nearest neighbor (kNN) method is a popular classification method in data mining and statistics because of its simple implementation and significant classification performance. However, it is impractical for traditional kNN methods to assign a fixed value (even though set by experts) to all test samples. Previous solutions assign different values to different test samples by the cross validation method but are usually time-consuming. This paper proposes a kTree method to learn different optimal values for different test/new samples, by involving a training stage in the kNN classification. Specifically, in the training stage, kTree method first learns optimal values for all training samples by a new sparse reconstruction model, and then constructs a decision tree (namely, kTree) using training samples and the learned optimal values. In the test stage, the kTree fast outputs the optimal value for each test sample, and then, the kNN classification can be conducted using the learned optimal value and all training samples. As a result, the proposed kTree method has a similar running cost but higher classification accuracy, compared with traditional kNN methods, which assign a fixed value to all test samples. Moreover, the proposed kTree method needs less running cost but achieves similar classification accuracy, compared with the newly kNN methods, which assign different values to different test samples. This paper further proposes an improvement version of kTree method (namely, k*Tree method) to speed its test stage by extra storing the information of the training samples in the leaf nodes of kTree, such as the training samples located in the leaf nodes, their kNNs, and the nearest neighbor of these kNNs. We call the resulting decision tree as k*Tree, which enables to conduct kNN classification using a subset of the training samples in the leaf nodes rather than all training samples used in the newly kNN methods. This actually reduces running cost of test stage. Finally, the experimental results on 20 real data sets showed that our proposed methods (i.e., kTree and k*Tree) are much more efficient than the compared methods in terms of classification tasks.

Colloidal quantum dots (CQDs) are fast-improving materials for next-generation solution-processed optoelectronic devices such as solar cells, photocatalysis, light emitting diodes, and photodetectors.

The root economics spectrum (RES), a common hypothesis postulating a tradeoff between resource acquisition and conservation traits, is being challenged by conflicting relationships between root diameter, tissue density (RTD) and root nitrogen concentration (RN). Here, we analyze a global trait dataset of absorptive roots for over 800 plant species. For woody species (but not for non-woody species), we find nonlinear relationships between root diameter and RTD and RN, which stem from the allometric relationship between stele and cortical tissues. These nonlinear relationships explain how sampling bias from different ends of the nonlinear curves can result in conflicting trait relationships. Further, the shape of the relationships varies depending on evolutionary context and mycorrhizal affiliation. Importantly, the observed nonlinear trait relationships do not support the RES predictions. Allometry-based nonlinearity of root trait relationships improves our understanding of the ecology, physiology and evolution of absorptive roots.

Oral preexposure prophylaxis (PrEP) trials report disparate efficacy attributed to variable adherence. HPTN 066 was conducted to establish objective, quantitative benchmarks for discrete, regular levels of adherence using directly observed dosing of tenofovir (TFV) disoproxil fumarate (TDF)/emtricitabine (FTC). Healthy, HIV-uninfected men and women were randomized to one of four oral regimens of fixed-dose TDF 300 mg/FTC 200 mg tablet for 5 weeks with all doses observed: one tablet weekly (one/week), one tablet twice weekly (two/week), two tablets twice weekly (four/week), or one tablet daily (seven/week). Trough serum TFV and FTC, peripheral blood mononuclear cell (PBMC), and CD4(+) TFV-diphosphate (TFV-DP) and FTC-triphosphate (FTC-TP) concentrations were determined throughout dosing and 2 weeks after the last dose. Rectosigmoidal, semen, and cervicovaginal samples were collected for drug assessment at end of dosing and 2 weeks later in a subset of participants. The 49 enrolled participants tolerated the regimens well. All regimens achieved steady-state concentrations by the second dose for serum TFV/FTC and by 7 days for PBMC TFV-DP/FTC-TP. Steady-state median TFV-DP predose concentrations demonstrated dose proportionality: one/week 1.6 fmol/10(6) PBMCs, two/week 9.1, four/week 18.8, seven/week, 36.3. Further, TFV-DP was consistently quantifiable 2 weeks after the last dose for the ≥4/week regimens. Adherence benchmarks were identified using receiver operating characteristic curves, which had areas under the curve ≥0.93 for all analytes in serum and PBMCs. Intersubject and intrasubject coefficients of variation (%CV) ranged from 33% to 63% and 14% to 34%, respectively, for all analytes in serum and PBMCs. Steady-state PBMC TFV-DP was established earlier and at lower concentrations than predicted and was the only analyte demonstrating predose concentration dose proportionality. Steady-state daily dosing serum TFV and PBMC TFV-DP was consistent with highly effective PrEP clinical trials. HPTN 066 provides adherence benchmarks for oral TFV/FTC regimens to assist interpreting study outcomes.

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Dental resins represent an important family of biomaterials that have been evolving in response to the needs in biocompatibility and mechanical properties. They are composite materials consisting of mostly inorganic fillers and additives bound together with a polymer matrix. A large number of fillers in a variety of forms (spheroidal, fibrous, porous, etc.) along with other additives have been studied to enhance the performance of the composites. Silane derivatives are attached as coupling agents to the fillers to improve their interfacial properties. A review of the literature on dental composite fillers seems to suggest that each of the fillers tested presents its own strengths and weaknesses, and often combinations of these yield resin composites with the desired balance of properties. Additives such as nanotubes, whiskers, fibers, and nanoclusters have been shown to enhance the properties of these hybrid materials, and their use in small fractions may enhance the overall performance of the dental resin materials.

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