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María José Valencia López,1,* Brigitte Stephan,1,* Anna Meineke,1 Sandra Wolf,1 Diamant Thaci,2 Ulrich Mrowietz,3 Valerie Andrees,1 Stephan Jeff Rustenbach,1 Kristian Reich,1 Linus Thalmann,1 Henriette Bogena,1 Petra Staubach,4 Ralph Michael von Kiedrowski,5 Matthias Augustin1 1Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany; 2Institute and Comprehensive Center for Inflammation Medicine, University of Lübeck, Lübeck, Germany; 3Psoriasis-Center, Department of Dermatology, University Medical Center Schleswig-Holstein, Kiel, Germany; 4Department of Dermatology and Allergy, University Medical Center Mainz, Mainz, Germany; 5Dermatological Practice Selters, Selters, Germany*These authors contributed equally to this workCorrespondence: Matthias Augustin, Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, Hamburg, 20246, Germany, Tel +49 040 7410 55428, Fax +49 040 7410 55348, Email m.augustin@uke.deBackground: Limited data are available characterizing the impact of the SARS-CoV-2 pandemic on psoriasis care for patients in Germany.Objective: To analyze patient perception and impact of the pandemic on well-being and psoriasis management of German patients with moderate-to-severe psoriasis or psoriasis arthritis under systemic therapies.Methods: The CoronaBest registry captures events of SARS-CoV-2 infections and analyzes the impact of the pandemic on patients with psoriasis or psoriasis arthritis. In June 2020, and independently in February 2022, patients with psoriasis or psoriasis arthritis received a standardized questionnaire for current treatment, protective measures, well-being, and individual risks for COVID-19, among others.Results: Included were 4,194 patients in 2020 (mean age of 47.7 years and 41.8% women) and 4,818 patients in 2022 (mean age of 56.4 and 42.9% women). Treatment discontinuations were observed in 2.7% and 1.7% of patients in 2020 and 2022, respectively. In the vast majority of the cases (> 92%), no additional measures were taken concerning the management of psoriasis treatments in either 2020 or 2022. Those patients with changes reported most frequently: telephone calls instead of face-to-face visits (80.2%, in 2020 vs 40.5% in 2022) or more frequent controls (27.1%, 2020 vs 22.0%, 2022). A majority (66.7%, 2020, and 70.6%, 2022) did not perceive the virus as a considerable threat. The proportion of patients feeling well informed about COVID-19 by physicians increased from 42.6% in 2020 to 51.8% in 2022. About 81.1% of patients in 2020 and 67.5% in 2022 stated that their overall personal condition was not affected due to the pandemic. Physicians attributed no special risk of contracting SARS-CoV-2 in most of the patients.Conclusion: A high rate of systemic treatment persistence and awareness of risks and protective measures indicate that health care for psoriasis largely followed current national and international recommendations during the COVID-19 pandemic.Keywords: psoriasis, COVID-19, SARS-CoV-2, pandemic, patient perception, risk factors

PURPOSE: The objective of this study was to perform a retrospective cohort analysis, in which we measured the association of an acute pain service (APS)-driven multimodal analgesia protocol that included preoperative intrathecal morphine (ITM) compared to historic controls (i.e., surgeon-driven analgesia protocol without ITM) with postoperative opioid use. METHODS: This was a retrospective cohort study in which the primary objective was to determine whether there was a decrease in median 24-h opioid consumption (intravenous morphine equivalents [MEQ]) among robotic nephrectomy patients whose pain was managed by the surgical team prior to the APS, versus pain managed by APS. Secondary outcomes included opioid consumption during the 24-48 h and 48-72 h period and hospital length of stay. To create matched cohorts, we performed 1:1 (APS:non-APS) propensity score matching. Due to the cohorts occurring at the different time periods, we performed a segmented regression analysis of an interrupted time series. RESULTS: There were 76 patients in the propensity-matched cohorts, in which 38 (50.0%) were in the APS cohort. The median difference in 24-h opioid consumption in the pre-APS versus APS cohort was 23.0 mg [95% CI 15.0, 31.0] (p

