Separating Mangrove Species and Conditions Using Laboratory Hyperspectral Data: A Case Study of a Degraded Mangrove Forest of the Mexican Pacific

Given the scale and rate of mangrove loss globally, it is increasingly important to map and monitor mangrove forest health in a timely fashion. This study aims to identify the conditions of mangroves in a coastal lagoon south of the city of Mazatlán, Mexico, using proximal hyperspectral remote sensing techniques. The dominant mangrove species in this area includes the red (Rhizophora mangle), the black (Avicennia germinans) and the white (Laguncularia racemosa) mangrove. Moreover, large patches of poor condition black and red mangrove and healthy dwarf black mangrove are commonly found. Mangrove leaves were collected from this forest representing all of the aforementioned species and conditions. The leaves were then transported to a laboratory for spectral measurements using an ASD FieldSpec® 3 JR spectroradiometer (Analytical Spectral Devices, Inc., USA). R2 plot, principal components analysis and stepwise discriminant analyses were then used to select wavebands deemed most appropriate for further mangrove classification. Specifically, the wavebands at 520, 560, 650, 710, 760, 2100 and 2230 nm were selected, which correspond to chlorophyll absorption, red edge, starch, cellulose, nitrogen and protein regions of the spectrum. The classification and validation indicate that these wavebands are capable of identifying mangrove species and mangrove conditions common to this degraded forest with an overall accuracy and Khat coefficient higher than 90% and 0.9, respectively. Although lower in accuracy, the classifications of the stressed (poor condition and dwarf) mangroves were found to be satisfactory with accuracies higher than 80%. The results of this study indicate that it could be possible to apply laboratory hyperspectral data for classifying mangroves, not only at the species level, but also according to their health conditions

Examining the influence of seasonality, condition, and species composition on mangrove leaf pigment contents and laboratory based spectroscopy data

The purpose of this investigation was to determine the seasonal relationships (dry vs. rainy) between reflectance (400-1000 nm) and leaf pigment contents (chlorophyll-a (chl-a), chlorophyll-b (chl-b), total carotenoids (tcar), chlorophyll a/b ratio) in three mangrove species (Avicennia germinans (A. germinans), Laguncularia racemosa (L. racemosa), and Rhizophora mangle (R. mangle)) according to their condition (stressed vs. healthy). Based on a sample of 360 leaves taken from a semi-arid forest of the Mexican Pacific, it was determined that during the dry season, the stressed A. germinans and R. mangle show the highest maximum correlations at the green (550 nm) and red-edge (710 nm) wavelengths (r = 0.8 and 0.9, respectively) for both chl-a and chl-b and that much lower values (r = 0.7 and 0.8, respectively) were recorded during the rainy season. Moreover, it was found that the tcar correlation pattern across the electromagnetic spectrum was quite different from that of the chl-a, the chl-b, and chl a/b ratio but that their maximum correlations were also located at the same two wavelength ranges for both seasons. The stressed L. racemosa was the only sample to exhibit minimal correlation with chl-a and chl-b for either season. In addition, the healthy A. germinans and R. mangle depicted similar patterns of chl-a and chl-b, but the tcar varied depending on the species. The healthy L. racemosa recorded higher correlations with chl-b and tcar at the green and red-edge wavelengths during the dry season, and higher correlation with chl-a during the rainy season. Finally, the vegetation index Red Edge Inflection Point Index (REIP) was found to be the optimal index for chl-a estimation for both stressed and healthy classes. For chl-b, both the REIP and the Vogelmann Red Edge Index (Vog1) index were found to be best at prediction. Based on the results of this investigation, it is suggested that caution be taken as mangrove leaf pigment contents from spectroscopy data have been shown to be sensitive to seasonality, species, and condition. The authors suggest potential reasons for the observed variability in the reflectance and pigment contents relationships

The Efficacy and Effectiveness of Education for Preventing and Treating Non-Specific Low Back Pain in the Hispanic Cultural Setting: A Systematic Review

A systematic review was conducted to assess the efficacy and effectiveness of education programs to prevent and treat low back pain (LBP) in the Hispanic cultural setting. Electronic and manual searches identified 1148 unique references. Nine randomized clinical trials (RCTs) were included in this review. Methodological quality assessment and data extraction followed the recommendations from the Cochrane Back Pain Review Group. Education programs which were assessed focused on active management (3 studies), postural hygiene (7), exercise (4) and pain neurophysiology (1). Comparators were no intervention, usual care, exercise, other types of education, and different combinations of these procedures. Five RCTs had a low risk of bias. Results show that: (a) education programs in the school setting can transmit potentially useful knowledge for LBP prevention and (b) education programs for patients with LBP improve the outcomes of usual care, especially in terms of disability. Education on pain neurophysiology improves the results of education on exercise, and education on active management is more effective than “sham” education and education on postural hygiene. Future studies should assess the comparative or summatory effects of education on exercise, education on pain neurophysiology and education on active management, as well as explore their efficiency

Acupuncture and radioactive pathways of hypodermically injected technetium-99m

To the editor: In our opinion, some of the interpretations of Scott and Vernejoul and associates are not supported by the data contained in a recently published study by our group

Debugging of Web Applications with Web-TLR

Web-TLR is a Web verification engine that is based on the well-established Rewriting Logic--Maude/LTLR tandem for Web system specification and model-checking. In Web-TLR, Web applications are expressed as rewrite theories that can be formally verified by using the Maude built-in LTLR model-checker. Whenever a property is refuted, a counterexample trace is delivered that reveals an undesired, erroneous navigation sequence. Unfortunately, the analysis (or even the simple inspection) of such counterexamples may be unfeasible because of the size and complexity of the traces under examination. In this paper, we endow Web-TLR with a new Web debugging facility that supports the efficient manipulation of counterexample traces. This facility is based on a backward trace-slicing technique for rewriting logic theories that allows the pieces of information that we are interested to be traced back through inverse rewrite sequences. The slicing process drastically simplifies the computation trace by dropping useless data that do not influence the final result. By using this facility, the Web engineer can focus on the relevant fragments of the failing application, which greatly reduces the manual debugging effort and also decreases the number of iterative verifications.Comment: In Proceedings WWV 2011, arXiv:1108.208

The prognostic value of catastrophizing for predicting the clinical evolution of low back pain patients: a study in routine clinical practice within the Spanish National Health Service

Francisco Javier Cano García es miembro de la Spanish Back Pain Research Network (Red Española de Investigadores en Dolencias de la Espalda (REIDE) y coautor del artículoBackground context Experimental studies suggest that catastrophizing may worsen the prognosis of low back pain (LBP) and LBP-related disability and increase the risk of chronicity. Purpose To assess the prognostic value of baseline catastrophizing for predicting the clinical evolution of LBP patients in routine clinical practice and the association between the evolution of pain and catastrophizing. Study design/setting Prospective study in routine clinical practice of the Spanish National Health Service. Patient sample One thousand four hundred twenty-two acute and chronic adult LBP patients treated in primary and hospital care. Outcome measures Pain, disability, and catastrophizing measured through validated instruments. Methods Patients were managed according to routine clinical practice. Outcome measures were assessed at baseline and 3 months later. Logistic regression models were developed to estimate the association between baseline catastrophizing score and the improvement of LBP and disability, adjusting for baseline LBP and leg pain (LP) severity, disability, duration of the pain episode, workers' compensation coverage, radiological findings, failed back surgery, and diagnostic procedures and treatments undertaken throughout the study. Another model was developed to estimate the association between the evolution of LBP and the change in catastrophizing, adjusting for the same possible confounders plus the evolution of LP and disability. Models were repeated excluding the treatments undergone after the baseline assessment. Results Regression models showed that the degree of baseline catastrophizing does not predict the evolution of LBP and disability. Conversely, as the degree of pain improvement increases, so does the odds ratio for improvement in catastrophizing, ranging from three (95% confidence interval [95% CI], 2.00–4.50; p<.001) for improvements in pain between 1.1 and 4 visual analog scale (VAS) points, to 7.3 (95% CI, 3.49–15.36; p<.001) for improvements in pain more than 6.1 VAS points. Similar results were obtained when treatments were excluded from the models. Conclusions In routine practice, assessing the baseline score for catastrophizing does not help clinicians to predict the evolution of LBP and disability at 3 months.Spanish Ministry of Health’s Agency for QualityKovacs Foundatio

Automatic Variable Selection Algorithms in Prognostic Factor Research in Neck Pain.

This study aims to compare the variable selection strategies of different machine learning (ML) and statistical algorithms in the prognosis of neck pain (NP) recovery. A total of 3001 participants with NP were included. Three dichotomous outcomes of an improvement in NP, arm pain (AP), and disability at 3 months follow-up were used. Twenty-five variables (twenty-eight parameters) were included as predictors. There were more parameters than variables, as some categorical variables had >2 levels. Eight modelling techniques were compared: stepwise regression based on unadjusted p values (stepP), on adjusted p values (stepPAdj), on Akaike information criterion (stepAIC), best subset regression (BestSubset) least absolute shrinkage and selection operator [LASSO], Minimax concave penalty (MCP), model-based boosting (mboost), and multivariate adaptive regression splines (MuARS). The algorithm that selected the fewest predictors was stepPAdj (number of predictors, p = 4 to 8). MuARS was the algorithm with the second fewest predictors selected (p = 9 to 14). The predictor selected by all algorithms with the largest coefficient magnitude was "having undergone a neuroreflexotherapy intervention" for NP (β = from 1.987 to 2.296) and AP (β = from 2.639 to 3.554), and "Imaging findings: spinal stenosis" (β = from -1.331 to -1.763) for disability. Stepwise regression based on adjusted p-values resulted in the sparsest models, which enhanced clinical interpretability. MuARS appears to provide the optimal balance between model sparsity whilst retaining high predictive performance across outcomes. Different algorithms produced similar performances but resulted in a different number of variables selected. Rather than relying on any single algorithm, confidence in the variable selection may be increased by using multiple algorithms

Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain.

Purpose Prognostic models play an important clinical role in the clinical management of neck pain disorders. No study has compared the performance of modern machine learning (ML) techniques, against more traditional regression techniques, when developing prognostic models in individuals with neck pain. Methods A total of 3001 participants suffering from neck pain were included into a clinical registry database. Three dichotomous outcomes of a clinically meaningful improvement in neck pain, arm pain, and disability at 3 months follow-up were used. There were 26 predictors included, five numeric and 21 categorical. Seven modelling techniques were used (logistic regression, least absolute shrinkage and selection operator [LASSO], gradient boosting [Xgboost], K nearest neighbours [KNN], support vector machine [SVM], random forest [RF], and artificial neural networks [ANN]). The primary measure of model performance was the area under the receiver operator curve (AUC) of the validation set. Results The ML algorithm with the greatest AUC for predicting arm pain (AUC=0.765), neck pain (AUC=0.726), and disability (AUC=0.703) was Xgboost. The improvement in classification AUC from stepwise logistic regression to the best performing machine learning algorithms was 0.081, 0.103, and 0.077 for predicting arm pain, neck pain, and disability, respectively. Conclusion The improvement in prediction performance between ML and logistic regression methods in the present study, could be due to the potential greater nonlinearity between baseline predictors and clinical outcome. The benefit of machine learning in prognostic modelling may be dependent on factors like sample size, variable type, and disease investigated

Experimental study on radioactive pathways of hypodermically injected technetium-99m

The objective of this study was to investigate the biological substrate of radioactive pathways of migration of hypodermically injected 99mTc into points of low electrical resistance. Sixteen anesthetized adult male beagles were used. Control and test points were defined by comparing their electrical resistance to that of the pinna. Seventy-three experiments of three different types were performed: (1) separate hypodermic injections of [99mTc] sodium pertechnetate, 201Tl-chloride, 131INa and 99mTc-rhenium sulfide into control and test points; (2) simultaneous injections of [99mTc]sodium pertechnetate and 201Tl chloride into control and test points; and (3) intravascular injections of 99mTcO4 into blood vessels underlying test points. Only the hypodermic injection of 99mTc into points of low electrical resistance gave rise to a specific radioactive pathway characterized by rapid and longitudinal migration, clearly independent of background activity. The specific radioactive pathway detected is not the result of diffusion of the radiotracer through nerves, veins or lymphatic vessels, but its trajectory coincides with that described for one of the acupuncture meridians in the dog

Minimum detectable and minimal clinically important changes for pain in patients with nonspecific neck pain

<p>Abstract</p> <p>Background</p> <p>The minimal detectable change (MDC) and the minimal clinically important changes (MCIC) have been explored for nonspecific low back pain patients and are similar across different cultural settings. No data on MDC and MCIC for pain severity are available for neck pain patients. The objectives of this study were to estimate MDC and MCIC for pain severity in subacute and chronic neck pain (NP) patients, to assess if MDC and MCIC values are influenced by baseline values and to explore if they are different in the subset of patients reporting referred pain, and in subacute versus chronic patients.</p> <p>Methods</p> <p>Subacute and chronic patients treated in routine clinical practice of the Spanish National Health Service for neck pain, with or without pain referred to the arm, and a pain severity ≥ 3 points on a pain intensity number rating scale (PI-NRS), were included in this study. Patients' own "global perceived effect" over a 3 month period was used as the external criterion. The minimal detectable change (MDC) was estimated by means of the standard error of measurement in patients who self-assess as unchanged. MCIC were estimated by the mean value of change score in patients who self-assess as improved (mean change score, MCS), and by the optimal cutoff point in receiver operating characteristics curves (ROC). The effect on MDC and MCIC of initial scores, duration of pain, and existence of referred pain were assessed.</p> <p>Results</p> <p>658 patients were included, 487 of them with referred pain. MDC was 4.0 PI-NRS points for neck pain in the entire sample, 4.2 for neck pain in patients who also had referred pain, and 6.2 for referred pain. MCS was 4.1 and ROC was 1.5 for referred and for neck pain, both in the entire sample and in patients who also complained of referred pain. ROC was lower (0.5 PI-NRS points) for subacute than for chronic patients (1.5 points). MCS was higher for patients with more intense baseline pain, ranging from 2.4 to 4.9 PI-NRS for neck pain and from 2.4 to 5.3 for referred pain.</p> <p>Conclusion</p> <p>In general, improvements ≤ 1.5 PI-NRS points could be seen as irrelevant. Above that value, the cutoff point for clinical relevance depends on the methods used to estimate MCIC and on the patient's baseline severity of pain. MDC and MCIC values in neck pain patients are similar to those for low back pain and other painful conditions.</p