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Abstract According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning the number of events that may be used to optimise seizure prediction approaches is limited. To overcome such constraint, we analysed the possibility of using data from patients from an external database to improve patient-specific seizure prediction models. We present seizure prediction models trained using a transfer learning procedure. We trained a deep convolutional autoencoder using electroencephalogram data from 41 patients collected from the EPILEPSIAE database. Then, a bidirectional long short-term memory and a classifier layers were added on the top of the encoder part and were optimised for 24 patients from the Universitätsklinikum Freiburg individually. The encoder was used as a feature extraction module. Therefore, its weights were not changed during the patient-specific training. Experimental results showed that seizure prediction models optimised using pretrained weights present about four times fewer false alarms while maintaining the same ability to predict seizures and achieved more 13% validated patients. Therefore, results evidenced that the optimisation using transfer learning was more stable and faster, saving computational resources. In summary, adopting transfer learning for seizure prediction models represents a significant advancement. It addresses the data limitation seen in the seizure prediction field and offers more efficient and stable training, conserving computational resources. Additionally, despite the compact size, transfer learning allows to easily share data knowledge due to fewer ethical restrictions and lower storage requirements. The convolutional autoencoder developed in this study will be shared with the scientific community, promoting further research.

Cervical cancer is a significant public health issue in Mexico and many developing countries. Early detection is crucial for combating this disease. The official screening test for cervical cancer is cytology, but this technique faces several barriers, including methodological, educational, and sociocultural challenges. Liquid-based cytology is an improved version of this test, however it does not address the aforementioned complications. Biomarkers for cervical precursor lesions and cervical cancer can improve timely detection of the disease. A previous study from our group identified four circulating human proteins as potential biomarkers for these conditions. For molecular screening, we selected GAPDH as the biomarker for cervical precursor lesions and HNRNPA1 as the biomarker for cervical cancer -chosen from the three previously identified options based on antibody availability- to be detected in sera. Participants underwent a comprehensive panel of tests, including liquid-based cytology, PCR detection of Human papillomavirus (HPV), colposcopy, and histopathology -when applicable-. The last two tests were used as references for determining sensitivity and specificity, with histopathology being the gold standard for cervical cancer diagnosis. All the participants successfully received colposcopies (n = 99) and only those women with visible or suspected cervical lesions/malignancies were biopsied (n = 62). A subset of randomly selected biopsies underwent p16INK4a immunohistochemistry (n = 36). This study compares the performance of liquid-based cytology with the molecular screening. With colposcopy as reference, liquid-based cytology showed 30% sensitivity and 96% specificity, while the molecular screening showed 90% sensitivity and 43% specificity. With histopathology as reference, liquid-based cytology showed 21% sensitivity and 93% specificity, while the molecular screening showed 85% sensitivity and 61% specificity. The molecular screening outperformed the liquid-based cytology in several areas, including detecting true-positive cases, reducing false-negative cases by 34.62%, application time, simplicity of result´s categories, and acceptance among participants. An ideal screening test requires high sensitivity, maintains moderate specificity, and minimizes false negatives. Our proposed screening test meets these criteria, making it an ideal complement -or alternative- for cervical cancer screening.

Abstract Epilepsy affects around 1% of the population worldwide. Anti-epileptic drugs are an excellent option for controlling seizure occurrence but do not work for around one-third of patients. Warning devices employing seizure prediction or forecasting algorithms could bring patients new-found comfort and quality of life. These algorithms would attempt to detect a seizure’s preictal period, a transitional moment between regular brain activity and the seizure, and relay this information to the user. Over the years, many seizure prediction studies using Electroencephalogram-based methodologies have been developed, triggering an alarm when detecting the preictal period. Recent studies have suggested a shift in view from prediction to forecasting. Seizure forecasting takes a probabilistic approach to the problem in question instead of the crisp approach of seizure prediction. In this field of study, the triggered alarm to symbolize the detection of a preictal period is substituted by a constant risk assessment analysis. The present work aims to explore methodologies capable of seizure forecasting and establish a comparison with seizure prediction results. Using 40 patients from the EPILEPSIAE database, we developed several patient-specific prediction and forecasting algorithms with different classifiers (a Logistic Regression, a 15 Support Vector Machines ensemble, and a 15 Shallow Neural Networks ensemble). Results show an increase of the seizure sensitivity in forecasting relative to prediction of up to 146% and in the number of patients that displayed an improvement over chance of up to 300%. These results suggest that a seizure forecasting methodology may be more suitable for seizure warning devices than a seizure prediction one.

