An early Little Ice Age brackish water invasion along the south coast of the Caspian Sea (sediment of Langarud wetland) and its wider impacts on environment and people

Caspian Sea level has undergone significant changes through time with major impacts not only on the surrounding coasts, but also offshore. This study reports a brackish water invasion on the southern coast of the Caspian Sea constructed from a multi-proxy analysis of sediment retrieved from the Langarud wetland. The ground surface level of wetland is >6 m higher than the current Caspian Sea level (at -27.41 m in 2014) and located >11 km far from the coast. A sequence covering the last millennium was dated by three radiocarbon dates. The results from this new study suggest that Caspian Sea level rose up to at least -21.44 m (i.e. >6 m above the present water level) during the early Little Ice Age. Although previous studies in the southern coast of the Caspian Sea have detected a high-stand during the Little Ice Age period, this study presents the first evidence that this high-stand reached so far inland and at such a high altitude. Moreover, it confirms one of the very few earlier estimates of a high-stand at -21 m for the second half of the 14th century. The effects of this large-scale brackish water invasion on soil properties would have caused severe disruption to regional agriculture, thereby destabilizing local dynasties and facilitating a rapid Turko-Mongol expansion of Tamerlane’s armies from the east.N Ghasemi (INIOAS), V Jahani (Gilan Province Cultural Heritage and Tourism Organisation) and A Naqinezhad (University of Mazandaran), INQUA QuickLakeH project (no. 1227) and to the European project Marie Curie, CLIMSEAS-PIRSES-GA-2009-24751

German to Spanish translation of Einstein’s work on the formation of meanders in rivers

In 1926 Albert Einstein gave a clear explanation of the physical processes involved in the meander formation and evolution in open channels (Einstein, 1926). Although this work is far from being recognized as one of his greatest achievements, such as his annus mirabilis papers in 1905, he shows a truly remarkable didactic skills that make it easy to understand even to the non- specialist. In particular, a brilliant explanation of the tea leaf paradox can be found in this paper of 1926, presented as a simple experiment for clarifying the role of Earth rotation and flow curvature in the differential river banks erosion. This work deserves to be considered as a pioneering work that has laid a basic knowledge in currently very active research fields in fluvial geomorphology, estuarine physics, and hydraulic engineering. In response to the curiosity aroused and transmitted to the authors over the years by undergraduates and MSc. students, and also due to its historical and scientific significance, we present here the Spanish translation of Einstein’s original work published in German in 1926 in Die Naturwissenschaften (Einstein, 1926). Einstein’s drawings have not been interpreted, but just updated preserving their original spirit

Conservation assessments and Red Listing of the endemic Moroccan flora (monocotyledons)

Morocco constitutes an important centre of plant diversity and speciation in the Mediterranean Basin. However, numerous species are threatened by issues ranging from human activities to global climatic change. In this study, we present the conservation assessments and Red Listing of the endemic Moroccan monocotyledons according to International Union for Conservation of Nature (IUCN) criteria and categories. For each species, we include basic taxonomic information, local names and synonyms, uses, a distribution map, extent of occurrence, area of occupancy, population size and trend, a description of habitats and ecological requirements, and a discussion of the threats affecting the species and habitats. We assessed the threatened status of the endemic Moroccan monocotyledons at the species level (59 species) using the IUCN Red List criteria and categories (Version 3.1). This study shows the high extinction risk to the Moroccan monocotyledon flora, with 95% of threatened species (20% Critically Endangered, 50% Endangered, 25% Vulnerable) and only 5% not threatened (2% Near Threatened and 3% Least Concern). The flora is thus of conservation concern, which is poorly recognized, both nationally and internationally. The study presents the first part and so far the only national IUCN Red Data List for a large group of Moroccan plants, and thus provides an overview of the threatened Moroccan flora. This IUCN Red List is an important first step towards the recognition of the danger to Moroccan biodiversity hotspots, conservation of threatened species and the raising of public awareness at national and international levels

Partial Trisomy 1q41 Syndrome Delineated by Whole Genomic Array Comparative Genome Hybridization

Partial trisomy 1q syndrome is a rare chromosomal abnormality. We report on a male infant with 46,XY,der(11)t(1;11)(q41;p15.5) due to unbalanced segregation of the maternal reciprocal balanced translocation 46,XX,t(1;11)(q41;p15.5). The baby presented with a mild phenotype, characterized by a triangular face, almond-shaped eyes, low ears, short stature with relatively long legs, and mild psychomotor retardation. We utilized whole genomic array comparative genome hybridization (CGH) with 4,000 selected bacterial artificial chromosomes (BACs) to define the chromosomal breakpoints and to delineate the extent of the partial trisomy in more detail. To our knowledge, this is the first case of nearly pure "partial trisomy 1q41" defined by whole genomic array CGH

Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning

To achieve state-of-the-art jet energy resolution for Particle Flow, sophisticated energy clustering algorithms must be developed that can fully exploit available information to separate energy deposits from charged and neutral particles. Three published neural network-based shower separation models were applied to simulation and experimental data to measure the performance of the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL) technological prototype in distinguishing the energy deposited by a single charged and single neutral hadron for Particle Flow. The performance of models trained using only standard spatial and energy and charged track position information from an event was compared to models trained using timing information available from AHCAL, which is expected to improve sensitivity to shower development and, therefore, aid in clustering. Both simulation and experimental data were used to train and test the models and their performances were compared. The best-performing neural network achieved significantly superior event reconstruction when timing information was utilised in training for the case where the charged hadron had more energy than the neutral one, motivating temporally sensitive calorimeters. All models under test were observed to tend to allocate energy deposited by the more energetic of the two showers to the less energetic one. Similar shower reconstruction performance was observed for a model trained on simulation and applied to data and a model trained and applied to data

Software Compensation for Highly Granular Calorimeters using Machine Learning

A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied

Software compensation for highly granular calorimeters using machine learning

A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied