Low Fouling Nanostructured Cellulose Membranes for Ultrafiltration in Wastewater Treatment.

Ultrafiltration (UF) is a common technique used in wastewater treatments. However, the issue of membrane fouling in UF can greatly hinder the effectiveness of the treatments. This study demonstrated a low-fouling composite cellulose membrane system based on microfibrillated cellulose (MFC) and silica nanoparticle additives. The incorporation of 'non-spherical' silica nanoparticles was found to exhibit better structural integration in the membrane (i.e., minimal aggregation of silica nanoparticles in the membrane scaffold) as compared to spherical silica. The resulting composite membranes were tested for UF using local wastewater, where the best-performing membrane exhibited higher permeation flux than commercial polyvinylidene difluoride (PVDF) and polyether sulfone (PES) membranes while maintaining a high separation efficiency (~99.6%) and good flux recovery ratio (>90%). The analysis of the fouling behavior using different models suggested that the processes of cake layer formation and pore-constriction were probably two dominant fouling mechanisms, likely due to the presence of humic substances in wastewater. The demonstrated cellulose composite membrane system showed low-fouling and high restoration capability by a simple hydraulic cleaning method due to the super hydrophilic nature of the cellulose scaffold containing silica nanoparticles.

Modeling nation-wide U.S. swine movement networks at the resolution of the individual premises.

The spread of infectious livestock diseases is a major cause for concern in modern agricultural systems. In the dynamics of the transmission of such diseases, movements of livestock between herds play an important role. When constructing mathematical models used for activities such as forecasting epidemic development, evaluating mitigation strategies, or determining important targets for disease surveillance, including between-premises shipments is often a necessity. In the United States (U.S.), livestock shipment data is not routinely collected, and when it is, it is not readily available and mostly concerned with between-state shipments. To bridge this gap in knowledge and provide insight into the complete livestock shipment network structure, we have developed the U.S. Animal Movement Model (USAMM). Previously, USAMM has only existed for cattle shipments, but here we present a version for domestic swine. This new version of USAMM consists of a Bayesian model fit to premises demography, county-level livestock industry variables, and two limited data sets of between-state swine movements. The model scales up the data to simulate nation-wide networks of both within- and between-state shipments at the level of individual premises. Here we describe this shipment model in detail and subsequently explore its usefulness with a rudimentary predictive model of the prevalence of porcine epidemic diarrhea virus (PEDv) across the U.S. Additionally, in order to promote further research on livestock disease and other topics involving the movements of swine in the U.S., we also make 250 synthetic premises-level swine shipment networks with complete coverage of the entire conterminous U.S. freely available to the research community as a useful surrogate for the absent shipment data.

The Importance of Livestock Demography and Infrastructure in Driving Foot and Mouth Disease Dynamics.

Transboundary animal diseases, such as foot and mouth disease (FMD) pose a significant and ongoing threat to global food security. Such diseases can produce large, spatially complex outbreaks. Mathematical models are often used to understand the spatio-temporal dynamics and create response plans for possible disease introductions. Model assumptions regarding transmission behavior of premises and movement patterns of livestock directly impact our understanding of the ecological drivers of outbreaks and how to best control them. Here, we investigate the impact that these assumptions have on model predictions of FMD outbreaks in the U.S. using models of livestock shipment networks and disease spread. We explore the impact of changing assumptions about premises transmission behavior, both by including within-herd dynamics, and by accounting for premises type and increasing the accuracy of shipment predictions. We find that the impact these assumptions have on outbreak predictions is less than the impact of the underlying livestock demography, but that they are important for investigating some response objectives, such as the impact on trade. These results suggest that demography is a key ecological driver of outbreaks and is critical for making robust predictions but that understanding management objectives is also important when making choices about model assumptions.

Highly reinforced and degradable lignocellulose biocomposites by polymerization of new polyester oligomers.