Abstract 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.

Despite the abundance of research investigating the efficacy of caffeine supplementation on exercise performance, the physiological and biochemical responses to caffeine supplementation during intermittent activities are less evident. This study investigated the acute effects of caffeine supplementation on measures of exercise performance, ratings of perceived exertion, and biomarkers of oxidative stress induced by an acute bout of sprint interval training. In a randomized crossover design, 12 healthy males (age: 26 ± 4 years, height: 177.5 ± 6 cm, body mass: 80.7 ± 7.6 kg) ingested 6 mg/kg of caffeine or placebo 60 min prior to performing sprint interval training (12 × 6 s "all-out sprints" interspersed by 60 s of rest). Performance scores and ratings of perceived exertion were assessed after every sprint. Blood samples were collected before supplementation, prior to and following each sprint, and 5 and 60 min after the last sprint. Caffeine had no effect on any performance measures, ratings of perceived exertion, or biomarkers of oxidative stress (p >.05). In conclusion, caffeine supplementation does not improve performance or decrease oxidative stress after an acute bout of sprint interval training. [ABSTRACT FROM AUTHOR]

Abstract 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.

Modified nucleobases are potentially useful building blocks when containing catalytically active functionalities and could be introduced in chiral tridimensional molecules such as nucleic acids, which creates the premises for the development of novel catalytic species. Herein, we describe the synthesis of a novel β-C-nucleoside bearing a diol group at anomeric position, amenable as a metal ligand or organocatalyst. An abasic ligand was successfully prepared and inserted into a complementary DNA strand.

Abstract 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.

Road network connectivity determines the accessibility of urban activities for pedestrians, while streetscape characteristics have an impact on route attractiveness. Methods used to measure the influence of connectivity and streetscape characteristics on walking behavior differ substantially, while trip purpose is a key factor. This paper explores the respective contributions of network connectivity and streetscape features to explain leisure walking and utilitarian trips on 740 street segments in Santarém (Portugal). The indicators cover the most commonly used factors in walkability indexes, such as density, diversity, design, and accessibility. The streetscape features measure imageability, enclosure, human scale, transparency, and complexity. The walking trip information was collected via survey. The results show that connectivity measures have a greater overall explanatory power for both trip motives. However, the findings highlight the need to consider a variety of design indicators to explain walking behavior due to the higher explanation power of the model with two types of indicators.

Summary: Invasive freshwater mussels, such as the zebra (Dreissena polymorpha), quagga (Dreissena rostriformis bugensis), and golden (Limnoperna fortunei) mussel have spread outside their native ranges throughout many regions of the North American, South American, and European continents in recent decades, damaging infrastructure and the environment. This review describes ongoing efforts by multiple groups to develop genetic biocontrol methods for invasive mussels. First, we provide an overview of genetic biocontrol strategies that have been applied in other invasive or pest species. Next, we summarize physical and chemical methods that are currently in use for invasive mussel control. We then describe the multidisciplinary approaches our groups are employing to develop genetic biocontrol tools for invasive mussels. Finally, we discuss the challenges and limitations of applying genetic biocontrol tools to invasive mussels. Collectively, we aim to openly share information and combine expertise to develop practical tools to enable the management of invasive freshwater mussels.

