A comprehensive overview of grain development in Brachypodium distachyon variety Bd21

A detailed and comprehensive understanding of seed reserve accumulation is of great importance for agriculture and crop improvement strategies. This work is part of a research programme aimed at using Brachypodium distachyon as a model plant for cereal grain development and filling. The focus was on the Bd21-3 accession, gathering morphological, cytological, and biochemical data, including protein, lipid, sugars, starch, and cell-wall analyses during grain development. This study highlighted the existence of three main developmental phases in Brachypodium caryopsis and provided an extensive description of Brachypodium grain development. In the first phase, namely morphogenesis, the embryo developed rapidly reaching its final morphology about 18 d after fertilization (DAF). Over the same period the endosperm enlarged, finally to occupy 80% of the grain volume. During the maturation phase, carbohydrates were continuously stored, mainly in the endosperm, switching from sucrose to starch accumulation. Large quantities of β-glucans accumulated in the endosperm with local variations in the deposition pattern. Interestingly, new β-glucans were found in Brachypodium compared with other cereals. Proteins (i.e. globulins and prolamins) were found in large quantities from 15 DAF onwards. These proteins were stored in two different sub-cellular structures which are also found in rice, but are unusual for the Pooideae. During the late stage of development, the grain desiccated while the dry matter remained fairly constant. Brachypodium exhibits some significant differences with domesticated cereals. Beta-glucan accumulates during grain development and this cell wall polysaccharide is the main storage carbohydrate at the expense of starch

Multigas survey from low-T° fumaroles in a tropical environment.: Effects from internal and external forcing: example from La Soufriere de Guadeloupe (FWI).

International audienceFumarolic gas survey of dormant volcanoes is fundamental because the compositional and flux changes in gas emissions have actually been recognised as signals of unrest or even precursors of eruptions on several dormant volcanoes in hydrothermal unrest [1-5]. Here we report on the chemical compositions (CO2, H2S, SO2, H2) and mass fluxes of fumarolic gas emissions from the low-temperature (from 97° to 104°C) volcanic-hydrothermal system of La Soufrière de Guadeloupe (Lesser Antilles). These data, since 2017, are acquired from portable MultiGAS (measurements performed monthly) and two permanent MultiGAS stations (4 automated 20’ measurements per day). These MultiGAS data are discussed along with other geochemical and geophysical parameters monitored at OVSG, such as the complete chemical gas composition sampled by Giggenbach bottles, fumarole temperature and volcanic seismicity in order to track the deep-sourced magmatic signal and detect potential signs of unrest [6]. However, dealing with the MultiGAS data in a low-T fumarolic system in a tropical environment is not straightforward due to external forcing. Hence, interpretation of the observed chemical changes must consider the dynamics of (i) scrubbing processes by the hydrothermal system and the perched volcanic pond [7], (ii) rainfall and the groundwater circulation (i.e. rainy vs non-rainy seasons, extreme events), (iii) water-gas-rock interactions [7], (iv) plume condensation, (v) sulphur deposition and remobilization, and (vi) gas-atmosphere chemical interaction. [1] Giggenbach and Sheppard, 1989; [2] Symonds et al., 1994; [3] Hammouya et al., 1998; [4] De Moor et al., 2016; [5] Allard et al., 2014; [6] Moretti et al., submitted; [7] Symonds et al., 200

A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe

The classification of seismo-volcanic signals is performed manually at La Soufrière Volcano, which is time consuming and can be biased by subjectivity of the operator. We propose here a machine-learning-based model for classification of these signals, to handle large datasets and provide objective and reproducible results. To describe the properties of the signals, we used 104 statistical, entropy, and shape descriptor features computed from the time waveform, the spectrum, and the cepstrum. First, we trained a random forest classifier with a dataset provided by the Observatoire Volcanologique et Sismologique de Guadeloupe that consisted of 845 labeled events that were recorded from 2013 to 2018: 542 volcano-tectonic (VT); 217 Nested; and 86 long period (LP). We obtained an overalll accuracy of 72%. We determined that the VT class includes a variety of signals that cover the VT, Nested and LP classes. After visual inspection of the waveforms and spectral characteristics of the data set, we introduced two new classes: Hybrid and Tornillo. A new random forest classifier was trained with this new information, and we obtained a much better overall accuracy of 82%. The model is very good for recognition of all event classes, except Hybrid events (67% accuracy, 70% precision). Hybrid events are often considered to be a mix of VT and LP events. This can be explained by the nature of this class and the physical processes that include both fracturing and resonating components with different modal frequencies. By analyzing the feature weights and by training a model with the most important features, we show that a subset of the 14 best features is sufficient to obtain a performance that is close to that of the model with the whole feature set. However, these best features are different from the 13 best features obtained for another volcano in Peru, with only one feature common to both sets of best features. Therefore, the model is not universal and it must be trained for each volcano, or it is too specific to the one station used here

The 2018 unrest phase at La Soufrière of Guadeloupe (French West Indies) andesitic volcano: Scrutiny of a failed but prodromal phreatic eruption

After 25 years of gradual increase, volcanic unrest at La Soufrière of Guadeloupe reached its highest seismic energy level on 27 April 2018, with the largest felt volcano-tectonic (VT) earthquake (ML 4.1 or MW 3.7) recorded since the 1976–1977 phreatic eruptive crisis. This event marked the onset of a seismic swarm (180 events, 2 felt) occurring after three previous swarms on 3–6 January (70 events), 1 st February (30 events, 1 felt) and 16–17 April (140 events, 1 felt). Many events were hybrid VTs with long-period codas, located 2–4 km below the volcano summit and clustered within 2 km along a regional NW-SE fault cross-cutting La Soufrière. Elastic energy release increased with each swarm whereas inter-event time shortened. At the same time, summit fractures continued to open and thermal anomalies to extend. Summit fumarolic activity increased significantly until 20 April, with a maximum temperature of 111.4 °C and gas exit velocity of 80 m/s, before declining to ~95 °C and ~33 m/s on 25 April. Gas compositions revealed increasing C/S and CO2/CH4 ratios and indicate hydrothermal P-T conditions that reached the critical point of pure water. Repeated MultiGAS analysis of fumarolic plumes showed increased CO2/H2S ratios and SO2 contents associated with the reactivation of degassing fractures (T = 93 °C, H2S/SO2 ≈ 1). While no direct evidence of upward magma migration was detected, we attribute the above phenomena to an increased supply of deep magmatic fluids that heated and pressurized the La Soufrière hydrothermal system, triggering seismogenic hydro-fracturing, and probable changes in deep hydraulic properties (permeability) and drainage pathways, which ultimately allowed the fumarolic fluxes to lower. Although this magmatic fluid injection was modulated by the hydrothermal system, the unprecedented seismic energy release and the critical point conditions of hydrothermal fluids suggest that the 2018 sequence of events can be regarded as a failed phreatic eruption. Should a similar sequence repeat, we warn that phreatic explosive activity could result from disruption of the shallow hydrothermal system that is currently responsible for 3–9 mm/y of nearly radial horizontal displacements within 1 km from the dome. Another potential hazard is partial collapse of the dome's SW flank, already affected by basal spreading above a detachment surface inherited from past collapses. Finally, the increased magmatic fluid supply evidenced by geochemical indicators in 2018 is compatible with magma replenishment of the 6–7 km deep crustal reservoir feeding La Soufrière and, therefore, with a potential evolution of the volcano's activity towards magmatic conditions