How strongly does diet variation explain variation in isotope values of animal consumers?

Analysis of stable isotopes in consumers is used commonly to study their ecological and/or environmental niche. There is, however, considerable debate regarding how isotopic values relate to diet and how other sources of variation confound this link, which can undermine the utility. From the analysis of a simple, but general, model of isotopic incorporation in consumer organisms, we examine the relationship between isotopic variance among individuals, and diet variability within a consumer population. We show that variance in consumer isotope values is directly proportional to variation in diet (through Simpson indices), to the number of isotopically distinct food sources in the diet, and to the baseline variation within and among the isotope values of the food sources. Additionally, when considering temporal diet variation within a consumer we identify the interplay between diet turnover rates and tissue turnover rates that controls the sensitivity of stable isotopes to detect diet variation. Our work demonstrates that variation in the stable isotope values of consumers reflect variation in their diet. This relationship, however, can be confounded with other factors to the extent that they may mask the signal coming from diet. We show how simple quantitative corrections can recover a direct 1:1 correlation in some situations, and in others we can adjust our interpretation in light of the new understanding arising from our models. Our framework provides guidance for the design and analysis of empirical studies where the goal is to infer niche width from stable isotope data.

Machine Learning Applied to the Detection of Mycotoxin in Food: A Systematic Review.

Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and animals. Traditional lab analysis methods for mycotoxin detection can be time-consuming and may not always be suitable for large-scale screenings. However, in recent years, machine learning (ML) methods have gained popularity for use in the detection of mycotoxins and in the food safety industry in general due to their accurate and timely predictions. We provide a systematic review on some of the recent ML applications for detecting/predicting the presence of mycotoxin on a variety of food ingredients, highlighting their advantages, challenges, and potential for future advancements. We address the need for reproducibility and transparency in ML research through open access to data and code. An observation from our findings is the frequent lack of detailed reporting on hyperparameters in many studies and a lack of open source code, which raises concerns about the reproducibility and optimisation of the ML models used. The findings reveal that while the majority of studies predominantly utilised neural networks for mycotoxin detection, there was a notable diversity in the types of neural network architectures employed, with convolutional neural networks being the most popular.

The actin cytoskeleton plays multiple roles in structural colour formation in butterfly wing scales.

Vivid structural colours in butterflies are caused by photonic nanostructures scattering light. Structural colours evolved for numerous biological signalling functions and have important technological applications. Optically, such structures are well understood, however insight into their development in vivo remains scarce. We show that actin is intimately involved in structural colour formation in butterfly wing scales. Using comparisons between iridescent (structurally coloured) and non-iridescent scales in adult and developing H. sara, we show that iridescent scales have more densely packed actin bundles leading to an increased density of reflective ridges. Super-resolution microscopy across three distantly related butterfly species reveals that actin is repeatedly re-arranged during scale development and crucially when the optical nanostructures are forming. Furthermore, actin perturbation experiments at these later developmental stages resulted in near total loss of structural colour in H. sara. Overall, this shows that actin plays a vital and direct templating role during structural colour formation in butterfly scales, providing ridge patterning mechanisms that are likely universal across lepidoptera.

The Nanoscale Structure and Stability of Organic Photovoltaic Blends Processed with Solvent Additives.

Controlling the nanomorphology in bulk heterojunction photoactive blends is crucial for optimizing the performance and stability of organic photovoltaic (OPV) technologies. A promising approach is to alter the drying dynamics and consequently, the nanostructure of the blend film using solvent additives such as 1,8-diiodooctane (DIO). Although this approach is demonstrated extensively for OPV systems incorporating fullerene-based acceptors, it is unclear how solvent additive processing influences the morphology and stability of nonfullerene acceptor (NFA) systems. Here, small angle neutron scattering (SANS) is used to probe the nanomorphology of two model OPV systems processed with DIO: a fullerene-based system (PBDB-T:PC71BM) and an NFA-based system (PBDB-T:ITIC). To overcome the low intrinsic neutron scattering length density contrast in polymer:NFA blend films, the synthesis of a deuterated NFA analog (ITIC-d52) is reported. Using SANS, new insights into the nanoscale evolution of fullerene and NFA-based systems are provided by characterizing films immediately after fabrication, after thermal annealing, and after aging for 1 year. It is found that DIO processing influences fullerene and NFA-based systems differently with NFA-based systems characterized by more phase-separated domains. After long-term aging, SANS reveals both systems demonstrate some level of thermodynamic induced domain coarsening.

