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Spare parts management is a maintenance management function that aims to support maintenance activities. It intends to provide real-time information about the available quantities for each spare part and to adopt stock management policies that assure its availability at the lowest possible cost. This paper proposes a methodology for the stock management of spare parts that aims to assign stock management policies to each spare part in an industrial plant. Five groups and five associated stock management policies were defined. Considering that the policy to be allocated depends strongly on the characteristics of demand, two of the groups are intended for generic spare parts, and the other three for specific spare parts. The definition of the groups and the assignment method took into account the associated stock management policy, which in turn took into account the characteristics of the demand for the associated parts. A case study carried out in a manufacturing company that produces electronic systems for the automotive sector showed that the developed methodology allows, simply and intuitively, the assignment of policies that balance the cost of a part being out of stock and the cost of keeping it in stock, thus minimizing total cost.

Sea urchin processing practices by the canning industry result in a large volume of waste due to a high fraction of inedible parts, in special the gut, that can still be a valuable source of bioactive compounds. Therefore, this work aimed to thoroughly characterise gut biomass from Paracentrotus lividus evaluating its bioactive potential. The gut biomass was evaluated in terms of bioactive capacity, and biochemical composition in both males and females. Although no statistical differences were found between sexes in any of these parameters, this study provided a proof of concept on the potential of sea urchin's gut, obtained as a co-product in the sea urchin industry, for feed supplementation. The gut biomass exhibited a high antioxidant capacity (IC50 ≈ 0.5–1.0 mgDW mL−1 in four different assays) and bactericidal activity (IC50 < 1.0 mgDW mL−1 against Vibrio parahaemolyticus, Edwardsiella tarda, and Tenacibaculum maritimum). The gut co-product can also be used as a source of phenolic compounds, carotenoids, and PUFAs, with contents of 4.6 ± 0.4, 2.4 ± 0.8, and 93.0 ± 3.1 mg gDW−1, respectively. Overall, the sea urchin's gut seems a valuable product with a remarkable potential for use in aquafeeds as a source of bioactive compounds.

O presente trabalho discute os limites e possibilidades de implementação de práticas de promoção e vigilância da saúde, levando em conta a proposta de regionalização contida na Norma Operacional da Assistência à Saúde (NOAS), adotada pelo Ministério de Saúde em 2001. Caracteriza essa proposta como uma oportunidade para se avançar na construção de sistemas microrregionais que garantam a integralidade da atenção à saúde, de acordo com a capacidade operacional do Sistema Único de Saúde em cada região e Estado do país. Em seguida analisa a contribuição das diversas propostas de mudança nas práticas de saúde para o desenvolvimento da promoção, prevenção de riscos e agravos, e reorientação da assistência individual e coletiva, na medida em que venham a ser incorporadas ao processo de implementação da NOAS nos diversos Estados da Federação.

Durante o período experimental de novembro de 1995 a abril de 1996 variáveis como disponibilidade de MS da planta toda, do resíduo pós-pastejo e das perdas de pastejo de uma pastagem de capim Tobiatã foram avaliadas. O ciclo de pastejo foi de 34 dias (33 de descanso e 1 de ocupação) para o lote principal de vacas de leite. Os dados obtidos foram divididos em períodos de pastejo e esses comparados entre si pelos testes de variância do recurso Proc-GLM do pacote estatístico SAS. Observou-se uma queda crescente na eficiência de colheita da pastagem a medida que o ciclo avançou. Em contrapartida, a quantidade de resíduo pós-pastejo aumentou. Eficiências de pastejo de 68,57% e 18,82% foram observadas quando a quantidade de resíduo foi 1491,50 e 7917,90 kg MS/ha durante os meses de novembro e abril, respectivamente. A composição botânica mostrou-se menos favorável com o avançar da estação de pastejo. A relação haste/folha aumentou de 0,33 para 1,92 do período de novembro/dezembro para abril/maio, respectivamente. Concluiu-se que a pressão de pastejo para o capim Tobiatã deve ser maior do que as utilizadas nesse experimento (8,84%PV) para que a eficiência de colheita aumente. A quantidade de resíduo pós-pastejo parece uma melhor como referência de manejo, como ferramenta para minimizar as perdas de pastejo.

We report on the clinical characteristics of amyotrophic lateral sclerosis (ALS) in Fortaleza (Northeastern Brazil). For this, we analyzed retrospectively (from 1980 to 1999) 78 cases of ALS from the Service of Neurology of the University Hospital of Fortaleza diagnosed clinically and laboratorially (EMG, muscle biopsy, myelography, blood biochemistry, muscle enzymes and cranio-cervical X-ray). The results showed that they were mostly sporadic ALS (76/78), and they were divided into definite (n= 36), probable (n= 20), possible (n= 15) and suspected (n= 7), according to the level of diagnostic certainty. They were also subdivided into juvenile (n= 17), early-onset adult (n= 18), age-specific (n= 39) and late-onset (n= 4) groups. Clinically, they presented as initials symptoms, principally, asymmetrical (30/78) and symmetrical (24/78) weakness of extremities, besides bulbar signs, fasciculations, and atrophy. Curiously, pain as first symptom occurred in an expressive fashion (17/78). The predominant initial anatomic site, in this series, was the spinal cord, and mainly affecting the arms. As to the symptom accrual from region to region, this occurs more quickly in contiguous areas, and fasciculations are predominant when bulbar region was associated.

Abstract Background Reports of neoplasms in Panthera species are increasing, but they are still an uncommon cause of disease and death in captive wild felids. The presence of two or more primary tumor in large felids is rarely reported, and there are no documented cases of ocular melanoma and mammary mucinous carcinoma in African lions. Case presentation An ocular melanoma and a mammary mucinous carcinoma are described in an African lion (Panthera leo). The first tumour was histologically characterized by the presence of epithelioid and fusiform melanocytes, while the latter was composed of mucus-producing cells with an epithelial phenotype that contained periodic acid-Schiff (PAS) and Alcian blue staining mucins. Metastases of both tumor were identified in various organs and indirect immunohistochemistry was used to characterize them. Peribiliary cysts were observed in the liver. Conclusions This is the first description of these tumor in African lions.

Abstract Background The aim of this report was to describe the clinical signs, diagnostic approach, treatment and outcome in the case of a tiger with a deformity of the paw. Case presentation A 1.5-year-old tiger (Panthera tigris) was presented with lameness of the left thoracic limb. A deformity involving the first and second metacarpal bones, and a soft tissue separation between the second and third metacarpal bones of the left front paw were observed. The second digit constantly struck the ground during locomotion. Based on the physical and radiographic evaluations, a diagnosis of ectrodactyly was made. A soft tissue reconstruction of the cleft with excision of both the second digit and distal portion of the second metacarpal bone was performed. Marked improvement of the locomotion was observed after surgical treatment, although the tiger showed a low degree of lameness probably associated with the discrepancy in length between the thoracic limbs. Conclusion This report shows a rare deformity in an exotic feline that it is compatible to ectrodactyly. Reconstructive surgery of the cleft resulted in significant improvement of limb function.

