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We document a strong positive initial market reaction to merger announcements from bidders with either large earnings growth or significant earnings decline, relative to those with neutral earnings change, reflecting a U-shaped pattern between bidders’ earnings growth and announcement returns. However, the higher initial returns for bidders with earnings decline subsequently reverse, whereas the higher returns for bidders with high growth do not. We further show that the return patterns are driven by a tendency for retail investors to gamble that merger and acquisition deals initiated by poorly performing bidders will generate high synergies.

Background and ObjectivesThe aim of this work was to test the accuracy of Persyst commercially available automated seizure detection in critical care EEG by comparing automated seizure detections to human review in a manually reviewed cohort and on a large scale.MethodsAutomated seizure detections (Persyst versions 12 and 13) were compared to human review in a pilot cohort of 229 seizures from 85 EEG records and then in an expanded cohort of 7,924 EEG records. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for individual seizures (pilot cohort) and for entire records (pilot and expanded cohorts). We assessed EEG features associated with the accuracy of automated seizure detections.ResultsIn the pilot cohort, accuracy of automated detection for individual seizures was modest (sensitivity 0.50, PPV 0.60). At the record level (did the recording contain seizures or not?), sensitivity was higher (pilot cohort 0.78, expanded cohort 0.91), PPV was low (pilot cohort 0.40, expanded cohort 0.08), and NPV was high (pilot cohort 0.88, expanded cohort 0.97). Different software versions (version 12 vs 13) performed similarly. Sensitivity was higher for records containing focal-onset seizures compared to generalized-onset seizures (0.93 vs 0.85, p = 0.012).DiscussionIn critical care continuous EEG recordings, automated detection of individual seizures had rates of both false negatives and false positives that bring into question its utility as a seizure alarm in clinical practice. At the level of entire EEG records, the absence of automated detections accurately predicted EEG records without true seizures. The true value of Persyst automated seizure detection appears to lie in triaging of low-risk EEGs.Classification of EvidenceThis study provides Class II evidence that an automated seizure detection program cannot accurately identify EEG records that contain seizures.

Using merger documents filed with the SEC from 1994 to 2018, we show that being selected by investment banks as “comparable peers” are more than twice as likely to become a target themselves in the future compared to control firms matched for industry and size. They also experience an increase in analyst coverage and institutional ownership. Investment banks’ informational advantage as well as their ability to facilitate future takeover transactions, appear to contribute to peers’ significantly higher probability of becoming targets. Investors and equity analysts largely ignore the rich information contained in merger filings. A portfolio that longs peers and shorts non-peers matched for industry and size earns up to 15.6% alpha in the subsequent year. Alphas come entirely from the long-leg, which cannot be explained by short-sale constraints.

SummaryMany scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose CoCoA (Conditional Correlation Model with Association Size), a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale, while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semi-parametric estimators adapted from genome association studies, and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modelling and partitioned correlation analyses.

A central challenge of medical imaging studies is to extract quantitative biomarkers that characterize pathology or predict disease outcomes. In high-resolution, high-quality magnetic resonance images (MRI), state-of-the-art approaches have performed well. However, such methods may not translate to low resolution, lower quality images acquired on MRI scanners with lower magnetic field strength. Therefore, in low-resource settings where low field scanners are more common and there is a shortage of available radiologists to manually interpret MRI scans, it is essential to develop automated methods that can accommodate lower quality images and augment or replace manual interpretation. Motivated by a project in which a cohort of children with cerebral malaria were imaged using 0.35 Tesla MRI to evaluate the degree of diffuse brain swelling, we introduce a fully automated framework to translate radiological diagnostic criteria into image-based biomarkers. We integrate multi-atlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We further propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers are found to have excellent classification performance for determining severe cerebral edema due to cerebral malaria.

Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS

With the increasing availability of neuroimaging data from multiple modalities—each providing a different lens through which to study brain structure or function—new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods.

