Phylogeny, diversification rates and species boundaries of Mesoamerican firs (Abies, Pinaceae) in a genus-wide context

The genus Abies is distributed discontinuously in the temperate and subtropical montane forests of the northern hemisphere. In Mesoamerica (Mexico and northern Central America), modern firs originated from the divergence of isolated mountain populations of migrating North American taxa. However, the number of ancestral species, migratory waves and diversification speed of these taxa is unknown. Here, variation in repetitive (Pt30204, Pt63718, and Pt71936) and non-repetitive (rbcL, rps18-rpl20 and trnL-trnF) regions of the chloroplast genome was used to reconstruct the phylogenetic relationships of the Mesoamerican Abies in a genus-wide context. These phylogenies and two fossil-calibrated scenarios were further employed to estimate divergence dates and diversification rates within the genus, and to test the hypothesis that, as in many angiosperms, conifers may exhibit accelerated speciation rates in the subtropics. All phylogenies showed five main clusters that mostly agreed with the currently recognized sections of Abies and with the geographic distribution of species. The Mesoamerican taxa formed a single group with species from southwestern North America of sections Oiamel and Grandis. However, populations of the same species were not monophyletic within this group. Divergence of this whole group dated back to the late Paleocene and the early Miocene depending on the calibration used, which translated in very low diversification rates (r_0.0 = 0.026-0.054, r_0.9 = 0.009-0.019 sp/Ma). Such low rates were a constant along the entire genus, including both the subtropical and temperate taxa. An extended phylogeographic analysis on the Mesoamerican clade indicated that Abies flinckii and A. concolor were the most divergent taxa, while the remaining species (A. durangensis, A. guatemalensis, A. hickelii, A. religiosa and A. vejari) formed a single group. Altogether, these results show that divergence of Mesoamerican firs coincides with a model of environmental stasis and decreased extinction rate, being probably prompted by a series of range expansions and isolation-by-distance.     

Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor

Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Genomic Best Linear Unbiased Prediction (G-BLUP, a ridge-regression type method) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal breeding populations. However, breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP. Using theory, simulations, and real data analysis, we study the performance of G-BLUP when applied to data from related and unrelated human subjects. Under perfect linkage disequilibrium (LD) between markers and QTL, the prediction R-squared (R²) of G-BLUP reaches trait-heritability, asymptotically. However, under imperfect LD between markers and QTL, prediction R² based on G-BLUP has a much lower upper bound. We show that the minimum decrease in prediction accuracy caused by imperfect LD between markers and QTL is given by (1−b)², where b is the regression of marker-derived genomic relationships on those realized at causal loci. For pairs of related individuals, due to within-family disequilibrium, the patterns of realized genomic similarity are similar across the genome; therefore b is close to one inducing small decrease in R². However, with distantly related individuals b reaches very low values imposing a very low upper bound on prediction R². Our simulations suggest that for the analysis of data from unrelated individuals, the asymptotic upper bound on R² may be of the order of 20% of the trait heritability. We show how PA can be enhanced with use of variable selection or differential shrinkage of estimates of marker effects.     

Time of Progression to Osteopenia/Osteoporosis in Chronically HIV-Infected Patients: Screening DXA Scan

Background

Algorithms for bone mineral density (BMD) management in HIV-infected patients are lacking. Our objective was to assess how often a dual-energy x-ray absorptiometry (DXA) scan should be performed by assessing time of progression to osteopenia/osteoporosis.

Methods

All DXA scans performed between 2000 and 2009 from HIV-infected patients with at least two DXA were included. Time to an event (osteopenia and osteoporosis) was assessed using the Kaplan–Meier method. Strata (tertiles) were defined using baseline minimum T scores. Differences between strata in time to an event were compared with the log-rank test.

Results

Of 391 patients (1,639 DXAs), 49.6% had osteopenia and 21.7% osteoporosis at their first DXA scan. Of the 112 (28.6%) with normal BMD, 35.7% progressed to osteopenia; median progression time was 6.7 years. These patients were stratified: “low-risk" (baseline minimum T score >−0.2 SD), “middle-risk" (between −0.2 and −0.6 SD), and “high-risk" (from −0.6 to −1 SD); median progression time to osteopenia was 8.7, >7.2, and 1.7 years, respectively (p<0.0001). Of patients with osteopenia, 23.7% progressed to osteoporosis; median progression time was >8.5 years. Progression time was >8.2 years in “low-risk" tertile (T score between −1.1 and −1.6 SD), >8.5 years in “middle-risk" (between −1.6 and −2), and 3.2 years in “high-risk" (from −2 to −2.4) (p<0.0001).

