quality, as it found optimal solutions in several cases, and in terms of shorter run times compared with the fastest available method. Furthermore, based on specific graph densities, we found that the difference in the value of solutions between methods was small, thus justifying the use of the fastest method. The proposed heuristic is scalable and is more affected by graph density than by size.

Quantitative assessment of aberrant P16INK4a methylation in ovarian cancer: a meta-analysis based on literature and TCGA datasets.

Epigenetic alteration of P16INK4a is conventionally thought to induce the initiation of carcinoma. However, the role of P16INK4a methylation in ovarian cancer still remains controversial. Therefore, we performed a meta-analysis to further elucidate the relationship between P16INK4a promoter methylation and ovarian cancer. A total of 24 studies, including 20 on risk, 10 on clinicopathological features, and 3 on prognosis, were included in our meta-analysis. Our results indicated that the frequency of P16INK4a methylation in cancer tissues was significantly higher than normal tissues and low malignant potential tumor tissues (odds ratio [OR] =5.01, 95% CI=1.55–16.14; OR =1.88, 95% CI=1.10–3.19, respectively), but similar to benign tissues (OR =1.18, 95% CI=0.52–2.65). Furthermore, P16INK4a promoter methylation was not strongly correlated with age, clinical stage, tumor differentiation, or histological subtype in patients with ovarian cancer. Additionally, survival analysis showed that patients with P16INK4a promoter methylation had a shorter progression-free survival in univariate and multivariate Cox regression models (hazard ratio =1.68, 95% CI=1.26–2.24; hazard ratio =1.55, 95% CI=1.15–2.08; respectively). In The Cancer Genome Atlas datasets, the methylation levels of seven out of nine CpG sites were significantly increased in the ovarian tumor tissues compared with the normal tissues. In conclusion, the present meta-analysis suggests that P16INK4a promoter methylation may be useful in distinguishing malignant cancer from healthy ovarian tissues, and it may be a potential predictive marker for prognosis in patients with ovarian cancer.

Relative Contributions of Solubility and Mobility to the Stability of Amorphous Solid Dispersions of Poorly Soluble Drugs: A Molecular Dynamics Simulation Study.

Amorphous solid dispersions are considered a promising formulation strategy for the oral delivery of poorly soluble drugs. The limiting factor for the applicability of this approach is the physical (in)stability of the amorphous phase in solid samples. Minimizing the risk of reduced shelf life for a new drug by establishing a suitable excipient/polymer-type from first principles would be desirable to accelerate formulation development. Here, we perform Molecular Dynamics simulations to determine properties of blends of eight different polymer–small molecule drug combinations for which stability data are available from a consistent set of literature data. We calculate thermodynamic factors (mixing energies) as well as mobilities (diffusion rates and roto-vibrational fluctuations). We find that either of the two factors, mobility and energetics, can determine the relative stability of the amorphous form for a given drug. Which factor is rate limiting depends on physico-chemical properties of the drug and the excipients/polymers. The methods outlined here can be readily employed for an in silico pre-screening of different excipients for a given drug to establish a qualitative ranking of the expected relative stabilities, thereby accelerating and streamlining formulation development.

A bibliometric analysis of climate change adaptation based on massive research literature data

To clarify the current situation, hotspots, and development trends, in the field of climate change adaptation, we analysed a massive literature dataset from the Web of Science database by bibliometric method. By characterising the data about each publication, the result indicate that the field of climate change adaptation has entered a stage of rapid development. The USA occupies a leading position in terms of comprehensive strength with the largest publications output as well as a greater influence therewith. The most productive journal, author, and institution are Climatic Change, Ford JD from Canada, and The Chinese Academy of Science, respectively. Collaboration in this field continues to strengthen, but the growth rates at national levels are relatively low. In addition, the frequency and co-occurrence analysis of keywords reveals ten important research topics: climate change, adaptation, vulnerability, ecosystem, socio-economic system, agriculture, region, extreme event, mitigation, and sustainability, as the foci of climate change adaptation. “Vulnerability” is in a core position in all keywords with the strongest betweenness therein. The results of this work will help researchers clarify the current situation in climate change adaptation science but also provide guidance for future research.

