Evaluating Models Of The Relationship Between Accounting Profitability Measures And Internal Rate Of Return

Researchers have investigated the relationship between the internal rate of return (IRR) and accounting-based profitability measures using analytical and indirect empirical methodologies.  The current study employs computer simulation to complement the other two methodologies and corroborate their results. The results indicate that the accounting rate of return (ARR) and the conditional estimate of internal rate of return (CIRR) are strongly associated with IRR; however, the length of the estimation period and formulation used for CIRR appear to affect its relationship to IRR.  ARR’s relationship to IRR appears to be unaffected by the length of the estimation period

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders 1 . They are heritable 2,3 and etiologically related 4,5 behaviors that have been resistant to gene discovery efforts 6–11 . In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures

Novel Common Genetic Susceptibility Loci for Colorectal Cancer

BACKGROUND: Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10-8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. METHODS: We conducted a GWAS in European descent CRC cases and control subjects using a discovery-replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10-8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. RESULTS: The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10-8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. CONCLUSIONS: This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screenin

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Bias And Random Measurement Error In Accounting-Based Surrogates For Internal Rate Of Return

Limitations inherent in alternative profitability measures as estimates of internal rate of return (IRR) often require that managers and researchers employ accounting-based profitability measures. Using published accounting and stock market data, this study models accounting rate of return (ARR) and conditional estimate of internal rate of return (CIRR) as functions of product market risk; growth (g) inventory cost flow assumption (INV), and depreciation method (DE). The models support inferences about the bias and efficiency (i.e. systematic and random error) in the relationships between the two accounting-based profitability measure and IRR, as estimated by the bias and efficiency in their relationships with a factor that is suggested in the finance literature as a determinant of systematic risk (product market risk). The results indicate that ARR estimates IRR with bias attributable to g; however, ARR is unaffected by INV and DEP. Whether g, INV, or DEP affect CIRRs ability to estimate IRR depends on the interval over which CIRR is estimated and the assumed cash flow pattern. On the other hand, CIRR generally estimates IRR with significantly greater efficiency. These results have research design implications, as well as implications for both accounting policy formulation and anti-trust policies

A Simulation-Based Investigation of Errors in Accounting-Based Surrogates for Internal Rate of Return

Accounting-based measures of a firm's ex post performance represent accessible, albeit imperfect, surrogates for its internal rate of return (IRR). Using a cross-sectional data set obtained via computer simulation, this study calculated the error with which the accounting rate of return (ARR) and conditional estimate of internal rate of return (CIRR) estimate IRR. The study compared the error with which both surrogates measure IRR, as well as the ability of growth in unit demand (gD), inventory cost flow assumption (INV) and depreciation method (DEP) to explain the measurement error in both surrogates. Copyright Blackwell Publishers Ltd 1997.

Pro-inflammatory immune responses are associated with clinical signs and symptoms of human anaplasmosis

<div><p>Human anaplasmosis (HA) is an emerging tick-borne disease that may present as a mild flu-like illness or a life threatening, sepsis-like condition. Although disease severity is hypothesized to relate to immunopathology and immune dysfunction in humans, studies to directly measure immune responses in infected humans have been very limited. We quantified cytokines in 80 confirmed HA patients using a multiplex chemiluminescence immunoassay system and compared similarly measured responses in 1000 control subjects. Pro-inflammatory cytokines were significantly elevated in HA patients (all seven p<0.0001). Interferon gamma (IFN-γ) concentrations were particularly high, with average concentrations 7.8 times higher in the HA patients than the controls. A subset of cytokines consisting of IL-1β, IL-8, IL-6, TNF-α, and IL-10 was also coordinately high and significantly associated with severity of thrombocytopenia in HA patients. Patients with infections in the very acute stage (≤ 4 days ill) tended to have the highest IFN-γ, IL-12p70, and IL-2 levels. Higher concentrations of IL-13 and IL-5 were associated with diarrhea and vomiting. Our findings support a pathophysiological role for a pro-inflammatory response in HA, especially with regard to the modulation of hematopoiesis and subsequent hematopoietic complications.</p></div

Demographics and clinical characteristics of patients with human anaplasmosis.

<p>Demographics and clinical characteristics of patients with human anaplasmosis.</p