Climate Change or Land Use Dynamics: Do We Know What Climate Change Indicators Indicate?

Different components of global change can have interacting effects on biodiversity and this may influence our ability to detect the specific consequences of climate change through biodiversity indicators. Here, we analyze whether climate change indicators can be affected by land use dynamics that are not directly determined by climate change. To this aim, we analyzed three community-level indicators of climate change impacts that are based on the optimal thermal environment and averagelatitude of the distribution of bird species present at local communities. We used multiple regression models to relate the variation in climate change indicators to: i) environmental temperature; and ii) three landscape gradients reflecting important current land use change processes (land abandonment, fire impacts and urbanization), all of them having forest areas at their positive extremes. We found that, with few exceptions, landscape gradients determined the figures of climate change indicators as strongly as temperature. Bird communities in forest habitats had colder-dwelling bird species with more northerndistributions than farmland, burnt or urban areas. Our results show that land use changes can reverse, hide or exacerbate our perception of climate change impacts when measured through community-level climate change indicators. We stress the need of an explicit incorporation of the interactions between climate change and land use dynamics to understand what are current climate change indicators indicating and be able to isolate real climate change impacts

Fallers in postacute rehabilitation have worse functional recovery and increased health services use.

OBJECTIVES: To determine characteristics associated with single and multiple fallers during postacute rehabilitation and to investigate the relationship among falls, rehabilitation outcomes, and health services use. DESIGN: Retrospective cohort study. SETTING: Geriatric postacute rehabilitation hospital. PARTICIPANTS: Patients (n = 4026) consecutively admitted over a 5-year period (2003-2007). MEASUREMENTS: All falls during hospitalization were prospectively recorded. Collected patients' characteristics included health, functional, cognitive, and affective status data. Length of stay and discharge destination were retrieved from the administrative database. RESULTS: During rehabilitation stay, 11.4% (458/4026) of patients fell once and an additional 6.3% (253/4026) fell several times. Compared with nonfallers, fallers were older and more frequently men. They were globally frailer, with lower Barthel score and more comorbidities, cognitive impairment, and depressive symptoms. In multivariate analyses, compared with 1-time fallers, multiple fallers were more likely to have lower Barthel score (adjOR: 2.45, 95% CI: 1.48-4.07; P = .001), cognitive impairment (adjOR: 1.43, 95% CI: 1.04-1.96; P = .026), and to have been admitted from a medicine ward (adjOR: 1.55, 95% CI: 1.03-2.32; P = .035). Odds of poor functional recovery and institutionalization at discharge, as well as length of stay, increased incrementally from nonfallers to 1-time and to multiple fallers. CONCLUSION: In these patients admitted to postacute rehabilitation, the proportion of fallers and multiple fallers was high. Multiple fallers were particularly at risk of poor functional recovery and increased health services use. Specific fall prevention programs targeting high-risk patients with cognitive impairment and low functional status should be developed in further studies.

A pipeline approach with spatial information for segmenting multiple sclerosis lesions on brain magnetic resonance imaging

Background: Conventional magnetic resonance imaging (MRI) techniques are highly sensitive to detect multiple sclerosis (MS) plaques, enabling a quantitative assessment of inflammatory activity and lesion load. In quantitative analyses of focal lesions, manual or semi-automated segmentations have been widely used to compute the total number of lesions and the total lesion volume. These techniques, however, are both challenging and time-consuming, being also prone to intra-observer and inter-observer variability.Aim: To develop an automated approach to segment brain tissues and MS lesions from brain MRI images. The goal is to reduce the user interaction and to provide an objective tool that eliminates the inter- and intra-observer variability.Methods: Based on the recent methods developed by Souplet et al. and de Boer et al., we propose a novel pipeline which includes the following steps: bias correction, skull stripping, atlas registration, tissue classification, and lesion segmentation. After the initial pre-processing steps, a MRI scan is automatically segmented into 4 classes: white matter (WM), grey matter (GM), cerebrospinal fluid (CSF) and partial volume. An expectation maximisation method which fits a multivariate Gaussian mixture model to T1-w, T2-w and PD-w images is used for this purpose. Based on the obtained tissue masks and using the estimated GM mean and variance, we apply an intensity threshold to the FLAIR image, which provides the lesion segmentation. With the aim of improving this initial result, spatial information coming from the neighbouring tissue labels is used to refine the final lesion segmentation.Results:The experimental evaluation was performed using real data sets of 1.5T and the corresponding ground truth annotations provided by expert radiologists. The following values were obtained: 64% of true positive (TP) fraction, 80% of false positive (FP) fraction, and an average surface distance of 7.89 mm. The results of our approach were quantitatively compared to our implementations of the works of Souplet et al. and de Boer et al., obtaining higher TP and lower FP values.Conclusion: Promising MS lesion segmentation results have been obtained in terms of TP. However, the high number of FP which is still a well-known problem of all the automated MS lesion segmentation approaches has to be improved in order to use them for the standard clinical practice. Our future work will focus on tackling this issue.

Internal arsenite bioassay calibration using multiple reporter cell lines

Bioassays with bioreporter bacteria are usually calibrated with analyte solutions of known concentrations that are analysed along with the samples of interest. This is done as bioreporter output (the intensity of light, fluorescence or colour) does not only depend on the target concentration, but also on the incubation time and physiological activity of the cells in the assay. Comparing the bioreporter output with standardized colour tables in the field seems rather difficult and error-prone. A new approach to control assay variations and improve application ease could be an internal calibration based on the use of multiple bioreporter cell lines with drastically different reporter protein outputs at a given analyte concentration. To test this concept, different Escherichia coli-based bioreporter strains expressing either cytochrome c peroxidase (CCP, or CCP mutants) or β-galactosidase upon induction with arsenite were constructed. The reporter strains differed either in the catalytic activity of the reporter protein (for CCP) or in the rates of reporter protein synthesis (for β-galactosidase), which, indeed, resulted in output signals with different intensities at the same arsenite concentration. Hence, it was possible to use combinations of these cell lines to define arsenite concentration ranges at which none, one or more cell lines gave qualitative (yes/no) visible signals that were relatively independent of incubation time or bioreporter activity. The discriminated concentration ranges would fit very well with the current permissive (e.g. World Health Organization) levels of arsenite in drinking water (10 µg l−1).

Association between substance use and psychosocial characteristics among adolescents of the Seychelles.

BACKGROUND: We examined the associations between substance use (cigarette smoking, alcohol drinking, and cannabis use) and psychosocial characteristics at the individual and family levels among adolescents of the Seychelles, a rapidly developing small island state in the African region. METHODS: A school survey was conducted in a representative sample of 1432 students aged 11-17 years from all secondary schools. Data came from a self-administered anonymous questionnaire conducted along a standard methodology (Global School-based Health Survey, GSHS). Risk behaviors and psychosocial characteristics were dichotomized. Association analyses were adjusted for a possible classroom effect. RESULTS: The prevalence of cigarette smoking, alcohol drinking and cannabis use was higher in boys than in girls and increased with age. Age-adjusted and multivariate analyses showed that several individual level characteristics (e.g. suicidal ideation and truancy) and family level characteristics (e.g. poor parental monitoring) were associated with substance use among students. CONCLUSIONS: Our results suggest that health promotion programs should simultaneously address multiple risk behaviors and take into account a wide range of psychosocial characteristics of the students at the individual and family levels.

Influence of multiple injections of human chorionic gonadotropin (hCG) on urine and serum endogenous steroids concentrations.

Since it is established that human chorionic gonadotropin (hCG) affects testosterone production and release in the human body, the use of this hormone as a performance enhancing drug has been prohibited by the World Anti-Doping Agency. Nowadays, the only validated biomarker of a hCG doping is its direct quantification in urine. However, this specific parameter is subjected to large inter-individual variability and its determination is directly dependent on the reliability of hCG immunoassays used. In order to counteract these weaknesses, new biomarkers need to be evidenced. To address this issue, a pilot clinical study was performed on 10 volunteers submitted to 3 subsequent hCG injections. Blood and urine samples were collected during two weeks in order to follow the physiological effects on related compounds such as the steroid profile or hormones involved in the hypothalamo-pituitary axis. The hCG pharmacokinetic observed in all subjects was, as expected, prone to important inter-individual variations. Using ROC plots, level of testosterone and testosterone on luteinizing hormone ratio in both blood and urine were found to be the most relevant biomarker of a hCG abuse, regardless of inter-individual variations. In conclusion, this study showed the crucial importance of reliable quantification methods to assess low differences in hormonal patterns. In regard to these results and to anti-doping requirements and constraints, blood together with urine matrix should be included in the anti-doping testing program. Together with a longitudinal follow-up approach it could constitute a new strategy to detect a hCG abuse, applicable to further forms of steroid or other forbidden drug manipulation.

