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During the 2015–2016 El Niño, the Amazon basin released almost one gigaton of carbon (GtC) into the atmosphere due to extreme temperatures and drought. The link between the drought impact and recovery of the total carbon pools and its biogeochemical drivers is still unknown. With satellite-constrained net carbon exchange and its component fluxes including gross primary production and fire emissions, we show that the total carbon loss caused by the 2015–2016 El Niño had not recovered by the end of 2018. Forest ecosystems over the Northeastern (NE) Amazon suffered a cumulative total carbon loss of ∼0.6 GtC through December 2018, driven primarily by a suppression of photosynthesis whereas southeastern savannah carbon loss was driven in part by fire. We attribute the slow recovery to the unexpected large carbon loss caused by the severe atmospheric aridity coupled with a water storage deficit during drought. We show the attenuation of carbon uptake is three times higher than expected from the pre-drought sensitivity to atmospheric aridity and ground water supply. Our study fills an important knowledge gap in our understanding of the unexpectedly enhanced response of carbon fluxes to atmospheric aridity and water storage deficit and its impact on regional post-drought recovery as a function of the vegetation types and climate perturbations. Our results suggest that the disproportionate impact of water supply and demand could compromise resiliency of the Amazonian carbon balance to future increases in extreme events.

Background: Sinus node dysfunction, atrioventricular (AV) block and atrial fibrillation (AF) are associated with advanced age. Required therapy commonly includes pacemaker implantation. Methods: We report the course of therapy for an 87-year-old with symptomatic sinus node dysfunction and paroxysmal atrial fibrillation who was intolerant of drug therapy. Results: The patient received a pacemaker for treatment of sick sinus syndrome. She continued to have symptomatic episodes of AF and was intolerant of pharmacologic therapy despite adequate rate support provided by the pacemaker. The AF suppression algorithm in the pacemaker was enabled, resulting in the elimination all AF episodes effectively eliminating the need for antiarrhythmic medication. If this continues to stabilize her atrium, withdrawal of anticoagulation therapy is anticipated. Conclusions: The clinical presentation of sinus node dysfunction and related conduction abnormalities is common in the elderly. Pharmacologic management is often a challenge in the presence of the advanced age and concomitant disease processes. In individuals who have paroxysmal atrial fibrillation or are likely to develop this and who need a pacemaker for standard indications, the availability of an AF Suppression™ algorithm may facilitate their management without needed to use medications or being able to utilize lower doses of those medications.

This case study by Dr. John1 demonstrates the care that one must take in programming an implantable cardioverter defibrillator, the complexity of these patients and the utility of the internal diagnostics in the system to facilitate the physician's understanding of device behavior. Even the single chamber ICDs have SVT discrimination algorithms that can be enabled in an effort to differentiate SVT from VT allowing the device to withhold ATP and shock therapy in the setting of an SVT. Many physicians, myself included, will initially utilize these discriminators in a monitoring mode preferring to deliver unnecessary therapy rather than inappropriately withholding needed therapy. This also provides an opportunity to better understand all the rhythms that may be occurring in a patient, many of which may not have been appreciated prior to the implant. After one or more SVT episodes have occurred and the clinician has a chance to review the stored electrograms (first introduced by Ventritex) along with the response of the device to any discriminators, a decision can be made as to how the ICD prescription might be adjusted.

Since the introduction of steroid eluting electrodes, the incidence of an early massive rise in the capture threshold that either exceeds or threatens to exceed the programmed output of the pacemaker has declined but has not totally disappeared1 If a persistent or massive threshold rise is encountered in the days to months post-implant, one consideration is microinstability of the lead. In this setting, there may be a change in the morphology of the pacemaker evoked depolarization on the ECG or a change in the physical location of the lead as assessed with a chest x-ray. Another marker is fluctuations in the capture threshold on repeated assessments at the same office or clinic visit. The options for this problem include an operative procedure to reposition or replace the lead or to closely observe the patient hoping that the lead settles into a secure location. Another potential totally reversible cause is the introduction of an new medication or herb. If the possible explanations for threshold increase cited above have been excluded and the high capture threshold is believed to be due to lead maturation, increasing the output or possible lead replacement or repositioning have been the usual options.

