Universidade Estadual do Oeste do Paraná

de Souza R.M., Seibert D., Quesada H.B., de Jesus Bassetti F., Fagundes-Klen M.R., Bergamasco R.

A review of the main pesticides employed in agriculture found that the pesticide groups present in the highest amounts are herbicides, fungicides, and insecticides. For this reason, their occurrence in surface waters around the world, as well as their adverse effects on non-target organisms were reviewed for the period 2012–2019. Among the most common vegetal herbicides is atrazine, followed by metalochlor, both of which are widely-used on soybean and corn crops. Insecticides are used to control insects by agonizing them. Although they present low toxicity for mammals, they are toxic to ecosystems and impact the environment when present. Fungicides are employed to prevent fungal infections by damaging the cellular membrane, causing damage to non-target organisms, tebuconazole and carbendazim were the most frequent fungicides identified in surface waters throughout the world. Once pesticides reach water bodies, they can impact the whole ecological food chain, since other animals, including humans, feed on aquatic animals that may be contaminated. Another concern is the mixing of pesticides, in which case the mixture may be more toxic than any one single compound. Because mixtures of pesticides are commonly found in surface water, the need for suitable water treatment is crucial.

Castelló E., Nunes Ferraz-Junior A.D., Andreani C., Anzola-Rojas M.D., Borzacconi L., Buitrón G., Carrillo-Reyes J., Gomes S.D., Maintinguer S.I., Moreno-Andrade I., Palomo-Briones R., Razo-Flores E., Schiappacasse-Dasati M., Tapia-Venegas E., Valdez-Vázquez I., et. al.

H2 production by dark fermentation using mixed cultures has been studied intensively during the last two decades, and its feasibility has been demonstrated. Different substrates, operational conditions, and reactor technologies have been widely studied and there is a general agreement that the use of non-sterile fermentable substrates is required to make the process feasible for scaling up. Nonetheless, stability problems during long term operation may hinder its application at large scale. This work, written by members of the Latin American Biohydrogen Network, analyse and discuss instability causes and possible solutions in the H2 production by dark fermentation. It is concluded that instability is mostly linked to the biotic aspects of the process (i.e., changes in the microbial community composition, presence of organisms that consume hydrogen and compete for the substrate, and accumulation of fermentation products); regardless of the reactor configuration. However, some problems like excessive growth of microorganisms and methanogens presence were mostly reported in fixed bed reactors and granular sludge reactors. The novelty of this work relies on the comprehensive revision of the main causes behind the unstable and low hydrogen production and how these causes are linked to the technology used. The strategies to overcome the problems as well as the potential implications are also analysed.

Dala‐Corte R.B., Melo A.S., Siqueira T., Bini L.M., Martins R.T., Cunico A.M., Pes A.M., Magalhães A.L., Godoy B.S., Leal C.G., Monteiro‐Júnior C.S., Stenert C., Castro D.M., Macedo D.R., Lima‐Junior D.P., et. al.

