Seronegative human T-cell lymphotropic virus 1 carriers in blood banks: A potential viral source for silent transmission?

BACKGROUND AND OBJECTIVES: Transfusion-transmitted viruses count among the greatest threats to blood safety. In Argentina, current laws oblige testing all donated blood for the presence of antibodies against human T-cell lymphotropic viruses 1 and 2 (HTLV-1/2). In endemic zones of the country, a high rate of seronegative HTLV-1 individuals with clear evidence of infection because of symptoms and/or presence of tax sequences of HTLV-1 and/or IgG anti-Tax antibodies has been recently described. Migration from endemic to nonendemic zones of Argentina is very frequent. MATERIALS AND METHODS: During a 1-year period, in the blood bank of Córdoba city, we performed molecular screening of all donors who were born in or arose from endemic zones for HTLV-1/2 in Argentina and neighbouring countries. RESULTS: By screening 219 bp of HTLV-1/2 tax gene, 0.6% (2/317) of the blood donors proved to be positive for HTLV-1 tax sequence. One of the donors presented anti-Tax antibodies, demonstrating the transcriptional activity of the tax gene, and the other donor was also positive for LTR and pol gene sequences. The HTLV-1 genetic analysis of the LTR sequence determined that it belonged to the Cosmopolitan subtype HTLV-1aA. CONCLUSION: These findings suggest potential limitations of some currently approved screening assays for HTLV-1 detection applied in some donor populations and the possibility of an HTLV-1 seronegative carrier state with the potential for silent transmission by blood.

Benchmarking Scientific Image Forgery Detectors.

The field of scientific image integrity presents a challenging research bottleneck given the lack of available datasets to design and evaluate forensic techniques. The sensitivity of data also creates a legal hurdle that restricts the use of real-world cases to build any accessible forensic benchmark. In light of this, there is no comprehensive understanding on the limitations and capabilities of automatic image analysis tools for scientific images, which might create a false sense of data integrity. To mitigate this issue, we present an extendable open-source algorithm library that reproduces the most common image forgery operations reported by the research integrity community: duplication, retouching, and cleaning. We create a large scientific forgery image benchmark (39,423 images) with enriched ground truth using this library and realistic scientific images. All figures within the benchmark are synthetically doctored using images collected from creative commons sources. While collecting the source images, we ensured that the they did not present any suspicious integrity problems. Because of the high number of retracted papers due to image duplication, this work evaluates the state-of-the-art copy-move detection methods in the proposed dataset, using a new metric that asserts consistent match detection between the source and the copied region. All evaluated methods had a low performance in this dataset, indicating that scientific images might need a specialized copy-move detector. The dataset and source code are available at https://github.com/phillipecardenuto/rsiil .

Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning.

COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease's pathophysiology and several discriminating features to patient's health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease's pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application.

Counteracting the contemporaneous proliferation of digital forgeries and fake news.

Fake news has been certainly the expression of the moment: from political round table discussions to newspapers to social and mainstream media. It is everywhere. With such an intense discussion and yet few effective ways to combat it, what can be done? Providing methods to fight back even the least harming hoax is a social responsibility. To look for authenticity in a wide sea of fake news, every detail is a lead. Image appearance and semantic content of text and images are some of the main properties, which can be analyzed to reveal even the slightest lie. In this vein, this work overviews some recent methods applicable to the verification of dubious content in text and images, and discusses how we can put them together as an option to curb away the proliferation of unverified and phony "facts". We briefly present the main idea behind each method, highlighting real situations where they can be applied and discussing expected results. Ultimately, we show how new research areas are working to seamlessly stitch together all these methods so as to provide a unified analysis and to establish the synchronization in space and time - the X-Coherence of heterogeneous sources of information documenting real-world events.

New records of the rare Troschel's Pampas Snake, Phimophis guianensis (Serpentes: Dipsadidae) in Brazil.

