Effects of climate and demography on reproductive phenology of a harvested marine fish population

Shifts in phenology are a well‐documented ecological response to changes in climate, which may or may not be adaptive for a species depending on the climate sensitivity of other ecosystem processes. Furthermore, phenology may be affected by factors in addition to climate, which may accentuate or dampen climate‐driven phenological responses. In this study, we investigate how climate and population demographic structure jointly affect spawning phenology of a fish species of major commercial importance: walleye pollock (Gadus chalcogrammus). We use 32 years of data from ichthyoplankton surveys to reconstruct timing of pollock reproduction in the Gulf of Alaska and find that the mean date of spawning has varied by over 3 weeks throughout the last >3 decades. Climate clearly drives variation in spawn timing, with warmer temperatures leading to an earlier and more protracted spawning period, consistent with expectations of advanced spring phenology under warming. However, the effects of temperature were nonlinear, such that additional warming above a threshold value had no additional effect on phenology. Population demographics were equally as important as temperature: An older and more age‐diverse spawning stock tended to spawn earlier and over a longer duration than a younger stock. Our models suggest that demographic shifts associated with sustainable harvest rates could shift the mean spawning date 7 days later and shorten the spawning season by 9 days relative to an unfished population, independent of thermal conditions. Projections under climate change suggest that spawn timing will become more stable for walleye pollock in the future, but it is unknown what the consequences of this stabilization will be for the synchrony of first‐feeding larvae with production of zooplankton prey in spring. With ongoing warming in the world’s oceans, knowledge of the mechanisms underlying reproductive phenology can improve our ability to monitor and manage species under changing climate conditions.     

Teaching and learning in ecology: a horizon scan of emerging challenges and solutions

We currently face significant, anthropogenic, global environmental challenges and the role of ecologists in mitigating these challenges is arguably more important than ever. Consequently there is an urgent need to recruit and train future generations of ecologists, both those whose main area is ecology, but also those involved in the geological, biological and environmental sciences. Here we present the results of a horizon scanning exercise that identified current and future challenges facing the teaching of ecology, through surveys of teachers, students and employers of ecologists. Key challenges identified were grouped in terms of the perspectives of three groups: students, for example the increasing disconnect between people and nature; teachers, for example the challenges associated with teaching the quantitative skills that are inherent to the study of ecology; and society, for example poor societal perceptions of the field of ecology. In addition to the challenges identified, we propose a number of solutions developed at a workshop by a team of ecology teaching experts, with supporting evidence of their potential to address many of the problems raised. These proposed solutions include developing living labs, teaching students to be ecological entrepreneurs and influencers, embedding skills-based learning and coding in the curriculum, an increased role for learned societies in teaching and learning, and using new technology to enhance fieldwork studies including virtual reality, artificial intelligence and real-time spoken language translation. Our findings are focused towards UK higher education, but they should be informative for students and teachers of a wide range of educational levels, policy makers and professional ecologists worldwide.     

Advanced glycation end products (AGEs) are cross-sectionally associated with insulin secretion in healthy subjects

It has been postulated that chronic exposure to high levels of advanced glycation end products (AGEs), in particular from dietary sources, can impair insulin secretion. In the present study, we investigated the cross-sectional relationship between AGEs and acute insulin secretion during an intravenous glucose tolerance test (IVGTT) and following a 75 g oral glucose tolerance test (OGTT) in healthy humans. We report the cross-sectional association between circulating AGE concentrations and insulin secretory function in healthy humans (17 F: 27 M, aged 30 ± 10 years) with a wide range of BMI (24.6–31.0 kg/m²). Higher circulating concentrations of AGEs were related to increased first phase insulin secretion during IVGTT (r = 0.43; p < 0.05) and lower 2-h glucose concentrations during OGTT (r = ?0.31; p < 0.05). In addition, fasting (r = ?0.36; p < 0.05) and 2-h glucose concentrations were negatively related to circulating levels of soluble receptor for AGE (RAGE) isoforms (r = ?0.39; p < 0.01). In conclusion, in healthy humans, we show a cross-sectional association between advanced glycation end products and acute insulin secretion during glucose tolerance testing.     

