Weighted Expectile Regression Neural Networks for Right Censored Data.

As a favorable alternative to the censored quantile regression, censored expectile regression has been popular in survival analysis due to its flexibility in modeling the heterogeneous effect of covariates. The existing weighted expectile regression (WER) method assumes that the censoring variable and covariates are independent, and that the covariates effects has a global linear structure. However, these two assumptions are too restrictive to capture the complex and nonlinear pattern of the underlying covariates effects. In this article, we developed a novel weighted expectile regression neural networks (WERNN) method by incorporating the deep neural network structure into the censored expectile regression framework. To handle the random censoring, we employ the inverse probability of censoring weighting (IPCW) technique in the expectile loss function. The proposed WERNN method is flexible enough to fit nonlinear patterns and therefore achieves more accurate prediction performance than the existing WER method for right censored data. Our findings are supported by extensive Monte Carlo simulation studies and a real data application.

Organic Geochemical Evidence for the Formation of Condensate from Coaly Source Rocks in the Wumaying Buried Hill of the Huanghua Depression, Bohai Bay Basin.

Although oil and gas from coaly source rocks have been widely discovered worldwide, the role of oil generated from coal measures in marine-continental coaly deposits during the Carboniferous-Permian period in the Bohai Bay Basin has long been a subject of debate. The recent discovery of a condensate reservoir in the Wumaying buried hill within the Huanghua Depression of the Bohai Bay Basin offers new potential insights into this issue. In this study, we employed organic geochemical methods to explore the possibility of the Carboniferous-Permian coal deposit being a primary source of the condensate. The distribution of light hydrocarbons and the biomarker assemblage indicate that the condensate did not undergo significant secondary alterations such as thermal cracking, gas invasion fractionation, or biodegradation. The hydrocarbon generation potential of the Carboniferous-Permian coaly source rocks suggests that they could be an important contributor to the formation of condensate. High pristine/phytane ratios (1.0-7.5), an abundant presence of benzene series, and the dominance of C29 steranes (>50%) within the condensate could be indicative of coaly organic matter. These features are comparable to those found in coaly source rocks. Moreover, the stable carbon isotopic compositions of n-alkanes in the condensate, ranging from -26.0 to -30.0‰, correlate well with those from coaly mudstone (-25.4 to -30.0‰). This suggests that the condensate of the Wumaying buried hill may predominantly originate from the Carboniferous-Permian coaly mudstone. When integrated with the geological background, the results distinctly demonstrate that the Carboniferous-Permian coaly source rocks have significantly contributed to the formation of the condensate reservoir in the Wumaying buried hill. This provides an essential reference for future exploration of oil and gas resources derived from the carboniferous-Permian coaly source rocks in the Bohai Bay Basin.

Evolutionary dynamics under partner preferences.

The fact that people often have preference rankings for their partners is a distinctive aspect of human behavior. Little is known, however, about how this talent as a powerful force shapes human behavioral traits, including those which should not have been favored by selection, such as cooperation in social dilemma situations. Here we propose a dynamic model in which network-structured individuals can switch their interaction partners within neighborhoods based on their preferences. For the partner switching, we propose two interruption regimes: dictatorial regime and negotiating regime. In the dictatorial regime, focal individuals are able to suspend interactions out of preferences unilaterally. In the negotiating regime, either focal individuals or the associated partners agree to suspend, then these interactions can be successfully suspended. We investigate the evolution of cooperation under both preference-driven partner switching regimes in the context of both the weakened variant of the donation game and the standard one. Specifically, we theoretically approximate the critical conditions for cooperation to be favored by weak selection in the weakened donation game where cooperators bear a unit cost to provide a benefit for each active neighbor and simulate the evolutionary dynamics of cooperation in the standard donation game to test the robustness of the analytical results. Under dictatorial regime, selection of cooperation becomes harder when individuals have preferences for either cooperator or defector partners, implying that the expulsion of defectors by cooperators is overwhelmed by the chasing of defectors towards cooperators. Under negotiating regime, both preferences for cooperator and defector partners can significantly favor the evolution of cooperation, yet underlying mechanisms differ greatly. For preferences over cooperator partners, cooperator-cooperator interaction relationships are reinforced and the associated mutual reciprocity can resist and assimilate defectors. For preferences over defector partners, defector-defector interaction relationships are anchored, weakening defectors' exploitation over cooperators. Cooperators are thus offered much time space to interact among cospecies and spread. Our work may help better understand the critical role of preference-based adaptive partner switching in promoting the evolution of cooperation.

