This is not an apple! Benefits and challenges of applying computer vision to museum collections

The application of computer vision on museum collection data is at an experimental stage with predictions that it will grow in significance and use in the coming years. This research, based on the analysis of five case studies and semi-structured interviews with museum professionals, examined the opportunities and challenges of these technologies, the resources and funding required, and the ethical implications that arise during these initiatives. The case studies examined in this paper are drawn from: The Metropolitan Museum of Art (USA), Princeton University Art Museum (USA), Museum of Modern Art (USA), Harvard Art Museums (USA), Science Museum Group (UK). The research findings highlight the possibilities of computer vision to offer new ways to analyze, describe and present museum collections. However, their actual implementation on digital products is currently very limited due to the lack of resources and the inaccuracies created by algorithms. This research adds to the rapidly evolving field of computer vision within the museum sector and provides recommendations to operationalize the usage of these technologies, increase the transparency on their application, create ethics playbooks to manage potential bias and collaborate across the museum sector

Single-cell analysis reveals regional reprogramming during adaptation to massive small bowel resection in mice

BACKGROUND & AIMS: The small intestine (SI) displays regionality in nutrient and immunological function. Following SI tissue loss (as occurs in short gut syndrome, or SGS), remaining SI must compensate, or adapt ; the capacity of SI epithelium to reprogram its regional identity has not been described. Here, we apply single-cell resolution analyses to characterize molecular changes underpinning adaptation to SGS. METHODS: Single-cell RNA sequencing was performed on epithelial cells isolated from distal SI of mice following 50% proximal small bowel resection (SBR) vs sham surgery. Single-cell profiles were clustered based on transcriptional similarity, reconstructing differentiation events from intestinal stem cells (ISCs) through to mature enterocytes. An unsupervised computational approach to score cell identity was used to quantify changes in regional (proximal vs distal) SI identity, validated using immunofluorescence, immunohistochemistry, qPCR, western blotting, and RNA-FISH. RESULTS: Uniform Manifold Approximation and Projection-based clustering and visualization revealed differentiation trajectories from ISCs to mature enterocytes in sham and SBR. Cell identity scoring demonstrated segregation of enterocytes by regional SI identity: SBR enterocytes assumed more mature proximal identities. This was associated with significant upregulation of lipid metabolism and oxidative stress gene expression, which was validated via orthogonal analyses. Observed upstream transcriptional changes suggest retinoid metabolism and proximal transcription factor Creb3l3 drive proximalization of cell identity in response to SBR. CONCLUSIONS: Adaptation to proximal SBR involves regional reprogramming of ileal enterocytes toward a proximal identity. Interventions bolstering the endogenous reprogramming capacity of SI enterocytes-conceivably by engaging the retinoid metabolism pathway-merit further investigation, as they may increase enteral feeding tolerance, and obviate intestinal failure, in SGS

Neurocalcin Delta Suppression Protects against Spinal Muscular Atrophy in Humans and across Species by Restoring Impaired Endocytosis

This document is the Accepted Manuscript version of the following article: Riessland et al., 'Neurocalcin Delta Suppression Protects against Spinal Muscular Atrophy in Humans and across Species by Restoring Impaired Endocytosis', The American Journal of Human Genetics, Vol. 100 (2): 297-315, first published online 26 January 2017. The final, published version is available online at doi: http://dx.doi.org/10.1016/j.ajhg.2017.01.005 © 2017 American Society of Human Genetics.Homozygous SMN1 loss causes spinal muscular atrophy (SMA), the most common lethal genetic childhood motor neuron disease. SMN1 encodes SMN, a ubiquitous housekeeping protein, which makes the primarily motor neuron-specific phenotype rather unexpected. SMA-affected individuals harbor low SMN expression from one to six SMN2 copies, which is insufficient to functionally compensate for SMN1 loss. However, rarely individuals with homozygous absence of SMN1 and only three to four SMN2 copies are fully asymptomatic, suggesting protection through genetic modifier(s). Previously, we identified plastin 3 (PLS3) overexpression as an SMA protective modifier in humans and showed that SMN deficit impairs endocytosis, which is rescued by elevated PLS3 levels. Here, we identify reduction of the neuronal calcium sensor Neurocalcin delta (NCALD) as a protective SMA modifier in five asymptomatic SMN1-deleted individuals carrying only four SMN2 copies. We demonstrate that NCALD is a Ca(2+)-dependent negative regulator of endocytosis, as NCALD knockdown improves endocytosis in SMA models and ameliorates pharmacologically induced endocytosis defects in zebrafish. Importantly, NCALD knockdown effectively ameliorates SMA-associated pathological defects across species, including worm, zebrafish, and mouse. In conclusion, our study identifies a previously unknown protective SMA modifier in humans, demonstrates modifier impact in three different SMA animal models, and suggests a potential combinatorial therapeutic strategy to efficiently treat SMA. Since both protective modifiers restore endocytosis, our results confirm that endocytosis is a major cellular mechanism perturbed in SMA and emphasize the power of protective modifiers for understanding disease mechanism and developing therapies.Peer reviewedFinal Accepted Versio

Stress errors in polysyllabic adjective words by the 6th semester students of English Letters Department in Sanata Dharma University

<p>Although clear separation between control and infected rat urine exosomes is seen, a fraction of overlap between the infected male and infected female rat urine exosome proteins is observed.</p

Computerized Visual Analysis for Classifying Chinese Paintings by Dynasty

A variety of machine learning methods have been used for art classification. These methods have predominately focused on Western art and neglected the incredibly rich and diverse field of Chinese art. Determining what dynasty a painting is from is one of the foremost tasks that art historians undertake to study Chinese paintings, but it can be difficult to pinpoint which dynasty a work is from. The goal of our research is to evaluate how well existing machine learning methods using deep learning and hand-crafted features can classify Chinese paintings based on dynasty. In our experiments, we aim to find the best-performing model from these methods. This will allow art historians and viewers to study Chinese paintings more efficiently by establishing a baseline for placing paintings in their art historical context

Computerized Visual Analysis for Classifying Chinese Paintings by Dynasty

A variety of machine learning methods have been used for art classification. These methods have predominately focused on Western art and neglected the incredibly rich and diverse field of Chinese art. Determining what dynasty a painting is from is one of the foremost tasks that art historians undertake to study Chinese paintings, but it can be difficult to pinpoint which dynasty a work is from. The goal of our research is to evaluate how well existing machine learning methods using deep learning and hand-crafted features can classify Chinese paintings based on dynasty. In our experiments, we aim to find the best-performing model from these methods. This will allow art historians and viewers to study Chinese paintings more efficiently by establishing a baseline for placing paintings in their art historical context

Computerized Visual Analysis for Classifying Chinese Paintings by Dynasty

A variety of machine learning methods have been used for art classification. These methods have predominately focused on Western art and neglected the incredibly rich and diverse field of Chinese art. Determining what dynasty a painting is from is one of the foremost tasks that art historians undertake to study Chinese paintings, but it can be difficult to pinpoint which dynasty a work is from. The goal of our research is to evaluate how well existing machine learning methods using deep learning and hand-crafted features can classify Chinese paintings based on dynasty. In our experiments, we aim to find the best-performing model from these methods. This will allow art historians and viewers to study Chinese paintings more efficiently by establishing a baseline for placing paintings in their art historical context