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Advances in high-throughput technologies have generated diverse informative molecular markers for cancer outcome prediction. Long non-coding RNA (lncRNA) and DNA methylation as new classes of promising markers are emerging as key molecules in human cancers; however, the prognostic utility of such diverse molecular data remains to be explored.We proposed a computational pipeline (IDFO) to predict patient survival by identifying prognosis-related biomarkers using multi-type molecular data (mRNA, microRNA, DNA methylation, and lncRNA) from 3198 samples of five cancer types. We assessed the predictive performance of both single molecular data and integrated multi-type molecular data in patient survival stratification, and compared their relative importance in each type of cancer, respectively. Survival analysis using multivariate Cox regression was performed to investigate the impact of the IDFO-identified markers and traditional variables on clinical outcome.Using the IDFO approach, we obtained good predictive performance of the molecular datasets (bootstrap accuracy: 0.71-0.97) in five cancer types. Impressively, lncRNA was identified as the best prognostic predictor in the validated cohorts of four cancer types, followed by DNA methylation, mRNA, and then microRNA. We found the incorporating of multi-type molecular data showed similar predictive power to single-type molecular data, but with the exception of the lncRNA + DNA methylation combinations in two cancers. Survival analysis of proportional hazard models confirmed a high robustness for lncRNA and DNA methylation as prognosis factors independent of traditional clinical variables.Our study provides insight into systematically understanding the prognostic performance of diverse molecular data in both single and aggregate patterns, which may have specific reference to subsequent related studies.

Abstract Background The automation of many common molecular biology techniques has resulted in the accumulation of vast quantities of experimental data. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system (e.g. knowledge of genes and their products, and the biological roles of proteins, their molecular functions, localizations and interaction networks). We present a technique called Global Mapping of Unknown Proteins (GMUP) which uses the Gene Ontology Index to relate diverse sources of experimental data by creation of an abstraction layer of evidence data. This abstraction layer is used as input to a neural network which, once trained, can be used to predict function from the evidence data of unannotated proteins. The method allows us to include almost any experimental data set related to protein function, which incorporates the Gene Ontology, to our evidence data in order to seek relationships between the different sets. Results We have demonstrated the capabilities of this method in two ways. We first collected various experimental datasets associated with yeast (Saccharomyces cerevisiae) and applied the technique to a set of previously annotated open reading frames (ORFs). These ORFs were divided into training and test sets and were used to examine the accuracy of the predictions made by our method. Then we applied GMUP to previously un-annotated ORFs and made 1980, 836 and 1969 predictions corresponding to the GO Biological Process, Molecular Function and Cellular Component sub-categories respectively. We found that GMUP was particularly successful at predicting ORFs with functions associated with the ribonucleoprotein complex, protein metabolism and transportation. Conclusion This study presents a global and generic gene knowledge discovery approach based on evidence integration of various genome-scale data. It can be used to provide insight as to how certain biological processes are implemented by interaction and coordination of proteins, which may serve as a guide for future analysis. New data can be readily incorporated as it becomes available to provide more reliable predictions or further insights into processes and interactions.

Space teleoperation systems, as complex giant systems, feature performance-influencing factors that are interrelated. Accurately describing the dependence between these factors is crucial for constructing a human factor reliability assessment (HRA) model. Moreover, data scarcity has consistently been a challenge in space HRA. There are primarily two types of data in this domain: expert judgment data and empirical data (simulation data, actual reports), each with complementary effects. The expert judgment data, although subjective, are readily accessible, while empirical data provide robust objectivity but are difficult to obtain. Addressing these challenges, this paper constructs an HRA model for space teleoperation that combines Interpretive Structural Modeling (ISM) with a two-stage Bayesian update method. This model reflects the dependencies between factors and accommodates multisource data (expert judgment and experimental data). With more empirical data, the model can be continuously updated and refined to yield increasingly accurate evaluations of human error probability (HEP). The validity of the model was verified through the analysis of 52 space incidents using the N-K model. The study provides a methodological foundation for HRA in other space missions. [ABSTRACT FROM AUTHOR]

AbstractWhile considerable research has been conducted, the field of neurodesign and human-computer interaction (N-HCI) has yet to be methodically delineated in light of contemporary studies. In this context, this paper undertakes a systematic review with dual objectives: (a) to uncover emerging research themes within N-HCI, and (b) to provide future directions for research on N-HCI. Employing robust systematic review methodologies, this paper scrutinized 122 pertinent documents, revealing four principal emerging research themes and constructing a conceptual framework that synthesized key concepts in N-HCI. The results underscored the relative underdevelopment in research that leverages neural evidence to enhance general human-computer interface design, and the notable absence of research at the intersections of the “user” and “environment” aspects of neurodesign with HCI. Accordingly, this paper identifies underdeveloped areas in existing research that require more depth and blank areas in the conceptual framework that call for exploration as future directions. [ABSTRACT FROM AUTHOR]

When designing situational maps, selecting distinct and visually comfortable movement speeds for dynamic elements is an ongoing challenge for designers. This study addresses this issue by conducting two experiments to measure the human eye's ability to discern moving speeds on a screen and examines how symbol movement speeds within situational maps affect users' subjective experiences, task performance, and visual comfort. The first experiment measured participants' speed discrimination capabilities for Landolt Ring of varying sizes moving at 0–256°/s, yielding speed discrimination thresholds of 7–23% and a sensitive velocity range of 1–64°/s. The second experiment evaluated observers' visual perceptions of moving elements within a cognitive task across the same range of 1–64°/s, identifying three significant benchmarks—8°/s, 16°/s, and 32°/s. These can be utilized to categorize slow-, moderate-, and fast-moving symbols in situational maps. The findings can aid in designing human–machine interface environments with improved viewer experience and visual comfort for both Air Traffic Control interfaces and situational maps. [ABSTRACT FROM AUTHOR]

User decision-making concerning critical operations is very important to nuclear power plant (NPP) safety. The NPP interface is the main information source that guides decision-making; thus, a good interface design is essential. Among the interface design factors such as interface complexity, layout and colour, interface complexity (the amount of information in the interface) has the greatest impact on NPP operator decision-making. This paper used the event-related potential (ERP) to evaluate the impact of interface complexity on user decision-making and found interface complexity has a specific range suitable for decision-making. Based on this important finding, a fast and economical method of evaluating NPP interfaces in all design phases was proposed. This method compensates for the shortcomings of traditional methods, such as heuristic evaluation and experimental evaluation, which are inconvenient for evaluating interfaces in initial design phase; it can also be applied to interfaces with similar features in other industrial fields. Practitioner summary: Evaluation of the impact of NPP interface complexity on user decision-making through an ERP experiment revealed a specific range of interface complexity that facilitates user decision-making. Based on this finding, a new, fast and inexpensive interface evaluation method was proposed. Abbreviations: NPP: nuclear power plant, it is a thermal power station in which the heat source is a nuclear reactor; ERP: event-related potential, it is the measured brain response that is the direct result of a specific cognitive, or motor event. [ABSTRACT FROM AUTHOR]