Optimal variance estimation without estimating the mean function

We study the least squares estimator in the residual variance estimation context. We show that the mean squared differences of paired observations are asymptotically normally distributed. We further establish that, by regressing the mean squared differences of these paired observations on the squared distances between paired covariates via a simple least squares procedure, the resulting variance estimator is not only asymptotically normal and root-$n$ consistent, but also reaches the optimal bound in terms of estimation variance. We also demonstrate the advantage of the least squares estimator in comparison with existing methods in terms of the second order asymptotic properties.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ432 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective.MethodsWe developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors.ResultFrom April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination.ConclusionAs found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward

Feasibility and safety of ultra-fast track anesthesia for totally thoracoscopic closure of ventricular septal defect: A randomized controlled trial

Objective: Ultra-fast channel anesthesia (UFTA) can reduce the dosage of opioid narcotic drugs, allow for a rapid postoperative extubation and reduce the harmful stress response during perioperative period. However, there has been limited information about the application of UFTA during thoracoscopic closure of ventricular septal defect (VSD). The aim of this study was to assess the feasibility and safety of UFTA technique in patients undergoing totally thoracoscopic closure of VSD. Methods: Seventy-eight patients were randomly divided into study (UFTA) and control (standard general anesthesia) group. Totally thoracoscopic closure of VSD was performed in all patients. Extubation in the study and control group was attempted in the operating room and the intensive care unit, respectively. Results: All patients in the study group were extubated in the operating room immediately after surgery, but 2 (6.1%) required reintubation. All the control group patients were extubated after a period of mechanical ventilation (3.0 ± 3.7 h vs 0 h in the study group, p = 0.001) in the intensive care unit. The intensive care and hospital stays in the study group were shorter than in the control group (4.3 ± 2.5 vs 13.4 ± 4.4 h, p = 0.003, and 5.8 ± 0.8 vs 6.5 ± 1.2 d, p = 0.047). The total costs for the treatment in the study group was lower than in the control group (5264 ± 514 vs 4662 ± 461 US dollars, p = 0.02). Conclusions: UFTA and operating room extubation was feasible and safe in the majority of patients following totally thoracoscopic closure of VSD. This technique was associated with a shorter intensive care stay and lower overall costs for the surgical treatment

Analytical model for pressure and rate analysis of multi-fractured horizontal wells in tight gas reservoirs

Abstract Multi-fractured horizontal wells (MFHWs) are effective for developing unconventional reservoirs. A complex fracture network around the well and hydraulic fractures form during fracturing. Hydraulic fractures and fracture network are sensitive to the effective stress. However, most existing models do not consider the effects of stress sensitivity. In this study, a new analytical model was established for an MFHW in tight gas reservoirs based on the trilinear flow model. Fractal porosity and permeability were employed to describe the heterogeneous distribution of the complex fracture network. The stress sensitivity of fractures was also considered in the model. Pedrosa substitution and perturbation method were applied to eliminate the nonlinearity of the model. Analytical solutions in the Laplace domain were obtained using Laplace transformation. The model was then validated and applied. Finally, sensitivity analyses of pressure and rate were discussed. The presented model provides a new approach to estimate the effect of fracturing. It can also be utilized to recognize formation properties and forecast the dynamics of pressure and the production of tight gas reservoirs