Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction.

Seizure prediction remains a challenge, with approximately 30% of patients unresponsive to conventional treatments. Addressing this issue is crucial for improving patients' quality of life, as timely intervention can mitigate the impact of seizures. In this research field, it is critical to identify the preictal interval, the transition from regular brain activity to a seizure. While previous studies have explored various Electroencephalogram (EEG) based methodologies for prediction, few have been clinically applicable. Recent studies have underlined the dynamic nature of EEG data, characterised by data changes with time, known as concept drifts, highlighting the need for automated methods to detect and adapt to these changes. In this study, we investigate the effectiveness of automatic concept drift adaptation methods in seizure prediction. Three patient-specific seizure prediction approaches with a 10-minute prediction horizon are compared: a seizure prediction algorithm incorporating a window adjustment method by optimising performance with Support Vector Machines (Backwards-Landmark Window), a seizure prediction algorithm incorporating a data-batch (seizures) selection method using a logistic regression (Seizure-batch Regression), and a seizure prediction algorithm with a dynamic integration of classifiers (Dynamic Weighted Ensemble). These methods incorporate a retraining process after each seizure and use a combination of univariate linear features and SVM classifiers. The Firing Power was used as a post-processing technique to generate alarms before seizures. These methodologies were compared with a control approach based on the typical machine learning pipeline, considering a group of 37 patients with Temporal Lobe Epilepsy from the EPILEPSIAE database. The best-performing approach (Backwards-Landmark Window) achieved results of 0.75 ± 0.33 for sensitivity and 1.03 ± 1.00 for false positive rate per hour. This new strategy performed above chance for 89% of patients with the surrogate predictor, whereas the control approach only validated 46%.

Classifier combination supported by the sleep-wake cycle improves EEG seizure prediction performance.

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 longterm 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.

EEG epilepsy seizure prediction: the post-processing stage as a chronology.

Almost one-third of epileptic patients fail to achieve seizure control through anti-epileptic drug administration. In the scarcity of completely controlling a patient's epilepsy, seizure prediction plays a significant role in clinical management and providing new therapeutic options such as warning or intervention devices. Seizure prediction algorithms aim to identify the preictal period that Electroencephalogram (EEG) signals can capture. However, this period is associated with substantial heterogeneity, varying among patients or even between seizures from the same patient. The present work proposes a patient-specific seizure prediction algorithm using post-processing techniques to explore the existence of a set of chronological events of brain activity that precedes epileptic seizures. The study was conducted with 37 patients with Temporal Lobe Epilepsy (TLE) from the EPILEPSIAE database. The designed methodology combines univariate linear features with a classifier based on Support Vector Machines (SVM) and two post-processing techniques to handle pre-seizure temporality in an easily explainable way, employing knowledge from network theory. In the Chronological Firing Power approach, we considered the preictal as a sequence of three brain activity events separated in time. In the Cumulative Firing Power approach, we assumed the preictal period as a sequence of three overlapping events. These methodologies were compared with a control approach based on the typical machine learning pipeline. We considered a Seizure Prediction horizon (SPH) of 5 mins and analyzed several values for the Seizure Occurrence Period (SOP) duration, between 10 and 55 mins. Our results showed that the Cumulative Firing Power approach may improve the seizure prediction performance. This new strategy performed above chance for 62% of patients, whereas the control approach only validated 49% of its models.

Loss of RAS Mutations in Liquid Biopsies of Patients With Multi-Treated Metastatic Colorectal Cancer.

BACKGROUND: Liquid biopsy (LB) is a non-invasive tool to evaluate the heterogeneity of tumors. Since RAS mutations (RAS-mut) play a major role in resistance to antiepidermal growth factor receptor inhibitors (EGFR) monoclonal antibodies (Mabs), serial monitoring of RAS-mut with LB may be useful to guide treatment. The main aim of this study was to evaluate the prognostic value of the loss of RAS-mut (NeoRAS-wt) in LB, during the treatment of metastatic colorectal cancer (mCRC). METHODS: A retrospective study was conducted on patients with mCRC between January 2018 and December 2021. RAS-mut were examined in tissue biopsy, at mCRC diagnosis, and with LB, during treatment. RESULTS: Thirty-nine patients with RAS-mut mCRC were studied. LB was performed after a median of 3 lines (0-7) of systemic treatment including anti-vascular endothelial growth factor (anti-VEGF) Mabs. NeoRAS-wt was detected in 13 patients (33.3%); 9 (69.2%) of them received further treatment with anti-EGFR Mabs with a disease control rate of 44.4%. Median overall survival (OS), from the date of LB testing, was 20 months in the NeoRAS-wt group and 9 months in the persistent RAS-mut group (log-rank 2.985; P = .08), with a 12-month OS of 84.6% and 57.7%, respectively. NeoRAS-wt was identified as a predictor of survival (HR = 0.29; P = .007), with an 11-month improvement in median OS and a 71% decrease in risk of death, in heavily pretreated patients. CONCLUSIONS: In conclusion, monitoring clonal evolution in mCRC by LB may provide an additional treatment line for patients with NeoRAS-wt in advanced disease.

Effects of an online program including mindfulness, exercise therapy and patient education compared to online exercise therapy and patient education for people with Patellofemoral Pain: protocol for a randomized clinical trial.

BACKGROUND: Patellofemoral pain (PFP) is a common knee disorder that causes persistent pain, lower self-reported function and quality of life. People with PFP also present with altered psychological factors, which are associated with higher levels of pain and dysfunction. Mindfulness-based interventions (MBI) generally consist of meditative practices developed to provide a holistic approach to chronic conditions. However, the effects of MBI on clinical and psychological outcomes for people with PFP remains understudied. METHODS: This assessor-blinded, parallel, two-arm randomized clinical trial aims to investigate the effects of adding an 8-week online MBI program to exercise therapy and patient education on clinical and psychological factors for people with PFP. We also aim to investigate whether psychological factors mediate changes in pain and function. Sixty-two participants with PFP will be recruited and randomized into one of two treatment groups (Mindfulness or Control group). Both groups will receive an 8-week intervention involving exercise therapy and education delivered through an online platform. The Mindfulness group will additionally receive a MBI component including formal and informal practices. Outcomes will be assessed online at baseline, intervention endpoint (follow-up 1) and 12 months after intervention completion (follow-up 2). Comparisons between groups will be performed at all time points with linear mixed models. A mediation analysis will be performed using a 3-variable framework. DISCUSSION: Exercise therapy and patient education are considered the "best management" options for PFP. However, unsatisfactory long-term prognosis remains an issue. It is known that people with PFP present with altered psychological factors, which should be considered during the evaluation and treatment of people with PFP. Adding a MBI to the current best treatment for PFP may improve short and long-term effects by addressing the underlying psychological factors. TRIAL REGISTRATION: Registro Brasileiro de Ensaios Clínicos (ReBEC) RBR-4yhbqwk, registered in April 6, 2021.

Impact of extreme wildfires from the Brazilian Forests and sugarcane burning on the air quality of the biggest megacity on South America.

Recently, extreme wildfires have damaged important ecosystems worldwide and have affected urban areas miles away due to long-range transport of smoke plumes. We performed a comprehensive analysis to clarify how smoke plumes from Pantanal and Amazon forests wildfires and sugarcane harvest burning also from interior of the state of São Paulo (ISSP) were transported and injected into the atmosphere of the Metropolitan Area of São Paulo (MASP), where they worsened air quality and increased greenhouse gas (GHG) levels. To classify event days, multiple biomass burning fingerprints as carbon isotopes, Lidar ratio and specific compounds ratios were combined with back trajectories modeling. During smoke plume event days in the MASP fine particulate matter concentrations exceeded the WHO standard (>25 µg m-3), at 99 % of the air quality monitoring stations, and peak CO2 excess were 100 % to 1178 % higher than non-event days. We demonstrated how external pollution events such as wildfires pose an additional challenge for cities, regarding public health threats associated to air quality, and reinforces the importance of GHG monitoring networks to track local and remote GHG emissions and sources in urban areas.

The goal of explaining black boxes in EEG seizure prediction is not to explain models' decisions.

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.

Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models.

The development of seizure prediction models is often based on long-term scalp electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive, and come at a relatively low-cost. However, they suffer from major shortcomings. First, long-term EEG is usually highly contaminated with artefacts. Second, changes in the EEG signal over long intervals, known as concept drift, are often neglected. We evaluate the influence of these problems on deep neural networks using EEG time series and on shallow neural networks using widely-used EEG features. Our patient-specific prediction models were tested in 1577 hours of continuous EEG, containing 91 seizures from 41 patients with temporal lobe epilepsy who were undergoing pre-surgical monitoring. Our results showed that cleaning EEG data, using a previously developed artefact removal method based on deep convolutional neural networks, improved prediction performance. We also found that retraining the models over time reduced false predictions. Furthermore, the results show that although deep neural networks processing EEG time series are less susceptible to false alarms, they may need more data to surpass feature-based methods. These findings highlight the importance of robust data denoising and periodic adaptation of seizure prediction models.

Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy.

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.

Activation of 5-HT3 receptors in the medulla oblongata is involved in the phasic control of urinary bladder.

The control of micturition depends on reflex mechanisms, however, it undergoes modulation from cortex, pons and medullary areas. This study investigated if the activation of 5-HT3 receptors in the medulla influences the urinary bladder (UB) regulation in rats. Isoflurane female Wistar rats were submitted to catheterization of the femoral artery and vein for mean arterial pressure (MAP) and heart rate (HR) recordings and injection of drugs, respectively. The UB was cannulated for intravesical pressure (IP) measurement. The Doppler flow probe was placed around the left renal artery for renal conductance (RC) recordings. Phenylbiguanide (PB) and granisetron (GN) were injected into the 4th brain ventricle in rats with guide cannulas implanted 5 days prior to the experiments; or PB and GN were randomly injected intravenously or applied topically (in situ) on the UB. PB injection into 4th V significantly increased IP (68.67 ± 11.70%) and decreased MAP (-29 ± 6 mmHg) compared to saline (0.34 ± 0.64% and -2 ± 2 mmHg), with no changes in the HR and RC. GN injection into the 4th V did not significantly change the IP and RC compared to saline, nevertheless, significantly increased MAP (25 ± 4 mmHg) and heart rate (36 ± 9 bpm) compared to saline. Intravenous PB and GN only produced cardiovascular effects, whilst PB but not GN in situ on the UB evoked increase in IP (111.60 ± 30.36%). Therefore, the activation of 5HT-3 receptors in medullary areas increases the intravesical pressure and these receptors are involved in the phasic control of UB. In contrast, 5-HT3 receptors in the medulla oblongata are involved in the pathways of the tonic control of the cardiovascular system. The activation of 5-HT3 receptors in the bladder cause increase in intravesical pressure and this regulation seem to be under phasic control as the blockade of such receptors elicits no changes in baseline intravesical pressure.

EPIC: Annotated epileptic EEG independent components for artifact reduction.

Scalp electroencephalogram is a non-invasive multi-channel biosignal that records the brain's electrical activity. It is highly susceptible to noise that might overshadow important data. Independent component analysis is one of the most used artifact removal methods. Independent component analysis separates data into different components, although it can not automatically reject the noisy ones. Therefore, experts are needed to decide which components must be removed before reconstructing the data. To automate this method, researchers have developed classifiers to identify noisy components. However, to build these classifiers, they need annotated data. Manually classifying independent components is a time-consuming task. Furthermore, few labelled data are publicly available. This paper presents a source of annotated electroencephalogram independent components acquired from patients with epilepsy (EPIC Dataset). This dataset contains 77,426 independent components obtained from approximately 613 hours of electroencephalogram, visually inspected by two experts, which was already successfully utilised to develop independent component classifiers.

Magnetoliposomes Containing Multicore Nanoparticles and a New Antitumor Thienopyridine Compound with Potential Application in Chemo/Thermotherapy.

Multicore magnetic nanoparticles of manganese ferrite were prepared using carboxymethyl dextran as an agglutinating compound or by an innovative method using melamine as a cross-coupling agent. The nanoparticles prepared using melamine exhibited a flower-shape structure, a saturation magnetization of 6.16 emu/g and good capabilities for magnetic hyperthermia, with a specific absorption rate (SAR) of 0.14 W/g. Magnetoliposome-like structures containing the multicore nanoparticles were prepared, and their bilayer structure was confirmed by FRET (Förster Resonance Energy Transfer) assays. The nanosystems exhibited sizes in the range of 250-400 nm and a low polydispersity index. A new antitumor thienopyridine derivative, 7-[4-(pyridin-2-yl)-1H-1,2,3-triazol-1-yl]thieno[3,2-b]pyridine, active against HeLa (cervical carcinoma), MCF-7 (breast adenocarcinoma), NCI-H460 (non-small-cell lung carcinoma) and HepG2 (hepatocellular carcinoma) cell lines, was loaded in these nanocarriers, obtaining a high encapsulation efficiency of 98 ± 2.6%. The results indicate that the new magnetoliposomes can be suitable for dual cancer therapy (combined magnetic hyperthermia and chemotherapy).