Synthesis and Characterization of Stimuli-Responsive Polymer Brushes in Nanofluidic Channels.

Nanochannels with controllable gating behavior are attractive features in a wide range of nanofluidic applications including viral detection, particle sorting, and flow regulation. Here, we use selective sidewall functionalization of nanochannels with a polyelectrolyte brush to investigate the channel gating response to variations in solution pH and ionic strength. The conformational and structural changes of the interfacial brush layer within the channels are interrogated by specular and off-specular neutron reflectometry. Simultaneous fits of the specular and off-specular signals, using a dynamical theory model and a fitting optimization protocol, enable detailed characterization of the brush conformations and corresponding channel geometry under different solution conditions. Our results indicate a collapsed brush state under basic pH, equivalent to an open gate, and an expanded brush state representing a partially closed gate upon decreasing the pH and salt concentration. These findings open new possibilities in noninvasive in situ characterization of tunable nanofluidics and lab-on-chip devices with advanced designs and improved functionality.

Tissue Equivalent Curved Organic X-ray Detectors Utilizing High Atomic Number Polythiophene Analogues.

Organic semiconductors are a promising material candidate for X-ray detection. However, the low atomic number (Z) of organic semiconductors leads to poor X-ray absorption thus restricting their performance. Herein, the authors propose a new strategy for achieving high-sensitivity performance for X-ray detectors based on organic semiconductors modified with high -Z heteroatoms. X-ray detectors are fabricated with p-type organic semiconductors containing selenium heteroatoms (poly(3-hexyl)selenophene (P3HSe)) in blends with an n-type fullerene derivative ([6,6]-Phenyl C71 butyric acid methyl ester (PC70 BM). When characterized under 70, 100, 150, and 220 kVp X-ray radiation, these heteroatom-containing detectors displayed a superior performance in terms of sensitivity up to 600 ± 11 nC Gy-1  cm-2 with respect to the bismuth oxide (Bi2 O3 ) nanoparticle (NP) sensitized organic detectors. Despite the lower Z of selenium compared to the NPs typically used, the authors identify a more efficient generation of electron-hole pairs, better charge transfer, and charge transport characteristics in heteroatom-incorporated detectors that result in this breakthrough detector performance. The authors also demonstrate flexible X-ray detectors that can be curved to a radius as low as 2 mm with low deviation in X-ray response under 100 repeated bending cycles while maintaining an industry-standard ultra-low dark current of 0.03 ± 0.01 pA mm-2 .

Optimal age-specific vaccination control for COVID-19: An Irish case study.

The outbreak of a novel coronavirus causing severe acute respiratory syndrome in December 2019 has escalated into a worldwide pandemic. In this work, we propose a compartmental model to describe the dynamics of transmission of infection and use it to obtain the optimal vaccination control. The model accounts for the various stages of the vaccination, and the optimisation is focused on minimising the infections to protect the population and relieve the healthcare system. As a case study, we selected the Republic of Ireland. We use data provided by Ireland's COVID-19 Data-Hub and simulate the evolution of the pandemic with and without the vaccination in place for two different scenarios, one representative of a national lockdown situation and the other indicating looser restrictions in place. One of the main findings of our work is that the optimal approach would involve a vaccination programme where the older population is vaccinated in larger numbers earlier while simultaneously part of the younger population also gets vaccinated to lower the risk of transmission between groups. We compare our simulated results with those of the vaccination policy taken by the Irish government to explore the advantages of our optimisation method. Our comparison suggests that a similar reduction in cases may have been possible even with a reduced set of vaccinations available for use.

