Clinical and ultrasound features correlated with a heavy axillary nodal tumor burden in colon cancer.

Aim: This study aimed to investigate the correlation between the pathologic and ultrasound (US) characteristics of colon cancer and the heavy axillary nodal burden. Methods: In total, 631 patients diagnosed with invasive colon cancer were recruited with ethical ratification. Results: The unitary pathologic features correlated with heavy axillary lymph nodal burden included the age of patient (p = 0.035), tumor size (p = 0.001), lymph node metastasis (p = 0.001), lymphovascular invasion (p = 0.020) and pathology type (p = 0.012). The independent US characteristics correlated with heavy axillary nodal burden included posterior acoustic enhancement (p = 0.006). Heavy axillary nodal burden was correlated with tumor size, lymph node metastasis, lymphovascular invasion and pathology type. Conclusion: Tumor size, lymph node metastasis and posterior acoustic can be used to predict the axillary lymph node tumor burden.

Anterior insular cortex is a bottleneck of cognitive control.

Cognitive control, with a limited capacity, is a core process in human cognition for the coordination of thoughts and actions. Although the regions involved in cognitive control have been identified as the cognitive control network (CCN), it is still unclear whether a specific region of the CCN serves as a bottleneck limiting the capacity of cognitive control (CCC). Here, we used a perceptual decision-making task with conditions of high cognitive load to challenge the CCN and to assess the CCC in a functional magnetic resonance imaging study. We found that the activation of the right anterior insular cortex (AIC) of the CCN increased monotonically as a function of cognitive load, reached its plateau early, and showed a significant correlation to the CCC. In a subsequent study of patients with unilateral lesions of the AIC, we found that lesions of the AIC were associated with a significant impairment of the CCC. Simulated lesions of the AIC resulted in a reduction of the global efficiency of the CCN in a network analysis. These findings suggest that the AIC, as a critical hub in the CCN, is a bottleneck of cognitive control.

Hick-Hyman Law is Mediated by the Cognitive Control Network in the Brain.

The Hick-Hyman law describes a linear increase in reaction time (RT) as a function of the information entropy of response selection, which is computed as the binary logarithm of the number of response alternatives. While numerous behavioral studies have provided evidence for the Hick-Hyman law, its neural underpinnings have rarely been examined and are still unclear. In this functional magnetic resonance imaging study, by utilizing a choice reaction time task to manipulate the entropy of response selection, we examined brain activity mediating the input and the output, as well as the connectivity between corresponding regions in human participants. Beyond confirming the Hick-Hyman law in RT performance, we found that activation of the cognitive control network (CCN) increased and activation of the default mode network (DMN) decreased, both as a function of entropy. However, only the CCN, but not the DMN, was involved in mediating the relationship between entropy and RT. The CCN was involved in both stages of uncertainty representation and response generation, while the DMN was mainly involved at the stage of uncertainty representation. These findings indicate that the CCN serves as a core entity underlying the Hick-Hyman law by coordinating uncertainty representation and response generation in the brain.

Empirical evaluation of scoring functions for Bayesian network model selection.

In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning algorithms to learn the network structures. The suboptimal structures found by the approximation methods have unknown quality and may affect the reliability of their conclusions. Our study uses an optimal algorithm to learn Bayesian network structures from datasets generated from a set of gold standard Bayesian networks. Because all optimal algorithms always learn equivalent networks, this ensures that only the choice of scoring function affects the learned networks. Another shortcoming of the previous studies stems from their use of random synthetic networks as test cases. There is no guarantee that these networks reflect real-world data. We use real-world data to generate our gold-standard structures, so our experimental design more closely approximates real-world situations. A major finding of our study suggests that, in contrast to results reported by several prior works, the Minimum Description Length (MDL) (or equivalently, Bayesian information criterion (BIC)) consistently outperforms other scoring functions such as Akaike's information criterion (AIC), Bayesian Dirichlet equivalence score (BDeu), and factorized normalized maximum likelihood (fNML) in recovering the underlying Bayesian network structures. We believe this finding is a result of using both datasets generated from real-world applications rather than from random processes used in previous studies and learning algorithms to select high-scoring structures rather than selecting random models. Other findings of our study support existing work, e.g., large sample sizes result in learning structures closer to the true underlying structure; the BDeu score is sensitive to the parameter settings; and the fNML performs pretty well on small datasets. We also tested a greedy hill climbing algorithm and observed similar results as the optimal algorithm.

Machine learning analysis of the relationship between changes in immunological parameters and changes in resistance to Listeria monocytogenes: a new approach for risk assessment and systems immunology.

No method has been reported to predict, even approximately, the impact of mild-to-moderate changes in several immunological parameters on resistance to infection. The ability to make such predictions would be useful in risk assessment. In addition, equations that predict host resistance on the basis of changes in components of a complex biological system (the immune system) would fulfill one of the major goals of systems biology. In this study, multiple machine learning classification methods were used to predict the effects of a series of drugs and chemicals on host resistance to Listeria monocytogenes in mice on the basis of changes in several holistic immunological parameters. A data set produced under the sponsorship of the National Toxicology Program (NTP) was used in this study. The NTP data set was found to have a high percentage of missing data and to be noisy (probably due to the intrinsically stochastic nature of immune responses). Data preprocessing steps were used to mitigate these problems. In evaluating the machine learning classifiers, we first randomly partitioned the NTP data set into 10 subsets. Each time, we used nine subsets of the data to train the machine learning classifiers, and the remaining single subset to predict outcomes with regard to host resistance. This process was repeated until all 10 combinations of the 9-1 split of the subsets have been tested. The best of the classifiers predicted host resistance outcome correctly for 94.7% of cases, a result which indicates it is possible to identify mathematical expressions that will be useful for risk assessment and to establish a basis for systems immunology.

Mixture model analysis reflecting dynamics of the population diversity of 2009 pandemic H1N1 influenza virus.

Influenza A viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. In April 2009, a novel swine-origin H1N1 virus emerged in North America and caused the first pandemic of the 21st century. Toward the end of 2009, two waves of outbreaks occurred, and then the disease moderated. It will be critical to understand how this novel pandemic virus invaded and adapted to a human population. To understand the molecular dynamics and evolution in this pandemic H1N1 virus, we applied an Expectation-Maximization algorithm to estimate the Gaussian mixture in the genetic population of the hemagglutinin (HA) gene of these H1N1 viruses from April of 2009 to January of 2010 and compared them with the viruses that cause seasonal H1N1 influenza. Our results show that, after it was introduced to human population, the 2009 H1N1 viral HA gene changed its population structure from a single Gaussian distribution to two major Gaussian distributions. The breadths of HA genetic diversity of 2009 H1N1 virus also increased from the first wave to the second wave of this pandemic. Phylogenetic analyses demonstrated that only certain HA sublineages of 2009 H1N1 viruses were able to circulate throughout the pandemic period. In contrast, the influenza HA population structure of seasonal H1N1 virus was relatively stable, and the breadth of HA genetic diversity within a single season population remained similar. This study revealed an evolutionary mechanism for a novel pandemic virus. After the virus is introduced to human population, the influenza virus would expand their molecular diversity through both random mutations (genetic drift) and selections. Eventually, multiple levels of hierarchical Gaussian distributions will replace the earlier single distribution. An evolutionary model for pandemic H1N1 influenza A virus was proposed and demonstrated with a simulation.