Garden Etiquette

Garden Etiquette is an ongoing project concerned with landscape photography, environmental conservation, and the way they have both served the settler colonialist agenda. I focus specifically on the conservation ideologies shaped in New South Wales (NSW) Australia and New England, United States of America (USA) in the late nineteenth century and the settler visualities that underwrote them. Both countries’ histories were marked by photography and conservation’s common function of mythologising land as empty space—to be invaded, extracted and occupied, and wilderness—to be territorialized and protected, albeit, in distinct ways. With British, German and Polish settler ancestry, born and raised on Ngunnawal and Ngambri Land / Canberra, I play a role in such structural violences—something that has compelled me to try to locate myself, my upbringing and the tools I use in my photographic work. For this project I visited and made images at conservation sites on Dharawal Land / NSW and Narragansett, Nipmuc, Wampanoag and Pokanoket Lands / Rhode Island and Massachusetts. I also engaged with material related to the 1989 Royal National Park (formerly just “National Park”) proclamation on Dharawal Land held by the NSW State Archives. I am interested in how imaging these conservation sites, and looking at related archival material, might render visible or invisible colonial logics: how the social, racial, gendered, political and scientific stratifications of land—that afforded settlers a sense of romantic communion with nature and sense of belonging—were obscured. Drawing connections between analogue photographic processes, hot glass and lenses, 3D scanning, and modeling has allowed me to extend on this and helped me to discursively locate Enlightenment aesthetics and politics within traditional photographic practices and contemporary imaging technologies. Doing so has served to de-familiarise myself with traditional photographic codes. Less so to find a solution or way around certain problematics in photographic and conservation practices, but to locate my own relations to the constitutive violences that colonialism attempts to veil as “common,” “ordinary,” or “inevitable,” and asserts through ideological constructions such as “civility” and “etiquette.” This thesis posits that a critical approach to photography—in practice, theory and as metaphor—has been of help to me in this undertaking

Combating the Class Imbalance Problem in Small Sample Data Sets

The class imbalance problem is a recent development in machine learning. It is frequently encountered when using a classifier to generalize on real-world application data sets, and it causes a classifier to perform sub-optimally. Researchers have rigorously studied resampling methods, new algorithms, and feature selection methods, but no studies have been conducted to understand how well these methods combat the class imbalance problem. In particular, feature selection has been rarely studied outside of text classification problems. Additionally, no studies have looked at the additional problem of learning from small samples. This paper develops a new feature selection metric, Feature Assessment by Sliding Thresholds (FAST), specifically designed to handle small sample imbalanced data sets. FAST is based on the area under the receiver operating characteristic (AUC) generated by moving the decision boundary of a single feature classifier with thresholds placed using an even-bin distribution. This paper also presents a first systematic comparison of the three types of methods developed for imbalanced data classification problems and of seven feature selection metrics evaluated on small sample data sets from different applications. We evaluated the performance of these metrics using AUC and area under the P-R curve (PRC). We compared each metric on the average performance across all problems and on the likelihood of a metric yielding the best performance on a specific problem. We examined the performance of these metrics inside each problem domain. Finally, we evaluated the efficacy of these metrics to see which perform best across algorithms. Our results showed that signal-to-noise correlation coefficient (S2N) and FAST are great candidates for feature selection in most applications

Post-Outburst Infrared Spectra of V1647 Ori, the Illuminating Star of McNeil\u27s Nebula

V1647 Ori is a low mass star in the L1630 star-forming region that underwent an outburst in late 2003/early 2004. We present post-outburst infrared spectra obtained with NIRSPEC (Keck II) and SpeX (IRTF) and compare these to spectra taken during the outburst. The results show that the temperature of the hot CO formed in the inner part of the disk has declined by ~800 K, while the water and CO ice and low-J CO gas features remained unchanged, consistent with previous assertions that the latter, low-temperature features arise in the foreground cloud. The P-Cygni profiles of the Paschen series that were present in the outburst spectra taken in March 2004 disappeared by late 2004. The equivalent width of the helium absorption line at 1.0830 µm decreased from 8.9 Å to 3.9 Å between March and November 2004, evidence that the hot, fast wind has decreased substantially. We discuss the implications for categorizing V1647 Ori among the known classes of outbursting young stars

Large-scale functional inference for skin-expressing lncRNAs using expression and sequence information

Long noncoding RNAs (lncRNAs) regulate the expression of protein-coding genes and have been shown to play important roles in inflammatory skin diseases. However, we still have limited understanding of the functional impact of lncRNAs in skin, partly due to their tissue specificity and lower expression levels compared with protein-coding genes. We compiled a comprehensive list of 18,517 lncRNAs from different sources and studied their expression profiles in 834 RNA-Seq samples from multiple inflammatory skin conditions and cytokine-stimulated keratinocytes. Applying a balanced random forest to predict involvement in biological functions, we achieved a median AUROC of 0.79 in 10-fold cross-validation, identifying significant DNA binding domains (DBDs) for 39 lncRNAs. G18244, a skin-expressing lncRNA predicted for IL-4/IL-13 signaling in keratinocytes, was highly correlated in expression with F13A1, a protein-coding gene involved in macrophage regulation, and we further identified a significant DBD in F13A1 for G18244. Reflecting clinical implications, AC090198.1 (predicted for IL-17 pathway) and AC005332.6 (predicted for IFN-γ pathway) had significant negative correlation with the SCORAD metric for atopic dermatitis. We also utilized single-cell RNA and spatial sequencing data to validate cell type specificity. Our research demonstrates lncRNAs have important immunological roles and can help prioritize their impact on inflammatory skin diseases.</p

