Exploratory Competitive Intelligence through Task Complexity Analysis

As one of the major resources for competitive intelligence (CI), the Internet not only provides a large amount of public data but also exposes a variety of business relations that may not otherwise be well-known. However, finding such information can be tedious and time-consuming for end-users without proper tools or expertise. In this paper, we examine the nature of CI tasks, classify and decompose them based on task complexity theories, and propose norms for a context-based approach to retrieve CI data. Our study provides a framework to further explore the relationships among CI tasks, interactive search, and context-based search systems design

Competitive Intelligence Task Analysis And Retrieval: An End-User Approach

The Internet, as one of the major resources for competitive intelligence (CI), not only provides a large amount of public data but also exposes a variety of business relations that may not otherwise be well-known. However, finding such information can be tedious and time-consuming for end-users without proper tools or expertise. In this paper, we examine the nature of CI tasks, classify and decompose them based on a task complexity theory, and propose norms for a context-based approach to retrieve CI data. We developed a meta-search engine called Competitive Intelligence Task Analysis and Retrieval (CITAR) to demonstrate the feasibility of the proposed approach. The present study provides a framework to further explore the relationships among CI tasks, interactive search, and context-based search systems design

Using Big Data for Predicting Freshmen Retention

Traditional research in student retention is survey-based, relying on data collected from questionnaires, which is not optimal for proactive prediction and real-time decision (student intervention) support. Machine learning approaches have their own limitations. Therefore, in this research, we propose a big data approach to formulating a predictive model. We used commonly available (student demographic and academic) data in academic institutions augmented by derived implicit social networks from students’ university smart card transactions. Furthermore, we applied a sequence learning method to infer students’ campus integration from their purchasing behaviors. Since student retention data is highly imbalanced, we built a new ensemble classifier to predict students at-risk of dropping out. For model evaluation, we use a real-world dataset of smart card transactions from a large educational institution. The experimental results show that the addition of campus integration and social behavior features refined using the ensemble method significantly improve prediction accuracy and recall

Using rank data to estimate health state utility models

In this paper we report the estimation of conditional logistic regression models for the Health Utilities Index Mark 2 and the SF-6D, using ordinal preference data. The results are compared to the conventional regression models estimated from standard gamble data, and to the observed mean standard gamble health state valuations. For both the HUI2 and the SF-6D, the models estimated using ordinal data are broadly comparable to the models estimated on standard gamble data and the predictive performance of these models is close to that of the standard gamble models. Our research indicates that ordinal data have the potential to provide useful insights into community health state preferences. However, important questions remain

Exosome Release Is Modulated by the Mitochondrial-Lysosomal Crosstalk in Parkinson’s Disease Stress Conditions

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the substantia nigra (SN) pars compacta region of the brain. The main pathological hallmark involves cytoplasmic inclusions of α-synuclein and mitochondrial dysfunction, which is observed in other part of the central nervous system other than SN suggesting the spread of pathogenesis to bystander neurons. The inter-neuronal communication through exosomes may play an important role in the spread of the disease; however, the mechanisms are not well elucidated. Mitochondria and its role in inter-organellar crosstalk with multivesicular body (MVB) and lysosome and its role in modulation of exosome release in PD is not well understood. In the current study, we investigated the mitochondria-lysosome crosstalk modulating the exosome release in neuronal and glial cells. We observed that PD stress showed enhanced release of exosomes in dopaminergic neurons and glial cells. The PD stress condition in these cells showed fragmented network and mitochondrial dysfunction which further leads to functional deficit of lysosomes and hence inhibition of autophagy flux. Neuronal and glial cells treated with rapamycin showed enhanced autophagy and inhibited the exosomal release. The results here suggest that maintenance of mitochondrial function is important for the lysosomal function and hence exosomal release which is important for the pathogenesis of PD

Parameter Bias from Unobserved Effects in the Multinomial Logit Model of Consumer Choice

Over the past two decades, validation of choice models has focused on predictive validity rather than parameter bias. In real-world validation of choice models, true parameter values are unknown, so examination of parameter bias is not possible. In contrast, the main focus of this study is parameter bias in simulated scanner-panel choice data with known parameter values. Study of parameter bias enables the assessment of a fundamental issue not addressed in the choice modeling literature-the extent to which the logit choice model is capable of distinguishing unobserved effects that give rise to persistence in observed choices (e.g., heterogeneity and state dependence). Although econometric theory provides some information about the causes of bias, the extent of such bias in typical scanner data applications remains unclear. The authors present an extensive simulation study that provides information on the extent of bias resulting from the misspecification of four unobserved effects that receive frequent attention in the literature-choice set effects, heterogeneity in preferences and market response, state dependence, and serial correlation. The authors outline implications for model builders and managers. In general, the potential for parameter bias in choice model applications appears to be high. Overall, a logit model with choice set effects and the Guadagni-Little loyalty variable produces the most valid parameter estimates

