Associations between Wastewater Microbiome and Population Smoking Rate Identified Using Wastewater-Based Epidemiology

Tobacco use is known to cause health damage, partly by changing the mouth, respiratory tract, and gut-related microbiomes. This study aims to identify the associations between the human microbiome detected in domestic wastewater and the population smoking rate. Metagenomic sequencing and a biomarker discovery algorithm were employed to identify microorganisms as potential microbial biomarkers of smoking through wastewater-based epidemiology. Wastewater samples were collected from selected catchments with low and high smoking rates, i.e., 11.2 ± 1.5% and 17.0 ± 1.6%, respectively. Using the linear discriminant analysis effect size (LEfSe) method, Neisseria, Desulfovibrio, Megamonas, Blautia, Fusicatenibacter, Granulicatella and Enterococcus were suggested as potential biomarker microorganisms. A higher abundance of pathogens, including Neisseria, Eikenella and Haemophilus, was associated with the high smoking rate, likely because of their colonization in smoking-disturbed human guts. The identified potential microbial biomarkers reflect the change of the human gut microbiome due to the long-term smoking behavior. The metagenomic analysis also indicates that smoking upregulates microbial gene expression of genetic information processing, environmental information processing, and cell wall peptidoglycan cleavage, while it downregulates amino acid, lipid, and galactose metabolisms. The findings demonstrate the potential of microbial biomarkers for the surveillance of smoking through a wastewater-based epidemiology approach

Phase Coexistence and Slow Mixing for the Hard-Core Model on Z 2

The hard-core model has attracted much attention across several disciplines, representing lattice gases in statistical physics and independent sets in the discrete setting. On nite graphs, we are given a parameter λ, and each independent set I arises with probability proportional to λ |I|. On in nite graphs the Gibbs distribution is de ned as a suitable limit with the correct conditional probabilities. In the in nite setting we are interested in determining when this limit is unique and when there is phase coexistence existence of multiple Gibbs states. In the nite setting, for example on nite regions of the square lattice Z2, we are interested in determining when local Markov chains are rapidly mixing. These problems are believed to be related and it is conjectured that both undergo a phase transition at some critical point λ = λc ≈ 3.79 [1]. It remains open whether there is a single critical point, although it was recently shown that on general graphs of maximum degree ∆, the computational complexity of computing the partition function (namely, the λ-weighted count of independent sets) undergoes a phase transition at the unique well-known critical point λc(T∆) at which the ∆-regular in nite tree T ∆ undergoes a transition from uniqueness to having multiple Gibbs states [25, 27]

Specific T cells for the treatment of cytomegalovirus and/or adenovirus in the context of hematopoietic stem cell transplantation

International audienc

Efficient pseudorandom generators based on the ddh assumption

A family of pseudorandom generators based on the decisional Diffie-Hellman assumption is proposed. The new construction is a modified and generalized version of the Dual Elliptic Curve generator proposed by Barker and Kelsey. Although the original Dual Elliptic Curve generator is shown to be insecure, the modified version is provably secure and very efficient in comparison with the other pseudorandom generators based on discrete log assumptions. Our generator can be based on any group of prime order provided that an additional requirement is met (i.e., there exists an efficiently computable function that in some sense enumerates the elements of the group). Two specific instances are presented. The techniques used to design the instances, for example, the new probabilistic randomness extractor are of independent interest for other applications

Acute Stroke Imaging Research Roadmap II.

The Stroke Imaging Research (STIR) Group, the American Society of Neuroradiology, and the Foundation of the American Society of Neuroradiology sponsored a series of working group meetings >12 months, with the final meeting occurring during the Stroke Treatment Academy Industry Roundtable (STAIR) on March 9 to 10, 2013, in Washington, DC. This process brought together vascular neurologists, neuroradiologists, neuroimaging research scientists, members of the National Institute of Neurological Disorders and Stroke, industry representatives, and members of the US Food and Drug Administration to discuss stroke imaging research priorities, especially in the light of the recent negative results of acute stroke clinical trials that tested the concept of penumbral imaging selection. The goal of this process was to propose a research roadmap for the next 5 years. STIR recommendations include (1) the use of standard terminology, aligned with the National Institute of Neurological Disorders and Stroke Common Data Elements. ; (2) a standardized imaging assessment of revascularization in acute ischemic stroke trials, including a modified Treatment In Cerebral Ischemia (mTICI) score. ; (3) a standardized process to assess whether ischemic core and penumbral imaging methods meet the requirements to be considered as an acceptable selection tool in acute ischemic stroke trials. ; (4) the characteristics of a clinical and imaging data repository to facilitate the development and testing process described in recommendation no. 3. ; (5) the optimal study design for a clinical trial to evaluate whether advanced imaging adds value in selecting acute ischemic stroke patients for revascularization therapy. ; (6) the structure of a stroke neuroimaging network to implement and coordinate the recommendations listed above. All of these recommendations pertain to research, not to clinical care