Accuracy of the third molar maturity index (I3M) for diagnosing the legal majority of young southern Brazilians.

Background: The numerous techniques for identifying adulthood require research testing the accuracy of each method in different populations. This study verified the accuracy of the third molar maturity index (I3M) proposed by Cameriere et al. (2008) for diagnosing the age of majority in a southern Brazilian population sample. Material and Methods: Panoramic radiographs of patients with dental element 38 treated at the School of Dentistry of the University of Passo Fundo (UPF), RS, Brazil, were analyzed. The patients were separated into age groups between 15.00 and 22.99 years. The Cameriere (2008) method was applied to each radiograph. The study sample comprised 671 individuals, with 385 women (mean age 19.67 ± 2.05) and 286 men (mean age 19.5 ± 2.11). Results: The original cut-off value of I3M≤0.08 classified individuals younger and older than 18 years. ROC curve plotting resulted in an overall accuracy of 0.69 and 0.84 for women and men, respectively. The most favorable cut-off value for southern Brazilian men was 0.06, and women showed better results with an I3M adjusted to 0.13. The new cut-off values produced an accuracy of 0.78 for women and 0.84 for men. The original cut-off point to the I3M (0.08) was not the most appropriate for the studied sample. Conclusions: Thus, index adjustments to 0.13 for women and 0.06 for men may improve method performance among southern Brazilian individuals. Key words:Molar, third, radiography, panoramic, forensic dentistry, age groups, imputability.

Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining.

The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system's efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods.

PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points.

Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat's performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.

State of the Art of Non-Invasive Technologies for Bladder Monitoring: A Scoping Review.

Bladder monitoring, including urinary incontinence management and bladder urinary volume monitoring, is a vital part of urological care. Urinary incontinence is a common medical condition affecting the quality of life of more than 420 million people worldwide, and bladder urinary volume is an important indicator to evaluate the function and health of the bladder. Previous studies on non-invasive techniques for urinary incontinence management technology, bladder activity and bladder urine volume monitoring have been conducted. This scoping review outlines the prevalence of bladder monitoring with a focus on recent developments in smart incontinence care wearable devices and the latest technologies for non-invasive bladder urine volume monitoring using ultrasound, optical and electrical bioimpedance techniques. The results found are promising and their application will improve the well-being of the population suffering from neurogenic dysfunction of the bladder and the management of urinary incontinence. The latest research advances in bladder urinary volume monitoring and urinary incontinence management have significantly improved existing market products and solutions and will enable the development of more effective future solutions.

Effects of flavonoids on membrane adaptation of food-associated bacteria.

The effects of naringenin and the biflavonoids amentoflavone and tetrahydroamentoflavone on select bacterial lipids (carotenoids, fatty acids, and menaquinones) and membrane fluidity based on Laurdan generalized polarization were investigated. For this purpose, the pigment-forming food-associated microorganisms Staphylococcus xylosus (DSM 20266T and J70), Staphylococcus carnosus DSM 20501T, and Micrococcus luteus (ATCC 9341 and J3) were studied. The results suggest an envelope stress response by microorganisms due to flavonoids and an employment of adaptive mechanisms using carotenoids, fatty acids, and menaquinones. The flavonoid monomer naringenin impacted carotenoids, fatty acids, menaquinones, and membrane fluidity. Naringenin significantly influenced the carotenoid profile, particularly by an increase in the relative proportion of 4,4'-diaponeurosporenoic acid in Staphylococcus xylosus. Amentoflavone caused changes mainly in the membrane of Micrococcus luteus and decreased the menaquinone content. Tetrahydroamentoflavone mainly affected the carotenoids in the investigated strains. The noticeably different CCS value of tetrahydroamentoflavone compared to naringenin and amentoflavone revealed further insights into the structure-dependent effects of flavonoids. This study provides valuable insights into the response of pigment-forming food-associated microorganisms to naringenin, amentoflavone, and tetrahydroamentoflavone, which is important for the targeted and safe application of the latter as natural preservatives and useful for further research on the mechanisms of action.

Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images.

