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.

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.

Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.

The epidemic increase in the incidence of Human Papilloma Virus (HPV) related Oropharyngeal Squamous Cell Carcinomas (OPSCCs) in several countries worldwide represents a significant public health concern. Although gender neutral HPV vaccination programmes are expected to cause a reduction in the incidence rates of OPSCCs, these effects will not be evident in the foreseeable future. Secondary prevention strategies are currently not feasible due to an incomplete understanding of the natural history of oral HPV infections in OPSCCs. The key parameters that govern natural history models remain largely ill-defined for HPV related OPSCCs and cannot be easily inferred from experimental data. Mathematical models have been used to estimate some of these ill-defined parameters in cervical cancer, another HPV related cancer leading to successful implementation of cancer prevention strategies. We outline a "double-Bayesian" mathematical modelling approach, whereby, a Bayesian machine learning model first estimates the probability of an individual having an oral HPV infection, given OPSCC and other covariate information. The model is then inverted using Bayes' theorem to reverse the probability relationship. We use data from the Surveillance, Epidemiology, and End Results (SEER) cancer registry, SEER Head and Neck with HPV Database and the National Health and Nutrition Examination Surveys (NHANES), representing the adult population in the United States to derive our model. The model contains 8,106 OPSCC patients of which 73.0% had an oral HPV infection. When stratified by age, sex, marital status and race/ethnicity, the model estimated a higher conditional probability for developing OPSCCs given an oral HPV infection in non-Hispanic White males and females compared to other races/ethnicities. The proposed Bayesian model represents a proof-of-concept of a natural history model of HPV driven OPSCCs and outlines a strategy for estimating the conditional probability of an individual's risk of developing OPSCC following an oral HPV infection.

Genome-wide association analyses of carcass traits using copy number variants and raw intensity values of single nucleotide polymorphisms in cattle.

BACKGROUND: The carcass value of cattle is a function of carcass weight and quality. Given the economic importance of carcass merit to producers, it is routinely included in beef breeding objectives. A detailed understanding of the genetic variants that contribute to carcass merit is useful to maximize the efficiency of breeding for improved carcass merit. The objectives of the present study were two-fold: firstly, to perform genome-wide association analyses of carcass weight, carcass conformation, and carcass fat using copy number variant (CNV) data in a population of 923 Holstein-Friesian, 945 Charolais, and 974 Limousin bulls; and secondly to perform separate association analyses of carcass traits on the same population of cattle using the Log R ratio (LRR) values of 712,555 single nucleotide polymorphisms (SNPs). The LRR value of a SNP is a measure of the signal intensity of the SNP generated during the genotyping process. RESULTS: A total of 13,969, 3,954, and 2,805 detected CNVs were tested for association with the three carcass traits for the Holstein-Friesian, Charolais, and Limousin, respectively. The copy number of 16 CNVs and the LRR of 34 SNPs were associated with at least one of the three carcass traits in at least one of the three cattle breeds. With the exception of three SNPs, none of the quantitative trait loci detected in the CNV association analyses or the SNP LRR association analyses were also detected using traditional association analyses based on SNP allele counts. Many of the CNVs and SNPs associated with the carcass traits were located near genes related to the structure and function of the spliceosome and the ribosome; in particular, U6 which encodes a spliceosomal subunit and 5S rRNA which encodes a ribosomal subunit. CONCLUSIONS: The present study demonstrates that CNV data and SNP LRR data can be used to detect genomic regions associated with carcass traits in cattle providing information on quantitative trait loci over and above those detected using just SNP allele counts, as is the approach typically employed in genome-wide association analyses.

X-Ray Ptychography with a Laboratory Source.

X-ray ptychography has revolutionized nanoscale phase contrast imaging at large-scale synchrotron sources in recent years. We present here the first successful demonstration of the technique in a small-scale laboratory setting. An experiment was conducted with a liquid metal-jet x-ray source and a single photon-counting detector with a high spectral resolution. The experiment used a spot size of 5 µm to produce a ptychographic phase image of a Siemens star test pattern with a submicron spatial resolution. The result and methodology presented show how high-resolution phase contrast imaging can now be performed at small-scale laboratory sources worldwide.

Concordance rate between copy number variants detected using either high- or medium-density single nucleotide polymorphism genotype panels and the potential of imputing copy number variants from flanking high density single nucleotide polymorphism haplotypes in cattle.