Learning about Bayesian networks for forensic interpretation : An example based on the "the problem of multiple propositions"

Both, Bayesian networks and probabilistic evaluation are gaining more and more widespread use within many professional branches, including forensic science. Notwithstanding, they constitute subtle topics with definitional details that require careful study. While many sophisticated developments of probabilistic approaches to evaluation of forensic findings may readily be found in published literature, there remains a gap with respect to writings that focus on foundational aspects and on how these may be acquired by interested scientists new to these topics. This paper takes this as a starting point to report on the learning about Bayesian networks for likelihood ratio based, probabilistic inference procedures in a class of master students in forensic science. The presentation uses an example that relies on a casework scenario drawn from published literature, involving a questioned signature. A complicating aspect of that case study - proposed to students in a teaching scenario - is due to the need of considering multiple competing propositions, which is an outset that may not readily be approached within a likelihood ratio based framework without drawing attention to some additional technical details. Using generic Bayesian networks fragments from existing literature on the topic, course participants were able to track the probabilistic underpinnings of the proposed scenario correctly both in terms of likelihood ratios and of posterior probabilities. In addition, further study of the example by students allowed them to derive an alternative Bayesian network structure with a computational output that is equivalent to existing probabilistic solutions. This practical experience underlines the potential of Bayesian networks to support and clarify foundational principles of probabilistic procedures for forensic evaluation.

A Robust Multiple Feature Approach To Endpoint Detection In Car Environment Based On Advanced Classifiers

In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.

Field Testing of Railroad Flatcar Bridges Volume II: Multiple Spans, TR-498, 2007

Based on the conclusions of IHRB Project TR-444, Demonstration Project Using Railroad Flat Car Bridges for Low Volume Road Bridges, additional research on the use of RRFC bridges was undertaken. This portion of the project investigated the following: (1) Different design and rating procedures; (2) Additional single span configurations plus multiple span configurations; (3) Different mechanisms for connecting adjacent RRFCs and the resulting lateral load distribution factors; (4) Sheet pile abutments; and (5) Behavior RRFCs that had been strengthened so that they could be used on existing abutments. A total of eight RRFC bridges were tested (five single span bridges, two two-span bridges, and one three-span bridge). Based on the results of this study a simplified design and rating procedure has been developed for the economical replacement bridge alternative. In Volume 1, the results from the testing of four single span RRFC bridges are presented, while in Volume 2,this volume, the results from the testing of the strengthened single span bridge plus the three multiple span bridges are presented.

Development of Non-Petroleum-Based Binders for Use in Flexible Pavements – Phase II, TR-650, 2015

Bio-binders can be utilized as asphalt modifiers, extenders, and replacements for conventional asphalt in bituminous binders. From the rheology results of Phase I of this project, it was found that the bio-binders tested had good performance, similar to conventional asphalt, except at low temperatures. Phase II of this project addresses this shortcoming and evaluates the Superpave performance of laboratory mixes produced with the enhanced bio-binders. The main objective of this research was to develop a bio-binder capable of replacing conventional asphalt in flexible pavements by incorporating ground tire rubber (GTR) into bio-oil derived from fast pyrolysis of agriculture and forestry residues. The chemical compatibility of the new bio-binder with GTR was assessed, and the low-temperature performance of the bio-binders was enhanced by the use of GTR. The newly developed binder, which consisted of 80 percent conventional binder and 20 percent rubber-modified bio-oil (85 percent bio-oil with 15 percent GTR), was used to produce mixes at two different air void contents, 4 and 7 percent. The laboratory performance test results showed that the performance of the newly developed bio-binder mixes is as good as or better than conventional asphalt mixes for fatigue cracking, rutting resistance, moisture sensitivity, and low-temperature cracking. These results need to be validated in field projects in order to demonstrate adequate performance for this innovative and sustainable technology for flexible pavements.

Improving T2 -weighted imaging at high field through the use of kT -points.