Indian Ornamental Tarantula (Poecilotheria regalis) Venom Affects Myoblast Function and Causes Skeletal Muscle Damage.

Envenomation by the Indian ornamental tarantula (Poecilotheria regalis) is medically relevant to humans, both in its native India and worldwide, where they are kept as pets. Muscle-related symptoms such as cramps and pain are commonly reported in humans following envenomation by this species. There is no specific treatment, including antivenom, for its envenomation. Moreover, the scientific knowledge of the impact of this venom on skeletal muscle function is highly limited. Therefore, we carried out this study to better understand the myotoxic properties of Poecilotheria regalis venom by determining its effects in cultured myoblasts and in the tibialis anterior muscle in mice. While there was no effect found on undifferentiated myoblasts, the venom affected differentiated multinucleated myotubes resulting in the reduction of fusion and atrophy of myotubes. Similarly, intramuscular administration of this venom in the tibialis anterior muscle in mice resulted in extensive muscle damage on day 5. However, by day 10, the regeneration was evident, and the regeneration process continued until day 20. Nevertheless, some tissue abnormalities including reduced dystrophin expression and microthrombi presence were observed on day 20. Overall, this study demonstrates the ability of this venom to induce significant muscle damage and affect its regeneration in the early stages. These data provide novel mechanistic insights into this venom-induced muscle damage and guide future studies to isolate and characterise individual toxic component(s) that induce muscle damage and their significance in developing better therapeutics.

NEAL: an open-source tool for audio annotation.

Passive acoustic monitoring is used widely in ecology, biodiversity, and conservation studies. Data sets collected via acoustic monitoring are often extremely large and built to be processed automatically using artificial intelligence and machine learning models, which aim to replicate the work of domain experts. These models, being supervised learning algorithms, need to be trained on high quality annotations produced by experts. Since the experts are often resource-limited, a cost-effective process for annotating audio is needed to get maximal use out of the data. We present an open-source interactive audio data annotation tool, NEAL (Nature+Energy Audio Labeller). Built using R and the associated Shiny framework, the tool provides a reactive environment where users can quickly annotate audio files and adjust settings that automatically change the corresponding elements of the user interface. The app has been designed with the goal of having both expert birders and citizen scientists contribute to acoustic annotation projects. The popularity and flexibility of R programming in bioacoustics means that the Shiny app can be modified for other bird labelling data sets, or even to generic audio labelling tasks. We demonstrate the app by labelling data collected from wind farm sites across Ireland.

Designing Effective Antimicrobial Nanostructured Surfaces: Highlighting the Lack of Consensus in the Literature.

Research into nanostructured materials, inspired by the topography of certain insect wings, has provided a potential pathway toward drug-free antibacterial surfaces, which may be vital in the ongoing battle against antimicrobial resistance. However, to produce viable antibacterial nanostructured surfaces, we must first understand the bactericidal mechanism of action and how to optimize them to kill the widest range of microorganisms. This review discusses the parameters of nanostructured surfaces that have been shown to influence their bactericidal efficiency and highlights the highly variable nature of many of the findings. A large-scale analysis of the literature is also presented, which further shows a lack of clarity in what is understood about the factors influencing bactericidal efficiency. The potential reasons for the ambiguity, including how the killing effect may be a result of multiple factors and issues with nonstandardized testing of the antibacterial properties of nanostructured surfaces, are then discussed. Finally, a standard method for testing of antimicrobial killing is proposed that will allow comparison between studies and enable a deeper understanding about nanostructured surfaces and how to optimize their bactericidal efficiency.

The influence of ambient cure chemistry and stoichiometry on epoxy coating surfaces.

