Development and eustatic control of an Upper Jurassic reef complex (Saint Germain-de-Joux, Eastern France)

This study consists of a sedimentological and diagenetical analysis of reef facies from the Upper Kimmeridgian (sensu gallico). The investigated deposits are situated in eastern France, about fifty kilometres west of the city of Geneva (Switzerland). The reef complex is a fine example of vertical development and facies differentiation. It is subdivided into two distinct sequences by a perforated hardground horizon and sand shoals. The onset of the first reef sequence is characterized by a pioneer growth stage followed by up to 20 m of reef-core and-flank facies. Corals forming the reef-core are typically the ramose variety ofCalamophylliopsis flabellum. The second reef sequence has a reef-core with an average thickness of about 5 m. Corals, however, display much more varied morphologies, and in some areas massive rudist (Heterodiceras) build-ups occur. Development of the second reef sequence was seriously weakened by a storm which produced a 2 m thick accumulation of coral rubble. A shallowing-upwards trend gradually leads to the formation of beach deposits, followed by a newly detected black-pebble horizon. Diagenesis is an important aspect of the reef complex. Especially noteworthy is the dolomitization of certain horizons. At the base of the reef formation, the passage of the phreatic mixing zone provoked invasive dolomitization in large irregular patches (probably deposits richer in Mg-calcite). Some of the beds above the black-pebble horizon, in particular a deposit of accumulated microbial mats, are also dolomitized. In this case, dolomitization is stratiform and is interpreted as having precipitated under conditions of evaporative pumping. The sedimentary record clearly shows the imprint of eustasy. The reef complex was initiated during a transgressive cycle and the hardground found between the two reef sequences is interpreted as a maximum flooding surface (mfs). At the top of the sequence, the horizon overlain by the black-pebble conglomerate is believed to represent the new sequence boundary SB140. Other significant features identified from the St. Germain-de-Joux deposits include the discovery of a new foraminifera,Troglotella incrustans, which is only marginally covered here but is the topic of another paper (Wernli &amp; Fookes, 1992); the subdivision of the first coralligenous level defined byPelletier (1953) into two reef sequences; and a proposition to redefine the ‘Calcaires de la Semine’ (Bernier, 1984). The investigations carried out in the past on the Kimmeridgian deposits in the area of St. Germain-de-Joux were mostly based on stratigraphy and palaeontology. These reefs are among the finest known in the Jura Mountains, but no thorough study had been made on their sedimentological aspects. The aim of this study is to fill this void and also to clarify the more confusing aspects of local stratigraphy (paper based onFookes, 1991).</p

3-D Bi-directional LSTM for Satellite Soil Moisture Downscaling

Soil moisture (SM) is a crucial parameter of hydrological processes as it affects the exchange of water and heat at the land/atmosphere interface. Regional hydrological applications (floods and modeling of small basins) and agricultural applications (irrigation and agricultural land mapping) require daily SM values having a spatial resolution of at least 1-km. This requirement is currently unmet by existing satellite missions. Notably, SM has variability over three dimensions. As such, accurate prediction of satellite SM requires multiple bidirectional spectra-spatiotemporal analyses. However, current state-of-the-art SM downscaling models cannot yet fulfill this requirement. This article proposes a new bidirectional long short-term memory (LSTM) model dubbed the 3-D bidirectional LSTM (3D-Bi-LSTM), which downscales the soil moisture active passive (SMAP) global daily 9-km SM to daily 1-km SM. In the proposed downscaling model, the region-specific soil moisture indices (SMIs) are first extracted using a covariance-adaptive convolutional neural network (CNN) to support the extraction of important distinctive information from multispectral data. Next, the CNN output is provided to the 3D-Bi-LSTM to perform the bidirectional analysis of spatial correlation within a feature and spectral correlation between features over multiple time instants. Experimental results demonstrate the proposed model outperforms the state-of-the-art networks. An ablation study, transferability assessment, and feature importance study further demonstrate the proposed 3D-Bi-LSTM's efficiency.</p

Dynamics of cholera epidemics from Benin to Mauritania.

The countries of West Africa are largely portrayed as cholera endemic, although the dynamics of outbreaks in this region of Africa remain largely unclear.To understand the dynamics of cholera in a major portion of West Africa, we analyzed cholera epidemics from 2009 to 2015 from Benin to Mauritania. We conducted a series of field visits as well as multilocus variable tandem repeat analysis and whole-genome sequencing analysis of V. cholerae isolates throughout the study region. During this period, Ghana accounted for 52% of the reported cases in the entire study region (coastal countries from Benin to Mauritania). From 2009 to 2015, we found that one major wave of cholera outbreaks spread from Accra in 2011 northwestward to Sierra Leone and Guinea in 2012. Molecular epidemiology analysis confirmed that the 2011 Ghanaian isolates were related to those that seeded the 2012 epidemics in Guinea and Sierra Leone. Interestingly, we found that many countries deemed "cholera endemic" actually suffered very few outbreaks, with multi-year lulls.This study provides the first cohesive vision of the dynamics of cholera epidemics in a major portion of West Africa. This epidemiological overview shows that from 2009 to 2015, at least 54% of reported cases concerned populations living in the three urban areas of Accra, Freetown, and Conakry. These findings may serve as a guide to better target cholera prevention and control efforts in the identified cholera hotspots in West Africa

Strains from Ghana, Togo, and Guinea situated on the maximum likelihood phylogenetic tree of the third wave of the seventh pandemic lineage of <i>V</i>. <i>cholerae</i>.

<p>The tree is based on the SNP differences across the whole core genome. An isolate from the first wave, Bangladesh 1975, was included as an outgroup to root the tree. An isolate from the second wave was also included (India 1990). The color of the branch tips indicates the country of origin, and the year of isolation is specified. The strains from Ghana, Togo, and Guinea are indicated using the same colors as in the Minimum Spanning Tree (Ghana in pink and red, Togo in orange and yellow, and Guinea in bright green). Labels A through G indicate the isolates from Ghana, Togo, and Guinea included on the MST in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006379#pntd.0006379.g003" target="_blank">Fig 3</a>. Scale is provided as the number of substitutions per variable site, and the SNPs are indicated on the branches.</p

Weekly evolution of cholera epidemics, rainfall levels, and the tested <i>V</i>. <i>cholerae</i> isolates in Greater Accra Region from 2011 to 2014.

<p>Suspected cholera cases are indicated in red (right y-axis), and rainfall is indicated in blue (left y-axis). The corresponding year is labeled on the x-axis. To integrate the epidemiological and MLVA/MST data, the three major MST clusters identified in Accra are indicated below the histogram of suspected cholera cases. GAR1 = the Ghana 2011 cluster (which gave rise to a few strains in 2012), GAR2 = the main Ghana 2012 cluster, and GAR3 = the Ghana 2014 cluster identified on the MST.</p

Minimum Spanning Tree based on the MLVA types of 257 <i>V</i>. <i>cholerae</i> isolates from several recent West African cholera outbreaks.

<p>Each MLVA type is represented by a node (and a unique number), and the size of the nodes reflects the number of isolates of each MLVA type. The solid lines indicate the most likely single locus variant, while dashed lines indicate the most likely double locus variant. The colors reflect the distinct country and year of isolate origin. Pie charts indicate strains from different time periods or countries displaying an identical MLVA type. The two strains represented by MLVA types #1 and #44 were isolated from environmental samples in Guinea (encircled in red). Labels A through G indicate the isolates from Ghana, Togo, and Guinea included on the phylogenic tree in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006379#pntd.0006379.g004" target="_blank">Fig 4</a>.</p

The number of suspected cholera cases reported in each country included in the study per year.

<p>The number of suspected cholera cases reported in each country included in the study per year.</p