Abstract An emerging theme in systems neurobiology is that even simple forms of memory depend on activity in a broad network of cortical and subcortical brain regions. One key challenge is to understand how different components of these complex networks contribute to memory. In a new study in Molecular Pain, Tang and colleagues use a novel set of approaches to characterize the role of the anterior cingulate cortex (ACC) in the formation of Pavlovian fear memories.

The leaves of Citrus deliciosa Tenore were collected in southern Brazil, dried, and subjected to the hydrodistillation process to obtain the essential oil. The extraction of essential oil yielded 0.97% ± 0.04. The chromatographic profile of this compound revealed 11 substances, being methyl N-methyl-anthranilate (75.1%), γ-terpinene (13.8%), and Limonene (7%) as major substances. The essential oil shows antifungal action against all tested yeasts, with promising action against Cryptococcus neoformans, Cryptococcus gattii, and Trichosporon asahii . This compound was also able to inhibit the biofilm production of Candida albicans, Candida glabrata, Candida parapsilosis, and T. asahii . The essential oil of tangerine showed weak antioxidant action. It did not show cytotoxicity in human mononuclear cells. It is hoped that these results may guide future studies for the production of formulations that can be used in the treatment of biofilms caused by yeasts, as well as in candidiasis, cryptococcosis, and/or trichosporonosis.

This paper presents a study that assesses the application of chemometrics for classifying coffee samples in a quality control context. High-resolution and accurate mass measurements were utilized as input for pixel-based orthogonal partial least squares discriminant analysis (OPLS-DA) models. The compositional data were acquired through a fully automated workflow combining headspace solid-phase microextraction and gas chromatography-high-resolution mass spectrometry (GC-HRMS) using an FT-Orbitrap® mass analyzer. A workflow centered on accurate mass measurements was successfully utilized for group-type analysis, offering an alternative to methods relying solely on MS similarity searches. The predictive models underwent thorough evaluation, demonstrating robust multivariate classification performance. Five key coffee attributes, bitterness, acidity, body, intensity, and roasting level were successfully predicted using GC-HRMS data. The results revealed strong predictive accuracy across all models, ranging from 88.9 % (bitterness) to 94.4 % (roasting level). This study represents a significant advancement in automating methods for coffee quality control, notably increasing the predictive ability of the models compared to existing literature., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: Seizure prediction is a promising solution to improve the quality of life for drug-resistant patients, which concerns nearly 30% of patients with epilepsy. The present study aimed to ascertain the impact of incorporating sleep-wake information in seizure prediction., Methods: We developed five patient-specific prediction approaches that use vigilance state information differently: i) using it as an input feature, ii) building a pool of two classifiers, each with different weights to sleep/wake training samples, iii) building a pool of two classifiers, each with only sleep/wake samples, iv) changing the alarm-threshold concerning each sleep/wake state, and v) adjusting the alarm-threshold after a sleep-wake transition. We compared these approaches with a control method that did not integrate sleep-wake information. Our models were tested with data (43 seizures and 482 hours) acquired during presurgical monitoring of 17 patients from the EPILEPSIAE database. As EPILEPSIAE does not contain vigilance state annotations, we developed a sleep-wake classifier using 33 patients diagnosed with nocturnal frontal lobe epilepsy from the CAP Sleep database., Results: Although different patients may require different strategies, our best approach, the pool of weighted predictors, obtained 65% of patients performing above chance level with a surrogate analysis (against 41% in the control method)., Conclusion: The inclusion of vigilance state information improves seizure prediction. Higher results and testing with long-term recordings from daily-life conditions are necessary to ensure clinical acceptance., Significance: As automated sleep-wake detection is possible, it would be feasible to incorporate these algorithms into future devices for seizure prediction.

The control of local heterogeneities in metallic glasses (MGs) represents an emerging field to improve their plasticity, preventing the propagation of catastrophic shear bands (SBs) responsible for the macroscopically brittle failure. To date, a nanoengineered approach aimed at finely tuning local heterogeneities controlling SB nucleation and propagation is still missing, hindering the potential to develop MGs with large and tunable strength/ductility balance and controlled deformation behavior. In this work, we exploited the potential of pulsed laser deposition (PLD) to synthesize a novel class of crystal/glass ultrafine nanolaminates (U-NLs) in which a ∼4 nm thick crystalline Al separates 6 and 9 nm thick Zr 50 Cu 50 glass nanolayers, while reporting a high density of sharp interfaces and large chemical intermixing. In addition, we tune the morphology by synthesizing compact and nanogranular U-NLs, exploiting, respectively, atom-by-atom or cluster-assembled growth regimes. For compact U-NLs, we report high mass density (∼8.35 g/cm 3 ) and enhanced and tunable mechanical behavior, reaching maximum values of hardness and yield strength of up to 9.3 and 3.6 GPa, respectively. In addition, we show up to 3.6% homogeneous elastoplastic deformation in compression as a result of SB blocking by the Al-rich sublayers. On the other hand, nanogranular U-NLs exhibit slightly lower yield strength (3.4 GPa) in combination with enhanced elastoplastic deformation (∼6%) followed by the formation of superficial SBs, which are not percolative even at deformations exceeding 15%, as a result of the larger free volume content within the cluster-assembled structure and the presence of crystal/glass nanointerfaces, enabling to accommodate SB events. Overall, we show how PLD enables the synthesis of crystal/glass U-NLs with ultimate control of local heterogeneities down to the atomic scale, providing new nanoengineered strategies capable of deep control of the deformation behavior, surpassing traditional trade-off between strength and ductility. Our approach can be extended to other combinations of metallic materials with clear interest for industrial applications such as structural coatings and microelectronics (MEMS and NEMS).

Produced water (PW) is a type of wastewater that arises during oil and gas production. Due to its potential environmental impact, PW is one of the most closely monitored forms of wastewater in the petroleum industry. The total oil and grease (TOG) content in the water is a crucial parameter for assessing the environmental impact of PW. Traditional methods for analyzing TOG in PW can be time-consuming and may not be compatible with green chemistry principles. In this study, an alternative method for classifying PW samples is proposed using a one-class classifier (OCC) model, which has proven useful for classification problems. To achieve this goal, headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography (GC×GC) were employed to obtain TOG profiles from PW. A series of simulated PW samples containing TOG were generated using a mixture design comprising four petrochemicals at concentrations ranging from 10 mg L -1 to 50 mg L -1 . The polydimethylsiloxane (PDMS) fiber showed the most representative extraction of analytes. The optimization of the HS-SPME method was performed using a Doehlert design with two variables, and the final conditions were set at 80 °C and 70 min for extraction temperature and time, respectively. A pixel-based data approach was used to implement data-driven soft independent modeling by class analogy (DD-SIMCA). Although DD-SIMCA is a developing area in GC×GC studies, the proposed model produced outstanding results with a sensitivity of 94.3 %, specificity of 95.0 %, and accuracy of 94.5 %, considering the complex and broad compositional range of the modeled mixtures. These findings demonstrated the effectiveness of the OCC model approach in classifying PW samples according to environmental regulations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The vaginal microbiome is dominated by Lactobacillus spp. and the depletion of these microorganisms have been associated with adverse conditions that can affect women's health. Disturbance of the vaginal niche with a non-lactobacillary microbiota is associated with susceptibility to some diseases, such as obstetric alterations and infertility, resulting in failure in natural pregnancies and increased demand for assisted reproduction treatments. The present study sought to understand the influence of Lactobacillus spp. and fertility female. A systematic search was performed in the following databases: PubMed, MEDLINE, SciELO and LILACS, using the keywords: "Microbiome"; "Lactobacillus" and "Female Infertility", published in the last five years. The search resulted in 92 articles; however, 38 articles were excluded due to duplicity, 23 articles were excluded in the selection title/abstract, leaving 31 articles for full reading. In the end, 18 articles were analyzed. The studies encompassed a total of 2,011 women, using 27 types of samples to verify the composition of the microbiome. The eighteen articles that reported the microbiome of fertile women were constituted by a dominance of Lactobacillus spp. who joined to positive predictive outcomes in reproduction, while infertile women showed a dysbiotic profile. Therefore, analyzing bacterial patterns would allow a personalized diagnosis, which could favor personalized therapy for prevention and treatment of certain diseases.

