Treating Erythromelalgia with Interosseous Membrane Stimulation: An Autonomic Basis for the Condition and Its Treatment.

Background: Erythromelalgia, which has primary and secondary presentations, causes heat, pain, and redness in the skin. The condition seems to have an autonomic basis, with vasomotor dysfunction causing dilatation of some blood vessels and constriction of others. No consistently effective treatments have been reported. Anticonvulsant, antidepressant, antihistamine, anti-inflammatory, antihypertensive, analgesic, nutritional, and topical approaches have been tried as were lidocaine infusions, nerve blocks, and thoracic and lumbar sympathectomies. Interosseous membrane stimulation appears to affect the local autonomic milieu in the extremity being treated. This approach was used on a patient with erythromelalgia. Case: A 36-year-old woman with erythromelalgia was treated with interosseous membrane stimulation. Eight treatments were given over a 1-year timeframe at 1-3-month intervals. Results: This patient repeatedly experienced much relief from her burning paresthesias, swelling, diaphoresis, and ruddy discoloration of her extremities for 6-8 hours following each treatment. The intensity of her discomfort subsided gradually over time. Conclusions: Interosseous membrane stimulation is a safe, simple, and effective treatment for erythromelalgia, which is notoriously refractory to treatment. This patient's response to treatment might have been a result of localized derangement of her autonomic nervous system. It is possible that manipulation of the autonomic milieu of an extremity is a significant factor in the mechanism of action of interosseous membrane stimulation.

Optimizing warfarin dosing for patients with atrial fibrillation using machine learning.

While novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range. We used a novel semi-Markov decision process formulation of the Batch-Constrained deep Q-learning algorithm to develop a reinforcement learning model to dynamically recommend optimal warfarin dosing to achieve INR of 2.0-3.0 for patients with atrial fibrillation. The model was developed using data from 22,502 patients in the warfarin treated groups of the pivotal randomized clinical trials of edoxaban (ENGAGE AF-TIMI 48), apixaban (ARISTOTLE) and rivaroxaban (ROCKET AF). The model was externally validated on data from 5730 warfarin-treated patients in a fourth trial of dabigatran (RE-LY) using multilevel regression models to estimate the relationship between center-level algorithm consistent dosing, time in therapeutic INR range (TTR), and a composite clinical outcome of stroke, systemic embolism or major hemorrhage. External validation showed a positive association between center-level algorithm-consistent dosing and TTR (R2 = 0.56). Each 10% increase in algorithm-consistent dosing at the center level independently predicted a 6.78% improvement in TTR (95% CI 6.29, 7.28; p < 0.001) and a 11% decrease in the composite clinical outcome (HR 0.89; 95% CI 0.81, 1.00; p = 0.015). These results were comparable to those of a rules-based clinical algorithm used for benchmarking, for which each 10% increase in algorithm-consistent dosing independently predicted a 6.10% increase in TTR (95% CI 5.67, 6.54, p < 0.001) and a 10% decrease in the composite outcome (HR 0.90; 95% CI 0.83, 0.98, p = 0.018). Our findings suggest that a deep reinforcement learning algorithm can optimize time in therapeutic range for patients taking warfarin. A digital clinical decision support system to promote algorithm-consistent warfarin dosing could optimize time in therapeutic range and improve clinical outcomes in atrial fibrillation globally.

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets.

Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 - 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data.

Optimizing Patient Record Linkage in a Master Patient Index Using Machine Learning: Algorithm Development and Validation.

BACKGROUND: To provide quality care, modern health care systems must match and link data about the same patient from multiple sources, a function often served by master patient index (MPI) software. Record linkage in the MPI is typically performed manually by health care providers, guided by automated matching algorithms. These matching algorithms must be configured in advance, such as by setting the weights of patient attributes, usually by someone with knowledge of both the matching algorithm and the patient population being served. OBJECTIVE: We aimed to develop and evaluate a machine learning-based software tool, which automatically configures a patient matching algorithm by learning from pairs of patient records previously linked by humans already present in the database. METHODS: We built a free and open-source software tool to optimize record linkage algorithm parameters based on historical record linkages. The tool uses Bayesian optimization to identify the set of configuration parameters that lead to optimal matching performance in a given patient population, by learning from prior record linkages by humans. The tool is written assuming only the existence of a minimal HTTP application programming interface (API), and so is agnostic to the choice of MPI software, record linkage algorithm, and patient population. As a proof of concept, we integrated our tool with SantéMPI, an open-source MPI. We validated the tool using several synthetic patient populations in SantéMPI by comparing the performance of the optimized configuration in held-out data to SantéMPI's default matching configuration using sensitivity and specificity. RESULTS: The machine learning-optimized configurations correctly detect over 90% of true record linkages as definite matches in all data sets, with 100% specificity and positive predictive value in all data sets, whereas the baseline detects none. In the largest data set examined, the baseline matching configuration detects possible record linkages with a sensitivity of 90.2% (95% CI 88.4%-92.0%) and specificity of 100%. By comparison, the machine learning-optimized matching configuration attains a sensitivity of 100%, with a decreased specificity of 95.9% (95% CI 95.9%-96.0%). We report significant gains in sensitivity in all data sets examined, at the cost of only marginally decreased specificity. The configuration optimization tool, data, and data set generator have been made freely available. CONCLUSIONS: Our machine learning software tool can be used to significantly improve the performance of existing record linkage algorithms, without knowledge of the algorithm being used or specific details of the patient population being served.

Nailfold capillaroscopy and deep learning in diabetes.

OBJECTIVE: To determine whether nailfold capillary images, acquired using video capillaroscopy, can provide diagnostic information about diabetes and its complications. RESEARCH DESIGN AND METHODS: Nailfold video capillaroscopy was performed in 120 adult patients with and without type 1 or type 2 diabetes, and with and without cardiovascular disease. Nailfold images were analyzed using convolutional neural networks, a deep learning technique. Cross-validation was used to develop and test the ability of models to predict five5 prespecified states (diabetes, high glycosylated hemoglobin, cardiovascular event, retinopathy, albuminuria, and hypertension). The performance of each model for a particular state was assessed by estimating areas under the receiver operating characteristics curves (AUROC) and precision recall curves (AUPR). RESULTS: A total of 5236 nailfold images were acquired from 120 participants (mean 44 images per participant) and were all available for analysis. Models were able to accurately identify the presence of diabetes, with AUROC 0.84 (95% confidence interval [CI] 0.76, 0.91) and AUPR 0.84 (95% CI 0.78, 0.93), respectively. Models were also able to predict a history of cardiovascular events in patients with diabetes, with AUROC 0.65 (95% CI 0.51, 0.78) and AUPR 0.72 (95% CI 0.62, 0.88) respectively. CONCLUSIONS: This proof-of-concept study demonstrates the potential of machine learning for identifying people with microvascular capillary changes from diabetes based on nailfold images, and for possibly identifying those most likely to have diabetes-related complications.

Machine learning for detecting centre-level irregularities in randomized controlled trials: A pilot study.

Centralized statistical monitoring is sometimes employed as an alternative to onsite monitoring for randomized control trials. Current central monitoring methods have limitations, in that they are relatively resource intensive and do not necessarily generalize to studies where an irregularity pattern has not been observed before. Machine learning has been effective in detecting irregularities in industries such as finance and manufacturing, but to date none have been applied to clinical trials. We conducted a pilot study for the use of machine learning to identify center-level irregularities in data from multicenter clinical trials. We employed unsupervised machine learning methods, which do not rely on labelled data, and therefore allow for the automated discovery of previously unseen irregularity patterns while maintaining flexibility when applied to new data with different structures. This pilot study employs unsupervised machine learning to compute distance matrices between centres, which we used to produce centre-level continuous features. We then used a one-class support vector machine to learn the underlying distribution of each data set to identify data that was substantially different from these distributions. We evaluated our approach against current automatable centralized monitoring methods on two trials with known irregularities. While current approaches performed well on one trial (AUROC 0.752 for monitoring vs. 0.584 for machine learning), our techniques performed substantially better on the other (AUROC 0.140 for monitoring vs 0.728 for machine learning). The results of this pilot study suggest both the feasibility and the potential value of a machine learning-based approach to irregularity detection in RCTs.

GWAS and ExWAS of blood mitochondrial DNA copy number identifies 71 loci and highlights a potential causal role in dementia.

Background: Mitochondrial DNA copy number (mtDNA-CN) is an accessible blood-based measurement believed to capture underlying mitochondrial (MT) function. The specific biological processes underpinning its regulation, and whether those processes are causative for disease, is an area of active investigation. Methods: We developed a novel method for array-based mtDNA-CN estimation suitable for biobank-scale studies, called 'automatic mitochondrial copy (AutoMitoC).' We applied AutoMitoC to 395,781 UKBiobank study participants and performed genome- and exome-wide association studies, identifying novel common and rare genetic determinants. Finally, we performed two-sample Mendelian randomization to assess whether genetically low mtDNA-CN influenced select MT phenotypes. Results: Overall, genetic analyses identified 71 loci for mtDNA-CN, which implicated several genes involved in rare mtDNA depletion disorders, deoxynucleoside triphosphate (dNTP) metabolism, and the MT central dogma. Rare variant analysis identified SAMHD1 mutation carriers as having higher mtDNA-CN (beta = 0.23 SDs; 95% CI, 0.18-0.29; p=2.6 × 10-19), a potential therapeutic target for patients with mtDNA depletion disorders, but at increased risk of breast cancer (OR = 1.91; 95% CI, 1.52-2.40; p=2.7 × 10-8). Finally, Mendelian randomization analyses suggest a causal effect of low mtDNA-CN on dementia risk (OR = 1.94 per 1 SD decrease in mtDNA-CN; 95% CI, 1.55-2.32; p=7.5 × 10-4). Conclusions: Altogether, our genetic findings indicate that mtDNA-CN is a complex biomarker reflecting specific MT processes related to mtDNA regulation, and that these processes are causally related to human diseases. Funding: No funds supported this specific investigation. Awards and positions supporting authors include: Canadian Institutes of Health Research (CIHR) Frederick Banting and Charles Best Canada Graduate Scholarships Doctoral Award (MC, PM); CIHR Post-Doctoral Fellowship Award (RM); Wellcome Trust Grant number: 099313/B/12/A; Crasnow Travel Scholarship; Bongani Mayosi UCT-PHRI Scholarship 2019/2020 (TM); Wellcome Trust Health Research Board Irish Clinical Academic Training (ICAT) Programme Grant Number: 203930/B/16/Z (CJ); European Research Council COSIP Grant Number: 640580 (MO); E.J. Moran Campbell Internal Career Research Award (MP); CISCO Professorship in Integrated Health Systems and Canada Research Chair in Genetic and Molecular Epidemiology (GP).

Our cells are powered by small internal compartments known as mitochondria, which host several copies of their own 'mitochondrial' genome. Defects in these semi-autonomous structures are associated with a range of severe, and sometimes fatal conditions: easily checking the health of mitochondria through cheap, quick and non-invasive methods can therefore help to improve human health. Measuring the concentration of mitochondrial DNA molecules in our blood cells can help to estimate the number of mitochondrial genome copies per cell, which in turn act as a proxy for the health of the compartment. In fact, having lower or higher concentration of mitochondrial DNA molecules is associated with diseases such as cancer, stroke, or cardiac conditions. However, current approaches to assess this biomarker are time and resource-intensive; they also do not work well across people with different ancestries, who have slightly different versions of mitochondrial genomes. In response, Chong et al. developed a new method for estimating mitochondrial DNA concentration in blood samples. Called AutoMitoC, the automated pipeline is fast, easy to use, and can be used across ethnicities. Applying this method to nearly 400,000 individuals highlighted 71 genetic regions for which slight sequence differences were associated with changes in mitochondrial DNA concentration. Further investigation revealed that these regions contained genes that help to build, maintain, and organize mitochondrial DNA. In addition, the analyses yield preliminary evidence showing that lower concentration of mitochondrial DNA may be linked to a higher risk of dementia. Overall, the work by Chong et al. demonstrates that AutoMitoC can be used to investigate how mitochondria are linked to health and disease in populations across the world, potentially paving the way for new therapeutic approaches.

The Toronto Postliver Transplantation Hepatocellular Carcinoma Recurrence Calculator: A Machine Learning Approach.

