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Unmanned aerial spraying systems (UASS), i.e., unmanned aerial vehicles designed for pesticide applications, are widely used in East Asia and increasingly prevalent in other regions of the world, including North America and Europe. However, according to a recent report of the Organization for Economic Co-operation and Development, spray drift and exposure caused by these systems are not yet fully understood. In particular, there are at present no peer-reviewed reports on direct exposure of residents and bystanders to spray drift following UASS applications. This lack of data results in regulatory concerns with respect to the environment and human safety. The objective of this study was to quantify environmental, resident and bystander exposure following the application of a plant protection product to an orchard using a commercial UASS under field conditions. Using a fluorescent tracer, horizontal and vertical downwind drift data were collected and direct exposure of residents and bystanders located downwind the sprayed area to spray drift was quantified using display mannequins equipped with personal air sampling pumps. Spray drift and exposure inversely correlated with sampling height and downwind distance. Furthermore, drift and exposure were strongly influenced by wind speed and direction, albeit hardly affected by the growth stage of the trees. In addition, substantially less tracer was extracted from the filters of the air sampling pumps than from the coveralls worn by mannequins, suggesting that direct resident/bystander exposure to spray drift may predominantly occur via the dermal route. This report provides essential data on UASS spray drift potential that are relevant for environmental and health risk assessments related to these systems. The results are compared to predicted values of current regulatory models and previously reported field data on drift and exposure caused by different spraying equipment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

We propose an interpretable and scalable model to predict likely diagnoses at an encounter based on past diagnoses and lab results. This model is intended to aid physicians in their interaction with the electronic health records (EHR). To accomplish this, we retrospectively collected and de-identified EHR data of 2,701,522 patients at Stanford Healthcare over a time period from January 2008 to December 2016. A population-based sample of patients comprising 524,198 individuals (44% M, 56% F) with multiple encounters with at least one frequently occurring diagnosis codes were chosen. A calibrated model was developed to predict ICD-10 diagnosis codes at an encounter based on the past diagnoses and lab results, using a binary relevance based multi-label modeling strategy. Logistic regression and random forests were tested as the base classifier, and several time windows were tested for aggregating the past diagnoses and labs. This modeling approach was compared to a recurrent neural network based deep learning method. The best model used random forest as the base classifier and integrated demographic features, diagnosis codes, and lab results. The best model was calibrated and its performance was comparable or better than existing methods in terms of various metrics, including a median AUROC of 0.904 (IQR [0.838, 0.954]) over 583 diseases. When predicting the first occurrence of a disease label for a patient, the median AUROC with the best model was 0.796 (IQR [0.737, 0.868]). Our modeling approach performed comparably as the tested deep learning method, outperforming it in terms of AUROC (p < 0.001) but underperforming in terms of AUPRC (p < 0.001). Interpreting the model showed that the model uses meaningful features and highlights many interesting associations among diagnoses and lab results. We conclude that the multi-label model performs comparably with RNN based deep learning model while offering simplicity and potentially superior interpretability. While the model was trained and validated on data obtained from a single institution, its simplicity, interpretability and performance makes it a promising candidate for deployment., (© 2023. The Author(s).)

Objective: To accelerate the use of outcome measures in rheumatology, we developed and evaluated a natural language processing (NLP) pipeline for extracting these measures from free-text outpatient rheumatology notes within the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) registry., Methods: We included all patients in RISE (2015-2018). The NLP pipeline extracted scores corresponding to 8 measures of rheumatoid arthritis (RA) disease activity (DA) and functional status (FS) documented in outpatient rheumatology notes. Score extraction performance was evaluated by chart review, and we assessed agreement with scores documented in structured data. We conducted an external validation of our NLP pipeline using data from rheumatology notes from an academic medical center that is not included in the RISE registry., Results: We processed over 34 million notes from 854,628 patients, 158 practices, and 24 electronic health record (EHR) systems from RISE. Manual chart review revealed a sensitivity, positive predictive value (PPV), and F1 score of 95%, 87%, and 91%, respectively. Substantial agreement was observed between scores extracted from RISE notes and scores derived from structured data (κ = 0.43-0.68 among DA and 0.86-0.98 among FS measures). In the external validation, we found a sensitivity, PPV, and F1 score of 92%, 69%, and 79%, respectively., Conclusion: We developed an NLP pipeline to extract RA outcome measures from a national registry of notes from multiple EHR systems and found it to have good internal and external validity. This pipeline can facilitate measurement of clinical- and patient-reported outcomes for use in research and quality measurement., (© 2022 American College of Rheumatology.)

