House Prices and Birth Rates: The Impact of the Real Estate Market on the Decision to Have a Baby

This project investigates how changes in Metropolitan Statistical Area (MSA)- level housing prices affect household fertility decisions. Recognizing that housing is a major cost associated with child rearing, and assuming that children are normal goods, we hypothesize that an increase in real estate prices will have a negative price effect on current period fertility. This applies to both potential first-time homeowners and current homeowners who might upgrade to a bigger house with the addition of a child. On the other hand, for current homeowners, an increase in MSA-level house prices will increase home equity, leading to a positive effect on birth rates. Controlling for MSA fixed effects, trends, and time-varying conditions, our analysis finds that indeed, short-term increases in house prices lead to a decline in births among non-owners and a net increase among owners. Our estimates suggest that a $10,000 increase in house prices leads to a 2.1 percent increase in births among home owners, and a 0.4 percent decrease among non-owners. At the mean U.S. home ownership rate, our estimates imply that the net effect of a$10,000 increase in house prices is a 0.8 percent increase in births. Given underlying differences in home ownership rates, the predicted net effect of house price changes varies across demographic groups. Our paper provides evidence that homeowners use some of their increased housing wealth, coming from increases in local area house prices, to fund their childbearing goals. In addition, we find that changes in house prices exert a larger effect on current period birth rates than do changes in unemployment rates.

Triple dehydrofluorination as a route to amidine-functionalized, aromatic phosphorus heterocycles

An unexpected route to hitherto unknown amidine-functionalized phosphinines has been developed that is rapid and simple. Starting from primary amines and CF3-substituted λ3,σ2-phosphinines, a cascade of dehydrofluorination reactions leads selectively to ortho-amidinephosphinines. DFT calculations reveal that this unusual transformation can take place via a series of nucleophilic attacks at the electrophilic, low-coordinate phosphorus atom

The impact of tinnitus distress on cognition

Tinnitus is the chronic perception of a phantom sound with different levels of related distress. Past research has elucidated interactions of tinnitus distress with audiological, affective and further clinical variables. The influence of tinnitus distress on cognition is underinvestigated. Our study aims at investigating specific influences of tinnitus distress and further associated predictors on cognition in a cohort of n = 146 out-ward clinical tinnitus patients. Age, educational level, hearing loss, Tinnitus Questionnaire (TQ) score, tinnitus duration, speech in noise (SIN), stress, anxiety and depression, and psychological well-being were included as predictors of a machine learning regression approach (elastic net) in three models with scores of a multiple choice vocabulary test (MWT-B), or two trail-making tests (TMT-A and TMT-B), as dependent variables. TQ scores predicted lower MWT-B scores and higher TMT-B test completion time. Stress, emotional, and psychological variables were not found to be relevant predictors in all models with the exception of small positive influences of SIN and depression on TMT-B. Effect sizes were small to medium for all models and predictors. Results are indicative of specific influence of tinnitus distress on cognitive performance, especially on general or crystallized intelligence and executive functions. More research is needed at the delicate intersection of tinnitus distress and cognitive skills needed in daily functioning

Identifiability of Gaussian structural equation models with equal error variances

We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding to each structural equation model, there is a directed acyclic graph describing the relationships between the variables. In Gaussian structural equation models with linear functions, the graph can be identified from the joint distribution only up to Markov equivalence classes, assuming faithfulness. In this work, we prove full identifiability if all noise variables have the same variances: the directed acyclic graph can be recovered from the joint Gaussian distribution. Our result has direct implications for causal inference: if the data follow a Gaussian structural equation model with equal error variances and assuming that all variables are observed, the causal structure can be inferred from observational data only. We propose a statistical method and an algorithm that exploit our theoretical findings

Lower Mortality with Andexanet Alfa vs 4-Factor Prothrombin Complex Concentrate for Factor Xa Inhibitor-Related Major Bleeding in a U.S. Hospital-Based Observational Study

BACKGROUND: Well-designed studies with sufficient sample size comparing andexanet alfa vs 4-factor prothrombin complex concentrate (4F-PCC) in routine clinical practice to evaluate clinical outcomes are limited. OBJECTIVES: To compare in-hospital mortality in patients hospitalized with rivaroxaban- or apixaban-related major bleeding who were treated with andexanet alfa or 4F-PCC. METHODS: An observational cohort study (ClinicalTrials.gov identifier: NCT05548777) was conducted using electronic health records between May 2018 and September 2022 from 354 U.S. hospitals. Inclusion criteria were age ≥18 years, inpatient admission with diagnosis code D68.32 (bleeding due to extrinsic anticoagulation), a record of use of the factor Xa inhibitors rivaroxaban or apixaban, andexanet alfa or 4F-PCC treatment during index hospitalization, and a documented discharge disposition. Multivariable logistic regression on in-hospital mortality with andexanet alfa vs 4F-PCC was performed. The robustness of the results was assessed via a supportive propensity score-weighted logistic regression. RESULTS: The analysis included 4395 patients (andexanet alfa, CONCLUSION: In this large observational study, treatment with andexanet alfa in patients hospitalized with rivaroxaban- or apixaban-related major bleeds was associated with 50% lower odds of in-hospital mortality than 4F-PCC. The magnitude of the risk reduction was similar in ICH and GI bleeds

