Search

The development of stand survival models can provide an important basis for the sustainable management of forest resources. In a new approach developed in this study, parameters of four survival quantile regression models were predicted from a quantile associated with a current stand density. The curves from these quantile regression models were then used to project future stand density for that stand. A three-fold cross-validation revealed that the quantile regression approach outperformed the least squares method based on three evaluation statistics, especially for longer projection lengths. These results were consistent for all four survival models evaluated. The best survival model is Clutter-Jones model, without constraints, but its ln(N)-ln(Dq) trajectories (N = stand density and Dq = quadratic mean diameter) from the quantile regression showed the linear self-thinning trend. Key words: Chinese fir, stand survival, quantile regression, three-fold cross-validation, self-thinning, Introduction A major goal to achieve sustainable management of forest is to develop and evaluate a model system for stand growth and yield prediction (Ozcelik et al. 2018). Mortality in [...]

Leaf area is an important ecophysiological variable for quantifying the potential production of trees, since it is closely related to tree growth. However, it is difficult to measure the leaf area completely because of the large number of leaves, so it is particularly important to develop accurate species-specific leaf area models. In this study, using 144 parse trees from 48 plots of different climate zones and ages of Chinese fir, tree leaf area models were developed based on sapwood area at breast height (SABH), diameter at breast height (DBH), and diameter at crown base (DCB), respectively. The results showed that the population-averaged levels of nonlinear mixed-effects (NLME) models were better than the plot-levels and base models, and the leaf area models based on DCB performed the best. Finally, the NLME model (16) based on DCB was used as the final model for tree leaf area of Chinese fir plantations, which was consistent with the pipe model theory. All the variables had certain biological and statistical significance and were easy to obtain in the field work (nondestructive). In addition, this study can also provide a reference for other tree species in predicting tree leaf area. [ABSTRACT FROM AUTHOR]

Bacterial biofilms are widely found in nature, and they can be a major factor in many local chronic infections inside the human body. To date, effective elimination of biofilms is intractable for conventional antibiotic therapies due to the existence of bacteria‐protecting extracellular polymeric substances. For biofilm eradication in tiny tubular structures, magnetic hydrogel micromachines (MHM) are designed that are capable of performing mechanical disruption of biofilms while releasing antibacterial agents in a controllable fashion. Two modes of motion controlled by external magnetic fields, namely planar rotation and wobbling modes, have been investigated to drive the micromachines to interact and destruct biofilms. In addition, the presented micromachines are composed of thermosensitive PNIPAm hydrogel that can load with the antibacterial agent (H2O2) and release them upon heating. The Fe3O4 nanoparticles embedded in the hydrogel matrix act as catalysts for the Fenton reaction, generating bacteria‐killing free radicals. The synergetic biofilm elimination effect has been experimentally demonstrated in well plates and small tubes. The presented MHMs may provide a new design to treat biofilm‐related infections in narrow and confined spaces. [ABSTRACT FROM AUTHOR]

Aggregation‐induced emission luminogens (AIEgens) as a new class of optical probes for two‐photon imaging (2PI) have attracted extensive attention in the research community. However, their non‐radiative energy dissipation plays a non‐negligible role in energy consumption, thereby weakening their performance in 2PI. Exploration of a facile and general approach to control the energy relaxation pathways is thus a fascinating yet significantly challenging task. To address this concern, an organic doping method is proposed herein by using TPE‐Br as the doping AIEgen and MeOTTMN as the functional AIEgen. With increased amount of doping molecules, a steadily strengthened brightness (up to 29‐fold of quantum yield) is observed at the expense of reactive oxygen species production. Remarkably, an ultradeep imaging depth of 1000 µm in a mouse brain is realized via 2PI using the heavily organic‐doped AIEgen(ODA) nanoparticles (NPs). Such result is far superior to the imaging depth of 156 µm enabled by MeOTTMN NPs. The safe use of ODA NPs is further evaluated by in vitro and in vivo toxicity assessment. This study thus provides an attractive paradigm to control the energy relaxation of AIEgen NPs and maximize their performance in 2PI while circumventing the difficulties in molecular engineering. [ABSTRACT FROM AUTHOR]