Predicting pathogen risks to aid beach management: The real value of quantitative microbial risk assessment (QMRA)

There has been an ongoing dilemma for agencies that set criteria for safe recreational waters in how to provide for a seasonal assessment of a beach site versus guidance for day-to-day management. Typically an overall ‘safe’ criterion level is derived from epidemiologic studies of sewage-impacted beaches. The decision criterion is based on a percentile value for a single sample or a moving median of a limited number (e.g. five per month) of routine samples, which are reported at least the day after recreator exposure has occurred. The focus of this paper is how to better undertake day-to-day recreational site monitoring and management. Internationally, good examples exist where predictive empirical regression models (based on rainfall, wind speed/direction, etc.) may provide an estimate of the target faecal indicator density for the day of exposure. However, at recreational swimming sites largely impacted by non-sewage sources of faecal indicators, there is concern that the indicator-illness associations derived from studies at sewage-impacted beaches may be inappropriate. Furthermore, some recent epidemiologic evidence supports the relationship to gastrointestinal (GI) illness with qPCR-derived measures of Bacteroidales/Bacteroides spp. as well as more traditional faecal indicators, but we understand less about the environmental fate of these molecular targets and their relationship to bather risk. Modelling pathogens and indicators within a quantitative microbial risk assessment framework is suggested as a way to explore the large diversity of scenarios for faecal contamination and hydrologic events, such as from waterfowl, agricultural animals, resuspended sediments and from the bathers themselves. Examples are provided that suggest that more site-specific targets derived by QMRA could provide insight, directly translatable to management actions.     

Evaluation of Product and Process Design Robustness

Critical design decisions are commonly made throughout the product development process assuming known material and process behavior. However, stochastic variation during manufacture can inadvertently result in inferior or unacceptable product performance and reduced production yields. Stochastic simulations have been developed to estimate the end-use performance distribution prior to the commitment of hard tooling. This article proposes a definition for integrated product and process robustness, and extends existing stochastic methods to model the important role of the manufacturing flexibility in elimination of defects and product optimization. The goal is to enable the designer to understand and account for not only the negative effects of manufacturing variation, but also the positive impact of manufacturing flexibility wherein instantaneous corrections in the manufacturing process can frequently improve the product quality and eliminate flaws in the product design. Then, a methodology is introduced and contrasted with conventional development methods in the evaluation of best practices for development of a molded plastic component.     

Assignment of glial brain tumors in humans byin vivo 1H-magnetic resonance spectroscopy and multidimensional metabolic classification

This study presents a simple approach for the noninvasive assignment of glial brain tumors according to malignancy by single-voxel proteon magnetic resonance spectroscopy at short echo times (TE≦50 milliseconds). Based on peak area ratios, a five-dimensional data set was obtained for each investigated subject. This vector was then projected along metabolic coordinates in a two-dimensional metabolic space. These coordinates had been determined in a previous study (Hagberg G et al., 1995,Magn Reson Med 34: 242–252). Tumor assignment was done without any knowledge of histology by comparing the location of the new cases to the features of the previous study. All 11 investigated glioblastomas multiforme, as well as 4 of 5 astrocytomas grade II, could easily be assigned to the groups of high- and low-grade tumors, respectively. Classification was more difficult in the case of a cystic astrocytoma grade II and one astrocytoma grade III. Two spectra measured in normal-appearing matter of glioblastoma patients were not classified as healthy. Using single-voxel proton magnetic resonance spectroscopy at short echo times with the knowledge of a base study, a straightforward, fast, and noninvasive differential diagnosis of glial brain tumors is possible. Supported by the Swiss National Science Foundation Research Grants No. 31-32533.91 (to J.S.) and 32.39773.93 (to W.S.).W.R. and E.W.R. have been supported by the Krebsliga beider Basel. A preliminary account of this work was given at the Third Annual Scientific Meeting of the International Society for Magnetic Resonance in Medicine, Nice, France, August 1995, abstract 1719.     

A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data

The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000-2004, and was validated using observed GPP over the period 2005-2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km × 1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33 Pg C yr^− 1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances.     