Vehicle pollution is a pervasive aspect of anthropogenic change across rural and urban habitats. The most common emissions are carbon- or nitrogen-based pollutants that may impact diverse interactions between plants and insect herbivores. However, the effects of vehicle pollution on plant-insect interactions are poorly understood. Here, we combine a city-wide experiment across the Sacramento Metropolitan Area and a laboratory experiment to determine how vehicle emissions affect insect herbivory and leaf nutritional quality. We demonstrate that leaf damage to a native oak species (Quercus lobata) commonly planted across the western US is substantially elevated on trees exposed to vehicle emissions. In the laboratory, caterpillars preferred leaves from highway-adjacent trees and performed better on leaves from those same trees. Synthesis and applications. Together, our studies demonstrate that the heterogeneity in vehicle emissions across cities may explain highly variable patterns of insect herbivory on street trees. Our results also indicate that trees next to highways are particularly vulnerable to multiple stressors, including insect damage. To combat these effects, urban foresters may consider planting trees that are less susceptible to insect herbivory along heavily travelled roadways.

BACKGROUND: Methods that can automate, support, and streamline the preanesthesia evaluation process may improve resource utilization and efficiency. Natural language processing (NLP) involves the extraction of relevant information from unstructured text data. We describe the utilization of a clinical NLP pipeline intended to identify elements relevant to preoperative medical history by analyzing clinical notes. We hypothesize that the NLP pipeline would identify a significant portion of pertinent history captured by a perioperative provider. METHODS: For each patient, we collected all pertinent notes from the institutions electronic medical record that were available no later than 1 day before their preoperative anesthesia clinic appointment. Pertinent notes included free-text notes consisting of history and physical, consultation, outpatient, inpatient progress, and previous preanesthetic evaluation notes. The free-text notes were processed by a Named Entity Recognition pipeline, an NLP machine learning model trained to recognize and label spans of text that corresponded to medical concepts. These medical concepts were then mapped to a list of medical conditions that were of interest for a preanesthesia evaluation. For each condition, we calculated the percentage of time across all patients in which (1) the NLP pipeline and the anesthesiologist both captured the condition; (2) the NLP pipeline captured the condition but the anesthesiologist did not; and (3) the NLP pipeline did not capture the condition but the anesthesiologist did. RESULTS: A total of 93 patients were included in the NLP pipeline input. Free-text notes were extracted from the electronic medical record of these patients for a total of 9765 notes. The NLP pipeline and anesthesiologist agreed in 81.24% of instances on the presence or absence of a specific condition. The NLP pipeline identified information that was not noted by the anesthesiologist in 16.57% of instances and did not identify a condition that was noted by the anesthesiologists review in 2.19% of instances. CONCLUSIONS: In this proof-of-concept study, we demonstrated that utilization of NLP produced an output that identified medical conditions relevant to preanesthetic evaluation from unstructured free-text input. Automation of risk stratification tools may provide clinical decision support or recommend additional preoperative testing or evaluation. Future studies are needed to integrate these tools into clinical workflows and validate its efficacy.

Fossilized leaves provide the longest running record of hyperdiverse plant-insect herbivore associations. Reconstructions of these relationships over deep time indicate strong links between environmental conditions, herbivore diversity, and feeding damage on leaves. However, herbivory has not been compared between the past and the modern era, which is characterized by intense anthropogenic environmental change. Here, we present estimates for damage frequencies and diversities on fossil leaves from the Late Cretaceous (66.8 Ma) through the Pleistocene (2.06 Ma) and compare these estimates with Recent (post-1955) leaves collected via paleobotanical methods from modern ecosystems: Harvard Forest, United States; the Smithsonian Environmental Research Center, United States; and La Selva, Costa Rica. Total damage frequency, measured as the percentage of leaves with any herbivore damage, within modern ecosystems is greater than any fossil locality within this record. This pattern is driven by increased frequencies across nearly all functional feeding groups within the Recent. Diversities of total, specialized, and mining damage types are elevated within the Recent compared with fossil floras. Our results demonstrate that plants in the modern era are experiencing unprecedented levels of insect damage, despite widespread insect declines. Human influence, such as the rate of global climate warming, influencing insect feeding and timing of life cycle processes along with urbanization and the introduction of invasive plant and insect species may drive elevated herbivory. This research suggests that the strength of human influence on plant-insect interactions is not controlled by climate change alone but rather, the way in which humans interact with terrestrial landscape.

