Immunostimulating activity of Uncaria tomentosa in RAW 264.7 macrophages

Uncaria tomentosa is a plant native to the Amazon that has immunomodulatory and antitumor properties due to the alkaloids found in the plant, being able to modify the immune response by potentiating or suspending the action of cytokines secreted by macrophages that induce the immune response, either by the classical route (M1) or through the alternative route (M2). Macrophages activated by M1 convert L-arginine into L-citrulline and nitric oxide (NO), whereas macrophages activated by the M2 pathway use the enzymatic activity of arginase to convert the same substrate into L-ornithine and urea. The aim of this work was to evaluate the immunostimulating activity of the crude hydroalcoholic extract from the bark of the U. tomentosa stem in RAW 264.7 macrophages. Concentrations of 0.2, 0.1 and 0.05 mg/mL of U. tomentosa extract associated with LPS, INF-γ and IL-4 inducers were tested by determining NO production and arginase enzyme activity. Nitric oxide production was enhanced by the extract when associated with LPS and LPS + INF-γ inducers. In the activity of the arginase enzyme, the extract decreased the stimulation of IL-4 on the enzyme, mainly at 0.2 mg/mL concentration. Therefore, it is concluded that the crude hydroalcoholic extract of the stem bark of U. tomentosa in RAW 264.7 cells, at a concentration of 0.2 mg/mL, showed considerable pro-inflammatory activity.

The carbon sink of secondary and degraded humid tropical forests

The globally important carbon sink of intact, old-growth tropical humid forests is declining because of climate change, deforestation and degradation from fire and logging^1-3. Recovering tropical secondary and degraded forests now cover about 10% of the tropical forest area⁴, but how much carbon they accumulate remains uncertain. Here we quantify the aboveground carbon (AGC) sink of recovering forests across three main continuous tropical humid regions: the Amazon, Borneo and Central Africa^5,6. On the basis of satellite data products^4,7, our analysis encompasses the heterogeneous spatial and temporal patterns of growth in degraded and secondary forests, influenced by key environmental and anthropogenic drivers. In the first 20 years of recovery, regrowth rates in Borneo were up to 45% and 58% higher than in Central Africa and the Amazon, respectively. This is due to variables such as temperature, water deficit and disturbance regimes. We find that regrowing degraded and secondary forests accumulated 107 Tg C year^-1 (90-130 Tg C year^-1) between 1984 and 2018, counterbalancing 26% (21-34%) of carbon emissions from humid tropical forest loss during the same period. Protecting old-growth forests is therefore a priority. Furthermore, we estimate that conserving recovering degraded and secondary forests can have a feasible future carbon sink potential of 53 Tg C year^-1 (44-62 Tg C year^-1) across the main tropical regions studied.

Co-limitation towards lower latitudes shapes global forest diversity gradients

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers.

Effect of capsaicin on the feed intake and immunoglobin concentration of sows, and performance of piglets

Capsaicin is a recognized alkaloid that can be used as a flavoring and palatability agent. These effects can increase feed intake in lactating sows during farrowing, especially under thermal stress conditions, and provide antioxidant and immunostimulant activities. The objective of this work was to find out the effects of a capsaicin-based product on the feed intake of sows, immunomodulation, and repercussions on litter performance and the control of piglet diarrhea conditions. A total of 132 pregnant sows and lactating sows and their respective litters were divided into 66 sows each and submitted to one of two possible treatments: a capsaicin-free diet (control group) or capsaicin per meal/day. Capsaicin was mixed with gestation and lactation diets in the proportion of 98.6 g of feed and 1.4 g of capsaicin/kg feed, with the dose administered "on top" of 100 g per treatment day on the first feeding. The sows were treated between 90 days of gestation and 21 days of lactation. Backfat thickness, feed intake during farrowing, colostrum production, IgG colostrum concentration, sow reproductive performance, piglet performance, and diarrhea were evaluated. Compared to the sows in the control group, those that received capsaicin had higher feed intake (+ 0.69 kg/day during lactation, P = 0.008), higher levels of IgG in colostrum (185.75 versus 153.80 mg/mL, P = 0.04), an 11.2% higher litter weight gain, with individual piglet weight gains greater than 5.24% (P = 0.045), and an effective reduction in the frequency of piglet diarrhea on the 10th and 17th days of age (P = 0.013 and P = 0.001, respectively). Capsaicin is an additive with potential effects on the sow's performance, with positive influences on the health and growth of suckling piglets.

Hormone therapy effect on menopausal systemic lupus erythematosus patients: a systematic review

Systemic lupus erythematosus (SLE) primarily affects women, who may need hormone therapy (HT) in menopause. There is, however, some concern as to its efficacy and safety. This systematic review aimed to determine the effect of HT on the activity of SLE and its safety. The study was a qualitative systematic review. Research was conducted with data retrieved from Embase, MEDLINE and Cochrane databases using MESH terms up to April 2021, with no bar on date or language. Sixteen studies were selected for analysis. Most of them showed HT to be effective in the treatment of menopausal symptoms with no impact in SLE activity, but one randomized clinical trial showed an increase in the number of thrombotic events. The present systematic review demonstrated the efficacy of HT for treating the menopausal symptoms of SLE patients. The risk of flare and thrombosis seems to be very low.

From Sound Perception to Automatic Detection of Schizophrenia: An EEG-Based Deep Learning Approach

Deep learning techniques have been applied to electroencephalogram (EEG) signals, with promising applications in the field of psychiatry. Schizophrenia is one of the most disabling neuropsychiatric disorders, often characterized by the presence of auditory hallucinations. Auditory processing impairments have been studied using EEG-derived event-related potentials and have been associated with clinical symptoms and cognitive dysfunction in schizophrenia. Due to consistent changes in the amplitude of ERP components, such as the auditory N100, some have been proposed as biomarkers of schizophrenia. In this paper, we examine altered patterns in electrical brain activity during auditory processing and their potential to discriminate schizophrenia and healthy subjects. Using deep convolutional neural networks, we propose an architecture to perform the classification based on multi-channels auditory-related EEG single-trials, recorded during a passive listening task. We analyzed the effect of the number of electrodes used, as well as the laterality and distribution of the electrical activity over the scalp. Results show that the proposed model is able to classify schizophrenia and healthy subjects with an average accuracy of 78% using only 5 midline channels (Fz, FCz, Cz, CPz, and Pz). The present study shows the potential of deep learning methods in the study of impaired auditory processing in schizophrenia with implications for diagnosis. The proposed design can provide a base model for future developments in schizophrenia research.

Coupled effects of climate teleconnections on drought, Santa Ana winds and wildfires in southern California

Projections of future climate change impacts suggest an increase of wildfire activity in Mediterranean ecosystems, such as southern California. This region is a wildfire hotspot and fire managers are under increasingly high pressures to minimize socio-economic impacts. In this context, predictions of high-risk fire seasons are essential to achieve adequate preventive planning. Regional-scale weather patterns and climatic teleconnections play a key role in modulating fire-conducive conditions across the globe, yet an analysis of the coupled effects of these systems onto the spread of large wildfires is lacking for the region. We analyzed seven decades (1953-2018) of documentary wildfire records from southern California to assess the linkages between weather patterns and large-scale climate modes using various statistical techniques, including Redundancy Analysis, Superposed Epoch Analysis and Wavelet Coherence. We found that high area burned is significantly associated with the occurrence of adverse weather patterns, such as severe droughts and Santa Ana winds. Further, we document how these fire-promoting events are mediated by climate teleconnections, particularly by the coupled effects of El Niño Southern Oscillation and Atlantic Multidecadal Oscillation.

Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls

The complexity and heterogeneity of schizophrenia symptoms challenge an objective diagnosis, which is typically based on behavioral and clinical manifestations. Moreover, the boundaries of schizophrenia are not precisely demarcated from other nosologic categories, such as bipolar disorder. The early detection of schizophrenia can lead to a more effective treatment, improving patients' quality of life. Over the last decades, hundreds of studies aimed at specifying the neurobiological mechanisms that underpin clinical manifestations of schizophrenia, using techniques such as electroencephalography (EEG). Changes in event-related potentials of the EEG have been associated with sensory and cognitive deficits and proposed as biomarkers of schizophrenia. Besides contributing to a more effective diagnosis, biomarkers can be crucial to schizophrenia onset prediction and prognosis. However, any proposed biomarker requires substantial clinical research to prove its validity and cost-effectiveness. Fueled by developments in computational neuroscience, automatic classification of schizophrenia at different stages (prodromal, first episode, chronic) has been attempted, using brain imaging pattern recognition methods to capture differences in functional brain activity. Advanced learning techniques have been studied for this purpose, with promising results. This review provides an overview of recent machine learning-based methods for schizophrenia classification using EEG data, discussing their potentialities and limitations. This review is intended to serve as a starting point for future developments of effective EEG-based models that might predict the onset of schizophrenia, identify subjects at high-risk of psychosis conversion or differentiate schizophrenia from other disorders, promoting more effective early interventions.

Combining unsupervised and supervised learning for predicting the final stroke lesion

Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-making process of treatment planning and intervention. Treatment selection is influenced by clinical diagnosis, which requires delineating the stroke lesion, as well as characterising cerebral blood flow dynamics using neuroimaging acquisitions. Nonetheless, predicting the final stroke lesion is an intricate task, due to the variability in lesion size, shape, location and the underlying cerebral haemodynamic processes that occur after the ischaemic stroke takes place. Moreover, since elapsed time between stroke and treatment is related to the loss of brain tissue, assessing and predicting the final stroke lesion needs to be performed in a short period of time, which makes the task even more complex. Therefore, there is a need for automatic methods that predict the final stroke lesion and support physicians in the treatment decision process. We propose a fully automatic deep learning method based on unsupervised and supervised learning to predict the final stroke lesion after 90 days. Our aim is to predict the final stroke lesion location and extent, taking into account the underlying cerebral blood flow dynamics that can influence the prediction. To achieve this, we propose a two-branch Restricted Boltzmann Machine, which provides specialized data-driven features from different sets of standard parametric Magnetic Resonance Imaging maps. These data-driven feature maps are then combined with the parametric Magnetic Resonance Imaging maps, and fed to a Convolutional and Recurrent Neural Network architecture. We evaluated our proposal on the publicly available ISLES 2017 testing dataset, reaching a Dice score of 0.38, Hausdorff Distance of 29.21 mm, and Average Symmetric Surface Distance of 5.52 mm.

Reproductive status effects of pair-housed male pigs on natural, agonistic and sexual behaviours

Boar rearing, which avoids pain and suffering caused by surgical castration, provides better performance, a greater deposition of muscle tissue and leaner carcasses and thus has beneficial effects on both animal welfare and the product. Some countries that do not slaughter boars must consider their boar taint and aggressive and sexual behaviours. Considering that pigs are housed in large groups, which may complicate the formation of social hierarchies and increase fighting and mounting behaviours, some studies have conducted research with reduced numbers of pigs per pen, but these behaviours continued to be observed. However, a study of the reproductive status of pair-housed male pigs has yet to be reported. The aim of this study was to determine whether the reproductive status of uncastrated, immunocastrated and surgically castrated pair-housed male pigs alters their natural, agonistic and sexual behaviours. A total of 48 male pigs from Agroceres PIC™ genetics were assigned to three groups: surgically castrated (barrows), immunocastrated and uncastrated (boars). Natural, aggressive and sexual behaviours of the pigs were assessed by direct observations during four periods of 12 h each (six, five and three weeks before slaughter and the slaughter week). The pigs were housed in pairs from the growing phase until slaughter. Animal behaviour was observed from the finishing phase to slaughter. Carcass lesions were assessed according to five different classes (one: no injury; two to five: severely injured). Overall, boars spent more time lying and less time eating and drinking than barrows. In total of all the periods (48 h), boars expressed more aggressive and sexual behaviours than barrows, whereas immunocastrated pigs displayed similar behaviours to boars, before and after the second vaccine dose. No differences in carcass lesions between treatments and no prevalence of carcasses with severe injuries were observed. In conclusion, the reproductive status of pair-housed male pigs did not change the natural behaviour of boars, immunocastrated pigs or barrows. The agonistic and sexual behaviours of boars and barrows remained unchanged. When housing pigs in pairs, immunocastrated pigs presented similar agonistic and sexual behaviours to boars before and after the second immunocastration vaccine dose. The use of pair-housed uncastrated male pigs has generated welfare benefits for these animals, as the number of carcasses with injuries did not differ from barrows and immunocastrated pigs.

Can severe drought periods increase metal concentrations in mangrove sediments? A case study in eastern Brazil

Mangrove ecosystems are essential to society, providing ecological and economic services, and play a crucial role in the geochemical land-ocean interface as a sink for potentially toxic metals. This study assessed metals (Al, Ba, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Sr and Zn) and arsenic in sediments from three mangrove zones (Tidal Flat, Rhizophora mangle L. and Avicennia schaueriana Stapf & Leechman ex Moldenke forests) during two seasons: spring of 2015 and autumn of 2016, with the latter being a severe drought year. Overall results suggest that Fe/Mn oxyhydroxides and clay minerals control the distribution of metal and arsenic in the Tidal Flat zone. In the mangrove forest however, sulfur and organic matter dominate complexation, with Fe mainly present as insoluble sulfide, and As, Cd, Cu and Zn as metal sulfides or organometallic complexes. In the autumn of 2016, all elements except Cd and Pb had lower concentrations compared to the spring of 2015. Cd and Pb were probably transferred from sources other than mangrove sediments, due to increased saline water intrusion, a consequence of reduced riverine flow, and precipitated in the Rhizophora mangle and Avicennia schaueriana mangrove forests. This increase of Cd and Pb in the mangrove forest suggests potential storage of metal contaminants in the organic rich areas, a change in availability and potential toxicity to fauna and flora and a need for regulatory responses to sediment quality. These results indicate a change in sediment metal contaminant dynamics with the increasing occurrence of extreme weather events - an increased risk to the ecosystem.

Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses

Deforestation is the primary driver of carbon losses in tropical forests, but it does not operate alone. Forest fragmentation, a resulting feature of the deforestation process, promotes indirect carbon losses induced by edge effect. This process is not implicitly considered by policies for reducing carbon emissions in the tropics. Here, we used a remote sensing approach to estimate carbon losses driven by edge effect in Amazonia over the 2001 to 2015 period. We found that carbon losses associated with edge effect (947 Tg C) corresponded to one-third of losses from deforestation (2592 Tg C). Despite a notable negative trend of 7 Tg C year^-1 in carbon losses from deforestation, the carbon losses from edge effect remained unchanged, with an average of 63 ± 8 Tg C year^-1 Carbon losses caused by edge effect is thus an additional unquantified flux that can counteract carbon emissions avoided by reducing deforestation, compromising the Paris Agreement's bold targets.