Phylogenetic structure of communities between temperate and tropical regions: Exploring patterns through literature datasets

Latitudinal patterns of diversity are one of the most striking large-scale biological phenomena and several hypotheses have been proposed to explain them. Using data from literature-surveys we investigated how phylogenetic patterns in microorganisms, plants, and, metazoans communities differ between the tropical and temperate regions and then explored possible ecological and evolutionary process that could shape such patterns. Using the Net Relatedness Index, we analyzed data from 1486 biological communities, collected in 32 articles that considered the phylogenetic structure of biological communities. We found a pattern of phylogenetic clustering in both regions for microorganisms, while for plants we found phylogenetic clustering in temperate regions and phylogenetic overdispersion in the tropics. We did not detect a clear pattern of clustering or overdispersion in tropical or temperate regions in metazoans. From these patterns we explore different ecological and evolutionary processes that have shaped these communities over space and time.

Magnetostructural correlations in S = 1 trans-[Ni{(OPPh2)(EPPh2)N}2(dmso)2], E = S, Se, and related complexes

In this work, the structural and spectroscopic characterization of trans-[Ni{(OPPh2)(EPPh2)N)}2(dmso)2], E = S, Se, complexes is described. These Ni(II) complexes contain two chelating (O,E) = [(OPPh2)(EPPh2)N)]− ligands, as well as two dimethyl sulfoxide (dmso) molecules coordinated via their O atoms. X-ray crystallography studies revealed a highly anisotropic pseudo-octahedral trans-NiO4E2 coordination environment, extending the literature dataset of their trans-[Ni(O,E)2(sol)2], sol = dmf, thf, counterparts. Collectively considered, these six complexes contain similar coordination spheres, exhibiting, however, subtle structural differences due to the nature of the donor atom (E) and solvent molecule (sol) coordinated to Ni(II). The spin Hamiltonian parameters of S = 1 trans-[Ni(O,Se)2(dmso)2], E = S, Se, were accurately determined by high-frequency and -field EPR spectroscopy, applying a methodology already used for trans-[Ni(O,E)2(sol)2], sol = dmf, thf. Magnetostructural correlations are explored between all members of this sub-family of Ni(II) complexes.

Case Based Interpretation of Windows 10 Registry Forensics

With the advancement in computer technologies, cybercrimes advanced too. As in today’s world, the technology knowledge to attack a computer is less than ever, with the help of advanced tools that does most of the work. Digital forensic investigations are crucial in solving this type of crimes, and it must be done professionally. Computer registries play a big part in the digital forensic investigation, it can help find artifacts that are left by the cybercrimes, dates of the crimes on the computer system and the user at the time of the crime. In this research, interpretation of these artifacts is the main focus, committees and jurors are the focus of the interpretations of the registries. Two types of cases are subject to investigation in this research. BitTorrent clients’ use for downloading illegal o copyrighted content, and three clients are chosen for this digital forensic investigation uTorrent, Vuze and BitComet. Theft using USB storage devices is the second type of case, where there are three types of USB devices Mass Storage Class, Picture Transfer Protocol and Media Transfer Protocol, each type of USB devices leaves different artifacts behind during insertion and removal. A web based dashboard will be developed to help with the process of interpretation the artifacts found in the registry of the computer system. A categorization process of each cybercrime case will be conduct to evaluate the severity of the case depending on the artifacts found in the digital forensics investigation process. The research methodology will consist of three phases. The first phase will be information gathering including literature review, requirements gathering and dataset gathering for the research. Performing digital forensics analysis will be the second phase and it includes planning, identification and reconnaissance. Last phase will include result analysis and discussion.