Segmented correspondence curve regression for quantifying covariate effects on the reproducibility of high-throughput experiments.

High-throughput biological experiments are essential tools for identifying biologically interesting candidates in large-scale omics studies. The results of a high-throughput biological experiment rely heavily on the operational factors chosen in its experimental and data-analytic procedures. Understanding how these operational factors influence the reproducibility of the experimental outcome is critical for selecting the optimal parameter settings and designing reliable high-throughput workflows. However, the influence of an operational factor may differ between strong and weak candidates in a high-throughput experiment, complicating the selection of parameter settings. To address this issue, we propose a novel segmented regression model, called segmented correspondence curve regression, to assess the influence of operational factors on the reproducibility of high-throughput experiments. Our model dissects the heterogeneous effects of operational factors on strong and weak candidates, providing a principled way to select operational parameters. Based on this framework, we also develop a sup-likelihood ratio test for the existence of heterogeneity. Simulation studies show that our estimation and testing procedures yield well-calibrated type I errors and are substantially more powerful in detecting and locating the differences in reproducibility across workflows than the existing method. Using this model, we investigated an important design question for ChIP-seq experiments: How many reads should one sequence to obtain reliable results in a cost-effective way? Our results reveal new insights into the impact of sequencing depth on the binding-site identification reproducibility, helping biologists determine the most cost-effective sequencing depth to achieve sufficient reproducibility for their study goals.

CLIMB: High-dimensional association detection in large scale genomic data.

Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology that learns patterns of condition-specificity present in genomic data. CLIMB provides a generic framework facilitating a host of analyses, such as clustering genomic features sharing similar condition-specific patterns and identifying which of these features are involved in cell fate commitment. We apply CLIMB to three sets of hematopoietic data, which examine CTCF ChIP-seq measured in 17 different cell populations, RNA-seq measured across constituent cell populations in three committed lineages, and DNase-seq in 38 cell populations. Our results show that CLIMB improves upon existing alternatives in statistical precision, while capturing interpretable and biologically relevant clusters in the data.

Ambient-Dried, Ultra-high Strength, Low Thermal Conductivity, High Char Residual Rate F-type Polybenzoxazine Aerogel.

The effect of the curing temperature (T c) on the properties of PBO aerogel was investigated in this paper. The compressive strength of PBO aerogel prepared was much higher than that of PBO aerogel of the same density in other kinds of literature. With the robust F-type polybenzoxazine (PBO) aerogels with ultra-high Young's modulus (733.7 MPa at 0.48 g/cm3 and 1070 MPa at 0.57 g/cm3), excellent properties were obtained through a facile and scalable room-temperature HCl-catalyzed sol-gel method, followed by the ambient pressure drying technique. It is found that T c plays a vital role in the polymerization process and the evolution of the microstructure of the 3D porous PBO network, where the necks between the nanoparticles become thick and strong when T c is up to 150 °C, resulting in a pearl necklace-to-worm transformation in the micro-structure and significant growth in mechanical properties, but if T c is higher than 180 °C, the pore volume and specific surface area will decrease sharply. Moreover, all synthetic PBO aerogels here possessed inherent flame retardancy and a high residual char rate in the volume density (0.32-0.57 g/cm3). These properties make the F-type PBO aerogels a candidate material in aerospace applications or other fields.

Design, Synthesis and Biological Activity Testing of Library of Sphk1 Inhibitors.