Benchmark maps of 33 years of secondary forest age for Brazil

The restoration and reforestation of 12 million hectares of forests by 2030 are amongst the leading mitigation strategies for reducing carbon emissions within the Brazilian Nationally Determined Contribution targets assumed under the Paris Agreement. Understanding the dynamics of forest cover, which steeply decreased between 1985 and 2018 throughout Brazil, is essential for estimating the global carbon balance and quantifying the provision of ecosystem services. To know the long-term increment, extent, and age of secondary forests is crucial; however, these variables are yet poorly quantified. Here we developed a 30-m spatial resolution dataset of the annual increment, extent, and age of secondary forests for Brazil over the 1986-2018 period. Land-use and land-cover maps from MapBiomas Project (Collection 4.1) were used as input data for our algorithm, implemented in the Google Earth Engine platform. This dataset provides critical spatially explicit information for supporting carbon emissions reduction, biodiversity, and restoration policies, enabling environmental science applications, territorial planning, and subsidizing environmental law enforcement.

On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities

As artificial intelligence (AI) systems begin to make their way into clinical radiology practice, it is crucial to assure that they function correctly and that they gain the trust of experts. Toward this goal, approaches to make AI "interpretable" have gained attention to enhance the understanding of a machine learning algorithm, despite its complexity. This article aims to provide insights into the current state of the art of interpretability methods for radiology AI. This review discusses radiologists' opinions on the topic and suggests trends and challenges that need to be addressed to effectively streamline interpretability methods in clinical practice. Supplemental material is available for this article. © RSNA, 2020 See also the commentary by Gastounioti and Kontos in this issue.

Standardizing Ecosystem Morphological Traits from 3D Information Sources

3D-imaging technologies provide measurements of terrestrial and aquatic ecosystems' structure, key for biodiversity studies. However, the practical use of these observations globally faces practical challenges. First, available 3D data are geographically biased, with significant gaps in the tropics. Second, no data source provides, by itself, global coverage at a suitable temporal recurrence. Thus, global monitoring initiatives, such as assessment of essential biodiversity variables (EBVs), will necessarily have to involve the combination of disparate data sets. We propose a standardized framework of ecosystem morphological traits - height, cover, and structural complexity - that could enable monitoring of globally consistent EBVs at regional scales, by flexibly integrating different information sources - satellites, aircrafts, drones, or ground data - allowing global biodiversity targets relating to ecosystem structure to be monitored and regularly reported.

Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks

Fully convolutional networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting several voxels in a single forward pass. So, there is a direct spatial correspondence between a unit in a feature map and the voxel in the same location. In a convolutional layer, the kernel spans over all channels and extracts information from them. We observe that linear recombination of feature maps by increasing the number of channels followed by compression may enhance their discriminative power. Moreover, not all feature maps have the same relevance for the classes being predicted. In order to learn the inter-channel relationships and recalibrate the channels to suppress the less relevant ones, squeeze and excitation blocks were proposed in the context of image classification with convolutional neural networks. However, this is not well adapted for segmentation with fully convolutional networks since they segment several objects simultaneously, hence a feature map may contain relevant information only in some locations. In this paper, we propose recombination of features and a spatially adaptive recalibration block that is adapted for semantic segmentation with fully convolutional networks- the SegSE block. Feature maps are recalibrated by considering the cross-channel information together with spatial relevance. The experimental results indicate that recombination and recalibration improve the results of a competitive baseline, and generalize across three different problems: brain tumor segmentation, stroke penumbra estimation, and ischemic stroke lesion outcome prediction. The obtained results are competitive or outperform the state of the art in the three applications.

Stochastic decision trigger modelling to assess the probability of wildland fire impact

The ability to estimate both the time and probability of a wildfire reaching an area to be protected is critically important to preventing loss of human life and property, and damage to ecological and economic assets. Wildfire decision trigger modelling has been used to assess fire exposure and create evacuation trigger buffers around the communities providing a specific amount of warning time. This approach has been applied in multiple scenarios including household-level and community-level evacuation planning and during suppression operations. However, little attention has been paid to input data uncertainty using this modelling approach. This study presents an innovative stochastic fire simulation decision trigger modelling method that produces a probability map of the fire arrival to areas to be protected by simulating (n) wildfire decision trigger buffers with varied input data according to a potential range of deviations. The Tubbs fire (USA) was used as case study to show the applicability of this approach to estimate the probability of wildland fire impact. Our results highlighted the importance of considering input data uncertainty in operational environments to estimate fire progression and decision trigger buffers to better develop suppression tactic and strategy. The method presented may be solved in real-time and used with any empirical fire propagation model as a core engine. Practical real-time implications of this fire simulation mode are discussed.

How does drought impact burned area in Mediterranean vegetation communities?

Drought and water stress are widely known to influence fuel moisture content and flammability, although differences do exist according to the response mechanisms and adaptive traits displayed by plant communities. In the Mediterranean basin, as a result of climate change, extreme drought events are expected to become more frequent and severe, envisaging episodes of increased fire risk. In this paper, we expand the scale of analysis on how does drought influence wildfire incidence exploring the joint influence on burned area of drought duration, magnitude and temporal distribution, and the affected vegetation communities (VCs). We leveraged the weekly adaptation of the Standardized Precipitation Evapotranspiration Index (SPEI) and historical fire perimeters to model complex interactions between drought and burned area mediated by VC composition and structure. We applied multivariate factor analysis (multi-group Principal Component Analysis) and non-parametric mixed regression models (GAMM) to a set of 1-to-48 weeks SPEI and 10 dominant VCs. We detected a significant influence of drought events (negative SPEI) on burned area, although differences in terms of seasonal distribution and VC were observed. Drought played a major role in explaining burned area in late spring and autumn by altering the usual positive rainfall-evapotranspiration balance, suggesting a potential lengthening of the fire season given the projected drought trends in the next decades.

Abstract P2005: Antioxidant Resveratrol Attenuates Cardiac Fibrosis and Reduces Matrix Metalloproteinase-2 Activity in Heart Failure Rats

Increased reactive oxygen species activate matrix metalloproteinases (MMP) playing a critical role in cardiovascular remodeling after myocardial infarction (MI). We evaluated the antioxidant effect of resveratrol (RESV) on cardiac remodeling and MMP-2 activity in heart failure (HF) rats. Wistar rats were divided into the following groups: Control; Control+RESV; HF; HF+RESV (10mg/kg/day, p.o., 4 wk). The MI was elicited by ligation of the coronary artery. After four weeks, the animals were anesthetized to evaluate the cardiac function and to collect the hearts and blood samples. Heart weight/body weight (HW/BW, mg/g) ratio and the infarct size were calculated. Histological assessment of the left ventricle was carried out with hematoxylin/eosin, and fibrosis was quantified with Masson trichrome-staining. Cardiac and plasma MMP-2 activities were determined by gel zymography. The oxidative stress was assessed measuring the thiobarbituric acid reactive substances (TBARS) from plasma. HF and HF+RESV showed similar infarct size. The HW/BW ratio increased (3.7±0.2 vs 3.8±0.2 vs 3.0±0.05). RESV improved the maximal slope of the systolic increment of left ventricular pressure in HF animals (9004±1120 vs 5849±595 vs 7181±533 mmHg/s) and reduced the end diastolic pressure (4±1 vs 18±4 vs 7±1 mmHg). HF increased TBARS, whereas RESV produced an antioxidant effect in HF+RESV group (11.2±0.9 vs 21.7±3 vs 8.2±1.4 μmol/L). HF augmented cardiac fibrosis (30,8±1,3 vs 8,8±0,5%) and myocyte hypertrophy (12,2±0,7 vs 8,8±0,3μm). RESV reduced collagen deposition in HF hearts (22,7±1,9%), but did not reduce the myocyte hypertrophy (11,7±0,6 μm). Cardiac and plasma MMP-2 activity were higher in HF animals (plasma: 1344±39 vs. 1164±47; heart: 536±54 vs. 262±18). RESV decreased plasma MMP-2 activity in control (992±27) and HF animals (1200±49) and reduced cardiac MMP-2 activity only in HF+RESV group (426±42). In conclusion, the antioxidant effect of RESV improved cardiac function and fibrosis and reduced MMP-2 levels in the heart and plasma from HF rats.

