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Reciprocity, evolution, and decision games in network and data science
\"Learn how to analyze and manage evolutionary and sequential user behaviors in modern networks, and how to optimize network performance by using indirect reciprocity, evolutionary games, and sequential decision-making. Understand the latest theory without the need to go through the details of traditional game theory. With practical management tools to regulate user behavior and simulations and experiments with real data sets, this is an ideal tool for graduate students and researchers working in networking, communications, and signal processing\"-- Provided by publisher.
Book
The Gonium pectorale genome demonstrates co-option of cell cycle regulation during the evolution of multicellularity
The transition to multicellularity has occurred numerous times in all domains of life, yet its initial steps are poorly understood. The volvocine green algae are a tractable system for understanding the genetic basis of multicellularity including the initial formation of cooperative cell groups. Here we report the genome sequence of the undifferentiated colonial alga,
Gonium pectorale,
where group formation evolved by co-option of the retinoblastoma cell cycle regulatory pathway. Significantly, expression of the
Gonium
retinoblastoma cell cycle regulator in unicellular
Chlamydomonas
causes it to become colonial. The presence of these changes in undifferentiated
Gonium
indicates extensive group-level adaptation during the initial step in the evolution of multicellularity. These results emphasize an early and formative step in the evolution of multicellularity, the evolution of cell cycle regulation, one that may shed light on the evolutionary history of other multicellular innovations and evolutionary transitions.
The undifferentiated
Gonium pectorale
represents the initial transition to multicellularity. Here, Bradley Olson, Erik Hanschen and colleagues describe the genome of
Gonium pectorale
, demonstrating that co-option of the retinoblastoma cell cycle regulatory pathway was a key genetic change in the evolution of multicellularity.
Journal Article
Numerical algorithms for personalized search in self-organizing information networks
\"This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks.\" The book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding web-scale data.--[book cover]
Book
Parameter Uncertainty Analysis of the Life Cycle Inventory Database: Application to Greenhouse Gas Emissions from Brown Rice Production in IDEA
The objective of this paper is to develop a simple method for analyzing the parameter uncertainty of the Japanese life cycle inventory database (LCI DB), termed the inventory database for environmental analysis (IDEA). The IDEA has a weakness of poor data quality because over 60% of datasets in IDEA were compiled based on secondary data (non-site-specific data sources). Three different approaches were used to estimate the uncertainty of the brown rice production dataset, including the stochastic modeling approach, the semi-quantitative DQI (Data Quality Indicator) approach, and a modification of the semi-quantitative DQI approach (including two alternative approaches for modification). The stochastic modeling approach provided the best estimate of the true mean of the sample space and its results were used as the reference for comparison with the other approaches. A simple method for the parameter uncertainty analysis of the agriculture industry DB was proposed by modifying the beta distribution parameters (endpoint range, shape parameter) in the semi-quantitative DQI approach using the results from the stochastic modeling approach. The effect of changing the beta distribution parameters in the semi-quantitative DQI approach indicated that the proposed method is an efficient method for the quantitative parameter uncertainty analysis of the brown rice production dataset in the IDEA.
Journal Article
Pharmacovigilance studies without a priori hypothesis: systematic review highlights inappropriate multiple testing correction procedures
The purpose of this study was to systematically review the statistical methods used in pharmacovigilance studies without a priori hypotheses.
A systematic review was performed on studies published in the MEDLINE database between 2012 and 2021. The included studies were analyzed for database name and type, statistical methods, anatomical therapeutic chemical class for the studied drug(s), and SOC MedDRA classification for the studied adverse drug reaction.
Ninety-two studies were included, with pharmacovigilance databases being the most used type. Disproportionality analysis using frequentist or Bayesian methods was the most common statistical method employed. The most studied drug classes were anti-infectives, nervous system drugs, and antineoplastics and immunomodulators. However, no common procedure was implemented to correct for multiple testing.
This review highlights the limited number of statistical methods employed for pharmacovigilance studies without a priori hypotheses, with no established consensus-based method and a lack of interest in multiple testing correction. The establishment of guidelines is recommended to improve the performance of such studies.
[Display omitted]
•Lack of rationale for managing multiple tests.•No correction for multiple testing for 43% of papers using the reporting odds ratio.•Arbitrary choice of a threshold.•Value of 2 for 54% of papers using proportional reporting ratio.•No explanation on how the chosen threshold reflects correction for tests.
