Mortgage Transition Model Based on LoanPerformance Data

The unexpected increase in loan default on the mortgage market is widely considered to be one of the main cause behind the economic crisis. To provide some insight on loan delinquency and default, I analyze the mortgage performance data from Fannie Mae website and investigate how economic factors and individual loan and borrower information affect the events of default and prepaid. Various delinquency status including default and prepaid are treated as discrete states of a Markov chain. One-step transition probabilities are estimated via multinomial logistic models. We find that in general current loan-to-value ratio, credit score, unemployment rate, and interest rate significantly affect the transition probabilities to different delinquency states, which lead to further default or prepaid events

Manpower Constraints and Corporate Policies

Manpower constraints are the pervasive lack of specialized high- and low-skill workers, irrespective of the wage firms might offer

Where has the rum gone? The impact of maritime piracy on trade and transport

Despite a general agreement that piracy poses a significant threat to maritime ship - ping, empirical evidence regarding its economic consequences remains scarce. This paper combines firm-level Chinese customs data and ship position data with infor- mation on pirate attacks to investigate how exporting firms and cargo ships respond to maritime piracy. It finds that overall exports along affected shipping routes fall following an increase in pirate activity. In addition, piracy induces firms to switch from ocean to air shipping, while remaining ocean shipments become larger. At the ship-level, the paper provides evidence for re-routing, as container ships avoid regions prone to pirate attacks

Developing New Oligo Probes to Distinguish Specific Chromosomal Segments and the A, B, D Genomes of Wheat (Triticum aestivum L.) Using ND-FISH

Non-denaturing FISH (ND-FISH) technology has been widely used to study the chromosomes of Triticeae species because of its convenience. The oligo probes for ND-FISH analysis of wheat (Triticum aestivum L.) chromosomes are still limited. In this study, the whole genome shotgun assembly sequences (IWGSC WGA v0.4) and the first version of the reference sequences (IWGSC RefSeq v1.0) of Chinese Spring (T. aestivum L.) were used to find new tandem repeats. One hundred and twenty oligo probes were designed according to the new tandem repeats and used for ND-FISH analysis of chromosomes of wheat Chinese Spring. Twenty nine of the 120 oligo probes produce clear or strong signals on wheat chromosomes. Two of the 29 oligo probes can be used to conveniently distinguish wheat A-, B-, and D-genome chromosomes. Sixteen of the 29 oligo probes only produce clear or strong signals on the subtelomeric regions of 1AS, 5AS, 7AL, 4BS, 5BS, and 3DS arms, on the telomeric regions of 1AL, 5AL, 2BS, 3BL, 6DS, and 7DL arms, on the intercalary regions of 4AL and 2DL arms, and on the pericentromeric regions of 3DL and 6DS arms. Eleven of the 29 oligo probes generate distinct signal bands on several chromosomes and they are different from those previously reported. In addition, the short and long arms of 6D chromosome have been confirmed. The new oligo probes developed in this study are useful and convenient for distinguishing wheat chromosomes or specific segments of wheat chromosomes

PDE4 inhibitors: potential protective effects in inflammation and vascular diseases

Phosphodiesterase 4 (PDE4) inhibitors are effective therapeutic agents for various inflammatory diseases. Roflumilast, apremilast, and crisaborole have been developed and approved for the treatment of chronic obstructive pulmonary disease psoriatic arthritis, and atopic dermatitis. Inflammation underlies many vascular diseases, yet the role of PDE4 inhibitors in these diseases remains inadequately explored. This review elucidates the clinical applications and anti-inflammatory mechanisms of PDE4 inhibitors, as well as their potential protective effects on vascular diseases. Additionally, strategies to mitigate the adverse reactions of PDE4 inhibitors are discussed. This article emphasizes the need for further exploration of the therapeutic potential and clinical applications of PDE4 inhibitors in vascular diseases

Development and external validation of a nomogram for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage

BackgroundPostoperative pneumonia (POP) is a common complication after aneurysmal subarachnoid hemorrhage (aSAH) associated with increased mortality rates, prolonged hospitalization, and high medical costs. It is currently understood that identifying pneumonia early and implementing aggressive treatment can significantly improve patients' outcomes. The primary objective of this study was to explore risk factors and develop a logistic regression model that assesses the risks of POP.MethodsAn internal cohort of 613 inpatients with aSAH who underwent surgery at the Neurosurgical Department of First Affiliated Hospital of Wenzhou Medical University was retrospectively analyzed to develop a nomogram for predicting POP. We assessed the discriminative power, accuracy, and clinical validity of the predictions by using the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA). The final model was validated using an external validation set of 97 samples from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.ResultsAmong patients in our internal cohort, 15.66% (n = 96/613) of patients had POP. The least absolute shrinkage and selection operator (LASSO) regression analysis identified the Glasgow Coma Scale (GCS), mechanical ventilation time (MVT), albumin, C-reactive protein (CRP), smoking, and delayed cerebral ischemia (DCI) as potential predictors of POP. We then used multivariable logistic regression analysis to evaluate the effects of these predictors and create a final model. Eighty percentage of patients in the internal cohort were randomly assigned to the training set for model development, while the remaining 20% of patients were allocated to the internal validation set. The AUC values for the training, internal, and external validation sets were 0.914, 0.856, and 0.851, and the corresponding Brier scores were 0.084, 0.098, and 0.143, respectively.ConclusionWe found that GCS, MVT, albumin, CRP, smoking, and DCI are independent predictors for the development of POP in patients with aSAH. Overall, our nomogram represents a reliable and convenient approach to predict POP in the patient population

Re-ID done right: towards good practices for person re-identification

Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism

Factors contributing to rapid decline of Arctic sea ice in autumn

Autumn Arctic sea ice has been declining since the beginning of the era of satellite sea ice observations. In this study, we examined the factors contributing to the decline of autumn sea ice concentration. From the Beaufort Sea to the Barents Sea, autumn sea ice concentration has decreased considerably between 1982 and 2020, and the rates of decline were the highest around the Beaufort Sea. We calculated the correlation coefficients between sea ice extent (SIE) anomalies and anomalies of sea surface temperature (SST), surface air temperature (SAT) and specific humidity (SH). Among these coefficients, the largest absolute value was found in the coefficient between SIE and SAT anomalies for August to October, which has a value of −0.9446. The second largest absolute value was found in the coefficient between SIE and SH anomalies for September to November, which has a value of −0.9436. Among the correlation coefficients between SIE and SST anomalies, the largest absolute value was found in the coefficient for August to October, which has a value of −0.9410. We conducted empirical orthogonal function (EOF) analyses of sea ice, SST, SAT, SH, sea level pressure (SLP) and the wind field for the months where the absolute values of the correlation coefficient were the largest. The first EOFs of SST, SAT and SH account for 39.07%, 63.54% and 47.60% of the total variances, respectively, and are mainly concentrated in the area between the Beaufort Sea and the East Siberian Sea. The corresponding principal component time series also indicate positive trends. The first EOF of SLP explains 41.57% of the total variance. It is mostly negative in the central Arctic. Over the Beaufort, Chukchi and East Siberian seas, the zonal wind weakened while the meridional wind strengthened. Results from the correlation and EOF analyses further verified the effects of the ice–temperature, ice–SH and ice–SLP feedback mechanisms in the Arctic. These mechanisms accelerate melting and decrease the rate of formation of sea ice. In addition, stronger meridional winds favor the flow of warm air from lower latitudes towards the polar region, further promoting Arctic sea ice decline