Persistent effects of fragmentation on tropical rainforest canopy structure after 20 yr of isolation

Assessing the persistent impacts of fragmentation on aboveground structure of tropical forests is essential to understanding the consequences of land use change for carbon storage and other ecosystem functions. We investigated the influence of edge distance and fragment size on canopy structure, aboveground woody biomass (AGB), and AGB turnover in the Biological Dynamics of Forest Fragments Project (BDFFP) in central Amazon, Brazil, after 22+ yr of fragment isolation, by combining canopy variables collected with portable canopy profiling lidar and airborne laser scanning surveys with long-term forest inventories. Forest height decreased by 30% at edges of large fragments (>10 ha) and interiors of small fragments (<3 ha). In larger fragments, canopy height was reduced up to 40 m from edges. Leaf area density profiles differed near edges: the density of understory vegetation was higher and midstory vegetation lower, consistent with canopy reorganization via increased regeneration of pioneers following post-fragmentation mortality of large trees. However, canopy openness and leaf area index remained similar to control plots throughout fragments, while canopy spatial heterogeneity was generally lower at edges. AGB stocks and fluxes were positively related to canopy height and negatively related to spatial heterogeneity. Other forest structure variables typically used to assess the ecological impacts of fragmentation (basal area, density of individuals, and density of pioneer trees) were also related to lidar-derived canopy surface variables. Canopy reorganization through the replacement of edge-sensitive species by disturbance-tolerant ones may have mitigated the biomass loss effects due to fragmentation observed in the earlier years of BDFFP. Lidar technology offered novel insights and observational scales for analysis of the ecological impacts of fragmentation on forest structure and function, specifically aboveground biomass storage.

Characterization of membrane vesicles released by Mycobacterium avium in response to environment mimicking the macrophage phagosome

Aim:

To investigate the formation of Mycobacterium avium membrane vesicles (MVs) within macrophage phagosomes.

Materials & methods:

A phagosome model was utilized to characterize proteomics and lipidomics of MVs. A click chemistry-based enrichment assay was employed to examine the presence of MV proteins in the cytosol of host cells.

Results:

Exposure to metals at concentrations present in phagosomes triggers formation of bacterial MVs. Proteomics identified several virulence factors, including enzymes involved in the cell wall synthesis, lipid and fatty acid metabolism. Some of MV proteins were also identified in the cytosol of infected macrophages. MVs harbor dsDNA.

Conclusion:

M. avium produces MVs within phagosomes. MVs carry products with potential roles in modulation of host immune defenses and intracellular survival.

Munchausen by proxy syndrome mimicking childhood-onset systemic lupus erythematosus

Childhood-onset systemic lupus erythematosus (cSLE) is a chronic inflammatory multisystem autoimmune disease that requires multiple differential diagnoses. Munchausen by proxy syndrome (MBPS) is a form of child abuse, where a caregiver intentionally creates a medical history and induces or fabricates signs or disease in a patient. To our knowledge, there is no case report of MBPS mimicking cSLE diagnosis. We reported herein a 9-year-old male patient, with a history of multiple hospitalizations due to seizures with altered levels of consciousness. The mother reported malar rash, photosensitivity, alopecia, arthralgia, arterial hypertension, macroscopic hematuria, seizure and positive antinuclear antibodies. In the other service, he was treated with intravenous methylprednisolone, prednisone and mycophenolate mofetil. At 8 years and 8 months, he was admitted to our tertiary center with history of fever and macroscopic hematuria. Laboratory examinations were normal, including negative for antinuclear antibodies, anti-double stranded DNA, anticardiolipin, anti-Ro/SSA, anti-La/SSB, anti-RNP and anti-Sm antibodies. Multiple urine cultures revealed the presence of Enterococcus faecium, Acinetobacter sp., Stenotrophomonas maltophilia and Serratia marcescens, without any association with pyuria. At 8 years and 9 months, he was readmitted at emergency room with history of severe fever, headache, vomiting, photophobia, phonophobia and dizziness. The physical examination showed agitation, confusion, ataxic gait, slurred speech, horizontal nystagmus, painful facial expressions, tachycardia and weight loss. Brain magnetic resonance angiography and cerebrospinal fluid analysis were normal. During hospitalization, he had an acute episode of epistaxis and otalgia with excoriation in the auditory canal. At that moment, the suspicion of MBPS mimicking cSLE was raised and phenytoin intoxication was confirmed (peak phenytoin concentration was 45.4 mcg/mL, therapeutic range 10-20 mcg/mL). The mother and the patient were immediately separated, and she was replaced by another legal guardian. One week later, the neurological and other signs and symptoms were completely resolved. The child was placed under paternal custody with a court order and moved to another state. After that, the mother reported phenytoin use for her child and was referred to psychiatric follow-up. In conclusion, the first case of MBPS mimicking cSLE, resulting in multiple unnecessary examinations and treatments with delayed diagnosis was reported.

Stroke Lesion Outcome Prediction Based on MRI Imaging Combined With Clinical Information

In developed countries, the second leading cause of death is stroke, which has the ischemic stroke as the most common type. The preferred diagnosis procedure involves the acquisition of multi-modal Magnetic Resonance Imaging. Besides detecting and locating the stroke lesion, Magnetic Resonance Imaging captures blood flow dynamics that guides the physician in evaluating the risks and benefits of the reperfusion procedure. However, the decision process is an intricate task due to the variability of lesion size, shape, and location, as well as the complexity of the underlying cerebral hemodynamic process. Therefore, an automatic method that predicts the stroke lesion outcome, at a 3-month follow-up, would provide an important support to the physicians' decision process. In this work, we propose an automatic deep learning-based method for stroke lesion outcome prediction. Our main contribution resides in the combination of multi-modal Magnetic Resonance Imaging maps with non-imaging clinical meta-data: the thrombolysis in cerebral infarction scale, which categorizes the success of recanalization, achieved through mechanical thrombectomy. In our proposal, this clinical information is considered at two levels. First, at a population level by embedding the clinical information in a custom loss function used during training of our deep learning architecture. Second, at a patient-level through an extra input channel of the neural network used at testing time for a given patient case. By merging imaging with non-imaging clinical information, we aim to obtain a model aware of the principal and collateral blood flow dynamics for cases where there is no perfusion beyond the point of occlusion and for cases where the perfusion is complete after the occlusion point.