Protecting riparian vegetation around streams is vital in reducing the detrimental effects of environmental change on freshwater ecosystems and in maintaining aquatic biodiversity. Thus, identifying ecological thresholds is useful for defining regulatory limits and for guiding the management of riparian zones towards the conservation of freshwater biota. Using nationwide data on fish and invertebrates occurring in small Brazilian streams, we estimated thresholds of native vegetation loss in which there are abrupt changes in the occurrence and abundance of freshwater bioindicators and tested whether there are congruent responses among different biomes, biological groups and riparian buffer sizes. Mean thresholds of native vegetation cover loss varied widely among biomes, buffer sizes and biological groups: ranging from 0.5% to 77.4% for fish, from 2.9% to 37.0% for aquatic invertebrates and from 3.8% to 43.2% for a subset of aquatic invertebrates. Confidence intervals for thresholds were wide, but the minimum values of these intervals were lower for the smaller riparian buffers (50 and 100 m) than larger ones (200 and 500 m), indicating that land use should be kept away from the streams. Also, thresholds occurred at a lower percentage of riparian vegetation loss in the smaller buffers, and were critically lower for invertebrates: reducing only 6.5% of native vegetation cover within a 50-m riparian buffer is enough to cross thresholds for invertebrates. Synthesis and applications. The high variability in biodiversity responses to loss of native riparian vegetation suggests caution in the use of a single riparian width for conservation actions or policy definitions nationwide. The most sensitive bioindicators can be used as early warning signals of abrupt changes in freshwater biodiversity. In practice, maintaining at least 50-m wide riparian reserves on each side of streams would be more effective to protect freshwater biodiversity in Brazil. However, incentives and conservation strategies to protect even wider riparian reserves (~100 m) and also taking into consideration the regional context will promote a greater benefit. This information should be used to set conservation goals and to create complementary mechanisms and policies to protect wider riparian reserves than those currently required by the federal law.

Lopes A.R., Nihei O.K.

Background The COVID-19 pandemic raises concerns about the mental health of the world population. Protection measures to prevention the disease impacted education and undergraduate students were exposed to additional stressors. Objectives Analyze depression, anxiety and stress symptoms in undergraduates, their respective predictors and the association with satisfaction with life, psychological well-being and coping strategies. Methods An online cross-sectional study was conducted from September 14 to October 19, 2020, involving undergraduate students enrolled in 33 courses from 5 public university campuses in the state of Parana, Brazil, using: questionnaire with sociodemographic, academic, health and pandemic effects variables; Depression, Anxiety and Stress Scale-21 (DASS-21); Satisfaction with Life Scale (SWLS); Psychological Well-Being (PWB); BriefCOPE. The convenience sample was composed of 1,224 participants, with 18 years old or older, that completed all research instruments. Spearman correlation and logistic analysis (univariate and multivariate) were applied to the collected data. Results Most of the undergraduates presented symptoms of depression (60.5%), anxiety (52.5%) and stress (57.5%). Depression, anxiety and stress presented significant correlations in common: negative with satisfaction with life, all dimensions of psychological well-being, and 3 adaptive copings (active coping, planning, positive reframing); positive with 5 maladaptive copings (behavioral disengagement, denial, self-blame, self-distraction, substance use). In addition, there were 7 common predictors for symptoms of depression, anxiety and stress: female; age 18–24 years old; having a chronic disease; lower scores in 2 dimensions of psychological well-being (positive relations with others, self-acceptance); higher scores in 2 maladaptive copings (self-blame, substance use). Conclusions The data indicate a high prevalence of symptoms of depression, anxiety and stress, and suggest that higher scores of satisfaction with life, psychological well-being dimensions and adaptive copings may present protective effects in undergraduates during a pandemic crisis.

de Souza C.A., Westphall C.B., Machado R.B., Sobral J.B., Vieira G.D.

In the Internet of Things (IoT) systems, information of various kinds is continuously captured, processed, and transmitted by systems generally interconnected by the Internet and distributed solutions. Attacks to capture information and overload services are common. This fact makes security techniques indispensable in IoT environments. Intrusion detection is one of the vital security points, aimed at identifying attempted attacks. The characteristics of IoT devices make it impossible to apply these solutions in this environment. Also, the existing anomaly-based methods for multiclass detection do not present acceptable accuracy. We present an intrusion detection architecture that operates in the fog computing layer. It has two steps and aims to classify events into specific types of attacks or non-attacks, for the execution of countermeasures. Our work presents a relevant contribution to the state of the art in this aspect. We propose a hybrid binary classification method called DNN-kNN. It has high accuracy and recall rates and is ideal for composing the first level of the two-stage detection method of the presented architecture. The approach is based on Deep Neural Networks (DNN) and the k-Nearest Neighbor (kNN) algorithm. It was evaluated with the public databases NSL-KDD and CICIDS2017. We used the method of selecting attributes based on the rate of information gain. The approach proposed in this work obtained 99.77% accuracy for the NSL-KDD dataset and 99.85% accuracy for the CICIDS2017 dataset. The experimental results showed that the proposed hybrid approach was able to achieve greater precision about classic machine learning approaches and the recent advances in intrusion detection for IoT systems. In addition, the approach works with low overhead in terms of memory and processing costs.

