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.
In this paper we explain the background, workings, and results obtained from three recently developed statistical age-depth models implemented in freely available, general purpose software packages ...(Bpeat, OxCal and Bchron). These models aim to reconstruct the sedimentation rate in a single core (typically lake, peat or ocean) given a limited number of scientific date estimates (usually radiocarbon) and fixed depths. Most importantly, they provide a suitably qualified estimate of the uncertainty in the age-depth chronology and thus can be used in a variety of applications. We perform a large data-driven study of the three models and discuss their general utility in chronology construction for palaeoenvironmental research.
We perform Bayesian inference on historical and late Holocene (last 2000 years) rates of sea-level change. The input data to our model are tidegauge measurements and proxy reconstructions from cores ...of coastal sediment. These data are complicated by multiple sources of uncertainty, some of which arise as part of the data collection exercise. Notably, the proxy reconstructions include temporal uncertainty from dating of the sediment core using techniques such as radiocarbon. The model we propose places a Gaussian process prior on the rate of sea-level change, which is then integrated and set in an errors-in-variables framework to take account of age uncertainty. The resulting model captures the continuous and dynamic evolution of sea-level change with full consideration of all sources of uncertainty. We demonstrate the performance of our model using two real (and previously published) example data sets. The global tide-gauge data set indicates that sea-level rise increased from a rate with a posterior mean of 1.13 mm/yr in 1880 AD (0.89 to 1.28 mm/yr 95% credible interval for the posterior mean) to a posterior mean rate of 1.92 mm/yr in 2009 AD (1.84 to 2.03 mm/yr 95% credible interval for the posterior mean). The proxy reconstruction from North Carolina (USA) after correction for land-level change shows the 2000 AD rate of rise to have a posterior mean of 2.44 mm/yr (1.91 to 3.01 mm/yr 95% credible interval). This is unprecedented in at least the last 2000 years.
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. 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. 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.
We produced a relative sea-level (RSL) reconstruction from Connecticut (USA) spanning the last ∼2200 yrs that is free from the influence of sediment compaction. The reconstruction used a suite of ...vertically- and laterally-ordered sediment samples ≤2 cm above bedrock that were collected by excavating a trench along an evenly-sloped bedrock surface. Paleomarsh elevation was reconstructed using a regional-scale transfer function trained on the modern distribution of foraminifera on Long Island Sound salt marshes and supported by bulk-sediment δ13C measurements. The history of sediment accumulation was estimated using an age-elevation model constrained by radiocarbon dates and recognition of pollution horizons of known age. The RSL reconstruction was combined with regional tide-gauge measurements spanning the last ∼150 yrs before being quantitatively analyzed using an error-in-variables integrated Gaussian process model to identify sea-level trends with formal and appropriate treatment of uncertainty and the temporal distribution of data. RSL rise was stable (∼1 mm/yr) from ∼200 BCE to ∼1000 CE, slowed to a minimum rate of rise (0.41 mm/yr) at ∼1400 CE, and then accelerated continuously to reach a current rate of 3.2 mm/yr, which is the fastest, century-scale rate of the last 2200 yrs. Change point analysis identified that modern rates of rise in Connecticut began at 1850–1886 CE. This timing is synchronous with changes recorded at other sites on the U.S. Atlantic coast and is likely the local expression of a global sea-level change. Earlier sea-level trends show coherence north of Cape Hatteras that are contrasted with southern sites. This pattern may represent centennial-scale variability in the position and/or strength of the Gulf Stream. Comparison of the new record to three existing and reanalyzed RSL reconstructions from the same site developed using sediment cores indicates that compaction is unlikely to significantly distort RSL reconstructions produced from shallow (∼2–3 m thick) sequences of salt-marsh peat.
•2200 yr sea-level reconstruction that is free from the influence of sediment compaction.•Foraminifera and δ13C used as sea-level indicators from sediment overlying bedrock.•Modern sea-level rise is fastest of last 2000 yrs and began at 1850–1886 CE.•Gulf Stream variability could have driven multi-century sea-level trends.