The analog of cGAMP, c-di-AMP, activates STING mediated cell death pathway in estrogen-receptor negative breast cancer cells

Immune adaptor protein like STING/MITA regulate innate immune response and plays a critical role in inflammation in the tumor microenvironment and regulation of metastasis including breast cancer. Chromosomal instability in highly metastatic cells releases fragmented chromosomal parts in the cytoplasm, hence the activation of STING via an increased level of cyclic dinucleotides (cDNs) synthesized by cGMP-AMP synthase (cGAS). Cyclic dinucleotides 2’ 3’-cGAMP and it's analog can potentially activate STING mediated pathways leading to nuclear translocation of p65 and IRF-3 and transcription of inflammatory genes. The differential modulation of STING pathway via 2’ 3’-cGAMP and its analog and its implication in breast tumorigenesis is still not well explored. In the current study, we demonstrated that c-di-AMP can activate type-1 IFN response in ER negative breast cancer cell lines which correlate with STING expression. c-di-AMP binds to STING and activates downstream IFN pathways in STING positive metastatic MDA-MB-231/MX-1 cells. Prolonged treatment of c-di-AMP induces cell death in STING positive metastatic MDA-MB-231/MX-1 cells mediated by IRF-3. c-di-AMP induces IRF-3 translocation to mitochondria and initiates Caspase-9 mediated cell death and inhibits clonogenicity of triple-negative breast cancer cells. This study suggests that c-di-AMP can activate and modulates STING pathway to induce mitochondrial mediated apoptosis in estrogen-receptor negative breast cancer cells

TNF-α-induced E3 ligase, TRIM15 inhibits TNF-α-regulated NF-κB pathway by promoting turnover of K63 linked ubiquitination of TAK1

Ubiquitin E3-ligases are recruited at different steps of TNF-α-induced NF-κB activation; however, their role in temporal regulation of the pathway remains elusive. The study systematically identified TRIMs as potential feedback regulators of the TNF-α-induced NF-κB pathway. We further observed that TRIM15 is “late” response TNF-α-induced gene and inhibits the TNF-α-induced NF-κB pathway in several human cell lines. TRIM15 promotes turnover of K63-linked ubiquitin chains in a PRY/SPRY domain-dependent manner. TRIM15 interacts with TAK1 and inhibits its K63-linked ubiquitination, thus NF-κB activity. Further, TRIM15 interacts with TRIM8 and inhibits cytosolic translocation to antagonize TRIM8 modualted NF-κB. TRIM8 and TRIM15 also show functionally inverse correlation in psoriasis condition. In conclusion, TRIM15 is TNF-α-induced late response gene and inhibits TNF-α induced NF-κB pathway hence a feedback modulator to keep the proinflammatory NF-κB pathway under control

Expression of expanded FMR1-CGG repeats alters mitochondrial miRNAs and modulates mitochondrial functions and cell death in cellular model of FXTAS

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a progressive neurodegenerative disorder caused by an expansion of 55 to 200 CGG repeats located within 5′UTR of FMR1.These CGG repeats are transcribed into RNAs, which sequester several RNA binding proteins and alter the processing of miRNAs. CGG repeats are also translated into a toxic polyglycine-containing protein, FMRpolyG, that affects mitochondrial and nuclear functions reported in cell and animal models and patient studies. Nuclear-encoded small non-coding RNAs, including miRNAs, are transported to mitochondria; however, the role of mitochondrial miRNAs in FXTAS pathogenesis is not understood. Here, we analyzed mitochondrial miRNAs from HEK293 cells expressing expanded CGG repeats and their implication in the regulation of mitochondrial functions. The analysis of next generation sequencing (NGS) data of small RNAs from HEK293 cells expressing CGG premutation showed decreased level of cellular miRNAs and an altered pattern of association of miRNAs with mitochondria (mito-miRs). Among such mito-miRs, miR-320a was highly enriched in mitoplast and RNA immunoprecipitation of Ago2 (Argonaute-2) followed by Droplet digital PCR (ddPCR)suggested that miR-320a may form a complex with Ago2 and mitotranscripts. Finally, transfection of miR-320a mimic in cells expressing CGG permutation recovers mitochondrial functions and rescues cell death. Overall, this work reveals an altered translocation of miRNAs to mitochondria and the role of miR-320a in FXTAS pathology