The Cure: Making a game of gene selection for breast cancer survival prediction

Motivation: Molecular signatures for predicting breast cancer prognosis could greatly improve care through personalization of treatment. Computational analyses of genome-wide expression datasets have identified such signatures, but these signatures leave much to be desired in terms of accuracy, reproducibility and biological interpretability. Methods that take advantage of structured prior knowledge (e.g. protein interaction networks) show promise in helping to define better signatures but most knowledge remains unstructured. Crowdsourcing via scientific discovery games is an emerging methodology that has the potential to tap into human intelligence at scales and in modes previously unheard of. Here, we developed and evaluated a game called The Cure on the task of gene selection for breast cancer survival prediction. Our central hypothesis was that knowledge linking expression patterns of specific genes to breast cancer outcomes could be captured from game players. We envisioned capturing knowledge both from the players prior experience and from their ability to interpret text related to candidate genes presented to them in the context of the game. Results: Between its launch in Sept. 2012 and Sept. 2013, The Cure attracted more than 1,000 registered players who collectively played nearly 10,000 games. Gene sets assembled through aggregation of the collected data clearly demonstrated the accumulation of relevant expert knowledge. In terms of predictive accuracy, these gene sets provided comparable performance to gene sets generated using other methods including those used in commercial tests. The Cure is available at http://genegames.org/cure

Evaluation of a School Campaign to Reduce Hatred

Combating violent extremism can involve organizing Peer-to-Peer (P2P) preventing violent extremism (PVE) programs and campaigns. In recent years, hundreds of school campaigns have been launched around the world but very few have been evaluated. In this manuscript, we present the results of the evaluation of one of these initiatives.  Study objectives consisted of: 1) Assessing the impact of the campaign in increasing students’ exposure to messages of acceptance and decreasing exposure to hate messages in the school environment, 2) Assess the impact of the campaign in improving students’ attitudes towards ethnocultural diversity. We conducted a longitudinal cohort study with control groups. The study was implemented in Utah in schools of 8th and 9th-grade levels. Two schools were identified as campaign implementation sites, and two schools of similar socio-economic and ethnocultural characteristics were identified as the control sites.  We utilized univariate and multivariate regression analysis to assess changes in students’ exposure to hate messages and attitudes towards ethnocultural diversity. Our study findings can be useful for the development of future campaigns and educational programs as they highlight the importance of ethnocultural empathic awareness in improving students’ attitudes regarding ethnocultural diversity

Tune-In: Training Under Negative Environments with Interference for Attention Networks Simulating Cocktail Party Effect

We study the cocktail party problem and propose a novel attention network called Tune-In, abbreviated for training under negative environments with interference. It firstly learns two separate spaces of speaker-knowledge and speech-stimuli based on a shared feature space, where a new block structure is designed as the building block for all spaces, and then cooperatively solves different tasks. Between the two spaces, information is cast towards each other via a novel cross- and dual-attention mechanism, mimicking the bottom-up and top-down processes of a human's cocktail party effect. It turns out that substantially discriminative and generalizable speaker representations can be learnt in severely interfered conditions via our self-supervised training. The experimental results verify this seeming paradox. The learnt speaker embedding has superior discriminative power than a standard speaker verification method; meanwhile, Tune-In achieves remarkably better speech separation performances in terms of SI-SNRi and SDRi consistently in all test modes, and especially at lower memory and computational consumption, than state-of-the-art benchmark systems.Comment: Accepted in AAAI 202

Sandglasset: A Light Multi-Granularity Self-attentive Network For Time-Domain Speech Separation

One of the leading single-channel speech separation (SS) models is based on a TasNet with a dual-path segmentation technique, where the size of each segment remains unchanged throughout all layers. In contrast, our key finding is that multi-granularity features are essential for enhancing contextual modeling and computational efficiency. We introduce a self-attentive network with a novel sandglass-shape, namely Sandglasset, which advances the state-of-the-art (SOTA) SS performance at significantly smaller model size and computational cost. Forward along each block inside Sandglasset, the temporal granularity of the features gradually becomes coarser until reaching half of the network blocks, and then successively turns finer towards the raw signal level. We also unfold that residual connections between features with the same granularity are critical for preserving information after passing through the bottleneck layer. Experiments show our Sandglasset with only 2.3M parameters has achieved the best results on two benchmark SS datasets -- WSJ0-2mix and WSJ0-3mix, where the SI-SNRi scores have been improved by absolute 0.8 dB and 2.4 dB, respectively, comparing to the prior SOTA results.Comment: Accepted in ICASSP 202