The microbiota is composed of numerous resident microorganisms, which contribute to the health and illness of the individual. When the microbiota is in dysbiosis, it can cause some pathological processes and in men it can be correlated with male infertility, so the present study does a systematic review, identifying whether there is a correlation between the microbiota and seminal quality. We analyzed 7 papers published in PubMed, Medline and the Cochrane library databases, in English and published between 2012 and 2022. In men with normal semen parameters, a higher prevalence of Lactobacillus. There was a higher prevalence of Prevotella in patients who had some seminal alteration. We conclude that the microbiota is correlated with seminal quality, since the decrease in Lactobacillus and the increase in other species is seen in infertile men.

Objective: To verify from a systematic literature review the possible effects of thyroid diseases on assisted reproduction techniques., Data Sources: The studies were analyzed from the PubMed, Cochrane Library, LILACS databases., Selection of Studies: The articles selected for the review included: cross-sectional studies, cohort studies, and clinical trials that addressed the proposed theme and which were published within the period stipulated from January 1, 2012, to March 5, 2022, in English, Portuguese and Spanish. These would later have to go through stages of inclusion as a framework of the type of study and exclusion criteria that were review articles, case reports, abstracts, articles with animal models, and duplicate articles and letters to the editor., Data Collection: Author's name; Number of patients; Clinical outcome; Use of drugs; Control group (in case it had); Clinical outcome., Data Synthesis: In in vitro fertilization and intracytoplasmic sperm injection it was verified that thyroid diseases can lead to effects such as a reduction in the rate of recovered oocytes, a decrease in the number of embryos, lower pregnancy rates, and increased chances of congenital anomalies in these patients and a reduction in the rate of implantation. Levothyroxine can increase the number of cycle cancellations., Conclusions: Thyroid diseases may have deleterious effects on the clinical outcome of in vitro fertilization and intracytoplasmic sperm injection.

Many state-of-the-art methods for seizure prediction, using the electroencephalogram, are based on machine learning models that are black boxes, weakening the trust of clinicians in them for high-risk decisions. Seizure prediction concerns a multidimensional time-series problem that performs continuous sliding window analysis and classification. In this work, we make a critical review of which explanations increase trust in models' decisions for predicting seizures. We developed three machine learning methodologies to explore their explainability potential. These contain different levels of model transparency: a logistic regression, an ensemble of 15 support vector machines, and an ensemble of three convolutional neural networks. For each methodology, we evaluated quasi-prospectively the performance in 40 patients (testing data comprised 2055 hours and 104 seizures). We selected patients with good and poor performance to explain the models' decisions. Then, with grounded theory, we evaluated how these explanations helped specialists (data scientists and clinicians working in epilepsy) to understand the obtained model dynamics. We obtained four lessons for better communication between data scientists and clinicians. We found that the goal of explainability is not to explain the system's decisions but to improve the system itself. Model transparency is not the most significant factor in explaining a model decision for seizure prediction. Even when using intuitive and state-of-the-art features, it is hard to understand brain dynamics and their relationship with the developed models. We achieve an increase in understanding by developing, in parallel, several systems that explicitly deal with signal dynamics changes that help develop a complete problem formulation., (© 2023 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Food supplement authentication is an important concern worldwide due to the ascending consumption related to health benefits and its lack of effective regulation in underdeveloped countries, making it a target of fraudulent activities. In this context, this study evaluated fish oil supplements by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) to obtain fingerprints, which were used to build predictive models for automated authentication of the most popular products sold in Brazil. The authentication process relied on a one-class classifier model using data-driven soft independent modeling of class analogy (DD-SIMCA). The output of the model was a binary classifier: certified IFOS fish oils and non-certified ones - regardless of the source of adulteration. The compositional analysis showed a significant variation in the samples, which validated the need for reliable statistical models. The DD-SIMCA algorithm is still incipient in GC×GC studies, but it proved to be an excellent tool for authenticity purposes, achieving a chemometric model with a sensitivity of 100%, specificity of 98.6%, and accuracy of 99.0% for fish oil authentication. Finally, orthogonalized partial least square discriminant analysis (OPLS-DA) was used to identify the features that distinguished the groups, which ascertained the results of the DD-SIMCA model that IFOS-certified oils are positively correlated to omega-3 fatty acids, including eicosapentaenoic acid (EPA, C20:5 n-3) and docosahexaenoic acid (DHA, C22:6 n-3)., (© 2022. Springer-Verlag GmbH Germany, part of Springer Nature.)

Typical seizure prediction models aim at discriminating interictal brain activity from pre-seizure electrographic patterns. Given the lack of a preictal clinical definition, a fixed interval is widely used to develop these models. Recent studies reporting preictal interval selection among a range of fixed intervals show inter- and intra-patient preictal interval variability, possibly reflecting the heterogeneity of the seizure generation process. Obtaining accurate labels of the preictal interval can be used to train supervised prediction models and, hence, avoid setting a fixed preictal interval for all seizures within the same patient. Unsupervised learning methods hold great promise for exploring preictal alterations on a seizure-specific scale. Multivariate and univariate linear and nonlinear features were extracted from scalp electroencephalography (EEG) signals collected from 41 patients with drug-resistant epilepsy undergoing presurgical monitoring. Nonlinear dimensionality reduction was performed for each group of features and each of the 226 seizures. We applied different clustering methods in searching for preictal clusters located until 2 h before the seizure onset. We identified preictal patterns in 90% of patients and 51% of the visually inspected seizures. The preictal clusters manifested a seizure-specific profile with varying duration (22.9 ± 21.0 min) and starting time before seizure onset (47.6 ± 27.3 min). Searching for preictal patterns on the EEG trace using unsupervised methods showed that it is possible to identify seizure-specific preictal signatures for some patients and some seizures within the same patient., (© 2023. The Author(s).)

