VISPUR: Visual Aids for Identifying and Interpreting Spurious Associations in Data-Driven Decisions.

Big data and machine learning tools have jointly empowered humans in making data-driven decisions. However, many of them capture empirical associations that might be spurious due to confounding factors and subgroup heterogeneity. The famous Simpson's paradox is such a phenomenon where aggregated and subgroup-level associations contradict with each other, causing cognitive confusions and difficulty in making adequate interpretations and decisions. Existing tools provide little insights for humans to locate, reason about, and prevent pitfalls of spurious association in practice. We propose VISPUR, a visual analytic system that provides a causal analysis framework and a human-centric workflow for tackling spurious associations. These include a CONFOUNDER DASHBOARD, which can automatically identify possible confounding factors, and a SUBGROUP VIEWER, which allows for the visualization and comparison of diverse subgroup patterns that likely or potentially result in a misinterpretation of causality. Additionally, weproposea REASONING STORYBOARD, which uses a flow-based approach to illustrate paradoxical phenomena, as well as an interactive DECISION DIAGNOSIS panel that helps ensure accountable decision-making. Through an expert interview and a controlled user experiment, our qualitative and quantitative results demonstrate that the proposed "de-paradox" workflow and the designed visual analytic system are effective in helping human users to identify and understand spurious associations, as well as to make accountable causal decisions.

Optimizing respiratory virus surveillance networks using uncertainty propagation.

Infectious disease prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for diseases with limited records. The  proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens - human metapneumovirus and seasonal coronavirus - from 35 US states and can be used to guide systemic allocation of surveillance efforts.

StoCast: Stochastic Disease Forecasting With Progression Uncertainty.

Forecasting patients' disease progressions with rich longitudinal clinical data has drawn much attention in recent years due to its impactful application in healthcare and the medical field. Researchers have tackled this problem by leveraging traditional machine learning, statistical techniques and deep learning based models. However, existing methods suffer from either deterministic internal structures or over-simplified stochastic components, failing to deal with complex uncertain scenarios such as progression uncertainty (i.e., multiple possible trajectories) and data uncertainty (i.e., imprecise observations and misdiagnosis). To overcome these major uncertainty issues, we propose a novel deep generative model, Stochastic Disease Forecasting Model (StoCast), along with an associated neural network architecture StoCastNet, that can be trained efficiently via stochastic optimization techniques. Our StoCast model uses internal stochastic components to deal with departures of observed data from patients' true health states, and more importantly, is able to produce a comprehensive estimate of future disease progression trajectories. Based on two public datasets related to Alzheimer's disease and Parkinson's disease, we demonstrate how our StoCast model achieves robust and superior performance than deterministic baseline approaches, and conveys richer information that can potentially assist doctors to make decisions with greater confidence in a complex uncertain scenario.

Management and outcomes of pregnant patients with central nervous system hemangioblastoma.

The study aims to assess the management and maternal and fetal outcomes of pregnancies complicated by central nervous system (CNS) hemangioblastoma. Twenty-four female patients with CNS hemangioblastoma, who were pregnant in a tumor-burden status, were identified. Their medical charts, treatments, and follow-up materials were carefully reviewed. Of the included 24 CNS patients with hemangioblastoma (14 intracranial and 10 spinal hemangioblastomas), 5 patients (20.8%) were diagnosed with Von Hippel-Lindau disease (VHL). The median age of these patients at admission was 27.5 years. Intracranial hypertension was a common presenting symptom for patients with intracranial hemangioblastoma and was observed in 85.7% (12/14) of cases; the other 10 patients with spinal hemangioblastomas all suffered from paresthesia. Overall, 66.7% (16/24) of patients with CNS hemangioblastoma went through the gestational course with conventional observation; 16.6% (4/24) of patients accepted a ventriculo-peritoneal shunt (VPS) to delay the tumor resection; and 16.7% (4/24) of patients needed urgent tumor resection even when symptomatic treatments were given. Variable symptom improvement was seen when patients had follow-up visits at a median of 32.5 months. No maternal death or tumor recurrence was identified. For the fetal prognoses, one (4.2%) pregnancy ended in a spontaneous miscarriage and for (16.7%) pregnancies were interrupted; the other 19 (79.2%) live births were in good status without any congenital malformations. Symptomatic treatment was the first choice for pregnant patients with CNS hemangioblastoma. When needed, urgent tumor resection could be safely achieved with careful maternal and fetal monitoring. Both maternal and fetal prognoses were favorable during follow-up.

Intracranial Mesenchymal Chondrosarcoma: Report of 16 Cases.

OBJECTIVES: Limited data regarding intracranial mesenchymal chondrosarcoma (MCS) are available. The goal of this study was to report the clinical characteristics, challenges in management, and poor outcomes of intracranial MCS. METHODS: Clinical data for 16 patients with MCS were reviewed retrospectively to evaluate their clinical characteristics, management, and outcomes. RESULTS: This study included 11 male and 5 female patients with a mean age of 22.9 ± 14.4 years. The most common presentations were headache (n = 10; 62.5%), followed by cranial deficits (n = 7; 43.6%). The radiologic spectrum for MCS was broad, and only 18.8% (3/16) of MCSs were correctly diagnosed preoperatively. Aggressive resection (including subtotal resection and gross total resection) and partial resection was performed in 62.5% (10/16) and 37.50% (6/16) of patients. With a median follow-up of 34 months (range, 10-78 months), 5 patients (31.3%) died and 8 patients (50%) developed tumor recurrence. The 1-, 3-, and 5-year rates of progression-free survival and overall survival were 86%, 53%, and 42% and 93%, 70%, and 56%, respectively. Although the differences were not significantly different, aggressive resection and the use of radiotherapy tended to improve the prognosis of the patients. CONCLUSIONS: Clinical characteristics of MCS are variable. The current management of intracranial MCS referring to conventional chondrosarcoma could not yield satisfactory outcomes. Further study is needed to identify the optimal treatments.

Intratumoral Hemorrhage as an Unusual Manifestation of Intracranial Subependymoma.

BACKGROUND: Subependymoma is rare, and little is known about subependymoma with intratumoral hemorrhage. METHODS: A retrospective study of subependymoma was performed. Among 61 subependymomas, 4 cases of intratumoral hemorrhage were collected. All 4 cases were pathologically confirmed to be subependymoma and showed a benign character. RESULTS: After complete subependymoma resection, the 4 patients achieved favorable outcomes. Pathology showed that dilated thin-walled vessels and/or hyalinosis of the vessel walls existed in all 4 cases. CONCLUSIONS: The present series showed that subependymomas with hemorrhage and benign pathology are rare and that surgical treatment results in good prognosis. This series supports the hypothesis that the pathology of vascular degeneration may contribute to subependymoma hemorrhage.

Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data.

The increasing availability of spatiotemporal data continuously collected from various sources provides new opportunities for a timely understanding of the data in their spatial and temporal context. Finding abnormal patterns in such data poses significant challenges. Given that there is often no clear boundary between normal and abnormal patterns, existing solutions are limited in their capacity of identifying anomalies in large, dynamic and heterogeneous data, interpreting anomalies in their multifaceted, spatiotemporal context, and allowing users to provide feedback in the analysis loop. In this work, we introduce a unified visual interactive system and framework, Voila, for interactively detecting anomalies in spatiotemporal data collected from a streaming data source. The system is designed to meet two requirements in real-world applications, i.e., online monitoring and interactivity. We propose a novel tensor-based anomaly analysis algorithm with visualization and interaction design that dynamically produces contextualized, interpretable data summaries and allows for interactively ranking anomalous patterns based on user input. Using the "smart city" as an example scenario, we demonstrate the effectiveness of the proposed framework through quantitative evaluation and qualitative case studies.

Efficient collective influence maximization in cascading processes with first-order transitions.