Contrastive Separative Coding for Self-supervised Representation Learning

To extract robust deep representations from long sequential modeling of speech data, we propose a self-supervised learning approach, namely Contrastive Separative Coding (CSC). Our key finding is to learn such representations by separating the target signal from contrastive interfering signals. First, a multi-task separative encoder is built to extract shared separable and discriminative embedding; secondly, we propose a powerful cross-attention mechanism performed over speaker representations across various interfering conditions, allowing the model to focus on and globally aggregate the most critical information to answer the "query" (current bottom-up embedding) while paying less attention to interfering, noisy, or irrelevant parts; lastly, we form a new probabilistic contrastive loss which estimates and maximizes the mutual information between the representations and the global speaker vector. While most prior unsupervised methods have focused on predicting the future, neighboring, or missing samples, we take a different perspective of predicting the interfered samples. Moreover, our contrastive separative loss is free from negative sampling. The experiment demonstrates that our approach can learn useful representations achieving a strong speaker verification performance in adverse conditions.Comment: Accepted in ICASSP 202

Fact sheet: The impacts of the woody biomass utilization program in Eastern Arizona

Utilizing woody biomass - small-diameter material and low-valued trees removed from forest restoration activities - on public lands may help reduce agency costs, enhance community wildfire protection, and create employment and economic activity. Yet small businesses adjacent to public land often lack the capacity to harvest and utilize biomass. Businesses face challenges such as limited access to capital and markets, technical assistance, and inconsistent material supply. From 2005-2010, the USDA Forest Service's Woody Biomass Utilization Grant (Woody BUG) program provided resources to address these barriers. We evaluated and compared the impacts of this program in eastern Oregon and in eastern Arizona. Here we summarize the findings from eastern Arizona

Cross-national vaccine concerns and predictors of vaccine hesitancy in not-fully vaccinated individuals: findings from USA, Canada, Sweden, and Italy

Vaccine hesitancy is a key contributor to reduced COVID-19 vaccine uptake and remains a threat to COVID-19 mitigation strategies as many countries are rolling out the campaign for booster shots. The goal of our study is to identify and compare the top vaccine concerns in four countries: Canada, Italy, Sweden, and the USA and how these concerns relate to vaccine hesitancy. While most individuals in these countries are now vaccinated, we expect our results to be helpful in guiding vaccination efforts for additional doses, and more in general for other vaccines in the future. We sought to empirically test whether vaccine related concerns followed similar thematic issues in the four countries included in this study, and then to see how these themes related to vaccine hesitancy using data from a cross-sectional survey conducted in May 2021. We applied CFA and created vaccine concern scales for analysis. We then utilized these results in regression-based modeling to determine how concerns related to vaccine hesitancy and whether there were similar or different concerns by country. The results quantitatively highlight that the same vaccine related concerns permeated multiple countries at the same point in time. This implies that COVID-19 vaccination communications could benefit from global collaboration

Identification of a candidate gene for a QTL for spikelet number per spike on wheat chromosome arm 7AL by high-resolution genetic mapping.

Key messageA high-resolution genetic map combined with haplotype analyses identified a wheat ortholog of rice gene APO1 as the best candidate gene for a 7AL locus affecting spikelet number per spike. A better understanding of the genes controlling differences in wheat grain yield components can accelerate the improvements required to satisfy future food demands. In this study, we identified a promising candidate gene underlying a quantitative trait locus (QTL) on wheat chromosome arm 7AL regulating spikelet number per spike (SNS). We used large heterogeneous inbred families ( > 10,000 plants) from two crosses to map the 7AL QTL to an 87-kb region (674,019,191-674,106,327 bp, RefSeq v1.0) containing two complete and two partial genes. In this region, we found three major haplotypes that were designated as H1, H2 and H3. The H2 haplotype contributed the high-SNS allele in both H1 × H2 and H2 × H3 segregating populations. The ancestral H3 haplotype is frequent in wild emmer (48%) but rare (~ 1%) in cultivated wheats. By contrast, the H1 and H2 haplotypes became predominant in modern cultivated durum and common wheat, respectively. Among the four candidate genes, only TraesCS7A02G481600 showed a non-synonymous polymorphism that differentiated H2 from the other two haplotypes. This gene, designated here as WHEAT ORTHOLOG OF APO1 (WAPO1), is an ortholog of the rice gene ABERRANT PANICLE ORGANIZATION 1 (APO1), which affects spikelet number. Taken together, the high-resolution genetic map, the association between polymorphisms in the different mapping populations with differences in SNS, and the known role of orthologous genes in other grass species suggest that WAPO-A1 is the most likely candidate gene for the 7AL SNS QTL among the four genes identified in the candidate gene region