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Abstract: In November 2021, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held, which involved hundreds of researchers from dozens of institutions. There were 17 sessions held at the workshop, including one on ecohydrology. The ecohydrology session included various breakout rooms that addressed specific topics, including 1) soils and belowground areas; 2) watersheds; 3) hydrology; 4) ecophysiology and plant hydraulics; 5) ecology; 6) extremes, disturbance and fire, and land-use and land-cover change; and 7) uncertainty quantification methods and techniques. In this paper, we investigate and report on the potential application of artificial intelligence and machine learning in ecohydrology, highlight outcomes of the ecohydrology session at the AI4ESP workshop, and provide visionary perspectives for future research in this area.

The seasonal-to-decadal terrestrial water balance on river basin scales depends on several well-characterized but uncertain soil physical processes, including soil moisture, plant available water, rooting depth, and recharge to lower soil layers. Reducing uncertainties in these quantities using observations is a key step toward improving the data fidelity and skill of land surface models. In this study, we quantitatively characterize the capability of Gravity Recovery and Climate Experiment (NASA-GRACE) measurements-a key constraint on total water storage (TWS)-to inform and constrain these processes. We use a reduced-complexity physically based model capable of simulating the hydrologic cycle, and we apply Bayesian inference on the model parameters using a Markov chain Monte Carlo algorithm, to minimize mismatches between model-simulated and GRACE-observed TWS anomalies. Based on the prior and posterior model parameter distributions, we further quantify information gain with regard to terrestrial water states, associated fluxes, and time-invariant process parameters. We show that the data-constrained terrestrial water storage model can capture basic physics of the hydrologic cycle for a watershed in the western Amazon during the period January 2003 through December 2012, with an r2 of 0.98 and root mean square error of 30.99 mm between observed and simulated TWS. Furthermore, we show a reduction of uncertainty in many of the parameters and state variables, ranging from a 2 % reduction in uncertainty for the porosity parameter to an 85 % reduction for the rooting depth parameter. The annual and interannual variability of the system are also simulated accurately, with the model simulations capturing the impacts of the 2005-2006 and 2010-2011 South American droughts. The results shown here suggest the potential of using gravimetric observations of TWS to identify and constrain key parameters in soil hydrologic models.

Future changes in the position of the intertropical convergence zone (ITCZ; a narrow band of heavy precipitation in the tropics) with climate change could affect the livelihood and food security of billions of people. Although models predict a future narrowing of the ITCZ, uncertainties remain large regarding its future position, with most past work focusing on zonal-mean shifts. Here we use projections from 27 state-of-the-art (CMIP6) climate models and document a robust zonally-varying ITCZ response to the SSP3-7.0 scenario by 2100, with a northward shift over eastern Africa and the Indian Ocean, and a southward shift in the eastern Pacific and Atlantic Oceans. The zonally-varying response is consistent with changes in the divergent atmospheric energy transport, and sector-mean shifts of the energy flux equator. Our analysis provides insight about mechanisms influencing the future position of the tropical rainbelt, and may allow for more robust projections of climate change impacts.

Future changes in the position of the intertropical convergence zone (ITCZ; a narrow band of heavy precipitation in the tropics) with climate change could affect the livelihood and food security of billions of people. Although models predict a future narrowing of the ITCZ, uncertainties remain large regarding its future position, with most past work focusing on zonal-mean shifts. Here we use projections from 27 state-of-the-art (CMIP6) climate models and document a robust zonally-varying ITCZ response to the SSP3-7.0 scenario by 2100, with a northward shift over eastern Africa and the Indian Ocean, and a southward shift in the eastern Pacific and Atlantic Oceans. The zonally-varying response is consistent with changes in the divergent atmospheric energy transport, and sector-mean shifts of the energy flux equator. Our analysis provides insight about mechanisms influencing the future position of the tropical rainbelt, and may allow for more robust projections of climate change impacts.