Lymph node yield in the pathological staging of resected nonmetastatic colon cancer: The more the better?

INTRODUCTION: Guidelines recommend regional lymphadenectomy with a lymph node yield (LNY) of at least 12 lymph nodes (LN) for adequate colon cancer (CC) staging. LNY ≥22LN may improve survival, especially in right-sided CC [Lee et al., Surg Oncol, 27(3), 2018]. This multicentric retrospective cohort study evaluated the impact of LNY and tumor laterality on CC staging and survival. MATERIALS AND METHODS: Patients with stage I-III CC that underwent surgery from 2012 to 2018 were grouped according to LNY: <22 and ≥ 22. Primary outcomes were LN positivity (N+ rate) and disease-free survival (DFS). Overall survival (OS) was the secondary outcome. Exploratory analyses were performed for laterality and stage. RESULTS: We included 795 patients (417 < 22LN, 378 ≥ 22LN); 53% had left-sided CC and 29%/37%/38% had stage I/II/III tumors. There was no association between LNY ≥22LN and N+ rate after adjustment for grade, T stage, lymphovascular invasion (LVI) and perineural invasion; a trend for a higher N+ rate in left-sided CC was identified (interaction p = 0.033). With a median follow-up of 63.6 months for DFS and 73.2 months for OS, 254 patients (31.9%) relapsed and 207 (26.0%) died. In multivariate analysis adjusted for age, ASA score, laparoscopic approach, T/N stage, mucinous histology, LVI and adjuvant chemotherapy, LNY ≥22LN was significantly associated with both DFS (HR 0.75, p = 0.031) and OS (HR 0.71, p = 0.025). Restricted cubic spline analysis showed a more significant benefit for right-sided CC. CONCLUSION: LNY ≥22LN was associated with longer DFS and OS in patients with operable CC, especially for right-sided CC.

On the clinical acceptance of black-box systems for EEG seizure prediction.

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.

Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm.

Seizure prediction might be the solution to tackle the apparent unpredictability of seizures in patients with drug-resistant epilepsy, which comprise about a third of all patients with epilepsy. Designing seizure prediction models involves defining the pre-ictal period, a transition stage between inter-ictal brain activity and the seizure discharge. This period is typically a fixed interval, with some recent studies reporting the evaluation of different patient-specific pre-ictal intervals. Recently, researchers have aimed to determine the pre-ictal period, a transition stage between regular brain activity and a seizure. Authors have been using deep learning models given the ability of such models to automatically perform pre-processing, feature extraction, classification, and handling temporal and spatial dependencies. As these approaches create black-box models, clinicians may not have sufficient trust to use them in high-stake decisions. By considering these problems, we developed an evolutionary seizure prediction model that identifies the best set of features while automatically searching for the pre-ictal period and accounting for patient comfort. This methodology provides patient-specific interpretable insights, which might contribute to a better understanding of seizure generation processes and explain the algorithm's decisions. We tested our methodology on 238 seizures and 3687 h of continuous data, recorded on scalp recordings from 93 patients with several types of focal and generalised epilepsies. We compared the results with a seizure surrogate predictor and obtained a performance above chance for 32% patients. We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients. In total, 54 patients performed above chance for at least one method: our methodology or the control one. Of these 54 patients, 21 ([Formula: see text]38%) were solely validated by our methodology, while 24 ([Formula: see text]44%) were only validated by the control method. These findings may evidence the need for different methodologies concerning different patients.

Ensemble Deep Neural Network for Automatic Classification of EEG Independent Components.

