Stochastically Perturbed Chains of Variable Memory

In this paper, we study inference for chains of variable order under two distinct contamination regimes. Consider we have a chain of variable memory on a finite alphabet containing zero. At each instant of time an independent coin is flipped and if it turns head a contamination occurs. In the first regime a zero is read independent of the value of the chain. In the second regime, the value of another chain of variable memory is observed instead of the original one. Our results state that the difference between the transition probabilities of the original process and the corresponding ones of the contaminated process may be bounded above uniformly. Moreover, if the contamination probability is small enough, using a version of the Context algorithm we are able to recover the context tree of the original process through a contaminated sample

On multifractals: a non-linear study of actigraphy data

This work aimed, to determine the characteristics of activity series from fractal geometry concepts application, in addition to evaluate the possibility of identifying individuals with fibromyalgia. Activity level data were collected from 27 healthy subjects and 27 fibromyalgia patients, with the use of clock-like devices equipped with accelerometers, for about four weeks, all day long. The activity series were evaluated through fractal and multifractal methods. Hurst exponent analysis exhibited values according to other studies ($H>0.5$) for both groups ($H=0.98\pm0.04$ for healthy subjects and $H=0.97\pm0.03$ for fibromyalgia patients), however, it is not possible to distinguish between the two groups by such analysis. Activity time series also exhibited a multifractal pattern. A paired analysis of the spectra indices for the sleep and awake states revealed differences between healthy subjects and fibromyalgia patients. The individuals feature differences between awake and sleep states, having statistically significant differences for $\alpha_{q-} - \alpha_{0}$ in healthy subjects ($p = 0.014$) and $D_{0}$ for patients with fibromyalgia ($p = 0.013$). The approach has proven to be an option on the characterisation of such kind of signals and was able to differ between both healthy and fibromyalgia groups. This outcome suggests changes in the physiologic mechanisms of movement control.Comment: Preprint accepted for publication at Physica A: Statistical Mechanics and its Application

Fire Training Fatalities and Firefighter Adherence to National fire Protection Association Standards

Sudden cardiac arrest continues to be a major cause of firefighter deaths during training due to a lack of individual firefighter adherence to National Fire Protection Association (NFPA) standards. These standards identify requirements for fire departments to create and maintain fitness programs. Existing research has not identified any relationships between training fatalities and individual firefighter adherence to NFPA 1583, Standard on Health-Related Fitness Programs for Fire Department Members. Using self-determination theory as the foundation, the purpose of this cross-sectional correlation study was to investigate whether individual firefighter adherence to NFPA 1583 has a measurable effect on training fatalities. Survey data were collected from 441 paid firefighters from 7 fire departments located in a rural county in a southern U.S. state. Data were analyzed using multiple linear regression. Results indicated that adherence to NFPA 1583 has a statistically significant relationship with reduced firefighter training fatalities (p = .000). Recommendations include examining adherence policies to all elements of the NFPA 1583 standard, not just chapters 5 through 8 in the publication. These include chapter 1 administration, chapter 2 referenced publications, chapter 3 definitions, and chapter 4 program organization specifications. The study results may be used by fire department training divisions to improve the health and safety of firefighters

Estudo da incidência de adenovírus humano e suíno, norovírus humano e circovírus suíno, em amostras de água de consumo e descedentação animal do município de Concórdia, Santa Catarina

TCC(graduação) - Universidade Federal de Santa Catarina. Centro de Ciências Biológicas. Biologia.Os dejetos provenientes da suinocultura e da população podem potencialmente contaminar mananciais de captação de água para consumo, já que esses efluentes nem sempre passam por tratamento adequado. Já a água utilizada para consumo humano geralmente passa por um processo de tratamento, mas este nem sempre é eficiente para remoção de todos os tipos de contaminantes. Dentre os contaminantes que podem estar presentes nessas águas estão o circovírus suíno tipo 2 (PVC2), o adenovírus suíno, PAdV, o adenovírus humanos (HAdV) e o norovírus humano (NoV). Diante da importância desses agentes infecciosos o presente estudo teve por objetivo avaliar a contaminação de águas de consumo humano e descendentação animal em Concórdia, na região oeste do Estado de Santa Catarina, por PCV2, PAdV, HAdV e NoV através de técnicas moleculares de PCR quantitativo. As amostras positivas para HAdV foram colocadas em cultura celular in vitro para estudos de infecciosidade viral pela técnica de formação de placas de lise (UFP). Das 36 amostras analisadas, a presença do material genético de PAdV e PCV2 foi verificada em todos os pontos de coleta, com prevalência acima de 30% de ambos os vírus, com uma média de 102 cg/mL para PAdV e 104 cg/mL para PCV2. Para HAdV, 100% das amostras foram positivas com média de 104 cg/mL. Contudo, no ensaio de infecciosidade, apenas 13 das 36 amostras apresentaram positividade com uma média de contaminação de 6 UFP/L. Para NoV os resultados de qPCR foram todos negativos. Os resultados obtidos confirmam que há liberação de dejetos suínos em rios e fontes de abastecimento de água de consumo humano, pondo em risco a qualidade das águas de mananciais. Além disso, há contaminação das águas de consumo humano por HAdV, uma vez que algumas amostras apresentaram viabilidade desses vírus, indicando um perigo real de veiculação de doenças.Animals and human wastewaters can potentially contaminate the water sources, since these effluents not usually receive proper treatment before discard. The treatment of drinking water may not effective to remove all contaminants. Among the contaminants that can be found in these waters are porcine circovirus type 2 (PVC2), porcine adenovirus (PAdV), human adenovirus (HAdV) and human norovirus (NoV). Given the impact of the infectious agents the present study aimed to evaluate the contamination of water for human and animal consumption from Concordia, in the western state of Santa Catarina, by porcine circovirus type 2 (PCV2), porcine adenovirus (PAdV) human adenovirus (HAdV) and human norovirus (NoV) by molecular techniques using quantitative PCR. HAdV-positive samples were placed in cell culture in vitro for viral infectivity by the technique of plaque-forming units assay (PFU). From the 36 samples analyzed, the presence of genetic material of PAdV and PCV2 was detected in all sampling sites, with prevalence above 30% for both viruses, with an average of 102 cg/mL for PAdV and 104 cg/mL for PCV2. For HAdV, 100% of the samples were positive with an average of 104 cg/mL. However, in the infectivity assay, only 13 of 36 samples were positive with an average contamination of 6 PFU/L. For NoV qPCR results were all negative. The results confirm that swine manure has been discharged into rivers and sources of drinking water, endangering the water quality. In addition, it was found to have a contamination of drinking water by viable HAdV, indicating a real risk of water related diseases