Soils contain more carbon than the atmosphere and vegetation combined. An increased flow of carbon from the atmosphere into soil pools could help mitigate anthropogenic emissions of carbon dioxide and climate change. Yet we do not know how quickly soils might respond because the age distribution of soil carbon is uncertain. Here we used 789 radiocarbon (∆14C) profiles, along with other geospatial information, to create globally gridded datasets of mineral soil ∆14C and mean age. We found that soil depth is a primary driver of ∆14C, whereas climate (for example, mean annual temperature) is a major control on the spatial pattern of ∆14C in surface soil. Integrated to a depth of 1 m, global soil carbon has a mean age of 4,830 ± 1,730 yr, with older carbon in deeper layers and permafrost regions. In contrast, vertically resolved land models simulate ∆14C values that imply younger carbon ages and a more rapid carbon turnover. Our data-derived estimates of older mean soil carbon age suggest that soils will accumulate less carbon than predicted by current Earth system models over the twenty-first century. Reconciling these models with the global distribution of soil radiocarbon will require a better representation of the mechanisms that control carbon persistence in soils.

BackgroundProspective cohort studies of circulating sex steroid hormones and prostate cancer risk have not provided a consistent association, despite evidence from animal and clinical studies. However, studies using male pattern baldness as a proxy of early-life or cumulative androgen exposure have reported significant associations with aggressive and fatal prostate cancer risk. Given that androgens underlie the development of patterned hair loss and chest hair, we assessed whether these two dermatological characteristics were associated with circulating and intraprostatic concentrations of sex steroid hormones among men diagnosed with localized prostate cancer.MethodsWe included 248 prostate cancer patients from the NCI Prostate Tissue Study, who answered surveys and provided a pre-treatment blood sample as well as fresh frozen adjacent normal prostate tissue. Male pattern baldness and chest hair density were assessed by trained nurses before surgery. General linear models estimated geometric means and 95% confidence intervals (95%CIs) of each hormone variable by dermatological phenotype with adjustment for potential confounding variables. Subgroup analyses were performed by Gleason score (

Background: Sex hormones have been implicated in prostate carcinogenesis, yet epidemiologic studies have not provided substantiating evidence. We tested the hypothesis that circulating concentrations of sex steroid hormones reflect intraprostatic concentrations using serum and adjacent microscopically verified benign prostate tissue from prostate cancer cases.Methods: Incident localized prostate cancer cases scheduled for surgery were invited to participate. Consented participants completed surveys, and provided resected tissues and blood. Histologic assessment of the ends of fresh frozen tissue confirmed adjacent microscopically verified benign pathology. Sex steroid hormones in sera and tissues were extracted, chromatographically separated, and then quantitated by radioimmunoassays. Linear regression was used to account for variations in intraprostatic hormone concentrations by age, body mass index, race, and study site, and subsequently to assess relationships with serum hormone concentrations. Gleason score (from adjacent tumor tissue), race, and age were assessed as potential effect modifiers.Results: Circulating sex steroid hormone concentrations had low-to-moderate correlations with, and explained small proportions of variations in, intraprostatic sex steroid hormone concentrations. Androstane-3α,17β-diol glucuronide (3α-diol G) explained the highest variance of tissue concentrations of 3α-diol G (linear regression r2 = 0.21), followed by serum testosterone and tissue dihydrotestosterone (r2 = 0.10), and then serum estrone and tissue estrone (r2 = 0.09). There was no effect modification by Gleason score, race, or age.Conclusions: Circulating concentrations of sex steroid hormones are poor surrogate measures of the intraprostatic hormonal milieu.Impact: The high exposure misclassification provided by circulating sex steroid hormone concentrations for intraprostatic levels may partly explain the lack of any consistent association of circulating hormones with prostate cancer risk. Cancer Epidemiol Biomarkers Prev; 26(11); 1660-6. ©2017 AACR.

