Confidence Candidate Research Articles

Identification of epigenetically regulated genes involved in plant-virus interaction and their role in virus-triggered induced resistance

BackgroundPlant responses to a wide range of stresses are known to be regulated by epigenetic mechanisms. Pathogen-related investigations, particularly against RNA viruses, are however scarce. It has been demonstrated that Arabidopsis thaliana plants defective in some members of the RNA-directed DNA methylation (RdDM) or histone modification pathways presented differential susceptibility to the turnip mosaic virus. In order to identify genes directly targeted by the RdDM-related RNA Polymerase V (POLV) complex and the histone demethylase protein JUMONJI14 (JMJ14) during infection, the transcriptomes of infected mutant and control plants were obtained and integrated with available chromatin occupancy data for various epigenetic proteins and marks.ResultsA comprehensive list of virus-responsive gene candidates to be regulated by the two proteins was obtained. Twelve genes were selected for further characterization, confirming their dynamic regulation during the course of infection. Several epigenetic marks on their promoter sequences were found using in silico data, raising confidence that the identified genes are actually regulated by epigenetic mechanisms. The altered expression of six of these genes in mutants of the methyltransferase gene CURLY LEAF and the histone deacetylase gene HISTONE DEACETYLASE 19 suggests that some virus-responsive genes may be regulated by multiple coordinated epigenetic complexes. A temporally separated multiple plant virus infection experiment in which plants were transiently infected with one virus and then infected by a second one was designed to investigate the possible roles of the identified POLV- and JMJ14-regulated genes in wild-type (WT) plants. Plants that had previously been stimulated with viruses were found to be more resistant to subsequent virus challenge than control plants. Several POLV- and JMJ14-regulated genes were found to be regulated in virus induced resistance in WT plants, with some of them poisoned to be expressed in early infection stages.ConclusionsA set of confident candidate genes directly regulated by the POLV and JMJ14 proteins during virus infection was identified, with indications that some of them may be regulated by multiple epigenetic modules. A subset of these genes may also play a role in the tolerance of WT plants to repeated, intermittent virus infections.

Robust Localization-Guided Dual-Branch Network for Camouflaged Object Segmentation

The existence of camouflage targets is widespread in the natural world, as they blend seamlessly or closely resemble their surrounding environment, making it difficult for the human eye to identify them accurately. In camouflage target segmentation, challenges often arise from the high similarity between the foreground and background, resulting in segmentation errors, imprecise edge detection, and overlooking of small targets. To address these issues, this paper presents a robust localization-guided dual-branch network for the recognition of camouflaged targets. Two crucial branches, i.e., a localization branch and an overall refinement branch are designed and incorporated. The localization branch achieves accurate preliminary localization of camouflaged targets by incorporating the robust localization module, which integrates different high-level feature maps in a partially decoded manner. The overall refinement branch optimizes segmentation accuracy based on the output predictions of the localization branch. Within this branch, the edge refinement module is devised to effectively reduce false negative and false positive interference. By conducting context exploration on each feature layer from top to bottom, this module further enhances the precision of target edge segmentation. Additionally, our network employs five jointly trained output prediction maps and introduces attention-guided heads for diverse prediction maps in the overall refinement branch. This design adjusts the spatial positions and channel weights of different prediction maps, generating output prediction maps based on the emphasis of each output, thereby further strengthening the perception and feature representation capabilities of the model. To improve its ability to generate highly confident and accurate prediction candidate regions, tailored loss functions are designed to cater to the objectives of different prediction maps. We conducted experiments on three publicly available datasets for camouflaged object detection to assess our methodology and compared it with state-of-the-art network models. On the largest dataset COD10K, our method achieved a Structure-measure of 0.827 and demonstrated superior performance in other evaluation metrics, outperforming recent network models.