Local Models for data-driven learning of control policies for complex systems

An approach based on local learning, relying on Nadaraya–Watson models (NWMs), is introduced for the problem of deriving an automatic controller able to exploit data collected during the operation of some complex plant or system by a reference teacher (e.g., a human operator). Such learning approach is particularly useful when the system is too complex to be modeled accurately and/or the task cannot be easily formalized by a cost function, a situation which rules out classic approaches based, e.g., on dynamic programming. Here it is proved that local models are a suitable solution for a real-time employment, since they allow to incorporate new information directly and efficiently without the need of offline training, and new data immediately reflect in improvement of performance. To this purpose, convergence analysis of the method is provided, also considering the case where the reference controller introduces random variations in the training data. Finally, a simulation test, concerning the control of a mechanical system, is provided to showcase the use of local models in an applicative scenario.     

Non-Woven Infection Prevention Fabrics Coated with Biobased Cranberry Extracts Inactivate Enveloped Viruses Such as SARS-CoV-2 and Multidrug-Resistant Bacteria

The Coronavirus Disease (COVID-19) pandemic is demanding the rapid action of the authorities and scientific community in order to find new antimicrobial solutions that could inactivate the pathogen SARS-CoV-2 that causes this disease. Gram-positive bacteria contribute to severe pneumonia associated with COVID-19, and their resistance to antibiotics is exponentially increasing. In this regard, non-woven fabrics are currently used for the fabrication of infection prevention clothing such as face masks, caps, scrubs, shirts, trousers, disposable gowns, overalls, hoods, aprons and shoe covers as protective tools against viral and bacterial infections. However, these non-woven fabrics are made of materials that do not exhibit intrinsic antimicrobial activity. Thus, we have here developed non-woven fabrics with antimicrobial coatings of cranberry extracts capable of inactivating enveloped viruses such as SARS-CoV-2 and the bacteriophage phi 6 (about 99% of viral inactivation in 1 min of viral contact), and two multidrug-resistant bacteria: the methicillin-resistant Staphylococcus aureus and the methicillin-resistant Staphylococcus epidermidis. The morphology, thermal and mechanical properties of the produced filters were characterized by optical and electron microscopy, differential scanning calorimetry, thermogravimetry and dynamic mechanical thermal analysis. The non-toxicity of these advanced technologies was ensured using a Caenorhabditis elegans in vivo model. These results open up a new prevention path using natural and biodegradable compounds for the fabrication of infection prevention clothing in the current COVID-19 pandemic and microbial resistant era.     

Study of selenocompounds from selenium-enriched culture of edible sprouts

Selenium is recognised as an essential micronutrient for humans and animals. One of the main sources of selenocompounds in the human diet is vegetables. Therefore, this study deals with the Se species present in different edible sprouts grown in Se-enriched media. We grew alfalfa, lentil and soy in a hydroponic system amended with soluble salts, containing the same proportion of Se, in the form of Se(VI) and Se(IV). Total Se in the sprouts was determined by acidic digestion in a microwave system and by ICP/MS. Se speciation was carried out by enzymatic extraction (Protease XIV) and measured by LC-ICP/MS. The study shows that the Se content of plants depends on the content in the growth culture, and that part of the inorganic Se was biotransformed mainly into SeMet. These results contribute to our understanding of the uptake of inorganic Se and its biotransformation by edible plants.     

Neural network and regression spline value function approximations for stochastic dynamic programming

Dynamic programming is a multi-stage optimization method that is applicable to many problems in engineering. A statistical perspective of value function approximation in high-dimensional, continuous-state stochastic dynamic programming (SDP) was first presented using orthogonal array (OA) experimental designs and multivariate adaptive regression splines (MARS). Given the popularity of artificial neural networks (ANNs) for high-dimensional modeling in engineering, this paper presents an implementation of ANNs as an alternative to MARS. Comparisons consider the differences in methodological objectives, computational complexity, model accuracy, and numerical SDP solutions. Two applications are presented: a nine-dimensional inventory forecasting problem and an eight-dimensional water reservoir problem. Both OAs and OA-based Latin hypercube experimental designs are explored, and OA space-filling quality is considered.     

Formation of carbon--carbon bonds under catalysis by transition-metal nanoparticles

This Account presents the fast-growing field of catalysis by transition metal nanoparticles, its present situation concerning scope and limitations, as well as future prospects. The Account is limited to reactions involving carbon-carbon bond formation.