Multimodel Predictive System for Carbon Dioxide Solubility in Saline Formation Waters

The prediction of carbon dioxide solubility in brine at conditions relevant to carbon sequestration (i.e., high temperature, pressure, and salt concentration (T-P-X)) is crucial when this technology is applied. Eleven mathematical models for predicting CO₂ solubility in brine are compared and considered for inclusion in a multimodel predictive system. Model goodness of fit is evaluated over the temperature range 304–433 K, pressure range 74–500 bar, and salt concentration range 0–7 <i>m</i> (NaCl equivalent), using 173 published CO₂ solubility measurements, particularly selected for those conditions. The performance of each model is assessed using various statistical methods, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Different models emerge as best fits for different subranges of the input conditions. A classification tree is generated using machine learning methods to predict the best-performing model under different T-P-X subranges, allowing development of a multimodel predictive system (MMoPS) that selects and applies the model expected to yield the most accurate CO₂ solubility prediction. Statistical analysis of the MMoPS predictions, including a stratified 5-fold cross validation, shows that MMoPS outperforms each individual model and increases the overall accuracy of CO₂ solubility prediction across the range of T-P-X conditions likely to be encountered in carbon sequestration applications

Data_Sheet_1_Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume.docx

ObjectiveSchizophrenia can be divided into deficient schizophrenia (DS) and non-deficient schizophrenia (NDS) according to the presence of primary and persistent negative symptoms. So far, there are few studies that have explored the differences in functional connectivity (FC) between the different subtypes based on the region of interest (ROI) from GMV (Gray matter volume), especially since the characteristics of brain networks are still unknown. This study aimed to investigate the alterations of functional connectivity between DS and NDS based on the ROI obtained by machine learning algorithms and differential GMV. Then, the relationships between the alterations and the clinical symptoms were analyzed. In addition, the thalamic functional connection imbalance in the two groups was further explored.MethodsA total of 16 DS, 31 NDS, and 38 health controls (HC) underwent resting-state fMRI scans, patient group will further be evaluated by clinical scales including the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS). Based on GMV image data, a support vector machine (SVM) is used to classify DS and NDS. Brain regions with high weight in the classification were used as seed points in whole-brain FC analysis and thalamic FC imbalance analysis. Finally, partial correlation analysis explored the relationships between altered FC and clinical scale in the two subtypes.ResultsThe relatively high classification accuracy is obtained based on the SVM. Compared to HC, the FC increased between the right inferior parietal lobule (IPL.R) bilateral thalamus, and lingual gyrus, and between the right inferior temporal gyrus (ITG.R) and the Salience Network (SN) in NDS. The FC between the right thalamus (THA.R) and Visual network (VN), between ITG.R and right superior occipital gyrus in the DS group was higher than that in HC. Furthermore, compared with NDS, the FC between the ITG.R and the left superior and middle frontal gyrus decreased in the DS group. The thalamic FC imbalance, which is characterized by frontotemporal-THA.R hypoconnectivity and sensory motor network (SMN)-THA.R hyperconnectivity was found in both subtypes. The FC value of THA.R and SMN was negatively correlated with the SANS score in the DS group but positively correlated with the SAPS score in the NDS group.ConclusionUsing an SVM classification method and based on an ROI from GMV, we highlighted the difference in functional connectivity between DS and NDS from the local to the brain network, which provides new information for exploring the neural physiopathology of the two subtypes of schizophrenic.</p

The evaluation of microsatellite primer pairs for different repeat classes.

<p>The evaluation of microsatellite primer pairs for different repeat classes.</p

Q-plot showing clustering of 336 tetraploid alfalfa genotypes based on analysis of genotypic data using STRUCTURE.

<p>Q-plot showing clustering of 336 tetraploid alfalfa genotypes based on analysis of genotypic data using STRUCTURE.</p

Dendrogram of 336 tetraploid alfalfa genotypes by NJ analysis.

<p>Colors in the dendrogram correspond to population structure as identified in structure analysis.</p

Summary frequencies of different SSR repeat motif types related to variation of repeat unit numbers in alfalfa EST-SSR loci.

<p>Summary frequencies of different SSR repeat motif types related to variation of repeat unit numbers in alfalfa EST-SSR loci.</p

Development and Characterization of Simple Sequence Repeat (SSR) Markers Based on RNA-Sequencing of <i>Medicago sativa</i> and <i>In silico</i> Mapping onto the <i>M. truncatula</i> Genome