203P Identification of high confidence candidate markers for macrophage infiltrating tumor microenvironment through single cell genomic atlases

203P Identification of high confidence candidate markers for macrophage infiltrating tumor microenvironment through single cell genomic atlases

Exploratory Inference Learning for Scribble Supervised Semantic Segmentation

Scribble supervised semantic segmentation has achieved great advances in pseudo label exploitation, yet suffers insufficient label exploration for the mass of unannotated regions. In this work, we propose a novel exploratory inference learning (EIL) framework, which facilitates efficient probing on unlabeled pixels and promotes selecting confident candidates for boosting the evolved segmentation. The exploration of unannotated regions is formulated as an iterative decision-making process, where a policy searcher learns to infer in the unknown space and the reward to the exploratory policy is based on a contrastive measurement of candidates. In particular, we devise the contrastive reward with the intra-class attraction and the inter-class repulsion in the feature space w.r.t the pseudo labels. The unlabeled exploration and the labeled exploitation are jointly balanced to improve the segmentation, and framed in a close-looping end-to-end network. Comprehensive evaluations on the benchmark datasets (PASCAL VOC 2012 and PASCAL Context) demonstrate the superiority of our proposed EIL when compared with other state-of-the-art methods for the scribble-supervised semantic segmentation problem.

Search for a Black Hole Binary in Gaia DR3 Astrometric Binary Stars with Spectroscopic Data

We report the discovery of a candidate binary system consisting of a black hole (BH) and a red giant branch star in Gaia DR3. This binary system was discovered from 64,108 binary solutions for which both astrometric and spectroscopic data are available. For this system, the astrometric and spectroscopic solutions are consistent with each other, making this system a confident candidate of a BH binary. The primary (visible) star in this system, Gaia DR3 5870569352746779008, is a red giant branch star whose mass is quite uncertain. Fortunately, despite the uncertainty of the primary’s mass, we can estimate the mass of the secondary (dark) object in this system to be >5.68 M ⊙ with a probability of 99%, based on the orbital parameters. The mass of the secondary object is much larger than the maximum neutron star mass (∼2.0 M ⊙), which indicates that the secondary object is likely a BH. We argue that, if this dark object is not a BH, this system must be a more exotic system, in which the primary red giant branch star orbits around a quadruple star system (or a higher-order multiple-star system) whose total mass is more than 5.68 M ⊙. If this is a genuine BH binary, this has the longest period (1352.22 ± 45.81 days) among those discovered so far. As our conclusion entirely relies on Gaia DR3 data, independent confirmation with follow-up observations (e.g., long-term time-series spectra) is desired.

Benchmarking differential abundance analysis methods for correlated microbiome sequencing data.

Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Current microbiome studies frequently generate correlated samples from different microbiome sampling schemes such as spatial and temporal sampling. In the past decade, a number of DAA tools for correlated microbiome data (DAA-c) have been proposed. Disturbingly, different DAA-c tools could sometimes produce quite discordant results. To recommend the best practice to the field, we performed the first comprehensive evaluation of existing DAA-c tools using real data-based simulations. Overall, the linear model-based methods LinDA, MaAsLin2 and LDM are more robust than methods based on generalized linear models. The LinDA method is the only method that maintains reasonable performance in the presence of strong compositional effects.

Multi-Omics Profiling of JMML HSPCs Reveals Onco-Fetal Reprogramming and Identifies Novel Prognostic Biomarkers and Therapeutic Targets in High-Risk JMML