BACKGROUND: The trading of individual animal genotype information often involves only the exchange of the called genotypes and not necessarily the additional information required to effectively call structural variants. The main aim here was to determine if it is possible to impute copy number variants (CNVs) using the flanking single nucleotide polymorphism (SNP) haplotype structure in cattle. While this objective was achieved using high-density genotype panels (i.e., 713,162 SNPs), a secondary objective investigated the concordance of CNVs called with this high-density genotype panel compared to CNVs called from a medium-density panel (i.e., 45,677 SNPs in the present study). This is the first study to compare CNVs called from high-density and medium-density SNP genotypes from the same animals. High (and medium-density) genotypes were available on 991 Holstein-Friesian, 1015 Charolais, and 1394 Limousin bulls. The concordance between CNVs called from the medium-density and high-density genotypes were calculated separately for each animal. A subset of CNVs which were called from the high-density genotypes was selected for imputation. Imputation was carried out separately for each breed using a set of high-density SNPs flanking the midpoint of each CNV. A CNV was deemed to be imputed correctly when the called copy number matched the imputed copy number. RESULTS: For 97.0% of CNVs called from the high-density genotypes, the corresponding genomic position on the medium-density of the animal did not contain a called CNV. The average accuracy of imputation for CNV deletions was 0.281, with a standard deviation of 0.286. The average accuracy of imputation of the CNV normal state, i.e. the absence of a CNV, was 0.982 with a standard deviation of 0.022. Two CNV duplications were imputed in the Charolais, a single CNV duplication in the Limousins, and a single CNV duplication in the Holstein-Friesians; in all cases the CNV duplications were incorrectly imputed. CONCLUSION: The vast majority of CNVs called from the high-density genotypes were not detected using the medium-density genotypes. Furthermore, CNVs cannot be accurately predicted from flanking SNP haplotypes, at least based on the imputation algorithms routinely used in cattle, and using the SNPs currently available on the high-density genotype panel.

Differential effects of early growth conditions on colour-producing nanostructures revealed through small angle X-ray scattering and electron microscopy.

The costs associated with the production and maintenance of colour patches is thought to maintain their honesty. Although considerable research on sexual selection has focused on structurally coloured plumage ornaments, the proximate mechanisms of their potential condition dependence, and thus their honesty, is rarely addressed, particularly in an experimental context. Blue tit (Cyanistes caeruleus) nestlings have ultraviolet (UV)-blue structurally coloured tail feathers, providing a unique opportunity for investigation of the causes of variation in their colour. Here, we examined the influence of early growing conditions on the reflectance and structural properties of UV-blue-coloured tail feathers of blue tit nestlings. We applied a two-stage brood size manipulation to determine which stage of development more strongly impacts the quality of tail feather colouration and microstructure. We used small-angle X-ray scattering (SAXS) and electron microscopy to characterise the nanoscale and microscale structure of tail feather barbs. Nestlings from the broods enlarged at a later stage of growth showed a sex-specific rectrix development delay, with males being more sensitive to this manipulation. Contrary to predictions, treatment affected neither the quality of the barbs' nanostructures nor the brightness and UV chroma of feathers. However, at the microscale, barbs' keratin characteristics were impaired in late-enlarged broods. Our results suggest that nanostructure quality, which determines the UV-blue colour in tail feathers, is not sensitive to early rearing conditions. Furthermore, availability of resources during feather growth seems to impact the quality of feather microstructure more than body condition, which is likely to be determined at an earlier stage of nestling growth.

Estimating contributions of pelagic and benthic pathways to consumer production in coupled marine food webs.

Pelagic and benthic systems usually interact, but their dynamics and production rates differ. Such differences influence the distribution, reproductive cycles, growth rates, stability and productivity of the consumers they support. Consumer preferences for, and dependence on, pelagic or benthic production are governed by the availability of these sources of production and consumer life history, distribution, habitat, behavioural ecology, ontogenetic stage and morphology. Diet studies may demonstrate the extent to which consumers feed on prey in pelagic or benthic environments. But they do not discriminate benthic production directly supported by phytoplankton from benthic production recycled through detrital pathways. The former will track the dynamics of phytoplankton production more closely than the latter. We develop and apply a new analytical method that uses carbon (C) and sulphur (S) natural abundance stable isotope data to assess the relative contribution of pelagic and benthic pathways to fish consumer production. For 13 species of fish that dominate community biomass in the northern North Sea (estimated >90% of total biomass), relative modal use of pelagic pathways ranged from <25% to >85%. Use of both C and S isotopes as opposed to just C reduced uncertainty in relative modal use estimates. Temporal comparisons of relative modal use of pelagic and benthic pathways revealed similar ranking of species dependency over 4 years, but annual variation in relative modal use within species was typically 10%-40%. For the total fish consumer biomass in the study region, the C and S method linked approximately 70% and 30% of biomass to pelagic and benthic pathways, respectively. As well as providing a new method to define consumers' links to pelagic and benthic pathways, our results demonstrate that a substantial proportion of fish biomass, and by inference production, in the northern North Sea is supported by production that has passed through transformations on the seabed.

Effect of fullerene acceptor on the performance of solar cells based on PffBT4T-2OD.