Journal Article
Comorbidities and healthcare costs and resource use of patients with nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) in the Japan medical data vision database
BackgroundThis study examined demographics, comorbidities and healthcare resource use (HCRU) and costs among Japanese patients with nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH).MethodsWe conducted a repeated cross-sectional analysis of the Medical Data Vision (MDV) claims database, from January 2011 to March 2018. Demographics were described at index date and by calendar year; a “NASH” subpopulation included patients with ≥ 1 claim for NASH at any time. Prevalence of pre-specified comorbidities of interest and data-emergent top comorbidities were estimated. All-cause HCRU and costs were quantified by calendar year. Outcomes were compared between 2011 and 2017 using partially overlapping t tests.Results58,958 patients (mean age 61.6 years; 55.5% male) were included. 1139 patients (2%) were in the NASH subpopulation. At baseline, comorbid cardiovascular disease (69.4%), diabetes (62.1%) and hyperlipidaemia (54.4%) were most prevalent; comorbidity prevalence increased with age. Mean outpatient visits decreased from 9.36 per patient in 2011 to 7.80 in 2017; mean inpatient admissions increased (both p < 0.001 for 2011 vs 2017). Mean total all-cause healthcare costs ranged from ¥322,206 to ¥340,399 per patient per year between 2011 and 2017. Although total all-cause healthcare costs did not change significantly (p = 0.552), cost burden shifted from the outpatient to inpatient setting between 2011 and 2017. All-cause healthcare resource use/costs were generally higher for the NASH subgroup compared with the overall population.ConclusionsThere is a high burden of disease among Japanese NAFLD/NASH patients, including a high prevalence of comorbidities which generally increase with age. Accordingly, substantial all-cause HCRU and costs were incurred.
Journal Article
Epidemiological Study of Adenoid Cystic Carcinoma and Its Outcomes: Insights from the Surveillance, Epidemiology, and End Results (SEER) Database
Objective: Adenoid cystic carcinoma (ACC) is a rare malignant tumor that mainly arises in the head and neck area. We aimed to compare the long-term survival of patients with ACC based on their geographic regions within the United States using the Surveillance, Epidemiology, and End Results (SEER) registry data. Methods: We queried the SEER database to evaluate the geographic distribution of ACC patients based on inpatient admissions. The states included in the study were divided into four geographical regions (Midwest, Northeast, South, and West) based on the U.S. Census Bureau-designated regions and divisions. Demographic and clinical variables were compared between the groups. Kaplan–Meier curves and Cox regression were used to assess late mortality. Results: A total of 5150 patients were included (4.2% from the Midwest, 17.2% from the Northeast, 22.5% from the South, and 56.1% from the West regions). The median follow-up was 12.3 (95% CI: 11.6–13.1 years). Median overall survival was 11.0 (95% CI: 9.2-NR years), 14.3 (95% CI: 12.4–16.4 years), 11.3 (95% CI: 9.7–14.8 years), and 12.0 (95% CI: 11.3–13.0 years) for Midwest, Northeast, South, and West regions, respectively. In multivariable analysis, older age, male sex, thoracic cancer, the presence of regional and distal disease, receiving chemotherapy, not undergoing surgical resection, and being treated in the West vs. Northeast region were found to be independent predictors of poor survival. We identified a significant survival difference between the different regions, with the West exhibiting the worst survival compared to the Northeast region. Conclusions: In addition to the well-known predictors of late mortality in ACC (tumor location, stage, and treatment modalities), our study identified a lack of social support (being unmarried) and geographic location (West region) as independent predictors of late mortality in multivariable analysis. Further research is needed to explore the causal relationships.