Magnetic resonance imaging is a fundamental tool in the diagnosis and management of neurological diseases such as multiple sclerosis (MS). New portable, low-field MRI scanners could potentially lower financial and technical barriers to neuroimaging and reach underserved or disabled populations. However, the sensitivity of low-field MRI for MS lesions is unknown. We sought to determine if white matter lesions can be detected on a 64mT low-field MRI, compare automated lesion segmentations and total lesion burden between paired 3T and 64mT scans, and identify features that contribute to lesion detection accuracy. In this prospective, cross-sectional study, same-day brain MRI (FLAIR, T1, and T2) scans were collected from 36 adults (32 women; mean age, 50 ± 14 years) with known or suspected MS using 3T (Siemens) and 64mT (Hyperfine) scanners at two centers. Images were reviewed by neuroradiologists. MS lesions were measured manually and segmented using an automated algorithm. Statistical analyses assessed accuracy and variability of segmentations across scanners and systematic scanner biases in automated volumetric measurements. Lesions were identified on 64mT scans in 94% (31/33) of patients with confirmed MS. The smallest lesions manually detected were 5.7 ± 1.3 mm in maximum diameter at 64mT vs 2.1 ± 0.6 mm at 3T. Automated lesion burden estimates were highly correlated between 3T and 64mT scans (r = 0.89, p < 0.001). Bland-Altman analysis identified bias in 64mT segmentations (mean = 1.6 ml, standard error = 5.2 ml, limits of agreement = -19.0–15.9 ml), which over-estimated low lesion burden and under-estimated high burden (r = 0.74, p < 0.001). Visual inspection revealed over-segmentation was driven by flow-related hyperintensities in veins on 64mT FLAIR. Lesion size drove segmentation accuracy, with 93% of lesions >1.0 ml and all lesions >1.5 ml being detected. These results demonstrate that in established MS, a portable 64mT MRI scanner can identify white matter lesions, and disease burden estimates are consistent with 3T scans.HighlightsPaired, same-day 3T and 64mT MRI studies were collected in 36 patients64mT MRI showed 94% sensitivity for detecting any lesions in established MS casesThe diameter of the smallest detected lesion was larger at 64mT compared to 3TDisease burden estimates were strongly correlated between 3T and 64mT scansLow-field MRI can detect white matter lesions, though smaller lesions may be missed

Correlations between cognitive ability and psychopathology are well recognized, but prior research has been limited by focusing on individuals with intellectual disability, single-diagnosis psychiatric populations, or few measures of psychopathology. Here, we quantify relationships between full-scale IQ and multiple dimensions of psychopathology in a diverse care-seeking population, with a novel focus on differential coupling between psychopathology dimensions as a function of IQ.A total of 70 dimensional measures of psychopathology, plus IQ and demographic data, were collated for 2,752 children and adolescents from the Healthy Brain Network dataset. We first examined univariate associations between IQ and psychopathology, and then characterized how the correlational architecture of psychopathology differs between groups at extremes of the IQ distribution.Associations with IQ vary in magnitude between different domains of psychopathology: IQ shows the strongest negative correlations with attentional and social impairments, but is largely unrelated to affective symptoms and psychopathy. Lower IQ is associated with stronger coupling between internalizing problems and aggression, repetitive behaviors, and hyperactivity/inattentiveness.Our analyses reveal that variation in general cognitive ability is associated not only with significant and selective shifts in severity of psychopathology, but also in the coupling between different dimensions of psychopathology. These findings have relevance for the clinical assessment of mental health in populations with varying IQ, and may also inform ongoing efforts to improve the measurement of psychopathology and to understand how relationships between cognition and behavior are reflected in brain organization.We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure sex balance in the selection of non-human subjects. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science.

Background. Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in newly diagnosed, treatment-naïve, IDH-wildtype GBM patients, by combining conventional and deep learning methods.Methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. SVM classifiers were trained on the discovery cohort (n=404) to categorize patient groups of high-risk (OSradiomics) was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information.Results. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed AUCs of 0.78 (95%CI:0.70–0.85)/0.75 (95%CI:0.64–0.79) and 0.75 (95%CI: 0.65–0.84)/0.63 (95%CI: 0.52–0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95%CI:0.6–0.7) for clinical data, 0.70 (95%CI:0.65–0.75) for clinical and radiomics, 0.72 (95%CI:0.68–0.77) for clinical, MGMT methylation, and radiomics, and 0.75 (95%CI:0.72–0.79) for the combination of all omics, i.e., clinical, MGMT methylation, radiomics, and genomics.Conclusions. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM. Our multi-omic survival prediction tool is easily scalable and can be used for more effective clinical trial stratification.

Magnetic resonance imaging (MRI) is a fundamental tool in the diagnosis and management of neurological diseases such as multiple sclerosis (MS). New portable, low-field strength, MRI scanners could potentially lower financial and technical barriers to neuroimaging and reach underserved or disabled populations, but the sensitivity of these devices for MS lesions is unknown. We sought to determine if white matter lesions can be detected on a portable 64mT scanner, compare automated lesion segmentations and total lesion volume between paired 3T and 64mT scans, identify features that contribute to lesion detection accuracy, and explore super-resolution imaging at low-field. In this prospective, cross-sectional study, same-day brain MRI (FLAIR, T1w, and T2w) scans were collected from 36 adults (32 women; mean age, 50 ± 14 years) with known or suspected MS using Siemens 3T (FLAIR: 1 mm isotropic, T1w: 1 mm isotropic, and T2w: 0.34-0.5 × 0.34-0.5 × 3-5 mm) and Hyperfine 64mT (FLAIR: 1.6 × 1.6 × 5 mm, T1w: 1.5 × 1.5 × 5 mm, and T2w: 1.5 × 1.5 × 5 mm) scanners at two centers. Images were reviewed by neuroradiologists. MS lesions were measured manually and segmented using an automated algorithm. Statistical analyses assessed accuracy and variability of segmentations across scanners and systematic scanner biases in automated volumetric measurements. Lesions were identified on 64mT scans in 94% (31/33) of patients with confirmed MS. The average smallest lesions manually detected were 5.7 ± 1.3 mm in maximum diameter at 64mT vs 2.1 ± 0.6 mm at 3T, approaching the spatial resolution of the respective scanner sequences (3T: 1 mm, 64mT: 5 mm slice thickness). Automated lesion volume estimates were highly correlated between 3T and 64mT scans (r = 0.89, p 0.001). Bland-Altman analysis identified bias in 64mT segmentations (mean = 1.6 ml, standard error = 5.2 ml, limits of agreement = -19.0-15.9 ml), which over-estimated low lesion volume and under-estimated high volume (r = 0.74, p 0.001). Visual inspection revealed over-segmentation was driven venous hyperintensities on 64mT T2-FLAIR. Lesion size drove segmentation accuracy, with 93% of lesions 1.0 ml and all lesions 1.5 ml being detected. Using multi-acquisition volume averaging, we were able to generate 1.6 mm isotropic images on the 64mT device. Overall, our results demonstrate that in established MS, a portable 64mT MRI scanner can identify white matter lesions, and that automated estimates of total lesion volume correlate with measurements from 3T scans.