Liver transplantation (LT) listing criteria for hepatocellular carcinoma (HCC) remain controversial. To optimize the utility of limited donor organs, this study aims to leverage machine learning to develop an accurate posttransplantation HCC recurrence prediction calculator. Patients with HCC listed for LT from 2000 to 2016 were identified, with 739 patients who underwent LT used for modeling. Data included serial imaging, alpha-fetoprotein (AFP), locoregional therapies, treatment response, and posttransplantation outcomes. We compared the CoxNet (regularized Cox regression), survival random forest, survival support vector machine, and DeepSurv machine learning algorithms via the mean cross-validated concordance index. We validated the selected CoxNet model by comparing it with other currently available recurrence risk algorithms on a held-out test set (AFP, Model of Recurrence After Liver Transplant [MORAL], and Hazard Associated with liver Transplantation for Hepatocellular Carcinoma [HALT-HCC score]). The developed CoxNet-based recurrence prediction model showed a satisfying overall concordance score of 0.75 (95% confidence interval [CI], 0.64-0.84). In comparison, the recalibrated risk algorithms' concordance scores were as follows: AFP score 0.64 (outperformed by the CoxNet model, 1-sided 95% CI, >0.01; P = 0.04) and MORAL score 0.64 (outperformed by the CoxNet model 1-sided 95% CI, >0.02; P = 0.03). The recalibrated HALT-HCC score performed well with a concordance of 0.72 (95% CI, 0.63-0.81) and was not significantly outperformed (1-sided 95% CI, ≥0.05; P = 0.29). Developing a comprehensive posttransplantation HCC recurrence risk calculator using machine learning is feasible and can yield higher accuracy than other available risk scores. Further research is needed to confirm the utility of machine learning in this setting.

Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology.

Many clinicians remain wary of machine learning because of longstanding concerns about "black box" models. "Black box" is shorthand for models that are sufficiently complex that they are not straightforwardly interpretable to humans. Lack of interpretability in predictive models can undermine trust in those models, especially in health care, in which so many decisions are- literally-life and death issues. There has been a recent explosion of research in the field of explainable machine learning aimed at addressing these concerns. The promise of explainable machine learning is considerable, but it is important for cardiologists who may encounter these techniques in clinical decision-support tools or novel research papers to have critical understanding of both their strengths and their limitations. This paper reviews key concepts and techniques in the field of explainable machine learning as they apply to cardiology. Key concepts reviewed include interpretability vs explainability and global vs local explanations. Techniques demonstrated include permutation importance, surrogate decision trees, local interpretable model-agnostic explanations, and partial dependence plots. We discuss several limitations with explainability techniques, focusing on the how the nature of explanations as approximations may omit important information about how black-box models work and why they make certain predictions. We conclude by proposing a rule of thumb about when it is appropriate to use black- box models with explanations rather than interpretable models.

Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review.

BACKGROUND: Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs-based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results. OBJECTIVE: This study aimed to identify, summarize, and evaluate the available research, current state of utility, and challenges with machine learning-based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings. METHODS: PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to "vital signs," "clinical deterioration," and "machine learning." Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines. RESULTS: We identified 24 peer-reviewed studies from 417 articles for inclusion; 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, intensive care units, emergency departments, step-down units, medical assessment units, postanesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods, and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97. CONCLUSIONS: In studies that compared performance, reported results suggest that machine learning-based early warning systems can achieve greater accuracy than aggregate-weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings.

A Quantitative Assessment of Organic Carbon Content as a Regional Sediment-Condition Indicator.

Organic carbon content of sediments, whether directly or indirectly assessed, has often been used as an indicator of marine benthic community condition both in site-specific and regional scale condition assessment studies. The conceptual framework underlying use of this indicator was developed based primarily on site-specific studies. A quantitative analysis of literature data on sediment organic matter impacts in marine systems was conducted to determine whether biotic metrics respond to abiotic indicators of sediment organic content, as predicted by conceptual models, at larger spatial scales. The ability to detect predicted decreases in community metrics (abundance, species richness, species diversity index H', biomass) varied among metrics, with best performance by species richness and H'. There was significant added variation both between and within analytical approaches (loss on ignition, total organic carbon methods), emphasizing the need for careful cross calibration and quality control in studies with multiple laboratory partners. High levels of variability for biotic metrics versus organic carbon metrics appear typical for large scale studies, and organic matter source, site depth, and individual estuarine system differences were important sources of variation. Covariation of organic matter content with percent fine sediments is another known source of variation, but various normalization methods may be inadequate due to inherent sources of variation at estuary level. While likely still useful for point-source studies, multiple major sources of variation appear to limit the usefulness of sediment organic content as a benthic condition indicator at larger spatial scales.

To Embed or Not: Network Embedding as a Paradigm in Computational Biology.

Current technology is producing high throughput biomedical data at an ever-growing rate. A common approach to interpreting such data is through network-based analyses. Since biological networks are notoriously complex and hard to decipher, a growing body of work applies graph embedding techniques to simplify, visualize, and facilitate the analysis of the resulting networks. In this review, we survey traditional and new approaches for graph embedding and compare their application to fundamental problems in network biology with using the networks directly. We consider a broad variety of applications including protein network alignment, community detection, and protein function prediction. We find that in all of these domains both types of approaches are of value and their performance depends on the evaluation measures being used and the goal of the project. In particular, network embedding methods outshine direct methods according to some of those measures and are, thus, an essential tool in bioinformatics research.

An evaluation of factors controlling the abundance of epiphytes on Zostera marina along an estuarine gradient in Yaquina Bay, Oregon, USA.

Epiphytes on seagrass (Zostera marina) growing in the lower intertidal were examined along an estuarine gradient within Yaquina Bay, Oregon over a period of 4 years. The Yaquina Estuary receives high levels of nutrients from the watershed during the wet season and from the ocean during the dry season. Mean epiphyte biomass per unit seagrass leaf surface area (epiphyte load) peaked during the summer, and thus epiphyte load was higher during dry season than wet season in both marine and riverine dominated regions. Epiphyte load was greater in marine than in riverine dominated areas in both wet and dry seasons, although only dry season differences were significant. There was no evidence that grazers controlled epiphyte load differences. Annual DIN concentration was inversely related to epiphyte load, principally because of elevated wet season dissolved inorganic nitrogen from river inputs. While there was a positive annual relation of epiphyte load to PO4 concentration, it is not clear that phosphorus becomes a limiting nutrient for epiphyte growth. Water column light attenuation tends to increase linearly with distance from the estuary mouth, while both epiphyte load and Z. marina biomass tend to decrease. Both seagrass and seagrass epiphytes may be increasingly light limited in the upper estuary, and thus, epiphyte loads may have proportionally more impact on seagrass occurrence in this estuarine region.

Assessment of spatial patterns in benthic macrofauna of the U.S. west coast continental shelf.

AIM: We assessed whether currently described marine biogeographic boundaries apply to shelf macrofauna and which environmental drivers were most associated with species differences among regions. LOCATION: Pacific coast of North America from the Strait of Juan de Fuca in Washington to the California-Mexico border. METHODS: Van Veen grab samples were collected from soft sediment 28-138 m deep and sieved using 1 mm mesh. Spatial patterns of species richness, diversity, and abundance were examined in relation to latitude and environmental parameters (temperature, salinity, dissolved oxygen, sediment % fines, and total organic carbon). Analyses of latitudinal distribution patterns of individual species were combined with multivariate analyses of community composition to determine biogeographic and habitat boundaries for mid-depth continental shelf macrofauna. RESULTS: Species richness, diversity, and abundance significantly decreased with increasing latitude, primarily between 32 and 40° N. There were positive associations of richness, diversity (H'), and abundance with upwelling index, sediment % fines, and TOC (<2%). Temperature and DO also were significant for richness and H' but not abundance. Assessment of individual species ranges found major faunal transitions at latitudes 33-34°, 37°, 44°, and 46-47°. Major assemblage differences were found at 34.5°, and 42°. Within each latitudinal region, significantly different macrofauna communities were found in sediment with <5% fines. MAIN CONCLUSIONS: The biogeographic boundaries proposed under the Marine Ecoregions of the World schema are more closely aligned with shelf fauna distributions than those developed using west coast rocky intertidal communities. However, the proposed province boundary at Cape Mendocino is not apparent in the shelf macrofauna, and a transition appears to occur closer to the Oregon-California border. Further, the shelf macrofauna indicate the Channel Islands should be a separate subregion from mainland southern California Bight. Multivariate community analyses minimizing the impact of rare species appeared more useful in determining macrofaunal community biogeographic boundaries than analysis of individual species range endpoints, which are strongly influenced by uncommon species.

Macrophyte Community Response to Nitrogen Loading and Thermal Stressors in Rapidly Flushed Mesocosm Systems.