BACKGROUND: Melanoma is the most dangerous and aggressive form among skin cancers, exhibiting a high mortality rate worldwide. Biopsy and histopathological analysis are standard procedures for skin cancer detection and prevention in clinical settings. A significant step in the diagnosis process is the deep understanding of the patterns, size, color, and structure of lesions based on images obtained through dermatoscopes for the infected area. However, the manual segmentation of the lesion region is time-consuming because the lesion evolves and changes its shape over time, making its prediction challenging. Moreover, it is challenging to predict melanoma at the initial stage as it closely resembles other skin cancer types that are not malignant as melanoma; thus, automatic segmentation techniques are required to design a computer-aided system for accurate and timely detection. METHODS: As deep learning approaches have gained significant attention in recent years due to their remarkable performance, therefore, in this work, we proposed a novel design of a convolutional neural network (CNN) framework based on atrous convolutions for automatic lesion segmentation. This architecture is built based on the concept of atrous/dilated convolutions which are effective for semantic segmentation. A deep neural network is designed from scratch employing several building blocks consisting of convolutional, batch normalization, leakyReLU layer, and fine-tuned hyperparameters contributing altogether towards higher performance. CONCLUSION: The network was tested on three benchmark datasets provided by International Skin Imaging Collaboration (ISIC), i.e., ISIC 2016, ISIC 2017, and ISIC 2018. The experimental results showed that the proposed network achieved an average Jaccard index of 90.4% on ISIC 2016, 81.8% on ISIC 2017, and 89.1% on ISIC 2018 datasets, respectively which is recorded as higher than the top three winners of the ISIC challenge and other state-of-the-art methods. Also, the model successfully extracts lesions from the whole image in one pass in less time, requiring no pre-processing step. The conclusions yielded that network is accurate in performing lesion segmentation on adopted datasets.

Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images.

Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC2017, and ISIC 2020). We evaluated the model based on accuracy, precision, recall, specificity, and F1-score. The proposed DCNN classifier achieved accuracies of 81.41%, 88.23%, and 90.42% on the ISIC 2016, 2017, and 2020 datasets, respectively, demonstrating high performance compared with the other state-of-the-art networks. Therefore, this proposed approach could provide a less complex and advanced framework for automating the melanoma diagnostic process and expediting the identification process to save a life.

A Systematic Review of Wearable Sensors for Monitoring Physical Activity.

This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing.

Evaluation of the Genotoxicity of Tobacco and Alcohol in Oral Mucosa Cells: A Pilot Study.

AIM AND OBJECTIVE: To define the genotoxic potential of tobacco and alcohol in the oral mucosa through a micronuclei (MN) test. MATERIALS AND METHODS: Samples of exfoliative cells from oral mucosa were collected using superficial scraping of the right- and left-cheek mucosa of 83 patients divided into four groups, namely: (G1) 24 individuals abstaining from tobacco and alcoholic beverages; (G2) 23 individuals who smoke and abstain from alcoholic beverages; (G3) 24 smokers and alcoholics; and (G4) 12 individuals who consume alcohol and abstain from tobacco. The samples were stained with Giemsa-Wright, and the frequencies of MN, binucleated cells, and metanuclear changes were recorded in the samples of each group (1,000 cells per patient). RESULTS: Analysis of variance (ANOVA) showed a difference between groups for changes concerning karyorrhexis (p = 0), pycnosis (p = 0.002), karyolysis (p = 0.003), and binucleated cells (p = 0.046). As for the total number of changes, G3, G2, and G4, respectively, were significantly higher than G1. CONCLUSION: It is suggested that the influence of smoking and drinking on exfoliating cells of oral mucosa may cause metanuclear changes due to genetic changes that these products cause, and the MN test is effective in detecting and monitoring such changes. CLINICAL SIGNIFICANCE: MN test may work for constantly monitoring the oral mucosa of smokers and/or alcoholic patients, so that early cell changes may be diagnosed, preventing the genesis of oral cancer.

Evaluation of a Series of Overdentures Made in a Higher Education Dental Institution in Southern Brazil.

AIM AND OBJECTIVE: This study aimed to evaluate the epidemiological profile, oral health self-perception index, and level of satisfaction of users of complete implant-supported overdentures that had been used for at least 1 year and were made at the School of Dentistry of the University of Passo Fundo, Rio Grande do Sul, Brazil (FO/UPF), between 2014 and 2019. MATERIALS AND METHODS: The sample consisted of 30 patients with overdentures, who were selected from the dental records filed at the institution. Data on general health and the dental implants involved (brand, type of prosthetic connection, number of implants, and additional overdenture retention system) were collected from the medical records. The 30 patients were invited to answer the Oral Health Impact Profile-14 (OHIP-14) and visual analog scale (VAS) questionnaires, and due to the coronavirus disease 2019 pandemic, it was possible to contact 15 patients. RESULTS: Most of the prostheses studied were mandibular overdentures, and 66.66% of the cases were retained by the O-ring system. As for the oral health self-perception of the individuals, it was concluded that male patients had a lower mean overall score (p = 0.047) and functional domain (p = 0.042) in the OHIP-14. The number of implants and the installation arch interfered with functional domain and psychological capacity (p <0.05). The VAS showed that women have greater esthetic satisfaction with prostheses (p = 0.048) and that the bar-clip retention system is more satisfactory than the O-ring (p = 0.017). CONCLUSION: Despite the limitations of overdentures, it was noted that, when properly indicated, they are a viable option for oral rehabilitation on implants. CLINICAL SIGNIFICANCE: Oral rehabilitation well-indicated with overdentures, especially those retained by the bar-clip system, results in an improvement in the patients' quality of life.

Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements.

Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.

Quality of life of patients following orthognathic surgery.

The aim of this study was to evaluate how patients with dentofacial deficiency who have undergone orthognathic surgery perceive their quality of life (QoL) with respect to functional, esthetic, and psychosocial issues. In an observational, cross-sectional, descriptive, and quantitative study, 10 patients who had undergone orthognathic surgery answered questionnaires used internationally for assessing QoL: the Short Form Health Survey (SF-36), Oral Health Impact Profile 14 (OHIP-14), and Orthognathic Quality of Life Questionnaire (OQLQ). In addition, the patients completed the Self-Perception Questionnaire of Esteem, Appearance, and Interpersonal Relationships (ASR-26), which explored the differences between their current self-esteem, appearance satisfaction, and interpersonal relationships and their memories of their presurgical feelings about those topics. The data were submitted to descriptive and multivariable statistical analyses. There was a statistically significant difference between the preoperative and postoperative periods regarding self-esteem, appearance satisfaction, and professional relationships (P < 0.05). The data collected with the SF-36, OHIP-14, and OQLQ questionnaires showed high internal consistency (Cronbach α coefficient). The index (mean) scores for the SF-36 (81.5), OHIP-14 (0.6), and OQLQ (5.0) were close to the conditions of high QoL. Principal component analysis revealed 3 distinct groups of patients, and 70% of patients composed a group with high QoL scores, showing no complaints of physical pain, functional limitation, psychological discomfort, social disability, or excessive concern about their oral condition. In this small sample of patients, orthognathic surgery resulted in improved health-related QoL with variations among patients regarding physical pain, psychological discomfort, oral function, facial esthetics, physical function, social function, and self-awareness of facial deformity. The results of this study indicate the importance of applying a questionnaire in individuals who have undergone orthognathic surgery to investigate their personal motivations for treatment and which physical, social, and psychological problems are limiting their QoL.

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications.

BACKGROUND: Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle. OBJECTIVES: This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications. METHODS: We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols. RESULTS: The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%. CONCLUSION: We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice.

A systematic review of rapid response applications based on early warning score for early detection of inpatient deterioration.

AIM: The aim of this study was to investigate the effectiveness of current rapid response applications available in acute care settings for escalation of patient deterioration. Current challenges and barriers, as well as key recommendations, were also discussed. METHODS: We adopted PRISMA review methodology and screened a total of 559 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2015 and 2019. The selection criteria were based on the inclusion of studies that report on the advancement made to the current practice for providing rapid response to the patient deterioration in acute care settings. RESULTS: We found that current rapid response applications are complicated and time-consuming for detecting inpatient deterioration. Existing applications are either siloed or challenging to use, where clinicians are required to move between two or three different applications to complete an end-to-end patient escalation workflow - from vital signs collection to escalation of deteriorating patients. We found significant differences in escalation and responses when using an electronic tool compared to the manual approach. Moreover, encouraging results were reported in extensive documentation of vital signs and timely alerts for patient deterioration. CONCLUSION: The electronic vital signs monitoring applications are proved to be efficient and clinically suitable if they are user-friendly and interoperable. As an outcome, several key recommendations and features were identified that would be crucial to the successful implementation of any rapid response system in all clinical settings.

Testing international techniques for the radiographic assessment of third molar maturation.

BACKGROUND: To assess the radiographic third molar maturation with internationally developed techniques for age estimation. MATERIAL AND METHODS: The study analyzed 1,062 panoramic radiographs of patients treated at the School of Dentistry of the University of Passo Fundo/RS/Brazil, between 2009 and 2020. The patients were separated into ages between 15.00 and 23.99 years and, for each radiograph, the third molars were classified into stages from 1 to 10, with the subsequent application of one of the formulae proposed by Gunst et al. A dichotomous variable indicating whether each individual was younger or older than 18 was calculated from the chronological age of the individuals. A logistic regression model was adjusted to assess whether the third molar stages are correlated with the age of individuals. RESULTS: The error indicators between estimated and chronological ages showed that mean errors closer to zero are seen in the 18-18.9 and 17-17.9 age groups, respectively. Male individuals were earlier in terms of dental mineralization but there were no significant differences between sexes regarding the applicability of the method. The ROC curve shows that the analysis of a single third molar for age estimation gives a maximum of 70.4% reliability. CONCLUSIONS: The moderate performance of the technique tested in the present study justify future country-specific corrections to improve age estimation from the radiographic assessment of third molar maturity. Key words:Dental age estimation, forensic dentistry, radiology, third molar.

Oral leukoplakia-epidemiological survey and histochemical analysis of 107 cases in Brazil.

