5-Chloro-2-methoxy­anilinium nitrate

The title salt, C7H9ClNO+·NO3 −, exhibits extensive hydrogen bonding between the ammonium functional group and the nitrate anion. A two-dimensional network of bifurcated N—H⋯O hydrogen bonds generates corrugated layers in the bc plane. The organic mol­ecules are stacked in a parallel orientation as a result of π–π inter­actions, with an inter-ring distance of 3.837 Å

Genetic Analysis of <b><i>TREM2</i></b> Variants in Tunisian Patients with Alzheimer&apos;s Disease

International audienc

Vegetation recovery in tidal marshes reveals critical slowing down under increased inundation

A declining rate of recovery following disturbance has been proposed as an important early warning for impending tipping points in complex systems. Despite extensive theoretical and laboratory studies, this \u27critical slowing down\u27 remains largely untested in the complex settings of real-world ecosystems. Here, we provide both observational and experimental support of critical slowing down along natural stress gradients in tidal marsh ecosystems. Time series of aerial images of European marsh development reveal a consistent lengthening of recovery time as inundation stress increases. We corroborate this finding with transplantation experiments in European and North American tidal marshes. In particular, our results emphasize the power of direct observational or experimental measures of recovery over indirect statistical signatures, such as spatial variance or autocorrelation. Our results indicate that the phenomenon of critical slowing down can provide a powerful tool to probe the resilience of natural ecosystems

Stability and Fluctuations in Complex Ecological Systems

From 08-12 August, 2022, 32 individuals participated in a workshop, Stability and Fluctuations in Complex Ecological Systems, at the Lorentz Center, located in Leiden, The Netherlands. An interdisciplinary dialogue between ecologists, mathematicians, and physicists provided a foundation of important problems to consider over the next 5-10 years. This paper outlines eight areas including (1) improving our understanding of the effect of scale, both temporal and spatial, for both deterministic and stochastic problems; (2) clarifying the different terminologies and definitions used in different scientific fields; (3) developing a comprehensive set of data analysis techniques arising from different fields but which can be used together to improve our understanding of existing data sets; (4) having theoreticians/computational scientists collaborate closely with empirical ecologists to determine what new data should be collected; (5) improving our knowledge of how to protect and/or restore ecosystems; (6) incorporating socio-economic effects into models of ecosystems; (7) improving our understanding of the role of deterministic and stochastic fluctuations; (8) studying the current state of biodiversity at the functional level, taxa level and genome level.Comment: 22 page

More than a meal… integrating non-feeding interactions into food webs

Organisms eating each other are only one of many types of well documented and important interactions among species. Other such types include habitat modification, predator interference and facilitation. However, ecological network research has been typically limited to either pure food webs or to networks of only a few (<3) interaction types. The great diversity of non-trophic interactions observed in nature has been poorly addressed by ecologists and largely excluded from network theory. Herein, we propose a conceptual framework that organises this diversity into three main functional classes defined by how they modify specific parameters in a dynamic food web model. This approach provides a path forward for incorporating non-trophic interactions in traditional food web models and offers a new perspective on tackling ecological complexity that should stimulate both theoretical and empirical approaches to understanding the patterns and dynamics of diverse species interactions in nature.Keywords: Ecosystem engineering, Non-trophic interactions, Ecological network, Food web, Interaction modification, Facilitation, Trophic interaction

Integrating ecological feedbacks across scales and levels of organization

In ecosystems, species interact in various ways with other species, and with their local environment. In addition, ecosystems are coupled in space by diverse types of flows. From these links connecting different ecological entities can emerge circular pathways of indirect effects: feedback loops. This contributes to creating a nested set of ecological feedbacks operating at different organizational levels as well as spatial and temporal scales in ecological systems: species modifying and being affected by their local abiotic environment, demographic and behavioral feedbacks within populations and communities, and spatial feedbacks occurring at the landscape scale. Here, we review how ecological feedbacks vary in space and time, and discuss the emergent properties they generate such as species coexistence or the spatial heterogeneity and stability of ecological systems. With the aim of identifying similarities across scales, we identify the abiotic and biotic modulators that can change the sign and strength of feedback loops and show that these feedbacks can interact in space or time. Our review shows that despite acting at different scales and emerging from different processes, feedbacks generate similar macroscopic properties of ecological systems across levels of organization. Ultimately, our contribution emphasizes the need to integrate such feedbacks to improve our understanding of their joint effects on the dynamics, patterns, and stability of ecological systems

Founder mutations in Tunisia: implications for diagnosis in North Africa and Middle East

Abstract Background Tunisia is a North African country of 10 million inhabitants. The native background population is Berber. However, throughout its history, Tunisia has been the site of invasions and migratory waves of allogenic populations and ethnic groups such as Phoenicians, Romans, Vandals, Arabs, Ottomans and French. Like neighbouring and Middle Eastern countries, the Tunisian population shows a relatively high rate of consanguinity and endogamy that favor expression of recessive genetic disorders at relatively high rates. Many factors could contribute to the recurrence of monogenic morbid trait expression. Among them, founder mutations that arise in one ancestral individual and diffuse through generations in isolated communities. Method We report here on founder mutations in the Tunisian population by a systematic review of all available data from PubMed, other sources of the scientific literature as well as unpublished data from our research laboratory. Results We identified two different classes of founder mutations. The first includes founder mutations so far reported only among Tunisians that are responsible for 30 genetic diseases. The second group represents founder haplotypes described in 51 inherited conditions that occur among Tunisians and are also shared with other North African and Middle Eastern countries. Several heavily disabilitating diseases are caused by recessive founder mutations. They include, among others, neuromuscular diseases such as congenital muscular dystrophy and spastic paraglegia and also severe genodermatoses such as dystrophic epidermolysis bullosa and xeroderma pigmentosa. Conclusion This report provides informations on founder mutations for 73 genetic diseases either specific to Tunisians or shared by other populations. Taking into account the relatively high number and frequency of genetic diseases in the region and the limited resources, screening for these founder mutations should provide a rapid and cost effective tool for molecular diagnosis. Indeed, our report should help designing appropriate measures for carrier screening, better evaluation of diseases burden and setting up of preventive measures at the regional level.</p

Adjacency matrix for the positive non-trophic layer

Same format as chilean-TI.txt. A link between species i and j means that species i is the target of a positive interaction and species j is the source

Python code for figures 1a and 2

Python code allowing to generate figure 1a and the data for figure 2 of the paper. It requires the package tethne: https://diging.github.io/tethne/ (works with python 2). It uses the data base of the paper "merged_db_final.json" and generates 4 output files: figure_1a.pdf and perturbation/metric_table.png (i.e. the tables used to generate figure 2)

Adjacency matrix for the positive non-trophic layer

Same format as chilean-TI.txt. A link between species i and j means that species i is the target of a positive interaction and species j is the source