Drinking Water Quality Monitoring: An Alternative Approach to Microbial Contamination Events

The nonstop growing of world population and consequent increase in water demands, as raise the interest in continuous and real-time drinking water monitoring systems. The following study aims to study Tryptophan Intrinsic Fluorescence as a method for detecting microbial contamination events in drinking water.

Abstract P285: Carotid Sinus Nerve Stimulation Attenuates Alveolar Bone Loss in Rats With Induced Periodontitis

Introduction: Few studies have focused on the impact of the progression of periodontitis on hypertension. Baroreflex Activation Therapy (BAT) has been used to treat patients with resistant hypertension. Our laboratory has investigated the role of electrical carotid sinus nerve (CSN) stimulation in unanesthetized rats, on local and systemic inflammation. Since periodontitis is a chronic inflammatory disease, the aim of this study was to evaluate the role of the baro- and chemoreflex activation, by means of electrical stimulation of the CSN, on alveolar bone loss in rats with periodontitis.

Methods and Results: Under ketamine/xylazine anesthesia Wistar Hannover rats were implanted with electrodes around the CSN and a catheter inserted into the abdominal aorta for blood pressure recording. After 48h periodontitis was induced by the ligation of the bilateral mandibular first molar, followed by the electrical stimulation of the CSN (1,5-4V, 1ms, 30Hz for 10 min); which was continued during the next eight days. At the 8 th day after the induction of periodontitis, the rats were euthanized and the jaws were resected; besides, microtomographic analysis was performed by bi and three-dimensional quantification using micro-computerized tomography. As compared to baseline results the electrical stimulation of the CSN (N=7) promoted a decrease in mean arterial pressure (108± 8 vs. 91± 5 mmHg; p<0.05) and heart rate (374±10 vs. 319±15 bpm; p<0.05). The CSN stimulated rats (N=7) with periodontitis showed greater bone volume and bone surface than the non-stimulated control rats (N=4) with periodontitis, evaluated by three-dimensional analysis (0.5±0.005 vs. 0.7±0.03 mm 3 ; 16.54±0.22 vs. 23.30±0.66 mm 2 , respectively; p<0.05). Moreover, the CSN stimulated rats with periodontitis presented a decrease of the furcation area and interproximal region as compared to the non-stimulated control rats (1.14±0.06 vs. 0.5±0.07 mm; 3.25±0.03 vs. 2,48±0,13 mm, respectively; p<0.05).

Conclusions: Overall, these results demonstrate that stimulation of the CSNs promotes a protective effect on alveolar bone loss elicited by periodontitis involving the activation of the baro- and chemoreflex.

Characteristics of 1555 childhood-onset lupus in three groups based on distinct time intervals to disease diagnosis: a Brazilian multicenter study

Objective The objective of this study was to compare demographic data, clinical/laboratorial features and disease activity at diagnosis in three different groups with distinct time intervals between onset of signs/symptoms and disease diagnosis. Methods A multicenter study was performed in 1555 childhood-onset systemic lupus erythematosus (American College of Rheumatology criteria) patients from 27 pediatric rheumatology services. Patients were divided into three childhood-onset systemic lupus erythematosus groups: A: short time interval to diagnosis (<1 month); B: intermediate time interval (≥1 and <3 months); and C: long time interval (≥3 months). An investigator meeting was held to define the protocol. Demographic data, SLICC classification criteria and SLEDAI-2 K were evaluated. Results The number of patients in each group was: A = 60 (4%); B = 522 (33.5%); and C = 973 (62.5%). The median age at diagnosis (11.1 (4.2-17) vs. 12 (1.9-17.7) vs. 12.5 (3-18) years, P = 0.025) was significantly lower in group A compared with groups B and C. The median number of diagnostic criteria according to SLICC (7 (4-12) vs. 6 (4-13) vs. 6 (4-12), P < 0.0001) and SLEDAI-2 K (18 (6-57) vs. 16 (2-63) vs. 13 (1-49), P < 0.0001) were significantly higher in group A than the other two groups. The frequency of oral ulcers in the palate (25% vs. 15% vs. 11%, P = 0.003), pleuritis (25% vs. 24% vs. 14%, P < 0.0001), nephritis (52% vs. 47% vs. 40%, P = 0.009), neuropsychiatric manifestations (22% vs. 13% vs. 10%, P = 0.008), thrombocytopenia (32% vs. 18% vs. 19%, P = 0.037), leucopenia/lymphopenia (65% vs. 46% vs. 40%, P < 0.0001) and anti-dsDNA antibodies (79% vs. 66% vs. 61%, P = 0.01) were significantly higher in group A compared with the other groups. In contrast, group C had a less severe disease characterized by higher frequencies of synovitis (61% vs. 66% vs. 71%, P = 0.032) and lower frequencies of serositis (37% vs. 33% vs. 25%, P = 0.002), proteinuria >500 mg/day (48% vs. 45% vs. 36%, P = 0.002) and low complement levels (81% vs. 81% vs. 71%, P < 0.0001) compared with groups A or B. Conclusions Our large Brazilian multicenter study demonstrated that for most childhood-onset systemic lupus erythematosus patients, diagnosis is delayed probably due to mild disease onset. Conversely, the minority has a very short time interval to diagnosis and a presentation with a more severe and active multisystemic condition.

Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features

Gliomas are among the most common and aggressive brain tumours. Segmentation of these tumours is important for surgery and treatment planning, but also for follow-up evaluations. However, it is a difficult task, given that its size and locations are variable, and the delineation of all tumour tissue is not trivial, even with all the different modalities of the Magnetic Resonance Imaging (MRI). We propose a discriminative and fully automatic method for the segmentation of gliomas, using appearance- and context-based features to feed an Extremely Randomized Forest (Extra-Trees). Some of these features are computed over a non-linear transformation of the image. The proposed method was evaluated using the publicly available Challenge database from BraTS 2013, having obtained a Dice score of 0.83, 0.78 and 0.73 for the complete tumour, and the core and the enhanced regions, respectively. Our results are competitive, when compared against other results reported using the same database.

Sparse high order potentials for extending multi-surface segmentation of OCT images with drusen

Drusen quantification is important for evaluating age-related macular degeneration (AMD) progress. Most methods for retinal layers segmentation in optical coherence tomography (OCT) depend heavily on prior information. This improves robustness, but also has the downside of increasing surface rigidity. Hence, those algorithms normally smooth drusen borders, as significant local variations are not expected. In this work, we propose to integrate sparse higher order potentials (SHOPs) into a multi-surface segmentation framework to cope with local boundary variations caused by drusen. The algorithm was evaluated in a database of 20 patients with AMD. The mean unsigned error for the inner retinal pigment epithelium (IRPE) and Bruch's membrane (BM) was 5.65±6.26 and 4.37±5.25 μm, respectively. These results are relative to the average of two experts, whose inter-observer variability was 7.30±6.87 μm for IRPE and 5.03±4.37 μm for BM. The use SHOPs resulted in a successful segmentation of the IRPE. The remaining boundaries were also successfully segmented.

