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.

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.

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.

A Bayesian ensemble approach for epidemiological projections.

Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, ensemble modeling has been used to combine multiple projections. We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting. We exemplify the implementation with single model ensembles based on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions. This allows us to investigate the effect that discrepancy among projections based on different modeling assumptions has on the ensemble prediction. A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for ensembles with large discrepancy among projections. However, by using a hierarchical extension of the method we show that prior sensitivity can be circumvented. We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections. We propose that the method is a promising analytical tool for ensemble modeling of disease outbreaks.

Rapid shifts in dispersal behavior on an expanding range edge.

Dispersal biology at an invasion front differs from that of populations within the range core, because novel evolutionary and ecological processes come into play in the nonequilibrium conditions at expanding range edges. In a world where species' range limits are changing rapidly, we need to understand how individuals disperse at an invasion front. We analyzed an extensive dataset from radio-tracking invasive cane toads (Rhinella marina) over the first 8 y since they arrived at a site in tropical Australia. Movement patterns of toads in the invasion vanguard differed from those of individuals in the same area postcolonization. Our model discriminated encamped versus dispersive phases within each toad's movements and demonstrated that pioneer toads spent longer periods in dispersive mode and displayed longer, more directed movements while they were in dispersive mode. These analyses predict that overall displacement per year is more than twice as far for toads at the invasion front compared with those tracked a few years later at the same site. Studies on established populations (or even those a few years postestablishment) thus may massively underestimate dispersal rates at the leading edge of an expanding population. This, in turn, will cause us to underpredict the rates at which invasive organisms move into new territory and at which native taxa can expand into newly available habitat under climate change.

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.

Surface Chemistry of Nanocellulose Fibers Directs Monocyte/Macrophage Response.

The effect of surface functionalization of nanofibrillated cellulose (NFC) on monocyte/macrophage (MM) behavior is investigated to understand how the physicochemical properties of nanocelluloses influence the interactions of such materials with biological systems. Films of anionic (a-), cationic (c-), and unmodified (u-) NFC were synthesized and characterized in terms of surface charge. THP-1 monocytes were cultured on the surface of the films for 24 h in the presence and absence of lipopolysaccharide, and the cell response was evaluated in terms of cell adhesion, morphology, and secretion of TNF-α, IL-10, and IL-1ra. The results show that MMs cultured on carboxymethylated-NFC films (a-NFC) are activated toward a proinflammatory phenotype, whereas u-NFC promotes a mild activation of the studied cells. The presence of hydroxypropyltrimethylammonium groups on c-NFC, however, does not promote the activation of MMs, indicating that c-NFC closely behaves as an inert material in terms of MM activation. None of the materials is able to directly activate the MMs toward an anti-inflammatory response. These results may provide a foundation for the design of future NFC-based materials with the ability to control MM activation and may expand the use of NFC in biomedical applications.

Egg incubation effects generate positive correlations between size, speed and learning ability in young lizards.

Previous studies have suggested that body size and locomotor performance are targets of Darwinian selection in reptiles. However, much of the variation in these traits may derive from phenotypically plastic responses to incubation temperature, rather than from underlying genetic variation. Intriguingly, incubation temperature may also influence cognitive traits such as learning ability. Therefore, we might expect correlations between a reptile's size, locomotor speed and learning ability either due to selection on all of these traits or due to environmental effects during egg incubation. In the present study, we incubated lizard eggs (Scincidae: Bassiana duperreyi) under 'hot' and 'cold' thermal regimes and then assessed differences in hatchling body size, running speed and learning ability. We measured learning ability using a Y-maze and a food reward. We found high correlations between size, speed and learning ability, using two different metrics to quantify learning (time to solution, and directness of route), and showed that environmental effects (incubation temperature) cause these correlations. If widespread, such correlations challenge any simple interpretation of fitness advantages due to body size or speed within a population; for example, survivors may be larger and faster than nonsurvivors because of differences in learning ability, not because of their size or speed.

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.

Effective Young's modulus of bacterial and microfibrillated cellulose fibrils in fibrous networks.

