Relationship between Continuum of Hurst Exponents of Noise-like Time Series and the Cantor Set.

In this paper, we have modified the Detrended Fluctuation Analysis (DFA) using the ternary Cantor set. We propose a modification of the DFA algorithm, Cantor DFA (CDFA), which uses the Cantor set theory of base 3 as a scale for segment sizes in the DFA algorithm. An investigation of the phenomena generated from the proof using real-world time series based on the theory of the Cantor set is also conducted. This new approach helps reduce the overestimation problem of the Hurst exponent of DFA by comparing it with its inverse relationship with α of the Truncated Lévy Flight (TLF). CDFA is also able to correctly predict the memory behavior of time series.

Mesenchymal stem cells instruct a beneficial phenotype in reactive astrocytes.

Transplanted mesenchymal stromal/stem cells (MSC) ameliorate the clinical course of experimental autoimmune encephalomyelitis (EAE), a mouse model of multiple sclerosis (MS), reducing inflammation and demyelination. These effects are mediated by instructive cross-talk between MSC and immune and neural cells. Astroglial reaction to injury is a prominent feature of both EAE and MS. Astrocytes constitute a relevant target to control disease onset and progression and, based on their potential to acquire stem cell properties in situ, to foster recovery in the post-acute phase of pathology. We have assessed how MSC impact astrocytes in vitro and ex vivo in EAE. Expression of astroglial factors implicated in EAE pathogenesis was quantified by real-time PCR in astrocytes co-cultured with MSC or isolated from EAE cerebral cortex; astrocyte morphology and expression of activation markers were analyzed by confocal microscopy. The acquisition of neural stem cell properties by astrocytes was evaluated by neurosphere assay. Our study shows that MSC prevented astrogliosis, reduced mRNA expression of inflammatory cytokines that sustain immune cell infiltration in EAE, as well as protein expression of endothelin-1, an astrocyte-derived factor that inhibits remyelination and contributes to neurodegeneration and disease progression in MS. Moreover, our data reveal that MSC promoted the acquisition of progenitor traits by astrocytes. These data indicate that MSC attenuate detrimental features of reactive astroglia and, based on the reacquisition of stem cell properties, also suggest that astrocytes may be empowered in their protective and reparative actions by MSC.

Supervised machine learning models applied to disease diagnosis and prognosis.

This work analyses the diagnosis and prognosis of cancer and heart disease data using five Machine Learning (ML) algorithms. We compare the predictive ability of all the ML algorithms to breast cancer and heart disease. The important variables that causes cancer and heart disease are also studied. We predict the test data based on the important variables and compute the prediction accuracy using the Receiver Operating Characteristic (ROC) curve. The Random Forest (RF) and Principal Component Regression (PCR) provides the best performance in analyzing the breast cancer and heart disease data respectively.