An Environmental Science and Engineering Framework for Combating Antimicrobial Resistance

On June 20, 2017, members of the environmental engineering and science (EES) community convened at the Association of Environmental Engineering and Science Professors (AEESP) Biennial Conference for a workshop on antimicrobial resistance. With over 80 registered participants, discussion groups focused on the following topics: risk assessment, monitoring, wastewater treatment, agricultural systems, and synergies. In this study, we summarize the consensus among the workshop participants regarding the role of the EES community in understanding and mitigating the spread of antibiotic resistance via environmental pathways. Environmental scientists and engineers offer a unique and interdisciplinary perspective and expertise needed for engaging with other disciplines such as medicine, agriculture, and public health to effectively address important knowledge gaps with respect to the linkages between human activities, impacts to the environment, and human health risks. Recommendations that propose priorities for research within the EES community, as well as areas where interdisciplinary perspectives are needed, are highlighted. In particular, risk modeling and assessment, monitoring, and mass balance modeling can aid in the identification of “hot spots” for antibiotic resistance evolution and dissemination, and can help identify effective targets for mitigation. Such information will be essential for the development of an informed and effective policy aimed at preserving and protecting the efficacy of antibiotics for future generations

The organizational change in the italian public personnel management

Premise. The importance of the new human resources management. Evolutionary trends in the personnel management. The Italian Public Administration: structural and organizational differences. The organizational public supply's system. The personnel management in the Public Sector. Evolutionary lines of public personnel management. Conclusive considerations. Bibliograph

Jefferson Digital Commons quarterly report: July-September 2018

This quarterly report includes: New Collection Alert Articles Code Red: Battling the Plague of Gun Violence Dissertations From the Archives Grand Rounds and Lectures House Staff Quality Improvement and Patient Safety Posters Journals and Newsletters Nexus Maximus Posters Third Annual Sepsis Symposium What People are Sayin

Analysis of the waste selective collection at drop-off systems: Case study including the income level and the seasonal variation

There are several factors which have an influence in the selective collection of the municipal waste. To define a selective collection system, the waste generation pattern should be firstly determined and these factors should be analyzed in depth. This paper tries to analyze the economic income level and the seasonal variation on the collection and the purity of light-packaging waste to determine actions to improve the waste management plan of a town. In the first stage of the work, waste samples of the light-packaging containers were collected in two zones of the town with different economic characteristics in different seasons during one year. In the second stage, the samples were characterized to analyze the composition and purity of the waste. They were firstly separated into four fractions: metals; plastic; beverage cartons; and misplaced materials. The misplaced fraction was in its turn separated into cardboard, rubber and leather, inert waste, organic matter, paper, hazardous waste, clothes and shoes, glass and others. The plastic fraction was separated into five types of plastics and the metal fraction into three. In the third stage, the data have been analyzed and conclusions have been extracted. The main result is that the quality of the light-packaging fraction collected in these zones during both seasons were similar. This methodology can be extrapolated to towns with similar characteristics. It will be useful when implementing a system to collect the waste selectively and to develop actions to achieve a good participation in the selective collection of the waste

Radiation-Induced Error Criticality in Modern HPC Parallel Accelerators

In this paper, we evaluate the error criticality of radiation-induced errors on modern High-Performance Computing (HPC) accelerators (Intel Xeon Phi and NVIDIA K40) through a dedicated set of metrics. We show that, as long as imprecise computing is concerned, the simple mismatch detection is not sufficient to evaluate and compare the radiation sensitivity of HPC devices and algorithms. Our analysis quantifies and qualifies radiation effects on applications’ output correlating the number of corrupted elements with their spatial locality. Also, we provide the mean relative error (dataset-wise) to evaluate radiation-induced error magnitude. We apply the selected metrics to experimental results obtained in various radiation test campaigns for a total of more than 400 hours of beam time per device. The amount of data we gathered allows us to evaluate the error criticality of a representative set of algorithms from HPC suites. Additionally, based on the characteristics of the tested algorithms, we draw generic reliability conclusions for broader classes of codes. We show that arithmetic operations are less critical for the K40, while Xeon Phi is more reliable when executing particles interactions solved through Finite Difference Methods. Finally, iterative stencil operations seem the most reliable on both architectures.This work was supported by the STIC-AmSud/CAPES scientific cooperation program under the EnergySFE research project grant 99999.007556/2015-02, EU H2020 Programme, and MCTI/RNP-Brazil under the HPC4E Project, grant agreement n° 689772. Tested K40 boards were donated thanks to Steve Keckler, Timothy Tsai, and Siva Hari from NVIDIA.Postprint (author's final draft

IAMBEE : a web-service for the identification of adaptive pathways from parallel evolved clonal populations

IAMBEE is a web server designed for the Identification of Adaptive Mutations in Bacterial Evolution Experiments (IAMBEE). Input data consist of genotype information obtained from independently evolved clonal populations or strains that show the same adapted behavior (phenotype). To distinguish adaptive from passenger mutations, IAMBEE searches for neighborhoods in an organism-specific interaction network that are recurrently mutated in the adapted populations. This search for recurrently mutated network neighborhoods, as proxies for pathways is driven by additional information on the functional impact of the observed genetic changes and their dynamics during adaptive evolution. In addition, the search explicitly accounts for the differences in mutation rate between the independently evolved populations. Using this approach, IAMBEE allows exploiting parallel evolution to identify adaptive pathways. The web-server is freely available at http://bioinformatics.intec.ugent.be/iambee/ with no login requirement

Proteomics: in pursuit of effective traumatic brain injury therapeutics

Effective traumatic brain injury (TBI) therapeutics remain stubbornly elusive. Efforts in the field have been challenged by the heterogeneity of clinical TBI, with greater complexity among underlying molecular phenotypes than initially conceived. Future research must confront the multitude of factors comprising this heterogeneity, representing a big data challenge befitting the coming informatics age. Proteomics is poised to serve a central role in prescriptive therapeutic development, as it offers an efficient endpoint within which to assess post-TBI biochemistry. We examine rationale for multifactor TBI proteomic studies and the particular importance of temporal profiling in defining biochemical sequences and guiding therapeutic development. Lastly, we offer perspective on repurposing biofluid proteomics to develop theragnostic assays with which to prescribe, monitor and assess pharmaceutics for improved translation and outcome for TBI patients