It is estimated by the year 2030, over 40% of the K-12 population in U.S. schools will be children whose first language is not English (Shin & Ortman, 2011; U.S. Census Bureau, 2007;). This shift has negative academic consequences for emergent bilingual students (EBS). Schools (K-12) unable to meet the needs of EBS contribute to these consequences and the social and cultural cycle of oppression for this marginalized group. Thus, integrating knowledge, skills, and dispositions (KSDs) beneficial for meeting the needs of EBS becomes imperative for pre-service program models, though often blocked by subjective thinking. This article presents the learning opportunities offered by three distinct pre-service program coursework models. Differences coursework models shaped pre-service teachers' ability to acquire KSDs required for working with EBS, which in turn, informed what they enacted in classrooms. This finding provides evidence and rationale for pre-service programs to develop and integrate KSDs beneficial to EBS.

Biological collections are arguably the most important resources for investigations into the impacts of human activities on biodiversity. However, the apparent opportunities presented by museum-derived datasets have not resulted in consistent or widespread use of specimens in ecology outside phenological research and species distribution modeling. We attribute this gap between opportunity and application to biases introduced by collectors, curators, and preservation practices and an imperfect understanding of these biases and how to mitigate them. To facilitate broader use of specimen-based data, we characterize collection biases across key axes and explore interactions among them. We then present a framework for determining the bias assessments needed when extracting data from biological collections. We show that bias assessments required by particular ecological studies will depend on the response variables being measured and the predictor axes of interest. We argue that quantification of biases in specimen-derived datasets is needed to facilitate the widespread application of these data.

Native biodiversity decline and non-native species spread are major features of the Anthropocene. Both processes can drive biotic homogenization by reducing trait and phylogenetic differences in species assemblages between regions, thus diminishing the regional distinctiveness of biotas and likely have negative impacts on key ecosystem functions. However, a global assessment of this phenomenon is lacking. Here, using a dataset of >200,000 plant species, we demonstrate widespread and temporal decreases in species and phylogenetic turnover across grain sizes and spatial extents. The extent of homogenization within major biomes is pronounced and is overwhelmingly explained by non-native species naturalizations. Asia and North America are major sources of non-native species; however, the species they export tend to be phylogenetically close to recipient floras. Australia, the Pacific and Europe, in contrast, contribute fewer species to the global pool of non-natives, but represent a disproportionate amount of phylogenetic diversity. The timeline of most naturalisations coincides with widespread human migration within the last ~500 years, and demonstrates the profound influence humans exert on regional biotas beyond changes in species richness.

Species interactions drive ecosystem processes and are a major focus of global change research. Among the most consequential interactions expected to shift with climate change are those between insect herbivores and plants, both of which are highly sensitive to temperature. Insect herbivores and their host plants display varying levels of synchrony that could be disrupted or enhanced by climate change, yet empirical data on changes in synchrony are lacking. Using evidence of herbivory on herbarium specimens collected from the northeastern United States and France from 1900 to 2015, we provide evidence that plant species with temperature-sensitive phenologies experience higher levels of insect damage in warmer years, while less temperature-sensitive, co-occurring species do not. While herbivory might be mediated by interactions between warming and phenology through multiple pathways, we suggest that warming might lengthen growing seasons for phenologically sensitive plant species, exposing their leaves to herbivores for longer periods of time in warm years. We propose that elevated herbivory in warm years may represent a previously underappreciated cost to phenological tracking of climate change over longer timescales.

Cities and towns are complex ecosystems with features that can vary dramatically in space and time. Our knowledge of the spatial structure of urban land and ecological systems is expanding. These systems have been investigated across spatial scales, urban to rural gradients, networks of urban macrosystems, and global megalopolises. However, the temporal dimensions of urban ecosystems – such as those related to ecological cycles and historical legacies – are far less understood and investigated. Here, we outline the main dimensions of time that can shape how events in urban ecosystems unfold, which we categorize as: (i) time flows and duration, (ii) synchrony, lags, and delays, (iii) trends and transitions, (iv) cycles and hysteresis, (v) legacies and priming, (vi) temporal hotspots and hot moments, and (vii) stochastic vs. deterministic processes affecting our ability to forecast the future of cities and the species that live in them. First, we demonstrate the roles of these understudied dimensions by discussing exemplary studies. We then propose key future research directions for investigating how processes over time may regulate the structure and functioning of urban land and biodiversity, as well as its effects on and implications for urban ecology. Our analysis and conceptual framework highlights that several temporal dimensions of urban ecosystems – like those related to temporal hotspots/moments and stochastic vs. deterministic processes – are understudied. This offers important research opportunities to further urban ecology and a comprehensive research agenda valuing the “Urban Chronos” – the change of urban ecosystems through time.

