פרסומים

Abstract Grapevine leafroll disease (GLD) is a globally spreading viral infection that causes major economic losses by reducing crop yield, plant longevity, and berry quality, with no effective treatment. Grapevine leafroll associated virus-3 (GLRaV-3) is the most severe, prevalent GLD strain affecting wine production. Here, the ability of RNA interference (RNAi), a non-GMO gene-silencing pathway, to treat GLRaV-3 in infected Cabernet Sauvignon grapevines is evaluated. Lipid-modified polyethylenimine (lmPEI) is synthesized as the carrier for long double-stranded RNA (dsRNA, 250-bp-long) that targets RNA polymerase and coat protein is a gene target that are conserved in the GLRaV-3 genome. Self-assembled dsRNA–lmPEI particles, 220 nm in diameter, display inner ordered domains spaced 7.3 ± 2 nm from one another, correlating to lmPEI wrapping spirally around the dsRNA. The particles effectively protect RNA from degradation by ribonucleases and show to increase uptake rate into plant cells as a result of the lipid component comprising the RNA carrier. In three field experiments, a single dose of foliar sprayed treatment of the RNA-particles knocks down GLRaV-3 titer, and multiple doses of the treatment keep the viral titer at baseline and trigger recovery of the vine and berries. This study demonstrates RNAi as a promising platform for treating viral diseases in agriculture.

Fire risk mapping - mapping the probability of fire occurrence and spread - is essential for pre-fire management as well as for efficient firefighting efforts. Most fire risk maps are generated using static information on variables such as topography, vegetation density, and fuel instantaneous wetness. Satellites are often used to provide such information. However, long-term vegetation dynamics and the cumulative dryness status of the woody vegetation, which may affect fire occurrence and spread, are rarely considered in fire risk mapping. Here, we investigate the impact of two satellite-derived metrics that represent long-term vegetation status and dynamics on fire risk mapping - the long-term mean normalized difference vegetation index (NDVI) of the woody vegetation (NDVIW) and its trend (NDVIT). NDVIW represents the mean woody density at the grid cell, while NDVIT is the 5-year trend of the woody NDVI representing the long-term dryness status of the vegetation. To produce these metrics, we decompose Lime-series of satellite-derived NDVI following a method adjusted for Mediterranean woodlands and forests. We tested whether these metrics improve fire risk mapping using three machine learning (ML) algorithms (Logistic Regression, Random Forest, and XGBoost). We chose the 2007 wildfires in Greece for the analysis. Our results indicate that XGBoost, which accounts for variable interactions and non-linear effects, was the ML model that produced the best results. NDVIW improved the model performance, while NDVIT was significant only when NDVIW was high. This NDVIW-NDVIT interaction means that the long-term dryness effect is meaningful only in places of dense woody vegetation. The proposed method can produce more accurate fire risk maps than conventional methods and can supply important dynamic information that may be used in fire behavior models. (C) 2020 Published by Elsevier B.V.

Deliberative democracy fosters greater involvement of the public in policymaking. However, psychological challenges involved in eliciting policy preferences receive little attention in this context. This study addresses the implications of opportunity cost neglect (OCN) and impact bias for policy preferences. Utilizing a survey experiment among residents of peripheral towns in Israel, we examine preferences regarding investment in rail infrastructure in peripheral areas. In line with psychological studies on OCN, we find evidence that priming awareness to alternatives can de-bias OCN in policy preferences. However, this method is less effective for people who exhibit impact bias (respondents for whom the policy is new), presenting a serious challenge to the validity of policy preferences of those who are expected to be most affected by the considered policy. This paper offers a theoretical contribution to the relationship between OCN and impact bias, and discusses the practical implications for public participation in policymaking.

Deliberative democracy fosters greater involvement of the public in policymaking. However, psychological challenges involved in eliciting policy preferences receive little attention in this context. This study addresses the implications of opportunity cost neglect (OCN) and impact bias for policy preferences. Utilizing a survey experiment among residents of peripheral towns in Israel, we examine preferences regarding investment in rail infrastructure in peripheral areas. In line with psychological studies on OCN, we find evidence that priming awareness to alternatives can de-bias OCN in policy preferences. However, this method is less effective for people who exhibit impact bias (respondents for whom the policy is new), presenting a serious challenge to the validity of policy preferences of those who are expected to be most affected by the considered policy. This paper offers a theoretical contribution to the relationship between OCN and impact bias, and discusses the practical implications for public participation in policymaking.

This paper characterizes Medieval Hebrew and Aramaic as literary languages and seeks to explain how a 'literary language' namely a language used mainly in literary contexts arises, while utilizing three types of research: comparative philological research, which compares different languages and texts in terms of their vocabulary and grammar; sociolinguistic research, which examines the social functions of language use; and psycholinguistic research, which (in this particular case) examines issues of language acquisition. The paper builds on philological studies of literary languages to explain how the grammar of these languages evolves. It assumes that the acquisition of such languages is similar to second-language acquisition, while taking into account that these languages are both acquired and used in a strictly literary context. The main argument of the paper is that literary languages should be studied the same way as other languages, because ultimately after making some adjustments motivated by their particular functions they are compatible with the standard models of second-language acquisition.

Long-lived anticyclonic eddies (ACs) have been repeatedly observed over several North Atlantic basins characterized by bowl-like topographic depressions. Motivated by these previous findings, the authors conduct numerical simulations of the spin-down of eddies initialized in idealized topographic bowls. In experiments with 1 or 2 isopycnal layers, it is found that a bowl-trapped AC is an emergent circulation pattern under a wide range of parameters. The trapped AC, often formed by repeated mergers of ACs over the bowl interior, is characterized by anomalously low potential vorticity (PV). Several PV segregation mechanisms that can contribute to the AC formation are examined. In one-layer experiments, the dynamics of the AC are largely determined by a nonlinearity parameter () that quantifies the vorticity of the AC relative to the bowl’s topographic PV gradient. The AC is trapped in the bowl for low . 1, but for moderate values (0.5 . . 1) partial PV segregation allows the AC to reside at finite distances from the center of the bowl. For higher & 1, eddies freely cross the topography and the AC is not confined to the bowl. These regimes are characterized across a suite of model experiments using and a PV homogenization parameter. Two-layer experiments show that the trapped AC can be top- or bottom-intensified, as determined by the domain-mean initial vertical energy distribution. These findings contrast with previous theories of mesoscale turbulence over topography that predict the formation of a prograde slope current, but do not predict a trapped AC.

Soil erosion affects agricultural landscapes worldwide, threatening food security and ecosystem viability. In arable environments, soil loss is primarily caused by short, intense rainstorms, typically characterized by high spatiotemporal variability. The complexity of erosive events challenges modeling efforts and explicit inclusion of extreme events in long-term risk assessment is missing. This study is intended to bridge this gap by quantifying the discrete and cumulative impacts of rainstorms on plot-scale soil erosion and providing storm-scale erosion risk analyses for a cropland region in northern Israel. Central to our analyses is the coupling of (1) a stochastic rainfall generator able to reproduce extremes down to 5-minute temporal resolutions; (2) a processes-based event-scale cropland erosion model (Dynamic WEPP, DWEPP); and, (3) a state-of-the-art frequency analysis method that explicitly accounts for rainstorms occurrence and properties. To our knowledge, this is the first study in which DWEPP runoff and soil loss are calibrated at the plot-scale on cropland (NSE is 0.82 and 0.79 for event runoff and sediment, respectively). We generated 300-year stochastic simulations of event runoff and sediment yield based on synthetic precipitation time series. Based on this data, the mean annual soil erosion in the study site is 0.1 kg m-2 [1.1 t ha-1]. Results of the risk analysis indicate that individual extreme rainstorms (>50 return period), characterized by high rainfall intensities (30-minute maximal intensity > $\sim$60 mm h-1) and high rainfall depth (>$\sim$200 mm), can trigger soil losses even one order of magnitude higher than the annual mean. The erosion efficiency of these rainstorms is mainly controlled by the short-duration (<=30 min) maximal intensities. The results demonstrate the importance of incorporating the impact of extreme events into soil conservation and management tools. We expect our methodology to be valuable for investigating future changes in soil erosion with changing climate.

Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current ‘state of the art’ from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of ‘soft recommendations’ about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage ‘open science’ practices.