A solution to treat mixed-type human datasets from socio-ecological systems

Coupled human and natural systems (CHANS) are frequently represented by large datasets with varied data including continuous, ordinal, and categorical variables. Conventional multivariate analyses cannot handle these mixed data types. In this paper, our goal was to show how a clustering method that has not before been applied to understanding the human dimension of CHANS: a Gower dissimilarity matrix with partitioning around medoids (PAM) can be used to treat mixed-type human datasets. A case study of land managers responsible for invasive plant control projects across rivers of the southwestern U.S. was used to characterize managers’ backgrounds and decisions, and project properties through clustering. Results showed that managers could be classified as “federal multitaskers” or as “educated specialists”. Decisions were characterized by being either “quick and active” or “thorough and careful”. Project goals were either comprehensive with ecological goals or more limited in scope. This study shows that clustering with Gower and PAM can simplify the complex human dimension of this system, demonstrating the utility of this approach for systems frequently composed of mixed-type data such as CHANS. This clustering approach can be used to direct scientific recommendations towards homogeneous groups of managers and project types

A Solution to Treat Mixed-Type Human Datasets from Socio-Ecological Systems

Coupled human and natural systems (CHANS) are frequently represented by large datasets with varied data including continuous, ordinal, and categorical variables. Conventional multivariate analyses cannot handle these mixed data types. In this paper, our goal was to show how a clustering method that has not before been applied to understanding the human dimension of CHANS: a Gower dissimilarity matrix with partitioning around medoids (PAM) can be used to treat mixed-type human datasets. A case study of land managers responsible for invasive plant control projects across rivers of the southwestern U.S. was used to characterize managers’ backgrounds and decisions, and project properties through clustering. Results showed that managers could be classified as “federal multitaskers” or as “educated specialists”. Decisions were characterized by being either “quick and active” or “thorough and careful”. Project goals were either comprehensive with ecological goals or more limited in scope. This study shows that clustering with Gower and PAM can simplify the complex human dimension of this system, demonstrating the utility of this approach for systems frequently composed of mixed-type data such as CHANS. This clustering approach can be used to direct scientific recommendations towards homogeneous groups of managers and project types

Vegetation response to invasive Tamarix control in southwestern U.S. rivers: a collaborative study including 416 sites

Most studies assessing vegetation response following control of invasive Tamarix trees along southwestern U.S. rivers have been small in scale (e.g., river reach), or at a regional scale but with poor spatial-temporal replication, and most have not included testing the effects of a now widely used biological control. We monitored plant composition following Tamarix control along hydrologic, soil, and climatic gradients in 244 treated and 172 reference sites across six U.S. states. This represents the largest comprehensive assessment to date on the vegetation response to the four most common Tamarix control treatments. Biocontrol by a defoliating beetle (treatment 1) reduced the abundance of Tamarix less than active removal by mechanically using hand and chain-saws (2), heavy machinery (3) or burning (4). Tamarix abundance also decreased with lower temperatures, higher precipitation, and follow-up treatments for Tamarix resprouting. Native cover generally increased over time in active Tamarix removal sites, however, the increases observed were small and was not consistently increased by active revegetation. Overall, native cover was correlated to permanent stream flow, lower grazing pressure, lower soil salinity and temperatures, and higher precipitation. Species diversity also increased where Tamarix was removed. However, Tamarix treatments, especially those generating the highest disturbance (burning and heavy machinery), also often promoted secondary invasions of exotic forbs. The abundance of hydrophytic species was much lower in treated than in reference sites, suggesting that management of southwestern U.S. rivers has focused too much on weed control, overlooking restoration of fluvial processes that provide habitat for hydrophytic and floodplain vegetation. These results can help inform future management of Tamarix-infested rivers to restore hydrogeomorphic processes, increase native biodiversity and reduce abundance of noxious species

A Quantitative Systems Pharmacology perspective on the importance of parameter identifiability

There is an inherent tension in Quantitative Systems Pharmacology (QSP) between the need to incorporate mathematical descriptions of complex physiology and drug targets with the necessity of developing robust, predictive and well-constrained models. In addition to this there is no "gold standard" for model development and assessment in QSP. Moreover, there can be confusion over terminology such as model and parameter identifiability; complex and simple models; virtual populations; and other concepts, which leads to potential miscommunication and misapplication of methodologies within modelling communities, both the QSP community and related disciplines. This perspective article highlights the pros and cons of using simple (often identifiable) vs. complex (more physiologically detailed but often non-identifiable) models, as well as aspects of parameter identifiability, sensitivity and inference methodologies for model development and analysis. The paper distills the central themes of the issue of identifiability and optimal model size and discusses open challenges

Addressing the Burden and Management Strategies for Disparities and Inequities Among Liver Transplant Professionals: The ILTS Experience.