In many social and biological networks, the collective dynamics of the entire system can be shaped by a small set of influential units through a global cascading process, manifested by an abrupt first-order transition in dynamical behaviors. Despite its importance in applications, efficient identification of multiple influential spreaders in cascading processes still remains a challenging task for large-scale networks. Here we address this issue by exploring the collective influence in general threshold models of cascading process. Our analysis reveals that the importance of spreaders is fixed by the subcritical paths along which cascades propagate: the number of subcritical paths attached to each spreader determines its contribution to global cascades. The concept of subcritical path allows us to introduce a scalable algorithm for massively large-scale networks. Results in both synthetic random graphs and real networks show that the proposed method can achieve larger collective influence given the same number of seeds compared with other scalable heuristic approaches.

Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks.

Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called "Collective Influence (CI)" has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes' significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct "virtual" information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes' importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community.

[Effects of chronic amiodarone therapy on L-type calcium current recovery and action potential duration of rabbit ventricular myocytes].

OBJECTIVE: To investigate the effects of chronic amiodarone therapy on L-type calcium current recovery and action potential duration of rabbit ventricular myocytes. METHODS: Healthy rabbits (1.6-1.8 kg) were treated with amiodarone (80 mg x kg(-1) x d(-1)) for four weeks. Action potential duration (APD) was recorded under isolated arterially perfused left ventricular wedge preparation, then single myocytes were isolated using enzyme digestion. L-type calcium current recovery (time constant, tau) were determined by fitting data with monoexponential. Tau/APD90 were compared in cells treated with saline, amiodarone and sotalol (3 x 10(-5) mmol/L). RESULTS: In chronic amiodarone treated myocytes, tau [(164 +/- 8) ms vs. (98 +/- 8) ms, P<0.05], APD90 [(321 +/- 12) ms vs. (220 +/- 10) ms, P<0.05] and tau/APD90 (0.51 +/- 0.03 vs. 0.44 +/- 0.03, P<0.05) were significantly increased than those in control myocytes. Sotalol significantly increased tau [(128 +/- 7) ms vs. (98 +/- 8) ms, P<0.05] and ADP90 [(405 +/- 13) ms vs. (220 +/- 10) ms, P<0.05] while reduced the tau/APD90 (0.32 +/- 0.05 vs. 0.44 +/- 0.03, P<0.05) compared to control myocytes. CONCLUSION: The differential effect of amiodarone and sotalol on ventricular myocytes tau/APD90 ratio might be responsible for the safety profile of these two drugs.

Effects of chronic amiodarone therapy on L-type calcium current recovery and action potential duration of rabbit ventricular myocytes / 中华心血管病杂志

<p><b>OBJECTIVE</b>To investigate the effects of chronic amiodarone therapy on L-type calcium current recovery and action potential duration of rabbit ventricular myocytes.</p><p><b>METHODS</b>Healthy rabbits (1.6-1.8 kg) were treated with amiodarone (80 mg x kg(-1) x d(-1)) for four weeks. Action potential duration (APD) was recorded under isolated arterially perfused left ventricular wedge preparation, then single myocytes were isolated using enzyme digestion. L-type calcium current recovery (time constant, tau) were determined by fitting data with monoexponential. Tau/APD90 were compared in cells treated with saline, amiodarone and sotalol (3 x 10(-5) mmol/L).</p><p><b>RESULTS</b>In chronic amiodarone treated myocytes, tau [(164 +/- 8) ms vs. (98 +/- 8) ms, P<0.05], APD90 [(321 +/- 12) ms vs. (220 +/- 10) ms, P<0.05] and tau/APD90 (0.51 +/- 0.03 vs. 0.44 +/- 0.03, P<0.05) were significantly increased than those in control myocytes. Sotalol significantly increased tau [(128 +/- 7) ms vs. (98 +/- 8) ms, P<0.05] and ADP90 [(405 +/- 13) ms vs. (220 +/- 10) ms, P<0.05] while reduced the tau/APD90 (0.32 +/- 0.05 vs. 0.44 +/- 0.03, P<0.05) compared to control myocytes.</p><p><b>CONCLUSION</b>The differential effect of amiodarone and sotalol on ventricular myocytes tau/APD90 ratio might be responsible for the safety profile of these two drugs.</p>