Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many ...cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB), the Hue, Saturation and Value (HSV), and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components) color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates.
The deployment of cryptocurrencies in e-commerce has reached a significant number of transactions and continuous increases in monetary circulation; nevertheless, they face two impediments: a lack of ...awareness of the technological utility, and a lack of trust among consumers. E-commerce carried out through social networks expands its application to a new paradigm called social commerce. Social commerce uses the content generated within social networks to attract new consumers and influence their behavior. The objective of this paper is to analyze the role played by social media in increasing trust and intention to use cryptocurrencies in making electronic payments. It develops a model that combines constructs from social support theory, social commerce, and the technology acceptance model. This model is evaluated using the partial least square analysis. The obtained results show that social commerce increases the trust and intention to use cryptocurrencies. However, mutual support among participants does not generate sufficient trust to adequately promote the perceived usefulness of cryptocurrencies. This research provides a practical tool for analyzing how collaborative relationships that emerge in social media can influence or enhance the adoption of a new technology in terms of perceived trust and usefulness. Furthermore, it provides a significant contribution to consumer behavior research by applying the social support theory to the adoption of new information technologies. These theoretical and practical contributions are detailed in the final section of the paper.
Smart monitoring and surveillance systems have become one of the fundamental areas in the context of security applications in Smart Cities. In particular, video surveillance for Human Activity ...Recognition (HAR) applied to the recognition of potential offenders and to the detection and prevention of violent acts is a challenging task that is still undergoing. This paper presents a method based on deep learning for face recognition at a distance for security applications. Due to the absence of available datasets on face recognition at a distance, a methodology to generate a reliable dataset that relates the distance of the individuals from the camera, the focal length of the image sensors and the size in pixels of the target face is introduced. To generate the extended dataset, the Georgia Tech Face and Quality Dataset for Distance Faces databases were chosen. Our method is then tested and applied to a set of commercial image sensors for surveillance cameras using this dataset. The system achieves an average accuracy above 99% for several sensors and allows to calculate the maximum distance for a sensor to get the required accuracy in the recognition, which could be crucial in security applications in smart cities.
Background
As COVID-19 became a pandemic, the urgent need to find an effective treatment vaccine has been a major objective. Vaccines contain adjuvants which are not exempt from adverse effects and ...can trigger the autoimmune/inflammatory syndrome induced by adjuvants (ASIA). There is very little information about autoimmune endocrine disease and the ASIA after the use of mRNA-based SARS-CoV2 vaccination.
Case series
We report three cases and also review the literature showing that the thyroid gland can be involved in the ASIA induced by the mRNA-based SARS-CoV2 vaccination. We present the first case to date of silent thyroiditis described in the context of SARS-CoV2 vaccination with Pfizer/BioNTech. Also, we discuss the first subacute thyroiditis in the context of SARS-CoV2 vaccination with the Moderna’s vaccine. Finally, we provide another case to be added to existing evidence on Graves’ disease occurring post-vaccination with the Pfizer/BioNTech vaccine.
Discussion
Adjuvants play an important role in vaccines. Their ability to increase the immunogenicity of the active ingredient is necessary to achieve the desired immune response. Both the Moderna and the Pfizer/BioNTech vaccines use mRNA coding for the SARS-CoV2 S protein enhanced by adjuvants. In addition, the cross-reactivity between SARS-CoV2 and thyroid antigens has been reported. This would explain, at least, some of the autoimmune/inflammatory reactions produced during and after SARS-CoV2 infection and vaccination.
Conclusion
The autoimmune/inflammatory syndrome induced by adjuvants involving the thyroid could be an adverse effect of SARS-CoV2 vaccination and could be underdiagnosed.
Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine ...learning models using gene expression data from patients' primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study's limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Cryptocurrencies have the potential to become a disruptive innovation because they define a new paradigm: the decentralization of trust in secure electronic transactions without the need for a ...central control authority. Cryptocurrencies arouse interest in society because they reformulate the generation and transference of money. The aim of this paper is to investigate the role of the disruptive innovation of cryptocurrencies in the acceptance and trust perceived by users with regard to the monetary transactions generated in e-commerce. This paper defines a model using constructs from the technology acceptance model, trust and perceived risk. This model is evaluated using partial least squares analysis. The findings affirm that perceived trust, perceived risk, and perceived ease of use are not strong predictors of the intention to use cryptocurrencies and that the strength of their effects on the intention to use is determined by the perceived usefulness of adopting the mentioned disruptive innovation. This preliminary study makes a significant contribution to consumer behaviour research by analysing a cryptocurrency acceptance model for C2C e-commerce. The theoretical and practical contributions are detailed in the final section of the paper.
Abstract
STUDY QUESTION
Does time to ICSI affect reproductive outcomes?
SUMMARY ANSWER
Biochemical and clinical pregnancy diminish progressively as time between oocyte pick up (OPU) and ICSI ...increases after fresh embryo transfer.
WHAT IS KNOWN ALREADY
Appropriate oocyte cytoplasmic and nuclear maturation are of paramount importance to ensure an optimal embryonic developmental competence. While nuclear maturation is usually attained by the time an oocyte reaches OPU, cytoplasmic maturation cannot be readily assessed and might be incomplete. On the other hand, excessive in vitro culture of mature human oocytes can affect their ultrastructural characteristics and, in mice, induces alterations in gene expression and changes of chromatin and histone modification patterns.
