BackgroundProstate-specific membrane antigen (PSMA; folate hydrolase) prostate cancer (PC) expression has theranostic utility.ObjectiveTo elucidate PC PSMA expression and associate this with defective DNA damage repair (DDR).Design, setting, and participantsMembranous PSMA (mPSMA) expression was scored immunohistochemically from metastatic castration-resistant PC (mCRPC) and matching, same-patient, diagnostic biopsies, and correlated with next-generation sequencing (NGS) and clinical outcome data.Outcome measurements and statistical analysisExpression of mPSMA was quantitated by modified H-score. Patient DNA was tested by NGS. Gene expression and activity scores were determined from mCRPC transcriptomes. Statistical correlations utilised Wilcoxon signed rank tests, survival was estimated by Kaplan-Meier test, and sample heterogeneity was quantified by Shannon's diversity index.Results and limitationsExpression of mPSMA at diagnosis was associated with higher Gleason grade (p = 0.04) and worse overall survival (p = 0.006). Overall, mPSMA expression levels increased at mCRPC (median H-score [interquartile range]: castration-sensitive prostate cancer [CSPC] 17.5 [0.0–60.0] vs mCRPC 55.0 [2.8–117.5]). Surprisingly, 42% (n = 16) of CSPC and 27% (n = 16) of mCRPC tissues sampled had no detectable mPSMA (H-score <10). Marked intratumour heterogeneity of mPSMA expression, with foci containing no detectable PSMA, was observed in all mPSMA expressing CSPC (100%) and 37 (84%) mCRPC biopsies. Heterogeneous intrapatient mPSMA expression between metastases was also observed, with the lowest expression in liver metastases. Tumours with DDR had higher mPSMA expression (p = 0.016; 87.5 [25.0–247.5] vs 20 [0.3–98.8]; difference in medians 60 [5.0–95.0]); validation cohort studies confirmed higher mPSMA expression in patients with deleterious aberrations in BRCA2 (p < 0.001; median H-score: 300 [165–300]; difference in medians 195.0 [100.0–270.0]) and ATM (p = 0.005; 212.5 [136.3–300]; difference in medians 140.0 [55.0–200]) than in molecularly unselected mCRPC biopsies (55.0 [2.75–117.5]). Validation studies using mCRPC transcriptomes corroborated these findings, also indicating that SOX2 high tumours have low PSMA expression.ConclusionsMembranous PSMA expression is upregulated in some but not all PCs, with mPSMA expression demonstrating marked inter- and intrapatient heterogeneity. DDR aberrations are associated with higher mPSMA expression and merit further evaluation as predictive biomarkers of response for PSMA-targeted therapies in larger, prospective cohorts.Patient summaryThrough analysis of prostate cancer samples, we report that the presence of prostate-specific membrane antigen (PSMA) is extremely variable both within one patient and between different patients. This may limit the usefulness of PSMA scans and PSMA-targeted therapies. We show for the first time that prostate cancers with defective DNA repair produce more PSMA and so may respond better to PSMA-targeting treatments.

[1] High-resolution space-time stochastic models for precipitation are crucial for hydrological applications related to flood risk and water resources management. In this study, we present a new stochastic space-time model, STREAP, which is capable of reproducing essential features of the statistical structure of precipitation in space and time for a wide range of scales, and at the same time can be used for continuous simulation. The model is based on a three-stage hierarchical structure that mimics the precipitation formation process. The stages describe the storm arrival process, the temporal evolution of areal mean precipitation intensity and wet area, and the evolution in time of the twodimensional storm structure. Each stage of the model is based on appropriate stochastic modeling techniques spanning from point processes, multivariate stochastic simulation and random fields. Details of the calibration and simulation procedures in each stage are provided so that they can be easily reproduced. STREAP is applied to a case study in Switzerland using 7 years of high-resolution (2 3 2 km 2 ; 5 min) data from weather radars. The model is also compared with a popular parsimonious space-time stochastic model based on point processes (space-time Neyman-Scott) which it outperforms mainly because of a better description of spatial precipitation. The model validation and comparison is based on an extensive evaluation of both areal and point scale statistics at hydrologically relevant temporal scales, focusing mainly on the reproduction of the probability distributions of rainfall intensities, correlation structure, and the reproduction of intermittency and wet spell duration statistics. The results shows that a more accurate description of the space-time structure of precipitation fields in stochastic models such as STREAP does indeed lead to a better performance for properties and at scales which are not used in model calibration.