Our team discovered a moderate SphK1 inhibitor, SAMS10 (IC50 = 9.8 µM), which was screened by computer-assisted screening. In this study, we developed a series of novel diaryl derivatives with improved antiproliferative activities by modifying the structure of the lead compound SAMS10. A total of 50 new compounds were synthesized. Among these compounds, the most potent compound, named CHJ04022Rb, has significant anticancer activity in melanoma A375 cell line (IC50 = 2.95 µM). Further underlying mechanism studies indicated that CHJ04022R exhibited inhibition effect against PI3K/NF-κB signaling pathways, inhibited the migration of A375 cells, promoted apoptosis and exerted antiproliferative effect by inducing G2/M phase arrest in A375 cells. Furthermore, acute toxicity experiment indicated CHJ04022R exhibited good safety in vivo. Additionally, it showed a dose-dependent inhibitory effect on the growth of xenograft tumor in nude mice. Therefore, CHJ04022R may be a potential candidate for the treatment of melanoma.

Assessing reproducibility of high-throughput experiments in the case of missing data.

High-throughput experiments are an essential part of modern biological and biomedical research. The outcomes of high-throughput biological experiments often have a lot of missing observations due to signals below detection levels. For example, most single-cell RNA-seq (scRNA-seq) protocols experience high levels of dropout due to the small amount of starting material, leading to a majority of reported expression levels being zero. Though missing data contain information about reproducibility, they are often excluded in the reproducibility assessment, potentially generating misleading assessments. In this article, we develop a regression model to assess how the reproducibility of high-throughput experiments is affected by the choices of operational factors (eg, platform or sequencing depth) when a large number of measurements are missing. Using a latent variable approach, we extend correspondence curve regression, a recently proposed method for assessing the effects of operational factors to reproducibility, to incorporate missing values. Using simulations, we show that our method is more accurate in detecting differences in reproducibility than existing measures of reproducibility. We illustrate the usefulness of our method using a single-cell RNA-seq dataset collected on HCT116 cells. We compare the reproducibility of different library preparation platforms and study the effect of sequencing depth on reproducibility, thereby determining the cost-effective sequencing depth that is required to achieve sufficient reproducibility.

Ultrasonic and enzymatic pretreatments of Monascus fermentation byproduct for a sustainable production of Bacillus subtilis.

BACKGROUND: Monascus fermentation byproduct (MFB) is a biowaste generated after food colorants are extracted. Using MFB to produce probiotics (Bacillus subtilis) is a sustainable way for the entire production to be used as food or animal feed additives. However, due to the rigidity of the Monascus mycelium cell wall, B. subtilis cannot sufficiently utilize the nutrients in MFB, leading to low biomass production efficiency. We studied the effects of ultrasonic treatment, papain, ß-glucanase, and chitosanase, and their combinations on improving the levels of soluble components from MFB. The effects of these treatments on mycelium cell walls were visualized using scanning electron microscopy, and their influence on B. subtilis production was analyzed. RESULTS: Ultrasonic treatment increased the soluble components by 210 g kg-1 , including 50 g kg-1 protein and 120 g kg-1 carbohydrates. An enzyme mixture increased the soluble components by 160 g kg-1 , including 30 g kg-1 protein and 90 g kg-1 carbohydrates. The combination of the two methods achieved the highest increase of soluble components (up to 400 g kg-1 ) leading to a maximum B. subtilis production of 1 × 1011 colony-forming unit mL-1 . This yield was about 20 times greater than that using untreated MFB and about eight times greater than treatments using only ultrasonic or enzymatic methods. CONCLUSION: The productivity of B. subtilis production using MFB as the sole medium can be greatly improved by ultrasound or enzymes, which cause the release of intercellular components or cell wall components. © 2020 Society of Chemical Industry.

A kernel nonparametric quantile estimator for right-censored competing risks data.

In medical and epidemiological studies, it is often interest to study time-to-event distributions under competing risks that involve two or more failure types. Nonparametric analysis of competing risks is typically focused on the cumulative incidence function or nonparametric quantile function. However, the existing estimators may be very unstable due to their unsmoothness. In this paper, we propose a kernel nonparametric quantile estimator for right-censored competing risks data, which is a smoothed version of Peng and Fine's nonparametric quantile estimator. We establish the Bahadur representation of the proposed estimator. The convergence rate of the remainder term for the proposed estimator is substantially faster than Peng and Fine's quantile estimator. The pointwise confidence intervals and simultaneous confidence bands of the quantile functions are also derived. Simulation studies illustrate the good performance of the proposed estimator. The methodology is demonstrated with two applications of the Supreme Court Judge data and AIDSSI data.