Peripheral blood gene expression profile of infants with atopic dermatitis

To enhance the understanding of molecular mechanisms and mine previously unidentified biomarkers of pediatric atopic dermatitis, PBMC gene expression profiles were generated by RNA sequencing in infants with atopic dermatitis and age-matched controls. A total of 178 significantly differentially expressed genes (DEGs) (115 upregulations and 63 downregulations) were seen, compared with those in healthy controls. The DEGs identified included IL1β, TNF, TREM1, IL18R1, and IL18RAP. DEGs were validated by real-time RT- qPCR in a larger number of samples from PBMCs of infants with atopic dermatitis aged <12 months. Using the DAVID (Database for Annotation, Visualization and Integrated Discovery) database, functional and pathway enrichment analyses of DEGs were performed. Gene ontology enrichment analysis showed that DEGs were associated with immune responses, inflammatory responses, regulation of immune responses, and platelet activation. Pathway analysis indicated that DEGs were enriched in cytokine‒cytokine receptor interaction, immunoregulatory interactions between lymphoid and nonlymphoid cells, hematopoietic cell lineage, phosphoinositide 3-kinase‒protein kinase B signaling pathway, NK cell‒mediated cytotoxicity, and platelet activation. Furthermore, the protein‒protein interaction network was predicted using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database and visualized with Cytoscape software. Finally, on the basis of the protein‒protein interaction network, 18 hub genes were selected, and two significant modules were obtained. In conclusion, this study sheds light on the molecular mechanisms of pediatric atopic dermatitis and may provide diagnostic biomarkers and therapeutic targets

Aspergillus fumigatus preexposure worsens pathology and improves control of Mycobacterium abscessus pulmonary infection in mice

ABSTRACT Cystic fibrosis (CF) is an autosomal recessive disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. Mutations in this chloride channel lead to mucus accumulation, subsequent recurrent pulmonary infections, and inflammation, which, in turn, cause chronic lung disease and respiratory failure. Recently, rates of nontuberculous mycobacterial (NTM) infections in CF patients have been increasing. Of particular relevance is infection with Mycobacterium abscessus , which causes a serious, life-threatening disease and constitutes one of the most antibiotic-resistant NTM species. Interestingly, an increased prevalence of NTM infections is associated with worsening lung function in CF patients who are also coinfected with Aspergillus fumigatus . We established a new mouse model to investigate the relationship between A. fumigatus and M. abscessus pulmonary infections. In this model, animals exposed to A. fumigatus and coinfected with M. abscessus exhibited increased lung inflammation and decreased mycobacterial burden compared with those of mice infected with M. abscessus alone. This increased control of M. abscessus infection in coinfected mice was mucus independent but dependent on both transcription factors T-box 21 (Tbx21) and retinoic acid receptor (RAR)-related orphan receptor gamma t (RORγ-t), master regulators of type 1 and type 17 immune responses, respectively. These results implicate a role for both type 1 and type 17 responses in M. abscessus control in A. fumigatus -coinfected lungs. Our results demonstrate that A. fumigatus , an organism found commonly in CF patients with NTM infection, can worsen pulmonary inflammation and impact M. abscessus control in a mouse model. </jats:p

Risk register and risk intelligence: the challenge of operational risks in the energy sector

This paper presents the needs and the challenges encountered in developing a company-wide risk register in the energy sector. The study presented comes from an electricity generation company and it was useful to indicate areas where the concept of risk registers could be extended to make better use of existing data and to support continuous improvement of risk management. Six key areas are discussed 1) aggregation of risks across the business, 2) supporting controls over mitigation measures, 3) improved estimation of event likelihood, 4) integrating with critical asset registers, 5) improving risk communication, and 6) linking with day-to-day operational practice. The paper concludes with a framework for placing risk registers at the heart of Process Safety