Objectives. To characterize the distribution profile of dengue, chikungunya, and Zika virus infections in Latin America and the Caribbean and to identify possible factors associated with the risk of dissemination and severity of these arboviruses. Methods. The protocol of this review was registered on the PROSPERO platform. Searches were carried out in the following databases: Virtual Health Library, MEDLINE/PubMed, and Embase. The search terms were: Zika virus, Zika virus infection, dengue, dengue virus, chikungunya virus, chikungunya fever, epidemiology, observational study, Latin America, and Caribbean region. Studies that addressed the distribution of these arboviruses and the risk factors associated with dengue, Zika virus disease, and chikungunya, published between January 2000 and August 2020 in English, Portuguese, and Spanish, were included. Results. Of 95 studies included, 70 identified risk factors, clinical manifestations, and outcomes for arbovirus infections and 25 described complications and/or deaths. The highest frequency of confirmed cases was for dengue. Brazil reported most cases of the three arboviruses in the period analyzed. Environmental and socioeconomic factors facilitated the proliferation and adaptation of vectors, and host-related factors were reported to aggravate dengue. Most deaths were due to chikungunya, Zika virus disease caused most neurological alterations, and dengue resulted in greater morbidity leading to more frequent hospitalization. Conclusions. The review provides a broad view of the three arboviruses and the intrinsic aspects of infections, and highlights the factors that influence the spread of these viruses in the populations studied.

Abstract 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.

We have recently developed a new synthetic methodology that provided both N-aryl-5-hydroxytriazoles and N-pyridine-4-alkyl triazoles. A selection of these products was carried through virtual screening towards targets that are contemporary and validated for drug discovery and development. This study determined a number of potential structure target dyads of which N-pyridinium-4-carboxylic-5-alkyl triazole displayed the highest score specificity towards KAT2A. Binding affinity tests of abovementioned triazole and related analogs towards KAT2A confirmed the predictions of the in-silico assay. Finally, we have run in vitro inhibition assays of selected triazoles towards KAT2A; the ensemble of binding and inhibition assays delivered pyridyl-triazoles carboxylates as the prototype of a new class of inhibitors of KAT2A.

The main candidate to be a practical and low cost high power THz source is the intersubband-based quantum cascade laser, which can have a tremendous impact in many practical applications, including last mile and indoor telecommunication systems. In this review we discuss current challenges for THz intersubband device development from a microscopic point of view. Next summarize the search for new mechanisms and structure designs that can lead to intersubband gain without population inversion. This is a very important topic of current research, since is both an extremely elegant phenomenon from the basic physics of view and crucial for effective lasing in the THz range. The reason is that scattering phenomena can lead to level broadenings of the same order of magnitude of the lasing transitions, making population inversion by carrier injection in upper lasing subbands extremely difficult. Previous work in the literature is compared and contrasted with a new scheme that may lead to high temperature lasing by engineering the nonequilibrium population inversion with a combination of band structure and many body effects mediated by a k-space filter.

Abstract The golden mussel (Limnoperna fortunei) is an aggressive invasive species in South America, where it endangers native species and freshwater ecosystems, in addition to causing extensive economic losses, mainly to the hydroelectric sector. Currently, there’s no efficient control method available and the invasion has progressed across the continent. Its high reproduction rate is one of the key factors of the golden mussel’s high invasive potential and, recently, efforts have been done in order to understand the reproduction and the sexual features of this species. However, its cytogenetics characterization is incipient and the possible occurrence of sex-specific cytogenetic features was never investigated. In this study, we aimed to characterize the chromosomal morphometry, the distribution profile of heterochromatin, and to detect possible sex-related epigenetic marks in the golden mussel. Results revealed that the karyotypic structure is similar in both sexes and no chromosome heteromorphism was observed between males and females specimens. The data increment the cytogenetic characterization of Limnoperna fortunei and contribute for future studies that aim to further investigate its reproduction and underlying sex determination processes.

Background Chagas disease (CD) presents an ominous prognosis. The predictive value of biomarkers and new echocardiogram parameters in adjusted models have not been well studied. Methods and Results There were 361 patients with chronic CD (57.6% men, 61±11 years of age, clinical forms: indeterminate 27.1%, cardiac 56.6%, digestive 3.6%, cardiodigestive 12.7%) included in this single‐center, observational, prospective longitudinal study. Echocardiographic evaluation included strain analyses of left atrial, left ventricular (LV), and right ventricular and 3‐dimensional analyses of left atrial and LV volumes. Biomarkers included cardiac troponin I, brain natriuretic peptide, transforming growth factor β1, tumor necrosis factor, matrix metalloproteinases, and Trypanosoma cruzi polymerase chain reaction. The studied end point was a composite of CD‐related mortality, heart transplant, hospital admission due to worsening heart failure, or new cardiac device insertion. Event‐free survival was analyzed by multivariable regression analyses adjusted for competing risks. P values