PURPOSE: At high magnetic field strengths (B0 ≥ 3 T), the shorter radiofrequency wavelength produces an inhomogeneous distribution of the transmit magnetic field. This can lead to variable contrast across the brain which is particularly pronounced in T2 -weighted imaging that requires multiple radiofrequency pulses. To obtain T2 -weighted images with uniform contrast throughout the whole brain at 7 T, short (2-3 ms) 3D tailored radiofrequency pulses (kT -points) were integrated into a 3D variable flip angle turbo spin echo sequence. METHODS: The excitation and refocusing "hard" pulses of a variable flip angle turbo spin echo sequence were replaced with kT -point pulses. Spatially resolved extended phase graph simulations and in vivo acquisitions at 7 T, utilizing both single channel and parallel-transmit systems, were used to test different kT -point configurations. RESULTS: Simulations indicated that an extended optimized k-space trajectory ensured a more homogeneous signal throughout images. In vivo experiments showed that high quality T2 -weighted brain images with uniform signal and contrast were obtained at 7 T by using the proposed methodology. CONCLUSION: This work demonstrates that T2 -weighted images devoid of artifacts resulting from B1 (+) inhomogeneity can be obtained at high field through the optimization of extended kT -point pulses. Magn Reson Med 71:1478-1488, 2014. © 2013 Wiley Periodicals, Inc.

Mapping the human connectome at multiple scales with diffusion spectrum MRI.

The global structural connectivity of the brain, the human connectome, is now accessible at millimeter scale with the use of MRI. In this paper, we describe an approach to map the connectome by constructing normalized whole-brain structural connection matrices derived from diffusion MRI tractography at 5 different scales. Using a template-based approach to match cortical landmarks of different subjects, we propose a robust method that allows (a) the selection of identical cortical regions of interest of desired size and location in different subjects with identification of the associated fiber tracts (b) straightforward construction and interpretation of anatomically organized whole-brain connection matrices and (c) statistical inter-subject comparison of brain connectivity at various scales. The fully automated post-processing steps necessary to build such matrices are detailed in this paper. Extensive validation tests are performed to assess the reproducibility of the method in a group of 5 healthy subjects and its reliability is as well considerably discussed in a group of 20 healthy subjects.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

In vitro selection of bacteria with potential for use as probiotics in marine shrimp culture

The objective of this work was to isolate strains of lactic acid bacteria with probiotic potential from the digestive tract of marine shrimp (Litopenaeus vannamei), and to carry out in vitro selection based on multiple characters. The ideotype (ideal proposed strain) was defined by the highest averages for the traits maximum growth velocity, final count of viable cells, and inhibition halo against nine freshwater and marine pathogens, and by the lowest averages for the traits duplication time and resistance of strains to NaCl (1.5 and 3%), pH (6, 8, and 9), and biliary salts (5%). Mahalanobis distance (D²) was estimated among the evaluated strains, and the best ones were those with the shortest distances to the ideotype. Ten bacterial strains were isolated and biochemically identified as Lactobacillus plantarum (3), L. brevis (3), Weissella confusa (2), Lactococcus lactis (1), and L. delbrueckii (1). Lactobacillus plantarum strains showed a wide spectrum of action and the largest inhibition halos against pathogens, both Gram-positive and negative, high growth rate, and tolerance to all evaluated parameters. In relation to ideotype, L. plantarum showed the lowest Mahalanobis (D²) distance, followed by the strains of W. confusa, L. brevis, L. lactis, and L. delbrueckii. Among the analyzed bacterial strains, those of Lactobacillus plantarum have the greatest potential for use as a probiotic for marine shrimp.

Efficiency and significance of multiple vocal signals in sibling competition

Animals can compete for resources by displaying various acoustic signals that may differentially affect the outcome of competition. We propose the hypothesis that the most efficient signal to deter opponents should be the one that most honestly reveals motivation to compete. We tested this hypothesis in the barn owl (Tyto alba) in which nestlings produce more calls of longer duration than siblings to compete for priority access to the indivisible prey item their parents will deliver next. Because nestlings increase call rate to a larger extent than call duration when they become hungrier, call rate would signal more accurately hunger level. This leads us to propose three predictions. First, a high number of calls should be more efficient in deterring siblings to compete than long calls. Second, the rate at which an individual calls should be more sensitive to variation in the intensity of the sibling vocal competition than the duration of its calls. Third, call rate should influence competitors' vocalization for a longer period of time than call duration. To test these three predictions we performed playback experiments by broadcasting to singleton nestlings calls of varying durations and at different rates. According to the first prediction, singleton nestlings became less vocal to a larger extent when we broadcasted more calls compared to longer calls. In line with the second prediction, nestlings reduced vocalization rate to a larger extent than call duration when we broadcasted more or longer calls. Finally, call rate had a longer influence on opponent's vocal behavior than call duration. Young animals thus actively and differentially use multiple signaling components to compete with their siblings over parental resources.