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.

Preparation Mechanism and Properties of Orthoboric Acid Nanowires with Honeycomb-Like Structure.

Orthoboric acid (H3BO3) nanowires with honeycomb-like structure are successfully synthesized on boron nitride (BN) substrate using Fe powder as a catalyst. The formation of the nanowires is a two-step process (solid-liquid-solid growth mechanism), including the formation of B2O3 and its reaction with water to form H3BO3. The fundamental properties of H3BO3 nanowires, including their morphology, crystal structure, optical properties, thermal stability, and electrochemical properties, were systematically studied in order to expand their application fields. When innovatively investigated as anode materials for lithium ion batteries, the nanowires show a high discharge specific capacity of 243 mA h g-1 in the first cycle at a current density of 100 mA g-1. Further modification is essential for improving its cycling stability, such as surface coating and doping ions. Orthoboric acid nanowires with high first discharge capacity are believed to have great prospects for the development of novel anode materials for lithium ion batteries.

Condition-adaptive fused graphical lasso (CFGL): An adaptive procedure for inferring condition-specific gene co-expression network.

Co-expression network analysis provides useful information for studying gene regulation in biological processes. Examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. One challenge in this type of analysis is that the sample sizes in each condition are usually small, making the statistical inference of co-expression patterns highly underpowered. A joint network construction that borrows information from related structures across conditions has the potential to improve the power of the analysis. One possible approach to constructing the co-expression network is to use the Gaussian graphical model. Though several methods are available for joint estimation of multiple graphical models, they do not fully account for the heterogeneity between samples and between co-expression patterns introduced by condition specificity. Here we develop the condition-adaptive fused graphical lasso (CFGL), a data-driven approach to incorporate condition specificity in the estimation of co-expression networks. We show that this method improves the accuracy with which networks are learned. The application of this method on a rat multi-tissue dataset and The Cancer Genome Atlas (TCGA) breast cancer dataset provides interesting biological insights. In both analyses, we identify numerous modules enriched for Gene Ontology functions and observe that the modules that are upregulated in a particular condition are often involved in condition-specific activities. Interestingly, we observe that the genes strongly associated with survival time in the TCGA dataset are less likely to be network hubs, suggesting that genes associated with cancer progression are likely to govern specific functions or execute final biological functions in pathways, rather than regulating a large number of biological processes. Additionally, we observed that the tumor-specific hub genes tend to have few shared edges with normal tissue, revealing tumor-specific regulatory mechanism.

A regression framework for assessing covariate effects on the reproducibility of high-throughput experiments.

The outcome of high-throughput biological experiments is affected by many operational factors in the experimental and data-analytical procedures. Understanding how these factors affect the reproducibility of the outcome is critical for establishing workflows that produce replicable discoveries. In this article, we propose a regression framework, based on a novel cumulative link model, to assess the covariate effects of operational factors on the reproducibility of findings from high-throughput experiments. In contrast to existing graphical approaches, our method allows one to succinctly characterize the simultaneous and independent effects of covariates on reproducibility and to compare reproducibility while controlling for potential confounding variables. We also establish a connection between our model and certain Archimedean copula models. This connection not only offers our regression framework an interpretation in copula models, but also provides guidance on choosing the functional forms of the regression. Furthermore, it also opens a new way to interpret and utilize these copulas in the context of reproducibility. Using simulations, we show that our method produces calibrated type I error and is more powerful in detecting difference in reproducibility than existing measures of agreement. We illustrate the usefulness of our method using a ChIP-seq study and a microarray study.

A Continuous Threshold Expectile Model.

Expectile regression is a useful tool for exploring the relation between the response and the explanatory variables beyond the conditional mean. A continuous threshold expectile regression is developed for modeling data in which the effect of a covariate on the response variable is linear but varies below and above an unknown threshold in a continuous way. The estimators for the threshold and the regression coefficients are obtained using a grid search approach. The asymptotic properties for all the estimators are derived, and the estimator for the threshold is shown to achieve root-n consistency. A weighted CUSUM type test statistic is proposed for the existence of a threshold at a given expectile, and its asymptotic properties are derived under both the null and the local alternative models. This test only requires fitting the model under the null hypothesis in the absence of a threshold, thus it is computationally more efficient than the likelihood-ratio type tests. Simulation studies show that the proposed estimators and test have desirable finite sample performance in both homoscedastic and heteroscedastic cases. The application of the proposed method on a Dutch growth data and a baseball pitcher salary data reveals interesting insights. The proposed method is implemented in the R package cthreshER.

HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient.

Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance dependence. We present HiCRep, a framework for assessing the reproducibility of Hi-C data that systematically accounts for these features. In particular, we introduce a novel similarity measure, the stratum adjusted correlation coefficient (SCC), for quantifying the similarity between Hi-C interaction matrices. Not only does it provide a statistically sound and reliable evaluation of reproducibility, SCC can also be used to quantify differences between Hi-C contact matrices and to determine the optimal sequencing depth for a desired resolution. The measure consistently shows higher accuracy than existing approaches in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages. The proposed measure is straightforward to interpret and easy to compute, making it well-suited for providing standardized, interpretable, automatable, and scalable quality control. The freely available R package HiCRep implements our approach.

Robust bent line regression.

We introduce a rank-based bent linear regression with an unknown change point. Using a linear reparameterization technique, we propose a rank-based estimate that can make simultaneous inference on all model parameters, including the location of the change point, in a computationally efficient manner. We also develop a score-like test for the existence of a change point, based on a weighted CUSUM process. This test only requires fitting the model under the null hypothesis in absence of a change point, thus it is computationally more efficient than likelihood-ratio type tests. The asymptotic properties of the test are derived under both the null and the local alternative models. Simulation studies and two real data examples show that the proposed methods are robust against outliers and heavy-tailed errors in both parameter estimation and hypothesis testing.

Association of genetic polymorphisms with laryngeal carcinoma prognosis in a Chinese population.

We analyzed the effects of single-nucleotide polymorphisms (SNPs) on laryngeal carcinoma (LC) risk and overall survival (OS) in 170 Chinese male LC patients followed for 10 years. After assessment of clinical characteristics (age, laryngectomy, neck dissection, tumor differentiation, TNM status), the patients were genotyped for 24 SNPs associated with risk in multiple cancers. LC risk was assessed using log-rank test and Cox proportional hazard models. The median OS time was 48 months. By the follow-up deadline, OS was 41.2%. Kaplan-Meier analysis indicated 1-, 3-, and 5-year survival rates to be 84.7%, 57.2%, and 47.1%, respectively. Five LC clinicopathological characteristics, namely total laryngectomy (TL), low differentiation (LD), T3-T4, N1-N2, and clinical stage III-IV were associated with worse OS (HR: 2.35, p < 0.001; HR: 2.39, p = 0.02; HR: 2.17, p < 0.001; HR: 2.39, p < 0.001; and HR: 3.29, p < 0.001, respectively). Univariate cox regression analysis indicated that four SNPs were associated (p < 0.05) with LC OS in the codominant genetic model compared to patients with the homozygous wild-type genotype: rs10088262 G/A (HR = 1.57), rs1665650 A/G (HR = 0.65); rs3802842 C/C (HR = 2.18), and rs59336 T/A and T/T (HR = 0.61 and 2.61, respectively).

A note on estimating the bent line quantile regression model.

This paper considers a new estimating method for the bent line quantile regression model. By a simple linearization technique, the proposed method can simultaneously obtain the estimates of the regression coefficients and the change-point location. Moreover, it can be readily implemented by current software. Simulation studies demonstrate that the proposed method has good finite sample performance. Two empirical applications are also presented to illustrate the method.

Inactivated chimeric porcine circovirus (PCV) 1-2 vaccines based on genotypes 2b and 2d exhibit similar immunological effectiveness in protecting pigs against challenge with PCV2b strain 0233.

