Optimisation of the predictive ability of NIR models to estimate nutritional parameters in elephant grass through LOCAL algorithms.

Elephant grass is a tropical forage widely used for livestock feed. The analytical techniques traditionally used for its nutritional evaluation are costly and time consuming. Alternatively, Near Infrared Spectroscopy (NIRS) technology has been used as a rapid analysis technique. However, in crops with high variability due to genetic improvement, predictive models quickly lose accuracy and must be recalibrated. The use of non-linear models such as LOCAL calibrations could mitigate these issues, although a number of parameters need to be optimized to obtain accurate results. The objective of this work was to compare the predictive results obtained with global NIRS calibrations and with LOCAL calibrations, paying special attention to the configuration parameters of the models. The results obtained showed that the prediction errors with the LOCAL models were between 1.6 and 17.5 % lower. The best results were obtained in most cases with a low number of selected samples (n = 100-250) and a high number of PLS terms (n = 20). This configuration allows a reduced computation time with high accuracy, becoming a valuable alternative for analytical determinations that require ruminal fluid, which would improve the welfare of the animals by avoiding the need to surgically prepare animals to estimate the nutritional value of the feeds.

Machine-Learning Approach for Predicting the Discharging Capacities of Doped Lithium Nickel-Cobalt-Manganese Cathode Materials in Li-Ion Batteries.

Understanding the governing dopant feature for cyclic discharge capacity is vital for the design and discovery of new doped lithium nickel-cobalt-manganese (NCM) oxide cathodes for lithium-ion battery applications. We herein apply six machine-learning regression algorithms to study the correlations of the structural, elemental features of 168 distinct doped NCM systems with their respective initial discharge capacity (IC) and 50th cycle discharge capacity (EC). First, a Pearson correlation coefficient study suggests that the lithium content ratio is highly correlated to both discharge capacity variables. Among all six regression algorithms, gradient boosting models have demonstrated the best prediction power for both IC and EC, with the root-mean-square errors calculated to be 16.66 mAhg-1 and 18.59 mAhg-1, respectively, against a hold-out test set. Furthermore, a game-theory-based variable-importance analysis reveals that doped NCM materials with higher lithium content, smaller dopant content, and lower-electronegativity atoms as the dopant are more likely to possess higher IC and EC. This study has demonstrated the exciting potentials of applying cutting-edge machine-learning techniques to accurately capture the complex structure-property relationship of doped NCM systems, and the models can be used as fast screening tools for new doped NCM structures with more superior electrochemical discharging properties.

Probabilistic classification models for the in situ authentication of iberian pig carcasses using near infrared spectroscopy.

Iberian pig ham is one of several high value European food products that are the subject of significant attempts at fraud because of the high price differences between commercial categories. Iberian pig products are classified by the Spanish regulations into different categories, mainly depending on the feeding regime during the fattening phase and the race involved, being of Premium quality those products obtained from the animals fed with acorns and other natural resources. Most of the previous NIRS studies related to the Iberian pig have involved the use of at-line instruments to predict quantitative quality parameters. This paper explores the use of the NIR spectra (369 for training and 199 for validation) to classify samples according to the categories Premium (animals fed with acorn) and Non Premium (animals fed with compound feeds), using a MicroNIR™ Pro1700 microspectrometer to analyse individual carcasses in situ at the slaughterhouse line. Four discriminant methods were explored: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), Kernel Bayes and Logistic Regression. These are all discriminant methods that naturally produce classification probabilities to quantify the uncertainty of the results. Rules were tuned and methods compared using both classification error rates and a probability scoring rule. LDA gave the best results, attaining an overall accuracy of 93% and providing well-calibrated classification probabilities.

Parsimonious test of dynamic interaction.

In recent years, there have been significant advances in the technology used to collect data on the movement and activity patterns of humans and animals. GPS units, which form the primary source of location data, have become cheaper, more accurate, lighter and less power-hungry, and their accuracy has been further improved with the addition of inertial measurement units. The consequence is a glut of geospatial time series data, recorded at rates that range from one position fix every several hours (to maximize system lifetime) to ten fixes per second (in high dynamic situations). Since data of this quality and volume have only recently become available, the analytical methods to extract behavioral information from raw position data are at an early stage of development. An instance of this lies in the analysis of animal movement patterns. When investigating solitary animals, the timing and location of instances of avoidance and association are important behavioral markers. In this paper, a novel analytical method to detect avoidance and association between individuals is proposed; unlike existing methods, assumptions about the shape of the territories or the nature of individual movement are not needed. Simulations demonstrate that false positives (type I error) are rare (1%-3%), which means that the test rarely suggests that there is an association if there is none.

Elastic scattering spectroscopy for early detection of breast cancer: partially supervised Bayesian image classification of scanned sentinel lymph nodes.

Sentinel lymph node biopsy is a standard diagnosis procedure to determine whether breast cancer has spread to the lymph glands in the armpit (the axillary nodes). The metastatic status of the sentinel node (the first node in the axillary chain that drains the affected breast) is the determining factor in surgery between conservative lumpectomy and more radical mastectomy including axillary node excision. The traditional assessment of the node requires sample preparation and pathologist interpretation. An automated elastic scattering spectroscopy (ESS) scanning device was constructed to take measurements from the entire cut surface of the excised sentinel node and to produce ESS images for cancer diagnosis. Here, we report on a partially supervised image classification scheme employing a Bayesian multivariate, finite mixture model with a Markov random field (MRF) spatial prior. A reduced dimensional space was applied to represent the scanning data of the node by a statistical image, in which normal, metastatic, and nonnodal-tissue pixels are identified. Our results show that our model enables rapid imaging of lymph nodes. It can be used to recognize nonnodal areas automatically at the same time as diagnosing sentinel node metastases with sensitivity and specificity of 85% and 94%, respectively. ESS images can help surgeons by providing a reliable and rapid intraoperative determination of sentinel nodal metastases in breast cancer.

Classifying Degraded Modern Polymeric Museum Artefacts by Their Smell.

The use of VOC analysis to diagnose degradation in modern polymeric museum artefacts is reported. Volatile organic compound (VOC) analysis is a successful method for diagnosing medical conditions but to date has found little application in museums. Modern polymers are increasingly found in museum collections but pose serious conservation difficulties owing to unstable and widely varying formulations. Solid-phase microextraction gas chromatography/mass spectrometry and linear discriminant analysis were used to classify samples according to the length of time they had been artificially degraded. Accuracies in classification of 50-83 % were obtained after validation with separate test sets. The method was applied to three artefacts from collections at Tate to detect evidence of degradation. This approach could be used for any material in heritage collections and more widely in the field of polymer degradation.

Parent-dependent stressors and the onset of anxiety disorders in children: links with parental psychopathology.

Exposure to stressors is associated with an increased risk for child anxiety. Investigating the family origins of stressors may provide promising avenues for identifying and intervening with children at risk for the onset of anxiety disorders and their families. The aim of this study was to compare the frequency of parent-dependent negative life events and chronic adversities experienced by children with an anxiety disorder (n = 34) in the 12 months prior to the onset of the child's most recent episode, compared to healthy controls (n = 34). Life events and chronic adversities were assessed using maternal report during an investigator-based interview, which provided independent panel ratings of the extent that reported experiences were related to parent behaviour. There were no group differences in the number of parent-dependent negative life events for anxious children compared to controls. However, significantly more parent-dependent chronic adversities were present for anxious children compared to controls. Findings suggest that parents contribute to an increased frequency of chronic adversities but not negative life events prior to their child's most recent onset of anxiety. Furthermore, increased child exposure to parent-dependent chronic adversities was related to parental history of mental disorder.

Near-Infrared Spectroscopy and Geostatistical Analysis for Modeling Spatial Distribution of Analytical Constituents in Bulk Animal By-Product Protein Meals.