A mesocosm system was developed to simulate estuarine conditions characteristic of short water-residence time ecosystems of the Pacific Coast of North America, and used to evaluate the response of multiple macrophyte metrics to gradients of NO3 loading and temperature. Replicated experiments found that few responses could be directly attributed to NO3 loading up to 6 x ambient. Some response metrics exhibited weak relationships with nutrient loading but could not be resolved with available statistical power. While direct nutrient responses were found for some species-specific metrics (e.g. green macroalgal growth and biomass, tissue N%, etc.), many patterns were confounded with temperature. Temperature generally had a larger effect on response metrics than did nutrient load. Experimental macrophyte communities exhibited community shifts consistent with the predicted effects of nutrient loading at 20 °C, but there was no evidence of other eutrophication symptoms (phytoplankton blooms or hypoxia) due to the short system-residence time. The Z. marina Nutrient Pollution Index (NPI) tracked the NO3 gradient at 10 °C, but exhibited no response at 20 °C, which may limit the utility of this metric in areas with marked thermal seasonality. Results suggest that teasing apart the influence of temperature and nutrients on the expression of eutrophication symptoms will require complex multi-stressor experiments and the use of indicators that are sensitive across a broad range of conditions.

Evaluación de factores que afectan la bioacidulación de roca fosfórica bajo condiciones in vitro / Evaluation of factors that affect bioacidulation rock phosphate under in vitro conditions

Resumen El fósforo (P) es un nutriente esencial para el desarrollo de las plantas, desafortunadamente, su disponibilidad en muchos suelos es baja. Consecuentemente, los agricultores aplican altas cantidades de fertilizantes fosfóricos solubles, pero esto es ineficiente y costoso. El uso directo de roca fosfórica (RP) es muy atractivo por su bajo costo; sin embargo, es poco soluble y de baja eficiencia agronómica. Para superar esta limitación, hay un creciente interés en el uso de microorganismos del suelo capaces de disolverla y mejorar su valor como fertilizante. El objetivo de este trabajo fue evaluar el efecto que tienen algunos factores sobre la capacidad del hongo Mortierella sp. para disolver RP bajo condiciones in vitro. Estos factores son: (i) tiempo de incubación, (ii) tipo de RP, (iii) concentración inicial de P soluble y (iv) adición de vitaminas y micronutrientes. Despues del periodo de incubación se midió P en solución y pH. Los resultados indican que producto de la biodisolución de RP la más alta concentración de P en solución se alcanzó al día 5. Por otro lado, la biodisolución de RP fue reducida por la adición de vitaminas y micronutrientes y por el incremento en la concentración inicial de P soluble en el medio. Aunque la disolución microbiana fue más efectiva con la RP de Carolina del Norte, las RP del Huila y Santander presentaron un buen nivel de disolución en un periodo de tiempo corto. La bioacidulación mejorara la efectividad agronómica de la RP para su uso directo o a través de un proceso biotecnológico previo.

Abstract Phosphorus (P) is an essential nutrient for plant development, unfortunately, its availability in many soils is low. Consequently, farmers apply high quantities of soluble P fertilizers, but this is an inefficient and costly practice. The direct use of rock phosphate (RP) is a highly attractive option because its low cost, but this material has low solubility and low agronomic efficiency. In order to overcome this limitation, there is a growing interest in the use of soil microorganisms capable of dissolving RP and improving its value as a P fertilizer. The objective of this study was to evaluate the effect of some factors on the effectiveness of the fungus Mortierella sp. to dissolve RP under in vitro conditions. These factors included: (i) incubation time, (ii) type of RP, (iii) initial concentration of soluble P, and (iv) addition of vitamins and micronutrients. After the incubation period, P and pH were measured in solution. The results indicated that as a consequence of the biodissolution of RP, the highest concentration of soluble P in the medium was reached on the day 5th. The biodissolution of RP was reduced by the addition of vitamins and micronutrients and by the increase in the initial concentration of soluble P. Although microbial dissolution was more effective with North Carolina RP, RPs from Huila and Santander showed a good level of dissolution in a short period of time. Bioacidulation will improve the agronomic effectiveness of RP for its direct use or through a previous biotechnological process.

Development of an epiphyte indicator of nutrient enrichment: threshold values for seagrass epiphyte load.

Metrics of epiphyte load on macrophytes were evaluated for use as quantitative biological indicators for nutrient impacts in estuarine waters, based on review and analysis of the literature on epiphytes and macrophytes, primarily seagrasses, but including some brackish and freshwater rooted macrophyte species. An approach is presented that empirically derives threshold epiphyte loads which are likely to cause specified levels of decrease in macrophyte response metrics such as biomass, shoot density, percent cover, production and growth. Data from 36 studies of 10 macrophyte species were pooled to derive relationships between epiphyte load and -25 and -50% seagrass response levels, which are proposed as the primary basis for establishment of critical threshold values. Given multiple sources of variability in the response data, threshold ranges based on the range of values falling between the median and the 75th quantiles of observations at a given seagrass response level are proposed rather than single, critical point values. Four epiphyte load threshold categories - low, moderate, high, very high, are proposed. Comparison of values of epiphyte loads associated with 25 and 50% reductions in light to macrophytes suggest that the threshold ranges are realistic both in terms of the principle mechanism of impact to macrophytes and in terms of the magnitude of resultant impacts expressed by the macrophytes. Some variability in response levels was observed among climate regions, and additional data collected with a standardized approach could help in the development of regionalized threshold ranges for the epiphyte load indicator.

Patterns of shading tolerance determined from experimental light reduction studies of seagrasses.

A review and analysis of the experimental literature on seagrass shading evaluated the relationships among experimental light reduction, experimental duration, additional modifying factors and common meadow-scale seagrass response metrics to determine whether there were consistent statistical relationships. Modifying factors included study latitude, field site depth, season of experiment initiation, rhizome connectivity (severed, intact), experiment type (field, mesocosm), and seagrass life history strategy. Highly significant, best fit linear regression models were found for both biomass and shoot density reduction that included light reduction, duration and other modifying variables, although unexplained variation in the data were high. Duration of light limitation affected extent of response for both metrics, and unexplained variance was greatly reduced by analysis of data from durations >60d for shoot density and for >60d <120d for biomass. Life history strategy was also a significant factor in three of four regression models. While the slopes of the responses were relatively similar for biomass and shoot density, unexplained variation was generally greater for shoot density than biomass in models for data pooled across species. There were highly significant, best fit regression models found for both biomass and shoot density for both genus and species level analyses, with the extent and duration of light reduction the most important model factors. Season of experiment, rhizome status, latitude, and experiment type all were also included in multiple models. Biomass regression models again tended to have lesser unexplained variation than shoot density models. Life history was invariant within genus and species, and separate analyses for data divided among Colonizing, Opportunistic, and Persistent strategies found relatively similar, best fit regression models among strategies. However, the mean percent reduction of both biomass and shoot density was generally lower for the Persistent strategy than for the other two life histories, suggesting a greater buffering capacity against effects of light reduction for such species. Overall, biomass based models explained more of the variance in seagrass response to light reduction than shoot density, and may be the preferred response variable for meadow-scale impact assessments. The relationships observed may inform management decisions by helping define the scope of expected responses of seagrasses in general to the range of factors that may reduce light availability to seagrasses.

Development of an epiphyte indicator of nutrient enrichment: a critical evaluation of observational and experimental studies.

An extensive review of the literature describing epiphytes on submerged aquatic vegetation (SAV), especially seagrasses, was conducted in order to evaluate the evidence for response of epiphyte metrics to increased nutrients. Evidence from field observational studies, together with laboratory and field mesocosm experiments, was assembled from the literature and evaluated for a hypothesized positive response to nutrient addition. There was general consistency in the results to confirm that elevated nutrients tended to increase the load of epiphytes on the surface of SAV, in the absence of other limiting factors. In spite of multiple sources of uncontrolled variation, positive relationships of epiphyte load to nutrient concentration or load (either nitrogen or phosphorus) often were observed along strong anthropogenic or natural nutrient gradients in coastal regions. Such response patterns may only be evident for parts of the year. Results from both mesocosm and field experiments also generally support the increase of epiphytes with increased nutrients, although outcomes from field experiments tended to be more variable. Relatively few studies with nutrient addition in mesocosms have been done with tropical or subtropical species, and more such controlled experiments would be helpful. Experimental duration influenced results, with more positive responses of epiphytes to nutrients at shorter durations in mesocosm experiments versus more positive responses at longer durations in field experiments. In the field, response of epiphyte biomass to nutrient additions was independent of climate zone. Mesograzer activity was a critical covariate for epiphyte response under experimental nutrient elevation, but the epiphyte response was highly dependent on factors such as grazer identity and density, as well as nutrient and ambient light levels. The balance of evidence suggests that epiphytes on SAV will be a useful indicator of persistent nutrient enhancement in many situations. Careful selection of appropriate temporal and spatial constraints for data collection, and concurrent evaluation of confounding factors will help increase the signal to noise ratio for this indicator.