Achyrocline flaccida aqueous extract was obtained by macerating wildflowers. The phytochemical profile present in the A. flaccida aqueous extract was elucidated by HPLC-ESI-MS/MS. Toxicity was evaluated in vitro by comet assay in peripheral blood mononuclear cells (PBMCs) and in vivo using Caenorhabditis elegans as a model. The antioxidant activity was also evaluated, and antimycobacterial activity was assessed by the broth microdilution method. The compounds present in the aqueous extract mainly belonged to the flavonoid class (89%). The concentrations that showed protective effects in C. elegans against oxidative stress and antimycobacterial activity had no toxic effects. The antimycobacterial activity test demonstrated that the concentration of 1,560 µg mL -1 inhibited the growth and eradication of the mycobacterial tested strains. Based on our findings, the A. flaccida aqueous extract presents a viable potential in developing new phytotherapeutic drugs against mycobacteria of clinical relevance.

Seizure prediction may be the solution for epileptic patients whose drugs and surgery do not control seizures. Despite 46 years of research, few devices/systems underwent clinical trials and/or are commercialized, where the most recent state-of-the-art approaches, as neural networks models, are not used to their full potential. The latter demonstrates the existence of social barriers to new methodologies due to data bias, patient safety, and legislation compliance. In the form of literature review, we performed a qualitative study to analyze the seizure prediction ecosystem to find these social barriers. With the Grounded Theory, we draw hypotheses from data, while with the Actor-Network Theory we considered that technology shapes social configurations and interests, being fundamental in healthcare. We obtained a social network that describes the ecosystem and propose research guidelines aiming at clinical acceptance. Our most relevant conclusion is the need for model explainability, but not necessarily intrinsically interpretable models, for the case of seizure prediction. Accordingly, we argue that it is possible to develop robust prediction models, including black-box systems to some extent, while avoiding data bias, ensuring patient safety, and still complying with legislation, if they can deliver human- comprehensible explanations. Due to skepticism and patient safety reasons, many authors advocate the use of transparent models which may limit their performance and potential. Our study highlights a possible path, by using model explainability, on how to overcome these barriers while allowing the use of more computationally robust models., (© 2022 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

GC × GC investigations are well known to generate a substantial amount of information-rich and structurally complex data, requiring advanced data processing strategies like chemometrics. Many workflows are available for data handling and processing, such as the peak-table and pixel-based approaches. The goal of this work is to present a solution based on method development to solve the missing pixel problem that may be encountered in experiments performed with GC and GC × GC coupled to the Fourier transform orbital ion trap (FT-Orbitrap) mass analyzer. Data input is vital for pixel-based chemometric analyses, as some post-processing solutions may lead to significant loss of chemical information in the data set. Hence, a key requisite is that the chemical information is consistently indexed in the data arrays for proper pixel-based data handling and analysis. In this study, we carefully evaluated the ion management parameters to preserve the intrinsic structure and information of the data arrays of the GC × GC-FT-Orbitrap for future pixel-oriented chemometric analysis. The most acceptable conditions yielded acquisition rates up to 42.6 spectra per s, while a routine setting of 24.7 Hz was successfully employed in analyses of different petroleum fractions, producing both consistent tensor sizes and acceptable peak reconstructions. A data acquisition rate of 24.7 spectra per s and a mass resolving power of 15 000 allowed the resolution of a mass split of only 0.004 Da - which is an interesting configuration for challenging applications in petroleomics. Using such advanced settings, the missing pixel problem was reduced from up to 30% to much less than 0.04% of the data array dimension. Thus, the proposed configuration can be employed in studies that require pixel-oriented multivariate data analysis.

Electrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure's clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state.

With the increase of animal slurry produced from livestock production, the monitoring and mitigation of greenhouse gas (GHG) and ammonia (NH 3 ) emissions represent a major issue. Life cycle assessment (LCA) has been used to evaluate the long-term environmental effects of applied strategies and technologies on cattle slurry management for mitigation of environmental harmful gases. This study was carried on two main aims: first, the effect of the addition of sulphuric acid (SA), biochar (SBi) or A + Bi to liquid cattle-slurry (treated systems) on gas emissions during storage compared to the untreated system (S) was investigated in a laboratory-controlled experiment; second, the environmental implications of each treated or untreated system were assessed through a LCA approach according to ISO 14040/44. Five CML 2001 impact categories were used: eutrophication potential (EP), acidification potential (AP), global warming potential (GWP), human toxicity potential (HTP) and Ozone Layer Depletion Potential (ODP). Comparisons were based on 1 ton fresh dairy cattle slurry. The environmental profile of untreated system showed lower efficiency in mitigation of total GHG and NH 3 emissions (0.0312 and 0.0001 kg CO 2 -eq respectively), during storage period and greater impact on GWP and HTP categories. The electricity consumption in mechanical separation dominated the environmental impacts. From the three proposed treated systems, SA showed the highest efficiency on mitigation of gas emissions compared to the other treatments, reducing NH 3 , CH 4 and CO 2 emissions respectively in 61%, 98% and 15%, when compared to the SBi system. In all categories, acidified slurry also showed the lowest environmental impact relative to other treated systems and the benefit was more evident when the impacts were expressed per kilogram of nitrogen in the slurry. Therefore, LCA methodology was of great importance to assess the environmental implications of the treatments and acidification can be considered as an effective technique on the mitigation of environmental implications of livestock production and cattle-effluent valorization. Optimization and uniformity of performed studies are essential to validate new strategies to improve the sustainability of this sector in the management of animal wastewater., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Seizure prediction may improve the quality of life of patients suffering from drug-resistant epilepsy, which accounts for about 30% of the total epileptic patients. The pre-ictal period determination, characterized by a transitional stage between normal brain activity and seizure, is a critical step. Past approaches failed to attain real-world applicability due to lack of generalization capacity. More recently, deep learning techniques may outperform traditional classifiers and handle time dependencies. However, despite the existing efforts for providing interpretable insights, clinicians may not be willing to make high-stake decisions based on them. Furthermore, a disadvantageous aspect of the more usual seizure prediction pipeline is its modularity and significant independence between stages. An alternative could be the construction of a search algorithm that, while considering pipeline stages' synergy, fine-tunes the selection of a reduced set of features that are widely used in the literature and computationally efficient. With extracranial recordings from 19 patients suffering from temporal-lobe seizures, we developed a patient-specific evolutionary optimization strategy, aiming to generate the optimal set of features for seizure prediction with a logistic regression classifier, which was tested prospectively in a total of 49 seizures and 710 h of continuous recording and performed above chance for 32% of patients, using a surrogate predictor. These results demonstrate the hypothesis of pre-ictal period identification without the loss of interpretability, which may help understanding brain dynamics leading to seizures and improve prediction algorithms.