Juvenile myelomonocytic leukemia (JMML) is a myeloproliferative neoplasm of early childhood, which is characterized by highly heterogenous clinical outcomes ranging from spontaneous resolution to early relapse after hematopoietic stem cell transplantation (HSCT). Established clinical risk factors as well as characteristic mutational patterns do not fully explain this heterogeneity. Recently, we and others identified DNA methylation subgroups in JMML, which were shown to be the only significant factor for predicting overall survival (Schönung et al., Clinical Cancer Research 2021). Together, these findings suggest a functional role for DNA methylation in the molecular pathogenesis of JMML. To elucidate the impact of aberrant DNA methylation on pathogenesis and disease progression, we leveraged a multi-modal molecular analysis approach. We integrated flow cytometry data with single-cell RNA-seq and ultra-low input whole-genome bisulfite sequencing (WGBS) data in order to dissect the hematopoietic system across patients from all epigenetic subgroups (n=8). With this approach, we aimed to identify novel candidate prognostic biomarkers and therapeutic targets for high-risk JMML patients. We observed conservation of epigenetic subgroups already in the hematopoietic stem and progenitor cell (HSPC) compartment, suggesting that differential methylation might be key to understand pathogenesis and course of the disease in JMML. In addition, single-cell transcriptomics revealed disease-specific aberrations to be most pronounced in the HSPC compartment relative to all other hematopoietic cell types. Consequently, we focused our work on JMML HSPCs, which revealed a high degree of immunophenotypic, transcriptomic and methylomic heterogeneity across JMML risk groups. This included the expression of proto-oncogenes belonging to the AP-1 family, CTNNB1 and MECOM (EVI1). Furthermore, we observed divergent lineage priming across JMML subgroups which affected lymphoid, myeloid and erythroid differentiation programs. Remarkably, subgroup-specific aberrations comprised upregulation of fetal developmental programs in HSPCs from high-risk JMML patients. This is of particular interest regarding the cell-of-origin in JMML, since mutations activating the RAS signaling pathway have been identified in newborn blood samples from children who later developed JMML (Behnert et al., Leukemia 2022). Comparing the methylome data of HSPCs from JMML patients with HSPCs from healthy individuals at different developmental stages (fetal, perinatal, juvenile and adult) suggested progressive epigenomic reprogramming of leukemic HSPCs towards high-risk JMML. As a consequence, we observed signatures of both accelerated aging and aberrant activation of onco-fetal programs. This included the downregulation of postnatal HSPC markers such as AVP and vice versa the upregulation of prenatal markers such as HMGA2 in high-risk HSPCs, which was associated with differential methylation of these genes. Moreover, binding motifs of central hematopoietic and developmental transcription factors such as RUNX1, FLI1, CDX4, and several HOX genes were not only enriched in differentially methylated regions but were also found to be differentially methylated themselves. Systematic comparison of JMML HSPCs with their healthy nearest normal counterparts revealed disease-specific expression of several cell surface markers. The expression patterns appeared to be regulated in a subgroup-specific manner, which nominates these factors as novel prognostic biomarkers in JMML. Moreover, we identified surface markers that were not only differentially methylated but also known drug targets in other entities, which is why we selected these genes as high confidence candidates for further functional validation. In a preclinical patient-derived xenograft (PDX) mouse model, targeted treatment with a therapeutic antibody against one of these candidates efficiently depleted high-risk JMML HSPCs and improved survival of JMML-PDX mice. In conclusion, the personalized molecular analysis of JMML HSPCs disentangled disease-specific heterogeneity and provided novel insights into tumorigenesis. Subgroup-specific epigenetic dysregulation included onco-fetal reprogramming of JMML HSPCs and revealed novel prognostic biomarkers and promising novel drug targets specifically for high-risk JMML.

Metric assisted stochastic sampling search for gravitational waves from binary black hole mergers

We present a novel gravitational-wave detection algorithm that conducts a matched-filter search stochastically across the compact binary parameter space rather than relying on a fixed bank of template waveforms. This technique is competitive with standard template-bank-driven pipelines in both computational cost and sensitivity. However, the complexity of the analysis is simpler, allowing for easy configuration and horizontal scaling across heterogeneous grids of computers. To demonstrate the method we analyze approximately one month of public LIGO data from July 27 00:00 2017 UTC–Aug 25 22:00 2017 UTC and recover eight known confident gravitational-wave candidates. We also inject simulated binary black hole signals to demonstrate the sensitivity.Received 1 November 2021Accepted 4 October 2022DOI:https://doi.org/10.1103/PhysRevD.106.084033© 2022 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasClassical black holesGravitational wave detectionGravitational wave sourcesGravitation, Cosmology & Astrophysics

A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions

BackgroundDifferential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Numerous DAA tools have been proposed in the past decade addressing the special characteristics of microbiome data such as zero inflation and compositional effects. Disturbingly, different DAA tools could sometimes produce quite discordant results, opening to the possibility of cherry-picking the tool in favor of one’s own hypothesis. To recommend the best DAA tool or practice to the field, a comprehensive evaluation, which covers as many biologically relevant scenarios as possible, is critically needed.ResultsWe performed by far the most comprehensive evaluation of existing DAA tools using real data-based simulations. We found that DAA methods explicitly addressing compositional effects such as ANCOM-BC, Aldex2, metagenomeSeq (fitFeatureModel), and DACOMP did have improved performance in false-positive control. But they are still not optimal: type 1 error inflation or low statistical power has been observed in many settings. The recent LDM method generally had the best power, but its false-positive control in the presence of strong compositional effects was not satisfactory. Overall, none of the evaluated methods is simultaneously robust, powerful, and flexible, which makes the selection of the best DAA tool difficult. To meet the analysis needs, we designed an optimized procedure, ZicoSeq, drawing on the strength of the existing DAA methods. We show that ZicoSeq generally controlled for false positives across settings, and the power was among the highest. Application of DAA methods to a large collection of real datasets revealed a similar pattern observed in simulation studies.ConclusionsBased on the benchmarking study, we conclude that none of the existing DAA methods evaluated can be applied blindly to any real microbiome dataset. The applicability of an existing DAA method depends on specific settings, which are usually unknown a priori. To circumvent the difficulty of selecting the best DAA tool in practice, we design ZicoSeq, which addresses the major challenges in DAA and remedies the drawbacks of existing DAA methods. ZicoSeq can be applied to microbiome datasets from diverse settings and is a useful DAA tool for robust microbiome biomarker discovery.Ckj99NhoBCvpJt85XtgNgVVideo