Increasing concentrations of atmospheric carbon dioxide are expected to affect carbon assimilation and evapotranspiration (ET), ultimately driving changes in plant growth, hydrology, and the global carbon balance. Direct leaf biochemical effects have been widely investigated, whereas indirect effects, although documented, elude explicit quantification in experiments. Here, we used a mechanistic model to investigate the relative contributions of direct (through carbon assimilation) and indirect (via soil moisture savings due to stomatal closure, and changes in leaf area index) effects of elevated CO 2 across a variety of ecosystems. We specifically determined which ecosystems and climatic conditions maximize the indirect effects of elevated CO 2 . The simulations suggest that the indirect effects of elevated CO 2 on net primary productivity are large and variable, ranging from less than 10% to more than 100% of the size of direct effects. For ET, indirect effects were, on average, 65% of the size of direct effects. Indirect effects tended to be considerably larger in water-limited ecosystems. As a consequence, the total CO 2 effect had a significant, inverse relationship with the wetness index and was directly related to vapor pressure deficit. These results have major implications for our understanding of the CO 2 response of ecosystems and for global projections of CO 2 fertilization, because, although direct effects are typically understood and easily reproducible in models, simulations of indirect effects are far more challenging and difficult to constrain. Our findings also provide an explanation for the discrepancies between experiments in the total CO 2 effect on net primary productivity.he leaf-level response to elevated CO 2 (eCO 2 ) is well known: at current CO 2 levels, photosynthesis of C 3 plants is not saturated, whereas, for C 4 plants, it is close to saturation (e.g., refs. 1-4). If acclimation is limited, leaf-level carbon assimilation of C 3 plants will increase as the CO 2 concentration increases, as shown by, among others, observations in Free-Air CO 2 Enrichment (FACE) experiments (5-7). Concurrently, stomatal conductance decreases consistently with eCO 2 in most species (8-11). Even though the leaf-level responses are well characterized and quantifiable, the ecosystem response to eCO 2 remains considerably more uncertain and difficult to predict (12-17). This discrepancy is not simply a consequence of the uncertainty in scaling up from leaf to canopy and ecosystem but derives from indirect effects and feedbacks that may lead to an amplification or dampening of the direct leaf-level response to eCO 2 .Indirect effects may be related to (i) modifications of plant water status through changes in soil moisture within the root zone, which occur as a consequence of stomatal closure; (ii) changes in Leaf Area Index (LAI), root biomass and depth, and canopy structure; (iii) limitations due to soil nutrient scarcity or plant incapability to take up nutrients at a rate sufficient to support enhanced c...

Decision makers and consultants are particularly interested in "detailed" information on future climate to prepare adaptation strategies and adjust design criteria. Projections of future climate at local spatial scales and fine temporal resolutions are subject to the same uncertainties as those at the global scale but the partition among uncertainty sources (emission scenarios, climate models, and internal climate variability) remains largely unquantified. At the local scale, the uncertainty of the mean and extremes of precipitation is shown to be irreducible for mid and end-of-century projections because it is almost entirely caused by internal climate variability (stochasticity). Conversely, projected changes in mean air temperature and other meteorological variables can be largely constrained, even at local scales, if more accurate emission scenarios can be developed. The results were obtained by applying a comprehensive stochastic downscaling technique to climate model outputs for three exemplary locations. In contrast with earlier studies, the three sources of uncertainty are considered as dependent and, therefore, non-additive. The evidence of the predominant role of internal climate variability leaves little room for uncertainty reduction in precipitation projections; however, the inference is not necessarily negative, because the uncertainty of historic observations is almost as large as that for future projections with direct implications for climate change adaptation measures.

Androgen receptor (AR) splice variants (AR-Vs) have been implicated in the development and progression of metastatic prostate cancer. AR-Vs are truncated isoforms of the AR, a subset of which lack a ligand-binding domain and remain constitutively active in the absence of circulating androgens, thus promoting cancer cell proliferation. Consequently, AR-Vs have been proposed to contribute not only to resistance to anti-androgen therapies but also to resistance to radiotherapy in patients receiving combination therapy by promoting DNA repair. AR-Vs, such as AR-V7, have been associated with unfavourable clinical outcomes in patients; however, attempts to specifically inhibit or prevent the formation of AR-Vs have, to date, been unsuccessful. Thus, novel therapeutic strategies are desperately needed to address the oncogenic effects of AR-Vs, which can drive lethal forms of prostate cancer. Disruption of alternative splicing through modulation of the spliceosome is one such potential therapeutic avenue; however, our understanding of the biology of the spliceosome and how it contributes to prostate cancer remains incomplete, as reflected in the dearth of spliceosome-targeted therapeutic agents. In this Review, the authors outline the current understanding of the role of the spliceosome in the progression of prostate cancer and explore the therapeutic utility of manipulating alternative splicing to improve patient care.

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Serra received grants from Fundacion Cientifica AECC (LABAE16020PORTT) and an ERAPERMED2019-215. J. Mateo gratefully acknowledges funding from the European Union's Horizon 2020 research and innovation program (Marie Skłodowska-Curie grant 837900), Instituto de Salud Carlos III (Grant PI18/01384), Fundación AECC, CRIS Cancer Foundation and the US Department of Defense CDMRP (Impact Award PC170510P1). S. Arce-Gallego Research.

10Extreme rainfall is quantified in engineering practice using Intensity- Frequency curves (IDF) that are traditionally derived from rain-gauges and more 12 recently also from remote sensing instruments, such as weather radars. These 13 instruments measure rainfall at different spatial scales: rain-gauge samples rainfall at 14 the point scale while weather radar averages precipitation on a relatively large area,