Early dystrophin loss is coincident with the transition of compensated cardiac hypertrophy to heart failure

Hypertension causes cardiac hypertrophy, one of the most important risk factors for heart failure (HF). Despite the importance of cardiac hypertrophy as a risk factor for the development of HF, not all hypertrophied hearts will ultimately fail. Alterations of cytoskeletal and sarcolemma-associated proteins are considered markers cardiac remodeling during HF. Dystrophin provides mechanical stability to the plasma membrane through its interactions with the actin cytoskeleton and, indirectly, to extracellular matrix proteins. This study was undertaken to evaluate dystrophin and calpain-1 in the transition from compensated cardiac hypertrophy to HF. Wistar rats were subjected to abdominal aorta constriction and killed at 30, 60 and 90 days post surgery (dps). Cardiac function and blood pressure were evaluated. The hearts were collected and Western blotting and immunofluorescence performed for dystrophin, calpain-1, alpha-fodrin and calpastatin. Statistical analyses were performed and considered significant when p<0.05. After 90 dps, 70% of the animals showed hypertrophic hearts (HH) and 30% hypertrophic+dilated hearts (HD). Systolic and diastolic functions were preserved at 30 and 60 dps, however, decreased in the HD group. Blood pressure, cardiomyocyte diameter and collagen content were increased at all time points. Dystrophin expression was lightly increased at 30 and 60 dps and HH group. HD group showed decreased expression of dystrophin and calpastatin and increased expression of calpain-1 and alpha-fodrin fragments. The first signals of dystrophin reduction were observed as early as 60 dps. In conclusion, some hearts present a distinct molecular pattern at an early stage of the disease; this pattern could provide an opportunity to identify these failure-prone hearts during the development of the cardiac disease. We showed that decreased expression of dystrophin and increased expression of calpains are coincident and could work as possible therapeutic targets to prevent heart failure as a consequence of cardiac hypertrophy.

Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation

Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images.

Anti-ribosomal P antibody: a multicenter study in childhood-onset systemic lupus erythematosus patients

Objectives Anti-ribosomal P protein (anti-P) autoantibodies are highly specific for systemic lupus erythematosus (SLE). However, the evaluation of this autoantibody in childhood-onset SLE (cSLE) populations has been limited to a few small series, hampering the interpretation of the clinical and laboratorial associations. Therefore, the objective of this multicenter cohort study was to evaluate demographic, clinical/laboratorial features, and disease damage score in cSLE patients with and without the presence of anti-P antibody. Methods This was a retrospective multicenter study performed in 10 pediatric rheumatology services of São Paulo state, Brazil. Anti-P antibodies were measured by ELISA in 228 cSLE patients. Results Anti-P antibodies were observed in 61/228 (27%) cSLE patients. Frequencies of cumulative lymphadenopathy (29% vs. 15%, p = 0.014), acute confusional state (13% vs. 5%, p = 0.041), mood disorder (18% vs. 8%, p = 0.041), autoimmune hemolytic anemia (34% vs. 15%, p = 0.001), as well as presence of anti-Sm (67% vs. 40%, p = 0.001), anti-RNP (39% vs. 21%, p = 0.012) and anti-Ro/SSA antibodies (43% vs. 25%, p = 0.016) were significantly higher in cSLE patients with anti-P antibodies compared to those without these autoantibodies. A multiple regression model revealed that anti-P antibodies were associated with autoimmune hemolytic anemia (odds ratio (OR) = 2.758, 95% confidence interval (CI): 1.304-5.833, p = 0.008) and anti-Sm antibody (OR = 2.719, 95% CI: 1.365-5.418, p = 0.004). The SLICC/ACR damage index was comparable in patients with and without anti-P antibodies ( p = 0.780). Conclusions The novel association of anti-P antibodies and autoimmune hemolytic anemia was evidenced in cSLE patients and further studies are necessary to determine if anti-P titers may vary with this hematological manifestation.

Hypercapnia-induced active expiration increases in sleep and enhances ventilation in unanaesthetized rats

KEY POINTS

Expiratory muscles (abdominal and thoracic) can be recruited when respiratory drive increases under conditions of increased respiratory demand such as hypercapnia. Studying hypercapnia-induced active expiration in unanaesthetized rats importantly contributes to the understanding of how the control system is integrated in vivo in freely moving animals. In unanaesthetized rats, hypercapnia-induced active expiration was not always recruited either in wakefulness or in sleep, suggesting that additional factors influence the recruitment of active expiration. The pattern of abdominal muscle recruitment varied in a state-dependent manner with active expiration being more predominant in the sleep state than in quiet wakefulness. Pulmonary ventilation was enhanced in periods with active expiration compared to periods without it.

ABSTRACT

Expiration is passive at rest but becomes active through recruitment of abdominal muscles under increased respiratory drive. Hypercapnia-induced active expiration has not been well explored in unanaesthetized rats. We hypothesized that (i) CO₂ -evoked active expiration is recruited in a state-dependent manner, i.e. differently in sleep or wakefulness, and (ii) recruitment of active expiration enhances ventilation, hence having an important functional role in meeting metabolic demand. To test these hypotheses, Wistar rats (280-330 g) were implanted with electrodes for EEG and electromyography EMG of the neck, diaphragm (DIA) and abdominal (ABD) muscles. Active expiratory events were considered as rhythmic ABD_EMG activity interposed to DIA_EMG . Animals were exposed to room air followed by hypercapnia (7% CO₂ ) with EEG, EMG and ventilation ( V̇E ) recorded throughout the experimental protocol. No active expiration was observed during room air exposure. During hypercapnia, CO₂ -evoked active expiration was predominantly recruited during non-rapid eye movement sleep. Its increased occurrence during sleep was evidenced by the decreased DIA-to-ADB ratio (1:1 ratio means that each DIA event is followed by an ABD event, indicating a high occurrence of ABD activity). Moreover, V̇E was also enhanced (P < 0.05) in periods with active expiration. V̇E had a positive correlation (P < 0.05) with the peak amplitude of ABD_EMG activity. The data demonstrate strongly that hypercapnia-induced active expiration increases during sleep and provides an important functional role to support V̇E in conditions of increased respiratory demand.

Abstract P422: Two-kidney One Clip (2k1c) Hypertension Reduces Complexity of Heart Rate Variability in Mice

Introduction: Hypertension is the most common chronic cardiovascular disease, being multifactorial in origin and an important cause of morbidity and mortality worldwide. Complex behaviors of heart rate series have been widely recognized and the loss of complexity in heart rate variability (HRV) has been shown to predict adverse cardiovascular outcomes. We hypothesized that two-kidney one clip (2K1C) hypertension reduces the HRV complexity in mice.

Methods and Results: C57BL/6 mice were anesthetized with isoflurane and submitted to 2K1C hypertension by placing a silver clip (0.12 mm) around left renal artery. After 4 weeks, mice were implanted with subcutaneous electrocardiogram (ECG) electrodes and allowed to recover for 48 h. On the day of the experiment, the ECG was recorded for 30 minutes in conscious, unrestrained mice. At the end of the recording, arterial pressure (AP) was directly measured in each mouse under isoflurane anesthesia. RR interval time series were generated and the complexity of HRV was determined using detrending fluctuation analysis (DFA) and multiscale entropy (MSE). Mean AP was higher in 2K1C mice (133±2 vs 93±4 mmHg) while the HR was similar between groups. DFA scaling exponents were calculated in short (5 to 15), mid (30 to 200) and long (200 to 1500) window sizes, but only the long-term exponent was different between groups (1.27±0.09 vs 0.89±0.08 in 2K1C and sham mice, respectively). MSE was calculated up to scale 20 and averaged in short (1 to 5) and long (6 to 20) time scales. In both short (0.75±0.16 vs 1.25±0.11) and long (0.76±0.17 vs 1.22±0.09) ranges, entropy is lower in hypertensive mice.

