Middle Palaeolithic find spots with Nubian cores from the Southern Negev and the Arava, Israel

This is a report of results from a cursory survey of several Middle Paleolithic find spots from the Arava, Israel, conducted as part of a broader collaboration between the Dead Sea and Arava Science Center and the Israel Antiquities Authority.  A series of find spots were recorded on the eastern flanks of the Zehiha hills and on the northern terraces of Wadi Paran. These finds consist of mostly Middle Paleolithic artifacts including Levallois centripetal, bidirectional and Nubian cores. The presence of Nubian cores within this technological constellation is of interest in light of recent discussions regarding archaeological markers of modern human dispersals out of Africa and feasible routes into Eurasia and Arabia. The Nubian core technology, a specific variant of the Levallois technology is found within a defined and continuous geographic region and is perceived as penecontemporaneous. Sites with a similar technological package are found to the east at Al-Jawf, within the Arabian Peninsula, as well as to the North-West, within the central Negev highlands, at the localities of Har Oded and H2. The distinctive technological characteristics, geographical extent and chronology advocate its use as a possible marker for human dispersals and interactions between Eastern Africa, the Nile Valley, the southern Levant and Arabia

Heinrich Event 2 (ca. 24 ka BP) as a chrono-climatic anchor for the appearance of Epipaleolithic backed bladelets microlith industries in the Southern Levant

The Early Epipaleolithic (EEP) of the Southern Levant, roughly dated to 25-18 ka BP, is characterized by microlithic industries with highly variable synchronic and geographic techno-typological characteristics, the chronology of which remains poorly understood. Here, we present the results from excavations at Idan VII, a well-preserved site amongst a cluster of newly discovered EEP occurrences in the hyper-arid Arava Valley, Israel. The finds are embedded within the Late Pleistocene Lisan Formation lacustrine sediments, an extensively studied paleo-hydroclimatic archive in the Rift Valley. This unique situation enables contextualization of the archaeological finds within the detailed paleo-climatic chronology. The data presented include the stratigraphy (geomorphology and micro-geoarchaeology), relative (related to paleo-lake curve) and absolute (radiocarbon and U–Th) chronology, and archaeological (lithics, faunal and botanical) remains. The results demonstrate that the Idan EEP occurrences are situated within a localized relatively short-lived paleo-wetland area adjacent to Lake Lisan, during or immediately after the extremely cool and locally dry Heinrich Event 2 (H2), ca. 24 ka BP. The results are critically examined with respect to available radiocarbon dates from EEP archaeological sites in the Southern Levant. These, together with the geomorphological evidence, indicate that the Idan VII assemblage, while superficially resembling the so-called ‘Late Kebaran’ industry, actually significantly predates its most pertinent techno-typological analogs, highlighting the necessity of re-evaluating the “Kebaran complex”. Rather, it is coeval with the local, but unrelated ‘Masraqan’ and ‘Nebekian’ industries at the very onset of the EEP, demonstrating the high degree of Last Glacial Maximum hunter-gatherer cultural diversity then present in the Levant. In contextualizing the results within the Northern Hemisphere chrono-climatic framework, we conclude that within the Southern Levant, the H2 provides a solid chrono-climatic anchor for the appearance of fully-fledged backed bladelets microlithic industries, which probably reflects a technological change in composite projectile hunting gear that occurred during the EEP

Drought and Anthropogenic Effects on Acacia Populations: A Case Study from the Hyper-Arid Southern Israel

Acacia encompasses a keystone genus across the Middle Eastern and African drylands. This study assesses the dynamics of Acacia populations in two ephemeral stream channels (Nahal Naomi and Nahal Yael) in the hyper-arid Arava region following the establishment of a dam in the upstream channel of Nahal Yael and a long-term regional drought episode. The assessments were conducted at the individual and population levels, for a period of 45 years (during 1972, 1994 and 2017). In Nahal Naomi, the population increased by 35% during 1972–1994 (a relatively rainy period) and experienced low mortality (net change of +1.6% year−1). However, following a regional drought episode between 1995 and 2009, this population decreased by 57% (net change of −2.5% year−1). In Nahal Yael, the acacia population declined by 66% during 1972–1994 (net change of −1.6% year−1). Between 1994–2017, this population was co-affected by dam and drought, with no recruitment, and declined by 70% (net change of −2.0% year−1). By examining the tree’s specific location, species, age and state of preservation of dead individuals, we identified factors that influence tree mortality, and highlighted the adverse impacts of natural and anthropogenic disturbances on Acacia populations in hyper-arid environments

