CROMES - A fast and efficient machine learning emulator pipeline for gridded crop models

Global gridded crop models (GGCMs) have become state-of-the-art tools in large-scale climate impact and adaptation assessments. Yet, these combinations of large-scale spatial data frameworks and plant growth models have limitations in the volume of scenarios they can address due to computational demand and complex software structures. Emulators mimicking such models have therefore become an attractive option to produce reasonable predictions of GGCMs’ crop productivity estimates at much lower computational costs. However, such emulators’ flexibility is thus far typically limited in terms of crop management flexibility and spatial resolutions among others. Here we present a new emulator pipeline CROp model Machine learning Emulator Suite (CROMES) that serves for processing climate features from netCDF input files, combining these with site-specific features (soil, topography), and crop management specifications (planting dates, cultivars, irrigation) to train machine learning emulators and subsequently produce predictions. Presently built around the GGCM EPIC-IIASA and employing a boosting algorithm, CROMES is capable of producing predictions for EPIC-IIASA’s crop yield estimates with high accuracy and very high computational efficiency. Predictions require for a first used climate dataset about 45 min and 10 min for any subsequent scenario based on the same climate forcing in a single thread compared to approx. 14h for a GGCM simulation on the same system. Prediction accuracy is highest if modeling the case when crops receive sufficient nutrients and are consequently most sensitive to climate. When training an emulator on crop model simulations for rainfed maize and a single global climate model (GCM), the yield prediction accuracy for out-of-bag GCMs is R2=0.93-0.97, RMSE=0.5-0.7, and rRMSE=8-10% in space and time. Globally, the best agreement between predictions and crop model simulations occurs in (sub-)tropical regions, the poorest is in cold, arid climates where both growing season length and water availability limit crop growth. The performance slightly deteriorates if fertilizer supply is considered, more so at low levels of nutrient inputs than at the higher end. Importantly, emulators produced by CROMES are virtually scale-free as all training samples, i.e., pixels, are pooled and hence treated as individual locations solely based on features provided without geo-referencing. This allows for applications on increasingly available high-resolution climate datasets or in regional studies for which more granular data may be available than at global scales. Using climate features based on crop growing seasons and cardinal growth stages enables also adaptation studies including growing season and cultivar shifts. We expect CROMES to facilitate explorations of comprehensive climate projection ensembles, studies of dynamic climate adaptation scenarios, and cross-scale impact and adaptation assessments

Use of a Novel Imaging Technology for Remote Autism Diagnosis: A Reflection on Experience of Stakeholders

AbstractTimely diagnosis and early interventions are critical to improving the long term functioning of a child with ASD. However, a major challenge facing parents is difficulty in obtaining on-time access to appropriate diagnostic services. To address this need, an imaging technology, NODA® (Naturalistic Observation Diagnostic Assessment), has been successfully developed and field-tested. NODA® includes 1) NODA SmartCapture; a smart-phone based recording system for parents to capture and share in-home video evidence of their child behavior and 2) NODA Connect; a HIPPA compliant web-platform for diagnosticians to conduct remote autism diagnostic assessments based on in-home video evidence of behavior, developmental history and their clinical judgment. In the field study, parents captured and shared videos evidence from their homes via NODA SmartCapture and diagnosticians conducted remote diagnostic assessment via NODA Connect. Results show that parents were able to successfully collect video evidence of behavior as per given prescription and diagnosticians were able to complete remote diagnostic assessments. This paper is a reflection on the first hand experience of key stakeholders (parents and diagnosticians) using NODA® in the field

Excessive folate synthesis limits lifespan in the C. elegans: E. coli aging model

Background: Gut microbes influence animal health and thus, are potential targets for interventions that slow aging. Live E. coli provides the nematode worm Caenorhabditis elegans with vital micronutrients, such as folates that cannot be synthesized by animals. However, the microbe also limits C. elegans lifespan. Understanding these interactions may shed light on how intestinal microbes influence mammalian aging. Results: Serendipitously, we isolated an E. coli mutant that slows C. elegans aging. We identified the disrupted gene to be aroD, which is required to synthesize aromatic compounds in the microbe. Adding back aromatic compounds to the media revealed that the increased C. elegans lifespan was caused by decreased availability of para-aminobenzoic acid, a precursor to folate. Consistent with this result, inhibition of folate synthesis by sulfamethoxazole, a sulfonamide, led to a dose-dependent increase in C. elegans lifespan. As expected, these treatments caused a decrease in bacterial and worm folate levels, as measured by mass spectrometry of intact folates. The folate cycle is essential for cellular biosynthesis. However, bacterial proliferation and C. elegans growth and reproduction were unaffected under the conditions that increased lifespan. Conclusions: In this animal:microbe system, folates are in excess of that required for biosynthesis. This study suggests that microbial folate synthesis is a pharmacologically accessible target to slow animal aging without detrimental effects

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

Strong floristic distinctiveness across Neotropical successional forests.