Background: In Brazil, a substantial number of coronavirus disease (COVID-19) cases and deaths have been reported. It has become the second most affected country worldwide, as of June 9, 2020. Official Brazilian government sources present contradictory data on the impact of the disease; thus, it is possible that the actual number of infected individuals and deaths in Brazil is far larger than those officially reported. It is very likely that the actual spread of the disease has been underestimated., Objective: This study investigates the underreporting of cases and deaths related to COVID-19 in the most affected cities in Brazil, based on public data available from official Brazilian government internet portals, to identify the actual impact of the pandemic., Methods: We used data from historical deaths due to respiratory problems and other natural causes from two public portals: DATASUS (Department of Informatics of the Unified Healthcare System) (2010-2018) and the Brazilian Transparency Portal of Civil Registry (2019-2020). These data were used to build time-series models (modular regressions) to predict the expected mortality patterns for 2020. The forecasts were used to estimate the possible number of deaths that were incorrectly registered during the pandemic and posted on government internet portals in the most affected cities in the country., Results: Our model found a significant difference between the real and expected values. The number of deaths due to severe acute respiratory syndrome (SARS) was considerably higher in all cities, with increases between 493% and 5820%. This sudden increase may be associated with errors in reporting. An average underreporting of 40.68% (range 25.9%-62.7%) is estimated for COVID-19-related deaths., Conclusions: The significant rates of underreporting of deaths analyzed in our study demonstrate that officially released numbers are much lower than actual numbers, making it impossible for the authorities to implement a more effective pandemic response. Based on analyses carried out using different fatality rates, it can be inferred that Brazil's epidemic is worsening, and the actual number of infectees could already be between 1 to 5.4 million., (©Lena Veiga e Silva, Maria Da Penha de Andrade Abi Harb, Aurea Milene Teixeira Barbosa dos Santos, Carlos André de Mattos Teixeira, Vitor Hugo Macedo Gomes, Evelin Helena Silva Cardoso, Marcelino S da Silva, N L Vijaykumar, Solon Venâncio Carvalho, André Ponce de Leon Ferreira de Carvalho, Carlos Renato Lisboa Frances. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.08.2020.)

The assessment of pesticide residue levels demands fast, low cost and easy-to-use procedures which are not found in conventional methods. In this work, SERS substrates based on the deposition of gold nanoparticles (GNPs) on common office paper were prepared using a wax printer. These substrates combined with Data Driven Soft Independent Modelling of Class Analogies (DD-SIMCA), a one-class classifier algorithm, were used for detection of pesticide residues in water extracts of mango peels. Paper-based substrates made sample collection easier compared with conventional SERS methods, since few microliters of the pesticide aqueous extract from fruit peels needed to be deposited onto the substrate. Moreover, one-class classifiers dismiss the need for quantification or calibration curves. Classification of a fruit with residue levels in accordance to regulatory bodies' limits is based on a mathematical threshold. Just as in an authentication problem, all the possibilities for a given analysed fruit are now restricted to agreeing or not agreeing with current regulations. The performance of the one-class model was demonstrated by detecting thiabendazole (TBZ) residues at various mango samples, with all results being confirmed by HPLC-DAD analysis. The final model could distinguish samples with TBZ levels above the ones allowed by the Brazilian Health Regulatory Agency with 94% of selectivity and 92% of sensitivity, even in the presence of other pesticides., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier B.V. All rights reserved.)

There are different types of effluents from farming operations including untreated slurry (a mixture of manure, urine, split feed, and water), and treated slurry that normally is filtered to separate the solid fraction from the liquid fraction. With the amount of slurry applied on the soils as fertiliser every year, there are necessary to measure the leaching of microbial capable of transmitting infective agents that can be normally on slurry, because slurry can be a potential biohazard capable of transmitting infective agents. The aim of this study was to investigate the presence and survival of Escherichia coli (E. coli) on liquid fraction of dairy slurry, with the addition of different treatments during storage, such as addition of Biochar, beneficial microorganisms, sulphuric acid and the combinations of them. All the applied treatments to slurry show statistically significant differences (P < 0.001), according to the different sampling dates. Results showed that there are conditions and treatments that benefit the survival probability of E. coli, the treatments that include the acidification of slurry have the highest averages of CFU/ml (243288.3 at 37 °C; 136584 at 44 °C). These results can contribute to the improvement of the quality of slurry applied on soils, beneficiating agriculture but also public health., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

We describe the concept of a new imaging modality based on the tracking and dynamic modeling of local intensity changes (ICs) observed in conventional ultrasound images collected during a medium-temperature change. We computed the pixel-by-pixel IC from averaged B-mode images that exhibited different behaviors with varying temperature resulting from changes in the speed of sound, which consequently induce changes in the backscattered energy. Moreover, for each pixel, a first-order polynomial model was adjusted to the different temperature-dependent ICs. The representation of the polynomial angular parameter in 2D pixel space was used to obtain a parametric image. The results obtained by simulations and with real B-mode images indicated that this new ultrasound imaging modality was able to enhance the contrast and highlight structures that were poorly visible or even undetected in conventional images. A temperature change of 3°C was found to be sufficient to generate appropriate images with the proposed method. In addition, if a temperature change of 6°C was considered, the thermal dose, measured as the cumulative number of equivalent minutes at 43°C (CEM43°C), was 2.4 CEM43°C, which is a value that is considered safe according to the literature. We provide a proof-of-concept of a new imaging modality that opens new opportunities for the enhancement of ultrasound images and consequently contributes to improvements in ultrasound-based diagnoses. Our approach is based on images returned by commercial ultrasound scanners. Therefore, it can be implemented in any ultrasound system and is independent of specific ultrasound hardware and software data acquisition characteristics.

Transformation of the river catchment and the river bed cause significant changes in the functioning of river ecosystems. The main effects of anthropogenic transformations are hydrological changes, such as lower current velocity or an increase of nutrient content, and higher temperature. Zooplankton reacts rapidly to the new environmental conditions in rivers, increasing its richness and abundance. We tried to answer two questions: what type of catchment use has a greater influence on the zooplankton communities in a river and how do dam impoundments influence the zooplankton communities downstream? The study was conducted in the Corgo river (drainage of the Douro river, Northern Portugal) at 17 sampling sites in the lotic, free-flowing sections. Crustaceans present in the Corgo can attain relatively high densities in the rural section, which offers them better trophic conditions. The urban catchment use and the presence of dams have a greater impact on the rotifer density and the increase of zooplankton density downstream. The results of this study confirm that zooplankton properties allow for the evaluation of the degree of river-bed transformation.

Background: Detection and quantification of cyclic alternating patterns (CAP) components has the potential to serve as a disease bio-marker. Few methods exist to discriminate all the different CAP components, they do not present appropriate sensitivities, and often they are evaluated based on accuracy (AC) that is not an appropriate measure for imbalanced datasets., Methods: We describe a knowledge discovery methodology in data (KDD) aiming the development of automatic CAP scoring approaches. Automatic CAP scoring was faced from two perspectives: the binary distinction between A-phases and B-phases, and also for multi-class classification of the different CAP components. The most important KDD stages are: extraction of 55 features, feature ranking/transformation, and classification. Classification is performed by (i) support vector machine (SVM), (ii) k-nearest neighbors (k-NN), and (iii) discriminant analysis. We report the weighted accuracy (WAC) that accounts for class imbalance., Results: The study includes 30 subjects from the CAP Sleep Database of Physionet. The best alternative for the discrimination of the different A-phase subtypes involved feature ranking by the minimum redundancy maximum relevance algorithm (mRMR) and classification by SVM, with a WAC of 51%. Concerning the binary discrimination between A-phases and B-phases, k-NN with mRMR ranking achieved the best WAC of 80%., Conclusions: We describe a KDD that, to the best of our knowledge, was for the first time applied to CAP scoring. In particular, the fully discrimination of the three different A-phases subtypes is a new perspective, since past works tried multi-class approaches but based on grouping of different sub-types. We also considered the weighted accuracy, in addition to simple accuracy, resulting in a more trustworthy performance assessment. Globally, better subtype sensitivities than other published approaches were achieved.