The surface properties of epoxy resin coatings influence their function as substrates for subsequent coats. Variation in ambient cure conditions (temperature and relative humidity, RH), stoichiometry (ratio of epoxy: amine) and delay time between epoxy component mixing and film casting ("induction time") significantly altered the surface properties of ambient cured epoxy resin coatings (Dow Epoxy Novolac D.E.N. 431, resorcinol diglycidyl ether and 4,4-diaminodicyclohexylmethane). Gravimetric analysis showed that increasing induction time significantly reduced surface layer formation (carbamation) of cured epoxy resin coatings at 80% RH but had no measurable effect at 40% RH and below. RMS surface roughness increased with increasing RH and decreased with increasing induction time and ambient cure temperature, at two stoichiometric extremes. However, the net change in surface area arising from these conditions was not sufficient to significantly alter the equilibrium contact angles or wetting regime. We conclude that the observed significant variation in surface wettability was more likely to depend on variation in surface chemistry than roughness; stoichiometry was the variable which most significantly influenced surface wettability, average void volume and fractional free volume, while cure temperature significantly influenced the extent of cure at both stoichiometries. Off-stoichiometry formulation and elevated ambient cure temperature significantly increased system average void volume while fractional free volume decreased, which may be significant for the barrier properties of the final coating.

Exploring Nanoscale Structure in Perovskite Precursor Solutions Using Neutron and Light Scattering.

Tailoring the solution chemistry of metal halide perovskites requires a detailed understanding of precursor aggregation and coordination. In this work, we use various scattering techniques, including dynamic light scattering (DLS), small angle neutron scattering (SANS), and spin-echo SANS (SESANS) to probe the nanostructures from 1 nm to 10 µm within two different lead-halide perovskite solution inks (MAPbI3 and a triple-cation mixed-halide perovskite). We find that DLS can misrepresent the size distribution of the colloidal dispersion and use SANS/SESANS to confirm that these perovskite solutions are mostly comprised of 1-2 nm-sized particles. We further conclude that if there are larger colloids present, their concentration must be <0.005% of the total dispersion volume. With SANS, we apply a simple fitting model for two component microemulsions (Teubner-Strey), demonstrating this as a potential method to investigate the structure, chemical composition, and colloidal stability of perovskite solutions, and we here show that MAPbI3 solutions age more drastically than triple cation solutions.

The genetic basis of structural colour variation in mimetic Heliconius butterflies.

Structural colours, produced by the reflection of light from ultrastructures, have evolved multiple times in butterflies. Unlike pigmentary colours and patterns, little is known about the genetic basis of these colours. Reflective structures on wing-scale ridges are responsible for iridescent structural colour in many butterflies, including the Müllerian mimics Heliconius erato and Heliconius melpomene. Here, we quantify aspects of scale ultrastructure variation and colour in crosses between iridescent and non-iridescent subspecies of both of these species and perform quantitative trait locus (QTL) mapping. We show that iridescent structural colour has a complex genetic basis in both species, with offspring from crosses having a wide variation in blue colour (both hue and brightness) and scale structure measurements. We detect two different genomic regions in each species that explain modest amounts of this variation, with a sex-linked QTL in H. erato but not H. melpomene. We also find differences between species in the relationships between structure and colour, overall suggesting that these species have followed different evolutionary trajectories in their evolution of structural colour. We then identify genes within the QTL intervals that are differentially expressed between subspecies and/or wing regions, revealing likely candidates for genes controlling structural colour formation. This article is part of the theme issue 'Genetic basis of adaptation and speciation: from loci to causative mutations'.

Inhibition of activin A receptor signalling attenuates age-related pathological cardiac remodelling.

