Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch

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Famílies botàniques de plantes medicinals

Facultat de Farmàcia, Universitat de Barcelona. Ensenyament: Grau de Farmàcia, Assignatura: Botànica Farmacèutica, Curs: 2013-2014, Coordinadors: Joan Simon, Cèsar Blanché i Maria Bosch.Els materials que aquí es presenten són els recull de 175 treballs d’una família botànica d’interès medicinal realitzats de manera individual. Els treballs han estat realitzat per la totalitat dels estudiants dels grups M-2 i M-3 de l’assignatura Botànica Farmacèutica durant els mesos d’abril i maig del curs 2013-14. Tots els treballs s’han dut a terme a través de la plataforma de GoogleDocs i han estat tutoritzats pel professor de l’assignatura i revisats i finalment co-avaluats entre els propis estudiants. L’objectiu principal de l’activitat ha estat fomentar l’aprenentatge autònom i col·laboratiu en Botànica farmacèutica

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch

Silver birch (Betula pendula) is a pioneer boreal tree that can be induced to flower within 1 year. Its rapid life cycle, small (440-Mb) genome, and advanced germplasm resources make birch an attractive model for forest biotechnology. We assembled and chromosomally anchored the nuclear genome of an inbred B. pendula individual. Gene duplicates from the paleohexaploid event were enriched for transcriptional regulation, whereas tandem duplicates were overrepresented by environmental responses. Population resequencing of 80 individuals showed effective population size crashes at major points of climatic upheaval. Selective sweeps were enriched among polyploid duplicates encoding key developmental and physiological triggering functions, suggesting that local adaptation has tuned the timing of and cross-talk between fundamental plant processes. Variation around the tightly-linked light response genes PHYC and FRS10 correlated with latitude and longitude and temperature, and with precipitation for PHYC. Similar associations characterized the growth-promoting cytokinin response regulator ARR1, and the wood development genes KAK and MED5A.Peer reviewe

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Author Correction: Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch.

In the version of this article initially published, there was a mistake in the calculation of the nucleotide mutation rate per site per generation: 1 × 10−9 mutations per site per generation was used, whereas 9.5 × 10−9 was correct. This error affects the interpretation of population-size changes over time and their possible correspondence with known geological events, as shown in the original Fig. 4 and supporting discussion in the text, as well as details in the Supplementary Note. Neither the data themselves nor any other results are affected. Figure 4 has been revised accordingly. Images of the original and corrected figure panels are shown in the correction notice

Does an eHealth Intervention Reduce Complications and Healthcare Resources? A mHeart Single-Center Randomized-Controlled Trial

(1) Background: In the mHeart trial, we showed that an eHealth intervention, mHeart, improved heart transplant (HTx) recipients’ adherence to immunosuppressive therapy compared with the standard of care. Herein, we present the analysis assessing whether mHeart reduces complication frequency and healthcare resource use, and whether this reduction depends on patients’ adherence. (2) Methods: The mHeart was a single-center randomized-controlled trial (IIBSP-MHE-2014-55) in 134 adult HTx recipients (n = 71 intervention; n = 63 controls). The endpoints were mortality, complications, and resource use during follow-up (mean 1.6 ± 0.6 years). (3) Results: A significantly lower proportion of HTx recipients in mHeart had echocardiographic alteration (2.8% vs. 13.8%; p = 0.02), cardiovascular events (0.35% vs. 2.4%; p = 0.006), infections (17.2% vs. 56%; p = 0.03), and uncontrolled Hba1c (40.8% vs. 59.6%; p = 0.03) than controls. In addition, a significantly lower proportion of patients in the intervention needed hospital (32.4% vs. 56.9%; p = 0.004) or urgent admissions (16.9% vs. 41.4%; p = 0.002) and emergency room visits (50.7% vs. 69.0%; p = 0.03). Adherence status (measured by the self-reported SMAQ) influenced only controls regarding hospitalizations and emergency room visits. Differences were not significant on deaths (intervention 4.2% vs. control 9.5%; p = 0.4) (4) Conclusions: the mHeart strategy significantly reduced the occurrence of the studied post-transplant complications and the need for medical attention in HTx recipients. Adherence status influenced controls in their need for medical care

Does an eHealth Intervention Reduce Complications and Healthcare Resources? A mHeart Single-Center Randomized-Controlled Trial

(1) Background: In the mHeart trial, we showed that an eHealth intervention, mHeart, improved heart transplant (HTx) recipients’ adherence to immunosuppressive therapy compared with the standard of care. Herein, we present the analysis assessing whether mHeart reduces complication frequency and healthcare resource use, and whether this reduction depends on patients’ adherence. (2) Methods: The mHeart was a single-center randomized-controlled trial (IIBSP-MHE-2014-55) in 134 adult HTx recipients (n = 71 intervention; n = 63 controls). The endpoints were mortality, complications, and resource use during follow-up (mean 1.6 ± 0.6 years). (3) Results: A significantly lower proportion of HTx recipients in mHeart had echocardiographic alteration (2.8% vs. 13.8%; p = 0.02), cardiovascular events (0.35% vs. 2.4%; p = 0.006), infections (17.2% vs. 56%; p = 0.03), and uncontrolled Hba1c (40.8% vs. 59.6%; p = 0.03) than controls. In addition, a significantly lower proportion of patients in the intervention needed hospital (32.4% vs. 56.9%; p = 0.004) or urgent admissions (16.9% vs. 41.4%; p = 0.002) and emergency room visits (50.7% vs. 69.0%; p = 0.03). Adherence status (measured by the self-reported SMAQ) influenced only controls regarding hospitalizations and emergency room visits. Differences were not significant on deaths (intervention 4.2% vs. control 9.5%; p = 0.4) (4) Conclusions: the mHeart strategy significantly reduced the occurrence of the studied post-transplant complications and the need for medical attention in HTx recipients. Adherence status influenced controls in their need for medical care