Mækelæ M.J., Reggev N., Dutra N., Tamayo R.M., Silva-Sobrinho R.A., Klevjer K., Pfuhl G.

The COVID-19 pandemic forced millions of people to drastically change their social life habits as governments employed harsh restrictions to reduce the spread of the virus. Although beneficial to physical health, the perception of physical distancing and related restrictions could impact mental health. In a pre-registered online survey, we assessed how effective a range of restrictions were perceived, how severely they affected daily life, general distress and paranoia during the early phase of the outbreak in Brazil, Colombia, Germany, Israel, Norway and USA. Most of our over 2000 respondents rated the restrictions as effective. School closings were perceived as having the strongest effect on daily life. Participants who believed their country reacted too mildly perceived the risk of contracting SARS-CoV-2 to be higher, were more worried and expressed reduced beliefs in the ability to control the outbreak. Relatedly, dissatisfaction with governmental reactions corresponded with increased distress levels. Together, we found that satisfaction with one's governmental reactions and fear appraisal play an important role in assessing the efficacy of restrictions during the pandemic and their related psychological outcomes. These findings inform policy-makers on the psychological factors that strengthen resilience and foster the well-being of citizens in times of global crisis.

Alabi R.O., Elmusrati M., Sawazaki‐Calone I., Kowalski L.P., Haglund C., Coletta R.D., Mäkitie A.A., Salo T., Almangush A., Leivo I.

The proper estimate of the risk of recurrences in early-stage oral tongue squamous cell carcinoma (OTSCC) is mandatory for individual treatment-decision making. However, this remains a challenge even for experienced multidisciplinary centers.We compared the performance of four machine learning (ML) algorithms for predicting the risk of locoregional recurrences in patients with OTSCC. These algorithms were Support Vector Machine (SVM), Naive Bayes (NB), Boosted Decision Tree (BDT), and Decision Forest (DF).The study cohort comprised 311 cases from the five University Hospitals in Finland and A.C. Camargo Cancer Center, São Paulo, Brazil. For comparison of the algorithms, we used the harmonic mean of precision and recall called F1 score, specificity, and accuracy values. These algorithms and their corresponding permutation feature importance (PFI) with the input parameters were externally tested on 59 new cases. Furthermore, we compared the performance of the algorithm that showed the highest prediction accuracy with the prognostic significance of depth of invasion (DOI).The results showed that the average specificity of all the algorithms was 71% . The SVM showed an accuracy of 68% and F1 score of 0.63, NB an accuracy of 70% and F1 score of 0.64, BDT an accuracy of 81% and F1 score of 0.78, and DF an accuracy of 78% and F1 score of 0.70. Additionally, these algorithms outperformed the DOI-based approach, which gave an accuracy of 63%. With PFI-analysis, there was no significant difference in the overall accuracies of three of the algorithms; PFI-BDT accuracy increased to 83.1%, PFI-DF increased to 80%, PFI-SVM decreased to 64.4%, while PFI-NB accuracy increased significantly to 81.4%.Our findings show that the best classification accuracy was achieved with the boosted decision tree algorithm. Additionally, these algorithms outperformed the DOI-based approach. Furthermore, with few parameters identified in the PFI analysis, ML technique still showed the ability to predict locoregional recurrence. The application of boosted decision tree machine learning algorithm can stratify OTSCC patients and thus aid in their individual treatment planning.

Ruaro R., Gubiani É.A., Hughes R.M., Mormul R.P.