Porcine circovirus type 2 (PCV2) is subdivided into four genotypes: PCV2a, PCV2b, PCV2c and PCV2d. Here, for the first time, we compared the efficacy of two experimental inactivated chimeric PCV1-2 vaccines based on genotypes 2b and 2d. Seventeen 3-week-old pigs were divided randomly into four groups. Group 1 and 2 pigs were inoculated with genotype 2b- and 2d-based inactivated vaccines, respectively. At 28 days post-vaccination (DPV), pigs in groups 1-3 were challenged with the PCV2b 0233 strain. All experimental pigs were necropsied at 21 days post-challenge (DPC). Pigs vaccinated with the genotype 2b- or 2d-based vaccine had high antibody titres and lower PCV2b copy numbers in samples of sera, faeces and nasal secretions compared with pigs in the unvaccinated challenge group. Interestingly, we detected no DNA from the challenge strain in the superficial inguinal lymph nodes of the pigs immunized with the PCV2b vaccine, while one pig in the PCV2d- immunized group had detectable DNA from the challenge strain at 21 DPC. We found no significant differences in the humoral immune response, PCV2b load, or PCV-related microscopic lesions between the two vaccinated groups post-challenge. Therefore, both vaccines were equally effective at inducing immunity against challenge with PCV2b strain 0233.

An improved substrate cocktail for assessing direct inhibition and time-dependent inhibition of multiple cytochrome P450s.

AIM: The substrate cocktail is frequently used to evaluate cytochrome P450 (CYP) enzyme-mediated drug interactions and potential interactions among the probe substrates. Here, we re-optimized the substrate cocktail method to increase the reliability and accuracy of screening for candidate compounds and expanded the method from a direct CYP inhibition assay to a time-dependent inhibition (TDI) assay. METHODS: In the reaction mixtures containing human liver microsome (0.1 mg/mL), both the concentrations of a substrate cocktail (phenacetin for 1A2, coumarin for 2A6, bupropion for 2B6, diclofenac for 2C9, dextromethorphan for 2D6, and testosterone for 3A4) and the incubation time were optimized. Metabolites of the substrate probes were simultaneously analyzed by multiple-reaction monitoring (MRM) using a routine LC/MS/MS. Direct CYP inhibition was validated using 7 inhibitors (α-naphthoflavone, tranylcypromine, ticlopidine, fluconazole, quinidine, ketoconazole and 1-ABT). The time-dependent inhibition was partially validated with 5 inhibitors (ketoconazole, verapamil, quinidine, paroxetine and 1-ABT). RESULTS: The inhibition curve profiles and IC50 values of 7 CYP inhibitors were approximate when a single substrate and the substrate cocktail were tested, and were consistent with the previously reported values. Similar results were obtained in the IC50 shifts of 5 inhibitors when a single substrate and the substrate cocktail were tested in the TDI assay. CONCLUSION: The 6-in-1 substrate cocktail (for 1A2, 2A6, 2B6, 2C9, 2D6 and 3A) is reliable for assessing CYP inhibition and time-dependent inhibition of drug candidates.

Comparative efficacy of experimental inactivated and live-attenuated chimeric porcine circovirus (PCV) 1-2b vaccines derived from PCV1 and PCV2b isolates originated in China.

BACKGROUND: Porcine circovirus type-2b (PCV2b) is recognized as the etiological agent of the various clinical manifestations of porcine circovirus-associated disease (PCVAD). Previous studies have demonstrated effectiveness of chimeric PCV1-2 vaccines against PCV2b challenge. In this study, the efficacy of inactivated and live-attenuated (2 × 10(3.5) or 2 × 10(4.0) 50% tissue culture infective dose [TCID50] dose) chimeric PCV1-2b vaccines was compared side-by-side in conventional pigs. METHODS: Twenty-seven non-PCV2 viremic pigs without PCV2-specific antibody were randomly divided into six groups, including four vaccinated and challenged groups, a nonvaccinated challenged group, and a mock group. All pigs except those in the mock group were challenged at 28 days post vaccination (DPV) using PCV2b. RESULTS: Both inactivated and live-attenuated chimeric PCV1-2b vaccines induced a robust antibody responses, and significantly decreased microscopic lesion and lower viral loads in serum or superficial inguinal lymph nodes (SILN) compared with that in the nonvaccinated challenged group. PCV2 antibody titers decreased after 7 days post challenge (DPC) in pigs administered the inactivated PCV1-2b vaccine and they were lower than those in pigs inoculated with live-attenuated PCV1-2b on the day of necropsy. Moreover, no viremia was present in pigs inoculated with live-attenuated PCV1-2b vaccine at 21 DPC regardless of the dose difference. CONCLUSIONS: The results demonstrated that both inactivated and live-attenuated chimeric PCV1-2b vaccines were effective to induce protective immunity against PCV2b infection.