S wave velocity structure below central Mexico using high-resolution surface wave tomography

Shear wave velocity of the crust below central Mexico is estimated using surface wave dispersion measurements from regional earthquakes recorded on a dense, 500 km long linear seismic network. Vertical components of regional records from 90 well-located earthquakes were used to compute Rayleigh-wave group-velocity dispersion curves. A tomographic inversion, with high resolution in a zone close to the array, obtained for periods between 5 and 50 s reveals significant differences relative to a reference model, especially at larger periods (>30 s). A 2-D S wave velocity model is obtained from the inversion of local dispersion curves that were reconstructed from the tomographic solutions. The results show large differences, especially in the lower crust, among back-arc, volcanic arc, and fore-arc regions; they also show a well-resolved low-velocity zone just below the active part of the Trans Mexican Volcanic Belt (TMVB) suggesting the presence of a mantle wedge. Low densities in the back arc, inferred from the low shear wave velocities, can provide isostatic support for the TMVB

A Source Study of the Bhuj, India, Earthquake of 26 January 2001 (M_w 7.6)

We study the source time function (STF) and radiated seismic energy (E_R) of the M_w 7.6 Bhuj earthquake using the empirical Green's function (EGF) technique. Our estimations of the STF and E_R are based on teleseismic P waves and regional seismograms, respectively. We find that the STFs as a function of azimuth have a similar shape and nearly constant duration of 18 sec. This suggests that the rupture propagation was essentially radial. The STFs show a sharp rise in the first 6 sec. The E_R estimated from the EGF technique is 2.1 × 10^(23) erg. We find that E_R's computed from integration of corrected velocity-squared spectra of teleseismic P waves and regional seismograms are in excellent agreement with the ER obtained from the EGF technique. Since the seismic moment, M_0, is 3.4 × 10^(27) dyne cm, we obtain E_R/M_0 = 6.2 × 10^(-5). The radiation efficiency, η_R, during the Bhuj earthquake was low, about 0.23. The sharp rise of the STFs and η_R = 0.23 can be explained by Sato and Hirasawa's (1973) quasi-dynamic, circular source model with an effective stress of ∼ 300 bar and the ratio of rupture to shear-wave velocity, V_R/β, of ∼ 0.5. The corresponding estimate of slip velocity at the center of the fault is 156 cm/sec. V_R/β ∼ 0.5 is in reasonable agreement with the duration of the STF and the reported dimension of the aftershocks, as well as with the results of inversion of teleseismic body waves. The observations may also be explained by a frictional sliding model, with gradual frictional stress drop and significant dissipation of energy on the fault plane. This model requires an average dynamic stress drop of about 120 bar and V_R/β ∼ 0.7 to explain both the rapid rise in the first 6 sec of the STFs and, along with a static stress drop of 200 bar, the observed E_R/M_0. High static stress drop is a common feature of most crustal earthquakes in stable continental regions. An examination of the available data, however, does not suggest that most of them also have relatively low radiation efficiency

Predicting progression of cognitive decline to dementia using dyadic patterns of subjective reporting: evidence from the CompAS longitudinal study

ObjectiveTo analyze the validity of self and informant reports, depressive symptomatology, and some sociodemographic variables to predict the risk of cognitive decline at different follow-up times.MethodsA total of 337 participants over 50 years of age included in the CompAS and classified as Cognitively Unimpaired (CU), Subjective Cognitive Decline (SCD) and Mild Cognitive Impairment (MCI) groups were assessed at baseline and three follow-ups. A short version of the QAM was administered to assess the severity of subjective cognitive complaints (SCCs), and the GDS-15 was used to evaluate the depressive symptoms. At each follow-up assessment, participants were reclassified according to the stability, regression or progression of their conditions. Logistic regression analysis was used to predict which CU, SCD and MCI participants would remain stable, regress or progress at a 3rd follow-up by using self- and informant-reported complaints, depressive symptomatology, age and education at baseline and 2nd follow-ups as the predictive variables.ResultsOverall, self-reported complaints predicted progression between the asymptomatic and presymptomatic stages. As the objective deterioration increased, i.e., when SCD progressed to MCI or dementia, the SCCs reported by informants proved the best predictors of progression. Depressive symptomatology was also a predictor of progression from CU to SCD and from SCD to MCI.ConclusionA late increase in self-reported complaints make valid estimates to predict subjective decline at asymptomatic stages. However, an early increase in complaints reported by informants was more accurate in predicting objective decline from asymptomatic stages. Both, early and late decrease in self-reported complaints successfully predict dementia from prodromic stage. Only late decrease in self-reported complaints predict reversion from prodromic and pre-symptomatic stages

Genomic prediction in CIMMYT maize and wheat breeding programs

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.J Crossa, P Pérez, J Hickey, J Burgueño, L Ornella, J Cerón-Rojas, X Zhang, S Dreisigacker, R Babu, Y Li, D Bonnett and K Mathew

RTP801 regulates motor cortex synaptic transmission and learning.

BACKGROUND: RTP801/REDD1 is a stress-regulated protein whose upregulation is necessary and sufficient to trigger neuronal death in in vitro and in vivo models of Parkinson's and Huntington's diseases and is up regulated in compromised neurons in human postmortem brains of both neurodegenerative disorders. Indeed, in both Parkinson's and Huntington's disease mouse models, RTP801 knockdown alleviates motor-learning deficits. RESULTS: We investigated the physiological role of RTP801 in neuronal plasticity and we found RTP801 in rat, mouse and human synapses. The absence of RTP801 enhanced excitatory synaptic transmission in both neuronal cultures and brain slices from RTP801 knock-out (KO) mice. Indeed, RTP801 KO mice showed improved motor learning, which correlated with lower spine density but increased basal filopodia and mushroom spines in the motor cortex layer V. This paralleled with higher levels of synaptosomal GluA1 and TrkB receptors in homogenates derived from KO mice motor cortex, proteins that are associated with synaptic strengthening. CONCLUSIONS: Altogether, these results indicate that RTP801 has an important role modulating neuronal plasticity and motor learning. They will help to understand its role in neurodegenerative disorders where RTP801 levels are detrimentally upregulated

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17–34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important

Effect of a single nutritional intervention previous to a critical period of fat gain in university students with overweight and obesity: A randomized controlled trial

Indexación: ScopusBackground: the present study aimed to investigate the effects of a single nutritional preventive session previous to a critical period linked to fat gain in university students with overweightness and obesity, emulating a nutritional session of a public health system. Methods: In this single-blind randomized controlled trial, 23 students met all the criteria to be included (20.91 ± 2.52-year-old; 52.2% women) who were divided into two groups: intervention group (IG) and control group (CG). Fat mass (FM) by dual-energy X-ray absorptiometry (DXA), physical activity by accelerometry, feeding evaluation through three questionnaires, and a set of healthy lifestyle recommendations were evaluated before and after the national holidays (NH). Results: Our findings showed that FM increased significantly in the CG, but not in the IG (CG = 428.1g; IG = 321.9g; ∆ = 106.2g; p = 0.654 [95% CI = −379.57, 591.92]). However, no differences were found during the NH between them (Hedges’ g effect size = 0.19; p = 0.654). In addition, no statistical differences were observed between groups in feeding evaluations, the set of recommendations performed, and physical activity. Conclusion: a single preventive session before a critical period, using a similar counselling approach as used in the public health system, might not be enough to promote changes in eating and physical activity patterns and preventing fat gain in overweight/obese university students. Long-term interventions are a must. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.https://www.mdpi.com/1660-4601/17/14/514

Genome-based trait prediction in multi- environment breeding trials in groundnut

Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut