Event studies are based on the theoretical framework of efficient capital markets and the notion that security prices include all information available to the market. As a result, announcements made by firms provide to market participants information that can be impounded into the market price. This paper investigates the use of event studies in information systems and accounting information systems research using a three-pronged approach. First, this paper provides a comprehensive survey of research that uses event study methodologies, where the events are announcements made by firms about issues related to information systems, e.g., announcements of the adoption of enterprise resource planning systems and of the effect of security breaches in firms' information systems. Second, this paper summarizes event study methodologies used in prior research, along with some of the key parameters and concerns associated with their implementation. Third, this paper provides remarks on key event study modeling issues, and it offers recommendations to researchers. © 2011 Elsevier Inc.

A 61-year-old man with metastatic renal cell carcinoma on cabozantinib developed hand-foot skin reaction with predominantly dorsal involvement including painful violaceous plaques over the joints and keratotic yellow plaques on the palmar fingers. The medication was discontinued with resolution of the plaques and later reinitiated at a lower dose uneventfully.

Researchers have used the stock price reaction to firms' disclosures of investment in information technology to investigate the value of those investments. This paper extends that research to include knowledge management (KM). In particular, we test whether and how KM is valued by market participants by examining the stock market reaction and future performance of companies receiving the "Most Admired Knowledge Enterprise" (MAKE) award, which recognizes companies that excel at KM. MAKE awards are generated based on opinions gathered from experts using the Delphi method, a well-known group decision support tool. We find that MAKE winners: (1) experience positive abnormal returns around the award announcement, (2) report superior operating performance relative to their peers subsequent to the receipt of the award, (3) receive upward analyst forecast revisions following the award, (4) experience a positive upward stock price drift following the award, and (5) that the market has taken time to learn how to process and interpret information useful in valuing KM. Thus, our findings contribute to the literature by finding that market participants value KM and KM apparently positively influences accounting performance indicators. In addition, a unique feature of our study is that we investigate the market's response to information gathered using the Delphi method, an information source not previously investigated in stock price reaction literature. © 2013 Elsevier B.V.

The mechanism of C-H activation at metathesis-relevant ruthenium(II) benzylidene complexes was studied both experimentally and computationally. Synthesis of a ruthenium dicarboxylate at a low temperature allowed for direct observation of the C-H activation step, independent of the initial anionic ligand-exchange reactions. A first-order reaction supports an intramolecular concerted metalation-deprotonation mechanism with ΔG(‡)(298K) = 22.2 ± 0.1 kcal·mol(-1) for the parent N-adamantyl-N'-mesityl complex. An experimentally determined ΔS(‡) = -5.2 ± 2.6 eu supports a highly ordered transition state for carboxylate-assisted C(sp(3))-H activation. Experimental results, including measurement of a large primary kinetic isotope effect (k(H)/k(D) = 8.1 ± 1.7), agree closely with a computed six-membered carboxylate-assisted C-H activation mechanism where the deprotonating carboxylate adopts a pseudo-apical geometry, displacing the aryl ether chelate. The rate of cyclometalation was found to be influenced by both the electronics of the assisting carboxylate and the ruthenium ligand environment.

Objective

We evaluated family history as a predictor of incident and progressive coronary artery calcium (CAC) using data from the Multi-Ethnic Study of Atherosclerosis (MESA).

Background

MESA is a multi-center prospective study of 6814 asymptomatic individuals. The relationship between family history of coronary heart disease (CHD) and CAC incidence or progression has not been described previously.

Methods

A total of 5099 participants had detailed information about family history of CHD (late versus premature and parental versus sibling history). The mean time between CAC scans was 3.1 ± 1.3 years. The association of late versus premature family history was assessed against CAC change using multivariate regression model adjusted for demographics and cardiac risk factors.

Results

A family history of premature CHD was associated with an odds ratio (OR) of 1.55 (p < 0.01) for incident development of CAC after adjusting for risk factors and demographics. A premature family history was associated with 14.4 units (p < 0.01) greater volume scores compared to those with no family history in similarly adjusted models by median regression analysis. A combined parental and sibling family history was associated with the greatest incidence and progression in demographic-adjusted models. Caucasians demonstrated the most consistent predictive relationship between family history of premature CHD and incidence (p < 0.01) and progression (p < 0.05) of CAC, though no significant interaction with ethnicity was noted.

Conclusions

Family history of premature CHD is associated with enhanced development and progression of subclinical disease, independent of other risk factors, in a multiethnic, population-based study.

