Volatility interactions between equity and crude oil markets: Evidence from intraday ETF data

This study replicates and extends the study done by Phan, Sharma and Narayan (2015) using intraday data from two widely available exchange traded funds (SPY and USO), before and after the recent regime change, which was represented by the drop in crude oil prices of 2014. Phan, Sharma and Narayan (2015) use data from futures contracts to demonstrate that lagged trading information like bid-ask spread, number of shares traded and price volatility, from the same market and cross-market, when incorporated in a single volatility prediction setup, can significantly improve future volatility prediction for equity and crude oil markets. The main findings of our study confirms the conclusion reached by the reference paper and also demonstrate that these results hold before and after the drop in crude oil prices, which occurred in 2014

Discovery of Dihydroartemisinin and Dasatinib Drug Combination to Cure Pooroutcome BCR-ABL+ Acute Lymphoblastic Leukemia

Oncogenic signaling by the Philadelphia chromosome-encoded BCR-ABL fusion kinase initiates and drives both Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL) and chronic myelogenous leukemia (CML). Food and Drug Administration (FDA)- approved BCR-ABL-specific kinase inhibitors (BCR-ABL–KIs) imatinib, dasatinib and nilotinib induce prolonged remissions in CML but poor leukemia-reduction and relapse-control in Ph+ ALL. The relative primary BCR-ABL–KI-resistance in Ph+ ALL patients carrying predominantly BCR-ABLWT disease cannot be attributed to drug-resistant BCR-ABL mutations (BCR-ABLMUTANTS), and remains poorly understood. We established a cell-based platform to evaluate the modulation of anti-Ph+ ALL activity of drugs by both tumor-extrinsic cytokines normally present in the leukemia microenvironment and tumor-intrinsic vector-mediated alteration of candidate genes. We identified that BCR-ABLWT-driven Ph+ ALL cells are rendered significantly resistant against all FDAapproved BCR-ABL–KIs by 1) Several host-cytokines, but most dominantly by Interleukin7 (IL7), and 2) Cell-intrinsic functional-loss of IKAROS. Utilizing IL7-deficient recipient mice, we demonstrated that physiological levels of IL7 significantly attenuate the survival benefit derived from dasatinib monotherapy against Ph+ ALL. Follow-up mechanistic studies using cell signaling and gene expression comparisons indicated that IL7 imparts chemo-refractoriness by initiating IL7-pSTAT5-c-MYC signaling. Interestingly, IKAROS haploinsufficiency, which has been previously associated with poor clinical prognosis in patients with Ph+ ALL, was recently demonstrated to directly de-repress c-MYC expression. This suggested that both cell-extrinsic IL7 and cell-intrinsic IKAROS loss converge on c-MYC, which may act as node of leukemia BCR-ABL–KI-refractoriness. We confirmed that vector-mediated modifications imitating IL7 induction of STAT5 activity, functional-loss of IKAROS, and c-MYC over-expression, all selectively enrich Ph+ ALL cells during prolonged exposures to imatinib and dasatinib, thus revealing new chemo-refractory phenotypes. Contemporary medicine advocates co-treatment with agents of complementary mechanism of drug-action to tackle drug resistance. To identify combination agents that could overcome the dasatinib-resistant phenotypes of Ph+ ALL, we screened a library of 3200 agents including known anti-infective and chemotherapy drugs. We discovered that a well known antimalarial drug dihydroartemisinin (DHA) killed host-IL7-protected BCR-ABLWT, c-MYC-overexpressing BCR-ABLWT and BCR-ABLMUTANT Ph+ ALL cells in vitro. In vivo, DHA displayed weak activity as a single agent but its addition synergistically augmented the leukemia reduction by dasatinib, relative to either of the two drugs alone. Remarkably, DHA and dasatinib combination regimen eliminated host-protected dasatinib-refractory persistent leukemia and improved long-term survival from 0 to \u3e90% in a murine model that faithfully captures the BCR-ABL–KI drug-refractoriness of human Ph+ ALL. This study: 1) Uncovers novel mechanisms of clinical drug-resistance against BCR-ABL–KIs, 2) Identifies increased levels of IL7, pSTAT5 and c-MYC protein, and IKAROS haploinsufficiency as potential biomarkers of BCR-ABL-targeted drug-resistance, 3) Strongly supports clinical exploration of the BCR-ABL–KI and DHA combinations for treating patients with Ph+ ALL, and 4) Establishes a paradigm for investigating frequently overlooked host-tumor-drug interactions

Associative pattern mining for supervised learning

The Internet era has revolutionized computational sciences and automated data collection techniques, made large amounts of previously inaccessible data available and, consequently, broadened the scope of exploratory computing research. As a result, data mining, which is still an emerging field of research, has gained importance because of its ability to analyze and discover previously unknown, hidden, and useful knowledge from these large amounts of data. One aspect of data mining, known as frequent pattern mining, has recently gained importance due to its ability to find associative relationships among the parts of data, thereby aiding a type of supervised learning known as associative learning . The purpose of this dissertation is two-fold: to develop and demonstrate supervised associative learning in non-temporal data for multi-class classification and to develop a new frequent pattern mining algorithm for time varying (temporal) data which alleviates the current issues in analyzing this data for knowledge discovery. In order to use associative relationships for classification, we have to algorithmically learn their discriminatory power. While it is well known that multiple sets of features work better for classification, we claim that the isomorphic relationships among the features work even better and, therefore, can be used as higher order features. To validate this claim, we exploit these relationships as input features for classification instead of using the underlying raw features. The next part of this dissertation focuses on building a new classifier using associative relationships as a basis for the multi-class classification problem. Most of the existing associative classifiers represent the instances from a class in a row-based format wherein one row represents features of one instance and extract association rules from the entire dataset. The rules formed in this way are known as class constrained rules, as they have class labels on the right side of the rules. We argue that this class constrained representation schema lacks important information that is necessary for multi-class classification. Further, most existing works use either the intraclass or inter-class importance of the association rules, both of which sets of techniques offer empirical benefits. We hypothesize that both intra-class and inter-class variations are important for fast and accurate multi-class classification. We also present a novel weighted association rule-based classification mechanism that uses frequent relationships among raw features from an instance as the basis for classifying the instance into one of the many classes. The relationships are weighted according to both their intra-class and inter-class importance. The final part of this dissertation concentrates on mining time varying data. This problem is known as inter-transaction association rule mining in the data-mining field. Most of the existing work transforms the time varying data into a static format and then use multiple scans over the new data to extract patterns. We present a unique index-based algorithmic framework for inter-transaction association rule mining. Our proposed technique requires only one scan of the original database. Further, the proposed technique can also provide the location information of each extracted pattern. We use mathematical induction to prove that the new representation scheme captures all underlying frequent relationships