Due to wearables’ popularity, human activity recognition (HAR) plays a significant role in people’s routines. Many deep learning (DL) approaches have studied HAR to classify human activities. ...Previous studies employ two HAR validation approaches: subject-dependent (SD) and subject-independent (SI). Using accelerometer data, this paper shows how to generate visual explanations about the trained models’ decision making on both HAR and biometric user identification (BUI) tasks and the correlation between them. We adapted gradient-weighted class activation mapping (grad-CAM) to one-dimensional convolutional neural networks (CNN) architectures to produce visual explanations of HAR and BUI models. Our proposed networks achieved 0.978 and 0.755 accuracy, employing both SD and SI. The proposed BUI network achieved 0.937 average accuracy. We demonstrate that HAR’s high performance with SD comes not only from physical activity learning but also from learning an individual’s signature, as in BUI models. Our experiments show that CNN focuses on larger signal sections in BUI, while HAR focuses on smaller signal segments. We also use the grad-CAM technique to identify database bias problems, such as signal discontinuities. Combining explainable techniques with deep learning can help models design, avoid results overestimation, find bias problems, and improve generalization capability.
New DNA sequencing technologies have provided novel insights into eukaryotic genomes, epigenomes, and the transcriptome, including the identification of new non-coding RNA (ncRNA) classes such as ...promoter-associated RNAs and long RNAs. Moreover, it is now clear that up to 90% of eukaryotic genomes are transcribed, generating an extraordinary range of RNAs with no coding capacity. Taken together, these new discoveries are modifying the status quo in genomic science by demonstrating that the eukaryotic gene pool is divided into two distinct categories of transcripts: protein-coding and non-coding. The function of the majority of ncRNAs produced by the transcriptome is largely unknown; however, it is probable that many are associated with epigenetic mechanisms. The purpose of this review is to describe the most recent discoveries in the ncRNA field that implicate these molecules as key players in the epigenome.
Despite multiple associations between the microbiota and immune diseases, their role in autoimmunity is poorly understood. We found that translocation of a gut pathobiont,
, to the liver and other ...systemic tissues triggers autoimmune responses in a genetic background predisposing to autoimmunity. Antibiotic treatment prevented mortality in this model, suppressed growth of
in tissues, and eliminated pathogenic autoantibodies and T cells. Hepatocyte-
cocultures induced autoimmune-promoting factors. Pathobiont translocation in monocolonized and autoimmune-prone mice induced autoantibodies and caused mortality, which could be prevented by an intramuscular vaccine targeting the pathobiont.
-specific DNA was recovered from liver biopsies of autoimmune patients, and cocultures with human hepatocytes replicated the murine findings; hence, similar processes apparently occur in susceptible humans. These discoveries show that a gut pathobiont can translocate and promote autoimmunity in genetically predisposed hosts.
Aim
To evaluate the frequency of post‐treatment apical periodontitis associated with root filled teeth with at least one untreated root canal.
Methodology
Eight hundred and seven cone beam computed ...tomography images containing at least one root filled tooth were selected from a collection of 1543 images from Brazilian individuals. Scans were taken using ICAT Classic devices (Imaging Sciences, Hatfield, PA, USA) in a private oral radiology clinic from January to April 2015. All root filled teeth were analysed for the presence of missed canals and apical periodontitis. The chi‐square and odds ratio tests were used to verify if there were an association and risk relationship between the occurrence of untreated canals and apical periodontitis.
Results
A total of 2294 teeth with evidence of root fillings were identified. Two hundred and eighty‐one teeth had at least one untreated missed canal (12%). The frequency of apical periodontitis in teeth with at least one untreated canal was significantly greater in comparison to teeth with all canals treated (274/281, 98% versus 1736/2013, 86%) (P < 0.01). The odds for apical periodontitis to be present was 6.25 times greater for teeth with an untreated canal. The mesiobuccal roots of maxillary first molars had the greatest frequency of untreated canals (114/154, 74%), with the second mesiobuccal canal being the most frequently missed (n = 106/114, 93%).
Conclusion
Root filled teeth with at least one missed canal had a high prevalence of post‐treatment apical periodontitis.
Several aspects of epigenetics are strongly linked to non-coding RNAs, especially small RNAs that can direct the cytosine methylation and histone modifications that are implicated in gene expression ...regulation in complex organisms. A fundamental characteristic of epigenetics is that the same genome can show alternative phenotypes, which are based in different epigenetic states. Some of the most studied complex epigenetic phenomena including transposon activity and silencing recently exemplified by piRNAs (piwi-interacting RNAs), position effect variegation, X-chromosome inactivation, parental imprinting, and paramutation have direct or indirect participation of an RNA component. Conceivably, most of the non-coding RNAs with no described function yet, are players in epigenetic mechanisms that are still not completely understood. In that regard, RNAs were recently implicated in new mechanisms of genetic information transfer in yeast, plants and mice. In this review article, the hypothesis that non-coding RNAs might be the main component of complex organisms acquired during evolution will be explored. The question of how evolutionary theories have been challenged by these molecules in association with epigenetic mechanisms will also be discussed here.
