Plastid DNA is a major source of nuclear genome complexity and of RNA genes in the orphan crop moringa.

Background: Unlike Transposable Elements (TEs) and gene/genome duplication, the role of the so-called nuclear plastid DNA sequences (NUPTs) in shaping the evolution of genome architecture and function remains poorly studied. We investigate here the functional and evolutionary fate of NUPTs in the orphan crop Moringa oleifera (moringa), featured by the highest fraction of plastid DNA found so far in any plant genome, focusing on (i) any potential biases in their distribution in relation to specific nuclear genomic features, (ii) their contribution to the emergence of new genes and gene regions, and (iii) their impact on the expression of target nuclear genes.

Results: In agreement with their potential mutagenic effect, NUPTs are underrepresented among structural genes, although their overall transcription levels and broadness were only lower when involved exonic regions; the occurrence of plastid DNA generally did not result in a broader expression, except among those affected in introns by older NUPTs.

Spatial and temporal characterization of the rich fraction of plastid DNA present in the nuclear genome of Moringa oleifera reveals unanticipated complexity in NUPTs´ formation.

Background: Beyond the massive amounts of DNA and genes transferred from the protoorganelle genome to the nucleus during the endosymbiotic event that gave rise to the plastids, stretches of plastid DNA of varying size are still being copied and relocated to the nuclear genome in a process that is ongoing and does not result in the concomitant shrinking of the plastid genome. As a result, plant nuclear genomes feature small, but variable, fraction of their genomes of plastid origin, the so-called nuclear plastid DNA sequences (NUPTs). However, the mechanisms underlying the origin and fixation of NUPTs are not yet fully elucidated and research on the topic has been mostly focused on a limited number of species and of plastid DNA.

Probabilistic Models with Deep Neural Networks.

Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical physics, have enabled probabilistic modeling to overcome these limitations: (i) Approximate probabilistic inference is now possible over a broad class of probabilistic models containing a large number of parameters, and (ii) scalable inference methods based on stochastic gradient descent and distributed computing engines allow probabilistic modeling to be applied to massive data sets.

Analyzing Uncertainty in Complex Socio-Ecological Networks.

Socio-ecological systems are recognized as complex adaptive systems whose multiple interactions might change as a response to external or internal changes. Due to its complexity, the behavior of the system is often uncertain. Bayesian networks provide a sound approach for handling complex domains endowed with uncertainty.

Validated stability-indicating HPLC method for paricalcitol in pharmaceutical dosage form according to ICH guidelines: application to stability studies.

A stability-indicating HPLC method with diode array detection for the determination of paricalcitol, a synthetic vitamin D2 analog, was developed. Analytical parameters were studied according to International Conference on Harmonization guidelines. A C18 column (250 x 4.