Unbleached wood fibers and nanofibers are environmentally friendly bio-based candidates for material production, in particular, as reinforcements in polymer matrix biocomposites due to their low density and potential as carbon sink during the materials production phase. However, producing high reinforcement content biocomposites with degradable or chemically recyclable matrices is troublesome. Here, we address this issue with a new concept for facile and scalable in-situ polymerization of polyester matrices based on functionally balanced oligomers in pre-formed lignocellulosic networks. The idea enabled us to create high reinforcement biocomposites with well-dispersed mechanically undamaged fibers or nanocellulose. These degradable biocomposites have much higher mechanical properties than analogs in the literature. Reinforcement geometry (fibers at 30 µm or fibrils at 10-1000 nm diameter) influenced the polymerization and degradation of the polyester matrix. Overall, this work opens up new pathways toward environmentally benign materials in the context of a circular bioeconomy.

Nanocellulose for Sustainable Water Purification.

Nanocelluloses (NC) are nature-based sustainable biomaterials, which not only possess cellulosic properties but also have the important hallmarks of nanomaterials, such as large surface area, versatile reactive sites or functionalities, and scaffolding stability to host inorganic nanoparticles. This class of nanomaterials offers new opportunities for a broad spectrum of applications for clean water production that were once thought impractical. This Review covers substantial discussions based on evaluative judgments of the recent literature and technical advancements in the fields of coagulation/flocculation, adsorption, photocatalysis, and membrane filtration for water decontamination through proper understanding of fundamental knowledge of NC, such as purity, crystallinity, surface chemistry and charge, suspension rheology, morphology, mechanical properties, and film stability. To supplement these, discussions on low-cost and scalable NC extraction, new characterizations including solution small-angle X-ray scattering evaluation, and structure-property relationships of NC are also reviewed. Identifying knowledge gaps and drawing perspectives could generate guidance to overcome uncertainties associated with the adaptation of NC-enabled water purification technologies. Furthermore, the topics of simultaneous removal of multipollutants disposal and proper handling of post/spent NC are discussed. We believe NC-enabled remediation nanomaterials can be integrated into a broad range of water treatments, greatly improving the cost-effectiveness and sustainability of water purification.

Sustainable Development of Hot-Pressed All-Lignocellulose Composites-Comparing Wood Fibers and Nanofibers.

Low-porosity materials based on hot-pressed wood fibers or nanocellulose fibrils (no polymer matrix) represent a new concept for eco-friendly materials with interesting mechanical properties. For the replacement of fossil-based materials, physical properties of wood fiber materials need to be improved. In addition, the carbon footprint and cumulative energy required to produce the material also needs to be reduced compared with fossil-based composites, e.g., glass fiber composites. Lignin-containing fibers and nanofibers are of high yield and special interest for development of more sustainable materials technologies. The present mini-review provides a short analysis of the potential. Different extraction routes of lignin-containing wood fibers are discussed, different processing methods, and the properties of resulting fiber materials. Comparisons are made with analogous lignin-containing nanofiber materials, where mechanical properties and eco-indicators are emphasized. Higher lignin content may promote eco-friendly attributes and improve interfiber or interfibril bonding in fiber materials, for improved mechanical performance.

Assessing intrastate shipments from interstate data and expert opinion.

Live animal shipments are a potential route for transmitting animal diseases between holdings and are crucial when modelling spread of infectious diseases. Yet, complete contact networks are not available in all countries, including the USA. Here, we considered a 10% sample of Interstate Certificate of Veterinary Inspections from 1 year (2009). We focused on distance dependence in contacts and investigated how different functional forms affect estimates of unobserved intrastate shipments. To further enhance our predictions, we included responses from an expert elicitation survey about the proportion of shipments moving intrastate. We used hierarchical Bayesian modelling to estimate parameters describing the kernel and effects of expert data. We considered three functional forms of spatial kernels and the inclusion or exclusion of expert data. The resulting six models were ranked by widely applicable information criterion (WAIC) and deviance information criterion (DIC) and evaluated through within- and out-of-sample validation. We showed that predictions of intrastate shipments were mildly influenced by the functional form of the spatial kernel but kernel shapes that permitted a fat tail at large distances while maintaining a plateau-shaped behaviour at short distances better were preferred. Furthermore, our study showed that expert data may not guarantee enhanced predictions when expert estimates are disparate.