Unravelling consensus genomic regions associated with quality traits in wheat using meta-analysis of quantitative trait loci.

Meta-analysis in wheat for three major quality traits identified 110 meta-QTL (MQTL) with reduced confidence interval (CI). Five GWAS validated MQTL (viz., 1A.1, 1B.2, 3B.4, 5B.2, and 6B.2), each involving more than 20 initial QTL and reduced CI (95%) (< 2cM), were selected for quality breeding programmes. Functional characterization including candidate gene mining and expression analysis discovered 44 high confidence candidate genes associated with quality traits. A meta-analysis of quantitative trait loci (QTL) associated with dough rheology properties, nutritional traits, and processing quality traits was conducted in wheat. For this purpose, as many as2458 QTL were collected from 50 interval mapping studies published during 2013-2020. Of the total QTL, 1126 QTL were projected onto the consensus map saturated with 249,603 markers which led to the identification of 110 meta-QTL (MQTL). These MQTL exhibited an 18.84-fold reduction in the average CI compared to the average CI of the initial QTL (ranging from 14.87 to 95.55cM with an average of 40.35cM). Of the 110, 108 MQTL were physically anchored to the wheat reference genome, including 51 MQTL verified with marker-trait associations (MTAs) reported from earlier genome-wide association studies. Candidate gene (CG) mining allowed the identification of 2533 unique gene models from the MQTL regions. In-silico expression analysis discovered 439 differentially expressed gene models with > 2 transcripts per million expressions in grains and related tissues, which also included 44 high-confidence CGs involved in the various cellular and biochemical processes related to quality traits. Nine functionally characterized wheat genes associated with grain protein content, high-molecular-weight glutenin, and starch synthase enzymes were also found to be co-localized with some of the MQTL. Synteny analysis between wheat and rice MQTL regions identified 23 wheat MQTL syntenic to 16 rice MQTL associated with quality traits. Furthermore, 64 wheat orthologues of 30 known rice genes were detected in 44 MQTL regions. Markers flanking the MQTL identified in the present study can be used for marker-assisted breeding and as fixed effects in the genomic selection models for improving the prediction accuracy during quality breeding. Wheat orthologues of rice genes and other CGs available from MQTLs can be promising targets for further functional validation and to better understand the molecular mechanism underlying the quality traits in wheat.

TAILS Identifies Candidate Substrates and Biomarkers of ADAMTS7, a Therapeutic Protease Target in Coronary Artery Disease

Loss-of-function mutations in the secreted enzyme ADAMTS7 (a disintegrin and metalloproteinase with thrombospondin motifs 7) are associated with protection for coronary artery disease. ADAMTS7 catalytic inhibition has been proposed as a therapeutic strategy for treating coronary artery disease; however, the lack of an endogenous substrate has hindered the development of activity-based biomarkers. To identify ADAMTS7 extracellular substrates and their cleavage sites relevant to vascular disease, we used TAILS (terminal amine isotopic labeling of substrates), a method for identifying protease-generated neo–N termini. We compared the secreted proteome of vascular smooth muscle and endothelial cells expressing either full-length mouse ADAMTS7 WT, catalytic mutant ADAMTS7 E373Q, or a control luciferase adenovirus. Significantly enriched N-terminal cleavage sites in ADAMTS7 WT samples were compared to the negative control conditions and filtered for stringency, resulting in catalogs of high confidence candidate ADAMTS7 cleavage sites from our three independent TAILS experiments. Within the overlap of these discovery sets, we identified 24 unique cleavage sites from 16 protein substrates, including cleavage sites in EFEMP1 (EGF-containing fibulin-like extracellular matrix protein 1/Fibulin-3). The ADAMTS7 TAILS preference for EFEMP1 cleavage at the amino acids 123.124 over the adjacent 124.125 site was validated using both endogenous EFEMP1 and purified EFEMP1 in a binary in vitro cleavage assay. Collectively, our TAILS discovery experiments have uncovered hundreds of potential substrates and cleavage sites to explore disease-related biological substrates and facilitate activity-based ADAMTS7 biomarker development.