Experimental and Numerical Study of Free-Surface Flows in a Corrugated Pipe

A new discharge computational model is proposed on the basis of the integration of the velocity profile across the flow cross-section in an internally corrugated pipe flowing partially full. The model takes into account the velocity profiles in the pressurised pipe to predict the flow rate under free-surface flow conditions. The model was evaluated through new laboratory experiments as well as a literature datasets. The results show that flow depth and pipe slope may affect the model accuracy; nevertheless, a prediction error smaller than 20% is expected from the model. Experimental results reveal the influence of the pipe slope and flow depth on the friction factor and the stage-discharge curves: the friction factor may increase with pipe slope, while it reduces as flow depth increases. Hence, a notable change of pipe slope may lead to the variation of the stage-discharge curve. A part of this study deals with numerical simulation of the velocity profiles and the stage-discharge curves. Using the Reynolds-Averaged Navier-Stokes (RANS) equations, numerical solutions were obtained to simulate four experimental tests, obtaining enough accurate results as to velocity profiles and water depths. The results of the simulated flow velocity were used to estimate the flow discharge, confirming the potential of numerical techniques for the prediction of stage-discharge curves.

Rational design of isonicotinic acid hydrazide derivatives with antitubercular activity: Machine learning, molecular docking, synthesis and biological testing.

The problem of designing new antitubercular drugs against multiple drug-resistant tuberculosis (MDR-TB) was addressed using advanced machine learning methods. As there are only few published measurements against MDR-TB, we collected a large literature data set and developed models against the non-resistant H37Rv strain. The predictive accuracy of these models had a coefficient of determination q2 =.7-.8 (regression models) and balanced accuracies of about 80% (classification models) with cross-validation and independent test sets. The models were applied to screen a virtual chemical library, which was designed to have MDR-TB activity. The seven most promising compounds were identified, synthesized and tested. All of them showed activity against the H37Rv strain, and three molecules demonstrated activity against the MDR-TB strain. The docking analysis indicated that the discovered molecules could bind enoyl reductase, InhA, which is required in mycobacterial cell wall development. The models are freely available online (http://ochem.eu/article/103868) and can be used to predict potential anti-TB activity of new chemicals.

An efficient matheuristic for the robust multiple allocation p-hub median problem under polyhedral demand uncertainty

This paper addresses the robust multiple allocation p-hub median problem under polyhedral demand uncertainty. Three variants of polyhedral uncertainty models are used in the problem, namely the hose, the hybrid, and the budget uncertainty models. The problems are formulated as linear mixed integer programming problems and a Tabu Search (TS) based matheuristic approach is proposed to solve the three variants of the problem. Extensive computational experiments are conducted based on three well-known data sets in the hub location literature and the results show the capability of the proposed solution algorithm to obtain the optimal solutions in short computational times.

Meta-analysis of endophyte-infected tall fescue effects on cattle growth rates.

The objective of this study was to quantitatively summarize literature reporting endophyte-infected (Neotyphodium coenophialum) tall fescue (Festuca arundinacea) effects on cattle ADG. This meta-analysis evaluated endophyte infection level, climate, and forage yield using a literature dataset of 138 treatments from 20 articles. Three infection level measurements were tested: endophyte infection as a percentage of infected tillers (E%); ergovaline concentration in ppb ([E]); and total ergot alkaloid concentration ([TEA]). Three types of climate variables were used: base values (temperature, humidity, and relative humidity), climate indices (heat index and temperature-heat index [THI]), and novel climate variables accounting for duration of climate effects. Mixed effect models, weighted by 1/SEM, including a random effect of study were built for each factorial combination of measurement method and climate variable group. Because many studies were missing SEM, two datasets were used: one containing only data with SEM reported and one that also included missing-SEM data. For the complete-SEM dataset (CSD), models were weighted by 1/SEM. In the missing-SEM dataset (MSD) the mean reported 1/SEM was assigned as the weight for all missing SEM treatments. Although 18 initial models were created (2 × 3 × 3 factorial approach), the backward stepwise derivation resulted in models that included only endophyte infection level, suggesting a negative relationship between infection level and ADG. The CSD models predicted ADG to decrease 39 and 33 g/d with each increase of 100 ppb of [TEA] and [E], and by 39 g/d for each increase of 10% E%. In the MSD dataset, predicted ADG decreased by 39 and 33 g/d with each increase of 100 ppb of [TEA] and [E], and by 47 g/d for each increase of 10% E%. All relationships reported had P < 0.05. After visual inspection of the data, piecewise regression was used to identify an infection threshold (IT) of 60 ppb [E] and 11 E%, where the effect of infection level was constant on either side of the IT. The ADG was 40% and 49% greater for infection levels below the IT for [E] and E%, respectively. Across THI values in the analysis, ADG decreases ranged from 11.2% to 45.0% for cattle grazing endophyte-infected tall fescue compared to non-ergot alkaloid endophyte infected tall fescue. Pasture E%, [E], and [TEA] have a negative relationship with ADG in growing cattle, and increasing temperature decreases ADG when infection level is greater than the IT.

