Background:Foxp3+ regulatory T (Treg) cells and M2 macrophages are associated with increased tumour progression. However, the interaction between Treg cells and M2 macrophages remains unclear.Methods:The expression of FoxP3 and CD163 was detected by immunohistochemistry in 65 cases of laryngeal squamous cell carcinoma (LSCC). In vitro, the generation of activated Treg (aTreg) cells and M2 macrophages by interactions with their precursor cells were analysed by flow cytometry and ELISA. In vivo, the antitumour effects were assessed by combined targeting aTreg cells and M2 macrophages, and intratumoural immunocytes were analysed by flow cytometry.Results:In LSCC tissue, accumulation of aTreg cells and M2 macrophages predicted a poor prognosis and were positively associated with each other. In vitro, aTreg cells were induced from CD4+CD25− T cells by cancer cell-activated M2-like macrophages. Consequently, these aTreg cells skewed the differentiation of monocytes towards an M2-like phenotype, thereby forming a positive-feedback loop. Combined targeting aTreg cells and M2 macrophages led to potent antitumour immunity in vivo.Conclusions:The positive-feedback loop between aTreg cells and M2 macrophages is essential to maintain or promote immunosuppression in the tumour microenvironment and may be a potential therapeutic target to inhibit tumour progression.

Supervised learning algorithms are a recent trend for the prediction of mechanical properties of concrete. This paper presents AdaBoost, random forest (RF), and decision tree (DT) models for predicting the compressive strength of concrete at high temperature, based on the experimental data of 207 tests. The cement content, water, fine and coarse aggregates, silica fume, nano silica, fly ash, super plasticizer, and temperature were used as inputs for the models’ development. The performance of the AdaBoost, RF, and DT models are assessed using statistical indices, including the coefficient of determination (R2), root mean squared error-observations standard deviation ratio (RSR), mean absolute percentage error, and relative root mean square error. The applications of the above-mentioned approach for predicting the compressive strength of concrete at high temperature are compared with each other, and also to the artificial neural network and adaptive neuro-fuzzy inference system models described in the literature, to demonstrate the suitability of using the supervised learning methods for modeling to predict the compressive strength at high temperature. The results indicated a strong correlation between experimental and predicted values, with R2 above 0.9 and RSR lower than 0.5 during the learning and testing phases for the AdaBoost model. Moreover, the cement content in the mix was revealed as the most sensitive parameter by sensitivity analysis.

Crosses between certain genotypes of common bean result in dwarfing of F1 plants and lethal dwarfing in a proportion of the F2 population. This is under the control of the semi-dominant alleles, DL " and DL # at two complementary loci which are expressed in the root and shoot respectively. The various DL genotypes can be simulated by grafting. The graft combination DL " DL " dl # dl # \dl " dl " DL # DL # was found to have a significantly higher root dry matter fraction than either parent. Lethally dwarfed plants (DL " DL " DL # DL # ) and the analogous lethal graft combination (dl " dl " DL # DL # \DL " DL " dl # dl # ) exhibit failure of root growth and have very low root fractions. Hybrids or graft combinations with failed roots ceased growth and accumulated large amounts of starch throughout their hypocotyls. In sterile culture, both lethal dwarfs and lethal graft combinations were able to grow roots if sucrose was added to the growth medium. This indicates that a failure of sucrose translocation to the roots is probably responsible for failed root growth. Data from screening the DL genotypes of 49 cultivars could be fully explained using the DL system hypothesis, and grafting proved to be efficient for identifying DL genotype. The DL system might be of fundamental importance in root-shoot partitioning. Current evidence favours the hypothesis that failure of root growth is the outcome of excessively high sink strength of shoots compared to roots, which might arise from signalling incompatibilities between the genotypes.