OBJECTIVE: To perform an epidemiological survey comparing the cell proliferative activity of 107 cases of oral leukoplakia with their clinical and histopathological characteristics. MATERIALS AND METHODS: A cross-sectional, observational, and histological-histochemical study. The cases came from the Histopathological Diagnostic Service of UPF/RS and the School of Dentistry of Araçatuba FOA/UNESP/SP (1986-2016). The histopathological sections were stained using the silver staining (AgNOR) technique and the nuclei of 100 epithelial cells selected randomly were recorded to count the number of nucleolar organizer regions (NORs). The mean NORs per lesion were correlated to clinical and histological characteristics using ANOVA, at 5% significance. RESULTS: Most of the cases included men (62.62%), white (64.49%), and with an average age of 53.94 years. The most probable etiological factors were smoking (44.7%) and alcohol consumption (9.85%). The evolution time of most lesions was fast (33.65%), manifesting mainly in the form of plaques (70.37%) and without symptoms (58.88%). They were located mainly in the cheek mucosa (26.62%) and presented white color (66.35%), well-defined edges (59.81%), firm consistency (47.5%), and keratinized surface (49.53%). Etiological factor (p = 0.003), evolution time (p = 0.006), symptoms (p = 0.029), location (p = 0.020), consistency (p = 0.047), histopathological characteristics (p = 0.004), and superficial keratinization (p = 0.001) were statistically significant regarding the mean NORs of the leukoplakias studied. CLINICAL RELEVANCE: Oral leukoplakias caused by alcohol consumption and/or tobacco use, considering an evolution time of fewer than 12 months, asymptomatic, located in the lower lip or tongue, and with a firm consistency and increased superficial keratinization should be treated more aggressively by the clinician to avoid cancerization.

Histopathological Classifications of Oral Leukoplakia and its Relation to Cell Proliferative Activity: A Case Series.

AIM: This study relates the average number of nucleolar organizing regions (NORs) obtained in a series of cases of oral leukoplakia, with three methods of histopathological classification of such lesion. MATERIALS AND METHODS: This is a histopathological-histochemical laboratory cross-sectional study. The 18 cases of leukoplakia analyzed were filed at Pathology Service of the Biological Sciences Institute of the University of Passo Fundo, Rio Grande do Sul, Brazil (SDH/ICB/UPF) (2017 and 2018), from which epidemiological data were extracted. New histological sections were performed for impregnation by the argyrophilic nucleolar organizer regions (AgNOR) technique. The histopathological slides were analyzed by photon microscopy (1,000×), and the nuclei of 100 epithelial cells were photographed to count the number of NORs. Three methods were used for the lesions' histopathological classification [World Health Organization (WHO), Brothwell, and binary system]. The means of NORs were compared with the three histopathological classifications by means of the t or analysis of variance (ANOVA) statistical tests, at a significance level of 5%. RESULTS: According to the WHO classification method, most cases (11-61.1%) had a moderate classification. Evaluations by the Brothwell method showed moderate and mild classification in 50 and 38.9% of cases, respectively. According to the binary system, most cases (10-55.6%) had low risk. The average NORs found in 100 nuclei of each of the 18 lesions ranged from 2 to 4. When crossing the average NORs with the histopathological classification methods of the lesions by means of the t test or ANOVA, no significant relationship was noted. CONCLUSION: The average of NORs is not associated with the histological classifications of leukoplakias. Thus, the AgNOR method should be used with caution when differentiating different histological grades of leukoplakias. CLINICAL SIGNIFICANCE: The AgNOR method should be used with caution to determine the clinical treatment of oral leukoplakias, since no agreement was observed between this method and the histopathological classifications available for such lesion.

A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition.

Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly's daily life and to help people suffering from cognitive disorders, Parkinson's disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity.

Effects of structural differences on the antibacterial activity of biflavonoids from fruits of the Brazilian peppertree (Schinus terebinthifolius Raddi).

Flavonoids, synthesized by plants across all families and therefore found in a huge variety, possess a diverse range of pharmacological properties. Direct antibacterial and synergistic activities as well as the inhibition of several bacterial virulence factors are known. Besides the mode of action, it is important to understand the structure-activity relationship to identify key structural characteristics. This study aimed to identify biflavonoids with antibacterial activity from Schinus terebinthifolius Raddi fruits. The purified biflavonoids were characterized in terms of their antibacterial effects. We found that the activity of biflavonoids, including agathisflavone (AGF), amentoflavone (AMF), and tetrahydroamentoflavone (THAF), was dependent on their chemical configuration and degree of oxidation, with THAF showing the highest activity on planktonic cells. Additionally, biofilm formation and composition were strongly influenced by THAF. Even slight differences in the chemical structure have fundamental effects on the activity of isolated biflavonoids. This suggests a specific binding of these substances in bacteria and thus enables detailed investigations of the mode of action in the future.