A Proposition for the Estimation of the Maximum Tensile Strength of Variously Charged Nanocellulosic Film Materials Provided by Vacuum Filtration.

This short investigation deals with a review of the tensile strength properties of six different types of nanocellulose films (carboxymethylated, carboxymethylcellulose-grafted, enzymatically pretreated, phosphorylated, sulfoethylated, and alkoxylated nanocellulose films) manufactured using identical protocols and the determination of the apparent nanocellulose yield of the same nanocelluloses and their tensile strength properties at different extents of delamination (microfluidization). The purpose was to test a previously suggested procedure to estimate the maximum tensile strength on these different procedures. A second goal was to investigate the impact of the nanocellulose yield on the tensile strength properties. The investigations were limited to the nanocellulose research activities at RISE in Stockholm, because these investigations were made with identical experimental laboratory protocols. The importance of such protocols is also stressed. This review shows that the suggested procedure to estimate the maximum tensile strength is a viable proposition, albeit not scientifically proven. Secondly, there is a relationship between the nanocellulose yield and tensile strength properties, although there may not be a linear relationship between the two measures.

Estimating hunting harvest from partial reporting: a Bayesian approach.

Quantifying hunting harvest is essential for numerous ecological topics, necessitating reliable estimates. We here propose novel analytical tools for this purpose. Using a hierarchical Bayesian framework, we introduce models for hunting reports that accounts for different structures of the data. Focusing on Swedish harvest reports of red fox (Vulpes vulpes), wild boar (Sus scrofa), European pine marten (Martes martes), and Eurasian beaver (Castor fiber), we evaluated predictive performance through training and validation sets as well as Leave One Out Cross Validation. The analyses revealed that to provide reliable harvest estimates, analyses must account for both random variability among hunting teams and the effect of hunting area per team on the harvest rate. Disregarding the former underestimated the uncertainty, especially at finer spatial resolutions (county and hunting management precincts). Disregarding the latter imposed a bias that overestimated total harvest. We also found support for association between average harvest rate and variability, yet the direction of the association varied among species. However, this feature proved less important for predictive purposes. Importantly, the hierarchical Bayesian framework improved previously used point estimates by reducing sensitivity to low reporting and presenting inherent uncertainties.

Cross-Sections of Nanocellulose from Wood Analyzed by Quantized Polydispersity of Elementary Microfibrils.

Bio-based nanocellulose has been shown to possess impressive mechanical properties and simplicity for chemical modifications. The chemical properties are largely influenced by the surface area and functionality of the nanoscale materials. However, finding the typical cross-sections of nanocellulose, such as cellulose nanofibers (CNFs), has been a long-standing puzzle, where subtle changes in extraction methods seem to yield different shapes and dimensions. Here, we extracted CNFs from wood with two different oxidation methods and variations in degree of oxidation and high-pressure homogenization. The cross-sections of CNFs were characterized by small-angle X-ray scattering and wide-angle X-ray diffraction in dispersed and freeze-dried states, respectively, where the results were analyzed by assuming that the cross-sectional distribution was quantized with an 18-chain elementary microfibril, the building block of the cell wall. We find that the results agree well with a pseudosquare unit having a size of about 2.4 nm regardless of sample, while the aggregate level strongly depends on the extraction conditions. Furthermore, we find that aggregates have a preferred cohesion of phase boundaries parallel to the (110)-plane of the cellulose fibril, leading to a ribbon shape on average.

Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States.