STUDY DESIGN, SIZE, DURATION
Retrospective consecutive cohort study including 1468 ICSI cycles carried out in a single center between December 2012 and September 2015. All cycles were with patient's own oocytes and fresh embryo transfer (ET). A radiofrequency-based system was used to record exact culture times, namely, OPU-denudation (DN); DN-ICSI and OPU-ICSI. We analyzed the effect of total and partial time intervals between procedures, from OPU to ICSI, on fertilization rate and biochemical, clinical, ongoing pregnancy and live birth rates.
PARTICIPANTS/MATERIALS, SETTING, METHODS
Differences in laboratory times between positive and negative biochemical, clinical, ongoing pregnancies and birth results were tested by Mann-Whitney U test. The likelihood of positive clinical outcomes was further modeled by locally weighted scatterplot smoothing (LOWESS) regression and logistic regression, adjusting for woman's age and BMI, number of transferred embryos; mean embryo morphological score, sperm origin and status, and number of mature oocytes obtained at OPU. Effect of time on fertilization rate was modeled by Generalized Linear Modeling (GLM) and LOWESS regression.
MAIN RESULTS AND THE ROLE OF CHANCE
The mean woman's age was 38.4 years (SD 4.6). Biochemical, clinical, ongoing pregnancy and live birth rates after the fresh ET were: 39.6, 33.1, 25.7 and 20.8%, respectively. Cumulative values for biochemical pregnancy and live birth were 46.4 and 26.3%, respectively. Mean times in hours for OPU-DN, DN-ICSI and OPU-ICSI were: 1.00 (SD 0.20); 3.86 (SD 1.93) and 4.87 (SD 1.96), respectively, and were not different for pregnant and non-pregnant patients. However, multivariate analyses showed that on average (anti-log transformed), each 1-h increase in the OPU-ICSI time reduced the likelihood of biochemical pregnancy by 7.3% (95% CI: 0.7-13.5%) and of clinical pregnancy by 7.7% (95% CI 0.8-14.1%), after the fresh ET. No effect of time was observed for ongoing pregnancy or live birth rates. Increasing OPU-ICSI time increased the fertilization rate (B = 0.052, 95% CI: 0.022, 0.082).
LIMITATIONS, REASONS FOR CAUTION
The lack of relationship between incubation time of oocytes and live birth rates might be due to uncontrolled variables. Given the population analyzed, these results should not be extended to other ART protocols such as in vitro maturation of oocytes or classical IVF fertilization.
WIDER IMPLICATIONS OF THE FINDINGS
This study indicates that in vitro ageing of mature oocytes significantly affects the chances to become pregnant. Effect on live birth rates, although not evident in this study, cannot be excluded. Limiting incubation time of mature oocytes in the embryology laboratory should improve reproductive results for patients using their own oocytes and with a transfer of fresh embryos.
STUDY FUNDING/COMPETING INTEREST(S)
None.
TRIAL REGISTRATION NUMBER
NA.
X-linked adrenoleukodystrophy (X-ALD) is an inherited metabolic disorder of the nervous system characterized by axonopathy in spinal cords and/or cerebral demyelination, adrenal insufficiency and ...accumulation of very long-chain fatty acids (VLCFAs) in plasma and tissues. The disease is caused by malfunction of the ABCD1 gene, which encodes a peroxisomal transporter of VLCFAs or VLCFA-CoA. In the mouse, Abcd1 loss causes late onset axonal degeneration in the spinal cord, associated with locomotor disability resembling the most common phenotype in patients, adrenomyeloneuropathy. We have formerly shown that an excess of the VLCFA C26:0 induces oxidative damage, which underlies the axonal degeneration exhibited by the Abcd1(-) mice. In the present study, we sought to investigate the noxious effects of C26:0 on mitochondria function. Our data indicate that in X-ALD patients' fibroblasts, excess of C26:0 generates mtDNA oxidation and specifically impairs oxidative phosphorylation (OXPHOS) triggering mitochondrial ROS production from electron transport chain complexes. This correlates with impaired complex V phosphorylative activity, as visualized by high-resolution respirometry on spinal cord slices of Abcd1(-) mice. Further, we identified a marked oxidation of key OXPHOS system subunits in Abcd1(-) mouse spinal cords at presymptomatic stages. Altogether, our results illustrate some of the mechanistic intricacies by which the excess of a fatty acid targeted to peroxisomes activates a deleterious process of oxidative damage to mitochondria, leading to a multifaceted dysfunction of this organelle. These findings may be of relevance for patient management while unveiling novel therapeutic targets for X-ALD.
Aims
The molecular cross‐talk between commensal bacteria and the gut play an important role in the maintenance of the intestinal homeostasis and general health. Here, we studied the impact of a major ...Gram‐positive anaerobic bacterium of the human gut microbiota, that is, Ruminococcus gnavus on the glycosylation pattern and the production of intestinal mucus by the goblet cells.
Methods and Results
Our results showed that R. gnavus E1 specifically increases the expression and the glycosylation level of the intestinal glyco‐conjugates by goblet cells in the colonic mucosa of mono‐associated mice with R. gnavus E1 as well as in human HT29‐MTX cells. Such an effect was mediated through induction of the level of mRNA encoding for the major intestinal gel‐forming mucin such as MUC2 and various glycosyltransferase enzymes.
Conclusions
This study demonstrates for the first time that R. gnavus E1 possess the ability to modulate the glycosylation profile of the glyco‐conjugate molecules and mucus in goblet cells.
Significance and Impact of the Study
Furthermore, we demonstrated that R. gnavus E1 modified specifically the glycosylation pattern and MUC2 expression by means of a small soluble factor of peptidic nature (<3 kDa) and heat stable in the HT29‐MTX cell.
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