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Deep learning has proven capable of automating key aspects of histopathologic analysis. However, its context-specific nature and continued reliance on large expert-annotated training datasets hinders the development of a critical mass of applications to garner widespread adoption in clinical/research workflows. Here, we present an online collaborative platform that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/ ). Specifically, PHARAOH uses a weakly supervised, human-in-the-loop learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched annotation by human experts. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed efficiently and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Moreover, by using our PHARAOH pipeline, we showcase how correlation of cohort-level cytoarchitectural features with accompanying biological and outcome data can help systematically devise interpretable morphometric models of disease. Both the custom model design and feature extraction pipelines are amenable to crowdsourcing, positioning PHARAOH to become a fully scalable, systems-level solution for the expansion, generalization and cataloging of computational pathology applications. Faust, Chen, and colleagues present PHARAOH, a collaborative computational pathology platform that allows histologists to quickly develop custom labelled image datasets to train and catalogue a variety of machine learning models for histopathological analysis.

Deep learning has proven to be capable of automating key aspects of histopathologic analysis, but its continual reliance on large expert-annotated training datasets hinders widespread adoption. Here, we present an online collaborative portal that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/). PHARAOH uses a weakly supervised active learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched human annotation. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Both custom model design and feature extraction pipelines are amenable to crowdsourcing making PHARAOH a fully scalable systems-level solution for the systematic expansion and cataloging of computational pathology applications.

2-methylfuran is a significant organic chemical raw material which can be produced by hydrolysis, dehydration, and selective hydrogenation of biomass hemicellulose. 2-methylfuran can be converted into value-added chemicals and liquid fuels. This article reviews the latest progress in the synthesis of liquid fuel precursors through hydroxyalkylation/alkylation reactions of 2-methylfuran and biomass-derived carbonyl compounds in recent years. 2-methylfuran reacts with olefins through Diels–Alder reactions to produce chemicals, and 2-methylfuran reacts with anhydrides (or carboxylic acids) to produce acylated products. In the future application of 2-methylfuran, developing high value-added chemicals and high-density liquid fuels are two good research directions.

Stand dominant height has a close relationship with stand productivity and is not much affected by stand density and thinning within a reasonable density range, making it an excellent indicator for estimating stand site quality. Topographic factors (altitude, aspect, slope, etc.) have a significant influence on the growth process of stand level, and the combination of different site factors increases the randomness of the evaluation of forest productivity. In this paper, with one-way ANOVA, it was determined that the effects of density and management mode on the Masson pine stand dominant height were not significant. The data on the Masson pine stand dominant height in the southern subtropics in Guangxi, China, were analyzed, and the GADA model was established using the nonlinear least squares method, the Bayesian approach, and the one-level nonlinear mixed-effects model with the topographic factor as the random effect, respectively. The results indicated that the nonlinear mixed-effects model had the best fitting performance and the highest prediction accuracy for stand site quality (a 0.27% improvement in R² compared to the least squares method and a 1.30% improvement in R² compared to the Bayesian approach), while the model obtained by the Bayesian approach had more elasticity and biological significance. In summary, when the data distribution is uniform and comprehensive, introducing terrain factors into the establishment of site index models can provide a more scientific basis for estimating the productivity of southern subtropical Masson pine stands under different site conditions. When the data distribution is uneven, applying the Bayesian approach can make the site index model more biologically meaningful. The stand site quality model can predict the potential production capacity of forests, which is an important basis and can support forest management and harvest prediction. The results of this study provide a theoretical and practical basis for the establishment of a reasonable site index model for the Masson pine stand.

As the dominant height of the stand at the baseline age, the site index is an important index to evaluate site quality. However, due to the variability of environmental factors, the growth process of the dominant height of the same tree species was variable in different regions which influenced the estimation results of the site index. In this study, a methodology that established site index modeling of larch plantations with site types as a random effect in northern China was proposed. Based on 394 sample plots, nine common base models were developed, and the best model (M8) was selected (R2 = 0.5773) as the base model. Moreover, elevation, aspect, and slope position were the main site factors influencing stand dominant height through the random forest method. Then, the three site factors and their combinations (site types) were selected as random effects and simulated by the nonlinear mixed-effects model based on the model M8. The R2 values had raised from 0.5773 to 0.8678, and the model with combinations (94 kinds) of three site factors had the best performance (R2 = 0.8678). Considering the model accuracy and practical application, the 94 combinations were divided into three groups of site types (3, 5, and 8) by hierarchical clustering. Furthermore, a mixed-effects model considering the random effects of these three groups was established. All the three groups of site types got a better fitting effect (groups 3 R2 = 0.8333, groups 5 R2 = 0.8616, groups 8 R2 = 0.8683), and a better predictive performance (groups 3 R2 = 0.8157, groups 5 R2 = 0.8464, groups 8 R2 = 0.8479 for 20 percent of plots randomly selected per group in the calibration procedure) using the leave-one-out cross-validation approach. Therefore, groups 5 of site types had better applicability and estimation of forest productivity at the regional level and management plan design.