Enhanced biomethane production from Scenedesmus sp. using polymer harvesting and expired COVID-19 disinfectant for pretreatment.

This study evaluates the repurposing of expired isopropanol (IPA) COVID-19 disinfectant (64% w/w) to pretreat algal biomass for enhancing methane (CH4) yield. The impact of harvesting methods (centrifugation and polymer flocculation) and microwave pretreatment on CH4 production from Scenedesmus sp. microalgal biomass were also investigated. Results show minimal impact of harvesting methods on the CH4 yield, with wet centrifuged and polymer-harvested biomass exhibiting comparable and low CH4 production at 66 and 74 L/kgvolatile solid, respectively. However, microalgae drying significantly increased CH4 yield compared to wet biomass, attributed to cell shrinkage and enhanced digestibility. Consequently, microwave and IPA pretreatment significantly enhanced CH4 production when applied to dried microalgae, yielding a 135% and 212% increase, respectively, compared to non-pretreated wet biomass. These findings underscore the advantage of using dried Scenedesmus sp. over wet biomass and highlight the synergistic effect of combining oven drying with IPA treatment to boost CH4 production whilst reducing COVID-19 waste.

Estimation of spatial demographic maps from polymorphism data using a neural network.

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and source sink dynamics of dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity by descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology, and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN.

Mussel-inspired resilient hydrogels with strong skin adhesion and high-sensitivity for wearable device.

Stretchable and self-adhesive conductive hydrogels hold significant importance across a wide spectrum of applications, including human-machine interfaces, wearable devices, and soft robotics. However, integrating multiple properties, such as high stretchability, strong interfacial adhesion, self-healing capability, and sensitivity, into a single material poses significant technical challenges. Herein, we present a multifunctional conductive hydrogel based on poly(acrylic acid) (PAA), dopamine-functionalized pectin (PT-DA), polydopamine-coated reduction graphene oxide (rGO-PDA), and Fe3+ as an ionic cross-linker. This hydrogel exhibits a combination of high stretchability (2000%), rapid self-healing (~ 94% recovery in 5 s), and robust self-adhesion to various substrates. Notably, the hydrogel demonstrates a remarkable skin adhesion strength of 85 kPa, surpassing previous skin adhesive hydrogels. Furthermore, incorporating rGO within the hydrogel network creates electric pathways, ensuring excellent conductivity (0.56 S m-1). Consequently, these conductive hydrogels exhibit strain-sensing properties with a significant increase in gauge factor (GF) of 14.6, covering an extensive detection range of ~ 1000%, fast response (198 ms) and exceptional cycle stability. These multifunctional hydrogels can be seamlessly integrated into motion detection sensors capable of distinguishing between various strong or subtle movements of the human body.

Large effects and the infinitesimal model.

The infinitesimal model of quantitative genetics relies on the Central Limit Theorem to stipulate that under additive models of quantitative traits determined by many loci having similar effect size, the difference between an offspring's genetic trait component and the average of their two parents' genetic trait components is Normally distributed and independent of the parents' values. Here, we investigate how the assumption of similar effect sizes affects the model: if, alternatively, the tail of the effect size distribution is polynomial with exponent α<2, then a different Central Limit Theorem implies that sums of effects should be well-approximated by a "stable distribution", for which single large effects are often still important. Empirically, we first find tail exponents between 1 and 2 in effect sizes estimated by genome-wide association studies of many human disease-related traits. We then show that the independence of offspring trait deviations from parental averages in many cases implies the Gaussian aspect of the infinitesimal model, suggesting that non-Gaussian models of trait evolution must explicitly track the underlying genetics, at least for loci of large effect. We also characterize possible limiting trait distributions of the infinitesimal model with infinitely divisible noise distributions, and compare our results to simulations.

Evaluating evidence for co-geography in the Anopheles-Plasmodium host-parasite system.

The often tight association between parasites and their hosts means that under certain scenarios, the evolutionary histories of the two species can become closely coupled both through time and across space. Using spatial genetic inference, we identify a potential signal of common dispersal patterns in the Anopheles gambiae and Plasmodium falciparum host-parasite system as seen through a between-species correlation of the differences between geographic sampling location and geographic location predicted from the genome. This correlation may be due to coupled dispersal dynamics between host and parasite but may also reflect statistical artifacts due to uneven spatial distribution of sampling locations. Using continuous-space population genetics simulations, we investigate the degree to which uneven distribution of sampling locations leads to bias in prediction of spatial location from genetic data and implement methods to counter this effect. We demonstrate that while algorithmic bias presents a problem in inference from spatio-genetic data, the correlation structure between A. gambiae and P. falciparum predictions cannot be attributed to spatial bias alone and is thus likely a genetic signal of co-dispersal in a host-parasite system.

Shared evolutionary processes shape landscapes of genomic variation in the great apes.

For at least the past 5 decades, population genetics, as a field, has worked to describe the precise balance of forces that shape patterns of variation in genomes. The problem is challenging because modeling the interactions between evolutionary processes is difficult, and different processes can impact genetic variation in similar ways. In this paper, we describe how diversity and divergence between closely related species change with time, using correlations between landscapes of genetic variation as a tool to understand the interplay between evolutionary processes. We find strong correlations between landscapes of diversity and divergence in a well-sampled set of great ape genomes, and explore how various processes such as incomplete lineage sorting, mutation rate variation, GC-biased gene conversion and selection contribute to these correlations. Through highly realistic, chromosome-scale, forward-in-time simulations, we show that the landscapes of diversity and divergence in the great apes are too well correlated to be explained via strictly neutral processes alone. Our best fitting simulation includes both deleterious and beneficial mutations in functional portions of the genome, in which 9% of fixations within those regions is driven by positive selection. This study provides a framework for modeling genetic variation in closely related species, an approach which can shed light on the complex balance of forces that have shaped genetic variation.

Occurrence, spatiotemporal trends, fate, and treatment technologies for microplastics and organic contaminants in biosolids: A review.

This review provides a comprehensive overview of the occurrence, fate, treatment and multi-criteria analysis of microplastics (MPs) and organic contaminants (OCs) in biosolids. A meta-analysis was complementarily analysed through the literature to map out the occurrence and fate of MPs and 10 different groups of OCs. The data demonstrate that MPs (54.7% occurrence rate) and linear alkylbenzene sulfonate surfactants (44.2% occurrence rate) account for the highest prevalence of contaminants in biosolids. In turn, dioxin, polychlorinated biphenyls (PCBs) and phosphorus flame retardants (PFRs) have the lowest rates (<0.01%). The occurrence of several OCs (e.g., dioxin, per- and polyfluoroalkyl substances, polycyclic aromatic hydrocarbons, pharmaceutical and personal care products, ultraviolet filters, phosphate flame retardants) in Europe appear at higher rates than in Asia and the Americas. However, MP concentrations in biosolids from Australia are reported to be 10 times higher than in America and Europe, which required more measurement data for in-depth analysis. Amongst the OC groups, brominated flame retardants exhibited exceptional sorption to biosolids with partitioning coefficients (log Kd) higher than 4. To remove these contaminants from biosolids, a wide range of technologies have been developed. Our multicriteria analysis shows that anaerobic digestion is the most mature and practical. Thermal treatment is a viable option; however, it still requires additional improvements in infrastructure, legislation, and public acceptance.

Genetic architecture, spatial heterogeneity, and the coevolutionary arms race between newts and snakes.

Coevolution between two species can lead to exaggerated phenotypes that vary in a correlated manner across space. However, the conditions under which we expect such spatially varying coevolutionary patterns in polygenic traits are not well-understood. We investigate the coevolutionary dynamics between two species undergoing reciprocal adaptation across space and time, using simulations inspired by the Taricha newt - Thamnophis garter snake system. One striking observation from this system is that newts in some areas carry much more tetrodotoxin than in other areas, and garter snakes that live near more toxic newts tend to be more resistant to this toxin, a correlation seen across several broad geographic areas. Furthermore, snakes seem to be "winning" the coevolutionary arms race, i.e., having a high level of resistance compared to local newt toxicity, despite substantial variation in both toxicity and resistance across the range. We explore how possible genetic architectures of the toxin and resistance traits would affect the coevolutionary dynamics by manipulating both mutation rate and effect size of mutations across many simulations. We find that coevolutionary dynamics alone were not sufficient in our simulations to produce the striking mosaic of levels of toxicity and resistance observed in nature, but simulations with ecological heterogeneity (in trait costliness or interaction rate) did produce such patterns. We also find that in simulations, newts tend to "win" across most combinations of genetic architectures, although the species with higher mutational genetic variance tends to have an advantage.

