Colors, Emotions, and the Auction Value of Paintings

We study the impact of colors of paintings on prices in the art auction market and incorporate color attributes of non-figurative paintings in pricing models. A one standard deviation increase in the percentages of blue (red) hue leads to premiums of 10.63% (4.20%). We also conduct laboratory experiments in China, the Netherlands, and U.S., and elicit participants’ willingness-to-pay and emotions (pleasure-arousal). Blue (red) paintings command 18.57% (17.28%) higher bids and stronger intention to purchase. Although abstract art is visually arousing, it is the emotional pleasure channel that relates colors and prices. Our results are consistent across all three cultures

A close phylogenetic relationship between Sipuncula and Annelida evidenced from the complete mitochondrial genome sequence of Phascolosoma esculenta

<p>Abstract</p> <p>Background</p> <p>There are many advantages to the application of complete mitochondrial (mt) genomes in the accurate reconstruction of phylogenetic relationships in Metazoa. Although over one thousand metazoan genomes have been sequenced, the taxonomic sampling is highly biased, left with many phyla without a single representative of complete mitochondrial genome. Sipuncula (peanut worms or star worms) is a small taxon of worm-like marine organisms with an uncertain phylogenetic position. In this report, we present the mitochondrial genome sequence of <it>Phascolosoma esculenta</it>, the first complete mitochondrial genome of the phylum.</p> <p>Results</p> <p>The mitochondrial genome of <it>P</it>.<it>esculenta </it>is 15,494 bp in length. The coding strand consists of 32.1% A, 21.5% C, 13.0% G, and 33.4% T bases (AT = 65.5%; AT skew = -0.019; GC skew = -0.248). It contains thirteen protein-coding genes (PCGs) with 3,709 codons in total, twenty-two transfer RNA genes, two ribosomal RNA genes and a non-coding AT-rich region (AT = 74.2%). All of the 37 identified genes are transcribed from the same DNA strand. Compared with the typical set of metazoan mt genomes, sipunculid lacks <it>trnR </it>but has an additional <it>trnM</it>. Maximum Likelihood and Bayesian analyses of the protein sequences show that Myzostomida, Sipuncula and Annelida (including echiurans and pogonophorans) form a monophyletic group, which supports a closer relationship between Sipuncula and Annelida than with Mollusca, Brachiopoda, and some other lophotrochozoan groups.</p> <p>Conclusion</p> <p>This is the first report of a complete mitochondrial genome as a representative within the phylum Sipuncula. It shares many more similar features with the four known annelid and one echiuran mtDNAs. Firstly, sipunculans and annelids share quite similar gene order in the mitochondrial genome, with all 37 genes located on the same strand; secondly, phylogenetic analyses based on the concatenated protein sequences also strongly support the sipunculan + annelid clade (including echiurans and pogonophorans). Hence annelid "key-characters" including segmentation may be more labile than previously assumed.</p

Homozygosity Mapping and Genetic Analysis of Autosomal Recessive Retinal Dystrophies in 144 Consanguineous Pakistani Families.

PurposeThe Pakistan Punjab population has been a rich source for identifying genes causing or contributing to autosomal recessive retinal degenerations (arRD). This study was carried out to delineate the genetic architecture of arRD in the Pakistani population.MethodsThe genetic origin of arRD in a total of 144 families selected only for having consanguineous marriages and multiple members affected with arRD was examined. Of these, causative mutations had been identified in 62 families while only the locus had been identified for an additional 15. The remaining 67 families were subjected to homozygosity exclusion mapping by screening of closely flanking microsatellite markers at 180 known candidate genes/loci followed by sequencing of the candidate gene for pathogenic changes.ResultsOf these 67 families subjected to homozygosity mapping, 38 showed homozygosity for at least one of the 180 regions, and sequencing of the corresponding genes showed homozygous cosegregating mutations in 27 families. Overall, mutations were detected in approximately 61.8 % (89/144) of arRD families tested, with another 10.4% (15/144) being mapped to a locus but without a gene identified.ConclusionsThese results suggest the involvement of unmapped novel genes in the remaining 27.8% (40/144) of families. In addition, this study demonstrates that homozygosity mapping remains a powerful tool for identifying the genetic defect underlying genetically heterogeneous arRD disorders in consanguineous marriages for both research and clinical applications

Protective role of curcumin in disease progression from non-alcoholic fatty liver disease to hepatocellular carcinoma: a meta-analysis

Background: Pathological progression from non-alcoholic fatty liver disease (NAFLD) to liver fibrosis (LF) to hepatocellular carcinoma (HCC) is a common dynamic state in many patients. Curcumin, a dietary supplement derived from the turmeric family, is expected to specifically inhibit the development of this progression. However, there is a lack of convincing evidence.Methods: The studies published until June 2023 were searched in PubMed, Web of Science, Embase, and the Cochrane Library databases. The SYstematic Review Center for Laboratory animal Experimentation (SYRCLE) approach was used to evaluate the certainty of evidence. StataSE (version 15.1) and Origin 2021 software programs were used to analyze the critical indicators.Results: Fifty-two studies involving 792 animals were included, and three disease models were reported. Curcumin demonstrates a significant improvement in key indicators across the stages of NAFLD, liver fibrosis, and HCC. We conducted a detailed analysis of common inflammatory markers IL-1β, IL-6, and TNF-α, which traverse the entire disease process. The research results reveal that curcumin effectively hinders disease progression at each stage by suppressing inflammation. Curcumin exerted hepatoprotective effects in the dose range from 100 to 400 mg/kg and treatment duration from 4 to 10 weeks. The mechanistic analysis reveals that curcumin primarily exerts its hepatoprotective effects by modulating multiple signaling pathways, including TLR4/NF-κB, Keap1/Nrf2, Bax/Bcl-2/Caspase 3, and TGF-β/Smad3.Conclusion: In summary, curcumin has shown promising therapeutic effects during the overall progression of NAFLD–LF–HCC. It inhibited the pathological progression by synergistic mechanisms related to multiple pathways, including anti-inflammatory, antioxidant, and apoptosis regulation

