The novel E. coli cell division protein, YtfB, plays a role in eukaryotic cell adhesion.

Characterisation of protein function based solely on homology searches may overlook functions under specific environmental conditions, or the possibility of a protein having multiple roles. In this study we investigated the role of YtfB, a protein originally identified in a genome-wide screen to cause inhibition of cell division, and has demonstrated to localise to the Escherichia coli division site with some degree of glycan specificity. Interestingly, YtfB also shows homology to the virulence factor OapA from Haemophilus influenzae, which is important for adherence to epithelial cells, indicating the potential of additional function(s) for YtfB. Here we show that E. coli YtfB binds to N'acetylglucosamine and mannobiose glycans with high affinity. The loss of ytfB results in a reduction in the ability of the uropathogenic E. coli strain UTI89 to adhere to human kidney cells, but not to bladder cells, suggesting a specific role in the initial adherence stage of ascending urinary tract infections. Taken together, our results suggest a role for YtfB in adhesion to specific eukaryotic cells, which may be additional, or complementary, to its role in cell division. This study highlights the importance of understanding the possible multiple functions of proteins based on homology, which may be specific to different environmental conditions

Meropenem vs standard of care for treatment of neonatal late onset sepsis (NeoMero1): A randomised controlled trial.

BACKGROUND: The early use of broad-spectrum antibiotics remains the cornerstone for the treatment of neonatal late onset sepsis (LOS). However, which antibiotics should be used is still debatable, as relevant studies were conducted more than 20 years ago, recruited in single centres or countries, evaluated antibiotics not in clinical use anymore and had variable inclusion/exclusion criteria and outcome measures. Moreover, antibiotic-resistant bacteria have become a major problem in many countries worldwide. We hypothesized that efficacy of meropenem as a broad-spectrum antibiotic is superior to standard of care regimens (SOC) in empiric treatment of LOS and aimed to compare meropenem to SOC in infants aged 44 weeks meeting the Goldstein criteria of sepsis, were randomized in a 1:1 ratio to receive meropenem or one of the two SOC regimens (ampicillin+gentamicin or cefotaxime+gentamicin) chosen by each site prior to the start of the study for 8-14 days. The primary outcome was treatment success (survival, no modification of allocated therapy, resolution/improvement of clinical and laboratory markers, no need of additional antibiotics and presumed/confirmed eradication of pathogens) at test-of-cure visit (TOC) in full analysis set. Stool samples were tested at baseline and Day 28 for meropenem-resistant Gram-negative organisms (CRGNO). The primary analysis was performed in all randomised patients and in patients with culture confirmed LOS. Proportions of participants with successful outcome were compared by using a logistic regression model adjusted for the stratification factors. From September 3, 2012 to November 30th 2014, total of 136 patients (instead of planned 275) in each arm were randomized; 140 (52%) were culture positive. Successful outcome at TOC was achieved in 44/136 (32%) in the meropenem arm vs. 31/135 (23%) in the SOC arm (p = 0.087). The respective numbers in patients with positive cultures were 17/63 (27%) vs. 10/77 (13%) (p = 0.022). The main reason of failure was modification of allocated therapy. Treatment emergent adverse events occurred in 72% and serious adverse events in 17% of patients, the Day 28 mortality was 6%. Cumulative acquisition of CRGNO by Day 28 occurred in 4% of patients in the meropenem and 12% in the SOC arm (p = 0.052). CONCLUSIONS: Within this study population, we found no evidence that meropenem was superior to SOC in terms of success at TOC, short term hearing disturbances, safety or mortality were similar in both treatment arms but the study was underpowered to detect the planned effect. Meropenem treatment did not select for colonization with CRGNOs. We suggest that meropenem as broad-spectrum antibiotic should be reserved for neonates who are more likely to have Gram-negative LOS, especially in NICUs where microorganisms producing extended spectrum- and AmpC type beta-lactamases are circulating

Alpha-1 antitrypsin mitigates the inhibition of airway epithelial cell repair by neutrophil elastase

Copyright © 2016 by the American Thoracic Society. Neutrophil elastase (NE) activity is associated with many destructive lung diseases and is a predictor for structural lung damage in early cystic fibrosis (CF), which suggests normal maintenance of airway epitheliumis prevented byuninhibitedNE.However, limited data exist on how the NE activity in airways of very young children with CF affects function of the epithelia. The aimof this studywas to determine if NE activity could inhibit epithelial homeostasis and repair and whether any functional effect was reversible by antiprotease alpha-1 antitrypsin (a1AT) treatment. Viability, inflammation, apoptosis, and proliferation were assessed in healthy non-CF and CF pediatric primary airway epithelial cells (pAEC non-CF and pAEC CF , respectively) during exposure to physiologically relevant NE. The effect of NE activity on pAEC CF wound repair was also assessed.We report that viability after 48 hours was significantly decreased by 100 nM NE in pAEC non-CF and pAEC CF owing to rapid cellular detachment that was accompanied by inflammatory cytokine release. Furthermore, both phenotypes initiated an apoptotic response to 100 nM NE, whereas ≥50 nM NE activity significantly inhibited the proliferative capacity of cultures. Similar concentrations of NE also significantly inhibited wound repair of pAEC CF , but this effect was reversed by the addition of a1AT. Collectively, our results demonstrate free NE activity is deleterious for epithelial homeostasis and support the hypothesis that proteases inthe airway contribute directly toCF structural lung disease. Our results also highlight the need to investigate antiprotease therapies in early CF disease in more detail

