Estimation and Model Selection of Semiparametric Multivariate Survival Functions under General Censorship

Many models of semiparametric multivariate survival functions are characterized by nonparametric marginal survival functions and parametric copula functions, where different copulas imply different dependence structures. This paper considers estimation and model selection for these semiparametric multivariate survival functions, allowing for misspecified parametric copulas and data subject to general censoring. We first establish convergence of the two-step estimator of the copula parameter to the pseudo-true value defined as the value of the parameter that minimizes the KLIC between the parametric copula induced multivariate density and the unknown true density. We then derive its root--n asymptotically normal distribution and provide a simple consistent asymptotic variance estimator by accounting for the impact of the nonparametric estimation of the marginal survival functions. These results are used to establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application of the model selection test to the Loss-ALAE insurance data set is provided.Multivariate survival models, Misspecified copulas, Penalized pseudo-likelihood ratio, Fixed or random censoring, Kaplan-Meier estimator

Isobutanol production from cellobionic acid in Escherichia coli.

BackgroundLiquid fuels needed for the global transportation industry can be produced from sugars derived from plant-based lignocellulosics. Lignocellulosics contain a range of sugars, only some of which (such as cellulose) have been shown to be utilizable by microorganisms capable of producing biofuels. Cellobionic acid makes up a small but significant portion of lignocellulosic degradation products, and had not previously been investigated as an utilizable substrate. However, aldonic acids such as cellobionic acid are the primary products of a promising new group of lignocellulosic-degrading enzymes, which makes this compound group worthy of study. Cellobionic acid doesn't inhibit cellulose degradation enzymes and so its inclusion would increase lignocellulosic degradation efficiency. Also, its use would increase overall product yield from lignocellulose substrate. For these reasons, cellobionic acid has gained increased attention for cellulosic biofuel production.ResultsThis study describes the discovery that Escherichia coli are naturally able to utilize cellobionic acid as a sole carbon source with efficiency comparable to that of glucose and the construction of an E. coli strain able to produce the drop-in biofuel candidate isobutanol from cellobionic acid. The gene primarily responsible for growth of E. coli on cellobionic acid is ascB, a gene previously thought to be cryptic (expressed only after incurring specific mutations in nearby regulatory genes). In addition to AscB, the ascB knockout strain can be complemented by the cellobionic acid phosphorylase from the fungus Neurospora crassa. An E. coli strain engineered to express the isobutanol production pathway was successfully able to convert cellobionic acid into isobutanol. Furthermore, to demonstrate potential application of this strain in a sequential two-step bioprocessing system, E. coli was grown on hydrolysate (that was degraded by a fungus) and was successfully able to produce isobutanol.ConclusionsThese results demonstrate that cellobionic acid is a viable carbon source for biofuel production. This work suggests that with further optimization, a bacteria-fungus co-culture could be used in decreased-cost biomass-based biofuel production systems

Estimation and Model Selection of Semiparametric Multivariate Survival Functions under General Censorship

Many models of semiparametric multivariate survival functions are characterized by nonparametric marginal survival functions and parametric copula functions, where different copulas imply different dependence structures. This paper considers estimation and model selection for these semiparametric multivariate survival functions, allowing for misspecified parametric copulas and data subject to general censoring. We first establish convergence of the two-step estimator of the copula parameter to the pseudo-true value defined as the value of the parameter that minimizes the KLIC between the parametric copula induced multivariate density and the unknown true density. We then derive its root–n asymptotically normal distribution and provide a simple consistent asymptotic variance estimator by accounting for the impact of the nonparametric estimation of the marginal survival functions. These results are used to establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application of the model selection test to the Loss-ALAE insurance data set is provided

Ultra-narrowband interference circuits enable low-noise and high-rate photon counting for InGaAs/InP avalanche photodiodes

Afterpulsing noise in InGaAs/InP single photon avalanche photodiodes (APDs) is caused by carrier trapping and can be suppressed successfully through limiting the avalanche charge via sub-nanosecond gating. Detection of faint avalanches requires an electronic circuit that is able to effectively remove the gate-induced capacitive response while keeping photon signals intact. Here we demonstrate a novel ultra-narrowband interference circuit (UNIC) that can reject the capacitive response by up to 80 dB per stage with little distortion to avalanche signals. Cascading two UNIC's in a readout circuit, we were able to enable high count rate of up to 700 MC/s and low afterpulsing of 0.5 % at a detection efficiency of 25.3 % for 1.25 GHz sinusoidally gated InGaAs/InP APDs. At -30 degree C, we measured 1 % afterpulsing at a detection efficiency of 21.2 %

Conversion of paper sludge to ethanol, II: process design and economic analysis

Abstract Process design and economics are considered for conversion of paper sludge to ethanol. A particular site, a bleached kraft mill operated in Gorham, NH by Fraser Papers (15 tons dry sludge processed per day), is considered. In addition, profitability is examined for a larger plant (50 dry tons per day) and sensitivity analysis is carried out with respect to capacity, tipping fee, and ethanol price. Conversion based on simultaneous saccharification and fermentation with intermittent feeding is examined, with ethanol recovery provided by distillation and molecular sieve adsorption. It was found that the Fraser plant achieves positive cash flow with or without xylose conversion and mineral recovery. Sensitivity analysis indicates economics are very sensitive to ethanol selling price and scale; significant but less sensitive to the tipping fee, and rather insensitive to the prices of cellulase and power. Internal rates of return exceeding 15% are projected for larger plants at most combinations of scale, tipping fee, and ethanol price. Our analysis lends support to the proposition that paper sludge is a leading point-of-entry and proving ground for emergent industrial processes featuring enzymatic hydrolysis of cellulosic biomass

LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm

According to the latest research, lncRNAs (long non-coding RNAs) play a broad and important role in various biological processes by interacting with proteins. However, identifying whether proteins interact with a specific lncRNA through biological experimental methods is difficult, costly, and time-consuming. Thus, many bioinformatics computational methods have been proposed to predict lncRNA-protein interactions. In this paper, we proposed a novel approach called Long non-coding RNA-Protein Interaction Prediction based on Improved Bipartite Network Recommender Algorithm (LPI-IBNRA). In the proposed method, we implemented a two-round resource allocation and eliminated the second-order correlations appropriately on the bipartite network. Experimental results illustrate that LPI-IBNRA outperforms five previous methods, with the AUC values of 0.8932 in leave-one-out cross validation (LOOCV) and 0.8819 ± 0.0052 in 10-fold cross validation, respectively. In addition, case studies on four lncRNAs were carried out to show the predictive power of LPI-IBNRA

Elevated IL-6 Receptor Expression on CD4+ T Cells contributes to the increased Th17 Responses in patients with Chronic Hepatitis B

<p>Abstract</p> <p>Background</p> <p>Increased numbers of Interleukin-17-producing CD4⁺T cells (Th17) have been found in association with hepatitis B virus (HBV)-induced liver injury. However, the mechanism underlying the increase of Th17 responses in patients with HBV infection remains unclear. In this study, we investigate the possible regulatory mechanisms of increased Th17 responses in patients with chronic hepatitis B(CHB).</p> <p>Methods</p> <p>Th17 response and IL-6R expression on CD4⁺T cells in peripheral blood samples were determined by flow cytometry. Cytokines TGF-β, IL-1β, IL-6 and IL-17 in plasma and/or supernatant samples were determined by ELISA and the IL-17 and IL-6R mRNA levels were quantified by quantitative real-time reverse polymerase chain reaction.</p> <p>Results</p> <p>All these data indicated that the frequency of periphery Th17 cells is significantly correlated with the percentage of CD4<b>⁺</b>T cells expressing IL-6R in CHB patients. CD4⁺T cells from patients with CHB, but not those from healthy donors, produced higher levels of IL-17 and had more IL-6R expression upon stimulation with the HBV core antigen (HBcAg) in vitro. The PMA/ionomycin and HBcAg -stimulated up-regulation of IL-17 production by CD4⁺T cells could be reversed by a neutralizing antibody against IL-6R.</p> <p>Conclusion</p> <p>we showed that enhancement of IL-6R expression on CD4⁺T cells upon HBV infection contributes to increased Th17 response in patients with CHB.</p

Primary and potential secondary risks of landslide outburst floods

Outburst floods triggered by breaching of landslide dams may cause severe loss of life and property downstream. Accurate identification and assessment of such floods, especially when leading to secondary impacts, are critical. In 2018, the Baige landslide in the Tibetan Plateau twice blocked the Jinsha River, eventually resulting in a severe outburst flood. The Baige landslide remains active, and it is possible that a breach happens again. Based on numerical simulation using a hydrodynamic model, remote sensing, and field investigation, we reproduce the outburst flood process and assess the hazard associated with future floods. The results show that the hydrodynamic model could accurately simulate the outburst flood process, with overall accuracy and Kappa accuracy for the flood extent of 0.956 and 0.911. Three future dam break scenarios were considered with landslide dams of heights 30 m, 35 m, and 51 m. The potential storage capacity and length of upstream flow back up in the upstream valley for these heights were 142 × 106m3/32 km, 182 × 106m3/40 km, and 331 × 106m3/50 km. Failure of these three dams leads to maximum inundation extents of 0.18 km2, 0.34 km2, and 0.43 km2, which is significant out-of-bank flow and serious infrastructure impacts. These results demonstrate the seriousness of secondary hazards associated with this region