Reactor pressure vessel embrittlement: Insights from neural network modelling

Irradiation embrittlement of steel pressure vessels is an important consideration for the operation of current and future light water nuclear reactors. In this study we employ an ensemble of artificial neural networks in order to provide predictions of the embrittlement using two literature datasets, one based on US surveillance data and the second from the IVAR experiment. We use these networks to examine trends with input variables and to assess various literature models including compositional effects and the role of flux and temperature. Overall, the networks agree with the existing literature models and we comment on their more general use in predicting irradiation embrittlement.

Multidimensional BSS/WSS Criterion for Modified SBFS Feature Selection Method in Tumor Classification

To avoid the defect of BSS/WSS criterion, we propose a multidimensional BSS/WSS feature selection criterion and modify the sequential backward floating selection (SBFS) algorithm to deal with the case where the covariance matrix is singular in this study. Then, we use support vector machine (SVM) to classify the gene expression data based on the proposed feature selection algorithm. The performance of the proposed approach is compared with BSS/WSS criterion and some other popular methods in feature selection and classification via the wellknown colon cancer and prostate datasets in microarray literature, which demonstrates that the proposed criterion can take into account genes' joint discriminatory power, and the proposed feature selection method can obtain correct and informative gene subset for tumor classification.

Predicting Surface Tensions of Surfactant Solutions from Statistical Mechanics.

The importance of surfactants to various industries necessitates a predictive understanding of their surface tension and adsorption behavior in terms of molecular characteristics. Previous models are highly empirical, require fitting parameters, and have limited applicability at various temperatures. Here, we provide a surface tension model based on statistical mechanics that (1) is thermodynamically consistent, (2) provides a higher predictive power, wherein surface tension can be calculated for any tail length, concentration, and temperature from molecular parameters, and (3) provides a physical understanding of the important molecular interactions at play. This model is applicable to both nonionic and ionic surfactants, where the effects of the electric double layer have been taken into account in the latter case. For nonionic surfactants, we were able to extend our model to predict dynamic surface tension as well. We have validated our model with tensiometry experiments for various surfactants, concentrations, and temperatures. In addition, we have validated our model with a diverse set of literature data, wherein agreement within a few mN M-1 and a correct prediction of phase change behavior is shown. The model could enable a more informed design of surfactant systems and serve as the theoretical basis for theory on more complex surfactant systems such as mixtures.

Dealing with reciprocity in dynamic stochastic block models

A stochastic block model for dynamic network data is introduced, where directed relations among a set of nodes are observed at different time occasions and the blocks are represented by a sequence of latent variables following a Markov chain. Dyads are explicitly modeled conditional on the states occupied by both nodes involved in the relation. With respect to the approaches already available in the literature, the main focus is on reciprocity. In this regard, three different parameterizations are proposed in which: (i) reciprocity is allowed to depend on the blocks of the nodes in the dyad; (ii) reciprocity is assumed to be constant across blocks; and (iii) reciprocity is ruled out. The assumption of conditional independence between dyads (referred to different pairs of nodes and time occasions) given the latent blocks is always retained. Given the complexity of the model, inference on its parameters is based on a variational approach, where a lower bound of the log-likelihood function is maximized instead of the intractable full model log-likelihood. An approximate likelihood ratio test statistic is proposed which compares the value at convergence of this lower bound under different model specifications. This allows us to formally test for both the hypothesis of no reciprocity and that of constant reciprocity with respect to the latent blocks. The proposed approach is illustrated via a simulation study based on different scenarios. The application to two benchmark datasets in the social network literature is also proposed to illustrate the effectiveness of the proposal in studying reciprocity and identifying groups of nodes having a similar social behavior.