We have studied bulk-heterojunction (BHJ) solar cells composed of the polymer PffBT4T-2OD as electron donor and three different electron accepting fullerenes, namely PC71BM, PC61BM and indene-C60-bis-adduct (ICBA) in order to understand the impact of different fullerenes on the morphology and efficiency of the corresponding photovoltaic devices. Despite PffBT4T-2OD:ICBA devices being characterised by higher values of Voc, they display the lowest power conversion efficiency (PCE) due to their lower Jsc and FF values. We find that although all blend films have similar morphologies, X-ray scattering indicates a reduced degree of order within the fullerene domains in the ICBA-based film. Due to the high LUMO level of ICBA, the corresponding blends are characterised by a lower initial exciton dissociation and this associated with the reduced ordering within the ICBA domains results in increased geminate recombination of the photogenerated electrons in the fullerene-rich domains and a consequently reduced PCE of the corresponding devices.

Layer-By-Layer Self-Assembly of Polyelectrolytic Block Copolymer Worms on a Planar Substrate.

Cationic and anionic block copolymer worms are prepared by polymerization-induced self-assembly via reversible addition-fragmentation chain transfer (RAFT) aqueous dispersion copolymerization of 2-hydroxypropyl methacrylate and glycidyl methacrylate (GlyMA), using a binary mixture of a nonionic poly(ethylene oxide) macromolecular RAFT agent and either a cationic poly([2-(methacryloyloxy)ethyl]trimethylammonium chloride) or an anionic poly(potassium 3-sulfopropyl methacrylate) macromolecular RAFT agent. In each case, covalent stabilization of the worm cores was achieved via reaction of the epoxide groups on the GlyMA repeat units with 3-mercaptopropyltriethoxysilane. Aqueous electrophoresis studies indicated a pH-independent mean zeta potential of +40 mV and -39 mV for the cationic and anionic copolymer worms, respectively. These worms are expected to mimic the rigid rod behavior of water-soluble polyelectrolyte chains in the absence of added salt. The kinetics of adsorption of the cationic worms onto a planar anionic silicon wafer was examined at pH 5 and was found to be extremely fast at 1.0 w/w % copolymer concentration in the absence of added salt. Scanning electron microscopy (SEM) analysis indicated that a relatively constant worm surface coverage of 16% was achieved at 20 °C for adsorption times ranging from just 2 s up to 2 min. Furthermore, the successive layer-by-layer deposition of cationic and anionic copolymer worms onto planar surfaces was investigated using SEM, ellipsometry, and surface zeta potential measurements. These techniques confirmed that the deposition of oppositely charged worms resulted in a monotonic increase in the mean layer thickness, with a concomitant surface charge reversal occurring on addition of each new worm layer. Unexpectedly, two distinct linear regimes were observed when plotting the mean layer thickness against the total number of adsorbed worm layers, with a steeper gradient (corresponding to thicker layers) being observed after the deposition of six worm layers.

Control of the Porous Structure of Polystyrene Particles Obtained by Nonsolvent Induced Phase Separation.

Porous polystyrene microspheres were produced by a process of nonsolvent induced phase separation (NIPS) from ternary polymer-solvent-nonsolvent (polystyrene-toluene-ethanol) systems and characterized by scanning electron microscopy (SEM) and small-angle X-ray scattering (SAXS) techniques. This study provides evidence for a link between the structural morphology of the porous polystyrene particles and the polystyrene concentration in the initial solutions. A reciprocal relationship between pore diameter and polymer concentration was observed for the systems with the polymer amount below the critical chain overlap concentration, C*. Above C*, this relationship breaks down. The reciprocal relationship between porosity and polymer concentration can be used to facilitate the fine control of the void size. We demonstrate that the observed reciprocal relationship between pore diameter and polymer concentration correlates well with the relative amount of nonsolvent present in the system at the onset of the phase separation process. The pore size can be reduced and, consequently, the pore surface area can be increased either by reducing the polymer concentration in the initial solution or by decreasing the polymer molecular weight in the sample composition.

GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data.

BACKGROUND: Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors. RESULTS: We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples. CONCLUSIONS: GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.

Coassembled nanostructured bioscaffold reduces the expression of proinflammatory cytokines to induce apoptosis in epithelial cancer cells.

The local inflammatory environment of the cell promotes the growth of epithelial cancers. Therefore, controlling inflammation locally using a material in a sustained, non-steroidal fashion can effectively kill malignant cells without significant damage to surrounding healthy cells. A promising class of materials for such applications is the nanostructured scaffolds formed by epitope presenting minimalist self-assembled peptides; these are bioactive on a cellular length scale, while presenting as an easily handled hydrogel. Here, we show that the assembly process can distribute an anti-inflammatory polysaccharide, fucoidan, localized to the nanofibers within the scaffold to create a biomaterial for cancer therapy. We show that it supports healthy cells, while inducing apoptosis in cancerous epithelial cells, as demonstrated by the significant down-regulation of gene and protein expression pathways associated with epithelial cancer progression. Our findings highlight an innovative material approach with potential applications in local epithelial cancer immunotherapy and drug delivery.

Why have so few proteomic biomarkers "survived" validation? (Sample size and independent validation considerations).

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC-MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC-MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.

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.

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.