Conclusions: The complexity of HRV dynamics was found lower in renovascular hypertensive mice. Both sympathetic and vagal control of the heart seems to be involved in this process, as predictability (MSE) and fractality (DFA) is affected in various temporal scales. Nevertheless, the greatest entropy difference between groups is found at scale 6, which is closely related to respiration.

Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data

Basal area (BA) is a good predictor of timber stand volume and forest growth. This study developed predictive models using field and airborne LiDAR (Light Detection and Ranging) data for estimation of basal area in Pinus taeda plantation in south Brazil. In the field, BA was collected from conventional forest inventory plots. Multiple linear regression models for predicting BA from LiDAR-derived metrics were developed and evaluated for predictive power and parsimony. The best model to predict BA from a family of six models was selected based on corrected Akaike Information Criterion (AICc) and assessed by the adjusted coefficient of determination (adj. R²) and root mean square error (RMSE). The best model revealed an adj. R²=0.93 and RMSE=7.74%. Leave one out cross-validation of the best regression model was also computed, and revealed an adj. R² and RMSE of 0.92 and 8.31%, respectively. This study showed that LiDAR-derived metrics can be used to predict BA in Pinus taeda plantations in south Brazil with high precision. We conclude that there is good potential to monitor growth in this type of plantations using airborne LiDAR. We hope that the promising results for BA modeling presented herein will stimulate to operate this technology in Brazil.

Outcomes of 847 childhood-onset systemic lupus erythematosus patients in three age groups

Objective The objective of this study was to assess outcomes of childhood systemic lupus erythematosus (cSLE) in three different age groups evaluated at last visit: group A early-onset disease (<6 years), group B school age (≥6 and <12 years) and group C adolescent (≥12 and <18 years). Methods An observational cohort study was performed in ten pediatric rheumatology centers, including 847 cSLE patients. Results Group A had 39 (4%), B 395 (47%) and C 413 (49%). Median disease duration was significantly higher in group A compared to groups B and C (8.3 (0.1-23.4) vs 6.2 (0-17) vs 3.3 (0-14.6) years, p < 0.0001). The median Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SLICC/ACR-DI) (0 (0-9) vs 0 (0-6) vs 0 (0-7), p = 0.065) was comparable in the three groups. Further analysis of organ/system damage revealed that frequencies of neuropsychiatric (21% vs 10% vs 7%, p = 0.007), skin (10% vs 1% vs 3%, p = 0.002) and peripheral vascular involvements (5% vs 3% vs 0.3%, p = 0.008) were more often observed in group A compared to groups B and C. Frequencies of severe cumulative lupus manifestations such as nephritis, thrombocytopenia, and autoimmune hemolytic anemia were similar in all groups ( p > 0.05). Mortality rate was significantly higher in group A compared to groups B and C (15% vs 10% vs 6%, p = 0.028). Out of 69 deaths, 33/69 (48%) occurred within the first two years after diagnosis. Infections accounted for 54/69 (78%) of the deaths and 38/54 (70%) had concomitant disease activity. Conclusions This large multicenter study provided evidence that early-onset cSLE group had distinct outcomes. This group was characterized by higher mortality rate and neuropsychiatric/vascular/skin organ damage in spite of comparable frequencies of severe cumulative lupus manifestations. We also identified that overall death in cSLE patients was an early event mainly attributed to infection associated with disease activity.

Multi-surface segmentation of OCT images with AMD using sparse high order potentials

In age-related macular degeneration (AMD), the quantification of drusen is important because it is correlated with the evolution of the disease to an advanced stage. Therefore, we propose an algorithm based on a multi-surface framework for the segmentation of the limiting boundaries of drusen: the inner boundary of the retinal pigment epithelium + drusen complex (IRPEDC) and the Bruch's membrane (BM). Several segmentation methods have been considerably successful in segmenting retinal layers of healthy retinas in optical coherence tomography (OCT) images. These methods are successful because they incorporate prior information and regularization. Nonetheless, these factors tend to hinder the segmentation for diseased retinas. The proposed algorithm takes into account the presence of drusen and geographic atrophy (GA) related to AMD by excluding prior information and regularization just valid for healthy regions. However, even with this algorithm, prior information and regularization still cause the oversmoothing of drusen in some locations. Thus, we propose the integration of local shape prior in the form of a sparse high order potentials (SHOPs) into the algorithm to reduce the oversmoothing of drusen. The proposed algorithm was evaluated in a public database. The mean unsigned errors, relative to the average of two experts, for the inner limiting membrane (ILM), IRPEDC and BM were 2.94±2.69, 5.53±5.66 and 4.00±4.00 µm, respectively. Drusen areas measurements were evaluated, relative to the average of two expert graders, by the mean absolute area difference and overlap ratio, which were 1579.7 ± 2106.8 µm² and 0.78 ± 0.11, respectively.

Autoimmune hemolytic anemia in systemic lupus erythematosus at diagnosis: differences between pediatric and adult patients

Objective To determine the overall prevalence of autoimmune hemolytic anemia (AIHA), and to compare clinical and laboratory features in a large population of children and adult lupus patients at diagnosis. Methods This retrospective study evaluated the medical charts of 336 childhood-onset systemic lupus erythematosus (cSLE) and 1830 adult SLE (aSLE) patients followed in the same tertiary hospital. Demographic data, clinical features and disease activity were recorded. AIHA was defined according to the presence of anemia (hemoglobin <10 g/dL) and evidence of hemolysis (reticulocytosis and positive direct antiglobulin test (DAT)/Coombs test) at SLE diagnosis. Evans syndrome (ES) was defined by the combination of immune thrombocytopenia (platelet count <100,000/mm³) and AIHA. Results The frequency of AIHA at diagnosis was significantly higher in cSLE patients compared to aSLE (49/336 (14%) vs 49/1830 (3%), p = 0.0001), with similar frequency of ES (3/336 (0.9%) vs 10/1830 (0.5%), p = 0.438). The median of hemoglobin levels was reduced in cSLE vs aSLE patients (8.3 (2.2-10) vs 9.5 (6.6-10) g/dL, p = 0.002) with a higher frequency of multiple hemorrhagic manifestations (41% vs 7%, p = 0.041) and erythrocyte transfusion due to bleeding (24% vs 5%, p = 0.025). cSLE patients also had more often constitutional involvement (84% vs 31%, p < 0.001), fever (65% vs 26%, p < 0.001), weight loss > 2 kg (39% vs 6%, p < 0.001), reticuloendothelial manifestations (48% vs 8%, p < 0.001), hepatomegaly (25% vs 2%, p < 0.001) and splenomegaly (21% vs 2%, p = 0.004). Other major organ involvements were common but with similar frequencies in cSLE and aSLE ( p > 0.05). Median systemic lupus erythematosus disease activity index 2000 (SLEDAI-2 K) was comparable in cSLE and aSLE (p = 0.161). Conclusions We identified that AIHA was not a common condition in cSLE and aSLE, with distinct features characterized by a higher prevalence/severity in children and concomitant constitutional symptoms in the majority of them.

