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0001 and p = 0.0006, respectively). In cohort 1, univariate analysis showed that FGFR3 was associated with DFS but not OS. Kaplan-Meier analysis showed that higher FGFR3 and mp53 level correlated with worse DFS (p = 0.025) and OS (p = 0.009). As expected, p16 positive status was associated with improved OS and DFS (p less then 0.001 for both). Our results suggest that high FGFR3 expression is associated with p16 negative status and mp53 expression in OPSCC and correlates with a worse clinical outcome. The biological relationship between FGFR3 and mp53 in OPSCC deserves further investigation.Leaf-cutting ants of the genera Atta and Acromyrmex are at constant risk of epizootics due to their dense living conditions and frequent social interactions between genetically related individuals. To help mitigate the risk of epizootics, these ants display individual and collective immune responses, including associations with symbiotic bacteria that can enhance their resistance to pathogenic infections. For example, Acromyrmex spp. harbor actinobacteria that control infection by Escovopsis in their fungal gardens. Although Atta spp. Oleic manufacturer do not maintain symbiosis with protective actinobacteria, the evidence suggests that these insects are colonized by bacterial microbiota that may play a role in their defense against pathogens. The potential role of the bacterial microbiome of Atta workers in enhancing host immunity remains unexplored. We evaluated multiple parameters of the individual immunity of Atta cephalotes (Linnaeus, 1758) workers, including hemocyte count, encapsulation response, and the antimicrobial acinnate immunity, either at baseline or after exposure to the entomopathogen M. anisopliae. Further, upon infection, workers rely on mechanisms of humoral immunity to respond to this threat. Overall, our findings indicate that the bacterial microbiota associated with A. cephalotes workers does not play a defensive role.Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and social interaction and restricted, repetitive patterns of behavior, interests, or activities. Given the lack of specific pharmacological therapy for ASD and the clinical heterogeneity of the disorder, current biomarker research efforts are geared mainly toward identifying markers for determining ASD risk or for assisting with a diagnosis. A wide range of putative biological markers for ASD is currently being investigated. Proteomic analyses indicate that the levels of many proteins in plasma/serum are altered in ASD, suggesting that a panel of proteins may provide a blood biomarker for ASD. Serum samples from 76 boys with ASD and 78 typically developing (TD) boys, 18 months-8 years of age, were analyzed to identify possible early biological markers for ASD. Proteomic analysis of serum was performed using SomaLogic’s SOMAScanTM assay 1.3K platform. A total of 1,125 proteins were analyzed. There were 86 downregulated proteins and 52 upregulated proteins in ASD (FDR less then 0.05). Combining three different algorithms, we found a panel of 9 proteins that identified ASD with an area under the curve (AUC) = 0.8599±0.0640, with specificity and sensitivity of 0.8217±0.1178 and 0.835±0.1176, respectively. All 9 proteins were significantly different in ASD compared with TD boys, and were significantly correlated with ASD severity as measured by ADOS total scores. Using machine learning methods, a panel of serum proteins was identified that may be useful as a blood biomarker for ASD in boys. Further verification of the protein biomarker panel with independent test sets is warranted.In this study, an extension of the generalized Lindley distribution using the Marshall-Olkin method and its own sub-models is presented. This new model for modelling survival and lifetime data is flexible. Several statistical properties and characterizations of the subject distribution along with its reliability analysis are presented. Statistical inference for the new family such as the Maximum likelihood estimators and the asymptotic variance covariance matrix of the unknown parameters are discussed. A simulation study is considered to compare the efficiency of the different estimators based on mean square error criterion. Finally, a real data set is analyzed to show the flexibility of our proposed model compared with the fit attained by some other competitive distributions.This article investigates the effects of the COVID-19 outbreak on electoral participation. We study the French municipal elections that took place at the very beginning of the ongoing pandemic and held in over 9,000 municipalities on March 15, 2020. In addition to the simple note that turnout rates decreased to a historically low level, we establish a robust relationship between the depressed turnout rate and the disease. Using various estimation strategies and employing a large number of potential confounding factors, we find that the participation rate decreases with city proximity to COVID-19 clusters. Furthermore, the proximity has conditioned impacts according to the proportion of elderly -who are the most threatened- within the city. Cities with higher population density, where the risk of infection is higher, and cities where only one list ran at the election, which dramatically reduces competitiveness, experienced differentiated effects of distance.Face masks are an important tool for preventing the spread of COVID-19. However, it is unclear how different types of masks affect speech recognition in different levels of background noise. To address this, we investigated the effects of four masks (a surgical mask, N95 respirator, and two cloth masks) on recognition of spoken sentences in multi-talker babble. In low levels of background noise, masks had little to no effect, with no more than a 5.5% decrease in mean accuracy compared to a no-mask condition. In high levels of noise, mean accuracy was 2.8-18.2% lower than the no-mask condition, but the surgical mask continued to show no significant difference. The results demonstrate that different types of masks generally yield similar accuracy in low levels of background noise, but differences between masks become more apparent in high levels of noise.