Experts have noted a concerning gap between clinical natural language processing (NLP) research and real-world applications, such as clinical decision support. To help address this gap, in this viewpoint, we enumerate a set of practical considerations for developing an NLP system to support real-world clinical needs and improve health outcomes. They include determining (1) the readiness of the data and compute resources for NLP, (2) the organizational incentives to use and maintain the NLP systems, and (3) the feasibility of implementation and continued monitoring. These considerations are intended to benefit the design of future clinical NLP projects and can be applied across a variety of settings, including large health systems or smaller clinical practices that have adopted electronic medical records in the United States and globally., (©Suzanne Tamang, Marie Humbert-Droz, Milena Gianfrancesco, Zara Izadi, Gabriela Schmajuk, Jinoos Yazdany. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 03.01.2023.)

Biliary tract cancers (BTCs) have poor prognosis and limited therapeutic options. The impact of O 6 -methylguanine-DNA methyltransferase (MGMT) inactivation in advanced BTC patients is not established. We investigated the prevalence, prognostic, and predictive impact of MGMT inactivation in two multicenter cohorts. MGMT inactivation was assessed through PCR and immunohistochemistry (IHC) in an Italian cohort; the results were then externally validated using RNA sequencing (RNA-seq) data from the BTC subcohort of the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) precision oncology program of the National Center for Tumor Diseases Heidelberg and the German Cancer Consortium. Among 164 Italian cases, 18% presented MGMT promoter hypermethylation (> 14%) and 73% had negative MGMT protein expression. Both were associated with worse overall survival (OS; HR 2.31; P < 0.001 and HR 1.99, P = 0.012, respectively). In the MASTER cohort, patients with lower MGMT mRNA expression showed significantly poorer OS (median OS [mOS] 20.4 vs 31.7 months, unadjusted HR 1.89; P = 0.043). Our results suggest that MGMT inactivation is a frequent epigenetic alteration in BTC, with a significant prognostic impact, and provide the rationale to explore DNA-damaging agents in MGMT-inactivated BTCs., (© 2022 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Background: Automated extraction of symptoms from clinical notes is a challenging task owing to the multidimensional nature of symptom description. The availability of labeled training data is extremely limited owing to the nature of the data containing protected health information. Natural language processing and machine learning to process clinical text for such a task have great potential. However, supervised machine learning requires a great amount of labeled data to train a model, which is at the origin of the main bottleneck in model development., Objective: The aim of this study is to address the lack of labeled data by proposing 2 alternatives to manual labeling for the generation of training labels for supervised machine learning with English clinical text. We aim to demonstrate that using lower-quality labels for training leads to good classification results., Methods: We addressed the lack of labels with 2 strategies. The first approach took advantage of the structured part of electronic health records and used diagnosis codes (International Classification of Disease-10th revision) to derive training labels. The second approach used weak supervision and data programming principles to derive training labels. We propose to apply the developed framework to the extraction of symptom information from outpatient visit progress notes of patients with cardiovascular diseases., Results: We used >500,000 notes for training our classification model with International Classification of Disease-10th revision codes as labels and >800,000 notes for training using labels derived from weak supervision. We show that the dependence between prevalence and recall becomes flat provided a sufficiently large training set is used (>500,000 documents). We further demonstrate that using weak labels for training rather than the electronic health record codes derived from the patient encounter leads to an overall improved recall score (10% improvement, on average). Finally, the external validation of our models shows excellent predictive performance and transferability, with an overall increase of 20% in the recall score., Conclusions: This work demonstrates the power of using a weak labeling pipeline to annotate and extract symptom mentions in clinical text, with the prospects to facilitate symptom information integration for a downstream clinical task such as clinical decision support., (©Marie Humbert-Droz, Pritam Mukherjee, Olivier Gevaert. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 14.03.2022.)

