Summary Sorting and degradation of receptors and associated signaling molecules maintain homeostasis of conserved signaling pathways during cell specification and tissue development. Yet, whether machineries that sort signaling proteins act preferentially on different receptors and ligands in different contexts remains mysterious. Here we show that Vacuolar protein sorting 25, Vps25, a component of ESCRT-II (Endosomal Sorting Complex Required for Transport II), directs preferential endosome-mediated modulation of FGF signaling in limbs. By ENU-induced mutagenesis we isolated a polydactylous mouse line carrying a hypomorphic mutation of Vps25 (Vps25ENU). Unlike Vps25-null embryos we generated, Vps25ENU/ENU mutants survive until late gestation. Their limbs display FGF signaling enhancement and consequent hyper-activation of the FGF-SHH feedback loop causing polydactyly, whereas WNT and BMP signaling remain unperturbed. Notably, Vps25ENU/ENU Mouse Embryonic Fibroblasts exhibit aberrant FGFR trafficking and degradation; however SHH signaling is unperturbed. These studies establish that the ESCRT-II machinery selectively limits FGF signaling in vertebrate skeletal patterning.

In this study, a ground-based mobile measurement system was developed to provide rapid and cost-effective emission surveillance of both methane (CH 4 ) and volatile organic compounds (VOCs) from oil and gas (O&G) production sites. After testing in several controlled release experiments, the system was deployed in a field campaign in the Eagle Ford basin, TX. We found fat-tail distributions for both methane and total VOC (C4−C12) emissions (e.g., the top 20% sites ranked according to methane and total VOC (C4−C12) emissions were responsible for ∼60 and ∼80% of total emissions, respectively) and a good correlation between them (Spearman's R = 0.74). This result suggests that emission controls targeting relatively large emitters may help significantly reduce both methane and VOCs in oil and wet gas basins, such as the Eagle Ford. A strong correlation (Spearman's R = 0.84) was found between total VOC (C4−C12) emissions estimated using SUMMA canisters and data reported from a local ambient air monitoring station. This finding suggests that this system has the potential for rapid emission surveillance targeting relatively large emitters, which can help achieve emission reductions for both greenhouse gas (GHG) and air toxics from O&G production well pads in a cost-effective way.

We investigate the predictability of East African short rains at long (up to 12 month) lead times by relating seasonal rainfall anomalies to climate anomalies associated with the predominant Walker circulation, including sea surface temperatures (SST), geopotential heights, zonal and meridional winds, and vertical velocities. The underlying teleconnections are examined using a regularized regression model that shows two periods of high model skill (0–3-month lead and 7–9-month lead) with similar spatial patterns of predictability. We observe large-scale circulation anomalies consistent with the Walker circulation at short lead times (0–3 months) and dipoles of SST and height anomalies over the Mascarene high region at longer lead times (7–9 months). These two patterns are linked in time by anticyclonic winds in the dipole region associated with a perturbed meridional circulation (4–6-month lead). Overall, these results suggest that there is potential to extend forecast lead times beyond a few months for drought impact mitigation applications.

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Skillful long-lead climate forecast is of great importance in managing large water systems and can be made possible using teleconnections between regional climate and large-scale circulations. Recent innovations in machine learning provide powerful tools in exploring linear/nonlinear associations between climate variables. However, while it is hard to give physical interpretation of the more complex models, the simple models can be vulnerable to over-fitting, especially when dealing with the highly “non-square” climate data. Here, as a compromise of interpretability and complexity, we proposed a regression model by coupling pooling and a generalized regression with regularization. Performance of the model is tested in estimating the Three-Rivers Headwater Region wet-season precipitation using the sea surface temperatures at lead times of 0–24 months. The model shows better predictive skill for certain long lead times when compared with some commonly used regression methods including the Ordinary Least Squares (OLS), Empirical Orthogonal Function (EOF), and Canonical Correlation Analysis (CCA) regressions. The high skill is found to relate to the persistent regional correlation patterns between the predictand precipitation and predictor SSTs as also confirmed by a correlation analysis. Furthermore, flexibility of the model is demonstrated using a multinomial regression model which shows good skill around the long lead time of 22 months. Consistent clusters of SSTs are found to contribute to both models. Two SST indices are defined based on the major clusters of predictors and are found to be significantly correlated with the predictand precipitation at corresponding lead times. In conclusion, the proposed regression model demonstrates great flexibility and advantages in dealing with collinearity while preserving simplicity and interpretability, and shows potential as a cheap preliminary analysis tool to guide further study using more complex models.