Ants as Bioindicators of Riparian Ecological Health in Catalonian Rivers

In this study, we assess the potential of ants as bioindicators of riparian ecological health in two river types (upland and lowland type) located in the Catalonian region. We proposed to understand to what extent do metrics based on ant responses provide useful information that cannot be presented by traditional biophysical assessments while attempting an approach to creating an ant-based multimetric index (ant-based MMI) of the riparian ecological health. A total of 22 ant species were identified, and 42 metrics related to ant foraging activity, species richness, and functional traits were evaluated as potential core metrics of the index. Riparian features and proximal land use land cover (LULC) were used to distinguish disturbed from less disturbed sites. We found that ant communities strongly responded to human disturbance. When compared with an exclusively physical-based index for the assessment of the riparian health, the ant-based MMI was more sensitive to human disturbance, by also reacting to the effects of the surrounding LULC pressure. This study provides a preliminary approach for an ant-based assessment tool to evaluate the health of riparian corridors although additional research is required to include other river types and a wider stressor gradient before a wider application.

Using Log Data to Measure Provider EHR Activity at a Cancer Center during Rapid Telemedicine Deployment

Objectives Accurate metrics of provider activity within the electronic health record (EHR) are critical to understand workflow efficiency and target optimization initiatives. We utilized newly described, log-based core metrics at a tertiary cancer center during rapid escalation of telemedicine secondary to initial coronavirus disease-2019 (COVID-19) peak onset of social distancing restrictions at our medical center (COVID-19 peak). These metrics evaluate the impact on total EHR time, work outside of work, time on documentation, time on prescriptions, inbox time, teamwork for orders, and undivided attention patients receive during an encounter. Our study aims were to evaluate feasibility of implementing these metrics as an efficient tool to optimize provider workflow and to track impact on workflow to various provider groups, including physicians, advanced practice providers (APPs), and different medical divisions, during times of significant policy change in the treatment landscape. Methods Data compilation and analysis was retrospectively performed in Tableau utilizing user and schedule data obtained from Cerner Millennium PowerChart and our internal scheduling software. We analyzed three distinct time periods: the 3 months prior to the initial COVID-19 peak, the 3 months during peak, and 3 months immediately post-peak. Results Application of early COVID-19 restrictions led to a significant increase of telemedicine encounters from baseline <1% up to 29.2% of all patient encounters. During initial peak period, there was a significant increase in total EHR time, work outside of work, time on documentation, and inbox time for providers. Overall APPs spent significantly more time in the EHR compared with physicians. All of the metrics returned to near baseline after the initial COVID-19 peak in our area. Conclusion Our analysis showed that implementation of these core metrics is both feasible and can provide an accurate representation of provider EHR workflow adjustments during periods of change, while providing a basis for cross-vendor and cross-institutional analysis.

The Effect of Article Characteristics on Citation Number in a Diachronic Dataset of the Biomedical Literature on Chronic Inflammation: An Analysis by Ensemble Machines

Citations are core metrics to gauge the relevance of scientific literature. Identifying features that can predict a high citation count is therefore of primary importance. For the present study, we generated a dataset of 121,640 publications on chronic inflammation from the Scopus database, containing data such as titles, authors, journal, publication date, type of document, type of access and citation count, ranging from 1951 to 2021. Hence we further computed title length, author count, title sentiment score, number of colons, semicolons and question marks in the title and we used these data as predictors in Gradient boosting, Bagging and Random Forest regressors and classifiers. Based on these data, we were able to train these machines, and Gradient Boosting achieved an F1 score of 0.552 on classification. These models agreed that document type, access type and number of authors were the best predicting factors, followed by title length.

Transmol: Repurposing Language Model for Molecular Generation

Recent advances in convolutional neural networks have inspired the application of deep learning to other disciplines. Even though image processing and natural language processing have turned out to be the most successful, there are many other areas that have benefited, like computational chemistry in general and drug design in particular. From 2018 the scientific community has seen a surge of methodologies related to the generation of diverse molecular libraries using machine learning. However, no algorithm used an attention mechanisms for <i>de novo</i> molecular generation. Here we employ a variant of transformers, a recent NLP architecture, for this purpose. We have achieved a statistically significant increase in some of the core metrics of the MOSES benchmark. Furthermore, a novel way of generating libraries fusing two molecules as seeds has been described.

Transmol: Repurposing Language Model for Molecular Generation

Recent advances in convolutional neural networks have inspired the application of deep learning to other disciplines. Even though image processing and natural language processing have turned out to be the most successful, there are many other areas that have benefited, like computational chemistry in general and drug design in particular. From 2018 the scientific community has seen a surge of methodologies related to the generation of diverse molecular libraries using machine learning. However, no algorithm used an attention mechanisms for <i>de novo</i> molecular generation. Here we employ a variant of transformers, a recent NLP architecture, for this purpose. We have achieved a statistically significant increase in some of the core metrics of the MOSES benchmark. Furthermore, a novel way of generating libraries fusing two molecules as seeds has been described.