Conclusions

Our results may help to define the BMD testing interval. The lowest T score tertiles would suggest recommending a subsequent DXA in 1–2 years; in the highest tertiles, ≥6 years. Early intervention in patients with bone demineralization could reduce fracture–related morbidity/mortality.     

Enhancing estimation methods for integrating probability and nonprobability survey samples with machine-learning techniques. An application to a Survey on the impact of the COVID-19 pandemic in Spain

Web surveys have replaced Face-to-Face and computer assisted telephone interviewing (CATI) as the main mode of data collection in most countries. This trend was reinforced as a consequence of COVID-19 pandemic-related restrictions. However, this mode still faces significant limitations in obtaining probability-based samples of the general population. For this reason, most web surveys rely on nonprobability survey designs. Whereas probability-based designs continue to be the gold standard in survey sampling, nonprobability web surveys may still prove useful in some situations. For instance, when small subpopulations are the group under study and probability sampling is unlikely to meet sample size requirements, complementing a small probability sample with a larger nonprobability one may improve the efficiency of the estimates. Nonprobability samples may also be designed as a mean for compensating for known biases in probability-based web survey samples by purposely targeting respondent profiles that tend to be underrepresented in these surveys. This is the case in the Survey on the impact of the COVID-19 pandemic in Spain (ESPACOV) that motivates this paper. In this paper, we propose a methodology for combining probability and nonprobability web-based survey samples with the help of machine-learning techniques. We then assess the efficiency of the resulting estimates by comparing them with other strategies that have been used before. Our simulation study and the application of the proposed estimation method to the second wave of the ESPACOV Survey allow us to conclude that this is the best option for reducing the biases observed in our data.     

Ecological indicators based on trophic spectrum as a tool to assess ecosystems fishing impacts

Trophic indicators were used to compare two Malian freshwater reservoirs whose main differences are based on their different fishing pressures. Data were collected from a scientific survey of small-scale fishery landings conducted in 2002/2003. The trophic levels of fish species caught by artisanal fisheries are estimated from observations of scientific fishing or from the metabase Fishbase. Important differences exist in the trophic structure of both reservoirs. In Selingue (with high fishing pressure), very few top predators are found in the catches while the low trophic level fishes increase in total catches. In Manantali (with low fishing pressure), the top predators contribute twice as much to catches compared to Selingue. Hence, the mean trophic level of catches in Selingue (2.80) is lower than in Manantali (2.97). When comparing these results with those of study made in 1994/1995, it clearly appears that the effects of the fishing pressure in Selingue are obvious through a decrease of 0.12 in the mean trophic level while in Manantali this mean level has increased by 0.33 due to a recent strategic targeting of top predators. Trophic spectra seem to be relevant tools to characterize exploited fish communities from multi-specific and multi-gear small-scale fisheries catch data.     

��-Amyloid Causes Depletion of Synaptic Vesicles Leading to Neurotransmission Failure

Alzheimer disease is a progressive neurodegenerative brain disorder that leads to major debilitating cognitive deficits. It is believed that the alterations capable of causing brain circuitry dysfunctions have a slow onset and that the full blown disease may take several years to develop. Therefore, it is important to understand the early, asymptomatic, and possible reversible states of the disease with the aim of proposing preventive and disease-modifying therapeutic strategies. It is largely unknown how amyloid β-peptide (Aβ), a principal agent in Alzheimer disease, affects synapses in brain neurons. In this study, we found that similar to other pore-forming neurotoxins, Aβ induced a rapid increase in intracellular calcium and miniature currents, indicating an enhancement in vesicular transmitter release. Significantly, blockade of these effects by low extracellular calcium and a peptide known to act as an inhibitor of the Aβ-induced pore prevented the delayed failure, indicating that Aβ blocks neurotransmission by causing vesicular depletion. This new mechanism for Aβ synaptic toxicity should provide an alternative pathway to search for small molecules that can antagonize these effects of Aβ.     

Molecular physiology of glutamine and glutamate biosynthesis in developing seedlings of conifers

Nitrogen is a limiting factor in tree growth and development. The incorporation of ammonium ions in carbon skeletons is catalyzed by the sequential action of the enzymes glutamine synthetase (GS) and glutamate synthase (GOGAT). Most studies on nitrogen‐assimilating enzymes have been reported for annual crop plants. Knowledge of these enzymes in woody plants is much more limited, particularly at the molecular level. Here, we review current available information on glutamine/glutamate biosynthesis and chloroplast development in conifers.