Random mutagenesis of Phaeodactylum tricornutum using ultraviolet, chemical, and X-radiation demonstrates the need for temporal analysis of phenotype stability.

We investigated two non-ionising mutagens in the form of ultraviolet radiation (UV) and ethyl methanosulfonate (EMS) and an ionising mutagen (X-ray) as methods to increase fucoxanthin content in the model diatom Phaeodactylum tricornutum. We implemented an ultra-high throughput method using fluorescence-activated cell sorting (FACS) and live culture spectral deconvolution for isolation and screening of potential pigment mutants, and assessed phenotype stability by measuring pigment content over 6 months using high-performance liquid chromatography (HPLC) to investigate the viability of long-term mutants. Both UV and EMS resulted in significantly higher fucoxanthin within the 6 month period after treatment, likely as a result of phenotype instability. A maximum fucoxanthin content of 135 ± 10% wild-type found in the EMS strain, a 35% increase. We found mutants generated using all methods underwent reversion to the wild-type phenotype within a 6 month time period. X-ray treatments produced a consistently unstable phenotype even at the maximum treatment of 1000 Grays, while a UV mutant and an EMS mutant reverted to wild-type after 4 months and 6 months, respectively, despite showing previously higher fucoxanthin than wild-type. This work provides new insights into key areas of microalgal biotechnology, by (i) demonstrating the use of an ionising mutagen (X-ray) on a biotechnologically relevant microalga, and by (ii) introducing temporal analysis of mutants which has substantial implications for strain creation and utility for industrial applications.

Symbiodiniaceae photophysiology and stress resilience is enhanced by microbial associations.

Symbiodiniaceae form associations with extra- and intracellular bacterial symbionts, both in culture and in symbiosis with corals. Bacterial associates can regulate Symbiodiniaceae fitness in terms of growth, calcification and photophysiology. However, the influence of these bacteria on interactive stressors, such as temperature and light, which are known to influence Symbiodiniaceae physiology, remains unclear. Here, we examined the photophysiological response of two Symbiodiniaceae species (Symbiodinium microadriaticum and Breviolum minutum) cultured under acute temperature and light stress with specific bacterial partners from their microbiome (Labrenzia (Roseibium) alexandrii, Marinobacter adhaerens or Muricauda aquimarina). Overall, bacterial presence positively impacted Symbiodiniaceae core photosynthetic health (photosystem II [PSII] quantum yield) and photoprotective capacity (non-photochemical quenching; NPQ) compared to cultures with all extracellular bacteria removed, although specific benefits were variable across Symbiodiniaceae genera and growth phase. Symbiodiniaceae co-cultured with M. aquimarina displayed an inverse NPQ response under high temperatures and light, and those with L. alexandrii demonstrated a lowered threshold for induction of NPQ, potentially through the provision of antioxidant compounds such as zeaxanthin (produced by Muricauda spp.) and dimethylsulfoniopropionate (DMSP; produced by this strain of L. alexandrii). Our co-culture approach empirically demonstrates the benefits bacteria can deliver to Symbiodiniaceae photochemical performance, providing evidence that bacterial associates can play important functional roles for Symbiodiniaceae.

Utilizing proteomics to identify and optimize microalgae strains for high-quality dietary protein: a review.

Algae-derived protein has immense potential to provide high-quality protein foods for the expanding human population. To meet its potential, a broad range of scientific tools are required to identify optimal algal strains from the hundreds of thousands available and identify ideal growing conditions for strains that produce high-quality protein with functional benefits. A research pipeline that includes proteomics can provide a deeper interpretation of microalgal composition and biochemistry in the pursuit of these goals. To date, proteomic investigations have largely focused on pathways that involve lipid production in selected microalgae species. Herein, we report the current state of microalgal proteome measurement and discuss promising approaches for the development of protein-containing food products derived from algae.

link-ancestors: fast simulation of local ancestry with tree sequence software.