The deformation micromechanics of bacterial cellulose (BC) and microfibrillated cellulose (MFC) networks have been investigated using Raman spectroscopy. The Raman spectra of both BC and MFC networks exhibit a band initially located at ≈ 1095 cm(-1). We have used the intensity of this band as a function of rotation angle of the specimens to study the cellulose fibril orientation in BC and MFC networks. We have also used the change in this peak's wavenumber position with applied tensile deformation to probe the stress-transfer behavior of these cellulosic materials. The intensity of this Raman band did not change significantly with rotation angle, indicating an in-plane 2D network of fibrils with uniform random orientation; conversely, a highly oriented flax fiber exhibited a marked change in intensity with rotation angle. Experimental data and theoretical analysis shows that the Raman band shift rate arising from deformation of networks under tension is dependent on the angles between the axis of fibrils, the strain axis, the incident laser polarization direction, and the back scattered polarization configurations. From this analysis, the effective moduli of single fibrils of BC and MFC in the networks were estimated to be in the ranges of 79-88 and 29-36 GPa, respectively. It is shown also that for the model to fit the data it is necessary to use a negative Poisson's ratio for MFC networks and BC networks. Discussion of this in-plane "auxetic" behavior is given.

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.

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.

The shape of the spatial kernel and its implications for biological invasions in patchy environments.

Ecological and epidemiological invasions occur in a spatial context. We investigated how these processes correlate to the distance dependence of spread or dispersal between spatial entities such as habitat patches or epidemiological units. Distance dependence is described by a spatial kernel, characterized by its shape (kurtosis) and width (variance). We also developed a novel method to analyse and generate point-pattern landscapes based on spectral representation. This involves two measures: continuity, which is related to autocorrelation and contrast, which refers to variation in patch density. We also analysed some empirical data where our results are expected to have implications, namely distributions of trees (Quercus and Ulmus) and farms in Sweden. Through a simulation study, we found that kernel shape was not important for predicting the invasion speed in randomly distributed patches. However, the shape may be essential when the distribution of patches deviates from randomness, particularly when the contrast is high. We conclude that the speed of invasions depends on the spatial context and the effect of the spatial kernel is intertwined with the spatial structure. This implies substantial demands on the empirical data, because it requires knowledge of shape and width of the spatial kernel, and spatial structure.

Nanocelluloses: a new family of nature-based materials.

Cellulose fibrils with widths in the nanometer range are nature-based materials with unique and potentially useful features. Most importantly, these novel nanocelluloses open up the strongly expanding fields of sustainable materials and nanocomposites, as well as medical and life-science devices, to the natural polymer cellulose. The nanodimensions of the structural elements result in a high surface area and hence the powerful interaction of these celluloses with surrounding species, such as water, organic and polymeric compounds, nanoparticles, and living cells. This Review assembles the current knowledge on the isolation of microfibrillated cellulose from wood and its application in nanocomposites; the preparation of nanocrystalline cellulose and its use as a reinforcing agent; and the biofabrication of bacterial nanocellulose, as well as its evaluation as a biomaterial for medical implants.

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.

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.

Self-organized films from cellulose I Nanofibrils using the layer-by-layer technique.

The possibility of forming self-organized films using only charge-stabilized dispersions of cellulose I nanofibrils with opposite charges is presented, that is, the multilayers were composed solely of anionically and cationically modified microfibrillated cellulose (MFC) with a low degree of substitution. The build-up behavior and the properties of the layer-by-layer (LbL)-constructed films were studied using a quartz crystal microbalance with dissipation (QCM-D) and stagnation point adsorption reflectometry (SPAR). The adsorption behavior of cationic/anionic MFC was compared with that of polyethyleneimine (PEI)/anionic MFC. The water contents of five bilayers of cationic/anionic MFC and PEI/anionic MFC were approximately 70 and 50%, respectively. The MFC surface coverage was studied by atomic force microscopy (AFM) measurements, which clearly showed a more dense fibrillar structure in the five bilayer PEI/anionic MFC than in the five bilayer cationic/anionic MFC. The forces between the cellulose-based multilayers were examined using the AFM colloidal probe technique. The forces on approach were characterized by a combination of electrostatic and steric repulsion. The wet adhesive forces were very long-range and were characterized by multiple adhesive events. Surfaces covered by PEI/anionic MFC multilayers required more energy to be separated than surfaces covered by cationic/anionic MFC multilayers.

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.