Learning restricted Boolean network model by time-series data

Restricted Boolean networks are simplified Boolean networks that are required for either negative or positive regulations between genes. Higa et al. (BMC Proc 5:S5, 2011) proposed a three-rule algorithm to infer a restricted Boolean network from time-series data. However, the algorithm suffers from a major drawback, namely, it is very sensitive to noise. In this paper, we systematically analyze the regulatory relationships between genes based on the state switch of the target gene and propose an algorithm with which restricted Boolean networks may be inferred from time-series data. We compare the proposed algorithm with the three-rule algorithm and the best-fit algorithm based on both synthetic networks and a well-studied budding yeast cell cycle network. The performance of the algorithms is evaluated by three distance metrics: the normalized-edge Hamming distance ${\mathit{\mu }}_{\mathrm{ham}}^{\mathrm{e}}$, the normalized Hamming distance of state transition ${\mathit{\mu }}_{\mathrm{ham}}^{\mathrm{st}}$, and the steady-state distribution distance μ^ssd. Results show that the proposed algorithm outperforms the others according to both ${\mathit{\mu }}_{\mathrm{ham}}^{\mathrm{e}}$ and ${\mathit{\mu }}_{\mathrm{ham}}^{\mathrm{st}}$, whereas its performance according to μ^ssd is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate for inferring interactions between genes from time-series data.     

Genome Sequencing of Idiopathic Pulmonary Fibrosis in Conjunction with a Medical School Human Anatomy Course

Even in cases where there is no obvious family history of disease, genome sequencing may contribute to clinical diagnosis and management. Clinical application of the genome has not yet become routine, however, in part because physicians are still learning how best to utilize such information. As an educational research exercise performed in conjunction with our medical school human anatomy course, we explored the potential utility of determining the whole genome sequence of a patient who had died following a clinical diagnosis of idiopathic pulmonary fibrosis (IPF). Medical students performed dissection and whole genome sequencing of the cadaver. Gross and microscopic findings were more consistent with the fibrosing variant of nonspecific interstitial pneumonia (NSIP), as opposed to IPF per se. Variants in genes causing Mendelian disorders predisposing to IPF were not detected. However, whole genome sequencing identified several common variants associated with IPF, including a single nucleotide polymorphism (SNP), rs35705950, located in the promoter region of the gene encoding mucin glycoprotein MUC5B. The MUC5B promoter polymorphism was recently found to markedly elevate risk for IPF, though a particular association with NSIP has not been previously reported, nor has its contribution to disease risk previously been evaluated in the genome-wide context of all genetic variants. We did not identify additional predicted functional variants in a region of linkage disequilibrium (LD) adjacent to MUC5B, nor did we discover other likely risk-contributing variants elsewhere in the genome. Whole genome sequencing thus corroborates the association of rs35705950 with MUC5B dysregulation and interstitial lung disease. This novel exercise additionally served a unique mission in bridging clinical and basic science education.     

Activation of pattern recognition receptor-mediated innate immunity inhibits the replication of hepatitis B virus in human hepatocyte-derived cells

Recognition of virus infections by pattern recognition receptors (PRRs), such as Toll-like receptors (TLRs), retinoic acid-inducible gene I (RIG-I), and melanoma differentiation associated gene 5 (MDA5), activates signaling pathways, leading to the induction of inflammatory cytokines that limit viral replication. To determine the effects of PRR-mediated innate immune response on hepatitis B virus (HBV) replication, a 1.3mer HBV genome was cotransfected into HepG2 or Huh7 cells with plasmid expressing TLR adaptors, myeloid differentiation primary response gene 88 (MyD88), and TIR-domain-containing adaptor-inducing beta interferon (TRIF), or RIG-I/MDA5 adaptor, interferon promoter stimulator 1 (IPS-1). The results showed that expressing each of the three adaptors dramatically reduced the levels of HBV mRNA and DNA in both HepG2 and Huh7 cells. However, HBV replication was not significantly affected by treatment of HBV genome-transfected cells with culture media harvested from cells transfected with each of the three adaptors, indicating that the adaptor-induced antiviral response was predominantly mediated by intracellular factors rather than by secreted cytokines. Analyses of involved signaling pathways revealed that activation of NF-κB is required for all three adaptors to elicit antiviral response in both HepG2 and Huh7 cells. However, activation of interferon regulatory factor 3 is only essential for induction of antiviral response by IPS-1 in Huh7 cells, but not in HepG2 cells. Furthermore, our results suggest that besides NF-κB, additional signaling pathway(s) are required for TRIF to induce a maximum antiviral response against HBV. Knowing the molecular mechanisms by which PRR-mediated innate defense responses control HBV infections could potentially lead to the development of novel therapeutics that evoke the host cellular innate antiviral response to control HBV infections.     