OBJECTIVE: Independent component analysis (ICA) is commonly used to remove noisy artifacts from multi-channel scalp electroencephalogram (EEG) signals. ICA decomposes EEG into different independent components (ICs) and then, experts remove the noisy ones. This process is highly time-consuming and experts are not always available. To surpass this drawback, research is going on to develop models to automatically conduct IC classification. Current state-of-the-art models use power spectrum densities (PSDs) and topoplots to classify ICs. The performance of these methods may be limited by disregarding the IC time-series that would contribute to fully simulate the visual inspection performed by experts. METHODS: We present a novel ensemble deep neural network that combines time-series, PSDs, and topoplots to classify ICs. Moreover, we study the ability to use our model in transfer learning approaches. RESULTS: Experimental results showed that using time-series improves IC classification. Results also indicated that transfer learning obtained higher performance than simply training a new model from scratch. CONCLUSION: Researchers should develop IC classifiers using the three sources of information. Moreover, transfer learning approaches should be considered when producing new deep learning models. SIGNIFICANCE: This work improves IC classification, enhancing the automatic removal of EEG artifacts. Additionally, since labelled ICs are generally not publicly available, the possibility of using our model in transfer learning studies may motivate other researchers to develop their own classifiers.

Caracterização de atores e práticas de Redução de Danos na região Sul do país / Characterization of actors and practices of Harm Reduction in the South region of Brazil

Resumo O uso de conceitos e práticas de Redução de Danos (RD) na atenção às pessoas que usam drogas foi iniciado no Brasil nos anos 1990, como resposta preventiva à propagação do HIV/aids. Ao longo dos anos, ganhou espaço em outros campos de ação, como a saúde mental e a proteção social. Em 2019, com as mudanças no governo federal, esta prática foi retirada da definição da política sobre drogas. Com foco nos reflexos dessas mudanças, este estudo visou caracterizar quem atua e quais ações são desenvolvidas junto às pessoas que usam drogas, sob a perspectiva da RD, em municípios da região Sul do Brasil (RS, SC e PR). Trata-se de um estudo exploratório descritivo e transversal, que usou um questionário virtual composto por questões abertas, fechadas e métodos mistos de análise de dados. Os participantes foram recrutados por meio do método Snowball Sampling, de forma virtual, alcançando 72 questionários validados. Foi desenvolvida uma análise exploratória dos dados quantitativos. Para as respostas qualitativas foi usada a Análise Textual Discursiva e o software NVivo (versão 12). Os resultados demonstraram o perfil dos profissionais; mudanças ao longo dos anos e a necessidade de ampliar investimentos em pesquisas sobre RD.

Abstract Harm Reduction (HR) concepts and practices in the care of people who use drugs stared being used in Brazil in the 1990s, as a preventive response to the spread of HIV/AIDS. Over the years, it gained space in other fields, such as mental health and social protection. In 2019, with changes in the federal government, this practice was removed from the definitions of the drug policy. Focusing on the consequences of these changes, this study aimed to characterize who works and what actions are developed with the people who use drugs, in the HR perspective, in the southern region of Brazil. This is a descriptive and cross-sectional exploratory study, which used an online questionnaire with open and closed questions, and mixed methods of data analysis. Participants were recruited using the Snowball Sampling method, by virtual means, reaching 72 validated questionnaires. An exploratory analysis of the quantitative data was developed. For the qualitative data, the Discursive Textual Analysis and NVivo software (version 12) were used. The results showed the profile of professionals; the changes over the years; and the need to expand investments in HR research.

Cultural and technology elucidation of the Tupi-Guarani tradition through analysis of potsherds from Travessão do Rio Vermelho site (Santa Catarina - Brazil) by spectroscopy, SEM-EDS and chemometrics.

Ceramic fragments can provide an insight into the ancient culture and practices of groups of humans and their way of life (technology, cultural identity, social organization, habitation and economy). Scientific analysis can be used to obtain information on the ceramic production process, as well as the specificities of the material employed. In this research, all samples of archaeological potsherd from the Tupi-Guarani tradition were analyzed in order to identify and to characterize the structures, morphologies and the elemental composition by using by scanning electron microscopy (SEM-EDS) and multivariate statistical methods (PCA and HCA). FTIR spectroscopy revealed the presence of an organic residue in three samples along with carbonates, clay minerals, quartz and hematite. In addition, the presence of the stretching attributed to water molecules in crystalline systems was observed. Also, the presence of TiO2 in the anatase polymorphic form was detected using µ-Raman spectroscopy. These results indicate a firing temperature of between 800 and 1000 °C. In relation to the morphology, all samples revealed amorphous structures presenting isolated and heterogenic particles of different forms and sizes, and the EDS spectrum confirmed the elements present in the molecular structures elucidated by vibrational spectroscopy. The multivariate analysis has confirmed the correlation between the elemental compositions of ceramics collected from two different sites: a mountain region and a coastal area in Santa Catarina State, Brazil.

Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy.

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.

A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction.

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.