Genome variation in tick infestation and cryptic divergence in Tunisian indigenous sheep

BackgroundTicks are obligate haematophagous ectoparasites considered second to mosquitos as vectors and reservoirs of multiple pathogens of global concern. Individual variation in tick infestation has been reported in indigenous sheep, but its genetic control remains unknown.ResultsHere, we report 397 genome-wide signatures of selection overlapping 991 genes from the analysis, using ROH, LR-GWAS, XP-EHH, and FST, of 600 K SNP genotype data from 165 Tunisian sheep showing high and low levels of tick infestations and piroplasm infections. We consider 45 signatures that are detected by consensus results of at least two methods as high-confidence selection regions. These spanned 104 genes which included immune system function genes, solute carriers and chemokine receptor. One region spanned STX5, that has been associated with tick resistance in cattle, implicating it as a prime candidate in sheep. We also observed RAB6B and TF in a high confidence candidate region that has been associated with growth traits suggesting natural selection is enhancing growth and developmental stability under tick challenge. The analysis also revealed fine-scale genome structure indicative of cryptic divergence in Tunisian sheep.ConclusionsOur findings provide a genomic reference that can enhance the understanding of the genetic architecture of tick resistance and cryptic divergence in indigenous African sheep.

Constraints on the Galactic Centre environment from Gaia hyper-velocity stars

ABSTRACT Following a dynamical encounter with Sgr A*, binaries in the Galactic Centre (GC) can be tidally separated and one member star ejected as a hyper-velocity star (HVS) with a velocity beyond the escape speed of the Milky Way. As GC-born objects located in more observationally accessible regions of the sky, HVSs offer insight into the stellar population in the inner parsecs of the Milky Way. We perform a suite of simulations ejecting stars from the GC, exploring how detectable HVS populations depend on assumptions concerning the GC stellar population, focusing on HVSs that would appear in current and/or future data releases from the Gaia space mission with precise astrometry and measured radial velocities. We show that predictions are sensitive to two parameters in particular: the shape of the stellar initial mass function (IMF) in the GC and the ejection rate of HVSs. The absence of confident HVS candidates in Gaia Data Release 2 excludes scenarios in which the HVS ejection rate is $\gtrsim 3\times 10^{-2} \, \mathrm{yr^{-1}}$. Stricter constraints will be placed on these parameters when more HVS candidates are unearthed in future Gaia data releases – assuming recent determinations of the GC IMF shape, one confident HVS at minimum is expected in Gaia DR3 and DR4 as long as the HVS ejection rate is greater than $\sim 10^{-3}$ and $\sim 10^{-5} \, \mathrm{yr^{-1}}$, respectively.

Exome Sequencing Reveals Novel Variants and Expands the Genetic Landscape for Congenital Microcephaly.

Congenital microcephaly causes smaller than average head circumference relative to age, sex and ethnicity and is most usually associated with a variety of neurodevelopmental disorders. The underlying etiology is highly heterogeneous and can be either environmental or genetic. Disruption of any one of multiple biological processes, such as those underlying neurogenesis, cell cycle and division, DNA repair or transcription regulation, can result in microcephaly. This etiological heterogeneity manifests in a clinical variability and presents a major diagnostic and therapeutic challenge, leaving an unacceptably large proportion of over half of microcephaly patients without molecular diagnosis. To elucidate the clinical and genetic landscapes of congenital microcephaly, we sequenced the exomes of 191 clinically diagnosed patients with microcephaly as one of the features. We established a molecular basis for microcephaly in 71 patients (37%), and detected novel variants in five high confidence candidate genes previously unassociated with this condition. We report a large number of patients with mutations in tubulin-related genes in our cohort as well as higher incidence of pathogenic mutations in MCPH genes. Our study expands the phenotypic and genetic landscape of microcephaly, facilitating differential clinical diagnoses for disorders associated with most commonly disrupted genes in our cohort.