Amphibia, Anura, Hylidae Rafinesque, 1815 and Hylodidae Günther, 1858: distribution extension and new records for Santa Catarina, southern Brazil

In the present study we report new records of the anurans Hypsiboas curupi, Scinax littoralis, Dendropsophus elegans, and Crossodactylus schmidti for the state of Santa Catarina. These records expand the geographic distribution currently known for these species and contribute for the knowledge of the southern Brazilian anuran fauna

A review about lycopene-induced nuclear hormone receptor signalling in inflammation and lipid metabolism via still unknown endogenous apo-10´-lycopenoids

Lycopene is the red pigment in tomatoes and tomato products and is an important dietary carotenoid found in the human organism. Lycopene-isomers, oxidative lycopene metabolites and apo-lycopenoids are found in the food matrix. Lycopene intake derived from tomato consumption is associated with alteration of lipid metabolism and a lower incidence of cardiovascular diseases (CVD). Lycopene is mainly described as a potent antioxidant but novel studies are shifting towards its metabolites and their capacity to mediate nuclear receptor signalling. Di-/tetra-hydro-derivatives of apo-10´-lycopenoic acid and apo-15´-lycopenoic acids are potential novel endogenous mammalian lycopene metabolites which may act as ligands for nuclear hormone mediated activation and signalling. In this review, we postulate that complex lycopene metabolism results in various lycopene metabolites which have the ability to mediate transactivation of various nuclear hormone receptors like RARs, RXRs and PPARs. A new mechanistic explanation of how tomato consumption could positively modulate inflammation and lipid metabolism is discussed

Scalable multimodal convolutional networks for brain tumour segmentation

Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these models tend to generalise poorly to different imaging modalities than those for which they have been designed, thereby limiting their applications. For example, a network architecture initially designed for brain parcellation of monomodal T1 MRI can not be easily translated into an efficient tumour segmentation network that jointly utilises T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable multimodal deep learning architecture using new nested structures that explicitly leverage deep features within or across modalities. This aims at making the early layers of the architecture structured and sparse so that the final architecture becomes scalable to the number of modalities. We evaluate the scalable architecture for brain tumour segmentation and give evidence of its regularisation effect compared to the conventional concatenation approach.Comment: Paper accepted at MICCAI 201

A Nexus of Supportive Infrastructure to Foster Student Learning, Engagement, & Flourishing During the COVID-19 Pandemic

In 2020, educators and students were faced with a global pandemic that created unprecedented challenges to classrooms across the nation. For many students, the shift to online learning in a necessary effort to maintain educational continuity lasted for an entire academic year. Students attended online synchronous and asynchronous class sessions, interacted with their peers in exclusively online settings, and were isolated to the social and economic constraints of their own households. This study examines the dramatic impact that these virtual learning experiences had on middle school students’ learning, engagement, and development during the COVID-19 pandemic. By interviewing middle school students, teachers, and parents of students who participated in remote learning, researchers identified the necessity of a nexus of interconnected support founded on the relationships between the students, parents, and teachers both inside and outside of educational contexts to foster student engagement. This nexus of support was even more imperative for the success of low-income students and students who were children from immigrant families. Even when all conditions of this nexus were met, however, it was still necessary for students to display a remarkable level of intrinsic motivation and self-help behavior in order to maintain consistent engagement. These findings suggest the a radical reimagination of the educational landscape for students and educators’ return to the physical classroom: (1) the prioritization of a dynamic, personalized, and evolving curriculum, (2) community-focused, inquiry-based pedagogy, and (3) an audit system that ensures students are consistently supported in all three conditions of the support nexus

Detecting and locating trending places using multimodal social network data

This paper presents a machine learning-based classifier for detecting points of interest through the combined use of images and text from social networks. This model exploits the transfer learning capabilities of the neural network architecture CLIP (Contrastive Language-Image Pre-Training) in multimodal environments using image and text. Different methodologies based on multimodal information are explored for the geolocation of the places detected. To this end, pre-trained neural network models are used for the classification of images and their associated texts. The result is a system that allows creating new synergies between images and texts in order to detect and geolocate trending places that has not been previously tagged by any other means, providing potentially relevant information for tasks such as cataloging specific types of places in a city for the tourism industry. The experiments carried out reveal that, in general, textual information is more accurate and relevant than visual cues in this multimodal setting.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has been partially funded by project “Desarrollo de un ecosistema de datos abiertos para transformar el sector turístico” (GVA-COVID19/2021/103) funded by Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana, “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the “CHAN-TWIN” project (grant TED2021-130890B-C21) and the HORIZON-MSCA-2021-SE-0 action number: 101086387, REMARKABLE, Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated Learning. We also would like to thank Nvidia for their generous hardware donations that made these experiments possible