Medical professional environments are becoming increasingly multicultural, international, and diverse in terms of its specialists. Many transplant professionals face challenges related to gender, sexual orientation or racial background in their work environment or experience inequities involving access to leadership positions, professional promotion, and compensation. These circumstances not infrequently become a major source of work-related stress and burnout for these disadvantaged, under-represented transplant professionals. In this review, we aim to 1) discuss the current perceptions regarding disparities among liver transplant providers 2) outline the burden and impact of disparities and inequities in the liver transplant workforce 3) propose potential solutions and role of professional societies to mitigate inequities and maximize inclusion within the transplant community

Chemical Genetic Analysis and Functional Characterization of Staphylococcal Wall Teichoic Acid 2-Epimerases Reveals Unconventional Antibiotic Drug Targets

Here we describe a chemical biology strategy performed in Staphylococcus aureus and Staphylococcus epidermidis to identify MnaA, a 2-epimerase that we demonstrate interconverts UDP-GlcNAc and UDP-ManNAc to modulate substrate levels of TarO and TarA wall teichoic acid (WTA) biosynthesis enzymes. Genetic inactivation of mnaA results in complete loss of WTA and dramatic in vitro β-lactam hypersensitivity in methicillin-resistant S. aureus (MRSA) and S. epidermidis (MRSE). Likewise, the β-lactam antibiotic imipenem exhibits restored bactericidal activity against mnaA mutants in vitro and concomitant efficacy against 2-epimerase defective strains in a mouse thigh model of MRSA and MRSE infection. Interestingly, whereas MnaA serves as the sole 2-epimerase required for WTA biosynthesis in S. epidermidis, MnaA and Cap5P provide compensatory WTA functional roles in S. aureus. We also demonstrate that MnaA and other enzymes of WTA biosynthesis are required for biofilm formation in MRSA and MRSE. We further determine the 1.9Å crystal structure of S. aureus MnaA and identify critical residues for enzymatic dimerization, stability, and substrate binding. Finally, the natural product antibiotic tunicamycin is shown to physically bind MnaA and Cap5P and inhibit 2-epimerase activity, demonstrating that it inhibits a previously unanticipated step in WTA biosynthesis. In summary, MnaA serves as a new Staphylococcal antibiotic target with cognate inhibitors predicted to possess dual therapeutic benefit: as combination agents to restore β-lactam efficacy against MRSA and MRSE and as non-bioactive prophylactic agents to prevent Staphylococcal biofilm formation.publishe

Vegetation response to invasive Tamarix control in southwestern U.S. rivers: a collaborative study including 416 sites

Most studies assessing vegetation response following control of invasive Tamarix trees along southwestern U.S. rivers have been small in scale (e.g., river reach), or at a regional scale but with poor spatial-temporal replication, and most have not included testing the effects of a now widely used biological control. We monitored plant composition following Tamarix control along hydrologic, soil, and climatic gradients in 244 treated and 172 reference sites across six U.S. states. This represents the largest comprehensive assessment to date on the vegetation response to the four most common Tamarix control treatments. Biocontrol by a defoliating beetle (treatment 1) reduced the abundance of Tamarix less than active removal by mechanically using hand and chain-saws (2), heavy machinery (3) or burning (4). Tamarix abundance also decreased with lower temperatures, higher precipitation, and follow-up treatments for Tamarix resprouting. Native cover generally increased over time in active Tamarix removal sites, however, the increases observed were small and was not consistently increased by active revegetation. Overall, native cover was correlated to permanent stream flow, lower grazing pressure, lower soil salinity and temperatures, and higher precipitation. Species diversity also increased where Tamarix was removed. However, Tamarix treatments, especially those generating the highest disturbance (burning and heavy machinery), also often promoted secondary invasions of exotic forbs. The abundance of hydrophytic species was much lower in treated than in reference sites, suggesting that management of southwestern U.S. rivers has focused too much on weed control, overlooking restoration of fluvial processes that provide habitat for hydrophytic and floodplain vegetation. These results can help inform future management of Tamarix-infested rivers to restore hydrogeomorphic processes, increase native biodiversity and reduce abundance of noxious species

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe