Mind the gap: Epigenetic regulation of chromatin accessibility in plants.

Chromatin plays a crucial role in genome compaction and is fundamental for regulating multiple nuclear processes. Nucleosomes, the basic building blocks of chromatin, are central in regulating these processes, determining chromatin accessibility by limiting access to DNA for various proteins and acting as important signaling hubs. The association of histones with DNA in nucleosomes and the folding of chromatin into higher-order structures are strongly influenced by a variety of epigenetic marks, including DNA methylation, histone variants, and histone post-translational modifications. Additionally, a wide array of chaperones and ATP-dependent remodelers regulate various aspects of nucleosome biology, including assembly, deposition, and positioning. This review provides an overview of recent advances in our mechanistic understanding of how nucleosomes and chromatin organization are regulated by epigenetic marks and remodelers in plants. Furthermore, we present current technologies for profiling chromatin accessibility and organization.

P-Stereogenic Ir-MaxPHOX: A Step toward Privileged Catalysts for Asymmetric Hydrogenation of Nonchelating Olefins.

The Ir-MaxPHOX-type catalysts demonstrated high catalytic performance in the hydrogenation of a wide range of nonchelating olefins with different geometries, substitution patterns, and degrees of functionalization. These air-stable and readily available catalysts have been successfully applied in the asymmetric hydrogenation of di-, tri-, and tetrasubstituted olefins (ee's up to 99%). The combination of theoretical calculations and deuterium labeling experiments led to the uncovering of the factors responsible for the enantioselectivity observed in the reaction, allowing the rationalization of the most suitable substrates for these Ir-catalysts.

The TRIPLE PHD FINGERS proteins are required for SWI/SNF complex-mediated +1 nucleosome positioning and transcription start site determination in Arabidopsis.

Eukaryotes have evolved multiple ATP-dependent chromatin remodelers to shape the nucleosome landscape. We recently uncovered an evolutionarily conserved SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodeler complex in plants reminiscent of the mammalian BAF subclass, which specifically incorporates the MINUSCULE (MINU) catalytic subunits and the TRIPLE PHD FINGERS (TPF) signature subunits. Here we report experimental evidence that establishes the functional relevance of TPF proteins for the complex activity. Our results show that depletion of TPF triggers similar pleiotropic phenotypes and molecular defects to those found in minu mutants. Moreover, we report the genomic location of MINU2 and TPF proteins as representative members of this SWI/SNF complex and their impact on nucleosome positioning and transcription. These analyses unravel the binding of the complex to thousands of genes where it modulates the position of the +1 nucleosome. These targets tend to produce 5'-shifted transcripts in the tpf and minu mutants pointing to the participation of the complex in alternative transcription start site usage. Interestingly, there is a remarkable correlation between +1 nucleosome shift and 5' transcript length change suggesting their functional connection. In summary, this study unravels the function of a plant SWI/SNF complex involved in +1 nucleosome positioning and transcription start site determination.

Acid number, viscosity and end-point detection in a multiphase high temperature polymerisation process using an online miniaturised MEMS Fabry-Pérot interferometer.

Recent advances in the latest generation of MEMS (micro-electro-mechanical system) Fabry-Pérot interferometers (FPI) for near infrared (NIR) wavelengths has led to the development of ultra-fast and low cost NIR sensors with potential to be used by the process industry. One of these miniaturised sensors operating from 1350 to 1650 nm, was integrated into a software platform to monitor a multiphase solid-gas-liquid process, for the production of saturated polyester resins. Twelve batches were run in a 2 L reactor mimicking industrial conditions (24 h process, with temperatures ranging from 220 to 240 °C), using an immersion NIR transmission probe. Because of the multiphase nature of the reaction, strong interference produced by process disturbances such as temperature variations and the presence of solid particles and bubbles in the online spectra required robust pre-processing algorithms and a good long-term stability of the probe. These allowed partial least squares (PLS) regression models to be built for the key analytical parameters acid number and viscosity. In parallel, spectra were also used to build an end-point detection model based on principal component analysis (PCA) for multivariate statistical process control (MSPC). The novel MEMS-FPI sensor combined with robust chemometric analysis proved to be a suitable and affordable alternative for online process monitoring, contributing to sustainability in the process industry.

Process Monitoring of Moisture Content and Mass Transfer Rate in a Fluidised Bed with a Low Cost Inline MEMS NIR Sensor.

PURPOSE: The current trend for continuous drug product manufacturing requires new, affordable process analytical techniques (PAT) to ensure control of processing. This work evaluates whether property models based on spectral data from recent Fabry-Pérot Interferometer based NIR sensors can generate a high-resolution moisture signal suitable for process control. METHODS: Spectral data and offline moisture content were recorded for 14 fluid bed dryer batches of pharmaceutical granules. A PLS moisture model was constructed resulting in a high resolution moisture signal, used to demonstrate (i) endpoint determination and (ii) evaluation of mass transfer performance. RESULTS: The sensors appear robust with respect to vibration and ambient temperature changes, and the accuracy of water content predictions (±13 % ) is similar to those reported for high specification NIR sensors. Fusion of temperature and moisture content signal allowed monitoring of water transport rates in the fluidised bed and highlighted the importance water transport within the solid phase at low moisture levels. The NIR data was also successfully used with PCA-based MSPC models for endpoint detection. CONCLUSIONS: The spectral quality of the small form factor NIR sensor and its robustness is clearly sufficient for the construction and application of PLS models as well as PCA-based MSPC moisture models. The resulting high resolution moisture content signal was successfully used for endpoint detection and monitoring the mass transfer rate.

Determination of chemical properties in 'calçot' (Allium cepa L.) by near infrared spectroscopy and multivariate calibration.

'Calçots', the immature floral stems of second-year onion resprouts, are an economically important traditional crop in Catalonia (Spain). Classical approaches to evaluating the chemical properties of 'calçots' are time consuming and expensive; near-infrared spectroscopy (NIRS) may be faster and cheaper. We used NIRS to develop partial least square (PLS) models to predict dry matter, soluble solid content, titratable acidity, and ash content in cooked 'calçots'. To guarantee the robustness of the models, calibration samples were grown and analyzed in a first season (2014-15) and validation samples in a second season (2015-16). NIRS on puree spectra estimated dry matter and soluble solid content with excellent accuracy (R2pred = 0.953, 0.985 and RPD = 4.571, 8.068, respectively). However, good estimation of titratable acidity and ash content required using ground dried puree spectra (R2pred = 0.852, 0.820 and RPD = 2.590, 1.987, respectively). NIRS can be a helpful tool for 'calçots' breeding and quality control.

Prediction of olive oil sensory descriptors using instrumental data fusion and partial least squares (PLS) regression.

Headspace-Mass Spectrometry (HS-MS), Fourier Transform Mid-Infrared spectroscopy (FT-MIR) and UV-Visible spectrophotometry (UV-vis) instrumental responses have been combined to predict virgin olive oil sensory descriptors. 343 olive oil samples analyzed during four consecutive harvests (2010-2014) were used to build multivariate calibration models using partial least squares (PLS) regression. The reference values of the sensory attributes were provided by expert assessors from an official taste panel. The instrumental data were modeled individually and also using data fusion approaches. The use of fused data with both low- and mid-level of abstraction improved PLS predictions for all the olive oil descriptors. The best PLS models were obtained for two positive attributes (fruity and bitter) and two defective descriptors (fusty and musty), all of them using data fusion of MS and MIR spectral fingerprints. Although good predictions were not obtained for some sensory descriptors, the results are encouraging, specially considering that the legal categorization of virgin olive oils only requires the determination of fruity and defective descriptors.

Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA).

Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV-visible spectrophotometry (UV-vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually. A general model was applied to discriminate high-quality non-defective olive oils (extra-virgin) and the lowest-quality olive oils considered non-edible (lampante). A specific identification of key off-flavours, such as musty, winey, fusty and rancid, was also studied. The data fusion of the three techniques improved the classification results in most of the cases. Low-level data fusion was the best strategy to discriminate musty, winey and fusty defects, using HS-MS, MIR and UV-vis, and the rancid defect using only HS-MS and MIR. The mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be the best strategy for defective vs non-defective and edible vs non-edible oil discrimination. However, the data fusion did not sufficiently improve the results obtained by a single technique (HS-MS) to classify non-defective classes. These results indicate that instrumental data fusion can be useful for the identification of sensory defects in virgin olive oils.

Data fusion methodologies for food and beverage authentication and quality assessment - a review.

The ever increasing interest of consumers for safety, authenticity and quality of food commodities has driven the attention towards the analytical techniques used for analyzing these commodities. In recent years, rapid and reliable sensor, spectroscopic and chromatographic techniques have emerged that, together with multivariate and multiway chemometrics, have improved the whole control process by reducing the time of analysis and providing more informative results. In this progression of more and better information, the combination (fusion) of outputs of different instrumental techniques has emerged as a means for increasing the reliability of classification or prediction of foodstuff specifications as compared to using a single analytical technique. Although promising results have been obtained in food and beverage authentication and quality assessment, the combination of data from several techniques is not straightforward and represents an important challenge for chemometricians. This review provides a general overview of data fusion strategies that have been used in the field of food and beverage authentication and quality assessment.

Identification of olive oil sensory defects by multivariate analysis of mid infrared spectra.

Mid-infrared (MIR) spectra (4000-600 cm(-1)) of olive oils were analyzed using chemometric methods to identify the four main sensorial defects, musty, winey, fusty and rancid, previously evaluated by an expert sensory panel. Classification models were developed using partial least squares discriminant analysis (PLS-DA) to distinguish between extra-virgin olive oils (defect absent) and lower quality olive oils (defect present). The most important spectral ranges responsible for the discrimination were identified. PLS-DA models were able to discriminate between defective and high quality oils with predictive abilities around 87% for the musty defect and around 77% for winey, fusty and rancid defects. This methodology advances instrumental determination of results previously only achievable with a human test panel.

Biomonitoring exposure assessment to contemporary pesticides in a school children population of Spain.

The exposure to pesticides amongst school-aged children (6-11 years old) was assessed in this study. One hundred twenty-five volunteer children were selected from two public schools located in an agricultural and in an urban area of Valencia Region, Spain. Twenty pesticide metabolites were analyzed in children's urine as biomarkers of exposure to organophosphate (OP) insecticides, synthetic pyrethroid insecticides, and herbicides. These data were combined with a survey to evaluate the main predictors of pesticide exposure in the children's population. A total of 15 metabolites were present in the urine samples with detection frequencies (DF) ranging from 5% to 86%. The most frequently detected metabolites with DF>53%, were 3,5,6-trichloro-2-pyridinol (TCPy, metabolite of chlorpyrifos), diethyl phosphate (DEP, generic metabolite of OP insecticides), 2-isopropyl-4-methyl-6-hydroxypyrimidine (IMPY, metabolite of diazinon) and para-nitrophenol (PNP, metabolite of parathion and methyl parathion). The calculated geometric means ranged from 0.47 to 3.36 µg/g creatinine, with TCPy and IMPY showing the higher mean concentrations. Statistical significant differences were found between exposure subgroups (Mann-Whitney test, p<0.05) for TCPy, DEP, and IMPY. Children living in the agricultural area had significantly higher concentrations of DEP than those living in the urban area. In contrast, children aged 6-8 years from the urban area, showed statistically higher IMPY levels than those from agricultural area. Higher levels of TCPy were also found in children with high consumption of vegetables and higher levels of DEP in children whose parents did not have university degree studies. The multivariable regression analysis showed that age, vegetable consumption, and residential use of pesticides were predictors of exposure for TCPy, and IMPY; whereas location and vegetable consumption were factors associated with DEP concentrations. Creatinine concentrations were the most important predictors of urinary TCPy and PNP metabolites.

Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy.

Near infrared (NIR) spectroscopy and multivariate classification were applied to discriminate soybean oil samples into non-transgenic and transgenic. Principal Component Analysis (PCA) was applied to extract relevant features from the spectral data and to remove the anomalous samples. The best results were obtained when with Support Vectors Machine-Discriminant Analysis (SVM-DA) and Partial Least Squares-Discriminant Analysis (PLS-DA) after mean centering plus multiplicative scatter correction. For SVM-DA the percentage of successful classification was 100% for the training group and 100% and 90% in validation group for non transgenic and transgenic soybean oil samples respectively. For PLS-DA the percentage of successful classification was 95% and 100% in training group for non transgenic and transgenic soybean oil samples respectively and 100% and 80% in validation group for non transgenic and transgenic respectively. The results demonstrate that NIR spectroscopy can provide a rapid, nondestructive and reliable method to distinguish non-transgenic and transgenic soybean oils.

Classification of edible oils and modeling of their physico-chemical properties by chemometric methods using mid-IR spectroscopy.

This research work describes two studies for the classification and characterization of edible oils and its quality parameters through Fourier transform mid infrared spectroscopy (FT-mid-IR) together with chemometric methods. The discrimination of canola, sunflower, corn and soybean oils was investigated using SVM-DA, SIMCA and PLS-DA. Using FT-mid-IR, DPLS was able to classify 100% of the samples from the validation set, but SIMCA and SVM-DA were not. The quality parameters: refraction index and relative density of edible oils were obtained from reference methods. Prediction models for FT-mid-IR spectra were calculated for these quality parameters using partial least squares (PLS) and support vector machines (SVM). Several preprocessing alternatives (first derivative, multiplicative scatter correction, mean centering, and standard normal variate) were investigated. The best result for the refraction index was achieved with SVM as well as for the relative density except when the preprocessing combination of mean centering and first derivative was used. For both of quality parameters, the best results obtained for the figures of merit expressed by the root mean square error of cross validation (RMSECV) and prediction (RMSEP) were equal to 0.0001.

Outlier detection for the Generalized Rank Annihilation Method in HPLC-DAD analysis.

The Generalized Rank Annihilation Method (GRAM) is a second-order calibration method that is used in chromatography to quantify analytes that coelute with interferences. For a correct quantification, the peak of the analyte in the standard and in the test sample must be aligned and have the same shape (i.e., have a trilinear structure). Variations in retention time and shape between the two peaks may cause the test sample to behave as an outlier and produce an incorrect prediction. This situation cannot be detected by checking the coincidence of the recovered spectrum with the known spectrum of the analyte because the spectral domain is not affected. It cannot be detected either by checking if the recovered profile is correct (i.e., unimodal and positive). Several plots are presented to detect such outliers. The first plot compares the particular elution profiles in the standard and in the test sample that are recovered by least-squares regression from the spectra estimated by GRAM. The calculated elution profiles from both peaks should coincide. A second plot uses the elution profiles and spectra calculated by GRAM to define the vector space spanned by the interferences. The measured peaks in the standard and in the test sample are projected onto the space that is orthogonal to the space spanned by the interferences. These projections are proportional (up to the noise) if data are trilinear. The proportionality is checked graphically from the first singular vector of the projected peaks, or from the plot of the orthogonal signal versus the net sensitivity. The use of these graphs is shown for simulated data and for the determination of 4-nitrophenol in river water samples with liquid chromatography/UV-Vis detection.

Establishment of multivariate specifications for food commodities with discriminant partial least squares.

A novel method for establishing multivariate specifications of food commodities is proposed. The specifications are established for discriminant partial least squares (DPLS) by setting limits on the predictions of the DPLS model together with Hotelling T(2) and square error of prediction (SPE). These limits can be tuned depending on whether type I error (i.e. a correct sample is declared out-of-specification) or type II error (i.e. an out-of-specification sample is declared within specifications) need to be minimized. The methodology is illustrated with a set of NIR spectra of Italian olive oils, corresponding to five regions and the class Liguria is the class of interest. The results demonstrate the possibility of establishing multivariate specification for olive oils from the Liguria region on the basis of spectral data obtaining type I and type II errors lower than 5%.

Multi-class classification with probabilistic discriminant partial least squares (p-DPLS).

This work describes multi-classification based on binary probabilistic discriminant partial least squares (p-DPLS) models, developed with the strategy one-against-one and the principle of winner-takes-all. The multi-classification problem is split into binary classification problems with p-DPLS models. The results of these models are combined to obtain the final classification result. The classification criterion uses the specific characteristics of an object (position in the multivariate space and prediction uncertainty) to estimate the reliability of the classification, so that the object is assigned to the class with the highest reliability. This new methodology is tested with the well-known Iris data set and a data set of Italian olive oils. When compared with CART and SIMCA, the proposed method has better average performance of classification, besides giving a statistic that evaluates the reliability of classification. For the olive oil set the average percentage of correct classification for the training set was close to 84% with p-DPLS against 75% with CART and 100% with SIMCA, while for the test set the average was close to 94% with p-DPLS as against 50% with CART and 62% with SIMCA.

Classification from microarray data using probabilistic discriminant partial least squares with reject option.