The construction and use of buildings represent about half of the extracted materials and energy consumption, and around one third of the water consumption and waste produced in the European Union. Therefore it is becoming more important to use sustainable materials that reduce the environmental impacts of construction, by conserving and using resources more efficiently. Green walls can be used as a sustainable strategy to reduce the environmental impact of buildings. The aim of this study is to evaluate the environmental impact of a new modular system for green roofs and green walls (Geogreen) which uses waste and sustainable materials in its composition. A life cycle analysis (LCA) is used to evaluate the long term environmental benefits of this system. The life cycle analysis (LCA) is carried according to ISO 14040/44 using GaBi software and CML 2001 impact category indicators. The adopted functional unit is the square meter of each material required to assemble the Geogreen system. This study also compares the environmental performance of the Geogreen system with other living wall systems and other cladding materials using data from the literature. This LCA study of the Geogreen system became relevant to identify a curing process with a major impact on GWP due to the energy consumed in this process. A change on this process allowed reducing 74% of the overall GWP. After this change it can be noticed that the Geogreen System presents one of the lowest environmental burden when compared to other construction systems., (Copyright © 2018. Published by Elsevier B.V.)

A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

Objective: To identify the factors associated with the fear of falling in the older adultliving at home., Method: Cross-sectional study with probabilistic sampling of older adultenrolled in two Family Health Strategies (FHS). The fear of falling was measured by the Brazilian version of the Falls Efficacy Scale-International and by a household questionnairethat contained the explanatory variables. Multiple Linear Regression using the stepwise selection technique and the Generalized Linear Models were used in the statistical analyses., Results: A total of170 older adultsparticipated in the research, 85 from each FHS. The majority (57.1%) aged between 60 and 69; 67.6% were female; 46.1% fell once in the last year. The majority of the older adults(66.5%) had highfear of falling. In the final multiple linear regression model, it was identified that a higher number of previous falls, female gender, older age, and worse health self-assessment explained 37% of the fear of falling among the older adult., Conclusion: The findings reinforce the need to assess the fear of falling among the older adultliving at home, in conjunction with the development and use ofstrategies based on modifiable factors by professionalsto reduce falls and improve health status, which may contribute to the reduction of the fear of falling among the older adult., Objetivo: Identificar os fatores associados ao medo de cair em idosos residentes no domicílio., Métodos: Estudo transversal com amostragem probabilística de idosos cadastrados em duas Estratégias Saúde da Família (ESF). O medo de cair foi avaliado pela versão brasileira da escala Falls Efficacy Scale International e por um inquérito domiciliar que continha as variáveis explicativas.A Regressão Linear Múltipla por meio da técnica stepwise selectione osModelos Lineares Generalizados foram utilizados nas análises estatísticas., Resultados: Participaram da pesquisa170 idosos, 85 de cada ESF. A maioria (57,1%) tinha entre 60 e 69 anos de idade; 67,6% eram do sexo feminino; 46,1% tiveram queda no último ano. A maioria dos idosos (66,5%) tinha elevado medo de cair. No modelo final de regressão multivariada, identificou-se que maior número de quedas anteriores, sexo feminino, idade mais avançada, e pior autoavaliação de saúde explicaram 37% do medo de cair entre os idosos., Conclusão: Os achados reforçam a necessidade da avaliação do medo de cair entre os idosos que residem no próprio domicílio, assim como o desenvolvimento e a utilização de estratégias pelos profissionais voltadas para os fatores modificáveis,de modo a reduzir as quedas e melhorar o estado de saúde, o que pode contribuir para a diminuição do medo de cair entre os idosos.

An important strategy to promote a strong sustainable growth relies on an efficient municipal waste management, and phasing out waste landfilling through waste prevention and recycling emerges as a major target. For this purpose, effective collection schemes are required, in particular those regarding selective waste collection, pursuing a more efficient and high quality recycling of reusable materials. This paper addresses the assessment and benchmarking of selective collection schemes, relevant to guide future operational improvements. In particular, the assessment is based on the monitoring and statistical analysis of a core-set of performance indicators that highlights collection trends, complemented with a performance index that gathers a weighted linear combination of these indicators. This combined analysis underlines a potential tool to support decision makers involved in the process of selecting the collection scheme with best overall performance. The presented approach was applied to a case study conducted in Oporto Municipality, with data gathered from two distinct selective collection schemes., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The contamination, passivation, or fouling of the detection electrodes is a serious problem undermining the analytical performance of electroanalytical devices. The methods to regenerate the electrochemical activity of the solid electrodes involve mechanical, physical, or chemical surface treatments that usually add operational time, complexity, chemicals, and further instrumental requirements to the analysis. In this paper, we describe for the first time a reproducible method for renewing solid electrodes whenever their morphology or composition are nonspecifically changed without any surface treatment. These renewable electrodes are the closest analogue to the mercury drop electrodes. Our approach was applied in microfluidics, where the downsides related to nonspecific modifications of the electrode are more critical. The renewal consisted in manually sliding metal-coated microwires across a channel with the sample. For this purpose, the chip was composed of a single piece of polydimethylsiloxane (PDMS) with three parallel channels interconnected to one perpendicular and top channel. The microwires were inserted in each one of the parallel channels acting as working, counter, and pseudoreference electrodes for voltammetry. This assembly allowed the renewal of all the three electrodes by simply pulling the microwires. The absence of any interfaces in the chips and the elastomeric nature of the PDMS allowed us to pull the microwires without the occurrence of leakages for the electrode channels even at harsh flow rates of up to 40.0 mL min -1 . We expect this paper can assist the researchers to develop new microfluidic platforms that eliminate any steps of electrode cleaning, representing a powerful alternative for precise and robust analyses to real samples.