A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action

<div><p>The adaptation of the CRISPR-Cas9 system as a genome editing technique has generated much excitement in recent years owing to its ability to manipulate targeted genes and genomic regions that are complementary to a programmed single guide RNA (sgRNA). However, the efficacy of a specific sgRNA is not uniquely defined by exact sequence homology to the target site, thus unintended off-targets might additionally be cleaved. Current methods for sgRNA design are mainly concerned with predicting off-targets for a given sgRNA using basic sequence features and employ elementary rules for ranking possible sgRNAs. Here, we introduce CRISTA (CRISPR Target Assessment), a novel algorithm within the machine learning framework that determines the propensity of a genomic site to be cleaved by a given sgRNA. We show that the predictions made with CRISTA are more accurate than other available methodologies. We further demonstrate that the occurrence of bulges is not a rare phenomenon and should be accounted for in the prediction process. Beyond predicting cleavage efficiencies, the learning process provides inferences regarding patterns that underlie the mechanism of action of the CRISPR-Cas9 system. We discover that attributes that describe the spatial structure and rigidity of the entire genomic site as well as those surrounding the PAM region are a major component of the prediction capabilities.</p></div

Accuracy across different studies in a leave-study-out cross-validation.

<p>(a) Observed cleavage intensities versus predicted intensities. The top and bottom rows represent the nuclear targets of the ‘unique guides’ and ‘common guides’, respectively. Pearson <b><i>r</i></b>^<b>2</b> values are shown; "overall" represents the correlation calculated by taking all points, and "mean" is the average correlation calculated for each sgRNA individually. Different colors represent nuclear targets of different sgRNAs. (b, c) ROC and PRC curves. The ‘unique guides’ and ‘common guides’ of each study are represented by different curves. AUC values are denoted in the legend. Each column corresponds to a single experimental platform.</p

Fluvial Sediment Yields in Hyper-Arid Areas, Exemplified by Nahal Nehushtan, Israel

Floods rarely occur in hyper-arid deserts and little is known about the magnitude and frequency of sediment delivery from their basins, despite their importance to changes to the landscape, infrastructures and engineering activities. Sediment yield from the Nahal Nehushtan watershed (11.9 km2) located in the Timna Valley in southern Israel was determined by assessing stratigraphic sections in its 60-year reservoir deposits. Stratigraphic correlation between event couplets allowed for quantification of sediment yields representing 13 former flow and flood events. Based on the sediment volume in the reservoir, the 29.8 t km&minus;2 year&minus;1 average specific sediment yield is one of the lowest among other studied warm deserts. Among the event layers, the thickest layer, deposited by a flash flood caused by a single short rain event, contributed 31% of the total sediment yield. Based on event reservoir sedimentation from watersheds located in several hyper-arid areas in the Middle East and North America, we demonstrate that sediment yield increases with drainage area as expected and mean annual sediment yield increases in hyper-arid areas with flood frequency. Our quantitative results, together with previous studies of hyper-arid areas, provide complementary evidence of fluvial sediment transport&mdash;the main landscape designer in hyper-arid fluvial landscapes

Features importance.

<p>Clustering of top-ranked features and their relative importance. The nodes sizes represent the feature importance as calculated by CRISTA. Edges transparencies represent correlation such that strongly correlated features are connected by darker edges. Yellow and blue edges represent positively and negatively correlated features respectively. Abbreviations: YY- mismatches of type pyrimidine-pyrimidine; RR–mismatches of type purine-purine; MGW–minor groove width; ‘#’ represents counts (for further explanations of the features, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005807#pcbi.1005807.s016" target="_blank">S3 Table</a>). The graph was produced with Cytoscape [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005807#pcbi.1005807.ref055" target="_blank">55</a>] using the pairwise correlation for every pair of features and their importance scores.</p

Schematic flow of the cross-validation procedures.

<p>The main components of the learning pipeline for the leave-one-sgRNA-out and leave-study-out cross-validation procedures are presented. ¹ This step was applied to the leave-one-sgRNA-out procedure only. ² In each iteration, the samples of a single sgRNA (in the case of the leave-one-sgRNA-out procedure) or all samples from a single study (in the case of leave-study-out) were excluded from the training data and used as a test set. The algorithm was trained on the rest of the data. ³ Each set of cleaved samples (targets that correspond to a single sgRNA) was oversampled using bootstrapping, thus introducing a subset twice the size of the original one, and an equal-sized set of uncleaved samples was randomly chosen. ⁴ For each original set of cleaved samples in the test set (targets that correspond to a single sgRNA), an equal-sized set of uncleaved samples was randomly chosen.</p