Control and inspection operations within the context of safety and quality assessment of bulk foods and feeds are not only of particular importance, they are also demanding challenges, given the complexity of food/feed production systems and the variability of product properties. Existing methodologies have a variety of limitations, such as high costs of implementation per sample or shortcomings in early detection of potential threats for human/animal health or quality deviations. Therefore, new proposals are required for the analysis of raw materials in situ in a more efficient and cost-effective manner. For this purpose, a pilot laboratory study was performed on a set of bulk lots of animal by-product protein meals to introduce and test an approach based on near-infrared (NIR) spectroscopy and geostatistical analysis. Spectral data, provided by a fiber optic probe connected to a Fourier transform (FT) NIR spectrometer, were used to predict moisture and crude protein content at each sampling point. Variographic analysis was carried out for spatial structure characterization, while ordinary Kriging achieved continuous maps for those parameters. The results indicated that the methodology could be a first approximation to an approach that, properly complemented with the Theory of Sampling and supported by experimental validation in real-life conditions, would enhance efficiency and the decision-making process regarding safety and adulteration issues.

Evaluation of local approaches to obtain accurate near-infrared (NIR) equations for prediction of ingredient composition of compound feeds.

This research work investigated new methods to improve the accuracy of intact feed calibrations for the near-infrared (NIR) prediction of the ingredient composition. When NIR reflection spectroscopy, together with linear models, was used for the prediction of the ingredient composition, the results were not always acceptable. Therefore, other methods have been investigated. Three different local methods (comparison analysis using restructured near-infrared and constituent data [CARNAC]), locally weighed regression [LWR], and LOCAL) were applied to a large (N = 20 320) and heterogeneous population of non-milled feed compounds for the NIR prediction of the inclusion percentage of wheat and sunflower meal, as representative of two different classes of ingredients. Compared with partial least-squares regression, results showed considerable reductions of standard error of prediction values for all methods and ingredients: reductions of 59, 47, and 50% with CARNAC, LWR, and LOCAL, respectively, for wheat, and reductions of 49, 45, and 43% with CARNAC, LWR, and LOCAL, respectively, for sunflower meal. These results are a valuable achievement in coping with legislation and manufacture requirements concerning the labeling of intact feedstuffs.

Scanning elastic scattering spectroscopy detects metastatic breast cancer in sentinel lymph nodes.

A novel method for rapidly detecting metastatic breast cancer within excised sentinel lymph node(s) of the axilla is presented. Elastic scattering spectroscopy (ESS) is a point-contact technique that collects broadband optical spectra sensitive to absorption and scattering within the tissue. A statistical discrimination algorithm was generated from a training set of nearly 3000 clinical spectra and used to test clinical spectra collected from an independent set of nodes. Freshly excised nodes were bivalved and mounted under a fiber-optic plate. Stepper motors raster-scanned a fiber-optic probe over the plate to interrogate the node's cut surface, creating a 20x20 grid of spectra. These spectra were analyzed to create a map of cancer risk across the node surface. Rules were developed to convert these maps to a prediction for the presence of cancer in the node. Using these analyses, a leave-one-out cross-validation to optimize discrimination parameters on 128 scanned nodes gave a sensitivity of 69% for detection of clinically relevant metastases (71% for macrometastases) and a specificity of 96%, comparable to literature results for touch imprint cytology, a standard technique for intraoperative diagnosis. ESS has the advantage of not requiring a pathologist to review the tissue sample.

Elastic scattering spectroscopy for detection of cancer risk in Barrett's esophagus: experimental and clinical validation of error removal by orthogonal subtraction for increasing accuracy.

Elastic scattering spectroscopy (ESS) may be used to detect high-grade dysplasia (HGD) or cancer in Barrett's esophagus (BE). When spectra are measured in vivo by a hand-held optical probe, variability among replicated spectra from the same site can hinder the development of a diagnostic model for cancer risk. An experiment was carried out on excised tissue to investigate how two potential sources of this variability, pressure and angle, influence spectral variability, and the results were compared with the variations observed in spectra collected in vivo from patients with Barrett's esophagus. A statistical method called error removal by orthogonal subtraction (EROS) was applied to model and remove this measurement variability, which accounted for 96.6% of the variation in the spectra, from the in vivo data. Its removal allowed the construction of a diagnostic model with specificity improved from 67% to 82% (with sensitivity fixed at 90%). The improvement was maintained in predictions on an independent in vivo data set. EROS works well as an effective pretreatment for Barrett's in vivo data by identifying measurement variability and ameliorating its effect. The procedure reduces the complexity and increases the accuracy and interpretability of the model for classification and detection of cancer risk in Barrett's esophagus.