Microarrays are used to simultaneously determine the expressions of thousands of genes. An important application of microarrays is in the classification of samples into classes of interest (e.g. either healthy cells or tumour cells). Discriminant partial least squares (DPLS) has often been used for this purpose. In this paper, we describe an improvement to DPLS that uses kernel-based probability density functions and the Bayes rule to classify samples whilst keeping the option of not classifying the sample if this cannot be done with sufficient confidence. With this approach, those samples outside the boundaries of the known classes or from the ambiguity region between classes are rejected and only samples with a high probability of being correctly classified are indeed classified. The optimal model is found by simultaneously minimizing the misclassification and rejection costs. The method (p-DPLS with reject option) was tested with two datasets. For the human cancers dataset the accuracy (obtained by leave-one-out cross-validation) was improved from 97% to 99% when compared to p-DPLS without reject option. For the breast cancer dataset, p-DPLS with reject option was able to reject 100% of the test samples that did not belong to any of the modelled classes. These samples would have been misclassified if the reject option had not been considered.

Bagged k-nearest neighbours classification with uncertainty in the variables.

An analytical result should be expressed as x+/-U, where x is the experimental result obtained for a given variable and U is its uncertainty. This uncertainty is rarely taken into account in supervised classification. In this paper, we propose to include the information about the uncertainty of the experimental results to compute the reliability of classification. The method combines k-nearest neighbours (kNN) with a nested bootstrap scheme, in which a new bootstrap training set is generated using the classical bootstrap in the first level (B times) and a new bootstrap method, called U-bootstrap, in the second level (D times). Two bootstraps are used to reduce the effect of sampling in the first level and the effect of the uncertainty in the second one. These BxD new training bootstrap sets are used to compute the reliability of classification for an unknown object using kNN. The object is classified into the class with the highest reliability. In this method, unlike the classical kNN and Probabilistic Bagged k-nearest neighbours (PBkNN), the reliability of classification changes (increases or decreases) when the uncertainty is increased. These changes depend on the position of the unknown object with respect to the training objects. For the benchmark Wine dataset, we found similar values of classification error rate (CER) than for kNN (5.57%), but lower than Probabilistic Bagged k-nearest neighbours using Hamamoto's bootstrap (7.96%) or Efron's bootstrap (8.97%).

Excitation-emission fluorescence spectroscopy combined with three-way methods of analysis as a complementary technique for olive oil characterization.

This paper shows the potential of excitation-emission fluorescence spectroscopy (EEFS) and three-way methods of analysis [parallel factor analysis (PARAFAC) and multiway partial least-squares (N-PLS) regression] as a complementary technique for olive oil characterization. The fluorescence excitation-emission matrices of a set of Spanish extra virgin, virgin, pure, and olive pomace oils were measured, and the relationship between them and some of the quality parameters of olive oils (peroxide value, K232, and K270) was studied. N-PLS was found to be more suitable than PARAFAC combined with multiple linear regression for correlating fluorescence and quality parameters, yielding better fits and lower prediction errors. The best results were obtained for predicting K270. EEFS allowed detection of extra virgin olive oils highly degraded at early stages (with high peroxide value) and little oxidized pure olive oils (with low K270). The proposed methodology may be used as an aid to analyze doubtful samples.

Prediction of heterofullerene stabilities: a combined DFT and chemometric study of C56Pt2, C57Pt2 and C81Pt2.

A systematic search of the regioisomers of the heterofullerenes, C57Pt2 and C56Pt2, has been carried out by means of density functional calculations to find the most stable structures. Both heterofullerenes incorporate two metal atoms into the fullerene surface. In the case of C57Pt2, one platinum atom substitutes one carbon atom of C60 and the other platinum atom replaces a C--C bond, whereas in C56Pt2 each platinum atom replaces one C--C bond. Several geometric factors were studied, three of which have particularly important effects on the relative stabilities of the regioisomers: the Pt--Pt separation, the number of C--C bonds remaining after substitution, and the type of C--C bond that is substituted. All these factors indicate that the deformation of the carbon framework is a general factor that governs the relative stabilities of the regioisomers. Because a high number of factors affect the stability of the heterofullerenes we also used chemometric techniques in this study. Partial least-squares (PLS) regression was used to establish the structure-energy relationships of C57Pt2 and C56Pt2 heterofullerenes. The understanding gained of the factors that affect the relative isomers stabilities has allowed us to predict the stabilities of larger disubstituted carbon cages, for example, C81Pt2 heterofullerene.