Erythropoietin in the Locus coeruleus attenuates the ventilatory response to CO2 in rats

The Locus coeruleus (LC) is a pontine area that contributes to the CO₂/pH chemosensitivity. LC cells express erythropoietin (Epo) receptors (EpoR), and Epo in the brainstem is a potent normoxic and hypoxic respiratory stimulant. However, a recent study showed that the intra-cisternal injection (ICI) of Epo antagonist does not alter the hypercapnic ventilatory response in mice. As ICI leads to a widespread dispersal of the product throughout the brainstem, in this work we evaluated the specific impact of Epo in the LC-mediated ventilatory response to CO₂ (by whole body plethysmography) in juvenile male Wistar rats. Normocapnic and hypercapnic ventilation were evaluated before and after unilateral microinjection of Epo (1ng/100nL) into the LC. To evaluate the long-term effect of Epo, the HcVR was re-evaluated 24h later. Our results show that Epo attenuates the hypercapnic ventilation. We conclude that Epo in the LC tunes the hypercapnia-induced hyperpnea.

Abstract P237: Is Ischemia/Reperfusion an Efficient Method for Producing Heart Failure in Rats?

Myocardium infarction (MI) elicited by coronary artery ligation (CAL) is commonly used to induce chronic heart failure (HF) in rats. However, CAL shows high mortality rates. Given that ischemia-reperfusion (IR) may cause the development of HF, this approach may be useful for obtaining a model of HF with low mortality rates. Therefore, it was compared the model of CAL vs. IR in rats, evaluating the mortality and cardiac morphological and functional aspects. The IR consisted of 30 minutes of cardiac ischemia. Wistar rats were assigned into three groups: CAL: n=18; IR: n=7; SHAM (fictitious IR): n=7. After four weeks of CAL, the subjects were evaluated by echocardiography and ventriculography as well. The statistical analysis consisted of ANOVA combined with Tukey’s posthoc test (p<0.05). There were no deaths in the IR and SHAM groups, whereas in the CAL group the mortality rate was 33.33% (6 out of 18). In the CAL group echocardiography showed increased left ventricular (LV) cavity during systole (8.3 ± 1mm) and diastole (10.5 ± 1mm); decreased LV free wall during systole (1.4 ± 0.5 mm); increased left atrium/aorta (2.3 ± 0.4) ratio. These changes were not significant in IR (4.8 ± 0.5mm, 7.6 ± 0.6mm, 2.6 ± 0.3 mm, 1.6 ± 0.2) and SHAM (4.6 ± 0.6 mm, 7.7 ± 0.8mm, 2.8 ± 0.4mm, 1.5 ± 0.2) groups. There was also the reduction in the ejection fraction in the CAL group (41 ± 12 %) when compared with IR (65 ± 9%) and SHAM (69 ± 7%) groups. The tissue Doppler analysis from the lateral mitral annulus showed reduction in E′ in CAL (-29 ± 8 mm/s) and IR (-31± 9 mm/s) groups when compared with the SHAM (-48 ± 11 mm/s) group. The ventriculography in the CAL group showed smaller maximum dP/dt (6519 ± 1062) and greater end-diastolic pressure (33 ± 8 mmHg) when compared with IR (8716 ± 756 mmHg/s; 9 ± 9 mmHg) and SHAM (7989 ± 1230 mmHg/s; 9 ± 7 mmHg) groups. The CAL group presented transmural infarct size of 40% of the left ventricular wall, measured under histopathological examination. In conclusion, IR for 30 minutes caused only small changes in LV diastolic function, assessed by tissue Doppler; however, the IR was not effective for promoting HF, as observed with CAL. Thus, it is possible that prolonged IR is necessary for promoting significant HF in rats.

Evaluation of skeletal muscle regeneration in experimental model after malnutrition

The aim of this study was to analyze muscle regeneration after cryoinjury in the tibialis anterior muscle of young rats that were malnourished and then recovered. Forty Wistar rats were divided into a nourished group that received a normal protein diet (14% casein) for 90 days and a malnourished and recovered rats group (MR) that was submitted to 45 days of malnutrition with a hypoproteic diet (6% casein) followed by 45 days of a normal protein diet (14% casein). After the recovery period, all of the animals underwent cryoinjury in the right tibialis anterior muscle and euthanasia after 7, 14 and 21 days. The amount of connective tissue and the inflammation area was higher in the malnutrition recovered injury MR group (MRI) at 14 days post-injury (p < 0.05). Additionally, the cross-sectional area (CSA) of the regenerated fibers was decreased in the MRI (p < 0.05). The MyoD and myogenin protein levels were higher in the nourished injury group. Similar levels of TGF-β1 were found between groups. The proposed malnutrition protocol was effective in showing delayed changes in the regeneration process of the tibialis anterior muscle of young rats. Furthermore, we observed a delay in muscle repair even after nutritional recovery.

Antinucleosome Antibodies in Patients with Juvenile Systemic Lupus Erythematosus

Our objective was to evaluate the frequency of antinucleosome antibodies (anti-Ncs) in juvenile systemic lupus erythematosus (JSLE) comparing it to that observed for anti-DNA and to correlate the presence of these antibodies with clinical manifestations and disease activity. Anti-Ncs and anti-DNA were detected by ELISA in 74 patients with JSLE and 64 normal controls. Clinical records were reviewed. Disease activity was assessed by SLEDAI score. Anti-Ncs and anti-DNA showed sensitivity of 52.7% and 54% and specificity of 98.4% and 95.3%, respectively. Disagreement between the two assays was found in 25.7% of the cases: isolated positive Anti-Ncs in nine cases (12.2%) and isolated positive anti-DNA in 10 cases (13.5%). Agreement was found in 74.3%: both positive antibodies in 30 cases and both negative in 25. The presence of anti-Ncs was significantly associated with malar erythema, hemolytic anemia, anti-DNA and low complement levels, but not with renal manifestations. The presence of anti-Ncs was associated with a higher SLEDAI median ( P &lt; 0.001) and its titers correlated with the SLEDAI score ( r = 0.504; P &lt; 0.001). The frequency, sensitivity and specificity values were similar between anti-Ncs and anti-DNA antibodies in patients with JSLE. Nevertheless, the discordance of 25.7% between the two assays suggests that both antibodies may have a complementary diagnostic role. The association and correlation between anti-Ncs and several disease activity parameters demonstrated its usufulness in the follow-up of these patients.

Floristic and structural status of forests in permanent preservation areas of Moju river basin, Amazon region

The goal of this study is to analyze the floristic patterns and the structure of disturbed and undisturbed upland forests, in Permanent Preservation Areas (PPAs) along the Moju river, in the Brazilian state of Pará. Trees with a diameter equal to or larger than 10cm at 1.30m from the ground (DBH) ≥10cm were analyzed for the upper stratum. For the middle stratum, individuals with DBH between 4.99 and 9.99cm were sampled. Forty-five families and 221 species were found in disturbed forests, and 43 families and 208 species in undisturbed forests. Floristic similarity was high between strata and between forest types, with values above 50%. Similarity was highest between middle strata. The most species-abundant families in undisturbed forests were Fabaceae, Sapotaceae, Chrysobalanaceae and Myrtaceae; the species with the highest density there were Eschweilera grandiflora, Licania sclerophylla and Zygia cauliflora. In disturbed forests, the dominant families were Fabaceae, Sapotaceae, Lecythidaceae and Melastomataceae. The Shannon-Wiener diversity index was 3.21 for undisturbed forests and 2.85 for disturbed forests. Non-metric multidimensional scaling (MDS) analysis did not group the forests by their floristic composition in both upper and middle strata. Overall, the PPA forests along the Moju river, even if disturbed, did not show major floristic changes but substantially change their structural characteristics.