A methodology based on NIR-microscopy for the detection of animal protein by-products.

This study develops a methodology based on NIR-microscopy analysis and chemometric tools for the detection of animal protein by-products in mixtures, such as compound feeds and mixtures of ingredients, using a library of animal meal by-products only. The proposed methodology is a two-step strategy which worked better than the SIMCA approach it was compared with. In the first step, animal particles are identified using one of two methods, a global or a local distance measure. In the second, K-nearest-neighbours (KNN) is used to discriminate between terrestrial and fish particles. The models were developed using a training set comprising 11,727 spectra of pure terrestrial meals and 5843 of fish meals. KNN using second derivative spectra and five neighbours correctly classifies 98.5% of these samples under cross-validation. The procedure was validated using two external datasets, one made up of mixtures of species (fish and bovine), and a second of commercial compound feeds. The results obtained confirm that the procedure is able to reliably detect the presence of animal meals, although further work would be needed to develop it into an accurate quantitative method.

Taking NIR calibrations of feed compounds from the laboratory to the process: calibration transfer between predispersive and postdispersive instruments.

In the context of current demands in the animal feed industry for controls and analyses, the use of instruments that may be applied on the process line has acquired a significant interest. A key aspect is that the calibrations developed for quality control with instruments sited in the laboratory (at-line) must be transferred to instruments that will be used in the plant itself (online). This study evaluates the standardization and the calibration transfer between a grating monochromator instrument (predispersive) designed for laboratory analysis and a diode array instrument (postdispersive) more adapted to process conditions. Two procedures that correct differences between spectra of two instruments were tested: the patented algorithm by Shenk and Westerhaus and piecewise direct standardization (PDS). Although results were slightly better with PDS, both methods achieved good spectral matching between the two instruments, with levels of repeatability similar to that of the grating instrument itself. The calibration transfer was evaluated in terms of the standard error of prediction (SEP), which was considerably reduced after standardization. However, final calibration models to be used in the diode array instrument must contain spectra from both types of instruments to give acceptable prediction accuracy.

Principal component discriminant analysis.

The approach adopted involved two-stages. First the 11205 measurements in the mass spectrometry data were reduced to 14 scores by a principal component analysis of the centered but otherwise untreated and unscaled data matrix. Then a linear classifier was derived by linear discriminant analysis using these 14 scores as inputs. This number of scores was chosen by leave-one-out cross-validation on the training set, where it gave an overall error rate of 14%. Some indication of the information used in the classification may be obtained from an inspection of the coefficients of the linear classifier.

A Metropolis-Hastings algorithm for dynamic causal models.

Dynamic causal modelling (DCM) is a modelling framework used to describe causal interactions in dynamical systems. It was developed to infer the causal architecture of networks of neuronal populations in the brain [Friston, K.J., Harrison, L, Penny, W., 2003. Dynamic causal modelling. NeuroImage. Aug; 19 (4): 1273-302]. In current formulations of DCM, the mean structure of the likelihood is a nonlinear and numerical function of the parameters, which precludes exact or analytic Bayesian inversion. To date, approximations to the posterior depend on the assumption of normality (i.e., the Laplace assumption). In particular, two arguments have been used to motivate normality of the prior and posterior distributions. First, Gaussian priors on the parameters are specified carefully to ensure that activity in the dynamic system of neuronal populations converges to a steady state (i.e., the dynamic system is dissipative). Secondly, normality of the posterior is an approximation based on general asymptotic results, regarding the form of the posterior under infinite data [Friston, K.J., Harrison, L, Penny, W., 2003. Dynamic causal modelling. NeuroImage. Aug; 19 (4): 1273-302]. Here, we provide a critique of these assumptions and evaluate them numerically. We use a Bayesian inversion scheme (the Metropolis-Hastings algorithm) that eschews both assumptions. This affords an independent route to the posterior and an external means to assess the performance of conventional schemes for DCM. It also allows us to assess the sensitivity of the posterior to different priors. First, we retain the conventional priors and compare the ensuing approximate posterior (Laplace) to the exact posterior (MCMC). Our analyses show that the Laplace approximation is appropriate for practical purposes. In a second, independent set of analyses, we compare the exact posterior under conventional priors with an exact posterior under newly defined uninformative priors. Reassuringly, we observe that the posterior is, for all practical purposes, insensitive of the choice of prior.