Spatially explicit livestock disease models require demographic data for individual farms or premises. In the U.S., demographic data are only available aggregated at county or coarser scales, so disease models must rely on assumptions about how individual premises are distributed within counties. Here, we addressed the importance of realistic assumptions for this purpose. We compared modeling of foot and mouth disease (FMD) outbreaks using simple randomization of locations to premises configurations predicted by the Farm Location and Agricultural Production Simulator (FLAPS), which infers location based on features such as topography, land-cover, climate, and roads. We focused on three premises-level Susceptible-Exposed-Infectious-Removed models available from the literature, all using the same kernel approach but with different parameterizations and functional forms. By computing the basic reproductive number of the infection (R0) for both FLAPS and randomized configurations, we investigated how spatial locations and clustering of premises affects outbreak predictions. Further, we performed stochastic simulations to evaluate if identified differences were consistent for later stages of an outbreak. Using Ripley's K to quantify clustering, we found that FLAPS configurations were substantially more clustered at the scales relevant for the implemented models, leading to a higher frequency of nearby premises compared to randomized configurations. As a result, R0 was typically higher in FLAPS configurations, and the simulation study corroborated the pattern for later stages of outbreaks. Further, both R0 and simulations exhibited substantial spatial heterogeneity in terms of differences between configurations. Thus, using realistic assumptions when de-aggregating locations based on available data can have a pronounced effect on epidemiological predictions, affecting if, where, and to what extent FMD may invade the population. We conclude that methods such as FLAPS should be preferred over randomization approaches.

Effects of regional differences and demography in modelling foot-and-mouth disease in cattle at the national scale.

Foot-and-mouth disease (FMD) is a fast-spreading viral infection that can produce large and costly outbreaks in livestock populations. Transmission occurs at multiple spatial scales, as can the actions used to control outbreaks. The US cattle industry is spatially expansive, with heterogeneous distributions of animals and infrastructure. We have developed a model that incorporates the effects of scale for both disease transmission and control actions, applied here in simulating FMD outbreaks in US cattle. We simulated infection initiating in each of the 3049 counties in the contiguous US, 100 times per county. When initial infection was located in specific regions, large outbreaks were more likely to occur, driven by infrastructure and other demographic attributes such as premises clustering and number of cattle on premises. Sensitivity analyses suggest these attributes had more impact on outbreak metrics than the ranges of estimated disease parameter values. Additionally, although shipping accounted for a small percentage of overall transmission, areas receiving the most animal shipments tended to have other attributes that increase the probability of large outbreaks. The importance of including spatial and demographic heterogeneity in modelling outbreak trajectories and control actions is illustrated by specific regions consistently producing larger outbreaks than others.

Mutation dynamics of CpG dinucleotides during a recent event of vertebrate diversification.

DNA methylation in CpGs dinucleotides is associated with high mutability and disappearance of CpG sites during evolution. Although the high mutability of CpGs is thought to be relevant for vertebrate evolution, very little is known on the role of CpG-related mutations in the genomic diversification of vertebrates. Our study analysed genetic differences in chickens, between Red Junglefowl (RJF; the living closest relative to the ancestor of domesticated chickens) and domesticated breeds, to identify genomic dynamics that have occurred during the process of their domestication, focusing particularly on CpG-related mutations. Single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) between RJF and these domesticated breeds were assessed in a reduced fraction of their genome. Additionally, DNA methylation in the same fraction of the genome was measured in the sperm of RJF individuals to identify possible correlations with the mutations found between RJF and the domesticated breeds. Our study shows that although the vast majority of CpG-related mutations found relate to CNVs, CpGs disproportionally associate to SNPs in comparison to CNVs, where they are indeed substantially under-represented. Moreover, CpGs seem to be hotspots of mutations related to speciation. We suggest that, on the one hand, CpG-related mutations in CNV regions would promote genomic 'flexibility' in evolution, i.e., the ability of the genome to expand its functional possibilities; on the other hand, CpG-related mutations in SNPs would relate to genomic 'specificity' in evolution, thus, representing mutations that would associate with phenotypic traits relevant for speciation.

Need for speed: An optimized gridding approach for spatially explicit disease simulations.