Electrochemical therapy (EChT) entails treatment of solid tumors with direct electric current (DC). This work evaluated the specific effects of anodic flow generated by DC on biochemical and metabolic features of the A549 human lung cancer cell line. Apoptosis was evaluated on the basis of caspase-3 activity and mitochondrial transmembrane potential dissipation. Cell morphology was analyzed using transmission electron microscopy, and lipid droplets were studied through morphometric analysis and X-ray qualitative elemental microanalysis. High-resolution respirometry was used to assess mitochondrial respiratory parameters. Results indicated A549 viability decreased in a dose-dependent manner with a prominent drop between 18 and 24h after treatment (p<0.001), together with a two-fold increase in caspase-3 activity. AF-treatment induced a significantly increase (p<0.01) in the cell number with disrupted mitochondrial transmembrane potential. Furthermore, treated cells demonstrated important ultrastructural mitochondria damage and a three-fold increase in the cytoplasmic lipid bodies' number, quantified by morphometrical analyses. Conversely, 24h after treatment, the cells presented a two-fold increase of residual oxygen consumption, accounting for 45.3% of basal oxygen consumption. These results show remarkable alterations promoted by anodic flow on human lung cancer cells which are possibly involved with the antitumoral effects of EChT., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Transthyretin (TTR)-related amyloidoses are diseases characterized by extracellular deposition of amyloid fibrils and aggregates in tissues composed of insoluble misfolded TTR that becomes toxic. Previous studies have demonstrated the ability of small compounds in preventing and reversing TTR V30M deposition in transgenic mice gastrointestinal (GI) tract as well as lowering biomarkers associated with cellular stress and apoptotic mechanisms. In the present study we aimed to study TTR V30M aggregates effect in autophagy, a cellular mechanism crucial for cell survival that has been implicated in the development of several neurodegenerative diseases. We were able to demonstrate in cell culture that TTR V30M aggregates cause a partial impairment of the autophagic machinery as shown by p62 accumulation, whereas early steps of the autophagic flux remain unaffected as shown by autophagosome number evaluation and LC3 turnover assay. Our studies performed in TTR V30M transgenic animals demonstrated that tauroursodeoxycholic acid (TUDCA) and curcumin effectively reverse p62 accumulation in the GI tract pointing to the ability of both compounds to modulate autophagy additionally to mitigate apoptosis. Overall, our in vitro and in vivo studies establish an association between TTR V30M aggregates and autophagy impairment and suggest the use of autophagy modulators as an additional and alternative therapeutic approach for the treatment of TTR V30M-related amyloidosis., (© 2016 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.)

The global inhomogeneity (GI) index is a electrical impedance tomography (EIT) parameter that quantifies the tidal volume distribution within the lung. In this work the global inhomogeneity index was computed for twenty subjects in order to evaluate his potential use in the detection and follow up of chronic obstructive pulmonary disease (COPD) patients. EIT data of 17 subjects were acquired: 14 patients with the main diagnoses of COPD and 3 healthy subjects which served as a control group. Two or three datasets of around 30 seconds were acquired at 33 scans/s and analysed for each subject. After reconstruction, a tidal EIT image was computed for each breathing cycle and a GI index calculated from it. Results have shown significant differences in GI values between the two groups (0.745 ± 0.007 for COPD and 0.668 ± 0.006 for lung-healthy subject, p <; 0.005). The GI values obtained for each subject have shown small variance between them, which is a good indication of stability. The results suggested that the GI may be useful for the identification and follow up of ventilation problems in patients with COPD.

Yellow nail syndrome (YNS) is an uncommon condition characterized by nail changes, lymphedema, in addition to pulmonary disorders and pleural effusion. Pericarditis and non-cardiac disorders can evolve with pericardial effusions including autoimmune conditions, hypothyroidism, malignancies, tuberculosis, and uremia. A 72-year-old Brazilian woman under treatment for arterial hypertension and hypothyroidism was admitted with pericarditis and pericardial effusion concomitant with yellow nail syndrome. She denied tobacco smoking, alcohol abuse, and similar disorders in her family. Clinical and complementary evaluation ruled out infectious diseases, malignancies, and autoimmune disorders as etiologic factors in this case. Hypothyroidism is a well-known cause of pericardial effusion, the vast majority in the absence of pericarditis, and has been described as an associated condition in some individuals with YNS. Case studies might contribute to better understanding of these causal or casual relationships.

This work introduces a new methodology for the early detection of epileptic seizure based on the WiSARD weightless neural network model and a new approach in terms of preprocessing the electroencephalogram (EEG) data. WiSARD has, among other advantages, the capacity of perform the training phase in a very fast way. This speed in training is due to the fact that WiSARD's neurons work like Random Access Memories (RAM) addressed by input patterns. Promising results were obtained in the anticipation of seizure onsets in four representative patients from the European Database on Epilepsy (EPILEPSIAE). The proposed seizure early detection WNN architecture was explored by varying the detection anticipation (δ) in the 2 to 30 seconds interval, and by adopting 2 and 3 seconds as the width of the Sliding Observation Window (SOW) input. While in the most challenging patient (A) one obtained accuracies from 99.57% (δ=2s; SOW=3s) to 72.56% (δ=30s; SOW=2s), patient D seizures could be detected in the 99.77% (δ=2s; SOW=2s) to 99.93% (δ=30s; SOW=3s) accuracy interval.

A novel approach using neuronal potential similarity (NPS) of two intracranial electroencephalogram (iEEG) electrodes placed over the foci is proposed for automated early seizure detection in patients with refractory partial epilepsy. The NPS measure is obtained from the spectral analysis of space-differential iEEG signals. Ratio between the NPS values obtained from two specific frequency bands is then investigated as a robust generalized measure, and reveals invaluable information about seizure initiation trends. A threshold-based classifier is subsequently applied on the proposed measure to generate alarms. The performance of the method was evaluated using cross-validation on a large clinical dataset, involving 183 seizure onsets in 1785 h of long-term continuous iEEG recordings of 11 patients. On average, the results show a high sensitivity of 86.9% (159 out of 183), a very low false detection rate of 1.4 per day, and a mean detection latency of 13.1 s from electrographic seizure onsets, while in average preceding clinical onsets by 6.3 s. These high performance results, specifically the short detection latency, coupled with the very low computational cost of the proposed method make it adequate for using in implantable closed-loop seizure suppression systems.

Objective: to analize the relationship between depression and work stress in nursing professionals with technical education level of a teaching hospital in a city of the state of São Paulo., Methods: a cross-sectional study was carried out with 310 nursing technicians and nursing assistants, randomly selected. The outcome analyzed was the report of depression and its relationship with high levels of work stress, measured using the Job Stress Scale. Descriptive statistics and logistic regression were performed., Results: the prevalence of depression in this study was 20%, and it was more expressive in females, aged over 40 years, living without a partner and in smokers. The chance of depression was twice as high among professionals showing high levels of work stress, even after multiple regression adjusting., Conclusion: depressive symptoms were strongly associated with high stress levels among nursing assistants and nursing technicians, evidencing a problem to be considered along with the planning of specific intervention programs for this population, as well as the need for better cases management by the supervisors.

