Fungal communities in decaying sapwood and heartwood of K. evelyniana were demonstrated through construction of four 18 S rRNA gene libraries. The 210 sequenced clones were clustered into 11 subgroups, belonging to Basidiomycota (71.9%) and to Ascomycota (22.4%) and unclassified (1 subgroup; 5.7%). The heartwood displayed higher species richness than the sapwood. Basidiomycota were dominant in either the heartwood or the sapwood. Phylogenetically diverse Homobasidiomycetes were detected in the heartwood, contrary to the sapwood, where Heterobasidiomycetes were detected. Clones close to Spongipellis unicolor dominated in the heartwood (21 of 99 clones), while those close to Hydnochaete olivacea dominated in the sapwood (41 of 111 clones). The common species between the two parts were those related to S. unicolor, Calocera cornea, Debaryomyces hansenii, Davidiella tassiana, and Nomuraea rileyi and those from Chaetothyriomycetes.

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With growingly abundant data that relate to both space and time becoming available, spatiotemporal modeling has received increasing attention in the literature. This paper targets on developing a class of spatiotemporal autoregressive partially linear varying coefficient models that are sufficiently flexible to simultaneously capture the spatiotemporal dependence and nonstationarity often encountered in practice. When spatial observations are observed over time and exhibit dynamic and nonstationary behaviors, our models become very useful for analyzing such data. We develop a numerically stable and computationally efficient estimation procedure using the tensor product splines over triangular prisms to approximate the coefficient functions.The estimators of both the constant coefficients and varying coefficients are consistent. We also show that the estimators of the constant coefficients are asymptotically normal, which enables us to construct confidence intervals and make inferences. The method's performance is evaluated by Monte Carlo experiments and applied to model and forecast the spread of COVID-19 at the county level in the United States.

Some sufficient conditions ensuring that the subdirect sum of Nekrasov matrices is in the class of Nekrasov matrices are given. In particular, we obtain that the 1-subdirect sum A 1 B is a Nekrasov matrix, when A is a Nekrasov matrix and B is a strictly diagonally dominant matrix.