Fake News and Indifference to Truth

State of the Union Addresses (SOUA) by two recent US Presidents, President Obama (2016) and President Trump (2018), and a series of recent of tweets by President Trump, are analysed by means of the data mining technique, sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they dier, and their potential implications for the national mood and state of the economy. President Trump's 2018 SOUA and his sample tweets are identied as being more positive in sentiment than President Obama's 2016 SOUA. This is conrmed by bootstrapped t tests and non-parametric sign tests on components of the respective sentiment scores. The issue of whether overly positive pronouncements amount to self-promotion, rather than intrinsic merit or sentiment, is a topic for future research

Factors Influencing Firm Sustainability Report External Assurance and the Contingent Firm Choice of External Assurance Provider

Our study undertakes a global analysis of data corresponding to firm sustainability reports generated by various organizational types including for profit and non-profit entities, listed and non-listed companies and small and large firms. We attempt to understand those factors reasonably expected to influence organizational decisions to assure firm sustainability reports externally and the contingent firm choice of assurance provider. Our research applies a sequential binomial logistical regression analysis to a sample of 4,504 sustainability reports generated in 2015 by organizations from 31 countries. We except (and identify) several country, firm and report level factors to influence organizational decisions to assure firm sustainability reports externally and the contingent firm choice of external assurance provider. Our results contribute to the extant and inconclusive academic literature relating to external assurance of sustainability reports

Changes in nano-mechanical properties of human epidermal cornified cells in children with atopic dermatitis

Background: Impaired skin barrier is an important etiological factor in atopic dermatitis (AD). The structural protein filaggrin (FLG) plays a major role in maintenance of the competent skin barrier and its deficiency is associated with enhanced susceptibility to mechanical injury. Here we examined biomechanical characteristics of the corneocytes in children with AD and healthy controls. Methods: We recruited 20 children with AD and 7 healthy children. They were genotyped for filaggrin gene ( FLG) loss-of-function mutations. Stratum corneum was collected from clinically unaffected skin by adhesive tapes. Cell stiffness (apparent elastic modulus, Ea) was determined by atomic force microscopy and filaggrin degradation products (NMF) by liquid chromatography. Skin barrier function was assessed through trans-epidermal water loss (TEWL) and disease severity by the SCORing Atopic Dermatitis (SCORAD) tool. Results: Corneocytes collected from AD patients showed a decreased elastic modulus which was strongly correlated with NMF and TEWL, but not with SCORAD. As compared with healthy controls, AD patients had reduced TEWL and NMF levels regardless of FLG mutations. NMF was strongly correlated with TEWL. Conclusion: Our findings demonstrate that AD patients have decreased corneocyte stiffness which correlates with reduced levels of filaggrin degradation products, NMF and skin barrier function. Altered mechanical properties of the corneocytes likely contribute to the loss of mechanical integrity of the SC and to reduced skin barrier function in AD

Confronting Reward Model Overoptimization with Constrained RLHF

Large language models are typically aligned with human preferences by optimizing $\textit{reward models}$ (RMs) fitted to human feedback. However, human preferences are multi-faceted, and it is increasingly common to derive reward from a composition of simpler reward models which each capture a different aspect of language quality. This itself presents a challenge, as it is difficult to appropriately weight these component RMs when combining them. Compounding this difficulty, because any RM is only a proxy for human evaluation, this process is vulnerable to $\textit{overoptimization}$, wherein past a certain point, accumulating higher reward is associated with worse human ratings. In this paper, we perform, to our knowledge, the first study on overoptimization in composite RMs, showing that correlation between component RMs has a significant effect on the locations of these points. We then introduce an approach to solve this issue using constrained reinforcement learning as a means of preventing the agent from exceeding each RM's threshold of usefulness. Our method addresses the problem of weighting component RMs by learning dynamic weights, naturally expressed by Lagrange multipliers. As a result, each RM stays within the range at which it is an effective proxy, improving evaluation performance. Finally, we introduce an adaptive method using gradient-free optimization to identify and optimize towards these points during a single run