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.     

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.     

Regime shifts of Mediterranean forest carbon uptake and reduced resilience driven by multidecadal ocean surface temperatures

The mechanisms translating global circulation changes into rapid abrupt shifts in forest carbon capture in semi‐arid biomes remain poorly understood. Here, we report unprecedented multidecadal shifts in forest carbon uptake in semi‐arid Mediterranean pine forests in Spain over 1950–2012. The averaged carbon sink reduction varies between 31% and 37%, and reaches values in the range of 50% in the most affected forest stands. Regime shifts in forest carbon uptake are associated with climatic early warning signals, decreased forest regional synchrony and reduced long‐term carbon sink resilience. We identify the mechanisms linked to ocean multidecadal variability that shape regime shifts in carbon capture. First, we show that low‐frequency variations of the surface temperature of the Atlantic Ocean induce shifts in the non‐stationary effects of El Niño Southern Oscillation (ENSO) on regional forest carbon capture. Modelling evidence supports that the non‐stationary effects of ENSO can be propagated from tropical areas to semi‐arid Mediterranean biomes through atmospheric wave trains. Second, decadal changes in the Atlantic Multidecadal Oscillation (AMO) significantly alter sea–air heat exchanges, modifying in turn ocean vapour transport over land and land surface temperatures, and promoting sustained drought conditions in spring and summer that reduce forest carbon uptake. Third, we show that lagged effects of AMO on the winter North Atlantic Oscillation also contribute to the maintenance of long‐term droughts. Finally, we show that the reported strong, negative effects of ocean surface temperature (AMO) on forest carbon uptake in the last decades are unprecedented over the last 150 years. Our results provide new, unreported explanations for carbon uptake shifts in these drought‐prone forests and review the expected impacts of global warming on the profiled mechanisms.     

Targeted Elimination of Senescent Beta Cells Prevents Type 1 Diabetes

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Resilience indicators: prospects and limitations for early warnings of regime shifts

In the vicinity of tipping points—or more precisely bifurcation points—ecosystems recover slowly from small perturbations. Such slowness may be interpreted as a sign of low resilience in the sense that the ecosystem could easily be tipped through a critical transition into a contrasting state. Indicators of this phenomenon of ‘critical slowing down (CSD)’ include a rise in temporal correlation and variance. Such indicators of CSD can provide an early warning signal of a nearby tipping point. Or, they may offer a possibility to rank reefs, lakes or other ecosystems according to their resilience. The fact that CSD may happen across a wide range of complex ecosystems close to tipping points implies a powerful generality. However, indicators of CSD are not manifested in all cases where regime shifts occur. This is because not all regime shifts are associated with tipping points. Here, we review the exploding literature about this issue to provide guidance on what to expect and what not to expect when it comes to the CSD-based early warning signals for critical transitions.