Cities and towns are complex ecosystems with features that can vary dramatically in space and time. Our knowledge of the spatial structure of urban land and ecological systems is expanding. These systems have been investigated across spatial scales, urban to rural gradients, networks of urban macrosystems, and global megalopolises. However, the temporal dimensions of urban ecosystems – such as those related to ecological cycles and historical legacies – are far less understood and investigated. Here, we outline the main dimensions of time that can shape how events in urban ecosystems unfold, which we categorize as: (i) time flows and duration, (ii) synchrony, lags, and delays, (iii) trends and transitions, (iv) cycles and hysteresis, (v) legacies and priming, (vi) temporal hotspots and hot moments, and (vii) stochastic vs. deterministic processes affecting our ability to forecast the future of cities and the species that live in them. First, we demonstrate the roles of these understudied dimensions by discussing exemplary studies. We then propose key future research directions for investigating how processes over time may regulate the structure and functioning of urban land and biodiversity, as well as its effects on and implications for urban ecology. Our analysis and conceptual framework highlights that several temporal dimensions of urban ecosystems – like those related to temporal hotspots/moments and stochastic vs. deterministic processes – are understudied. This offers important research opportunities to further urban ecology and a comprehensive research agenda valuing the “Urban Chronos” – the change of urban ecosystems through time.

Summary: Stop codon suppression using dedicated tRNA/aminoacyl-tRNA synthetase (aaRS) pairs allows for genetically encoded, site-specific incorporation of non-canonical amino acids (ncAAs) as chemical handles for protein labeling and modification. Here, we demonstrate that piggyBac-mediated genomic integration of archaeal pyrrolysine tRNA (tRNAPyl)/pyrrolysyl-tRNA synthetase (PylRS) or bacterial tRNA/aaRS pairs, using a modular plasmid design with multi-copy tRNA arrays, allows for homogeneous and efficient genetically encoded ncAA incorporation in diverse mammalian cell lines. We assess opportunities and limitations of using ncAAs for fluorescent labeling applications in stable cell lines. We explore suppression of ochre and opal stop codons and finally incorporate two distinct ncAAs with mutually orthogonal click chemistries for site-specific, dual-fluorophore labeling of a cell surface receptor on live mammalian cells. Motivation: Genetic code expansion via stop codon suppression is a powerful strategy to engineer proteins. Pyrrolysine tRNA (tRNAPyl)/pyrrolysyl-tRNA synthetase (PylRS) pairs from methanogenic archaea and engineered bacterial tRNA/aminoacyl-tRNA synthetase (aaRS) pairs are used for site-specific incorporation of non-canonical amino acids (ncAAs) in response to stop codons in mammalian cells. Routinely, ncAA incorporation is achieved by transient expression of the tRNA/aaRS pair leading to heterogeneous suppression. Genomic integration of tRNA/aaRS expression cassettes for more homogeneous, adjustable, and reproducible levels of protein, containing one or more ncAAs, will greatly benefit synthetic protein production, functional protein engineering, chemical control, and imaging applications in mammalian cells.

Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens-preserved plant material curated in natural history collections-but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.