The Troschel's Pampas Snake, Phimophis guianensis (Troschel, 1848), is widely distributed in Amazonian Savannas at northern South America and a small portion of southern Central America, being recorded to Brazil based on three historical records, that ranged from 1997 to 2002, in Amapá and Pará states. In this study, we revise all known records of P. guianensis, providing an updated distribution map, and the first record to Roraima state.

Effect of dentifrice containing fTCP, CPP-ACP and fluoride in the prevention of enamel demineralization.

OBJECTIVE: To evaluate the effect of different fluoride- and calcium- and/or phosphate-containing products on their ability to prevent enamel demineralization under pH cycling conditions. MATERIAL AND METHODS: Enamel bovine specimens were assigned to the following groups: G1-MPP (MI Paste Plus, 0.2% NaF, Recaldent™, GC Corporation Tokyo, Japan); G2-FD (Crest™ Cavity Protection, 0.243% NaF, Procter & Gamble, USA); G3-CLP (Clinpro™ 5000, 1.1% NaF, 3M ESPE, USA); and G4-CO (Control without fluoride, Silica-based dentifrice; Daudt Ltda, Brazil). The specimens were soaked in demineralizing solution for 6 h and remineralizing solution for 18 h alternatively for 10 days. The toothpaste was prepared with deionized water in a 1:3 ratio (w/v) for three minutes daily. The solutions were renewed every 48 h. After cycling, enamel changes were analysed by percentage change of SMH (%SMH) and energy-dispersive X-ray spectroscopy (EDS). The %SMH value observed for G3-CLP (2.9 ± 39.2) was higher than that found in G4-CO (-13.0 ± 20.7), G1-MPP (-8.9 ± 20.9) and G2-FD (-3.9 ± 27.1). The %SMH was similar for all treatment groups (one-way ANOVA and Tukey's HSD; p < .05). The pH, Ca2+ and Ptotal in the remineralization solutions were not different among all groups (Kruskal-Wallis; p < .05). At 24 h, the Ca2+ concentration in the demineralization solution was significantly lower in G1-MPP. Ca2+ concentration increased in all groups after 48 h, except for G3-CLP. The EDX quantitative analysis showed that the atomic % of elements is lower level at G4-CO. CONCLUSIONS: The Clinpro™ 5000 demonstrated having the most protective effect against demineralization; however, the % SMH was similar for all groups.

Time series causal relationships discovery through feature importance and ensemble models.

Inferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algorithms used for forecasting have become more complex, leading to a less understandable path of how a decision is made by the model. To address this issue, we propose leveraging ensemble models, e.g., Random Forest, to assess which input features the trained model prioritizes when making a forecast and, in this way, establish causal relationships between the variables. The advantage of these algorithms lies in their ability to provide feature importance, which allows us to build the causal network. We present our methodology to estimate causality in time series from oil field production. As it is difficult to extract causal relations from a real field, we also included a synthetic oil production dataset and a weather dataset, which is also synthetic, to provide the ground truth. We aim to perform causal discovery, i.e., establish the existing connections between the variables in each dataset. Through an iterative process of improving the forecasting of a target's value, we evaluate whether the forecasting improves by adding information from a new potential driver; if so, we state that the driver causally affects the target. On the oil field-related datasets, our causal analysis results agree with the interwell connections already confirmed by tracer information; whenever the tracer data are available, we used it as our ground truth. This consistency between both estimated and confirmed connections provides us the confidence about the effectiveness of our proposed methodology. To our knowledge, this is the first time causal analysis using solely production data is employed to discover interwell connections in an oil field dataset.

Literature Review on Crotalus durissus terrificus Toxins: From a Perspective of Structural Biology and Therapeutic Applications.