Exploring synergetic effects of dimensionality reduction and resampling tools on hyperspectral imagery data classification

The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA and a supervised filter are applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of combining several techniques to tackle the imbalance and the high dimensionality problems, and also to evaluate the order of application that leads to the best classification performance. Experimental results demonstrate the significance of using together these two preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order corresponds to first a resampling strategy and then a feature (or extraction) selection algorithm, this is a question that still needs a much more thorough investigation in the futureThis work has partially been supported by the Spanish Ministry of Education and Science under grants CSD2007–00018, AYA2008–05965–0596 and TIN2009–14205, the Fundació Caixa Castelló–Bancaixa under grant P1–1B2009–04, and the Generalitat Valenciana under grant PROMETEO/2010/02

Single-cell sequencing reveals Hippo signaling as a driver of fibrosis in hidradenitis suppurativa

Hidradenitis suppurativa (HS) is a chronic inflammatory disease characterized by abscesses, nodules, dissecting/draining tunnels, and extensive fibrosis. Here, we integrate single-cell RNA sequencing, spatial transcriptomics, and immunostaining to provide an unprecedented view of the pathogenesis of chronic HS, characterizing the main cellular players and defining their interactions. We found a striking layering of the chronic HS infiltrate and identified the contribution of 2 fibroblast subtypes (SFRP4+ and CXCL13+) in orchestrating this compartmentalized immune response. We further demonstrated the central role of the Hippo pathway in promoting extensive fibrosis in HS and provided preclinical evidence that the profibrotic fibroblast response in HS can be modulated through inhibition of this pathway. These data provide insights into key aspects of HS pathogenesis with broad therapeutic implications.</p

Single-cell sequencing reveals Hippo signaling as a driver of fibrosis in hidradenitis suppurativa

Hidradenitis suppurativa (HS) is a chronic inflammatory disease characterized by abscesses, nodules, dissecting/draining tunnels, and extensive fibrosis. Here, we integrate single-cell RNA sequencing, spatial transcriptomics, and immunostaining to provide an unprecedented view of the pathogenesis of chronic HS, characterizing the main cellular players and defining their interactions. We found a striking layering of the chronic HS infiltrate and identified the contribution of 2 fibroblast subtypes (SFRP4+ and CXCL13+) in orchestrating this compartmentalized immune response. We further demonstrated the central role of the Hippo pathway in promoting extensive fibrosis in HS and provided preclinical evidence that the profibrotic fibroblast response in HS can be modulated through inhibition of this pathway. These data provide insights into key aspects of HS pathogenesis with broad therapeutic implications.</p

An insight into imbalanced Big Data classification: outcomes and challenges

Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a “de facto” solution. Basically, it carries out a “divide-and-conquer” distributed procedure in a fault-tolerant way to adapt for commodity hardware. Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit the MapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current research state of this area. Second, to analyze the behavior of standard pre-processing techniques in this particular framework. Finally, taking into account the experimental results obtained throughout this work, we will carry out a discussion on the challenges and future directions for the topic.This work has been partially supported by the Spanish Ministry of Science and Technology under Projects TIN2014-57251-P and TIN2015-68454-R, the Andalusian Research Plan P11-TIC-7765, the Foundation BBVA Project 75/2016 BigDaPTOOLS, and the National Science Foundation (NSF) Grant IIS-1447795

Double triage to identify poorly annotated genes in maize: The missing link in community curation

The sophistication of gene prediction algorithms and the abundance of RNA-based evidence for the maize genome may suggest that manual curation of gene models is no longer necessary. However, quality metrics generated by the MAKER-P gene annotation pipeline identified 17,225 of 130,330 (13%) protein-coding transcripts in the B73 Reference Genome V4 gene set with models of low concordance to available biological evidence. Working with eight graduate students, we used the Apollo annotation editor to curate 86 transcript models flagged by quality metrics and a complimentary method using the Gramene gene tree visualizer. All of the triaged models had significant errors-including missing or extra exons, non-canonical splice sites, and incorrect UTRs. A correct transcript model existed for about 60% of genes (or transcripts) flagged by quality metrics; we attribute this to the convention of elevating the transcript with the longest coding sequence (CDS) to the canonical, or first, position. The remaining 40% of flagged genes resulted in novel annotations and represent a manual curation space of about 10% of the maize genome (~4,000 protein-coding genes). MAKER-P metrics have a specificity of 100%, and a sensitivity of 85%; the gene tree visualizer has a specificity of 100%. Together with the Apollo graphical editor, our double triage provides an infrastructure to support the community curation of eukaryotic genomes by scientists, students, and potentially even citizen scientists. © 2019 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication