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Estimations and applications of factor models often rely on the crucial condition that the number of latent factors is consistently estimated, which in turn also requires that factors be relatively strong, data are stationary and weakly serially dependent, and the sample size be fairly large, although in practical applications, one or several of these conditions may fail. In these cases, it is difficult to analyze the eigenvectors of the data matrix. To address this issue, we propose simple estimators of the latent factors using cross-sectional projections of the panel data, by weighted averages with predetermined weights. These weights are chosen to diversify away the idiosyncratic components, resulting in "diversified factors." Because the projections are conducted cross-sectionally, they are robust to serial conditions, easy to analyze and work even for finite length of time series. We formally prove that this procedure is robust to over-estimating the number of factors, and illustrate it in several applications, including post-selection inference, big data forecasts, large covariance estimation, and factor specification tests. We also recommend several choices for the diversified weights. for this article are available online. [ABSTRACT FROM AUTHOR]

The complete mitochondrial genome (mitogenome) of Miroplana shenzhensis Yu & Wang, 2013 is reported in the present study, representing the second mitogenome recorded in the suborder Maricola. The circular mitogenome is 14,344 bp in length, containing 12 protein-coding genes, 2 ribosomal RNAs and 22 transfer RNAs. Comparative analysis on mitochondrial gene order reveals a rearrangement in the suborder Maricola, indicating that mitochondrial gene order is conserved only in Continenticola, and is divergent across Tricladida. Phylogenetic analysis shows M. shenzhensis is clustered with an another marine triclad, forming a well-supported monophyletic group of Maricloan. [ABSTRACT FROM AUTHOR]

5/6 Nephrectomy (PNx) on rat and mouse mimics renal failure after loss of kidney function in human, and it has been widely used in CKD researches. However, existing methods for PNx model construction present high mortality of animals after modeling due to hemorrhage and infection in or after surgery. Here, we report a novel and highly efficient PNx modeling method to simulate conventional 5/6 nephrectomy, which significantly reduced the mortality of animals and simplified the modeling procedures. In this novel modeling method, we directly ligated the upper and lower poles of left kidney after removal the right kidney 1 week later (l-PNx), which leads to necrosis of ligated upper and lower poles of the kidney and mimics the conventional 5/6 nephrectomy (c-PNx). After modeling 4 and 12 weeks, the serum creatinine, BUN and proteinuria levels were strongly increased in both c-PNx and l-PNx model. Importantly, compared with the c-PNx, l-PNx model present more severe renal fibrosis estimated by Masson staining, IHC and western blotting. The results showed that the protein levels of α-SMA were significantly increased in the kidney of c-PNx and l-PNx models, but more increase was found in l-PNx model. It is noteworthy that, compared with c-PNx model, the survival rate of l-PNx model was markedly increased. In summary, we established a novel and efficient 5/6 nephrectomy model, which can mimic conventional 5/6 nephrectomy to construct a renal fibrosis and renal failure mouse model, that is conducive to mechanism and treatment researches of CKD. [ABSTRACT FROM AUTHOR]

In this article, we consider a high-dimensional quantile regression model where the sparsity structure may differ between two sub-populations. We develop ℓ1-penalized estimators of both regression coefficients and the threshold parameter. Our penalized estimators not only select covariates but also discriminate between a model with homogenous sparsity and a model with a change point. As a result, it is not necessary to know or pretest whether the change point is present, or where it occurs. Our estimator of the change point achieves an oracle property in the sense that its asymptotic distribution is the same as if the unknown active sets of regression coefficients were known. Importantly, we establish this oracle property without a perfect covariate selection, thereby avoiding the need for the minimum level condition on the signals of active covariates. Dealing with high-dimensional quantile regression with an unknown change point calls for a new proof technique since the quantile loss function is nonsmooth and furthermore the corresponding objective function is nonconvex with respect to the change point. The technique developed in this article is applicable to a general M-estimation framework with a change point, which may be of independent interest. The proposed methods are then illustrated via Monte Carlo experiments and an application to tipping in the dynamics of racial segregation. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]