Deep Learning for Link Prediction in Dynamic Networks using Weak Estimators

Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks

Global Incidence and mortality of oesophageal cancer and their correlation with socioeconomic indicators temporal patterns and trends in 41 countries

Oesophageal cancers (adenocarcinomas [AC] and squamous cell carcinomas [SCC]) are characterized by high incidence/mortality in many countries. We aimed to delineate its global incidence and mortality, and studied whether socioeconomic development and its incidence rate were correlated. The age-standardized rates (ASRs) of incidence and mortality of this medical condition in 2012 for 184 nations from the GLOBOCAN database; national databases capturing incidence rates, and the WHO mortality database were examined. Their correlations with two indicators of socioeconomic development were evaluated. Joinpoint regression analysis was used to generate trends. The ratio between the ASR of AC and SCC was strongly correlated with HDI (r = 0.535 [men]; r = 0.661 [women]) and GDP (r = 0.594 [men]; r = 0.550 [women], both p < 0.001). Countries that reported the largest reduction in incidence in male included Poland (Average Annual Percent Change [AAPC] = −7.1, 95%C.I. = −12,−1.9) and Singapore (AAPC = −5.8, 95%C.I. = −9.5,−1.9), whereas for women the greatest decline was seen in Singapore (AAPC = −12.3, 95%C.I. = −17.3,−6.9) and China (AAPC = −5.6, 95%C.I. = −7.6,−3.4). The Philippines (AAPC = 4.3, 95%C.I. = 2,6.6) and Bulgaria (AAPC = 2.8, 95%C.I. = 0.5,5.1) had a significant mortality increase in men; whilst Columbia (AAPC = −6.1, 95%C.I. = −7.5,−4.6) and Slovenia (AAPC = −4.6, 95%C.I. = −7.9,−1.3) reported mortality decline in women. These findings inform individuals at increased risk for primary prevention

A summary of the 2012 JHU CLSP Workshop on Zero Resource Speech Technologies and Models of Early Language Acquisition

We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding zero resource (unsupervised) speech technologies and related models of early language acquisition. Centered around the tasks of phonetic and lexical discovery, we consider unified evaluation metrics, present two new approaches for improving speaker independence in the absence of supervision, and evaluate the application of Bayesian word segmentation algorithms to automatic subword unit tokenizations. Finally, we present two strategies for integrating zero resource techniques into supervised settings, demonstrating the potential of unsupervised methods to improve mainstream technologies.5 page(s

A large-scale in vivo analysis reveals that TALENs are significantly more mutagenic than ZFNs generated using context-dependent assembly

Zinc-finger nucleases (ZFNs) and TAL effector nucleases (TALENs) have been shown to induce targeted mutations, but they have not been extensively tested in any animal model. Here, we describe a large-scale comparison of ZFN and TALEN mutagenicity in zebrafish. Using deep sequencing, we found that TALENs are significantly more likely to be mutagenic and induce an average of 10-fold more mutations than ZFNs. We observed a strong correlation between somatic and germ-line mutagenicity, and identified germ line mutations using ZFNs whose somatic mutations rates are well below the commonly used threshold of 1%. Guidelines that have previously been proposed to predict optimal ZFN and TALEN target sites did not predict mutagenicity in vivo. However, we observed a significant negative correlation between TALEN mutagenicity and the number of CpG repeats in TALEN target sites, suggesting that target site methylation may explain the poor mutagenicity of some TALENs in vivo. The higher mutation rates and ability to target essentially any sequence make TALENs the superior technology for targeted mutagenesis in zebrafish, and likely other animal models

Paraneoplastic thrombocytosis in ovarian cancer

<p>Background: The mechanisms of paraneoplastic thrombocytosis in ovarian cancer and the role that platelets play in abetting cancer growth are unclear.</p> <p>Methods: We analyzed clinical data on 619 patients with epithelial ovarian cancer to test associations between platelet counts and disease outcome. Human samples and mouse models of epithelial ovarian cancer were used to explore the underlying mechanisms of paraneoplastic thrombocytosis. The effects of platelets on tumor growth and angiogenesis were ascertained.</p> <p>Results: Thrombocytosis was significantly associated with advanced disease and shortened survival. Plasma levels of thrombopoietin and interleukin-6 were significantly elevated in patients who had thrombocytosis as compared with those who did not. In mouse models, increased hepatic thrombopoietin synthesis in response to tumor-derived interleukin-6 was an underlying mechanism of paraneoplastic thrombocytosis. Tumorderived interleukin-6 and hepatic thrombopoietin were also linked to thrombocytosis in patients. Silencing thrombopoietin and interleukin-6 abrogated thrombocytosis in tumor-bearing mice. Anti–interleukin-6 antibody treatment significantly reduced platelet counts in tumor-bearing mice and in patients with epithelial ovarian cancer. In addition, neutralizing interleukin-6 significantly enhanced the therapeutic efficacy of paclitaxel in mouse models of epithelial ovarian cancer. The use of an antiplatelet antibody to halve platelet counts in tumor-bearing mice significantly reduced tumor growth and angiogenesis.</p> <p>Conclusions: These findings support the existence of a paracrine circuit wherein increased production of thrombopoietic cytokines in tumor and host tissue leads to paraneoplastic thrombocytosis, which fuels tumor growth. We speculate that countering paraneoplastic thrombocytosis either directly or indirectly by targeting these cytokines may have therapeutic potential. </p&gt

Evolutionarily conserved regulation of hypocretin neuron specification by Lhx9

Loss of neurons that express the neuropeptide hypocretin (Hcrt) has been implicated in narcolepsy, a debilitating disorder characterized by excessive daytime sleepiness and cataplexy. Cell replacement therapy, using Hcrt-expressing neurons generated in vitro, is a potentially useful therapeutic approach, but factors sufficient to specify Hcrt neurons are unknown. Using zebrafish as a high-throughput system to screen for factors that can specify Hcrt neurons in vivo, we identified the LIM homeobox transcription factor Lhx9 as necessary and sufficient to specify Hcrt neurons. We found that Lhx9 can directly induce hcrt expression and we identified two potential Lhx9 binding sites in the zebrafish hcrt promoter. Akin to its function in zebrafish, we found that Lhx9 is sufficient to specify Hcrt-expressing neurons in the developing mouse hypothalamus. Our results elucidate an evolutionarily conserved role for Lhx9 in Hcrt neuron specification that improves our understanding of Hcrt neuron development

Asking More Informative Questions for Grounded Retrieval

When a model is trying to gather information in an interactive setting, it benefits from asking informative questions. However, in the case of a grounded multi-turn image identification task, previous studies have been constrained to polar yes/no questions, limiting how much information the model can gain in a single turn. We present an approach that formulates more informative, open-ended questions. In doing so, we discover that off-the-shelf visual question answering (VQA) models often make presupposition errors, which standard information gain question selection methods fail to account for. To address this issue, we propose a method that can incorporate presupposition handling into both question selection and belief updates. Specifically, we use a two-stage process, where the model first filters out images which are irrelevant to a given question, then updates its beliefs about which image the user intends. Through self-play and human evaluations, we show that our method is successful in asking informative open-ended questions, increasing accuracy over the past state-of-the-art by 14%, while resulting in 48% more efficient games in human evaluations