Allometric Models for Estimating Aboveground Biomass in Short Rotation Crops of Acacia Species in Two Different Sites in Chile

We evaluated the ability of different allometric models to estimate the biomass production of short-rotation woody crops of Acacia dealbata, A. mearnsii and A. melanoxylon. Models considered the adjustment and validation of biomass functions and biological restrictions, such as the use of additive components of the biomass (stem, branches, and leaves). Adjustments of linear and nonlinear models of the three acacia species—established in two locations and of three densities in southern Chile—were utilized. Systems of equations were adjusted to guarantee the addition of the biomass components and the trees’ total biomass. The selection of models was performed based on their goodness of fit and predictive quality. Methods that accounted for the correlation between biomass components granted an additively consistent equations system with efficient estimates and reliable prediction intervals.

Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues

Non-synonymous single nucleotide polymorphisms (nsSNPs) may result in pathogenic changes that are associated with human diseases. Accurate prediction of these deleterious nsSNPs is in high demand. The existing predictors of deleterious nsSNPs secure modest levels of predictive performance, leaving room for improvements. We propose a new sequence-based predictor, DMBS, which addresses the need to improve the predictive quality. The design of DMBS relies on the observation that the deleterious mutations are likely to occur at the highly conserved and functionally important positions in the protein sequence. Correspondingly, we introduce two innovative components. First, we improve the estimates of the conservation computed from the multiple sequence profiles based on two complementary databases and two complementary alignment algorithms. Second, we utilize putative annotations of functional/binding residues produced by two state-of-the-art sequence-based methods. These inputs are processed by a random forests model that provides favorable predictive performance when empirically compared against five other machine-learning algorithms. Empirical results on four benchmark datasets reveal that DMBS achieves AUC > 0.94, outperforming current methods, including protein structure-based approaches. In particular, DMBS secures AUC = 0.97 for the SNPdbe and ExoVar datasets, compared to AUC = 0.70 and 0.88, respectively, that were obtained by the best available methods. Further tests on the independent HumVar dataset shows that our method significantly outperforms the state-of-the-art method SNPdryad. We conclude that DMBS provides accurate predictions that can effectively guide wet-lab experiments in a high-throughput manner.

A comparative study of permeability prediction for Eocene sandstones - Part 1: Application of modified Swanson models to MICP and NMR data

The mercury injection capillary pressure (MICP) method and nuclear magnetic resonance (NMR) relaxometry provide insight into the pore radius distribution (PRD) either of pore throats (MICP) or pore bodies (NMR) of rocks. A variety of permeability (k) prediction models is based on the knowledge of the PRD. We evaluate the quality of k-prediction models using a sample set of Eocene sandstones with known values of measured permeability. The Swanson method relates the apex point of the capillary pressure curve to k. Although this widely acknowledged method uses only a single point of the PRD, it provides a predictive quality with an average ratio between measured and predicted permeability lower than a factor 3. The pore throat radius of the apex point proves to be a good proxy of the effective hydraulic radius. We demonstrate that an improved k prediction can be achieved if a larger section of the PRD is considered in the proposed generalized model. Using reliable values of surface relaxivity, the NMR relaxation time distribution is transformed into a PRD. We show that a characteristic apex point can be determined from NMR data, too. This characteristic point enables a good k prediction for the set of Eocene sandstone samples. In contrast to MICP, the predictive quality cannot be improved by applying an integration over a larger section of the PRD. Further test with samples of different pore structure and lithology should demonstrate the potential of the proposed models.

Modulation of the Activity and Regioselectivity of a Glycosidase: Development of a Convenient Tool for the Synthesis of Specific Disaccharides

The synthesis of disaccharides, particularly those containing hexofuranoside rings, requires a large number of steps by classical chemical means. The use of glycosidases can be an alternative to limit the number of steps, as they catalyze the formation of controlled glycosidic bonds starting from simple and easy to access building blocks; the main drawbacks are the yields, due to the balance between the hydrolysis and transglycosylation of these enzymes, and the enzyme-dependent regioselectivity. To improve the yield of the synthesis of β-d-galactofuranosyl-(1→X)-d-mannopyranosides catalyzed by an arabinofuranosidase, in this study we developed a strategy to mutate, then screen the catalyst, followed by a tailored molecular modeling methodology to rationalize the effects of the identified mutations. Two mutants with a 2.3 to 3.8-fold increase in transglycosylation yield were obtained, and in addition their accumulated regioisomer kinetic profiles were very different from the wild-type enzyme. Those differences were studied in silico by docking and molecular dynamics, and the methodology revealed a good predictive quality in regards with the regioisomer profiles, which is in good agreement with the experimental transglycosylation kinetics. So, by engineering CtAraf51, new biocatalysts were enabled to obtain the attractive central motif from the Leishmania lipophosphoglycan core with a higher yield and regioselectivity.

