BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility (P. aureginosa only). We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that, while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. We finally report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens. 157 project. Predicting GO terms for a protein (protein-centric) and predicting which proteins are associated 158 with a given function (term-centric) are related but different computational problems: the former is a 159 multi-label classification problem with a structured output, while the latter is a binary classification task. 160Predicting the results of a genome-wide screen for a single or a small number of functions fits the term-centric 161 formulation. To see how well all participating CAFA methods perform term-centric predictions, we mapped 162 results from the protein-centric CAFA3 methods onto these terms. In addition we held a separate CAFA 163 challenge, CAFA-π whose purpose was to attract additional submissions from algorithms that specialize in 164 term-centric tasks. 165 We performed screens for three functions in three species, which we then used to assess protein function 166 prediction. In the bacterium Pseudomonas aeruginosa and the fungus Candida albicans we performed 167 genome-wide screens capable of uncovering genes with two functions, biofilm formation (GO:0042710) and 168 motility (for P. aeruginosa only) (GO:0001539), as described in Methods. In Drosophila melanogaster we 169 performed targeted assays, guided by previous CAFA submissions, of a ...

The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.

Allergen-specific immunotherapy is widely used for allergic rhinitis and asthma treatment worldwide. This study explored the efficacy and safety of sublingual immunotherapy (SLIT) with the extracts of Dermatophagoides Farinae (D. farinae Drops) on house dust mites (HDM)-induced atopic dermatitis (AD). 239 patients with HDM-induced AD were recruited and exposure to a multi-centre, randomized, double-blind, and placebo-controlled clinical trials for 36 weeks, which were randomly divided into placebo and sublingual D. farinae Drops groups (high-dose, medium-dose and low-dose), respectively. Statistical analysis was performed in three groups: Full Analysis Set, Per Protocol Set and Safety Set. 48 cases have withdrawn from the study before the end of study. As primary outcomes, significant decreases in scoring atopic dermatitis and total medication score were showed in medium-dose and high-dose D. farinae Drops groups. In the sixth visit, the skin lesion area showed a statistically significant difference between high-dose/medium-dose D. farinae Drops group and placebo group (p < .05). Most adverse events are slight, and no life-threatening adverse drug reaction happened. Our research demonstrates the beneficial effect of SLIT with high or medium dose D. farinae Drops on AD, and the treatment was well tolerated.

Acne vulgaris (AV) is a chronic skin disease involving inflammation of the pilosebaceous units. Propionibacterium acnes (P. acnes) hypercolonization is one pathogenic factor for AV. P. acnes that triggers interleukin-1β (IL-1β) by activating the pyrin domain-containing 3 protein (NLRP3) inflammasome of the NOD-like receptor family in human monocytes. Reactive oxygen species (ROS) acts as a trigger for the production of IL-8 and activates theNLRP3 inflammasome. IL-8 promotes the metastasis and multiplication of different cancerous cells, whereas keratinocyte proliferation and migration contribute to the progression of AV. A steroidal saponin called polyphyllin I (PPI) that is extracted from Paris polyphylla’s rhizomes has anti-inflammatory properties. This study investigates the regulatory role of P. acnes in the secretion of IL-8 mediated by the CD36/NADPH oxidase 1 (NOX1)/ROS/NLRP3/IL-1β pathway and the effects of PPI on the CD36/NOX1/ROS/NLRP3/IL-1β/IL-8 pathway and human keratinocyte proliferation and migration. HaCaT cells were cultured and stimulated with 108 CFU/ml of P. acnes for 0, 6, 12, 18, 24, 30, and 36 hours. P. acnes induced IL-8 secretion from HaCaT cells via the CD36/NOX1/ROS/NLRP3/IL-1β pathway. PPI inhibited the CD36/NLRP3/NOX1/ROS/IL-8/IL-1β pathway and HaCaT cell proliferation and migration. PPI alleviates P. acnes-induced inflammatory responses and human keratinocyte proliferation and migration, implying a novel potential therapy for AV.

The accuracy of machine learning tasks is critically dependent on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data points of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We explore the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, with no significant difference between the two MTurk worker types. The quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.

The perifollicular vasculature undergoes hair-cycle dependent expansion and degeneration. Multiple soluble factors derived from dermal papilla cells (DPCs) may act on surrounding blood vessels to influence angiogenesis, growth and differentiation, and thereby regulate cyclic hair growth. The goal of this study was to examine the expression of angiogenin, a potent angiogenic factor, in human DPCs, and to determine its role in hair growth. Reverse transcription polymerase chain reaction (RT-PCR), western blotting, immunofluorescence and ELISA analyzes were used to investigate the expression of angiogenin in human DPCs, while semi-quantitative RT-PCR was used to assess angiogenin mRNA expression in murine skin phased at different stages of the hair cycle. We detected angiogenin expression in DPCs, where it was found to be localized to the cytoplasm. Angiogenin mRNA was expressed in murine skin in a hair-cycle dependent manner, with maximum levels observed at the late anagen. Local injection of angiogenin promoted skin angiogenesis and induced anagen VI. In vitro studies showed that angiogenin significantly enhanced the elongation of hair follicles, and stimulated DPCs and ORS keratinocytes to proliferate. Taken together, these findings show that angiogenin is expressed in human DPCs, where it might contribute to hair growth directly, by stimulating DPCs and ORS keratinocytes to proliferate, or indirectly, by inducing local vascularization.

Running Title: Critical assessment finds novel genes in Drosophila learning and memory. Key Words • D. melanogaster • Parasitoid wasp • Learning and memory • Long-term memory • Behavior • Bioinformatics • Gene function prediction • Critical assessment ABSTRACT A major bottleneck to our understanding of the genetic and molecular foundation of life lies in the ability to assign function to a gene and, subsequently, a protein. Traditional molecular and genetic experiments can provide the most reliable forms of identification, but are generally low-throughput, making such discovery and assignment a daunting task. The bottleneck has led to an increasing role for computational approaches. The CriticalAssessment of Functional Annotation (CAFA) effort seeks to measure the performance of computational methods. In CAFA3 we performed selected screens, including an effort focused on long-term memory. We used homology and previous CAFA predictions to identify 29 key Drosophila genes, which we tested via a long-term memory screen. We identify 11 novel genes that are involved in long-term memory formation and show a high level of connectivity with previously identified learning and memory genes. Our study provides first higher-order behavioral assay and organism screen used for CAFA assessments and revealed previously uncharacterized roles of multiple genes as possible regulators of neuronal plasticity at the boundary of information acquisition and memory formation.