On the long-run sensitivity of probabilistic Boolean networks

Boolean networks and, more generally, probabilistic Boolean networks, as one class of gene regulatory networks, model biological processes with the network dynamics determined by the logic-rule regulatory functions in conjunction with probabilistic parameters involved in network transitions. While there has been significant research on applying different control policies to alter network dynamics as future gene therapeutic intervention, we have seen less work on understanding the sensitivity of network dynamics with respect to perturbations to networks, including regulatory rules and the involved parameters, which is particularly critical for the design of intervention strategies. This paper studies this less investigated issue of network sensitivity in the long run. As the underlying model of probabilistic Boolean networks is a finite Markov chain, we define the network sensitivity based on the steady-state distributions of probabilistic Boolean networks and call it long-run sensitivity. The steady-state distribution reflects the long-run behavior of the network and it can give insight into the dynamics or momentum existing in a system. The change of steady-state distribution caused by possible perturbations is the key measure for intervention. This newly defined long-run sensitivity can provide insight on both network inference and intervention. We show the results for probabilistic Boolean networks generated from random Boolean networks and the results from two real biological networks illustrate preliminary applications of sensitivity in intervention for practical problems.     

Unnatural amino acids as probes of protein structure and function

Nonsense suppression methodology, for incorporating unnatural amino acids into proteins, has enabled a wide range of studies into protein structure and function using both in vitro and in vivo translation systems. Although methodological challenges remain, scores of unnatural amino acids have been employed that include both subtle and dramatic variants of the natural set. A number of insights that would not have been possible using conventional site-directed mutagenesis have been gained.     

What should be expected from feature selection in small-sample settings

MOTIVATION: High-throughput technologies for rapid measurement of vast numbers of biological variables offer the potential for highly discriminatory diagnosis and prognosis; however, high dimensionality together with small samples creates the need for feature selection, while at the same time making feature-selection algorithms less reliable. Feature selection must typically be carried out from among thousands of gene-expression features and in the context of a small sample (small number of microarrays). Two basic questions arise: (1) Can one expect feature selection to yield a feature set whose error is close to that of an optimal feature set? (2) If a good feature set is not found, should it be expected that good feature sets do not exist? RESULTS: The two questions translate quantitatively into questions concerning conditional expectation. (1) Given the error of an optimal feature set, what is the conditionally expected error of the selected feature set? (2) Given the error of the selected feature set, what is the conditionally expected error of the optimal feature set? We address these questions using three classification rules (linear discriminant analysis, linear support vector machine and k-nearest-neighbor classification) and feature selection via sequential floating forward search and the t-test. We consider three feature-label models and patient data from a study concerning survival prognosis for breast cancer. With regard to the two focus questions, there is similarity across all experiments: (1) One cannot expect to find a feature set whose error is close to optimal, and (2) the inability to find a good feature set should not lead to the conclusion that good feature sets do not exist. In practice, the latter conclusion may be more immediately relevant, since when faced with the common occurrence that a feature set discovered from the data does not give satisfactory results, the experimenter can draw no conclusions regarding the existence or nonexistence of suitable feature sets. AVAILABILITY: http://ee.tamu.edu/~edward/feature_regression/