BackgroundSeveral hospitals have implemented a multidisciplinary Acute Pain Service (APS) to execute surgery-specific opioid sparing analgesic pathways. Implementation of an anesthesia attending-only APS has been associated with decreased postoperative opioid consumption, time to ambulation, and time to solid food intake for patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy. In this study, we evaluated the impact of introducing an APS trainee on postoperative opioid consumption in patients undergoing hyperthermic intraperitoneal chemotherapy during POD 0-3.MethodsWe performed a retrospective propensity-matched cohort study where we compared opioid consumption and hospital length of stay among two historical cohorts: attending-only APS service versus service involving a regional anesthesia fellow.ResultsIn the matched cohorts, the median postoperative day (POD) 0-3 opioid use [25%, 75% quartile] for the single attending and trainee involvement cohort were 38.5 mg morphine equivalents (MEQ) [14.1 mg, 106.3 mg] and 50.4 mg MEQ [28.4 mg, 91.2 mg], respectively. The median difference was -9.8 mg MEQ (95% CI -30.7-16.5 mg; P = 0.43). There was no difference in hospital length of stay between both cohorts (P = 0.67).ConclusionsWe found that the addition of a regional anesthesia fellow to the APS team was not associated with statistically significant differences in total opioid consumption or hospital length of stay in this surgical population. The addition of trainees to the infrastructure, with vigilant supervision, is not associated with change in outcomes.

Local community structure is shaped by processes acting at local and landscape scales. The relative importance of drivers operating across different spatial scales is difficult to test without observations across regional or latitudinal gradients. Cities exhibit strong but predictable environmental gradients overlaying a mosaic of highly variable but repeated habitat types within a constrained area. Thus, cities present a unique opportunity to explore how both local and landscape factors influence local biotic communities. We used insect communities to examine the interactions among local environmental variables (such as temperature and relative humidity), local habitat characteristics (such as plant community composition), and broad-scale patterns of urbanization (including biophysical, human-built, and socioeconomic variables) on local insect abundance, species richness, and species composition in Los Angeles, a hot, dry, near-desert city. After accounting for seasonal trends, insect species richness and abundance were highest in drier and hotter sites, but the magnitude of local environmental effects varied with the degree of urbanization. In contrast, insect species composition was best predicted by broad-scale urbanization trends, with the more native communities occurring in less urbanized sites and more cosmopolitan insects occurring in highly urbanized sites. However, insect species richness and abundance were >30% higher and insect composition was similar across sites that hosted either native or drought-tolerant plants, regardless of the degree of urbanization. These results demonstrate that urban insect biodiversity is a product of interacting mechanisms working at both local and landscape scales. However, local-scale changes to urban habitats, such as cultivating plants that are adapted to the natural environment nearest the city, can positively impact urban biodiversity regardless of location.

Premise:Despite the economic significance of insect damage to plants (i.e., herbivory), long-term data documenting changes in herbivory are limited. Millions of pressed plant specimens are now available online and can be used to collect big data on plant-insect interactions during the Anthropocene. Methods:We initiated development of machine learning methods to automate extraction of herbivory data from herbarium specimens by training an insect damage detector and a damage type classifier on two distantly related plant species (Quercus bicolor and Onoclea sensibilis). We experimented with (1) classifying six types of herbivory and two control categories of undamaged leaf, and (2) detecting two of the damage categories for which several hundred annotations were available. Results:Damage detection results were mixed, with a mean average precision of 45% in the simultaneous detection and classification of two types of damage. However, damage classification on hand-drawn boxes identified the correct type of herbivory 81.5% of the time in eight categories. The damage classifier was accurate for categories with 100 or more test samples. Discussion:These tools are a promising first step for the automation of herbivory data collection. We describe ongoing efforts to increase the accuracy of these models, allowing researchers to extract similar data and apply them to biological hypotheses.

Premise:Equisetum is a distinctive vascular plant genus with 15 extant species worldwide. Species identification is complicated by morphological plasticity and frequent hybridization events, leading to a disproportionately high number of misidentified specimens. These may be correctly identified by applying appropriate computer vision tools. Methods:We hypothesize that aerial stem nodes can provide enough information to distinguish among Equisetum hyemale, E. laevigatum, and E . ×ferrissii, the latter being a hybrid between the other two. An object detector was trained to find nodes on a given image and to distinguish E. hyemale nodes from those of E. laevigatum. A classifier then took statistics from the detection results and classified the given image into one of the three taxa. Both detector and classifier were trained and tested on expert manually annotated images. Results:In our exploratory test set of 30 images, our detector/classifier combination identified all 10 E. laevigatum images correctly, as well as nine out of 10 E. hyemale images, and eight out of 10 E. ×ferrissii images, for a 90% classification accuracy. Discussion:Our results support the notion that computer vision may help with the identification of herbarium specimens once enough manual annotations become available.