Summary: It is challenging to simulate realistic tracts of genetic ancestry on a scale suitable for simulation-based inference. We present an algorithm that enables this information to be extracted efficiently from tree sequences produced by simulations run with msprime and SLiM. Availability and implementation: A C-based implementation of the link-ancestors algorithm is in tskit (https://tskit.dev/tskit/docs/stable/). We also provide a user-friendly wrapper for link-ancestors in tspop, a Python-based utility package.

Exogenous and endogenous dsRNAs perceived by plant Dicer-like 4 protein in the RNAi-depleted cellular context.

BACKGROUND: In plants, RNase III Dicer-like proteins (DCLs) act as sensors of dsRNAs and process them into short 21- to 24-nucleotide (nt) (s)RNAs. Plant DCL4 is involved in the biogenesis of either functional endogenous or exogenous (i.e. viral) short interfering (si)RNAs, thus playing crucial antiviral roles. METHODS: In this study we expressed plant DCL4 in Saccharomyces cerevisiae, an RNAi-depleted organism, in which we could highlight the role of dicing as neither Argonautes nor RNA-dependent RNA polymerase is present. We have therefore tested the DCL4 functionality in processing exogenous dsRNA-like substrates, such as a replicase-assisted viral replicon defective-interfering RNA and RNA hairpin substrates, or endogenous antisense transcripts. RESULTS: DCL4 was shown to be functional in processing dsRNA-like molecules in vitro and in vivo into 21- and 22-nt sRNAs. Conversely, DCL4 did not efficiently process a replicase-assisted viral replicon in vivo, providing evidence that viral RNAs are not accessible to DCL4 in membranes associated in active replication. Worthy of note, in yeast cells expressing DCL4, 21- and 22-nt sRNAs are associated with endogenous loci. CONCLUSIONS: We provide new keys to interpret what was studied so far on antiviral DCL4 in the host system. The results all together confirm the role of sense/antisense RNA-based regulation of gene expression, expanding the sense/antisense atlas of S. cerevisiae. The results described herein show that S. cerevisiae can provide insights into the functionality of plant dicers and extend the S. cerevisiae tool to new biotechnological applications.

Evaluating evidence for co-geography in the Anopheles-Plasmodium host-parasite system.

The often tight association between parasites and their hosts means that under certain scenarios, the evolutionary histories of the two species can become closely coupled both through time and across space. Using spatial genetic inference, we identify a potential signal of common dispersal patterns in the Anopheles gambiae and Plasmodium falciparum host-parasite system as seen through a between-species correlation of the differences between geographic sampling location and geographic location predicted from the genome. This correlation may be due to coupled dispersal dynamics between host and parasite, but may also reflect statistical artifacts due to uneven spatial distribution of sampling locations. Using continuous-space population genetics simulations, we investigate the degree to which uneven distribution of sampling locations leads to bias in prediction of spatial location from genetic data and implement methods to counter this effect. We demonstrate that while algorithmic bias presents a problem in inference from spatio-genetic data, the correlation structure between A. gambiae and P. falciparum predictions cannot be attributed to spatial bias alone, and is thus likely a genetic signal of co-dispersal in a host-parasite system.

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations.

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

Looking forwards and backwards: dynamics and genealogies of locally regulated populations.

We introduce a broad class of mechanistic spatial models to describe how spatially heterogeneous populations live, die, and reproduce. Individuals are represented by points of a point measure, whose birth and death rates can depend both on spatial position and local population density, defined at a location to be the convolution of the point measure with a suitable non-negative integrable kernel centred on that location. We pass to three different scaling limits: an interacting superprocess, a nonlocal partial differential equation (PDE), and a classical PDE. The classical PDE is obtained both by a two-step convergence argument, in which we first scale time and population size and pass to the nonlocal PDE, and then scale the kernel that determines local population density; and in the important special case in which the limit is a reaction-diffusion equation, directly by simultaneously scaling the kernel width, timescale and population size in our individual based model. A novelty of our model is that we explicitly model a juvenile phase. The number of juveniles produced by an individual depends on local population density at the location of the parent; these juvenile offspring are thrown off in a (possibly heterogeneous, anisotropic) Gaussian distribution around the location of the parent; they then reach (instant) maturity with a probability that can depend on the local population density at the location at which they land. Although we only record mature individuals, a trace of this two-step description remains in our population models, resulting in novel limits in which the spatial dynamics are governed by a nonlinear diffusion. Using a lookdown representation, we are able to retain information about genealogies relating individuals in our population and, in the case of deterministic limiting models, we use this to deduce the backwards in time motion of the ancestral lineage of an individual sampled from the population. We observe that knowing the history of the population density is not enough to determine the motion of ancestral lineages in our model. We also investigate (and contrast) the behaviour of lineages for three different deterministic models of a population expanding its range as a travelling wave: the Fisher-KPP equation, the Allen-Cahn equation, and a porous medium equation with logistic growth.

