In Situ Formation of CoP/Co3 O4 Heterojunction for Efficient Overall Water Splitting.

Electrochemical water splitting is an environmentally friendly and effective energy storage method. However, it is still a huge challenge to prepare non-noble metal based electrocatalysts that possess high activity and long-term durability to realize efficient water splitting. Here, we present a novel method of low-temperature phosphating for preparing CoP/Co3 O4 heterojunction nanowires catalyst on titanium mesh (TM) substrate that can be used for oxygen evolution reaction (OER), hydrogen evolution reaction (HER), and overall water splitting. CoP/Co3 O4 @TM heterojunction showed an excellent catalytic performance and long-term durability in 1.0 M KOH electrolyte. The overpotential of CoP/Co3 O4 @TM heterojunction was only 257 mV at 20 mA cm-2 during the OER process, and it could work stably more than 40 h at 1.52 V versus reversible hydrogen electrode (vs. RHE). During the HER process, the overpotential of CoP/Co3 O4 @TM heterojunction was only 98 mV at -10 mA cm-2 . More importantly, when used as anodic and cathodic electrocatalyst, they achieved 10 mA cm-2 at 1.59 V. The Faradaic efficiencies of OER and HER were 98.4 % and 99.4 %, respectively, outperforming Ru/Ir-based noble metal electrocatalysts and other non-noble metal electrocatalysts for overall water splitting.

Aberrant global and local dynamic properties in schizophrenia with instantaneous phase method based on Hilbert transform.

BACKGROUND: Emerging functional imaging studies suggest that schizophrenia is associated with aberrant spatiotemporal interaction which may result in aberrant global and local dynamic properties. METHODS: We investigated the dynamic functional connectivity (FC) by using instantaneous phase method based on Hilbert transform to detect abnormal spatiotemporal interaction in schizophrenia. Based on resting-state functional magnetic resonance imaging, two independent datasets were included, with 114 subjects from COBRE [51 schizophrenia patients (SZ) and 63 healthy controls (HCs)] and 96 from OpenfMRI (36 SZ and 60 HCs). Phase differences and instantaneous coupling matrices were firstly calculated at all time points by extracting instantaneous parameters. Global [global synchrony and intertemporal closeness (ITC)] and local dynamic features [strength of FC (sFC) and variability of FC (vFC)] were compared between two groups. Support vector machine (SVM) was used to estimate the ability to discriminate two groups by using all aberrant features. RESULTS: We found SZ had lower global synchrony and ITC than HCs on both datasets. Furthermore, SZ had a significant decrease in sFC but an increase in vFC, which were mainly located at prefrontal cortex, anterior cingulate cortex, temporal cortex and visual cortex or temporal cortex and hippocampus, forming significant dynamic subnetworks. SVM analysis revealed a high degree of balanced accuracy (85.75%) on the basis of all aberrant dynamic features. CONCLUSIONS: SZ has worse overall spatiotemporal stability and extensive FC subnetwork lesions compared to HCs, which to some extent elucidates the pathophysiological mechanism of schizophrenia, providing insight into time-variation properties of patients with other mental illnesses.

Dirichlet composition distribution for compositional data with zero components: An application to fluorescence in situ hybridization (FISH) detection of chromosome.

Zeros in compositional data are very common and can be classified into rounded and essential zeros. The rounded zero refers to a small proportion or below detection limit value, while the essential zero refers to the complete absence of the component in the composition. In this article, we propose a new framework for analyzing compositional data with zero entries by introducing a stochastic representation. In particular, a new distribution, namely the Dirichlet composition distribution, is developed to accommodate the possible essential-zero feature in compositional data. We derive its distributional properties (e.g., its moments). The calculation of maximum likelihood estimates via the Expectation-Maximization (EM) algorithm will be proposed. The regression model based on the new Dirichlet composition distribution will be considered. Simulation studies are conducted to evaluate the performance of the proposed methodologies. Finally, our method is employed to analyze a dataset of fluorescence in situ hybridization (FISH) for chromosome detection.

A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data.

BACKGROUND: Identifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression. The present methods usually involve normalization of the data with a scaling factor, followed by detection of significant genes. However, more than one scaling factor may exist because of the complexity of real data. Consequently, methods that normalize data by a single scaling factor may deliver suboptimal performance or may not even work.The development of modern machine learning techniques has provided a new perspective regarding discrimination between differentially expressed (DE) and non-DE genes. However, in reality, the non-DE genes comprise only a small set and may contain housekeeping genes (in same species) or conserved orthologous genes (in different species). Therefore, the process of detecting DE genes can be formulated as a one-class classification problem, where only non-DE genes are observed, while DE genes are completely absent from the training data. RESULTS: In this study, we transform the problem to an outlier detection problem by treating DE genes as outliers, and we propose a scaling-free minimum enclosing ball (SFMEB) method to construct a smallest possible ball to contain the known non-DE genes in a feature space. The genes outside the minimum enclosing ball can then be naturally considered to be DE genes. Compared with the existing methods, the proposed SFMEB method does not require data normalization, which is particularly attractive when the RNA-seq data include more than one scaling factor. Furthermore, the SFMEB method could be easily extended to different species without normalization. CONCLUSIONS: Simulation studies demonstrate that the SFMEB method works well in a wide range of settings, especially when the data are heterogeneous or biological replicates. Analysis of the real data also supports the conclusion that the SFMEB method outperforms other existing competitors. The R package of the proposed method is available at https://bioconductor.org/packages/MEB .

Fast QLB algorithm and hypothesis tests in logistic model for ophthalmologic bilateral correlated data.

In ophthalmologic or otolaryngologic studies, bilateral correlated data often arise when observations involving paired organs (e.g., eyes, ears) are measured from each subject. Based on Donner's model , in this paper, we focus on investigating the relationship between the disease probability and covariates (such as ages, weights, gender, and so on) via the logistic regression for the analysis of bilateral correlated data. We first propose a new minorization-maximization (MM) algorithm and a fast quadratic lower bound (QLB) algorithm to calculate the maximum likelihood estimates of the vector of regression coefficients, and then develop three large-sample tests (i.e., the likelihood ratio test, Wald test, and score test) to test if covariates have a significant impact on the disease probability. Simulation studies are conducted to evaluate the performance of the proposed fast QLB algorithm and three testing methods. A real ophthalmologic data set in Iran is used to illustrate the proposed methods.

Identifying and quantifying robust risk factors for mortality in critically ill patients with COVID-19 using quantile regression.

OBJECTIVE: Many laboratory indicators form a skewed distribution with outliers in critically ill patients with COVID-19, for which robust methods are needed to precisely determine and quantify fatality risk factors. METHOD: A total of 192 critically ill patients (142 were discharged and 50 died in the hospital) with COVID-19 were included in the sample. Quantile regression was used to determine discrepant laboratory indexes between survivors and non-survivors and quantile shift (QS) was used to quantify the difference. Logistic regression was then used to calculate the odds ratio (OR) and the predictive power of death for each risk indicator. RESULTS: After adjusting for multiple comparisons and controlling numerous confounders, quantile regression revealed that the laboratory indexes of non-survivors were significantly higher in C-reactive protein (CRP; QS = 0.835, p < .001), white blood cell counts (WBC; QS = 0.743, p < .001), glutamic oxaloacetic transaminase (AST; QS = 0.735, p < .001), blood glucose (BG; QS = 0.608, p = .059), fibrin degradation product (FDP; QS = 0.730, p = .080), and partial pressure of carbon dioxide (PCO2), and lower in oxygen saturation (SO2; QS = 0.312, p < .001), calcium (Ca2+; QS = 0.306, p = .073), and pH. Most of these indexes were associated with an increased fatality risk, and predictive for the probability of death. Especially, CRP is the most prominent index with and odds ratio of 205.97 and predictive accuracy of 93.2%. CONCLUSION: Laboratory indexes provided reliable information on mortality in critically ill patients with COVID-19, which might help improve clinical prediction and treatment at an early stage.

Poisson item count techniques with noncompliance.

The Poisson item count technique (PICT) is a survey method that was recently developed to elicit respondents' truthful answers to sensitive questions. It simplifies the well-known item count technique (ICT) by replacing a list of independent innocuous questions in known proportions with a single innocuous counting question. However, ICT and PICT both rely on the strong "no design effect assumption" (ie, respondents give the same answers to the innocuous items regardless of the absence or presence of the sensitive item in the list) and "no liar" (ie, all respondents give truthful answers) assumptions. To address the problem of self-protective behavior and provide more reliable analyses, we introduced a noncompliance parameter into the existing PICT. Based on the survey design of PICT, we considered more practical model assumptions and developed the corresponding statistical inferences. Simulation studies were conducted to evaluate the performance of our method. Finally, a real example of automobile insurance fraud was used to demonstrate our method.

Confidence intervals for proportion ratios of stratified correlated bilateral data.

In stratified bilateral studies, responses from two paired body parts are correlated. Confidence intervals (CIs), which reveal various features of the data, should take the correlations into account. In this article, five CI methods (sample-size weighted naïve Maximum likelihood estimation (MLE)-based Wald-type CI, complete MLE-based Wald-type CI, profile likelihood CI, MLE-based score CI and pooled MLE-based Wald-type CI) are derived for proportion ratios under the assumption of equal correlation coefficient within each stratum. Monte Carlo simulation shows that the complete MLE-based Wald-type CI approach generally produces the shortest mean interval width and satisfactory empirical coverage probability with close form solution; while the profile likelihood CI and the MLE-based score CI provide preferred ratio of non coverage probability and are more symmetric. Two real examples are used to demonstrate the performance of the proposed methods.

Efficient statistical inference for a parallel study with missing data by using an exact method.

In a parallel group study comparing a new treatment with a standard of care, missing data often occur for various reasons. When the outcome is binary, the data from such studies can be summarized into a 2 × 3 contingency table, with the missing observations in the last column. When the missingness is neither related to the outcome of interest nor related to other outcomes from the study but it is covariate dependent with the sole covariate being treatment, this type of missing data mechanism is considered as missing at random. In 2016, Tian et al.  proposed three statistics to test the hypothesis that the response rate is equivalent for a parallel group study with missing data. The asymptotic limiting distributions of these test statistics were used for statistical inference. However, asymptotic approaches for testing proportions generally do not have satisfactory performance with regard to type I error rate control for a clinical trial with the sample size from small to medium. For this reason, we consider an exact approach based on maximization to provide valid and efficient statistical inference for a parallel group study with missing data. Exact approaches can guarantee the type I error rate and they are computationally feasible in this setting. We conduct extensive numerical studies to compare the performance of the exact approach based on the three statistics for a one-sided hypothesis testing problem. We conclude that the exact approach based on the likelihood ratio statistic is more powerful than the exact approach based on the other two statistics. Two real clinical trial data sets are used to illustrate the application of the proposed exact approach.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, 1989) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods.

Testing homogeneity of proportion ratios for stratified correlated bilateral data in two-arm randomized clinical trials.

Stratified data analysis is an important research topic in many biomedical studies and clinical trials. In this article, we develop five test statistics for testing the homogeneity of proportion ratios for stratified correlated bilateral binary data based on an equal correlation model assumption. Bootstrap procedures based on these test statistics are also considered. To evaluate the performance of these statistics and procedures, we conduct Monte Carlo simulations to study their empirical sizes and powers under various scenarios. Our results suggest that the procedure based on score statistic performs well generally and is highly recommended. When the sample size is large, procedures based on the commonly used weighted least square estimate and logarithmic transformation with Mantel-Haenszel estimate are recommended as they do not involve any computation of maximum likelihood estimates requiring iterative algorithms. We also derive approximate sample size formulas based on the recommended test procedures. Finally, we apply the proposed methods to analyze a multi-center randomized clinical trial for scleroderma patients.

Confidence-interval construction for rate ratio in matched-pair studies with incomplete data.

Matched-pair design is often used in clinical trials to increase the efficiency of establishing equivalence between two treatments with binary outcomes. In this article, we consider such a design based on rate ratio in the presence of incomplete data. The rate ratio is one of the most frequently used indices in comparing efficiency of two treatments in clinical trials. In this article, we propose 10 confidence-interval estimators for the rate ratio in incomplete matched-pair designs. A hybrid method that recovers variance estimates required for the rate ratio from the confidence limits for single proportions is proposed. It is noteworthy that confidence intervals based on this hybrid method have closed-form solution. The performance of the proposed confidence intervals is evaluated with respect to their exact coverage probability, expected confidence interval width, and distal and mesial noncoverage probability. The results show that the hybrid Agresti-Coull confidence interval based on Fieller's theorem performs satisfactorily for small to moderate sample sizes. Two real examples from clinical trials are used to illustrate the proposed confidence intervals.

A nitrous oxide/oxygen fixed mixture to reduce pain induced by the hypodermic injection: study protocol for a randomized, controlled trial.

BACKGROUND: Patients with hematological malignancies received multiple hypodermic injections of recombinant human granulocyte colony-stimulating factor. Procedural pain is one of the most common iatrogenic causes of pain in patients with hematological malignancies. It is also identified as the most commonly occurring problem in clinical care in the Department of Hematology and Oncology at Shenzhen University General Hospital. However, providing immediate relief from pain induced by hypodermic injection of recombinant human granulocyte colony-stimulating factor remains a major challenge. This trial aims to evaluate the safety and analgesic efficacy of a fixed nitrous oxide/oxygen mixture for patients with hematological malignancies and experiencing procedural pain caused by hypodermic injection of recombinant human granulocyte colony-stimulating factor in the department. METHODS: The nitrous oxide/oxygen study is a single-center, randomized, double-blind, placebo-controlled trial involving patients with hematological malignancies who require hypodermic injections of recombinant human granulocyte colony-stimulating factor for treatment. This trial was conducted in the Hematology and Oncology Department of Shenzhen University General Hospital. A total of 54 eligible patients were randomly allocated to either the fixed nitrous oxide/oxygen mixture group (n = 36) or the oxygen group (n = 18). Neither the investigators nor the patients known about the randomization list and the nature of the gas mixture in each cylinder. Outcomes were monitored at the baseline (T0), immediately after hypodermic injection of recombinant human granulocyte colony-stimulating factor (T1), and 5 min after hypodermic injection of recombinant human granulocyte colony-stimulating factor (T2) for each group. The primary outcome measure was the score in the numerical rating scale corresponding to the highest level of pain experienced during hypodermic injection of recombinant human granulocyte colony-stimulating factor. Secondary outcomes included the fear of pain, anxiety score, four physiological parameters, adverse effects, total time of gas administration, satisfaction from both patients and nurses, and the acceptance of the patients. DISCUSSION: This study focused on the safety and analgesic efficacy during hypodermic injection of recombinant human granulocyte colony-stimulating factor procedure. Data on the feasibility and safety of nitrous oxide/oxygen therapy was provided if proven beneficial to patients with hematological malignancies during hypodermic injection of recombinant human granulocyte colony-stimulating factor and widely administered to patients with procedural pain in the department. TRIAL REGISTRATION: Chinese Clinical Trial Register, ChiCTR2200061507. Registered on June 27, 2022. http://www.chictr.org.cn/edit.aspx?pid=170573&htm=4.

Enrichment, isolation and identification of sulfur-oxidizing bacteria from sulfide removing bioreactor.

Sulfur-oxidizing bacteria (SOB) are the main microorganisms that participate in the natural sulfur cycle. To obtain SOB with high sulfur-oxidizing ability under aerobic or anaerobic conditions, aerobic and anaerobic enrichments were carried out. Denaturing gradient gel electrophoresis (DGGE) profiles showed that the microbial community changed according to the thiosulfate utilization during enrichments, and Rhodopseudomonas and Halothiobacillus were the predominant bacteria in anaerobic enrichment and aerobic enrichment, respectively, which mainly contributed to the thiosulfate oxidization in the enrichments. Based on the enriched cultures, six isolates were isolated from the aerobic enrichment and four isolates were obtained from the anaerobic enrichment. Phylogenetic analysis suggested the 16S rRNA gene of isolates belonged to the genus Acinetobacter, Rhodopseudomonas, Pseudomonas, Halothiobacillus, Ochrobactrum, Paracoccus, Thiobacillus, and Alcaligenes, respectively. The tests suggested isolates related to Halothiobacillus and Rhodopseudomonas had the highest thiosulfate oxidizing ability under aerobic or anaerobic conditions, respectively; Paracoccus and Alcaligenes could aerobically and anaerobically oxidize thiosulfate. Based on the DGGE and thiosulfate oxidizing ability analysis, Rhodopseudomonas and Halothiobacillus were found to be the main SOB in the sulfide-removing reactor, and were responsible for the sulfur-oxidizing in the treatment system.

Identification of novel genes for triple-negative breast cancer with semiparametric gene-based analysis.

Triple-negative breast cancer (TNBC) is generally considered an aggressive breast cancer subtype associated with poor prognostic outcomes. Up to now, the molecular and cellular mechanisms underlying TNBC pathology have not been fully understood. In this manuscript, we propose a novel semiparametric model with kernel for gene-based analysis with a breast cancer GWAS data. The software of SPMGBA (semiparametric method for gene-based analysis) in MATLAB is available at GitHub (https://github.com/zliu3/SPMGBA). Genetic signatures associated with breast cancer are discovered. We further validate the prognostic power of the identified genes with a large cohort of expression data from the European Genome-Phenome Archive, and discover that SEL1L is associated with the overall survival of TNBC with the p-value of .0002. We conclude that gene SEL1L is down-regulated in TNBC and the expression of SEL1L is positively associated with patient survival.

A novel hyperbranched polyethyleneimine-graphene composite as shale inhibitor for drilling fluid.

One of the principal conundrums in drilling operations is addressing wellbore instability caused by shale hydration. Therefore, it is crucial to develop high-performance shale inhibitors. In this work, a hyperbranched polyethyleneimine/graphene composite (HPEI-G) was prepared by blending at 60 °C, and it was then used as a shale inhibitor. The inhibition performance of HPEI-G was verified using mud making test, linear swelling test and sedimentation test. The mechanism of HPEI-G was researched and determined using Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), particle size distribution test and scanning electron microscopy (SEM). The compatibility of HPEI-G with the basic water-based drilling mud (WBM) was also verified. It can be observed from the results of the linear swelling test that 0.5 wt% HPEI-G reduced the swelling rate of montmorillonite (MMT) to 30.36%, and 1 wt% of KCl only decreased the swelling rate of MMT to 43.83%. In addition, HPEI-G is compatible with WBDF. The inhibition mechanism of HPEI-G included chemical adsorption and physical blockage. HPEI-G was adsorbed on the surface and interlayer of MMT by hydrogen bonding and electrostatic attraction, reducing the diffuse electric double layer to inhibit the hydration of MMT. The sheets of graphene in HPEI-G allowed it to stick on the surface of the shale and plug the nanopores of the shale, preventing the access of water. The inhibition effect of HPEI-G over a temperature range from room temperature to 150 °C was considered to be excellent.

Model detection for semiparametric accelerated failure additive model with right-censored data.

Censored data frequently appeared in applications across a variety of different areas like epidemiology or medical research. Traditionally statistical inference on this data mechanism was based on some pre-assigned models that will suffer from the risk of model-misspecification. This article proposes a two-folded shrinkage procedure for simultaneous structure identification and variable selection of the semiparametric accelerated failure additive model with right-censored data, in which the nonparametric functions are addressed by spline approximation. Under some regularity conditions, the consistency of model structure identification is theoretically established in the sense that the proposed method can automatically separate the linear and zero components from the nonlinear ones with probability approaching to one. Detailed issues in computation and turning parameter selection are also discussed. Finally, we illustrate the proposed method by some simulation studies and two real data applications to the primary biliary cirrhosis data and skin cutaneous melanoma data.

[Microbial community in nitrogen cycle of aquaculture water of the Pearl River Delta].

OBJECTIVE: In order to study the characteristic of nitrogen transport, the community structure and diversity of related microorganisms in aquaculture water of the Pearl River Delta. METHODS: We established an artificial aquaculture ecosystem to study the microbial community of 15N-stable isotope probing (15N-SIP) labeled nitrogen transport microorganisms. The 15N-labeled DNA was separated by CsCl-ethidium bromide density gradient centrifugation, and was used to construct 16S rRNA gene clone libraries of bacteria and archaea. RESULTS: Phylogenetic analysis shows that 19 Operational Taxonomic Units (OTUs) from bacterial library were clustered in Proteobacteria and Planctomycetes. Proteobacteria (99.2%) was the dominant group, mainly consisted of Comamonas (15.7%), Nitrosomonas (12.4%), Enterobacteriaceae (11.5%) and Nitrobacter (11.5%). From archaeal library 9 OTUs were divided into 3 phyla: Thaumarchaeota, Crenarchaeota and Euryarchaeota. CONCLUSION: We successfully elucidated the microbial community of nitrogen transport microorganisms in aquaculture water of Pearl River Delta by using 15N-SIP. The data of the community will provide essential information for isolating nitrogen degrading microorganism, and provide scientific basis for creating a healthy aquaculture environment.

[Community structure of microbes involved in nitrification and denitrification in typical shrimp-farming water].

OBJECTIVE: We analyzed the community and diversity of microorganisms involved in nitrification and denitrification of nitrogen cycle in typical aquaculture water in order to manage microbiological degradation of NH4+ and NO2-, to control nitrogen pollution and nitrogen cycle in shrimp-farming water. METHODS: Samples were analyzed by Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis (PCR-DGGE) graph. We constructed clone libraries of the typical sample based on the functional gene ammonia monooxygenase gene (amoA), nitrite oxidoreductase gene (nxrA), and nitrite reductase gene (nirS). These three libraries were analyzed by using Restriction Fragment Length Polymorphism (RFLP). RESULTS: Phylogenetic analysis showed that all sequences from amoA library were clustered into beta-Proteobacteria, including Nitrosomonas (81%) and Nitrosospira (19%). Clones from nxrA library were clustered into alpha-Proteobacteria and delta-Proteobacteria, including Nitrobacter (92%) and Desulfobacteraceae (8%). Clones from nirS library were clustered into alpha-Proteobacteria, beta-Proteobacteria and Actinobacteria. Beta-Proteobacteria was the dominant group that consisted of Azoarcus (25%), Brachymonas (5%), and Thauera (20%). In alpha-Proteobacteria group, Sophophora (10%), Polymorphum (25%), Ruegeria (5%) were detected. In the Actinobacteria group, Streptomyces (10%) was detected. CONCLUSION: Microorganisms involved in nitrification and denitrification of nitrogen cycle were abundant. In the aquaculture water, Nitrosomonas was the main performer of ammoxidation, Nitrobacter was the main performer of nitrification, and many kinds of populations played important roles in the denitrification.

A multivariate zero-inflated binomial model for the analysis of correlated proportional data.

In this paper, a new multivariate zero-inflated binomial (MZIB) distribution is proposed to analyse the correlated proportional data with excessive zeros. The distributional properties of purposed model are studied. The Fisher scoring algorithm and EM algorithm are given for the computation of estimates of parameters in the proposed MZIB model with/without covariates. The score tests and the likelihood ratio tests are derived for assessing both the zero-inflation and the equality of multiple binomial probabilities in correlated proportional data. A limited simulation study is performed to evaluate the performance of derived EM algorithms for the estimation of parameters in the model with/without covariates and to compare the nominal levels and powers of both score tests and likelihood ratio tests. The whitefly data is used to illustrate the proposed methodologies.