Genome sequencing in families with congenital limb malformations

The extensive clinical and genetic heterogeneity of congenital limb malformation calls for comprehensive genome-wide analysis of genetic variation. Genome sequencing (GS) has the potential to identify all genetic variants. Here we aim to determine the diagnostic potential of GS as a comprehensive one-test-for-all strategy in a cohort of undiagnosed patients with congenital limb malformations. We collected 69 cases (64 trios, 1 duo, 5 singletons) with congenital limb malformations with no molecular diagnosis after standard clinical genetic testing and performed genome sequencing. We also developed a framework to identify potential noncoding pathogenic variants. We identified likely pathogenic/disease-associated variants in 12 cases (17.4%) including four in known disease genes, and one repeat expansion in HOXD13. In three unrelated cases with ectrodactyly, we identified likely pathogenic variants in UBA2, establishing it as a novel disease gene. In addition, we found two complex structural variants (3%). We also identified likely causative variants in three novel high confidence candidate genes. We were not able to identify any noncoding variants. GS is a powerful strategy to identify all types of genomic variants associated with congenital limb malformation, including repeat expansions and complex structural variants missed by standard diagnostic approaches. In this cohort, no causative noncoding SNVs could be identified.

THE RELATIONSHIP BETWEEN TIME MANAGEMENT, SELF-EFFICACY AND ACADEMIC SUCCESS LEVELS OF NURSING AND MIDWIFERY STUDENTS AND VARIOUS VARIABLES

Objective: This study was conducted to assess the relationship between time management skills, self-efficacy and average academic achievement levels of nursing and midwifery students and various variables. Methods: A total of 459 nursing and midwifery students participated in this descriptive study. The “Student Information Form”, “Time Management Questionnaire” and "Self-Efficacy Scale” were used to collect the data. Descriptive, t test, ANOVA and correlation analysis were used for the analysis of the data. Results: While the mean score of students Time Management Questionnaire 90.45 ± 12.87, Self-Efficacy Scale mean score was 86.69 ± 11.73. There was a positive correlation between the scores of Time Management Questionnaire, Self-Efficacy Scale with average academic achievement and between the scores of Self-Efficacy Scale with average academic achievement. There was a positive correlation between time management skills, academic achievement averages and self-efficacy levels, and between self-efficacy levels and academic achievement average scores. Moreover, it was found that the students' status of having free time activities affected Time Management, Self-efficacy and average academic achievement scores. Conclusion and Suggestions: It is necessary for nurses/midwives, who are professionals of the future, to be able to manage time well and to be confident candidates in order to provide better quality service. Therefore, it is necessary to evaluate these characteristics of nursing/midwifery students throughout their education period and to create necessary training programs in order to increase the service and quality they will offer in their professional lives.

Broad Kinase Inhibition Mitigates Early Neuronal Dysfunction in Tauopathy

Tauopathies are a group of more than twenty known disorders that involve progressive neurodegeneration, cognitive decline and pathological tau accumulation. Current therapeutic strategies provide only limited, late-stage symptomatic treatment. This is partly due to lack of understanding of the molecular mechanisms linking tau and cellular dysfunction, especially during the early stages of disease progression. In this study, we treated early stage tau transgenic mice with a multi-target kinase inhibitor to identify novel substrates that contribute to cognitive impairment and exhibit therapeutic potential. Drug treatment significantly ameliorated brain atrophy and cognitive function as determined by behavioral testing and a sensitive imaging technique called manganese-enhanced magnetic resonance imaging (MEMRI) with quantitative R1 mapping. Surprisingly, these benefits occurred despite unchanged hyperphosphorylated tau levels. To elucidate the mechanism behind these improved cognitive outcomes, we performed quantitative proteomics to determine the altered protein network during this early stage in tauopathy and compare this model with the human Alzheimer’s disease (AD) proteome. We identified a cluster of preserved pathways shared with human tauopathy with striking potential for broad multi-target kinase intervention. We further report high confidence candidate proteins as novel therapeutically relevant targets for the treatment of tauopathy. Proteomics data are available via ProteomeXchange with identifier PXD023562.

