How to prioritize species recovery after a megafire

Due to climate change, megafires are increasingly common and have sudden, extensive impacts on many species over vast areas, leaving decision makers uncertain about how best to prioritize recovery. We devised a decision-support framework to prioritize conservation actions to improve species outcomes immediately after a megafire. Complementary locations are selected to extend recovery actions across all fire-affected species' habitats. We applied our method to areas burned in the 2019-2020 Australian megafires and assessed its conservation advantages by comparing our results with outcomes of a site-richness approach (i.e., identifying areas that cost-effectively recover the most species in any one location). We found that 290 threatened species were likely severely affected and will require immediate conservation action to prevent population declines and possible extirpation. We identified 179 subregions, mostly in southeastern Australia, that are key locations to extend actions that benefit multiple species. Cost savings were over AU$300 million to reduce 95% of threats across all species. Our complementarity-based prioritization also spread postfire management actions across a wider proportion of the study area compared with the site-richness method (43% vs. 37% of the landscape managed, respectively) and put more of each species' range under management (average 90% vs. 79% of every species' habitat managed). In addition to wildfire response, our framework can be used to prioritize conservation actions that will best mitigate threats affecting species following other extreme environmental events (e.g., floods and drought)

Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale

Sensory processing is associated with gamma frequency oscillations (30–80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp. Author Summary Sensory processing of time-varying stimuli, such as speech, is associated with high-frequency oscillatory cortical activity, the functional significance of which is still unknown. One possibility is that the oscillations are part of a stimulus-encoding mechanism. Here, we investigate a computational model of such a mechanism, a spiking neuronal network whose intrinsic oscillations interact with external input (waveforms simulating short speech segments in a single acoustic frequency band) to encode stimuli that extend over a time interval longer than the oscillation's period. The network implements a temporally sparse encoding, whose robustness to time warping and neuronal noise we quantify. To our knowledge, this study is the first to demonstrate that a biophysically plausible model of oscillations occurring in the processing of auditory input may generate a representation of signals that span multiple oscillation cycles.National Science Foundation (DMS-0211505); Burroughs Wellcome Fund; U.S. Air Force Office of Scientific Researc

Bridging Alone: Religious Conservatism, Marital Homogamy, and Voluntary Association Membership

This study characterizes social insularity of religiously conservative American married couples by examining patterns of voluntary associationmembership. Constructing a dataset of 3938 marital dyads from the second wave of the National Survey of Families and Households, the author investigates whether conservative religious homogamy encourages membership in religious voluntary groups and discourages membership in secular voluntary groups. Results indicate that couples’ shared affiliation with conservative denominations, paired with beliefs in biblical authority and inerrancy, increases the likelihood of religious group membership for husbands and wives and reduces the likelihood of secular group membership for wives, but not for husbands. The social insularity of conservative religious groups appears to be reinforced by homogamy—particularly by wives who share faith with husbands

Distinguishing Type 2 Diabetes from Type 1 Diabetes in African American and Hispanic American Pediatric Patients

To test the hypothesis that clinical observations made at patient presentation can distinguish type 2 diabetes (T2D) from type 1 diabetes (T1D) in pediatric patients aged 2 to 18.Medical records of 227 African American and 112 Hispanic American pediatric patients diagnosed as T1D or T2D were examined to compare parameters in the two diseases. Age at presentation, BMI z-score, and gender were the variables used in logistic regression analysis to create models for T2D prediction.The regression-based model created from African American data had a sensitivity of 92% and a specificity of 89%; testing of a replication cohort showed 91% sensitivity and 93% specificity. A model based on the Hispanic American data showed 92% sensitivity and 90% specificity. Similarities between African American and Hispanic American patients include: (1) age at onset for both T1D and T2D decreased from the 1980s to the 2000s; (2) risk of T2D increased markedly with obesity. Racial/ethnic-specific observations included: (1) in African American patients, the proportion of females was significantly higher than that of males for T2D compared to T1D (p<0.0001); (2) in Hispanic Americans, the level of glycated hemoglobin (HbA1c) was significantly higher in T1D than in T2D (p<0.002) at presentation; (3) the strongest contributor to T2D risk was female gender in African Americans, while the strongest contributor to T2D risk was BMI z-score in Hispanic Americans.Distinction of T2D from T1D at patient presentation was possible with good sensitivity and specificity using only three easily-assessed variables: age, gender, and BMI z-score. In African American pediatric diabetes patients, gender was the strongest predictor of T2D, while in Hispanic patients, BMI z-score was the strongest predictor. This suggests that race/ethnic specific models may be useful to optimize distinction of T1D from T2D at presentation