In the heart, ageing is associated with DNA damage, oxidative stress, fibrosis and activation of the activin signalling pathway, leading to cardiac dysfunction. The cardiac effects of activin signalling blockade in progeria are unknown. This study investigated the cardiac effects of progeria induced by attenuated levels of Ercc1, which is required for DNA excision and repair, and the impact of activin signalling blockade using a soluble activin receptor type IIB (sActRIIB). DNA damage and oxidative stress were significantly increased in Ercc1Δ/- hearts, but were reduced by sActRIIB treatment. sActRIIB treatment improved cardiac systolic function and induced cardiomyocyte hypertrophy in Ercc1Δ/- hearts. RNA-sequencing analysis showed that in Ercc1Δ/- hearts, there was an increase in pro-oxidant and a decrease in antioxidant gene expression, whereas sActRIIB treatment reversed this effect. Ercc1Δ/- hearts also expressed higher levels of anti-hypertrophic genes and decreased levels of pro-hypertrophic ones, which were also reversed by sActRIIB treatment. These results show for the first time that inhibition of activin A receptor signalling attenuates cardiac dysfunction, pathological tissue remodelling and gene expression in Ercc1-deficient mice and presents a potentially novel therapeutic target for heart diseases.

Inferences to estimate consumer's diet using stable isotopes: Insights from a dynamic mixing model.

Stable isotope ratios are used to reconstruct animal diet in trophic ecology via mixing models. Several assumptions of stable isotope mixing models are critical, i.e., constant trophic discrimination factor and isotopic equilibrium between the consumer and its diet. The isotopic turnover rate (λ and its counterpart the half-life) affects the dynamics of isotopic incorporation for an organism and the isotopic equilibrium assumption: λ involves a time lag between the real assimilated diet and the diet estimated by mixing models at the individual scale. Current stable isotope mixing model studies consider neither this time lag nor even the dynamics of isotopic ratios in general. We developed a mechanistic framework using a dynamic mixing model (DMM) to assess the contribution of λ to the dynamics of isotopic incorporation and to estimate the bias induced by neglecting the time lag in diet reconstruction in conventional static mixing models (SMMs). The DMM includes isotope dynamics of sources (denoted δs), λ and frequency of diet-switch (ω). The results showed a significant bias generated by the SMM compared to the DMM (up to 50% of differences). This bias can be strongly reduced in SMMs by averaging the isotopic variations of the food sources over a time window equal to twice the isotopic half-life. However, the bias will persist (∼15%) for intermediate values of the ω/λ ratio. The inferences generated using a case study highlighted that DMM enhanced estimates of consumer's diet, and this could avoid misinterpretation in ecosystem functioning, food-web structure analysis and underlying biological processes.

Machine learning denoising of high-resolution X-ray nanotomography data.

High-resolution X-ray nanotomography is a quantitative tool for investigating specimens from a wide range of research areas. However, the quality of the reconstructed tomogram is often obscured by noise and therefore not suitable for automatic segmentation. Filtering methods are often required for a detailed quantitative analysis. However, most filters induce blurring in the reconstructed tomograms. Here, machine learning (ML) techniques offer a powerful alternative to conventional filtering methods. In this article, we verify that a self-supervised denoising ML technique can be used in a very efficient way for eliminating noise from nanotomography data. The technique presented is applied to high-resolution nanotomography data and compared to conventional filters, such as a median filter and a nonlocal means filter, optimized for tomographic data sets. The ML approach proves to be a very powerful tool that outperforms conventional filters by eliminating noise without blurring relevant structural features, thus enabling efficient quantitative analysis in different scientific fields.

Identification and Evaluation of Serum Protein Biomarkers That Differentiate Psoriatic Arthritis From Rheumatoid Arthritis.