Heterogeneous ensemble selection for evolving data streams

Ensemble learning has been widely applied to both batch data classification and streaming data classification. For the latter setting, most existing ensemble systems are homogenous, which means they are generated from only one type of learning model. In contrast, by combining several types of different learning models, a heterogeneous ensemble system can achieve greater diversity among its members, which helps to improve its performance. Although heterogeneous ensemble systems have achieved many successes in the batch classification setting, it is not trivial to extend them directly to the data stream setting. In this study, we propose a novel HEterogeneous Ensemble Selection (HEES) method, which dynamically selects an appropriate subset of base classifiers to predict data under the stream setting. We are inspired by the observation that a well-chosen subset of good base classifiers may outperform the whole ensemble system. Here, we define a good candidate as one that expresses not only high predictive performance but also high confidence in its prediction. Our selection process is thus divided into two sub-processes: accurate-candidate selection and confident-candidate selection. We define an accurate candidate in the stream context as a base classifier with high accuracy over the current concept, while a confident candidate as one with a confidence score higher than a certain threshold. In the first sub-process, we employ the prequential accuracy to estimate the performance of a base classifier at a specific time, while in the latter sub-process, we propose a new measure to quantify the predictive confidence and provide a method to learn the threshold incrementally. The final ensemble is formed by taking the intersection of the sets of confident classifiers and accurate classifiers. Experiments on a wide range of data streams show that the proposed method achieves competitive performance with lower running time in comparison to the state-of-the-art online ensemble methods.

Abstract 4396: A bioinformatics approach for cancer immunotherapeutic target identification by evaluating surfaceome gene and protein expression (CITIESGAPE) in tumor and pan-normal tissues

Abstract Targeted therapeutic strategies and some immunotherapies for cancer including but not limited to therapeutic antibodies, antibody-drug conjugates (ADCs) and CAR-T cells therapy demand precisely targeting cell-surface proteins that are distinctively expressed by cancer cells. We developed a robust analytical tool for cancer immunotherapeutic target identification by evaluating surfaceome gene and protein expression (CITIESGAPE) that are uniquely overrepresented by the tumor in contrast to pan-normal tissues. The first step was to run differential expression testing on RNA-seq transcriptome of cancer (for example, data from TARGET and TCGA) against each normal tissue (31 tissues from GTEx_v8), from which candidate genes were defined as those are higher expressed in tumor than all normal tissues (minimum LogFC &amp;gt; 1 and maximum q &amp;lt; 0.05). We then validated the protein expression level of candidate genes through the Human Protein Atlas (HPA) and ProteomicsDB (The latter includes mass spectrometry-based label-free proteomic data of some tumor cells and 39 normal tissues). The final and crucial step was to run surfaceome protein predicting using the COMPARTMENTS database, the HPA database and the Membranome database (v2.0), depending on an optimized confidence score. We have applied this analytical pipeline to search surfaceome proteins that are unique to Osteosarcoma, a common primary malignant bone tumor in children and young adults, and obtained 57 candidates that are potential immunotherapeutic targets. To date, eight of the top confident candidates have been further validated in clinical samples, patient-derived cell lines and xenograft models. We believe this robust analytical tool could be adaptable to other types/subtypes of cancer in searching of candidate surfaceome proteins for targeted therapy and immunotherapy, as well as precision medicine and personalized therapy. Citation Format: Xiangjun Tian, Jing Wang, Yifei Wang, Zhongting Zhang, Michael Roth, Jonathan B. Gill, Richard Gorlick. A bioinformatics approach for cancer immunotherapeutic target identification by evaluating surfaceome gene and protein expression (CITIESGAPE) in tumor and pan-normal tissues [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4396.

Confident and cautious candidates: Explaining under‐representation of women in Danish municipal politics

AbstractWhy are women under‐represented even in democratic and egalitarian countries? Previous research considers either demand‐side or supply‐side explanations. We integrate both perspectives in a least‐likely case for the under‐representation of women, namely the municipal councils in Denmark. The data stems from a candidate choice conjoint experiment, a survey among potential candidates, and data on the actual pool of nominated candidates. On the voter demand‐side, we show that there is no pro‐male bias in general or in combination with other candidate traits nor that traits evaluated positively by voters appear more frequently among actual male candidates. On the supply‐side, we find that women are less likely to be interested in running for political office. This is primarily because women assess their own political qualifications significantly lower than men. The under‐supply of female candidates seem to drive the disparity suggesting that we should focus more on supply‐side factors to overcome the gender imbalance.