Journal Article
Medical Insurance Cost Prediction Using Gradient Boosting Regression: A Machine Learning Approach
This paper highlights the point that correct forecasting of the expense of medical insurance is essential in the better decision-making of individuals, insurers, and policymakers to efficiently allocate resources in the dynamically changing environment of healthcare financing. While recent studies have extensively explored machine learning (ML) approaches for medical insurance cost prediction, there remains a critical need to improve their accuracy and reliability, driving the pursuit of more effective methods to enhance the precis. In the context of these caveats, there exists a research gap to which this investigation attempts to contribute by proffering an ML method using the Gradient Boosting Regressor (GBR), through which one can enhance the level and quality of prediction for medical insurance expenses. To deal with this, this study presents a GBR base approach for predicting medical insurance costs from a dataset of 1,339 samples with seven features, such as age, sex, BMI, smoking, and region. The dataset from Kaggle offers thorough coverage of the factors affecting medical insurance costs. Our approach involves extensive preprocessing of the data, including one-hot encoding for categorical features, followed by training, validation, and evaluation of the model using an 80/20 train-test split. We rigorously evaluated the performance of the GBR model using the metrics of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared (R?), Mean Absolute Percentage Error (MAPE), and Explained Variance Score (EVS). Experimental outcome further establishes that the best-performing model is the GBR model, based on obtained results as reflecting better predictive accuracy. Comparison with Linear Regression, Random Forest, Support Vector Regression, K-Nearest Neighbors, and Neural Networks further established that the best precision (0.908), recall (0.903), and F1-score (0.899) is achieved by the GBR model. These findings support the effectiveness of the GBR model as a powerful tool for capturing nonlinear patterns of relationship underlying the data, for predicting medical insurance costs. This research highlights the usefulness of sophisticated techniques of machine learning for improving predictive modeling of healthcare finances.
Journal Article
Potassium Concentration in Initial Fluid Therapy and In-Hospital Mortality of Patients with Diabetic Ketoacidosis
Abstract
Context
Guidelines worldwide recommend potassium replacement of 10 to 40 mmol/L in the initial fluid therapy for patients with diabetic ketoacidosis. However, evidence is lacking as to the association between infused potassium concentration and mortality.
Objective
We aimed to determine the association between infused potassium concentration and in-hospital mortality.
Methods
Using the Japanese Diagnosis Procedure Combination database, we retrospectively identified inpatients admitted for treatment of diabetic ketoacidosis from July 2010 to March 2018. Patients with kidney dysfunction or serum potassium abnormalities were excluded. We evaluated the association of the potassium concentration in the total infused solutions in the first 2 days of hospitalization with 28-day in-hospital mortality using multivariable regression analysis with a cubic spline model. We also assessed the association between potassium concentration and occurrence of hyperkalemia.
Results
We identified 14 216 patients with diabetic ketoacidosis and observed 261 deaths. The quartile cut-points for potassium concentration were 7.7, 11.4, and 16.1 mmol/L. Within the range of approximately 10 to 40 mmol/L, potassium concentration was not associated with occurrence of hyperkalemia or death. Lower potassium concentrations were associated with higher 28-day in-hospital mortality; the odds ratio for patients receiving 8 mmol/L was 1.69 (95% CI, 1.03 to 2.78; reference: 20 mmol/L), and the odds ratio increased monotonically as potassium concentration decreased further.
Conclusion
Patients receiving potassium replacement at concentrations of 10 to 40 mmol/L had similar in-hospital mortality rates, whereas lower concentrations were associated with higher mortality.
Journal Article
Creating a digital database of tephra fallout distribution and frequency in Japan
Tephra fallout is a potential hazard to livelihoods, critical infrastructure, and health, even in areas that are far from volcanoes. Therefore, it is important to quantitatively evaluate tephra fall hazards for both residents and infrastructure around hazardous volcanoes. Modern probabilistic volcanic hazard assessments of tephra fallout strongly rely on computer modeling; however, assessments based on isopach maps can also be also helpful in assisting decision-makers. To assess the tephra fall hazards in Japan, we created a digital database “Isopach map-Based Tephra fall Hazard Analysis (IB-THA)” and a tool to draw the cumulative number of tephra fallout events exceeding a specific thickness at a particular location. The database was re-digitized using an existing catalog of 551 tephra fall deposit distribution maps. The re-digitized datasets were utilized here to estimate the cumulative number of tephra fallout events exceeding a specific thickness at 47 prefectural offices for the last 150 kyr. This allowed the characterization of regional tephra fall hazards in Japan for the first time. High cumulative numbers (20) of tephra fall deposits > 0 mm were identified in the NE-E region (e.g., Maebashi), whereas low numbers (2) were recognized in the SW and W regions of Japan (e.g., Naha). The thickest tephra fall deposit (2850 mm) was observed at Kagoshima. We used IB-THA to create a hazard curve for Tokyo. This hazard curve provides the minimum frequency needed to exceed the tephra fall thickness at any location. To refine the digital database presented here, further studies are required to incorporate recent (i.e., 2003 or younger) tephra distributions, review questionable isopach maps, and improve the interpolation method for digitizing tephra fall distributions.
Journal Article