Following the signs: applying urban regime analysis to a UK case study

As the debate continues regarding the applicability of urban regime analysis in a UK context, three aspects stand out as highly significant: the target for analysis, the mode of scrutiny, and the context of local governing arrangements with its implications for interdependence as an impetus for co-operation. This article will examine urban regime analysis and the move from government to governance in order to answer why and how the private, voluntary and public sectors might be inclined to collaborate in regimes. In addition, the regime analysis will provide the parameters for examination whilst the issue of governance will afford context for local governing arrangements. Although some issues require slight reframing to reflect the UK context, the article will follow a rigorous framework for examination utilizing the full weight of regime analysis as articulated by Stone such that it could not be accused of “concept stretching.” Far from it: Through the examination of an informal partnership, a coalition of actors from the public, private, and voluntary sectors that has been in existence for more than 13 years, the article focuses, specifically, on the long-term, less visible aspects of local governance. As such, it is able to demonstrate how economic and political change can have a tangible effect on the manifestation of interdependence as an impetus for co-operation, not only for this specific locale but also for other cities facing similar challenges

Key signalling nodes in mammary gland development and cancer: Myc

Myc has been intensely studied since its discovery more than 25 years ago. Insight has been gained into Myc's function in normal physiology, where its role appears to be organ specific, and in cancer where many mechanisms contribute to aberrant Myc expression. Numerous signals and pathways converge on Myc, which in turn acts on a continuously growing number of identified targets, via transcriptional and nontranscriptional mechanisms. This review will concentrate on Myc as a signaling mediator in the mammary gland, discussing its regulation and function during normal development, as well as its activation and roles in breast cancer

Cohesin Proteins Promote Ribosomal RNA Production and Protein Translation in Yeast and Human Cells

Cohesin is a protein complex known for its essential role in chromosome segregation. However, cohesin and associated factors have additional functions in transcription, DNA damage repair, and chromosome condensation. The human cohesinopathy diseases are thought to stem not from defects in chromosome segregation but from gene expression. The role of cohesin in gene expression is not well understood. We used budding yeast strains bearing mutations analogous to the human cohesinopathy disease alleles under control of their native promoter to study gene expression. These mutations do not significantly affect chromosome segregation. Transcriptional profiling reveals that many targets of the transcriptional activator Gcn4 are induced in the eco1-W216G mutant background. The upregulation of Gcn4 was observed in many cohesin mutants, and this observation suggested protein translation was reduced. We demonstrate that the cohesinopathy mutations eco1-W216G and smc1-Q843Δ are associated with defects in ribosome biogenesis and a reduction in the actively translating fraction of ribosomes, eiF2α-phosphorylation, and 35S-methionine incorporation, all of which indicate a deficit in protein translation. Metabolic labeling shows that the eco1-W216G and smc1-Q843Δ mutants produce less ribosomal RNA, which is expected to constrain ribosome biogenesis. Further analysis shows that the production of rRNA from an individual repeat is reduced while copy number remains unchanged. Similar defects in rRNA production and protein translation are observed in a human Roberts syndrome cell line. In addition, cohesion is defective specifically at the rDNA locus in the eco1-W216G mutant, as has been previously reported for Roberts syndrome. Collectively, our data suggest that cohesin proteins normally facilitate production of ribosomal RNA and protein translation, and this is one way they can influence gene expression. Reduced translational capacity could contribute to the human cohesinopathies

Publisher Correction: Understanding the genetic complexity of puberty timing across the allele frequency spectrum

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