Supervised machine learning-based seizure prediction methods consider preictal period as an important prerequisite parameter during training. However, the exact length of the preictal state is unclear and varies from seizure to seizure. We propose a novel statistical approach for proper selection of the preictal period, which can also be considered either as a measure of predictability of a seizure or as the prediction capability of an understudy feature. The optimal preictal periods (OPPs) obtained from the training samples can be used for building a more accurate classifier model. The proposed method uses amplitude distribution histograms of features extracted from electroencephalogram (EEG) recordings. To evaluate this method, we extract spectral power features in different frequency bands from monopolar and space-differential EEG signals of 18 patients suffering from pharmacoresistant epilepsy. Furthermore, comparisons among monopolar channels with space-differential channels, as well as intracranial EEG (iEEG) and surface EEG (sEEG) signals, indicate that while monopolar signals perform better in iEEG recordings, no significant difference is noticeable in sEEG recordings., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Objective: Prediction of epileptic seizures can improve the living conditions for refractory epilepsy patients. We aimed to improve sensitivity and specificity of prediction methods, and to reduce the number of false alarms., Methods: Relative combinations of sub-band spectral powers of electroencephalogram (EEG) recordings across all possible channel pairs were utilized for tracking gradual changes preceding seizures. By using a specifically developed feature selection method, a set of best candidate features were fed to support vector machines in order to discriminate cerebral state as preictal or non-preictal., Results: Proposed algorithm was evaluated on continuous long-term multichannel scalp and invasive recordings (183 seizures, 3565 h). The best results demonstrated a sensitivity of 75.8% (66 out of 87 seizures) and a false prediction rate of 0.1h(-1). Performance was validated statistically, and was superior to that of analytical random predictor., Conclusion: Applying machine learning methods on a reduced subset of proposed features could predict seizure onsets with high performance., Significance: Our method was evaluated on long-term continuous recordings of overall about 5 months, contrary to majority of previous studies using short-term fragmented data. It is of very low computational cost, while providing acceptable levels of alarm sensitivity and specificity., (Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Bivariate features, obtained from multichannel electroencephalogram recordings, quantify the relation between different brain regions. Studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. A new bivariate approach using univariate features is proposed here. Differences and ratios of 22 linear univariate features were calculated using pairwise combination of 6 electroencephalograms channels, to create 330 differential, and 330 relative features. The feature subsets were classified using support vector machines separately, as one of the two classes of preictal and nonpreictal. Furthermore, minimum Redundancy Maximum Relevance feature reduction method is employed to improve the predictions and reduce the number of false alarms. The studies were carried out on features obtained from 10 patients. For reduced subset of 30 features and using differential approach, the seizures were on average predicted in 60.9% of the cases (28 out of 46 in 737.9 h of test data), with a low false prediction rate of 0.11 h(-1). Results of bivariate approaches were compared with those achieved from original linear univariate features, extracted from 6 channels. The advantage of proposed bivariate features is the smaller number of false predictions in comparison to the original 22 univariate features. In addition, reduction in feature dimension could provide a less complex and the more cost-effective algorithm. Results indicate that applying machine learning methods on a multidimensional feature space resulting from relative/differential pairwise combination of 22 univariate features could predict seizure onsets with high performance.

Objective: To analyze the association between strategies used to cope with occupational stress that are focused on the problem wand the personal characteristics of nursing technicians and assistants., Methodology: This quantitative and correlational study was conducted in a large teaching hospital in the São Paulo State, Brazil, in 2013. A randomized sample with 310 participants (198 nursing assistants and 112 nursing technicians) comprised the study population. Data were collected using a sociodemographic characteristics questionnaire and Scale of Ways of Coping with Problems. Data were submitted to univariate analysis, and variables with statistical significance (p<0.20) were submitted posteriorly in a multiple regression model., Results: Most employees were women (76.1%) older than 40 years (67.7%), had nine to 11 years of formal education (73.5%), had a partner (58.7%), were Catholic (53.2%), and had children (74.5%). The final multiple regression model consisted of variable years of formal education and number of children., Conclusion: In this study, formal education and number of children were more strongly associated with a greater use of coping strategies focused on the problem. Such a strategy is related to minimal vulnerability to stress related to the working environment.

This article describes an accurate methodology for an operational, economic, and environmental assessment of municipal solid waste collection. The proposed methodological tool uses key performance indicators to evaluate independent operational and economic efficiency and performance of municipal solid waste collection practices. These key performance indicators are then used in life cycle inventories and life cycle impact assessment. Finally, the life cycle assessment environmental profiles provide the environmental assessment. We also report a successful application of this tool through a case study in the Portuguese city of Porto. Preliminary results demonstrate the applicability of the methodological tool to real cases. Some of the findings focus a significant difference between average mixed and selective collection effective distance (2.14 km t(-1); 16.12 km t(-1)), fuel consumption (3.96 L t(-1); 15.37 L t(-1)), crew productivity (0.98 t h(-1) worker(-1); 0.23 t h(-1) worker(-1)), cost (45.90 € t(-1); 241.20 € t(-1)), and global warming impact (19.95 kg CO2eq t(-1); 57.47 kg CO2eq t(-1)). Preliminary results consistently indicate: (a) higher global performance of mixed collection as compared with selective collection; (b) dependency of collection performance, even in urban areas, on the waste generation rate and density; (c) the decline of selective collection performances with decreasing source-separated material density and recycling collection rate; and (d) that the main threats to collection route efficiency are the extensive collection distances, high fuel consumption vehicles, and reduced crew productivity., (© The Author(s) 2014.)

The increase of Municipal Solid Waste (MSW) generated over the last years forces waste managers pursuing more effective collection schemes, technically viable, environmentally effective and economically sustainable. The assessment of MSW services using performance indicators plays a crucial role for improving service quality. In this work, we focus on the relevance of regular system monitoring as a service assessment tool. In particular, we select and test a core-set of MSW collection performance indicators (effective collection distance, effective collection time and effective fuel consumption) that highlights collection system strengths and weaknesses and supports pro-active management decision-making and strategic planning. A statistical analysis was conducted with data collected in mixed collection system of Oporto Municipality, Portugal, during one year, a week per month. This analysis provides collection circuits' operational assessment and supports effective short-term municipality collection strategies at the level of, e.g., collection frequency and timetables, and type of containers., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

This paper assesses the potential of the average gray-level (AVGL) from ultrasonographic (B-mode) images to estimate temperature changes in time and space in a non-invasive way. Experiments were conducted involving a homogeneous bovine muscle sample, and temperature variations were induced by an automatic temperature regulated water bath, and by therapeutic ultrasound. B-mode images and temperatures were recorded simultaneously. After data collection, regions of interest (ROIs) were defined, and the average gray-level variation computed. For the selected ROIs, the AVGL-Temperature relation were determined and studied. Based on uniformly distributed image partitions, two-dimensional temperature maps were developed for homogeneous regions. The color-coded temperature estimates were first obtained from an AVGL-Temperature relation extracted from a specific partition (where temperature was independently measured by a thermocouple), and then extended to the other partitions. This procedure aimed to analyze the AVGL sensitivity to changes not only in time but also in space. Linear and quadratic relations were obtained depending on the heating modality. We found that the AVGL-Temperature relation is reproducible over successive heating and cooling cycles. One important result was that the AVGL-Temperature relations extracted from one region might be used to estimate temperature in other regions (errors inferior to 0.5 °C) when therapeutic ultrasound was applied as a heating source. Based on this result, two-dimensional temperature maps were developed when the samples were heated in the water bath and also by therapeutic ultrasound. The maps were obtained based on a linear relation for the water bath heating, and based on a quadratic model for the therapeutic ultrasound heating. The maps for the water bath experiment reproduce an acceptable heating/cooling pattern, and for the therapeutic ultrasound heating experiment, the maps seem to reproduce temperature profiles consistent with the pressure field of the transducer, and in agreement with temperature maps developed by COMSOL®MultiPhysics simulations., (Copyright © 2014 Elsevier B.V. All rights reserved.)