In the wild, mice have developed survival strategies to detect volatile cues that warn them of potential danger. Specific olfactory neurons found in the Grueneberg ganglion olfactory subsystem can detect alarm pheromones emitted by stressed conspecifics, as well as kairomones involuntarily released by their predators. These volatile chemical cues allow intra- and interspecies communication of danger, respectively. Alarm pheromones, kairomones and bitter taste ligands share a common chemical motif containing sulfur or nitrogen. Interestingly, three specific bitter taste receptors (TAS2Rs) have been found in the Grueneberg ganglion neurons that are implicated in danger signalling pathways. We have recently developed a TAS2R-expressing heterologous system that mimics the Grueneberg ganglion neuron responses after kairomone stimulation. Here, we demonstrated by in vitro, ex vivo and in vivo experiments that the biological secretions from the raccoon ( Procyon lotor ) and the skunk ( Mephitis mephitis ) were acting as potent sources of kairomones. They activated the Grueneberg ganglion neurons and induced fear-related behaviours in mice. Identification of new sources of semiochemicals is a first step towards an understanding of the interspecies danger communication that takes place in the Grueneberg ganglion.

Free-text clinical notes in electronic health records are more difficult for data mining while the structured diagnostic codes can be missing or erroneous. To improve the quality of diagnostic codes, this work extracts diagnostic codes from free-text notes: five old and new word vectorization methods were used to vectorize Stanford progress notes and predict eight ICD-10 codes of common cardiovascular diseases with logistic regression. The models showed good performance, with TF-IDF as the best vectorization model showing the highest AUROC (0.9499-0.9915) and AUPRC (0.2956-0.8072). The models also showed transferability when tested on MIMIC-III data with AUROC from 0.7952 to 0.9790 and AUPRC from 0.2353 to 0.8084. Model interpretability was shown by the important words with clinical meanings matching each disease. This study shows the feasibility of accurately extracting structured diagnostic codes, imputing missing codes, and correcting erroneous codes from free-text clinical notes for information retrieval and downstream machine-learning applications., Competing Interests: X.Z., M.-H.-D., and P.M. declare no competing interests. O.G. reports grants from National Institutes of Health, grants from Onc. AI, grants from Lucence Health Inc., and grants from Nividien Inc., outside the submitted work; in addition, O.G. has a Provisional patent filed on related work pending to Stanford., (© 2021 The Authors.)

Background: Representing 60% of medical consultations in Switzerland, primary care holds an important place in our medical system. Patients' values in family medicine (FM) are nowadays recognized as important factors to take into account in order to provide good quality of care. The aim of this study is to describe patients' most important values regarding FM and to assess their associations with socio-demographics factors in a fee-for-services health system., Methods: We analyzed the Swiss 2012 study on Quality and Costs of Primary Care (QUALICOPC). Two-hundred patients, randomly drawn, answered a questionnaire about their values regarding FM just after their consultation. Explored values were related to communication and patient-centeredness care, continuity and coordination, care access, and patients' activation. We described values reaching more than 50% of "very important". Then, multivariate analyses were performed for the most important value of each dimension., Results: Items related to "communication and patient-centeredness care" and "coordination and continuity of care" are the most recurrently mentioned as "very important". Items related to access and patients' activation are generally declared as "very important" by less than 50% of patients. Whatever the domain and the item, women systematically grant items more often as "very important" than men. Variations are observed according to the age, and the presence or not of a chronic disease., Conclusion: Such dimensions should be subject to a special attention by general practitioners and public health authorities as it might enhance the quality of care and the patients' satisfaction.

