Are Key Audit Matter Disclosures Useful in Assessing the Financial Distress Level of a Client Firm?

This study examines the usefulness of new expanded audit report key audit matters (KAM) disclosures in assessing the level of financial distress present at a client firm. Using six years of KAM disclosures for U.K. Premium-listed firms beginning in 2013, we investigate the relation between firm financial distress and the number, risk level, financial statement impact, and individual nature of auditor-disclosed KAMs. We expand on literatures examining audit report disclosures in gauging financial distress assessments as well as the utility of expanded audit reporting. We find the greater the number of KAMs disclosed, the higher a firm’s financial distress level. Additionally, results show entity-level KAMs, account-level KAMs with a primary impact on profitability and solvency, and certain types of individual KAMs are more likely to be disclosed when client firms face higher levels of financial distress. The results are robust to alternative measures of financial distress and to endogeneity tests. Our findings also indicate KAMs have predictive ability in assessing subsequent periods’ financial distress levels. In all, evidence from this study suggests a way financial statement users can use independent auditor disclosures to assess one of the main risks associated with a firm - the risk of failure

Quantifying downstream impacts of impoundment on flow regime and channel planform, lower Trinity River, Texas

TextThis study seeks to describe and quantify channel activity and flow regime, identifying effects of the 1968 closure of Livingston dam. Channel activity rates do not indicate a more stabilized planform following dam closure; rather they suggest that the Trinity River is adjusting itself to the stress of Livingston dam in a slow, gradual process that may not be apparent in a modern time scale.Texas Christian University Department of Geology; Tobacco Road Research Team, Department of Geography, University of Kentucky.Center for Research in Water Resource

Drugs and Bugs: The Gut-Brain Axis and Substance Use Disorders

Substance use disorders (SUDs) represent a significant public health crisis. Worldwide, 5.4% of the global disease burden is attributed to SUDs and alcohol use, and many more use psychoactive substances recreationally. Often associated with comorbidities, SUDs result in changes to both brain function and physiological responses. Mounting evidence calls for a precision approach for the treatment and diagnosis of SUDs, and the gut microbiome is emerging as a contributor to such disorders. Over the last few centuries, modern lifestyles, diets, and medical care have altered the health of the microbes that live in and on our bodies; as we develop, our diets and lifestyle dictate which microbes flourish and which microbes vanish. An increase in antibiotic treatments, with many antibiotic interventions occurring early in life during the microbiome's normal development, transforms developing microbial communities. Links have been made between the microbiome and SUDs, and the microbiome and conditions that are often comorbid with SUDs such as anxiety, depression, pain, and stress. A better understanding of the mechanisms influencing behavioral changes and drug use is critical in developing novel treatments for SUDSs. Targeting the microbiome as a therapeutic and diagnostic tool is a promising avenue of exploration. This review will provide an overview of the role of the gut-brain axis in a wide range of SUDs, discuss host and microbe pathways that mediate changes in the brain's response to drugs, and the microbes and related metabolites that impact behavior and health within the gut-brain axis

The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches.

A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience

The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches.

A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience