Effects of Lake Erie dredged material on microbiomes in a farm soil of northwestern Ohio.

Dredged materials are often considered as candidates for replenishing lost topsoils in the watersheds of rivers and lakes. This study aimed to investigate the impacts of Lake Erie dredged material on the microbial community in a farm soil of northwestern Ohio. Dredged material from the Toledo Harbor, OH was mixed with a local farm soil at ratios of 0:100, 10:90, 20:80, and 100:0 for soybean growth in a greenhouse for 123 days and was subject to 16S rRNA gene sequencing. α-Diversity analysis revealed that although the original dredged material hosted a highly diverse microbiome, soils blended with the dredged material had similar levels of bacterial diversity to 100% farm soil throughout the experiment. ß-Diversity analysis demonstrated that, given the same plant status, that is, with or without soybean, blended soils had similar bacterial communities to 100% farm soil during the experiment. Furthermore, by the end of the experiment, all soils with soybeans merged into one cluster distinctive from those without the plants, indicating that the growth of plants played a dominating role in defining the structure of soil microbiomes. The majority (73.8%) of the operational taxonomy units that were unique to the original dredged material were not detected by the end of the experiment. This study demonstrates that up to 20% of the dredged material can be safely blended into the farm soil without distorting the microbial communities of the latter, implying a potential beneficial use of the dredged material for topsoil restoration.

Bootstrap-adjusted quasi-likelihood information criteria for mixed model selection.

We propose two model selection criteria relying on the bootstrap approach, denoted by QAICb1 and QAICb2, in the framework of linear mixed models. Similar to the justification of Akaike Information Criterion (AIC), the proposed QAICb1 and QAICb2 are proved as asymptotically unbiased estimators of the Kullback-Leibler discrepancy between a candidate model and the true model. However, they are defined on the quasi-likelihood function instead of the likelihood and are proven to be asymptotically equivalent. The proposed selection criteria are constructed by the quasi-likelihood of a candidate model and a bias estimation term in which the bootstrap method is adopted to improve the estimation for the bias caused by using the candidate model to estimate the true model. The simulations across a variety of mixed model settings are conducted to demonstrate that the proposed selection criteria outperform some other existing model selection criteria in selecting the true model. Generalized estimating equations (GEE) are utilized to calculate QAICb1 and QAICb2 in the simulations. The effectiveness of the proposed selection criteria is also demonstrated in an application of Parkinson's Progression Markers Initiative (PPMI) data.

Feature Screening for High-Dimensional Variable Selection in Generalized Linear Models.

The two-stage feature screening method for linear models applies dimension reduction at first stage to screen out nuisance features and dramatically reduce the dimension to a moderate size; at the second stage, penalized methods such as LASSO and SCAD could be applied for feature selection. A majority of subsequent works on the sure independent screening methods have focused mainly on the linear model. This motivates us to extend the independence screening method to generalized linear models, and particularly with binary response by using the point-biserial correlation. We develop a two-stage feature screening method called point-biserial sure independence screening (PB-SIS) for high-dimensional generalized linear models, aiming for high selection accuracy and low computational cost. We demonstrate that PB-SIS is a feature screening method with high efficiency. The PB-SIS method possesses the sure independence property under certain regularity conditions. A set of simulation studies are conducted and confirm the sure independence property and the accuracy and efficiency of PB-SIS. Finally we apply PB-SIS to one real data example to show its effectiveness.

Estimation and selection in linear mixed models with missing data under compound symmetric structure.

It is quite appealing to extend existing theories in classical linear models to correlated responses where linear mixed-effects models are utilized and the dependency in the data is modeled by random effects. In the mixed modeling framework, missing values occur naturally due to dropouts or non-responses, which is frequently encountered when dealing with real data. Motivated by such problems, we aim to investigate the estimation and model selection performance in linear mixed models when missing data are present. Inspired by the property of the indicator function for missingness and its relation to missing rates, we propose an approach that records missingness in an indicator-based matrix and derive the likelihood-based estimators for all parameters involved in the linear mixed-effects models. Based on the proposed method for estimation, we explore the relationship between estimation and selection behavior over missing rates. Simulations and a real data application are conducted for illustrating the effectiveness of the proposed method in selecting the most appropriate model and in estimating parameters.

Synthesis and evaluation of novel 1-(((6-substitutedbenzo[d]thiazol-2-yl)amino)(heteroaryl)methyl)naphthalen-2-ol as pesticidal agents.

To discover new agrochemicals with prominent pesticidal properties, a series of novel ß-naphthol derivatives containing benzothiazolylamino and various heteroaryl groups (8a-q) were efficiently synthesised via Betti reaction. The bioassay results showed that most of the synthesised compounds exhibited favourable insecticidal potentials, particularly towards oriental armyworm (50-100% at 200 mg·L-1) and diamondback moth (50-95% at 10 mg·L-1). Compounds 8 b, 8f, 8 g, 8j, 8k, 8n, and 8o possessed LC50 values of 0.0988-5.8864 mg·L-1 against diamondback moth. Compounds 8i, 8 l, and 8 m also displayed lethality rates of 30-90% against spider mite at the concentration of 100 mg·L-1. Overall, some compounds could be considered as new insecticidal/acaricidal leading structures for further investigation. The calcium imaging experiments revealed that 8 h, 8i, and viii could activate the release of calcium ions in insect (M. separata) central neurons at a higher concentration (50 mg·L-1). The SAR analysis provided valuable information for further structural modifications.

Upregulation of long non­coding RNA CCAT2 indicates a poor prognosis and promotes proliferation and metastasis in intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (IHCC) is an aggressive cancer with a poor survival rate and is the second most common type of primary cancer of the hepatobiliary system. At present, the molecular mechanisms of IHCC initiation and progression remain unclear. Recent evidence has indicated that long non­coding RNAs (lncRNAs) serve a crucial role in cancer development; however, the functional role of lncRNAs in IHCC has not been investigated in detail. In the present study, a marked overexpression of lncRNA colon cancer­associated transcript 2 (CCAT2) was observed in IHCC cell lines and clinical specimens. Statistical analysis of IHCC clinicopathological characteristics and CCAT2 expression data revealed that high CCAT2 expression levels correlated with microvascular invasion, differentiation grade, tumor (T), lymph node (N), metastasis (M) and overall TNM stages of IHCC (P<0.05). Kaplan­Meier analysis demonstrated that CCAT2 upregulation was associated with poor overall survival and progression­free survival in IHCC. Furthermore, high CCAT2 expression was identified as an independent risk factor of IHCC poor prognosis in both univariate and multivariate Cox regression analyses. The role of CCAT2 in promoting IHCC cell proliferation, motility and invasion was further confirmed with in vitro assays. Therefore, CCAT2 may promote IHCC progression and metastasis, and may be a promising prognostic biomarker and therapeutic target in IHCC.