Association of systemic inflammation with shock severity, 30-day mortality, and therapy response in patients with cardiogenic shock

Background: Mortality in cardiogenic shock (CS) remains high even when mechanical circulatory support (MCS) restores adequate circulation. To detect a potential contribution of systemic inflammation to shock severity, this study determined associations between C-reactive protein (CRP) concentrations and outcomes in patients with CS. Methods: Unselected, consecutive patients with CS and CRP measurements treated at a single large cardiovascular center between 2009 and 2019 were analyzed. Adjusted regression models were fitted to evaluate the association of CRP with shock severity, 30-day in-hospital mortality and treatment response to MCS. Results: The analysis included 1116 patients [median age: 70 (IQR 58–79) years, 795 (71.3%) male, lactate 4.6 (IQR 2.2–9.5) mmol/l, CRP 17 (IQR 5–71) mg/l]. The cause of CS was acute myocardial infarction in 530 (48%) patients, 648 (58%) patients presented with cardiac arrest. Plasma CRP concentrations were equally distributed across shock severities (SCAI stage B–E). Higher CRP concentrations were associated with 30-day in-hospital mortality (8% relative risk increase per 50 mg/l increase in CRP, range 3–13%; p < 0.001), even after adjustment for CS severity and other potential confounders. Higher CRP concentrations were only associated with higher mortality in patients not treated with MCS [hazard ratio (HR) for CRP > median 1.50; 95%-CI 1.21–1.86; p < 0.001], but not in those treated with MCS (HR for CRP > median 0.92; 95%-CI 0.67–1.26; p = 0.59; p-interaction = 0.01). Conclusion: Elevated CRP concentrations are associated with increased 30-day in-hospital mortality in unselected patients with cardiogenic shock. The use of mechanical circulatory support attenuates this association

Triple dehydrofluorination as a route to amidine-functionalized, aromatic phosphorus heterocycles

Hitherto unknown amidine-functionalized phosphabenzenes selectively form by a cascade of dehydrofluorination reactions

ANMM4CBR: a case-based reasoning method for gene expression data classification

<p>Abstract</p> <p>Background</p> <p>Accurate classification of microarray data is critical for successful clinical diagnosis and treatment. The "curse of dimensionality" problem and noise in the data, however, undermines the performance of many algorithms.</p> <p>Method</p> <p>In order to obtain a robust classifier, a novel Additive Nonparametric Margin Maximum for Case-Based Reasoning (ANMM4CBR) method is proposed in this article. ANMM4CBR employs a case-based reasoning (CBR) method for classification. CBR is a suitable paradigm for microarray analysis, where the rules that define the domain knowledge are difficult to obtain because usually only a small number of training samples are available. Moreover, in order to select the most informative genes, we propose to perform feature selection via additively optimizing a nonparametric margin maximum criterion, which is defined based on gene pre-selection and sample clustering. Our feature selection method is very robust to noise in the data.</p> <p>Results</p> <p>The effectiveness of our method is demonstrated on both simulated and real data sets. We show that the ANMM4CBR method performs better than some state-of-the-art methods such as support vector machine (SVM) and <it>k </it>nearest neighbor (<it>k</it>NN), especially when the data contains a high level of noise.</p> <p>Availability</p> <p>The source code is attached as an additional file of this paper.</p

Improving Cancer Classification Accuracy Using Gene Pairs

Recent studies suggest that the deregulation of pathways, rather than individual genes, may be critical in triggering carcinogenesis. The pathway deregulation is often caused by the simultaneous deregulation of more than one gene in the pathway. This suggests that robust gene pair combinations may exploit the underlying bio-molecular reactions that are relevant to the pathway deregulation and thus they could provide better biomarkers for cancer, as compared to individual genes. In order to validate this hypothesis, in this paper, we used gene pair combinations, called doublets, as input to the cancer classification algorithms, instead of the original expression values, and we showed that the classification accuracy was consistently improved across different datasets and classification algorithms. We validated the proposed approach using nine cancer datasets and five classification algorithms including Prediction Analysis for Microarrays (PAM), C4.5 Decision Trees (DT), Naive Bayesian (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN)

Gene selection for classification of microarray data based on the Bayes error

<p>Abstract</p> <p>Background</p> <p>With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, e.g., disease diagnosis. Several widely used gene selection methods often select top-ranked genes according to their individual discriminative power in classifying samples into distinct categories, without considering correlations among genes. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analyses. Some latest studies show that incorporating gene to gene correlations into gene selection can remove redundant genes and improve classification accuracy.</p> <p>Results</p> <p>In this study, we propose a new method, Based Bayes error Filter (BBF), to select relevant genes and remove redundant genes in classification analyses of microarray data. The effectiveness and accuracy of this method is demonstrated through analyses of five publicly available microarray datasets. The results show that our gene selection method is capable of achieving better accuracies than previous studies, while being able to effectively select relevant genes, remove redundant genes and obtain efficient and small gene sets for sample classification purposes.</p> <p>Conclusion</p> <p>The proposed method can effectively identify a compact set of genes with high classification accuracy. This study also indicates that application of the Bayes error is a feasible and effective wayfor removing redundant genes in gene selection.</p