BACKGROUND: The venom of Crotalus durissus terrificus, as well as its fractions, has intrigued research groups worldwide who are working to isolate, characterize, and find possible biotechnological applications. A number of studies have elucidated that these fractions and their derivatives possess pharmacological properties, which can enable the development of new drug prototypes with anti-inflammatory, antinociceptive, antitumor, antiviral, and antiparasitic applications. OBJECTIVE: This review presents a systematic study on Crotalus durissus terrificus, the most notable crotalid subspecies in South America, focusing on the composition, toxicological mechanisms, structural aspects, and applications of the main venom toxins (convulxin, gyroxin, crotamine, crotoxin, and their subunits). CONCLUSION: The authors have found that research on this snake and its toxins is still an area of focus, despite that almost a century has passed since the isolation of crotoxin. Several applications of these proteins in the development of novel drugs and bioactive substances have also been demonstrated.

Carbonate system in the Cabo Frio upwelling.

The quantitative assessment of the carbonate system represents one of the biggest challenges toward the "Sustainable Development Goals" defined by the United Nations in 2015. In this sense, the present study investigated the Spatio-temporal dynamics of the carbonate system and the effects of the El Niño and La Niña phenomena over the Cabo Frio upwelling area. The physical characterization of the site was carried out through data on wind speed and sea surface temperature. Water samples were also collected during the oceanographic cruise onboard the Diadorim R/V (Research Vessel). From these samples, the parameters of absolute and practical salinity, density, pH, total alkalinity, carbonate, calcite, aragonite, bicarbonate dissolved inorganic carbon, carbon dioxide, partial pressure of carbon, calcium, and total boron were obtained. The highest average concentration of bicarbonate in S1 (2018 µmol/kg) seems to contribute to the dissolved inorganic carbon values (2203 µmol/kg). The values of calcite saturation state, aragonite saturation state, and carbonate were higher on the surface of each station (calcite saturation state = 4.80-5.48; aragonite saturation state = 3.10-3.63, and carbonate = 189-216 µmol/kg). The mean values of pH were similar in the day/night samples (7.96/7.97). The whole carbonate system was calculated through thermodynamic modeling with the Marine Chemical Analysis (AQM) program loaded with the results of the following parameters: temperature, salinity, total alkalinity, and pH parameters. This manuscript presents original data on the carbonate system and the "acidification" process influenced by the Cabo Frio upwelling, which directly depends on the El Niño and La Niña phenomena oscillations in the sea surface temperature.

Diversity of HLA-A2-Restricted and Immunodominant Epitope Repertoire of Human T-Lymphotropic Virus Type 1 (HTLV-1) Tax Protein: Novel Insights among N-Terminal, Central and C-Terminal Regions.

The present study sought to search for the immunodominance related to the N-terminal, Central and C-terminal regions of HTLV-1 Tax using novel, cutting-edge peptide microarray analysis. In addition, in silico predictions were performed to verify the presence of nine amino acid peptides present along Tax restricted to the human leukocyte antigen (HLA)-A2.02*01 haplotype, as well as to verify the ability to induce pro-inflammatory and regulatory cytokines, such as IFN-γ and IL-4, respectively. Our results indicated abundant dose-dependent reactivity for HLA-A*02:01 in all regions (N-terminal, Central and C-terminal), but with specific hotspots. Furthermore, the results of fold-change over the Tax11-19 reactivity obtained at lower concentrations of HLA-A*02:01 reveal that peptides from the three regions contain sequences that react 100 times more than Tax11-19. On the other hand, Tax11-19 has similar or superior HLA-A*02:01 reactivity at higher concentrations of this haplotype. The in silico analysis showed a higher frequency of IFN-γ-inducing peptides in the N-terminal portion, while the C-terminal portion showed a higher frequency of IL-4 inducers. Taken together, these results shed light on the search for new Tax immunodominant epitopes, in addition to the canonic Tax11-19, for the rational design of immunomodulatory strategies for HTLV-1 chronic diseases.

The State-of-the-Art of the Humoral Memory Response to Snakebites: Insights from the Yanomami Population.