Bias in data-driven artificial intelligence systems—An introductory survey

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

The regulation of television sports broadcasting: a comparative analysis

Based on seven different sports broadcasting markets (Australia, Brazil, Italy, India, South Africa, United Kingdom and the United States), this article provides a comparative analysis of the regulation of television sports broadcasting. The article examines how contrasting perspectives on television and sport – economic and sociocultural – have been reflected in two main approaches to the regulation of sports broadcasting, namely competition law and major events legislation. The results of this analysis suggest that in many cases, the balance between commerce and culture in sports broadcasting has shifted too far in favour of the commercial interests of dominant pay-TV operators and sports organisations. Here, the case is made for the pursuit of an approach to sports broadcasting regulation that seeks to balance the commercial priorities of broadcasters and sports organisations with the wider sociocultural benefits citizens gain from freeto- air sports broadcasting

Bias in data-driven artificial intelligence systems - An introductory survey

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

Colistin: recent data on pharmacodynamics properties and clinical efficacy in critically ill patients

Recent clinical studies performed in a large number of patients showed that colistin "forgotten" for several decades revived for the management of infections due to multidrug-resistant (MDR) Gram-negative bacteria (GNB) and had acceptable effectiveness and considerably less toxicity than that reported in older publications. Colistin is a rapidly bactericidal antimicrobial agent that possesses a significant postantibiotic effect against MDR Gram-negative pathogens, such as Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae. The optimal colistin dosing regimen against MDR GNB is still unknown in the intensive care unit (ICU) setting. A better understanding of the pharmacokinetic-pharmacodynamic relationship of colistin is urgently needed to determine the optimal dosing regimen. Although pharmacokinetic and pharmacodynamic data in ICU patients are scarce, recent evidence shows that the pharmacokinetics/pharmacodynamics of colistimethate sodium and colistin in critically ill patients differ from those previously found in other groups, such as cystic fibrosis patients. The AUC:MIC ratio has been found to be the parameter best associated with colistin efficacy. To maximize the AUC:MIC ratio, higher doses of colistimethate sodium and alterations in the dosing intervals may be warranted in the ICU setting. In addition, the development of colistin resistance has been linked to inadequate colistin dosing. This enforces the importance of colistin dose optimization in critically ill patients. Although higher colistin doses seem to be beneficial, the lack of colistin pharmacokinetic-pharmacodynamic data results in difficulty for the optimization of daily colistin dose. In conclusion, although colistin seems to be a very reliable alternative for the management of life-threatening nosocomial infections due to MDR GNB, it should be emphasized that there is a lack of guidelines regarding the ideal management of these infections and the appropriate colistin doses in critically ill patients with and without multiple organ failure

OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics

Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often leads to the need of employing specialized hardware accelerators, further increase the effort and cost needed to employ DL models in robotics. Also, most of the existing DL methods follow a static inference paradigm, as inherited by the traditional computer vision pipelines, ignoring active perception, which can be employed to actively interact with the environment in order to increase perception accuracy. In this paper, we present the Open Deep Learning Toolkit for Robotics (OpenDR). OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions to efficiently develop and deploy AI and cognition technologies to robotics applications, providing a solid step towards addressing the aforementioned challenges. We also detail the design choices, along with an abstract interface that was created to overcome these challenges. This interface can describe various robotic tasks, spanning beyond traditional DL cognition and inference, as known by existing frameworks, incorporating openness, homogeneity and robotics-oriented perception e.g., through active perception, as its core design principles.acceptedVersionPeer reviewe

Towards understanding global patterns of antimicrobial use and resistance in neonatal sepsis: insights from the NeoAMR network.

OBJECTIVE: To gain an understanding of the variation in available resources and clinical practices between neonatal units (NNUs) in the low-income and middle-income country (LMIC) setting to inform the design of an observational study on the burden of unit-level antimicrobial resistance (AMR). DESIGN: A web-based survey using a REDCap database was circulated to NNUs participating in the Neonatal AMR research network. The survey included questions about NNU funding structure, size, admission rates, access to supportive therapies, empirical antimicrobial guidelines and period prevalence of neonatal blood culture isolates and their resistance patterns. SETTING: 39 NNUs from 12 countries. PATIENTS: Any neonate admitted to one of the participating NNUs. INTERVENTIONS: This was an observational cohort study. RESULTS: The number of live births per unit ranged from 513 to 27 700 over the 12-month study period, with the number of neonatal cots ranging from 12 to 110. The proportion of preterm admissions <32 weeks ranged from 0% to 19%, and the majority of units (26/39, 66%) use Essential Medicines List 'Access' antimicrobials as their first-line treatment in neonatal sepsis. Cephalosporin resistance rates in Gram-negative isolates ranged from 26% to 84%, and carbapenem resistance rates ranged from 0% to 81%. Glycopeptide resistance rates among Gram-positive isolates ranged from 0% to 45%. CONCLUSION: AMR is already a significant issue in NNUs worldwide. The apparent burden of AMR in a given NNU in the LMIC setting can be influenced by a range of factors which will vary substantially between NNUs. These variations must be considered when designing interventions to improve neonatal mortality globally