Forests that regrow naturally on abandoned fields are important for restoring biodiversity and ecosystem services, but can they also preserve the distinct regional tree floras? Using the floristic composition of 1215 early successional forests (<20 years) in 75 human-modified landscapes across the Neotropic realm, we identified 14 distinct floristic groups, with a between-group dissimilarity of 0.97. Floristic groups were associated with location, bioregions, soil pH, temperature seasonality, and water availability. Hence, there is large continental-scale variation in the species composition of early successional forests, which is mainly associated with biogeographic and environmental factors but not with human disturbance indicators. This floristic distinctiveness is partially driven by regionally restricted species belonging to widespread genera. Early secondary forests contribute therefore to restoring and conserving the distinctiveness of bioregions across the Neotropical realm, and forest restoration initiatives should use local species to assure that these distinct floras are maintained

Strong floristic distinctiveness across Neotropical successional forests

Forests that regrow naturally on abandoned fields are important for restoring biodiversity and ecosystem services, but can they also preserve the distinct regional tree floras? Using the floristic composition of 1215 early successional forests (≤20 years) in 75 human-modified landscapes across the Neotropic realm, we identified 14 distinct floristic groups, with a between-group dissimilarity of 0.97. Floristic groups were associated with location, bioregions, soil pH, temperature seasonality, and water availability. Hence, there is large continental-scale variation in the species composition of early successional forests, which is mainly associated with biogeographic and environmental factors but not with human disturbance indicators. This floristic distinctiveness is partially driven by regionally restricted species belonging to widespread genera. Early secondary forests contribute therefore to restoring and conserving the distinctiveness of bioregions across the Neotropical realm, and forest restoration initiatives should use local species to assure that these distinct floras are maintained

Behavior Imaging®: Resolving Assessment Challenges for Autism Spectrum Disorder in Pharmaceutical Trials

Behavior Imaging® was incorporated into an NIMH-funded multisite pharmaceutical trial to remotely document clinical rater reliability within a standardized observer evaluation protocol. Behavior Imaging provided a centralized telehealth system capable of documenting observer agreement. Interviewer-observer training and ongoing maintenance of assessment quality was performed efficiently. Behavior Imaging provided an important new quality control tool previously not available for such multisite research studies. The results indicate the potential for successful applications also to retrospective studies of archived video data and the evaluation of recorded data obtained in natural settings

From waste to value - direct utilization of limonene from orange peel in a biocatalytic cascade reaction towards chiral carvolactone

In this proof of concept study we demonstrate direct utilization of limonene containing waste product orange peel as starting material for a biocatalytic cascade reaction. The product of this cascade is chiral carvolactone, a promising building block for thermoplastic polymers. Four different concepts were applied to augment limonene availability based on either water extraction solely, addition of extraction enhancers or biomass dissolution

Problematic prescription opioid use in a chronic pain treatment facility: the role of emotional processes

BACKGROUND: Factors associated with prescription opioid misuse in a chronic pain treatment population are limited, and increasing our understanding of associated factors could lead to improved targeting of prevention and intervention efforts. OBJECTIVE: The aim of this study was to evaluate factors associated with problematic prescription opioid use in patients with chronic pain, and whether assessing emotional processes - alexithymia, ambivalence over emotional expression (AEQ), and emotional approach coping - improves understanding of problematic prescription opioid use beyond traditional risk factors. METHODS: Participants were 100 patients with chronic pain (mean age = 47.57 years, SD = 11.57; 53% female; 81% African American) who were receiving a self-administered opioid medication through a local pain clinic. We assessed traditional risk factors (substance use history, pain, psychiatric distress, and pain catastrophizing), the three emotional processes, and problematic prescription opioid-related outcomes. RESULTS: Zero-order correlations revealed that alexithymia was significantly, positively related to problematic prescription opioid use behaviors (PDUQ), and AEQ was significantly positively related to both prescription opioid misuse behaviors and opioid use disorder symptoms. Multiple regressions that included traditional risk factors and the three emotional processes indicated that AEQ was a unique correlate of problematic opioid use behaviors (β=.27, p=.04) and prescription opioid-related symptoms of abuse and dependence (β=.37, p=.01); history of substance use disorders was also associated. CONCLUSIONS: In addition to personal history of substance use problems, AEQ is a modifiable risk factor - and thus potential treatment target - for prescription opioid misuse and opioid use disorders