Electric power management in a microgrid analyzing photovoltaic arrays and a turbine-generator system

One of the priority objectives of microgrids is to achieve energy self-sufficiency, generally resorting to distributed generation sources and backup systems; however, they are usually connected to conventional electrical networks that ensure supply to the loads. Addressing this problem, this work presents a proposal (managing elements) to minimize the dependence of power from the external electrical system in the months of greatest demand and thus guarantee the supply of the other months. The proposed methodology compares two statistical techniques: central composition design with 20 simulated experimental replicas and regression with 28. In both cases, the monthly average purchased power is analyzed as a primary response and its standard deviation as a secondary. The study variables are seven photovoltaic arrays and the feed characteristics of the turbine-generator storage of the microgrid of the Center for Development of Renewable Energies (CEDER), belonging to the Center for Energy Research, Environmental and Technological (CIEMAT). The results, with high predictive quality supported by indexes of approach to the real values of solar radiation and the operation of the turbine-generator binomial, provide regions where CEDER has the possibility of increasing the capacities of solar systems and/or modifying the geometry of the mini-hydraulics supply according to your specific conditions.

FDR2-BD: A Fast Data Reduction Recommendation Tool for Tabular Big Data Classification Problems

In this paper, a methodological data condensation approach for reducing tabular big datasets in classification problems is presented, named FDR2-BD. The key of our proposal is to analyze data in a dual way (vertical and horizontal), so as to provide a smart combination between feature selection to generate dense clusters of data and uniform sampling reduction to keep only a few representative samples from each problem area. Its main advantage is allowing the model’s predictive quality to be kept in a range determined by a user’s threshold. Its robustness is built on a hyper-parametrization process, in which all data are taken into consideration by following a k-fold procedure. Another significant capability is being fast and scalable by using fully optimized parallel operations provided by Apache Spark. An extensive experimental study is performed over 25 big datasets with different characteristics. In most cases, the obtained reduction percentages are above 95%, thus outperforming state-of-the-art solutions such as FCNN_MR that barely reach 70%. The most promising outcome is maintaining the representativeness of the original data information, with quality prediction values around 1% of the baseline.

The Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2): A Psychometric Alternative to Measure and Explain Supernatural Experiences

This paper presents the English adaptation of the Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2), a questionnaire developed specifically for psychological assessment and prediction of anomalous phenomena. The sample consisted of 613 respondents from England (47.6% were women and 52.4% men). All of them were of legal age (mean = 34.5; standard deviation = 8.15). An exploratory factor analysis was applied, and three confirmatory factor models were adjusted. Omega coefficients and test-retest designs were used for reliability analysis. The MMSI-2 has a valid internal structure consisting of five macrofactors: Clinical Personality Tendencies (CPT), Anomalous Perceived Phenomena (APP), Incoherent Manipulations (IMA), Altered States of Consciousness (ASC), and Openness (OP). Omega coefficients for CPT and OP factors were low but acceptable. Furthermore, test-retest trials were excellent for all scales and factors. The psychological factors CPT, IMA, and ASC predicted 18.3% of the variance of anomalous experiences (APP). The authors concluded the English MMSI-2 was a valid and reliable test for the evaluation of anomalous phenomena but recommend that subsequent research reviews the predictive quality of the underlying model.

Fear Extinction and Predictive Trait-Like Inter-Individual Differences in Rats Lacking the Serotonin Transporter

Anxiety disorders are associated with a failure to sufficiently extinguish fear memories. The serotonergic system (5-hydroxytryptamine, 5-HT) with the 5-HT transporter (5-HTT, SERT) is strongly implicated in the regulation of anxiety and fear. In the present study, we examined the effects of SERT deficiency on fear extinction in a differential fear conditioning paradigm in male and female rats. Fear-related behavior displayed during acquisition, extinction, and recovery, was measured through quantification of immobility and alarm 22-kHz ultrasonic vocalizations (USV). Trait-like inter-individual differences in novelty-seeking, anxiety-related behavior, habituation learning, cognitive performance, and pain sensitivity were examined for their predictive value in forecasting fear extinction. Our results show that SERT deficiency strongly affected the emission of 22-kHz USV during differential fear conditioning. During acquisition, extinction, and recovery, SERT deficiency consistently led to a reduction in 22-kHz USV emission. While SERT deficiency did not affect immobility during acquisition, genotype differences started to emerge during extinction, and during recovery rats lacking SERT showed higher levels of immobility than wildtype littermate controls. Recovery was reflected in increased levels of immobility but not 22-kHz USV emission. Prominent sex differences were evident. Among several measures for trait-like inter-individual differences, anxiety-related behavior had the best predictive quality.