Numerical models for simulating outbreaks of infectious diseases are powerful tools for informing surveillance and control strategy decisions. However, large-scale spatially explicit models can be limited by the amount of computational resources they require, which poses a problem when multiple scenarios need to be explored to provide policy recommendations. We introduce an easily implemented method that can reduce computation time in a standard Susceptible-Exposed-Infectious-Removed (SEIR) model without introducing any further approximations or truncations. It is based on a hierarchical infection process that operates on entire groups of spatially related nodes (cells in a grid) in order to efficiently filter out large volumes of susceptible nodes that would otherwise have required expensive calculations. After the filtering of the cells, only a subset of the nodes that were originally at risk are then evaluated for actual infection. The increase in efficiency is sensitive to the exact configuration of the grid, and we describe a simple method to find an estimate of the optimal configuration of a given landscape as well as a method to partition the landscape into a grid configuration. To investigate its efficiency, we compare the introduced methods to other algorithms and evaluate computation time, focusing on simulated outbreaks of foot-and-mouth disease (FMD) on the farm population of the USA, the UK and Sweden, as well as on three randomly generated populations with varying degree of clustering. The introduced method provided up to 500 times faster calculations than pairwise computation, and consistently performed as well or better than other available methods. This enables large scale, spatially explicit simulations such as for the entire continental USA without sacrificing realism or predictive power.

Model-guided suggestions for targeted surveillance based on cattle shipments in the U.S.

Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22-34% of imported cattle while surveillance at 50 counties is predicted to sample 43%-61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets - Interstate Certificates of Veterinary Inspection and brand inspection data - to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable.

Water sorption in microfibrillated cellulose (MFC): The effect of temperature and pretreatment.

Water sorption behavior of two different microfibrillated cellulose (MFC) films, produced by delamination of cellulose pulp after different pretreatment methods, is examined at various temperatures (16-65°C) and up to 70% RH. The effect of drying temperature of MFC films on the water uptake is also investigated. The obtained solubility isotherms showed the typical downward curvature at moderate RH, while no upturn is observed at higher RH; the uptakes are in line with characteristic values for cellulose fibers. Enzymatically pretreated MFC dispersion showed lower solubility than carboxymethylated MFC, likely due to the different material structure, which results from the different preparation methods The experimental results are analyzed by Park and GAB models, which proved suitable to describe the observed behaviors. Interestingly, while no significant thermal effect is detected on water solubility above 35°C, the uptake at 16 and 25°C, at a given RH, is substantially lower than that at higher temperature, indicating that, in such range, sorption process is endothermic. Such unusual behavior for a cellulose-based system seems to be related mainly to the structural characteristics of MFC films, and to relaxation phenomena taking place upon water sorption. The diffusion kinetics, indeed, showed a clear Fickian behavior at low temperature and RH, whereas a secondary process seems to occur at high temperature and higher RH, leading to anomalous diffusion behaviors.

Ensemble modelling and structured decision-making to support Emergency Disease Management.

Epidemiological models in animal health are commonly used as decision-support tools to understand the impact of various control actions on infection spread in susceptible populations. Different models contain different assumptions and parameterizations, and policy decisions might be improved by considering outputs from multiple models. However, a transparent decision-support framework to integrate outputs from multiple models is nascent in epidemiology. Ensemble modelling and structured decision-making integrate the outputs of multiple models, compare policy actions and support policy decision-making. We briefly review the epidemiological application of ensemble modelling and structured decision-making and illustrate the potential of these methods using foot and mouth disease (FMD) models. In case study one, we apply structured decision-making to compare five possible control actions across three FMD models and show which control actions and outbreak costs are robustly supported and which are impacted by model uncertainty. In case study two, we develop a methodology for weighting the outputs of different models and show how different weighting schemes may impact the choice of control action. Using these case studies, we broadly illustrate the potential of ensemble modelling and structured decision-making in epidemiology to provide better information for decision-making and outline necessary development of these methods for their further application.

Generating in-Plane Orientational Order in Multilayer Films Prepared by Spray-Assisted Layer-by-Layer Assembly.