Evaluation of the effects and interactions of mixing and oxygen transfer on the production of Fab' antibody fragments in Escherichia coli fermentation with gas blending.

Fermentations carried out at 450-L and 20-L scale to produce Fab' antibody fragments indicated a serious problem to control levels of dissolved oxygen in the broth due to the large oxygen demand at high cell densities. Dissolved oxygen tension (DOT) dropped to zero during the induction phase and it was hypothesised that this could limit product formation due to inadequate oxygen supply. A gas blending system at 20-L scale was employed to address this problem and a factorial 2(2) experimental design was executed to evaluate independently the effects and interaction of two main engineering factors: agitation rate and DOT level (both related to mixing and oxygen transfer in the broth) on Fab' yields. By comparison to the non-gas blending system, results in the gas blending system at same scale showed an increase in the production of Fab' by 77% independent of the DOT level when using an agitation rate of 500 rpm level and by 50% at an agitation rate of 1,000 rpm with 30% DOT. Product localisation in the cell periplasm of >90% was obtained in all fermentations. Results obtained encourage further studies at 450-L scale initially, to evaluate the potential of gas blending for the industrial production of Fab' antibody fragments.

The influence of physico-chemical and process conditions on the physical stability of plasmid DNA complexes using response surface methodology.

Research is progressing fast to find safe and effective methods of delivering therapeutic genes to patients afflicted with a range of genetic and acquired diseases that either do not respond at all, or respond poorly, to treatment with small-molecule drugs or protein-replacement therapy. A technical barrier that remains relates to the need for scalable operations that can consistently and reproducibly make large quantities of the therapeutic gene vectors under the current Good Manufacturing Practice ('cGMP'). The present investigation focuses on these issues and introduces a new method of assessing the engineering effects of process and material factors on the colloidal properties of plasmid-DNA delivery systems based on response surface methodology (RSM) and experimental techniques. Previously, experiments have shown that several factors can reduce the physical stability of non-viral delivery systems. Specifically, it has been demonstrated that the mean size and charge of plasmid DNA condensed by cationic agents are affected by many factors, including the pH and ionic strength of the buffer, and the method of preparation. For example, the method and intensity of mixing of the DNA with condensing and conjugating agents have been shown to be important. Using RSM to analyse new experimental data in the present paper, we report on the impact of these factors and, more crucially, the effects of interaction between the factors on the colloidal properties of the DNA-vector complexes. Specifically, for plasmid DNA condensed by poly-L-lysine, interactions between ionic strength, pH and DNA concentrations play a critical role. Whether poly-L-lysine should be used as a condensing agent in the final delivery system remains to be demonstrated. However, the use of RSM combined with the scaleable experimental approach described in this paper may be applied to other delivery systems.

A decision theory approach to fitness for purpose in analytical measurement.

The choice of an analytical procedure and the determination of an appropriate sampling strategy are here treated as a decision theory problem in which sampling and analytical costs are balanced against possible end-user losses due to measurement error. Measurement error is taken here to include both sampling and analytical variances, but systematic errors are not considered. The theory is developed in detail for the case exemplified by a simple accept or reject decision following an analytical measurement on a batch of material, and useful approximate formulae are given for this case. Two worked examples are given, one involving a batch production process and the other a land reclamation site.