The use of multimetric indices (MMIs) of biological condition has been globally applied because they are practical tools that incorporate various biological metrics at different levels of ecological organization. However, the methods for developing and evaluating MMIs have been criticized. We reviewed the scientific literature to assess global processes for MMI creation and validation. We found a lack of common criteria for metric selection and determination of reference conditions, which hinders making comparable assessments across different programs and regions as well as for developing or refining future MMIs. We also present the ten metrics most commonly used in MMIs worldwide. Finally, we determined that differentiating natural variability from anthropogenic impacts is the major challenge in creating and applying MMIs. We offer our review to further the advancement and improvement of MMIs as standard bioassesment tools.

Maran B.M., Matos T.D., de Castro A.D., Vochikovski L., Amadori A.L., Loguercio A.D., Reis A., Berger S.B.

To answer the following research question: “Do low/medium hydrogen peroxide (HP) concentrations used for in-office bleaching in patients with permanent dentition have similar color change and bleaching sensitivity (BS) to high HP concentrations?” Randomized controlled trials that compared low / medium vs. high concentrate HP were included. The risk of bias (RoB) was evaluated using the Cochrane Collaboration tool. Meta-analyses were conducted for color change (ΔE*ab, ΔSGU/SGU), risk, and intensity of BS, using the random-effects model. Heterogeneity was assessed with the Cochrane Q test, I 2 statistics, and prediction interval. The GRADE assessed the certainty of the evidence. Search was performed in PubMed, Cochrane Library, BBO, LILACS, Scopus, Web of Science and grey literature on 15th September 2018 (updated on 13th May 2020). Study selection: 25 studies remained. Five were at low RoB; thirteen were at unclear RoB, and seven were at high RoB. The risk of having BS was, on average, 33 % lower (RR = 0.67; 95 % CI 0.51 to 0.86) for low / medium concentrate HP than high HP. No significant difference in color change was detected among groups, except from the subgroup low vs. high HP for the immediate color change, but this difference is not clinically relevant. The certainty of evidence for color change was low and very low, and moderate for the BS. Low and medium hydrogen peroxide concentrate products for in-office bleaching have lower risk and intensity of bleaching sensitivity than the high concentrate hydrogen peroxide group, with no difference in color change efficacy. The use of low concentrate hydrogen peroxide products may produce the same color change efficacy with the bonus of having lower risk and intensity of bleaching sensitivity. However, the ideal concentration at which this occurs is yet unknown and deserves further investigations. No funding. PROSPERO CRD42018108266.

Panis C., Candiotto L.Z., Gaboardi S.C., Gurzenda S., Cruz J., Castro M., Lemos B.

Pesticides, which are associated with endocrine dysfunction, immunological dysregulation, and cancer, are widespread sources of drinking water contamination. The state of Paraná has a population of 11 million, is the second largest grain producer in Brazil and is a leading consumer of pesticides. In this study, we analyzed the extent of drinking water contamination from 11 proven, probable, or potentially carcinogenic pesticides (alachlor, aldrin-dieldrin, atrazine, chlordane, DDT-DDD-DDE, diuron, glyphosate-AMPA, lindane-γ-HCH, mancozeb-ETU, molinate, and trifluralin) in 127 grain-producing municipalities in the state of Paraná. Extensive contamination of drinking water was found, including legacy pesticides such as aldrin-dieldrin (mean 0.047 ppb), DDT-DDD-DDE (mean: 0.07), chlordane (mean: 0.181), and lindane-HCH (mean: 2.17). Most of the municipalities were significantly above the maximum limits for each one of the currently allowed pesticides (67% for alachlor, 9.44% for atrazine, 96.85% for diuron, 100% for glyphosate-AMPA, 80.31% for mancozeb-ETU, 91.33% for molinate, and 12.6% for trifluralin). Ninety-seven percent of municipalities presented a sum of all pesticides at levels significantly above (189.84 ppb) the European Union preconized limits (