Ranking species based on sensitivity to perturbations under non-equilibrium community dynamics

Managing ecological communities requires fast detection of species that are sensitive to perturbations. Yet, the focus on recovery to equilibrium has prevented us from assessing species responses to perturbations when abundances fluctuate over time. Here, we introduce two data-driven approaches (expected sensitivity and eigenvector rankings) based on the time-varying Jacobian matrix to rank species over time according to their sensitivity to perturbations on abundances. Using several population dynamics models, we demonstrate that we can infer these rankings from time-series data to predict the order of species sensitivities. We find that the most sensitive species are not always the ones with the most rapidly changing or lowest abundance, which are typical criteria used to monitor populations. Finally, using two empirical time series, we show that sensitive species tend to be harder to forecast. Our results suggest that incorporating information on species interactions can improve how we manage communities out of equilibrium.     

Crosslinking of theDrosophila chorion involves a peroxidase

Summary TheDrosophila chorion contains an endogenous peroxidase activity which remains inactive until late stage 14 when it catalyzes the crosslinking of the chorionic proteins. Using explanted follicles developing in vitro, premature, but otherwise normal crosslinking can be induced with hydrogen peroxide and normal crosslinking can be prevented with peroxidase inhibitors. Inhibition or premature activation of the shell peroxidase allows characterization of chorionic filament specific proteins and establishes new criteria for the identification of eggshell components.     

Field studies of seahorse population density,structure and habitat use in a semi-closed north-eastern Mediterranean marine area (Stratoni,North Aegean Sea)

The present study was carried out in the marine area of Stratoni, Greece, where two seahorse species are present (Hippocampus hippocampus and Hippocampus guttulatus). Two surveys were conducted (September 2016 and May 2019) to gather information regarding seahorse species' abundance, distribution and habitat characteristics. Four different seahorse natural and artificial habitat types were identified. The results revealed that the presence of H. hippocampus was relatively high, especially at sites with artificial structures, whereas the presence of H. guttulatus was rare. Data collected can provide baseline information for future population assessments.     

Aldehyde dehydrogenase (ALDH) superfamily in plants: gene nomenclature and comparative genomics

In recent years, there has been a significant increase in the number of completely sequenced plant genomes. The comparison of fully sequenced genomes allows for identification of new gene family members, as well as comprehensive analysis of gene family evolution. The aldehyde dehydrogenase (ALDH) gene superfamily comprises a group of enzymes involved in the NAD⁺- or NADP⁺-dependent conversion of various aldehydes to their corresponding carboxylic acids. ALDH enzymes are involved in processing many aldehydes that serve as biogenic intermediates in a wide range of metabolic pathways. In addition, many of these enzymes function as ‘aldehyde scavengers’ by removing reactive aldehydes generated during the oxidative degradation of lipid membranes, also known as lipid peroxidation. Plants and animals share many ALDH families, and many genes are highly conserved between these two evolutionarily distinct groups. Conversely, both plants and animals also contain unique ALDH genes and families. Herein we carried out genome-wide identification of ALDH genes in a number of plant species—including Arabidopsis thaliana (thale crest), Chlamydomonas reinhardtii (unicellular algae), Oryza sativa (rice), Physcomitrella patens (moss), Vitis vinifera (grapevine) and Zea mays (maize). These data were then combined with previous analysis of Populus trichocarpa (poplar tree), Selaginella moellindorffii (gemmiferous spikemoss), Sorghum bicolor (sorghum) and Volvox carteri (colonial algae) for a comprehensive evolutionary comparison of the plant ALDH superfamily. As a result, newly identified genes can be more easily analyzed and gene names can be assigned according to current nomenclature guidelines; our goal is to clarify previously confusing and conflicting names and classifications that might confound results and prevent accurate comparisons between studies.     

A nonlinear autoregressive Volterra model of the Hodgkin–Huxley equations

We propose a new variant of Volterra-type model with a nonlinear auto-regressive (NAR) component that is a suitable framework for describing the process of AP generation by the neuron membrane potential, and we apply it to input-output data generated by the Hodgkin–Huxley (H–H) equations. Volterra models use a functional series expansion to describe the input-output relation for most nonlinear dynamic systems, and are applicable to a wide range of physiologic systems. It is difficult, however, to apply the Volterra methodology to the H–H model because is characterized by distinct subthreshold and suprathreshold dynamics. When threshold is crossed, an autonomous action potential (AP) is generated, the output becomes temporarily decoupled from the input, and the standard Volterra model fails. Therefore, in our framework, whenever membrane potential exceeds some threshold, it is taken as a second input to a dual-input Volterra model. This model correctly predicts membrane voltage deflection both within the subthreshold region and during APs. Moreover, the model naturally generates a post-AP afterpotential and refractory period. It is known that the H–H model converges to a limit cycle in response to a constant current injection. This behavior is correctly predicted by the proposed model, while the standard Volterra model is incapable of generating such limit cycle behavior. The inclusion of cross-kernels, which describe the nonlinear interactions between the exogenous and autoregressive inputs, is found to be absolutely necessary. The proposed model is general, non-parametric, and data-derived.     

Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions

One key problem in computational neuroscience and neural engineering is the identification and modeling of functional connectivity in the brain using spike train data. To reduce model complexity, alleviate overfitting, and thus facilitate model interpretation, sparse representation and estimation of functional connectivity is needed. Sparsities include global sparsity, which captures the sparse connectivities between neurons, and local sparsity, which reflects the active temporal ranges of the input-output dynamical interactions. In this paper, we formulate a generalized functional additive model (GFAM) and develop the associated penalized likelihood estimation methods for such a modeling problem. A GFAM consists of a set of basis functions convolving the input signals, and a link function generating the firing probability of the output neuron from the summation of the convolutions weighted by the sought model coefficients. Model sparsities are achieved by using various penalized likelihood estimations and basis functions. Specifically, we introduce two variations of the GFAM using a global basis (e.g., Laguerre basis) and group LASSO estimation, and a local basis (e.g., B-spline basis) and group bridge estimation, respectively. We further develop an optimization method based on quadratic approximation of the likelihood function for the estimation of these models. Simulation and experimental results show that both group-LASSO-Laguerre and group-bridge-B-spline can capture faithfully the global sparsities, while the latter can replicate accurately and simultaneously both global and local sparsities. The sparse models outperform the full models estimated with the standard maximum likelihood method in out-of-sample predictions.     

Higher-Order Looping and Nuclear Organization of Tcra Facilitate Targeted RAG Cleavage and Regulated Rearrangement in Recombination Centers

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Highlights? RAG-dependent monoallelic loop formation is linked to monoallelic RAG cleavage ? RAG enrichment, cleavage, and higher-order loop formation occur at the 3′ end of Tcra ? Looping out is a determinant of directed RAG targeting ? ATM-mediated control of looping out is linked to the maintenance of genome stability     

Targeted Elimination of Senescent Beta Cells Prevents Type 1 Diabetes

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