OBJECTIVE: To identify serum protein biomarkers that might distinguish patients with early inflammatory arthritis (IA) with psoriatic arthritis (PsA) from those with rheumatoid arthritis (RA) and may be used to support appropriate early intervention. METHODS: The serum proteome of patients with PsA and patients with RA was interrogated using nano-liquid chromatography mass spectrometry (nano-LC-MS/MS) (n = 64 patients), an aptamer-based assay (SomaScan) targeting 1,129 proteins (n = 36 patients), and a multiplexed antibody assay (Luminex) for 48 proteins (n = 64 patients). Multiple reaction monitoring (MRM) assays were developed to evaluate the performance of putative markers using the discovery cohort (n = 60 patients) and subsequently an independent cohort of PsA and RA patients (n = 167). RESULTS: Multivariate machine learning analysis of the protein discovery data from the 3 platforms revealed that it was possible to differentiate PsA patients from RA patients with an area under the curve (AUC) of 0.94 for nano-LC-MS/MS, 0.69 for bead-based immunoassay measurements, and 0.73 for aptamer-based analysis. Subsequently, in the separate verification and evaluation studies, random forest models revealed that a subset of proteins measured by MRM could differentiate PsA and RA patients with AUCs of 0.79 and 0.85, respectively. CONCLUSION: We present a serum protein biomarker panel that can separate patients with early-onset IA with PsA from those with RA. With continued evaluation and refinement using additional and larger patient cohorts, including those with other arthropathies, we suggest that the panel identified here could contribute to improved clinical decision making.

Molecular Weight Tuning of Organic Semiconductors for Curved Organic-Inorganic Hybrid X-Ray Detectors.

Curved X-ray detectors have the potential to revolutionize diverse sectors due to benefits such as reduced image distortion and vignetting compared to their planar counterparts. While the use of inorganic semiconductors for curved detectors are restricted by their brittle nature, organic-inorganic hybrid semiconductors which incorporated bismuth oxide nanoparticles in an organic bulk heterojunction consisting of poly(3-hexylthiophene-2,5-diyl) (P3HT) and [6,6]-phenyl C71 butyric acid methyl ester (PC70 BM) are considered to be more promising in this regard. However, the influence of the P3HT molecular weight on the mechanical stability of curved, thick X-ray detectors remains less well understood. Herein, high P3HT molecular weights (>40 kDa) are identified to allow increased intermolecular bonding and chain entanglements, resulting in X-ray detectors that can be curved to a radius as low as 1.3 mm with low deviation in X-ray response under 100 repeated bending cycles while maintaining an industry-standard dark current of <1 pA mm-2 and a sensitivity of ≈ 0.17 µC Gy-1 cm-2 . This study identifies a crucial missing link in the development of curved detectors, namely the importance of the molecular weight of the polymer semiconductors used.

Graded Morphologies and the Performance of PffBT4T-2OD:PC71BM Devices Using Additive Choice.

The impact of several solvent processing additives (1-chloronaphthalene, methylnaphthalene, hexadecane, 1-phenyloctane, and p-anisaldehyde), 3% v/v in o-dichlorobenzene, on the performance and morphology of poly[(5,6-difluoro-2,1,3-benzothiadiazol-4,7-diyl)-alt-(3,3‴-di(2-octyldodecyl)-2,2',5',22033,5″,2‴-quaterthiophen-5,5‴-diyl)] (PffBT4T-2OD):[6,6]-phenyl-C71-butyric acid methyl ester (PC71BM)-based polymer solar cells was investigated. Some additives were shown to enhance the power conversion efficiency (PCE) by ~6%, while others decreased the PCE by ~17-25% and a subset of the additives tested completely eliminated any power conversion efficiency and the operation as a photovoltaic device. Grazing-Incidence Wide Angle X-ray Scattering (GIWAXS) revealed a clear stepwise variation in the crystallinity of the systems when changing the additive between the two extreme situations of maximum PCE (1-chloronaphthalene) and null PCE (hexadecane). Small-Angle Neutron Scattering (SANS) revealed that the morphology of devices with PCE ~0% was composed of large domains with correlation lengths of ~30 nm, i.e., much larger than the typical exciton diffusion length (~12 nm) in organic semiconductors. The graded variations in crystallinity and in nano-domain size observed between the two extreme situations (1-chloronaphthalene and hexadecane) were responsible for the observed graded variations in device performance.