Snakebite envenomation (SBE)-induced immunity refers to individuals who have been previously bitten by a snake and developed a protective immune response against subsequent envenomations. The notion stems from observations of individuals, including in the indigenous population, who present only mild signs and symptoms after surviving multiple SBEs. Indeed, these observations have engendered scientific interest and prompted inquiries into the potential development of a protective immunity from exposure to snake toxins. This review explores the evidence of a protective immune response developing following SBE. Studies suggest that natural exposure to snake toxins can trigger protection from the severity of SBEs, mediated by specific antibodies. However, the evaluation of the immune memory response in SBE patients remains challenging. Further research is needed to elucidate the immune response dynamics and identify potential targets for therapeutic interventions. Furthermore, the estimation of the effect of previous exposures on SBE epidemiology in hyperendemic areas, such as in the indigenous villages of the Amazon region (e.g., the Yanomami population) is a matter of debate.

Reliability and generalization of gait biometrics using 3D inertial sensor data and 3D optical system trajectories.

Particularities in the individuals' style of walking have been explored for at least three decades as a biometric trait, empowering the automatic gait recognition field. Whereas gait recognition works usually focus on improving end-to-end performance measures, this work aims at understanding which individuals' traces are more relevant to improve subjects' separability. For such, a manifold projection technique and a multi-sensor gait dataset were adopted to investigate the impact of each data source characteristics on this separability. Assessments have shown it is hard to distinguish individuals based only on their walking patterns in a subject-based identification scenario. In this setup, the subjects' separability is more related to their physical characteristics than their movements related to gait cycles and biomechanical events. However, this study's results also points to the feasibility of learning identity characteristics from individuals' walking patterns learned from similarities and differences between subjects in a verification setup. The explorations concluded that periodic components occurring in frequencies between 6 and 10 Hz are more significant for learning these patterns than events and other biomechanical movements related to the gait cycle, as usually explored in the literature.

A multi-sensor human gait dataset captured through an optical system and inertial measurement units.

Different technologies can acquire data for gait analysis, such as optical systems and inertial measurement units (IMUs). Each technology has its drawbacks and advantages, fitting best to particular applications. The presented multi-sensor human gait dataset comprises synchronized inertial and optical motion data from 25 participants free of lower-limb injuries, aged between 18 and 47 years. A smartphone and a custom micro-controlled device with an IMU were attached to one of the participant's legs to capture accelerometer and gyroscope data, and 42 reflexive markers were taped over the whole body to record three-dimensional trajectories. The trajectories and inertial measurements were simultaneously recorded and synchronized. Participants were instructed to walk on a straight-level walkway at their normal pace. Ten trials for each participant were recorded and pre-processed in each of two sessions, performed on different days. This dataset supports the comparison of gait parameters and properties of inertial and optical capture systems, whereas allows the study of gait characteristics specific for each system.

Link Prediction Based on Stochastic Information Diffusion.

Link prediction (LP) in networks aims at determining future interactions among elements; it is a critical machine-learning tool in different domains, ranging from genomics to social networks to marketing, especially in e-commerce recommender systems. Although many LP techniques have been developed in the prior art, most of them consider only static structures of the underlying networks, rarely incorporating the network's information flow. Exploiting the impact of dynamic streams, such as information diffusion, is still an open research topic for LP. Information diffusion allows nodes to receive information beyond their social circles, which, in turn, can influence the creation of new links. In this work, we analyze the LP effects through two diffusion approaches, susceptible-infected-recovered and independent cascade. As a result, we propose the progressive-diffusion (PD) method for LP based on nodes' propagation dynamics. The proposed model leverages a stochastic discrete-time rumor model centered on each node's propagation dynamics. It presents low-memory and low-processing footprints and is amenable to parallel and distributed processing implementation. Finally, we also introduce an evaluation metric for LP methods considering both the information diffusion capacity and the LP accuracy. Experimental results on a series of benchmarks attest to the proposed method's effectiveness compared with the prior art in both criteria.

Inventory of water masses and carbonate system from Brazilian's northeast coast: Monitoring ocean acidification.

