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Indoor Positioning Systems (IPSs) for emergency responders is a challenging field attracting researchers worldwide. When compared with traditional indoor positioning solutions, the IPSs for emergency responders stand out as they have to operate in harsh and unstructured environments. From the various technologies available for the localization process, ultra-wide band (UWB) is a promising technology for such systems due to its robust signaling in harsh environments, through-wall propagation and high-resolution ranging. However, during emergency responders’ missions, the availability of UWB signals is generally low (the nodes have to be deployed as the emergency responders enter a building) and can be affected by the non-line-of-sight (NLOS) conditions. In this paper, the performance of four typical distance-based positioning algorithms (Analytical, Least Squares, Taylor Series, and Extended Kalman Filter methods) with only three ranging measurements is assessed based on a COTS UWB transceiver. These algorithms are compared based on accuracy, precision and root mean square error (RMSE). The algorithms were evaluated under two environments with different propagation conditions (an atrium and a lab), for static and mobile devices, and under the human body’s influence. A NLOS identification and error mitigation algorithm was also used to improve the ranging measurements. The results show that the Extended Kalman Filter outperforms the other algorithms in almost every scenario, but it is affected by the low measurement rate of the UWB system.

COVID-19 has led to over 3.47 million deaths worldwide and continues to devastate primarily middle- and low-income countries. High-frequency testing has been proposed as a potential solution to prevent outbreaks. However, current tests are not sufficiently low-cost, rapid, or scalable to enable broad COVID-19 testing. Here, we describe LEAD (Low-cost Electrochemical Advanced Diagnostic), a diagnostic test that detects severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within 6.5 min and costs $1.50 per unit to produce using easily accessible and commercially available materials. LEAD is highly sensitive toward SARS-CoV-2 spike protein (limit of detection = 229 fg·mL−1) and displays an excellent performance profile using clinical saliva (100.0% sensitivity, 100.0% specificity, and 100.0% accuracy) and nasopharyngeal/oropharyngeal (88.7% sensitivity, 86.0% specificity, and 87.4% accuracy) samples. No cross-reactivity was detected with other coronavirus or influenza strains. Importantly, LEAD also successfully diagnosed the highly contagious SARS-CoV-2 B.1.1.7 UK variant. The device presents high reproducibility under all conditions tested and preserves its original sensitivity for 5 d when stored at 4 °C in phosphate-buffered saline. Our low-cost and do-it-yourself technology opens new avenues to facilitate high-frequency testing and access to much-needed diagnostic tests in resource-limited settings and low-income communities.

This study is aimed at establishing a connection between brand gamified experiences and brand value co-creation, based on a literature review and insights from exploratory research. Consumers’ motivations to get involved with gamified experiences and their impact on consumer engagement and brand experience are analyzed. Simultaneously, the study explores the motivations that drive firms to implement gamified systems with consequences for brand value co-creation. Seven managers and marketing professionals were interviewed through semi-structured interviews, and two focus groups were performed to collect primary information on consumers’ perceptions and motivations. Results suggest that gamification offers a platform for the development of brand engagement and relationship enhancement, through entertainment, challenges, continuous experiences, and brand involvement. Consumers seek gamified systems that provide them with fun, rewards, competition, social interactions and recognition, customization, and sense of community. Gamification also allows firms to collect spontaneous and valuable data on consumers’ opinions and interactions. In sum, gamification can be seen as an innovative branding tool to promote consumer interaction and participation in brand co-creation experiences, all of which is essential to managing brand-based innovation.

The effective monitoring and maintenance of power lines are becoming increasingly important due to a global growing dependence on electricity. The costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced by using UAVs with the appropriate sensors. However, this implies developing algorithms to make the power line inspection process reliable and autonomous. In order to overcome the limitations of visual methods in the presence of poor light and noisy backgrounds, we propose to address the problem of power line detection and modeling based on LiDAR. The PL 2 DM, Power Line LiDAR-based Detection and Modeling, is a novel approach to detect power lines. Its basis is a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. Using a real dataset, the algorithm showed promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

Seed exudates are plant-derived natural bioactive compounds consisting of a complex mixture of organic and inorganic molecules. Plant seed exudates have been poorly studied against parasite nematodes. This study was undertaken to identify proteins in the Myracrodruon urundeuva seed exudates and to assess the anthelmintic activity against Haemonchus contortus, an important parasite of small ruminants. M. urundeuva seed exudates (SEX) was obtained after immersion of seeds in sodium acetate buffer. SEX was fractionated with ammonium sulfate at 0-90% concentration to generate the ressuspended pellet (SEXF1) and the supernatant (SEXF2). SEX, SEXF1, and SEXF2 were exhaustively dialyzed against distilled water (cut-off: 12 kDa) and the protein contents determined. Mass spectrometry analyses of SEX, SEXF1, and SEXF2 were done to identify proteins and secondary metabolites. The seed exudates contained protease, protease inhibitor, peptidase, chitinase, and lipases as well as the low molecular weight secondary compounds ellagic acid and quercetin rhamnoside. SEX inhibited H. contortus larval development (LDA) (IC50 = 0.29 mg mL-1), but did not affect larval exsheathment (LEIA). On the other hand, although SEXF1 and SEXF2 inhibited H. contortus LEIA (IC50 = 1.04 and 0.93 mg mL-1, respectively), they showed even greater inhibition efficiency of H. contortus larval development (IC50 = 0.29 and 0.42 mg mL-1, respectively). To the best of our knowledge, this study is the first to show the anthelmintic activity of plant exudates against a gastrointestinal nematode. Moreover, it suggests the potential of exuded proteins as candidates to negatively interfere with H. contortus life cycle.

Infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been associated with leukopenia and uncontrolled inflammatory response in critically ill patients. A better comprehension of SARS-CoV-2-induced monocyte death is essential for the identification of therapies capable to control the hyper-inflammation and reduce viral replication in patients with 2019 coronavirus disease (COVID-19). Here, we show that SARS-CoV-2 engages inflammasome and triggers pyroptosis in human monocytes, experimentally infected, and from patients under intensive care. Pyroptosis associated with caspase-1 activation, IL-1ß production, gasdermin D cleavage, and enhanced pro-inflammatory cytokine levels in human primary monocytes. At least in part, our results originally describe mechanisms by which monocytes, a central cellular component recruited from peripheral blood to respiratory tract, succumb to control severe COVID-19.

In comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis of malignant tumors in adult patients. The acronym PIRDs was used and a comprehensive literature search was conducted on PubMed, Cochrane, Scopus, Web of Science, LILACS, Embase, Scielo, EBSCOhost, and grey literature through Proquest, Google Scholar, and JSTOR for systematic reviews of AI as a diagnostic model and/or detection tool for any cancer type in adult patients, compared to the traditional diagnostic radiographic imaging model. There were no limits on publishing status, publication time, or language. For study selection and risk of bias evaluation, pairs of reviewers worked separately. In total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 satisfied the full-text reading criterion, and 09 papers were considered for qualitative synthesis. Although there was heterogeneity in terms of methodological aspects, patient differences, and techniques used, the studies found that several AI approaches are promising in terms of specificity, sensitivity, and diagnostic accuracy in the detection and diagnosis of malignant tumors. When compared to other machine learning algorithms, the Super Vector Machine method performed better in cancer detection and diagnosis. Computer-assisted detection (CAD) has shown promising in terms of aiding cancer detection, when compared to the traditional method of diagnosis. The detection and diagnosis of malignant tumors with the help of AI seems to be feasible and accurate with the use of different technologies, such as CAD systems, deep and machine learning algorithms and radiomic analysis when compared with the traditional model, although these technologies are not capable of to replace the professional radiologist in the analysis of medical images. Although there are limitations regarding the generalization for all types of cancer, these AI tools might aid professionals, serving as an auxiliary and teaching tool, especially for less trained professionals. Therefore, further longitudinal studies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems. Systematic review registration. Prospero registration number: CRD42022307403.