New York City (NYC) is threatened by 21st-century relative sea-level (RSL) rise because it will experience a trend that exceeds the global mean and has high concentrations of low-lying infrastructure ...and socioeconomic activity. To provide a long-term context for anticipated trends, we reconstructed RSL change during the past ~1500 years using a core of salt-marsh sediment from Pelham Bay in The Bronx. Foraminifera and bulk-sediment δ13C values were used as sea-level indicators. The history of sediment accumulation was established by radiocarbon dating and recognition of pollution and land-use trends of known age in down-core elemental, isotopic, and pollen profiles. The reconstruction was generated within a Bayesian hierarchical model to accommodate multiple proxies and to provide a unified statistical framework for quantifying uncertainty. We show that RSL in NYC rose by ~1.70 m since ~575 CE (including ~0.38 m since 1850 CE). The rate of RSL rise increased markedly at 1812–1913 CE from ~1.0 to ~2.5 mm/yr, which coincides with other reconstructions along the US Atlantic coast. We investigated the possible influence of tidal-range change in Long Island Sound on our reconstruction using a regional tidal model, and we demonstrate that this effect was likely small. However, future tidal-range change could exacerbate the impacts of RSL rise in communities bordering Long Island Sound. The current rate of RSL rise is the fastest that NYC has experienced for >1500 years, and its ongoing acceleration suggests that projections of 21st-century local RSL rise will be realized.
Stable isotope mixing models are regularly used to provide probabilistic estimates of source contributions to dietary mixtures. Whilst Bayesian implementations of isotope mixing models have become ...prominent, the use of appropriate diet-tissue discrimination factors (DTDFs) remains as the least resolved aspect. The DTDFs are critical in providing accurate inferences from these models. Using both simulated and laboratory-based experimental data, this study provides conceptual and practical applications of isotope mixing models by exploring the role of DTDFs. The experimental study used Mozambique Tilapia Oreochromis mossambicus, a freshwater fish, to explore multi-tissue variations in isotopic incorporation patterns, and to evaluate isotope mixing model outputs based on the experiment- and literature-based DTDFs. Isotope incorporation patterns were variable for both muscle and fin tissues among the consumer groups that fed diet sources with different stable isotope values. Application of literature-based DTDFs in isotope mixing models consistently underestimated the dietary proportions of all single-source consumer groups. In contrast, application of diet-specific DTDFs provided better dietary estimates for single-source consumer groups. Variations in the proportional contributions of the individual sources were, nevertheless, observed for the mixed-source consumer group, which suggests that isotope assimilation of the individual food sources may have been influenced by other underlying physiological processes. This study provides evidence that stable isotope values from different diet sources exhibit large variations as they become incorporated into consumer tissues. This suggests that the application of isotope mixing models requires consideration of several aspects such as diet type and the associated biological processes that may influence DTDFs.
The food industry requires automatic methods to establish authenticity of food products. In this work, we address the problem of the certification of suckling lamb meat with respect to the rearing ...system. We evaluate the performance of neural network classifiers as well as different dimensionality reduction techniques, with the aim of categorizing lamb fat by means of spectroscopy and analyzing the features with more discrimination power. Assessing the stability of feature ranking algorithms also becomes particularly important. We assess six feature selection techniques (<inline-formula> <tex-math notation="LaTeX">\chi ^{2} </tex-math></inline-formula>, Information Gain, Gain Ratio, Relief and two embedded techniques based on the decision rule 1R and SVM (Support Vector Machine). Additionally, we compare them with common approaches in the chemometrics field like the Partial Least Square (PLS) model and Principal Component Analysis (PCA) regression. Experimental results with a fat sample dataset collected from carcasses of suckling lambs show that performing feature selection contributes to classification performance increasing accuracy from 89.70% with the full feature set to 91.80% and 93.89% with the SVM approach and PCA, respectively. Moreover, the neural classifiers yield a significant increase in the accuracy with respect to the PLS model (85.60% accuracy). It is noteworthy that unlike PCA or PLS, the feature selection techniques that select relevant wavelengths allow the user to identify the regions in the spectrum with the most discriminant power, which makes the understanding of this process easier for veterinary experts. The robustness of the feature selection methods is assessed via a visual approach.
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.
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.
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.
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