Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation.

Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.

Where to Invest Project Efforts for Greater Benefit: A Framework for Management Performance Mapping with Examples for Potato Seed Health.

Policymakers and donors often need to identify the locations where technologies are most likely to have important effects, to increase the benefits from agricultural development or extension efforts. Higher-quality information may help to target the high-benefit locations, but often actions are needed with limited information. The value of information (VOI) in this context is formalized by evaluating the results of decision making guided by a set of specific information compared with the results of acting without considering that information. We present a framework for management performance mapping that includes evaluating the VOI for decision making about geographic priorities in regional intervention strategies, in case studies of Andean and Kenyan potato seed systems. We illustrate the use of recursive partitioning, XGBoost, and Bayesian network models to characterize the relationships among seed health and yield responses and environmental and management predictors used in studies of seed degeneration. These analyses address the expected performance of an intervention based on geographic predictor variables. In the Andean example, positive selection of seed from asymptomatic plants was more effective at high altitudes in Ecuador. In the Kenyan example, there was the potential to target locations with higher technology adoption rates and with higher potato cropland connectivity, i.e., a likely more important role in regional epidemics. Targeting training to high management performance areas would often provide more benefits than would random selection of target areas. We illustrate how assessing the VOI can contribute to targeted development programs and support a culture of continuous improvement for interventions.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Reference Protocol to Assess Analytical Performance of Higher Order Structural Analysis Measurements: Results from an Interlaboratory Comparison.

Measurements of protein higher order structure (HOS) provide important information on stability, potency, efficacy, immunogenicity, and biosimilarity of biopharmaceuticals, with a significant number of techniques and methods available to perform these measurements. The comparison of the analytical performance of HOS methods and the standardization of the results is, however, not a trivial task, due to the lack of reference protocols and reference measurement procedures. Here, we developed a protocol to structurally alter and compare samples of somatropin, a recombinant biotherapeutic, and describe the results obtained by using a number of techniques, methods and in different laboratories. This, with the final aim to provide tools and generate a pool of data to compare and benchmark analytical platforms and define method sensitivity to structural changes. Changes in somatropin HOS, induced by the presence of zinc at increasing concentrations, were observed, both globally and at more localized resolution, across many of the methods utilized in this study and with different sensitivities, suggesting the suitability of the protocol to improve understanding of inter- and cross-platform measurement comparability and assess analytical performance as appropriate.

Functional assessment staging (FAST) in Korean patients with Alzheimer's disease.

Functional Assessment Staging (FAST) was devised to meet the need for a more brief patient-derived rating scale for evaluating changes in functional performance and activities of daily living skills in all the stages of Alzheimer's disease (AD). FAST was administered to 464 patients with probable AD according to the National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria. The patients were also evaluated using the Korean version of the Mini-Mental Status Examination (K-MMSE), the Clinical Dementia Rating (CDR), the Clinical Dementia Rating-Sum of Boxes (CDR-SB), the Global Deterioration Scale (GDS), the Barthel Activities of Daily Living (B-ADL), and the Seoul-Instrumental Activities of Daily Living (S-IADL). For patients with moderate to severe dementia, the Korean versions of the Severe Impairment Battery (SIB-Ko) and Baylor profound mental status examination (BPMSE-Ko) were also administered. There were significant correlations between the FAST and the K-MMSE scores (r= - 0.71, p< 0.001), between the FAST and the SIB-Ko scores (r= - 0.54, p< 0.001) and between the FAST and the BPMSE-Ko scores (r=- 0.46, p< 0.001). The FAST was also correlated with the CDR, the CDR-SB, the B-ADL, and the S-IADL (p< 0.001). Ultimately, FAST is a reliable and valid assessment technique for evaluating functional deterioration in AD patients throughout the disease course. Moreover, the findings of the present study suggest that the FAST elucidates a characteristic pattern of progressive, ordinal, and functional decline in AD in Korean AD patients with dementia.

Definition of the heterocyclic pharmacophore of bacterial methionyl tRNA synthetase inhibitors: potent antibacterially active non-quinolone analogues.

Potent inhibitors of bacterial methionyl tRNA synthetase (MRS) have previously been reported. Through SAR of the quinolone moiety, the right hand side pharmacophore for MRS inhibition has now been defined as an NH-C-NH functionality in the context of a bicyclic heteroaromatic system. Potent antibacterial fused-pyrimidone and fused-imidazole analogues have been obtained and enantioselective activity demonstrated. Compound 46 demonstrated very good antibacterial activity against panels of antibiotic-resistant staphylococci and enterococci.