<div><p>Sufficient codominant genetic markers are needed for various genetic investigations in alfalfa since the species is an outcrossing autotetraploid. With the newly developed next generation sequencing technology, a large amount of transcribed sequences of alfalfa have been generated and are available for identifying SSR markers by data mining. A total of 54,278 alfalfa non-redundant unigenes were assembled through the Illumina HiSeqTM 2000 sequencing technology. Based on 3,903 unigene sequences, 4,493 SSRs were identified. Tri-nucleotide repeats (56.71%) were the most abundant motif class while AG/CT (21.7%), AGG/CCT (19.8%), AAC/GTT (10.3%), ATC/ATG (8.8%), and ACC/GGT (6.3%) were the subsequent top five nucleotide repeat motifs. Eight hundred and thirty- seven EST-SSR primer pairs were successfully designed. Of these, 527 (63%) primer pairs yielded clear and scored PCR products and 372 (70.6%) exhibited polymorphisms. High transferability was observed for ssp <i>falcata</i> at 99.2% (523) and 71.7% (378) in <i>M. truncatula</i>. In addition, 313 of 527 SSR marker sequences were <i>in silico</i> mapped onto the eight <i>M. truncatula</i> chromosomes. Thirty-six polymorphic SSR primer pairs were used in the genetic relatedness analysis of 30 Chinese alfalfa cultivated accessions generating a total of 199 scored alleles. The mean observed heterozygosity and polymorphic information content were 0.767 and 0.635, respectively. The codominant markers not only enriched the current resources of molecular markers in alfalfa, but also would facilitate targeted investigations in marker-trait association, QTL mapping, and genetic diversity analysis in alfalfa.</p></div

Image_1_Genomic Prediction for 25 Agronomic and Quality Traits in Alfalfa (Medicago sativa).TIF

Agronomic and quality traits in alfalfa are very important to forage industry. Genomic prediction (GP) based on genotyping-by-sequencing (GBS) data could shorten the breeding cycles and accelerate the genetic gains of these complex traits, if they display moderate to high prediction accuracies. The aim of this study was to investigate the predictive potentials of these traits in alfalfa. A total of 322 genotypes from 75 alfalfa accessions were used for GP of the agronomic and quality traits, which were related to yield and nutrition value, respectively, using BayesA, BayesB, and BayesCπ methods. Ten-fold cross validation was used to evaluate the accuracy of GP represented by the correlation between genomic estimated breeding value (GEBV) and estimated breeding value (EBV). The accuracies ranged from 0.0021 to 0.6485 for different traits. For each trait, three GP methods displayed similar prediction accuracies. Among 15 quality traits, mineral element Ca had a moderate and the highest prediction accuracy (0.34). NDF digestibility after 48 h (NDFD 48 h) and 30 h (NDFD 30 h) and mineral element Mg had prediction accuracies varying from 0.20 to 0.25. Other traits, for example, fat and crude protein, showed low prediction accuracies (0.05 to 0.19). Among 10 agronomic traits, however, some displayed relatively high prediction accuracies. Plant height (PH) in fall (FH) had the highest prediction accuracy (0.65), followed by flowering date (FD) and plant regrowth (PR) with accuracies at 0.52 and 0.51, respectively. Leaf to stem ratio (LS), plant branch (PB), and biomass yield (BY) reached to moderate prediction accuracies ranging from 0.25 to 0.32. Our results revealed that a few agronomic traits, such as FH, FD, and PR, had relatively high prediction accuracies, therefore it is feasible to apply genomic selection (GS) for these traits in alfalfa breeding programs. Because of the limitations of population size and density of SNP markers, several traits displayed low accuracies which could be improved by a bigger reference population, higher density of SNP markers, and more powerful statistic tools.</p

Three-dimensional principal coordinate analysis (PCA) of 336 tetraploid alfalfa genotypes genotyped with SSR markers.

<p>Three-dimensional principal coordinate analysis (PCA) of 336 tetraploid alfalfa genotypes genotyped with SSR markers.</p

Data_Sheet_1_Apolipoprotein E ε4 Specifically Modulates the Hippocampus Functional Connectivity Network in Patients With Amnestic Mild Cognitive Impairment.docx

The presence of both apolipoprotein E (APOE) ε4 allele and amnestic mild cognitive impairment (aMCI) are considered to be risk factors for Alzheimer’s disease (AD). Numerous neuroimaging studies have suggested that the modulation of APOE ε4 affects intrinsic functional brain networks, both in healthy populations and in AD patients. However, it remains largely unclear whether and how ε4 allele modulates the brain’s functional network architecture in subjects with aMCI. Using resting-state functional magnetic resonance imaging (fMRI) and graph-theory approaches-functional connectivity strength (FCS), we investigate the topological organization of the whole-brain functional network in 28 aMCI ε4 carriers and 38 aMCI ε3ε3 carriers. In the present study, we first observe that ε4-related FCS increases in the right hippocampus/parahippocampal gyrus (HIP/PHG). Subsequent seed-based resting-state functional connectivity (RSFC) analysis revealed that, compared with the ε3ε3 carriers, the ε4 carriers had lower or higher RSFCs between the right HIP/PHG seed and the bilateral medial prefrontal cortex (MPFC) or the occipital cortex, respectively. Further correlation analyses have revealed that the FCS values in the right HIP/PHG and lower HIP/PHG-RSFCs with the bilateral MPFC were significantly correlated with the impairment of episodic memory and executive function in the aMCI ε4 carriers. Importantly, the logistic regression analysis showed that the HIP/PHG-RSFC with the bilateral MPFC predicted aMCI-conversion to AD. These findings suggest that the APOE ε4 allele may modulate the large-scale brain network in aMCI subjects, facilitating our understanding of how the entire assembly of the brain network reorganizes in response to APOE variants in aMCI. Further longitudinal studies need to be conducted, in order to examine whether these network measures could serve as primary predictors of conversion from aMCI ε4 carriers to AD.</p