Large spatio‐temporal gradients in the Congo basin vegetation and rainfall are observed. However, its water‐balance (evapotranspiration minus precipitation, or ET − P) is typically measured at basin‐scales, limited primarily by river‐discharge data, spatial resolution of terrestrial water storage measurements, and poorly constrained ET. We use observations of the isotopic composition of water vapor to quantify the spatio‐temporal variability of net surface water fluxes across the Congo Basin between 2003 and 2018. These data are calibrated at basin scale using satellite gravity and total Congo river discharge measurements and then used to estimate time‐varying ET − P over four quadrants representing the Congo Basin, providing first estimates of this kind for the region. We find that the multi‐year record, seasonality, and interannual variability of ET − P from both the isotopes and the gravity/river discharge based estimates are consistent. Additionally, we use precipitation and gravity‐based estimates with our water vapor isotope‐based ET − P to calculate time and space averaged ET and net river discharge within the Congo Basin. These quadrant‐scale moisture flux estimates indicate (a) substantial recycling of moisture in the Congo Basin (temporally and spatially averaged ET/P > 70%), consistent with models and visible light‐based ET estimates, and (b) net river outflow is largest in the Western Congo where there are more rivers and higher flow rates. Our results confirm the importance of ET in modulating the Congo water cycle relative to other water sources. Plain Language Summary: Rainfall and vegetation vary substantially across the Congo Basin. However, the spatial variations, seasonality, and interannual variability of the net water balance, (the difference between evapotranspiration and rainfall) is not well quantified. Atmospheric observations of the isotopic composition of water vapor are sensitive to the balance of evapotranspiration (ET) and precipitation (P). We calibrate new observations of the isotopic composition of water vapor to ET − P that is based on satellite gravity measurements and ground‐based river discharge measurements to quantify ET − P across four quadrants of the Congo basin. When combined with satellite measurements of rainfall, we show that ET is the largest source of Congo basin water vapor. As ET is about 70% of observed rainfall, vegetation therefore plays an outsized role on the Congo water cycle. Additionally, when combined with satellite measurements of gravity, we show that river discharge is higher in the western part of the basin, where there are more rivers and stronger flows. Key Points: Water vapor isotopes provide a reliable proxy for evapotranspiration (ET) minus precipitation (P) in the Congo BasinET contributes over 70% to P within the four quadrants of the Congo BasinRiver discharge is highest in the western part of the basin, where there are more rivers and higher flow [ABSTRACT FROM AUTHOR]

A mandate framework is proposed for delegating monetary policy to an instrument-independent, but goal-dependent central bank that emphasizes simplicity in both the objectives entering the welfare criterion and those in the instrument rule. It consists of: (i) a simple quadratic loss function penalizing deviations from target variables; (ii) a welfare-optimized, Taylor-type log-linear nominal interest-rate rule with targets that match those in the loss function; (iii) a zero-lower-bound (ZLB) constraint on the nominal interest rate imposing a low unconditional probability of ZLB episodes; and (iv) a long-run inflation target. In an estimated New Keynesian model with these features, we find that for a quarterly probability of 5%, an optimal annual inflation target is close to 2%, weights for real variables in the loss function are small compared with inflation except for the real wage growth mandate and the optimized rules mimic a price-level rule. [ABSTRACT FROM AUTHOR]

While droughts predominantly induce immediate reductions in plant carbon uptake, they can also exert long‐lasting effects on carbon fluxes through associated changes in leaf area, soil carbon, etc. Among other mechanisms, shifts in carbon allocation due to water stress can contribute to the legacy effects of drought on carbon fluxes. However, the magnitude and impact of these allocation shifts on carbon fluxes and pools remain poorly understood. Using data from a wet tropical flux tower site in French Guiana, we demonstrate that drought‐induced carbon allocation shifts can be reliably inferred by assimilating Net Biosphere Exchange (NBE) and other observations within the CARbon DAta MOdel fraMework. This model‐data fusion system allows inference of optimized carbon and water cycle parameters and states from multiple observational data streams. We then examined how these inferred shifts affected the duration and magnitude of drought's impact on NBE during and after the extreme event. Compared to a static allocation scheme analogous to those typically implemented in land surface models, dynamic allocation reduced average carbon uptake during drought recovery by a factor of 2.8. Additionally, the dynamic model extended the average recovery time by 5 months. The inferred allocation shifts influenced the post‐drought period by altering foliage and fine root pools, which in turn modulated gross primary productivity and heterotrophic respiration for up to a decade. These changes can create a bust‐boom cycle where carbon uptake is enhanced some years after a drought, compared to what would have occurred under drought‐free conditions. Overall, allocation shifts accounted for 65% [45%–75%] of drought legacy effects in modeled NBE. In summary, drought‐induced carbon allocation shifts can play a substantial role in the enduring influence of drought on cumulative land‐atmosphere CO2 exchanges and should be accounted for in ecosystem models. [ABSTRACT FROM AUTHOR]