Historicising perceptions and the national management framework for invasive alien plants in South Africa.

This article offers a historical framework for understanding changes to human perceptions and efforts to manage invasive alien plants and weeds in South Africa from the mid-nineteenth century until the present. The article argues that South African legislation and policy for managing invasive alien plants and weeds has historically been limited because people have held contradictory values about plants, many private land owners have lacked resources and have not been compelled to follow government legislation and because policy has reflected the interests of a small group of farmers or scientific experts who have had limited influence on most private land owners and traditional land users. Successful control efforts often relied on technical expertise that was applied controversially or could be implemented on government land without extensive public consultation or social conflict. The creation of a national framework for invasive alien plants through the Working for Water Programme in 1995 and National Environmental Management of Biodiversity Act (no. 10) of 2004 (NEMBA) has increased public awareness, but the Programme and NEMBA remain limited by many of the same institutional and social constraints that experts and institutions faced in the past. In conclusion, the article draws on history to provide insights to contemporary challenges.

Explaining people's perceptions of invasive alien species: A conceptual framework.

Human perceptions of nature and the environment are increasingly being recognised as important for environmental management and conservation. Understanding people's perceptions is crucial for understanding behaviour and developing effective management strategies to maintain, preserve and improve biodiversity, ecosystem services and human well-being. As an interdisciplinary team, we produced a synthesis of the key factors that influence people's perceptions of invasive alien species, and ordered them in a conceptual framework. In a context of considerable complexity and variation across time and space, we identified six broad-scale dimensions: (1) attributes of the individual perceiving the invasive alien species; (2) characteristics of the invasive alien species itself; (3) effects of the invasion (including negative and positive impacts, i.e. benefits and costs); (4) socio-cultural context; (5) landscape context; and (6) institutional and policy context. A number of underlying and facilitating aspects for each of these six overarching dimensions are also identified and discussed. Synthesising and understanding the main factors that influence people's perceptions is useful to guide future research, to facilitate dialogue and negotiation between actors, and to aid management and policy formulation and governance of invasive alien species. This can help to circumvent and mitigate conflicts, support prioritisation plans, improve stakeholder engagement platforms, and implement control measures.

Clinical Validation of a Machine-Learned, Point-of-Care System to IDENTIFY Functionally Significant Coronary Artery Disease.

Many clinical studies have shown wide performance variation in tests to identify coronary artery disease (CAD). Coronary computed tomography angiography (CCTA) has been identified as an effective rule-out test but is not widely available in the USA, particularly so in rural areas. Patients in rural areas are underserved in the healthcare system as compared to urban areas, rendering it a priority population to target with highly accessible diagnostics. We previously developed a machine-learned algorithm to identify the presence of CAD (defined by functional significance) in patients with symptoms without the use of radiation or stress. The algorithm requires 215 s temporally synchronized photoplethysmographic and orthogonal voltage gradient signals acquired at rest. The purpose of the present work is to validate the performance of the algorithm in a frozen state (i.e., no retraining) in a large, blinded dataset from the IDENTIFY trial. IDENTIFY is a multicenter, selectively blinded, non-randomized, prospective, repository study to acquire signals with paired metadata from subjects with symptoms indicative of CAD within seven days prior to either left heart catheterization or CCTA. The algorithm's sensitivity and specificity were validated using a set of unseen patient signals (n = 1816). Pre-specified endpoints were chosen to demonstrate a rule-out performance comparable to CCTA. The ROC-AUC in the validation set was 0.80 (95% CI: 0.78-0.82). This performance was maintained in both male and female subgroups. At the pre-specified cut point, the sensitivity was 0.85 (95% CI: 0.82-0.88), and the specificity was 0.58 (95% CI: 0.54-0.62), passing the pre-specified endpoints. Assuming a 4% disease prevalence, the NPV was 0.99. Algorithm performance is comparable to tertiary center testing using CCTA. Selection of a suitable cut-point results in the same sensitivity and specificity performance in females as in males. Therefore, a medical device embedding this algorithm may address an unmet need for a non-invasive, front-line point-of-care test for CAD (without any radiation or stress), thus offering significant benefits to the patient, physician, and healthcare system.

Spaceflight-Associated Vascular Remodeling and Gene Expression in Mouse Calvaria.

Astronauts suffer from a loss of bone mass at a rate of 1.5% per month from lower regions of the body during the course of long-duration (>30 days) spaceflight, a phenomenon that poses important risks for returning crew. Conversely, a gain in bone mass may occur in non-load bearing regions of the body as related to microgravity-induced cephalad fluid shift. Representing non-load bearing regions with mouse calvaria and leveraging the STS-131 (15-day) and BION-M1 (30-day) flights, we examined spatial and temporal calvarial vascular remodeling and gene expression related to microgravity exposure compared between spaceflight (SF) and ground control (GC) cohorts. We examined parasagittal capillary numbers and structures in calvaria from 16 to 23 week-old C57BL/6 female mice (GC, n = 4; SF, n = 5) from STS-131 and 19-20 week-old C57BL/6 male mice (GC, n = 6; SF, n = 6) from BION-M1 using a robust isolectin-IB4 vessel marker. We found that the vessel diameter reduces significantly in mice exposed to 15 days of spaceflight relative to control. Capillarization increases by 30% (SF vs. GC, p = 0.054) in SF mice compared to GC mice. The vessel numbers and diameter remain unchanged in BION-M1 mice calvarial section. We next analyzed the parietal pro-angiogenic (VEGFA) and pro-osteogenic gene (BMP-2, DMP1, RUNX2 and OCN) expression in BION-M1 mice using quantitative RT-PCR. VEGFA gene expression increased 15-fold while BMP-2 gene expression increased 11-fold in flight mice compared to GC. The linkage between vascular morphology and gene expression in the SF conditions suggests that angiogenesis may be important in the regulation of pathological bone growth in non-weight bearing regions of the body. Short-duration microgravity-mediated bone restructuring has implications in planning effective countermeasures for long-duration flights and extraterrestrial human habitation.

Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data.

Introduction: Elevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance. Materials and methods: Patients presenting with cardiovascular symptoms (N = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid. Results: The cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2. Conclusion: This analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions.

Development and validation of a machine learned algorithm to IDENTIFY functionally significant coronary artery disease.

Introduction: Multiple trials have demonstrated broad performance ranges for tests attempting to detect coronary artery disease. The most common test, SPECT, requires capital-intensive equipment, the use of radionuclides, induction of stress, and time off work and/or travel. Presented here are the development and clinical validation of an office-based machine learned algorithm to identify functionally significant coronary artery disease without radiation, expensive equipment or induced patient stress. Materials and methods: The IDENTIFY trial (NCT03864081) is a prospective, multicenter, non-randomized, selectively blinded, repository study to collect acquired signals paired with subject meta-data, including outcomes, from subjects with symptoms of coronary artery disease. Time synchronized orthogonal voltage gradient and photoplethysmographic signals were collected for 230 seconds from recumbent subjects at rest within seven days of either left heart catheterization or coronary computed tomography angiography. Following machine learning on a proportion of these data (N = 2,522), a final algorithm was selected, along with a pre-specified cut point on the receiver operating characteristic curve for clinical validation. An unseen set of subject signals (N = 965) was used to validate the algorithm. Results: At the pre-specified cut point, the sensitivity for detecting functionally significant coronary artery disease was 0.73 (95% CI: 0.68-0.78), and the specificity was 0.68 (0.62-0.74). There exists a point on the receiver operating characteristic curve at which the negative predictive value is the same as coronary computed tomographic angiography, 0.99, assuming a disease incidence of 0.04, yielding sensitivity of 0.89 and specificity of 0.42. Selecting a point at which the positive predictive value is maximized, 0.12, yields sensitivity of 0.39 and specificity of 0.88. Conclusion: The performance of the machine learned algorithm presented here is comparable to common tertiary center testing for coronary artery disease. Employing multiple cut points on the receiver operating characteristic curve can yield the negative predictive value of coronary computed tomographic angiography and a positive predictive value approaching that of myocardial perfusion imaging. As such, a system employing this algorithm may address the need for a non-invasive, no radiation, no stress, front line test, and hence offer significant advantages to the patient, their physician, and healthcare system.

Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care.

BACKGROUND: Phase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures non-invasively and at the point-of-care is unknown. OBJECTIVE: This study prospectively validated a phase space machine-learned approach based on a novel electro-mechanical pulse wave method of data collection through orthogonal voltage gradient (OVG) and photoplethysmography (PPG) for the prediction of elevated left ventricular end diastolic pressure (LVEDP). METHODS: Consecutive outpatients across 15 US-based healthcare centers with symptoms suggestive of coronary artery disease were enrolled at the time of elective cardiac catheterization and underwent OVG and PPG data acquisition immediately prior to angiography with signals paired with LVEDP (IDENTIFY; NCT #03864081). The primary objective was to validate a ML algorithm for prediction of elevated LVEDP using a definition of ≥25 mmHg (study cohort) and normal LVEDP ≤ 12 mmHg (control cohort), using AUC as the measure of diagnostic accuracy. Secondary objectives included performance of the ML predictor in a propensity matched cohort (age and gender) and performance for an elevated LVEDP across a spectrum of comparative LVEDP (<12 through 24 at 1 mmHg increments). Features were extracted from the OVG and PPG datasets and were analyzed using machine-learning approaches. RESULTS: The study cohort consisted of 684 subjects stratified into three LVEDP categories, ≤12 mmHg (N = 258), LVEDP 13-24 mmHg (N = 347), and LVEDP ≥25 mmHg (N = 79). Testing of the ML predictor demonstrated an AUC of 0.81 (95% CI 0.76-0.86) for the prediction of an elevated LVEDP with a sensitivity of 82% and specificity of 68%, respectively. Among a propensity matched cohort (N = 79) the ML predictor demonstrated a similar result AUC 0.79 (95% CI: 0.72-0.8). Using a constant definition of elevated LVEDP and varying the lower threshold across LVEDP the ML predictor demonstrated and AUC ranging from 0.79-0.82. CONCLUSION: The phase space ML analysis provides a robust prediction for an elevated LVEDP at the point-of-care. These data suggest a potential role for an OVG and PPG derived electro-mechanical pulse wave strategy to determine if LVEDP is elevated in patients with symptoms suggestive of cardiac disease.

A global history of Australian trees.

Scholars studying the globalization of Australian trees have previously emphasized the rapid natural propagation of Australian trees outside of their native habitats, believing their success to be a reversal of "ecological imperialism" from the "new world" to the "old world." This article argues that the expansion of Australian trees should not be viewed as a biological phenomenon, but as the result of a long-term attempt by powerful states and state-sponsored scientists to select and breed Australian species that could grow in a variety of climates and ecological conditions. Five non-biological factors largely determined the success of these attempts to grow Australian trees: the abundance or paucity of natural forests, state power, the amount of scientific research directed to planting Australian trees, the cost of labor, and the ability to utilize hardwood timbers and bark. This paper compares the use of Australian trees in Australia, India, and South Africa to demonstrate that biology was not the determining factor in the long-term success of many Australian genera and species.