Terpyridine derivatives are of great interest due to their unique photophysical properties when used as antennas in metallic complexes. Several experimental and theoretical studies indicate strong charge-transfer character of the lowest electronic excited state, which could be exploited for predicting fluorescence quantum yields from the magnitude of the charge separation induced by electronic transitions. Focusing on substituted 4'-phenyl-2,2':6'2''-terpyridyl, we report on two measures of the charge separation obtained from high-level calculations in ground and excited states (length of the change of the dipole moment and the electron-hole distance). Our refined model confirms that the fluorescence quantum yield shows a global S-shape dependence on the magnitude of the charge separation, which can be quantified either by the change in dipole moments between the ground and excited states or by the associated charge-hole distances. This approach provides a remarkable tool for the molecular design of a fluorescent polyaromatic antenna.

The basic concept of allosteric cooperativity used in biology, chemistry and physics states that any change in the intermolecular host-guest interactions operating in multisite receptors can be assigned to intersite interactions. Using lanthanide metals as guests and linear multi-tridentate linear oligomers of variable lengths and geometries as hosts, this work shows that the quantitative modeling of metal loadings requires the consideration of a novel phenomenon originating from solvation processes. It stepwise modulates the intrinsic affinity of each isolated site in multisite receptors, and this without resorting to allosteric cooperativity. An easy-to-handle additive model predicts a negative power law dependence of the intrinsic affinity on the length of the linear metallopolymer. Applied to lanthanidopolymers, the latter common analysis overestimates cooperativity factors by more than two orders of magnitude., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Objectives: The purpose of the study was to assess the hydrothermal resistance of a translucent zirconia with two clinical relevant surface textures by means of accelerated tests (LTD) and to compare predicted monoclinic fractions with experimental values measured after two years aging at 37°C., Methods: Polished (P) and ground (G) specimens were subjected to hydrothermal degradation by exposure to water steam at different temperatures and pressures. The t-m phase transformation was quantified by grazing incidence X-ray diffraction (GIXDR). The elastic modulus and hardness before- and after LTD were determined by nanoindentation., Results: G specimens presented a better resistance to hydrothermal degradation than P samples. Activation energies of 89 and 98kJ/mol and b coefficients of 2.0×10(-5) and 1.8×10(-6) were calculated for P and G samples respectively. The coefficients were subsequently used to predict transformed monoclinic fractions at 37°C. A good correlation was found between the predicted values and the experimental data obtained after aging at 37°C during 2 years. Hydrothermal degradation led to a significant decrease of the elastic moduli and hardness in both groups., Significance: The dependency of the t-m phase transformation rate on temperature must be determined to accurately predict the hydrothermal behavior of the zirconia ceramics at oral temperatures. The current prevailing assumption, that 5h aging at 134°C corresponds to 15-20 years at 37°C, will underestimate the transformed fraction of the translucent ceramic at 37°C. In this case, the mechanical surface treatment influences the ceramic's transformability. While mild grinding could potentially retard the hydrothermal transformation, polishing after occlusal adjustment is recommended to prevent wear of the antagonist teeth and maintain structural strength., (Copyright © 2015 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.)

We report that anion-π and cation-π interactions can occur on the same aromatic surface. Interactions of this type are referred to as ion pair-π interactions. Their existence, nature, and significance are elaborated in the context of spectral tuning, ion binding in solution, and activation of cell-penetrating peptides. The origin of spectral tuning by ion pair-π interactions is unraveled with energy-minimized excited-state structures: The solvent- and pH-independent red shift of absorption and emission of push-pull fluorophores originates from antiparallel ion pair-π attraction to their polarized excited state. In contrast, the complementary parallel ion pair-π repulsion is spectroscopically irrelevant, in part because of charge neutralization by intriguing proton and electron transfers on excited push-pull surfaces. With time-resolved fluorescence measurements, very important differences between antiparallel and parallel ion pair-π interactions are identified and quantitatively dissected from interference by aggregation and ion pair dissociation. Contributions from hydrogen bonding, proton transfer, π-π interactions, chromophore twisting, ion pairing, and self-assembly are systematically addressed and eliminated by concise structural modifications. Ion-exchange studies in solution, activation of cell-penetrating peptides in vesicles, and computational analysis all imply that the situation in the ground state is complementary to spectral tuning in the excited state; i.e., parallel rather than antiparallel ion pair-π interactions are preferred, despite repulsion from the push-pull dipole. The overall quite complete picture of ion pair-π interactions provided by these remarkably coherent yet complex results is expected to attract attention throughout the multiple disciplines of chemistry involved.

Backscattered Raman optical activity (ROA) spectra are measured for Δ- and Λ-tris-(ethylenediamine)rhodium(III) chloride in aqueous solution. In addition, the spectra of the four possible conformers in the Λ configuration are investigated by ab initio calculations. The Λ(δδδ) conformer is in best agreement with experimental spectra and examined in more details. The two most stable conformers according to the calculations are not compatible with the experimental ROA spectrum. Insights into the origin of observed band intensities are obtained by means of group coupling matrices. The influence of the first solvation shell is explored via an ab initio molecular dynamics simulation. Taking explicit solvent molecules into account further improves the agreement between calculation and experiment. Analysis of selected normal modes using group coupling matrices shows that solvent molecules lead to normal mode rotation and thus contribute to the ROA intensity, whereas the contribution of the Rh can be neglected.

Herein, we address the question whether anion-π and cation-π interactions can take place simultaneously on the same aromatic surface. Covalently positioned carboxylate-guanidinium pairs on the surface of 4-amino-1,8-naphthalimides are used as an example to explore push-pull chromophores as privileged platforms for such "ion pair-π" interactions. In antiparallel orientation with respect to the push-pull dipole, a bathochromic effect is observed. A red shift of 41 nm found in the least polar solvent is in good agreement with the 70 nm expected from theoretical calculations of ground and excited states. Decreasing shifts with solvent polarity, protonation, aggregation, and parallel carboxylate-guanidinium pairs imply that the intramolecular Stark effect from antiparallel ion pair-π interactions exceeds solvatochromic effects by far. Theoretical studies indicate that carboxylate-guanidinium pairs can also interact with the surfaces of π-acidic naphthalenediimides and π-basic pyrenes., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

An individual-based model describing the dynamics of one type of annual plants is presented. We use Monte Carlo simulations where each plant has its own history and the interactions among plants are between nearest neighbours. The character of the interaction (positive or negative) depends on local conditions. The plants compete for two external resources-water and light. The amount of water and/or light a plant receives depends on the external factor but also on local arrangement. Survival, growth and seed production of plants are determined by how well their demands for the resources are met. The survival and seeds production tests have a probabilistic character, which makes the dynamics more realistic than by using a deterministic approach. There is a non-linear coupling between the external supplies. Water evaporates from the soil at a rate depending on constant evaporation rate, local conditions and the amount of light. We examine the dynamics of the plant population along two environmental gradients, allowing also for surplus of water and/or light. We show that the largest number of plants is when the demands for both resources are equal to the supplies. We estimate also the role of evaporation and we find that it depends on the situation. It could be negative, but sometimes it has a positive character. We show that the link between the type of interaction (positive or negative) and external conditions has a complex character. In general in favourable environment plants have a stronger tendency for competitive interactions, leading to mostly isolated plants. When the conditions are getting more difficult, cooperation becomes the dominant type of interactions and the plants grow in clusters. The type of plants-sun-loving or shade tolerating, plays also an important role., (© 2013 Elsevier Ltd. All rights reserved.)