Phenomics: conceptualization and importance for plant physiology.

Phenomics is a relatively new discipline of biology that has been widely applied in several fields, mainly in crop sciences. We reviewed the concepts used in this discipline (particularly for plants) and found a lack of consensus on what defines a phenomic study. Furthermore, phenomics has been primarily developed around its technical aspects (operationalization), while the conceptual framework of the actual research lags behind. Each research group has given its own interpretation of this 'omic' and thus unwittingly created a 'conceptual controversy'. Addressing this issue is of particular importance, as the experimental designs and concepts of phenomics are so diverse that it is difficult to compare studies. In this opinion article, we evaluate the conceptual framework of phenomics.

Dispersal inference from population genetic variation using a convolutional neural network.

The geographic nature of biological dispersal shapes patterns of genetic variation over landscapes, making it possible to infer properties of dispersal from genetic variation data. Here, we present an inference tool that uses geographically distributed genotype data in combination with a convolutional neural network to estimate a critical population parameter: the mean per-generation dispersal distance. Using extensive simulation, we show that our deep learning approach is competitive with or outperforms state-of-the-art methods, particularly at small sample sizes. In addition, we evaluate varying nuisance parameters during training-including population density, demographic history, habitat size, and sampling area-and show that this strategy is effective for estimating dispersal distance when other model parameters are unknown. Whereas competing methods depend on information about local population density or accurate inference of identity-by-descent tracts, our method uses only single-nucleotide-polymorphism data and the spatial scale of sampling as input. Strikingly, and unlike other methods, our method does not use the geographic coordinates of the genotyped individuals. These features make our method, which we call "disperseNN," a potentially valuable new tool for estimating dispersal distance in nonmodel systems with whole genome data or reduced representation data. We apply disperseNN to 12 different species with publicly available data, yielding reasonable estimates for most species. Importantly, our method estimated consistently larger dispersal distances than mark-recapture calculations in the same species, which may be due to the limited geographic sampling area covered by some mark-recapture studies. Thus genetic tools like ours complement direct methods for improving our understanding of dispersal.

Save the planet with green industries using algae.

We can use photosynthesis to capture carbon and make industries greener. Algae-driven carbon capture and manufacturing offer the potential for reducing CO2 emissions while also producing commodities such as bioplastics.

Shared evolutionary processes shape landscapes of genomic variation in the great apes.

For at least the past five decades population genetics, as a field, has worked to describe the precise balance of forces that shape patterns of variation in genomes. The problem is challenging because modelling the interactions between evolutionary processes is difficult, and different processes can impact genetic variation in similar ways. In this paper, we describe how diversity and divergence between closely related species change with time, using correlations between landscapes of genetic variation as a tool to understand the interplay between evolutionary processes. We find strong correlations between landscapes of diversity and divergence in a well sampled set of great ape genomes, and explore how various processes such as incomplete lineage sorting, mutation rate variation, GC-biased gene conversion and selection contribute to these correlations. Through highly realistic, chromosome-scale, forward-in-time simulations we show that the landscapes of diversity and divergence in the great apes are too well correlated to be explained via strictly neutral processes alone. Our best fitting simulation includes both deleterious and beneficial mutations in functional portions of the genome, in which 9% of fixations within those regions is driven by positive selection. This study provides a framework for modelling genetic variation in closely related species, an approach which can shed light on the complex balance of forces that have shaped genetic variation.