We present a simple yet efficient method for orienting cellulose nanofibrils in layer-by-layer assembled films through spray-assisted alignment. While spraying at 90° against a receiving surface produces films with homogeneous in-plane orientation, spraying at smaller angles causes a macroscopic directional surface flow of liquid on the receiving surface and leads to films with substantial in-plane anisotropy when nanoscale objects with anisotropic shapes are used as components. First results with cellulose nanofibrils demonstrate that such fibrils are easily aligned by grazing incidence spraying to yield optically birefringent films over large surface areas. We show that the cellulosic nanofibrils are oriented parallel to the spraying direction and that the orientational order depends for example on the distance of the receiving surface from the spray nozzle. The alignment of the nanofibrils and the in-plane anisotropy of the films were independently confirmed by atomic force microscopy, optical microscopy between crossed polarizers, and the ellipsometric determination of the apparent refractive index of the film as a function of the in-plane rotation of the sample with respect to the plane of incidence of the ellipsometer.

Spatiotemporal Variation in Distance Dependent Animal Movement Contacts: One Size Doesn't Fit All.

The structure of contacts that mediate transmission has a pronounced effect on the outbreak dynamics of infectious disease and simulation models are powerful tools to inform policy decisions. Most simulation models of livestock disease spread rely to some degree on predictions of animal movement between holdings. Typically, movements are more common between nearby farms than between those located far away from each other. Here, we assessed spatiotemporal variation in such distance dependence of animal movement contacts from an epidemiological perspective. We evaluated and compared nine statistical models, applied to Swedish movement data from 2008. The models differed in at what level (if at all), they accounted for regional and/or seasonal heterogeneities in the distance dependence of the contacts. Using a kernel approach to describe how probability of contacts between farms changes with distance, we developed a hierarchical Bayesian framework and estimated parameters by using Markov Chain Monte Carlo techniques. We evaluated models by three different approaches of model selection. First, we used Deviance Information Criterion to evaluate their performance relative to each other. Secondly, we estimated the log predictive posterior distribution, this was also used to evaluate their relative performance. Thirdly, we performed posterior predictive checks by simulating movements with each of the parameterized models and evaluated their ability to recapture relevant summary statistics. Independent of selection criteria, we found that accounting for regional heterogeneity improved model accuracy. We also found that accounting for seasonal heterogeneity was beneficial, in terms of model accuracy, according to two of three methods used for model selection. Our results have important implications for livestock disease spread models where movement is an important risk factor for between farm transmission. We argue that modelers should refrain from using methods to simulate animal movements that assume the same pattern across all regions and seasons without explicitly testing for spatiotemporal variation.

Chronic hypoxia during development does not trigger pathologic remodeling of the chicken embryonic heart but reduces cardiomyocyte number.

Fetal growth restriction programs an increased risk of cardiovascular disease in adulthood, but the actual mechanisms of this developmental programming are not fully understood. Previous studies in mammalian models suggest that hearts of growth-restricted fetuses have reduced cardiomyocyte number due to reduced proliferation and premature cardiomyocyte maturation. Chicken embryos incubated under chronic hypoxia are also growth-restricted, have smaller hearts, and show signs of cardiac insufficiency posthatching. The aim of the present study was to investigate how chronic hypoxia (14% O2) during development affects cardiomyocyte mass and how myocardial structure is altered. Hypoxic incubation reproduced the well-characterized embryonic growth restriction and an increased ventricle-to-body mass ratio (at E11, E15, E17, and E19) with reduced absolute heart mass only at E19. Cell density, apoptosis, and cardiomyocyte size were insensitive to hypoxia at E15 and E19, and no signs of ventricular wall remodeling or myocardial fibrosis were detected. Bayesian modeling provided strong support for hypoxia affecting absolute mass and proliferation rates at E15, indicating that the growth impairment, at least partly, occurs earlier in development. Neither E15 nor E19 hearts contained binucleated cardiomyocytes, indicating that fetal hypoxia does not trigger early maturation of cardiomyocytes in the chicken, which contrasts with previous results from hypoxic rat pups. In conclusion, prenatal hypoxia in the chick embryo results in a reduction in the number of cardiomyocytes without inducing ventricular remodeling, cell hypertrophy, or premature cardiomyocyte maturation.