This manuscript presents an inventory of the carbonate system from the main water masses comprising the marine current system on Brazil's northeast coast (South Atlantic Ocean). For this purpose, four transects were conducted with an approximate length of 357 km (each one) through the platform and continental slope of the Sergipe-Alagoas sedimentary basin. Water samples were then collected in vertical profiles measuring from 5 to 1,799 meters depth, totaling 34 stations. Total alkalinity, calcium, and total boron were obtained analytically from these samples and by relationships with salinity. Speciation and concentration of the carbonate system were obtained by means of thermodynamic modeling. The results revealed that the empirical models used to calculate the concentrations of TA, calcium and total boron showed relevant variation when compared to the analytical values (TA: 5.0-6.5%; Ca: 0.4-4.8%; BT: 7.0-18.9%). However, the speciation and concentration of the carbonate system (CA, DIC, [Formula: see text], CO2(aq), ΩCalc, and ΩArag) obtained from the empirical values of TA, calcium and total boron did not differ significantly from those obtained analytically (0.0-6.1%). On the other hand, the parameters of pH, [Formula: see text], [Formula: see text], CO2(aq), ρCO2, ΩCalc, and ΩArag varied significantly within the different water masses (p < 0.05). This study supports and encourages acidification monitoring projects in the South Atlantic Ocean, based on modeling the carbonate system parameters generated in real-time.

SILA: a system for scientific image analysis.

A great deal of the images found in scientific publications are retouched, reused, or composed to enhance the quality of the presentation. In most instances, these edits are benign and help the reader better understand the material in a paper. However, some edits are instances of scientific misconduct and undermine the integrity of the presented research. Determining the legitimacy of edits made to scientific images is an open problem that no current technology can perform satisfactorily in a fully automated fashion. It thus remains up to human experts to inspect images as part of the peer-review process. Nonetheless, image analysis technologies promise to become helpful to experts to perform such an essential yet arduous task. Therefore, we introduce SILA, a system that makes image analysis tools available to reviewers and editors in a principled way. Further, SILA is the first human-in-the-loop end-to-end system that starts by processing article PDF files, performs image manipulation detection on the automatically extracted figures, and ends with image provenance graphs expressing the relationships between the images in question, to explain potential problems. To assess its efficacy, we introduce a dataset of scientific papers from around the globe containing annotated image manipulations and inadvertent reuse, which can serve as a benchmark for the problem at hand. Qualitative and quantitative results of the system are described using this dataset.

Multi-Epitope Protein as a Tool of Serological Diagnostic Development for HTLV-1 and HTLV-2 Infections.

A multi-epitope protein expressed in a prokaryotic system, including epitopes of Env, Gag, and Tax proteins of both HTLV-1 and HTLV-2 was characterized for HTLV-1/2 serological screening. This tool can contribute to support the implementation of public policies to reduce HTLV-1/2 transmission in Brazil, the country with the highest absolute numbers of HTLV-1/2 infected individuals. The chimeric protein was tested in EIA using serum/plasma of HTLV-infected individuals and non-infected ones from four Brazilian states, including the North and Northeast regions (that present high prevalence of HTLV-1/2) and Southeast region (that presents intermediate prevalence rates) depicting different epidemiological context of HTLV-1/2 infection in our country. We enrolled samples from Pará (n = 114), Maranhão (n = 153), Minas Gerais (n = 225) and São Paulo (n = 59) states; they are from blood donors' candidates (Pará and Minas Gerais), pregnant women (Maranhão) and HIV+/high risk for sexually transmitted infection (STI; São Paulo). Among the HTLV-1/2 positive sera, there were co-infections with viral (HTLV-1 + HTLV-2, HIV, HCV, and HBV), bacterial (Treponema pallidum) and parasitic (Trypanosoma cruzi, Schistosma mansoni, Strongyloides stercoralis, Entamoeba coli, E. histolytica, and Endolimax nana) pathogens related to HTLV-1/2 co-morbidities that can contribute to inconclusive diagnostic results. Sera positive for HIV were included among the HTLV-1/2 negative samples. Considering both HTLV-1 and HTLV-2-infected samples from all states and different groups (blood donor candidates, pregnant women, and individuals with high risk for STI), mono or co-infected and HTLV-/HIV+, the test specificity ranged from 90.09 to 95.19% and the sensitivity from 82.41 to 92.36% with high accuracy (ROC AUC = 0.9552). This multi-epitope protein showed great potential to be used in serological screening of HTLV-1 and HTLV-2 in different platforms, even taking into account the great regional variation and different profile of HTLV-1 and HTLV-2 mono or co-infected individuals.

Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval.

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the search query results should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. It is pertinent to design systems that retrieve images with these nuanced relationships in addition to providing more traditional results, such as duplicates and near-duplicates - and to do so with enough efficiency at large scale. We propose a new approach for spatial verification that aims at modeling object-level regions using image keypoints retrieved from an image index, which is then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method the Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations, which can run quickly on either CPUs or GPUs. It performs comparably to state-of-the-art methods on classic CBIR problems (Oxford 5K, Paris 6K, and Google-Landmarks), and outperforms them in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style imagery from Reddit.

Antimicrobial potential of resin matrices loaded with coffee compounds.

This study evaluated the biological behavior of the coffee compounds Trigonelline (T), chlorogenic acid (C), and nicotinic acid (N), correlating with their release from a resin matrix. Minimum inhibitory concentration (MIC) was evaluated against Streptococcus mutans UA159, and cytotoxicity was assessed by methyl tetrazolium salt on OD-21 cells. Resin matrices (bisphenol A-glycidyl-dimethacrylate/triethylene glycol-dimethacrylate 70/30 wt%, camphorquinone/ethyl 4-dimethyl aminobenzoate 0.5/1 wt%) were doped with coffee compounds in different concentrations (10/20/30/40/50 wt%), performing 15 groups (T10-T50, C10-C50, N10-N50), and a control group with no coffee compound. Degree of conversion (DC%) was analyzed by Fourier transform infrared spectroscopy. Antimicrobial properties were evaluated by bioluminescence (Luciferase assay). The release from loaded matrices was analyzed over time (24 hr, 6, 14, 21 and 28 days), using high-performance liquid chromatography (HPLC). Data were submitted to ANOVA/Tukey's test (α = 0.05). MIC for T and C was 6 mg/ml, and 4 mg/ml for N. None of them were cytotoxic. Only T50 and C50 showed lower DC% than control (α < 0.05). Some groups (T30/T40/T50/C40/C50/N50) were strongly antimicrobial, reducing bacterial activity approximately five times compared to control (α < 0.05). For T30, T40, T50, C40, and C50, the HPLC showed a release above or closer to MIC values mainly in 24 hr, but for N50, up to 28 days. In conclusion, the coffee compounds presented antimicrobial activity, depending on their concentration when added in resin matrices, being found a correlation with their release.

Crotalus Durissus Ruruima: Current Knowledge on Natural History, Medical Importance, and Clinical Toxinology.

Crotalus durissus ruruima is a rattlesnake subspecies mainly found in Roraima, the northernmost state of Brazil. Envenomings caused by this subspecies lead to severe clinical manifestations (e.g. respiratory muscle paralysis, rhabdomyolysis, and acute renal failure) that can lead to the victim's death. In this review, we comprehensively describe C. d. ruruima biology and the challenges this subspecies poses for human health, including morphology, distribution, epidemiology, venom cocktail, clinical envenoming, and the current and future specific treatment of envenomings by this snake. Moreover, this review presents maps of the distribution of the snake subspecies and evidence that this species is responsible for some of the most severe envenomings in the country and causes the highest lethality rates. Finally, we also discuss the efficacy of the Brazilian horse-derived antivenoms to treat C. d. ruruima envenomings in Roraima state.