Technology-aided assessment of functionally relevant sensorimotor impairments in arm and hand of post-stroke individuals

Background Assessing arm and hand sensorimotor impairments that are functionally relevant is essential to optimize the impact of neurorehabilitation interventions. Technology-aided assessments should provide a sensitive and objective characterization of upper limb impairments, but often provide arm weight support and neglect the importance of the hand, thereby questioning their functional relevance. The Virtual Peg Insertion Test (VPIT) addresses these limitations by quantifying arm and hand movements as well as grip forces during a goal-directed manipulation task requiring active lifting of the upper limb against gravity. The aim of this work was to evaluate the ability of the VPIT metrics to characterize arm and hand sensorimotor impairments that are relevant for performing functional tasks. Methods Arm and hand sensorimotor impairments were systematically characterized in 30 chronic stroke patients using conventional clinical scales and the VPIT. For the latter, ten previously established kinematic and kinetic core metrics were extracted. The validity and robustness of these metrics was investigated by analyzing their clinimetric properties (test-retest reliability, measurement error, learning effects, concurrent validity). Results Twenty-three of the participants, the ones with mild to moderate sensorimotor impairments and without strong cognitive deficits, were able to successfully complete the VPIT protocol (duration 16.6 min). The VPIT metrics detected impairments in arm and hand in 90.0% of the participants, and were sensitive to increased muscle tone and pathological joint coupling. Most importantly, significant moderate to high correlations between conventional scales of activity limitations and the VPIT metrics were found, thereby indicating their functional relevance when grasping and transporting objects, and when performing dexterous finger manipulations. Lastly, the robustness of three out of the ten VPIT core metrics in post-stroke individuals was confirmed. Conclusions This work provides evidence that technology-aided assessments requiring goal-directed manipulations without arm weight support can provide an objective, robust, and clinically feasible way to assess functionally relevant sensorimotor impairments in arm and hand in chronic post-stroke individuals with mild to moderate deficits. This allows for a better identification of impairments with high functional relevance and can contribute to optimizing the functional benefits of neurorehabilitation interventions.

Technology-aided assessment of functionally relevant sensorimotor impairments in arm and hand of post-stroke individuals

BackgroundAssessing arm and hand sensorimotor impairments that are functionally relevant is essential to optimize the impact of neurorehabilitation interventions. Technology-aided assessments should provide a sensitive and objective characterization of upper limb impairments, but often provide arm weight support and neglect the importance of the hand, thereby questioning their functional relevance. The Virtual Peg Insertion Test (VPIT) addresses these limitations by quantifying arm movements and grip forces during a goal-directed manipulation task without arm weight support. The aim of this work was to evaluate the potential and robustness of the VPIT metrics to inform on sensorimotor impairments in arm and hand, and especially identify the functional relevance of the detected impairments.MethodsArm and hand sensorimotor impairments were systematically characterized in 30 chronic stroke patients using conventional clinical scales and the VPIT. For the latter, ten previously established kinematic and kinetic core metrics were extracted and compared to conventional clinical scales of impairment and activity limitations. Additionally, the robustness of the VPIT metrics was investigated by analyzing their clinimetric properties (test-retest reliability, measurement error, and learning effects).ResultsTwenty-three of the participants, the ones with mild to moderate sensorimotor impairments and without strong cognitive deficits, were able to successfully complete the VPIT protocol (duration 16.6 min). The VPIT metrics detected impairments in arm and hand in 90.0% of the participants, and were sensitive to increased muscle tone and pathological joint coupling. Most importantly, moderate to high significant correlations between conventional scales of activity limitations and the VPIT metrics were found, thereby indicating their functional relevance when grasping and transporting lightweight objects as well as dexterous finger manipulations. Lastly, the robustness of three out of the ten VPIT core metrics in post-stroke individuals was confirmed.ConclusionsThis work provides evidence that technology-aided assessments requiring goal-directed manipulations without arm weight support can provide an objective, robust, and clinically feasible way to assess functionally relevant sensorimotor impairments in arm and hand in chronic post-stroke individuals with mild to moderate deficits. This allows better identifying impairments with high functional relevance and can contribute to optimizing the functional benefits of neurorehabilitation interventions.Retrospectively registered: clinicaltrials.gov/ct2/show/NCT03135093

Criteria for evaluating molecular markers: Comprehensive quality metrics to improve marker-assisted selection

Despite strong interest over many years, the usage of quantitative trait loci in plant breeding has often failed to live up to expectations. A key weak point in the utilisation of QTLs is the “quality” of markers used during marker-assisted selection (MAS): unreliable markers result in variable outcomes, leading to a perception that MAS products fail to achieve reliable improvement. Most reports of markers used for MAS focus on markers derived from the mapping population. There are very few studies that examine the reliability of these markers in other genetic backgrounds, and critically, no metrics exist to describe and quantify this reliability. To improve the MAS process, this work proposes five core metrics that fully describe the reliability of a marker. These metrics give a comprehensive and quantitative measure of the ability of a marker to correctly classify germplasm as QTL[+]/[-], particularly against a background of high allelic diversity. Markers that score well on these metrics will have far higher reliability in breeding, and deficiencies in specific metrics give information on circumstances under which a marker may not be reliable. The metrics are applicable across different marker types and platforms, allowing an objective comparison of the performance of different markers irrespective of the platform. Evaluating markers using these metrics demonstrates that trait-specific markers consistently out-perform markers designed for other purposes. These metrics also provide a superb set of criteria for designing superior marker systems for a target QTL, enabling the selection of an optimal marker set before committing to design.