On partial well-order for monotone grid classes of permutations

A monotone grid class is a permutation class (i.e., a downset of permutations under the containment order) defined by local monotonicity conditions. We give a simplified proof of a result of Murphy and Vatter that monotone grid classes of forests are partially well-ordered

High carbon dioxide requiring mutants of Chlamydomonas reinhardtti

Chlamydomonas reinhardtii is a photosynthetic alga that has the ability to concentrate CO2 around Rubisco to achieve enhanced rates of photosynthesis in a low CO2 environment. This dissertation presents results obtained from the generation and analysis of four high CO2 requiring mutants of C. reinhardtii. The use of reverse genetics is a very powerful tool to dissect out the individual components of metabolic pathways. Two reverse genetics methods were utilized in this study: a random insertional mutagenesis method to discover genes that are required for growth in a low CO2 environment, and a directed mutagenesis approach, RNA interference, to determine the role of two low CO2 inducible genes in the carbon concentrating mechanism. The first high CO2 requiring mutant was determined to be defective at the Rubisco activase locus. The second mutant, cia6, had an insertion in a SET domain containing protein that may be involved in the regulation of the carbon concentrating mechanism. The third mutant, slc23, had an insertion in a gene that encodes for multiple splice variants that encode for at least four distinct WD40 repeat proteins that vary in their number of WD40 repeats. A targeted mutagenesis approach was utilized to silence the expression of the two low CO2 inducible, nearly identical genes, Ccp1 and Ccp2. RNA interference was successfully used to reduce the expression of Ccp1 and Ccp2 mRNAs and proteins to undetectable levels. Results suggest that the Ccp1 and Ccp2 proteins are required for growth in a low CO2 environment, but that these two proteins are not required for efficient photosynthesis at low levels of CO2

Robust sound event detection in bioacoustic sensor networks

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 milliseconds) and long-term (30 minutes) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer. Combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019; revised August 2019; published October 201

A Generalized Nash-Cournot Model for the North-Western European Natural Gas Markets with a Fuel SubstitutionDemand Function: The GaMMES Model

This article presents a dynamic Generalized Nash-Cournot model to describe the evolution of the natural gas markets. The aim of this work is to provide a theoretical framework that would allow us to analyze future infrastructure and policy developments, while trying to answer some of the main criticisms addressed to Cournot-based models of natural gas markets. The major gas chain players are depicted including: producers, consumers, storage and pipeline operators, as well as intermediate local traders. Our economic structure description takes into account market power and the demand representation tries to capture the possible fuel substitution that can be made between the consumption of oil, coal and natural gas in the overall fossil energy consumption. We also take into account the long-term aspects inherent to some markets, in an endogenous way. This particularity of our description makes the model a Generalized Nash Equilibrium problem that needs to be solved using specialized mathematical techniques. Our model has been applied to represent the European natural gas market and forecast, until 2030, after a calibration process, consumption, prices, production and natural gas dependence. A comparison between our model, a more standard one that does not take into account energy substitution, and the European Commission natural gas forecasts is carried out to analyze our results. Finally, in order to illustrate the possible use of fuel substitution, we studied the evolution of the natural gas price as compared to the coal and oil prices. This paper mostly focuses on the model description.Energy markets modeling, Game theory, Generalized Nash-Cournot equilibria, Quasi-Variational Inequality

Headers and concussions in elite female and male football: a pilot study

Background: Heading is a risk factor for neurogenerative disease in football. However, the exposure to heading in elite football training is understudied. Objectives: The primary purpose of this study was to determine the exposure to headers in elite men’s and women’s football and to describe the effects of the headers on ocular markers. Methods: Exposure to headers was observed over three days of women’s and men’s football. The number of headers at each session was determined through video analysis, and the G-force was determined via an impact tracker. Ocular markers were assessed at the start and end of the three days, and the results were compared to determine if there were any changes. Self-reported exposure to heading was recorded after each session and compared to the number of headers observed through video analysis, to assess the validity of players’ self-reporting. Results: Female players made an average of 11 headers per player per session. Ninety percent of the headers were below 10G, and none were above 80G. Male players made an average of three headers per player per session, with 74% of the headers recording a G-force above 10G and 3% above 80G. No meaningful changes were observed post-session in the ocular markers, and no concussions were observed. Neither cohort was able to accurately self-report exposure to headers. Conclusion: Longitudinal studies should be designed and conducted across different levels of play in both women and men’s football as a prerequisite to develop evidence-based measures to prevent or mitigate the potential risks associated with headers and concussions in elite football