PURPOSE Multi-omics data integration captures tumor characteristics at multiple scales [i.e., microscopic (genomics and epigenetics), macroscopic (radiomics), clinical manifestation], provides a more comprehensive assessment of patient’s risk, and facilitates personalized therapies. In this work, we investigated the synergistic value of such multiple data sources for risk stratification and prediction of overall survival in IDH-wildtype glioblastoma tumors. METHODS Quantitative conventional and deep radiomics were extracted from pre-operative multi-parametric structural MRI (T1, T1Gd, T2, T2-FLAIR) of 501 patients with newly diagnosed glioblastoma. 389/501 and 112/501 patients formed our discovery and replication cohorts, respectively. Conventional radiomics were extracted from CaPTk, and deep radiomics from a pre-trained VGG-19 model. Multivariate SVM classification was performed on the discovery cohort to stratify patients in high, medium, and low-risk groups, using recursive feature elimination and 5-fold cross-validation. This model was independently tested on the replication cohort, and a radiomic-based survival prediction index (SPIradiomics) was calculated for each patient. Multi-stage integration of omics data, i.e., clinical (age, gender, extent of resection (EOR)), SPIradiomics, epigenetics (MGMT promoter methylation), and genomics (27 clinically relevant gene mutations via next-generation sequencing (NGS)), was performed using multivariate Cox proportional hazards (Cox-PH) model for stratification of the risk in the replication cohort. RESULTS Cox-PH modeling resulted in a concordance index (c-index) of 0.65 (95% CI:0.6–0.7) for clinical data, 0.67 (95% CI:0.62–0.72) for clinical and epigenetics, 0.70 (95% CI:0.65–0.75) for clinical and radiomics, 0.72 (95% CI:0.68–0.77) for clinical, epigenetics, and radiomics, and 0.75 (95% CI:0.71 – 0.78) for the multi-omics combination of all data; highlighting the added value of each layer of information in prediction of the patient’s risk. CONCLUSION Our results reinforce the synergistic value of integrated diagnostic methods for improving risk assessment of patients with glioblastoma that may pave the path towards a more personalized treatment planning.

We have previously demonstrated that angiotensin-converting enzyme (ACE) inhibition with enalapril produces persistent effects that protect against future nitric oxide synthase (NOS) inhibitor (L-arginine methyl ester, L-NAME)-induced cardiac dysfunction and outer wall collagen deposition in spontaneously hypertensive rats (SHR). In the present study, we dissect the cytokine/chemokine release profile during NOS inhibition, its correlation to pathological cardiac remodeling and the impact of transient ACE inhibition on these effects. Adult male SHR were treated with enalapril (E+L) or tap water (C+L) for 2 weeks followed by a 2-week washout period. Rats were then subjected to 0, 3, 7 or 10 days of L-NAME treatment. The temporal response to NOS inhibition was evaluated by measuring arterial pressure, cardiac remodeling and cytokine/chemokine levels. L-NAME equivalently increased blood pressure and myocardial and vascular injury in C+L and E+L rats. However, pulse pressure (PP) was only transiently altered in C+L rats. The levels of several inflammatory mediators were increased during L-NAME treatment. However, interleukin-6 (IL-6) and IL-10 and monocyte chemoattractant protein-1 were uniquely increased in C+L hearts; whereas IL-4 and fractalkine were only elevated in E+L hearts. By days 7 and 10 of L-NAME treatment, there was a significant increase in the cardiac density of macrophages and proliferating cells, respectively only in C+L rats. Although myocardial injury was similar in both treatment groups, PP was not changed and there was a distinct cardiac chemokine/cytokine signature in rats previously treated with enalapril that may be related to the lack of proliferative response and macrophage infiltration in these hearts.