The polarizable CL&Pol force field presented in our previous study, Transferable, Polarizable Force Field for Ionic Liquids (J. Chem. Theory Comput. 2019, 15, 5858, DOI: ...http://doi.org/10.1021/acs.jctc.9b0068910.1021/acs.jctc.9b00689), is extended to electrolytes, protic ionic liquids (PIL), deep eutectic solvents (DES), and glycols. These systems are problematic in polarizable simulations because they contain either small, highly charged ions or strong hydrogen bonds, which cause trajectory instabilities due to the pull exerted on the induced dipoles. We use a Tang–Toennies (TT) function to dampen, or smear, the interactions between charges and induced dipole at a short range involving small, highly charged atoms (such as hydrogen or lithium), thus preventing the “polarization catastrophe”. The new force field gives stable trajectories and is validated through comparison with experimental data on density, viscosity, and ion diffusion coefficients of liquid systems of the above-mentioned classes. The results also shed light on the hydrogen-bonding pattern in ethylammonium nitrate, a PIL, for which the literature contains conflicting views. We describe the implementation of the TT damping function, of the temperature-grouped Nosé–Hoover thermostat for polarizable molecular dynamics (MD) and of the periodic perturbation method for viscosity evaluation from non-equilibrium trajectories in the LAMMPS MD code. The main result of this work is the wider applicability of the CL&Pol polarizable force field to new, important classes of fluids, achieving robust trajectories and a good description of equilibrium and transport properties in challenging systems. The fragment-based approach of CL&Pol will allow ready extension to a wide variety of PILs, DES, and electrolytes.
Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While ...many classification methods have been proposed, there is no consensus on which methods are more suitable for a given dataset. As a consequence, it is important to comprehensively compare methods in many possible scenarios. In this context, we performed a systematic comparison of 9 well-known clustering methods available in the R language assuming normally distributed data. In order to account for the many possible variations of data, we considered artificial datasets with several tunable properties (number of classes, separation between classes, etc). In addition, we also evaluated the sensitivity of the clustering methods with regard to their parameters configuration. The results revealed that, when considering the default configurations of the adopted methods, the spectral approach tended to present particularly good performance. We also found that the default configuration of the adopted implementations was not always accurate. In these cases, a simple approach based on random selection of parameters values proved to be a good alternative to improve the performance. All in all, the reported approach provides subsidies guiding the choice of clustering algorithms.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Recently, research examining the occurrence of microplastics in the marine environment has substantially increased. Field and laboratory work regularly provide new evidence on the fate of ...microplastic debris. This debris has been observed within every marine habitat. In this study, at least 101 peer-reviewed papers investigating microplastic pollution were critically analysed (Supplementary material). Microplastics are commonly studied in relation to (1) plankton samples, (2) sandy and muddy sediments, (3) vertebrate and invertebrate ingestion, and (4) chemical pollutant interactions. All of the marine organism groups are at an eminent risk of interacting with microplastics according to the available literature. Dozens of works on other relevant issues (i.e., polymer decay at sea, new sampling and laboratory methods, emerging sources, externalities) were also analysed and discussed. This paper provides the first in-depth exploration of the effects of microplastics on the marine environment and biota. The number of scientific publications will increase in response to present and projected plastic uses and discard patterns. Therefore, new themes and important approaches for future work are proposed.
Driven by the potential applications of ionic liquids (ILs) in many emerging electrochemical technologies, recent research efforts have been directed at understanding the complex ion ordering in ...these systems, to uncover novel energy storage mechanisms at IL-electrode interfaces. Here, we discover that surface-active ILs (SAILs), which contain amphiphilic structures inducing self-assembly, exhibit enhanced charge storage performance at electrified surfaces. Unlike conventional non-amphiphilic ILs, for which ion distribution is dominated by Coulombic interactions, SAILs exhibit significant and competing van der Waals interactions owing to the non-polar surfactant tails, leading to unusual interfacial ion distributions. We reveal that, at an intermediate degree of electrode polarization, SAILs display optimum performance, because the low-charge-density alkyl tails are effectively excluded from the electrode surfaces, whereas the formation of non-polar domains along the surface suppresses undesired overscreening effects. This work represents a crucial step towards understanding the unique interfacial behaviour and electrochemical properties of amphiphilic liquid systems showing long-range ordering, and offers insights into the design principles for high-energy-density electrolytes based on spontaneous self-assembly behaviour.
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