The purpose of the presented Artificial Intelligence (AI)-tool was to automatically segment the mandibular molars on panoramic radiographs and extract the molar orientations in order to predict the third molars’ eruption potential. In total, 838 panoramic radiographs were used for training (n = 588) and validation (n = 250) of the network. A fully convolutional neural network with ResNet-101 backbone jointly predicted the molar segmentation maps and an estimate of the orientation lines, which was then iteratively refined by regression on the mesial and distal sides of the segmentation contours. Accuracy was quantified as the fraction of correct angulations (with predefined error intervals) compared to human reference measurements. Performance differences between the network and reference measurements were visually assessed using Bland−Altman plots. The quantitative analysis for automatic molar segmentation resulted in mean IoUs approximating 90%. Mean Hausdorff distances were lowest for first and second molars. The network angulation measurements reached accuracies of 79.7% [−2.5°; 2.5°] and 98.1% [−5°; 5°], combined with a clinically significant reduction in user-time of >53%. In conclusion, this study validated a new and unique AI-driven tool for fast, accurate, and consistent automated measurement of molar angulations on panoramic radiographs. Complementing the dental practitioner with accurate AI-tools will facilitate and optimize dental care and synergistically lead to ever-increasing diagnostic accuracies.

The objective of this study was to evaluate the effect of supplementation with 100ppm sodium monensin or 0.15% of a blend of functional oils (cashew nut oil + castor oil) on the intestinal microbiota of broilers challenged with three different Eimeria spp. The challenge was accomplished by inoculating broiler chicks with sporulated oocysts of Eimeria tenella, Eimeria acervulina, and Eimeria maxima via oral gavage. A total of 864, day-old male broiler chicks (Cobb) were randomly assigned to six treatments (eight pens/treatment; 18 broilers/pen) in a 3 x 2 factorial arrangement, composed of three additives (control, monensin or blend), with or without Eimeria challenge. Intestinal contents was collected at 28 days of age for microbiota analysis by sequencing 16s rRNA in V3 and V4 regions using the Illumina MiSeq platform. Taxonomy was assigned through the SILVA database version 132, using the QIIME 2 software version 2019.1. No treatment effects (p > 0.05) were observed in the microbial richness at the family level estimated by Chao1 and the biodiversity assessed by Simpson's index, except for Shannon's index (p < 0.05). The intestinal microbiota was dominated by members of the order Clostridiales and Lactobacillales, followed by the families Ruminococcaceae, Bacteroidaceae, and Lactobacillaceae, regardless of treatment. When the controls were compared, in the challenged control group there was an increase in Erysipelotrichaceae, Lactobacillaceae, Bacteroidaceae, Streptococcaceae, and Peptostreptococcaceae, and a decrease in Ruminococcaceae. Similar results were found for a challenged group that received monensin, while the blend partially mitigated this variation. Therefore, the blend alleviated the impact of coccidiosis challenge on the microbiome of broilers compared to monensin.

Ichthyoplankton monitoring is crucial for stock assessments, offering insights into spawning grounds, stock size, seasons, recruitment, and changes in regional ichthyofauna. This study evaluates the efficiency of multi-marker DNA metabarcoding using mitochondrial cytochrome c oxidase subunit I (COI), 12S rRNA and 16S rRNA gene markers, in comparison to morphology-based methods for fish species identification in ichthyoplankton samples. Two transects with four coastal distance categories were sampled along the southern coast of Portugal, being each sample divided for molecular and morphological analyses. A total of 76 fish species were identified by both approaches, with DNA metabarcoding overperforming morphology—75 versus 11 species-level identifications. Linking species-level DNA identifications with higher taxonomic morphological identifications resolved several uncertainties associated with traditional methods. Multi-marker DNA metabarcoding improved fish species detection by 20–36% compared to using a single marker/amplicon, and identified 38 species in common, reinforcing the validity of our results. PERMANOVA analysis revealed significant differences in species communities based on the primer set employed, transect location, and distance from the coast. Our findings underscore the potential of DNA metabarcoding to assess ichthyoplankton diversity and suggest that its integration into routine surveys could enhance the accuracy and comprehensiveness of fish stock assessments.