In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (NMI) that calculates the nonlinear relationship between different brain regions. We demonstrate our proposed approach using simulated data and then apply it to a dataset previously studied by Damaraju et al. This resting-state fMRI data included 151 schizophrenia patients and 163 age- and gender-matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in explicitly nonlinear functional network connectivity (FNC) between schizophrenia patients and healthy controls, particularly in the visual cortex, with controls showing more nonlinearity (i.e., higher normalized mutual information between time courses with linear relationships removed) in most cases. Certain domains, including subcortical and auditory, showed relatively less nonlinear FNC (i.e., lower normalized mutual information), whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Beyond this, we propose a method that captures both linear and nonlinear effects in a "boosted" approach. This method increases the sensitivity to group differences compared to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.

In the United States, the judicial system response to violence between intimate partners, or intimate partner violence (IPV), typically mandates that adjudicated perpetrators complete a batterer intervention program (BIP). The social science data has found that these programs, on the whole, are only minimally effective in reducing rates of IPV. The authors examined the social science literature on the characteristics and efficacy of BIPs. More than 400 studies were considered, including a sweeping, recently conducted survey of BIP directors across the United States and Canada. Results of this review indicate that the limitations of BIPs are due, in large part, to the limitations of current state standards regulating these programs and, furthermore, that these standards are not grounded in the body of empirical research evidence or best practices. The authors, all of whom have considerable expertise in the area of domestic violence perpetrator treatment, conducted an exhaustive investigation of the following key intervention areas: overall effectiveness of BIPs; length of treatment/length of group sessions; number of group participants and number of facilitators; group format and curriculum; assessment protocol and instruments; victim contact; modality of treatment; differential treatment; working with female perpetrators; working with perpetrators in racial and ethnic minority groups; working with lesbian, gay, bisexual, and transgender (LGBT) perpetrators; perpetrator treatment and practitioner–client relationships; and required practitioner education and training. Recommendations for evidence-based national BIP standards were made based on findings from this review.

AIMS:To provide quantitative conversions between commonly used scales for the assessment of negative symptoms in schizophrenia. METHOD:Linear regression analyses generated conversion equations between symptom scores from the Scale for the Assessment of Negative Symptoms (SANS), the Schedule for the Deficit Syndrome (SDS), the Positive and Negative Syndrome Scale (PANSS), or the Negative Symptoms Assessment (NSA) based on a cross sectional sample of 176 individuals with schizophrenia. Intraclass correlations assessed the rating conversion accuracy based on a separate sub-sample of 29 patients who took part in the initial study as well as an independent sample of 28 additional subjects with schizophrenia. RESULTS:Between-scale negative symptom ratings were moderately to highly correlated (r = 0.73-0.91). Intraclass correlations between the original negative symptom rating scores and those obtained via using the conversion equations were in the range of 0.61-0.79. CONCLUSIONS:While there is a degree of non-overlap, several negative symptoms scores reflect measures of similar constructs and may be reliably converted between some scales. The conversion equations are provided at http://www.converteasy.org and may be used for meta- and mega-analyses that examine negative symptoms.

Deep learning methods hold strong promise for identifying biomarkers for clinical application. However, current approaches for psychiatric classification or prediction do not allow direct interpretation of original features. In the present study, we introduce a sparse deep neural network (DNN) approach to identify sparse and interpretable features for schizophrenia (SZ) case-control classification. An L₀ -norm regularization is implemented on the input layer of the network for sparse feature selection, which can later be interpreted based on importance weights. We applied the proposed approach on a large multi-study cohort with gray matter volume (GMV) and single nucleotide polymorphism (SNP) data for SZ classification. A total of 634 individuals served as training samples, and the classification model was evaluated for generalizability on three independent datasets of different scanning protocols (N = 394, 255, and 160, respectively). We examined the classification power of pure GMV features, as well as combined GMV and SNP features. Empirical experiments demonstrated that sparse DNN slightly outperformed independent component analysis + support vector machine (ICA + SVM) framework, and more effectively fused GMV and SNP features for SZ discrimination, with an average error rate of 28.98% on external data. The importance weights suggested that the DNN model prioritized to select frontal and superior temporal gyrus for SZ classification with high sparsity, with parietal regions further included with lower sparsity, echoing previous literature. The results validate the application of the proposed approach to SZ classification, and promise extended utility on other data modalities and traits which ultimately may result in clinically useful tools.