Improved Minimum Miscibility Pressure Prediction for Gas Injection Process in Petroleum Reservoir

Determination of gas–oil minimum miscibility conditions is one of the important design parameters to improve the displacement efficiency of the hydrocarbon reservoir during enhanced oil recovery with gas injection. In this work, a support vector regression (SVR) model is developed using experimental data to estimate the minimum miscibility pressure (MMP) for various reservoir fluids and injection gases. Experimental MMP data taken from the reliable literature were used as input. Each data point input includes methane and intermediate components mole percent, plus fraction properties and reservoir temperature related to reservoir fluid and CO2, H2S, N2 and intermediate mole fractions, and intermediate properties of the injected gas. Experimental MMP is regarded as the model output. The database contains 135 datasets, from which 125 datasets were used for model development, and the rest were used for model evaluation. Genetic algorithm was implemented to optimize the SVR model parameters. The proposed data-driven model was verified by statistical validation data. The model results illustrate a correlation coefficient (R2) of 0.999. In addition, the SVR results demonstrate the proposed model to be a fast tool and a robust approach to map input space to output features. The SVR model was compared to popular data-driven MMP estimation models as well. This comparison presents an acceptable accuracy relative to this estimation model. Finally, the presented model was evaluated against a comprehensive theoretical model of slim tube compositional simulation on a trusted literature dataset.

Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids.

In this paper, we investigate the use of decentralized blockchain mechanisms for delivering transparent, secure, reliable, and timely energy flexibility, under the form of adaptation of energy demand profiles of Distributed Energy Prosumers, to all the stakeholders involved in the flexibility markets (Distribution System Operators primarily, retailers, aggregators, etc.). In our approach, a blockchain based distributed ledger stores in a tamper proof manner the energy prosumption information collected from Internet of Things smart metering devices, while self-enforcing smart contracts programmatically define the expected energy flexibility at the level of each prosumer, the associated rewards or penalties, and the rules for balancing the energy demand with the energy production at grid level. Consensus based validation will be used for demand response programs validation and to activate the appropriate financial settlement for the flexibility providers. The approach was validated using a prototype implemented in an Ethereum platform using energy consumption and production traces of several buildings from literature data sets. The results show that our blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced.

Maize Yield and Planting Date Relationship: A Synthesis-Analysis for US High-Yielding Contest-Winner and Field Research Data.

For maize (Zea mays L.), early planting date could be of advantage to high yields but a review of planting date effect on high-yielding data is not yet available. Following this rationale, a synthesis-analysis was conducted from the farmer annual maize contest-winner data (n = 16171 data points; 2011–2016 period); cordially provided by the National Corn Growers Association and a scientific literature dataset collected from research publications since the last three decades. The main objectives of this study were to: (i) identify spatial yield variability within the high-yielding maize dataset; (ii) understand the impacts of planting date on yield variability; (iii) explore the effect of management practices on maize yield-planting date relationship, and (iv) utilize the yield-planting date dataset collected via farmer contest-winner as a benchmarking data to be compared to the compendium of scientific literature available for yield-planting date relationship for the primary US maize producing regions. Major findings of this study are: (i) significant correlation between planting date and latitude, (ii) maize yield was maximized when planting window was 89–106 day of the year (DOY) for the 30–35°N, 107–118 DOY for the 35–40°N, <119 DOY for 40–45°N, and <129 DOY for 45–50°N, and (iii) both, yield contest and literature datasets portrayed that planting date becomes a more relevant factor when planting late, presenting a relatively smaller planting window in high-compared to low-latitudes.