Associations between body composition profile and hypertension in different fatty liver phenotypes

BackgroundIt is currently unclear whether and how the association between body composition and hypertension varies based on the presence and severity of fatty liver disease (FLD).MethodsFLD was diagnosed using ultrasonography among 6,358 participants. The association between body composition and hypertension was analyzed separately in the whole population, as well as in subgroups of non-FLD, mild FLD, and moderate/severe FLD populations, respectively. The mediation effect of FLD in their association was explored.ResultsFat-related anthropometric measurements and lipid metabolism indicators were positively associated with hypertension in both the whole population and the non-FLD subgroup. The strength of this association was slightly reduced in the mild FLD subgroup. Notably, only waist-to-hip ratio and waist-to-height ratio showed significant associations with hypertension in the moderate/severe FLD subgroup. Furthermore, FLD accounted for 17.26% to 38.90% of the association between multiple body composition indicators and the risk of hypertension.ConclusionsThe association between body composition and hypertension becomes gradually weaker as FLD becomes more severe. FLD plays a significant mediating role in their association

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Optimizing FPGA Design For Real Time Video Content Analysis

The rapid growth of camera and storage capabilities, over the past decade, has resulted in an exponential growth in the size of video repositories, such as YouTube. In 2015, 400 hours of videos are uploaded to YouTube every minute. At the same time, massive amount of images/videos are generated from monitoring cameras for elderly, sick assistance, satellites for earth science research, and telescopes for space exploration. Human annotation and manual manipulation of such videos are infeasible. Computer vision technology plays an essential role in automating the indexing, sorting, tagging, searching and analyzing huge amount of video data. Object detection and activity recognition in general are some of the most challenging topics in computer vision today. While the detection/recognition accuracy has increased dramatically over the past few years, it has not kept up with the complexity of detection/recognition tasks nor with the increased resolution of the video/image sources. As a result, the computation speed, and power consumption, of computer vision applications have become a major impediment to their wider use. Thus applications relying on real-time monitoring/feedback are not possible under current speeds. This thesis focuses on the use of Field Programmable Gate Arrays (FPGAs) to accelerate computer vision applications for embedded/real time applications while maintaining similar detection/recognition accuracy as the original processing. FPGAs are electronic devices on which an arbitrary digital circuit can be (re) configured under software control. To leverage the computational parallelism on FPGAs, fixed-point arithmetic is used for all implementations. The benefit of using fixed-point representation over floating point is the reduced bit-width, but the range and sometimes the precision are limited. Comprehensive studies are performed in this study to show that the classification system has some degree of tolerance to the reduced precision data representation. Hence FPGA programs are implemented accordingly in low bit-width fixed-point to achieve high computation throughput, low power consumption, and accurate classification.As a first step, the impact of reduced precision is studied for Viola-Jones face detection algorithm: whereas the reference OpenCV code uses double precision floating-point values, by using only five decimal digit (17 bits) fixed-point representation, the detection can achieve the same rates of false positives and false negatives as the reference OpenCV code. By reducing the necessary precision by a factor of 3X to 4X, the size of the circuit on FPGA is reduced by a factor of 12X; hence increasing the number of feature classifiers that can be fit on a single FPGA. A hybrid CPU-FPGA processing pipeline is proposed to reduce CPU work-load. As a second step, Histogram of Oriented Gradients (HOG), one of the most popular object detection algorithms, is evaluated by using the full-image evaluation methodology to explore the FPGA implementation of HOG using reduced bit-width. This approach lessens the required area resources on the FPGA and increases the clock frequency and hence the throughput per device through increased parallelism. Detection accuracy of the fixed-point HOG is evaluated by applying state-of-the-art computer vision pedestrian detection evaluation metrics. The reduced precision detection performs as well as the original floating-point code from OpenCV. This work then shows the single FPGA implementation achieves a 68.7x higher throughput than a high-end CPU, 5.1x higher than a high-end GPU, and 7.8x higher than the same implementation using floating-point on the same FPGA. A power consumption comparison for different platforms shows our fixed-point FPGA implementation uses 130x less power than CPU, and 31x less energy than GPU to process one image. In addition to object detection algorithms, this thesis also investigates the acceleration of action recognition, specifically a human action recognition (HAR) algorithm. In HAR, pedestrian detection is normally used as a pre-processing step to locate human in stream video. In this work, the possibility to perform feature extraction under reduced precision fixed-point arithmetic is evaluated to ease hardware resource requirements. The Histogram of Oriented Gradient in 3D (HOG3D) feature extraction is then compared with state-of-the-art Convolutional Neural Networks (CNNs) methods and result shows that the later is 75X slower than the former. The experiment shows that by re-training the classifier with reduced data precision, the classification performs as well as the original double-precision floating-point. Based on this result, an FPGA-based HAR feature extraction is implemented for near camera processing using fixed-point data representation and arithmetic. This implementation, using a single Xilinx Virtex 6 FPGA, achieves about 70x speedup over multicore CPU.Furthermore, a GPU implementation of HAR is introduced with 80x speedup over CPU (on an Nvidia Tesla K20)