Kernel and nonlinear canonical correlation analysis

We review a neural implementation of the statistical technique of Canonical Correlation Analysis (CCA) and extend it to nonlinear CCA. We then derive the method of kernel-based CCA and compare these two methods on real and artificial data sets before using both on the Blind Separation of Sources.

Antibacterial activity of eravacycline (TP-434), a novel fluorocycline, against hospital and community pathogens

Eravacycline (TP-434 or 7-fluoro-9-pyrrolidinoacetamido-6-demethyl-6-deoxytetracycline) is a novel fluorocycline that was evaluated for antimicrobial activity against panels of recently isolated aerobic and anaerobic Gram-negative and Gram-positive bacteria. Eravacycline showed potent broad-spectrum activity against 90% of the isolates (MIC90) in each panel at concentrations ranging from ≤0.008 to 2 μg/ml for all species panels except those of Pseudomonas aeruginosa and Burkholderia cenocepacia (MIC90 values of 32 μg/ml for both organisms). The antibacterial activity of eravacycline was minimally affected by expression of tetracycline-specific efflux and ribosomal protection mechanisms in clinical isolates. Furthermore, eravacycline was active against multidrug-resistant bacteria, including those expressing extended-spectrum β-lactamases and mechanisms conferring resistance to other classes of antibiotics, including carbapenem resistance. Eravacycline has the potential to be a promising new intravenous (i.v.)/oral antibiotic for the empirical treatment of complicated hospital/health care infections and moderate-to-severe community-acquired infections.

A neural implementation of canonical correlation analysis

We derive a new method of performing Canonical Correlation Analysis with Artificial Neural Networks. We demonstrate the network's capabilities on artificial data and then compare its effectiveness with that of a standard statistical method on real data. We demonstrate the capabilities of the network in two situations where standard statistical techniques are not effective: where we have correlations stretching over three data sets and where the maximum nonlinear correlation is greater than any linear correlation. The network is also applied to Becker's (Network: Computation in Neural Systems, 1996, 7:7-31) random dot stereogram data and shown to be extremely effective at detecting shift information.

A randomized crossover study to assess the effect of an oat-rich diet on glycaemic control, plasma lipids and postprandial glycaemia, inflammation and oxidative stress in Type 2 diabetes

AIMS

In the UK, lifestyle intervention is first-line management in Type 2 diabetes. It is unclear what type of diet is most efficacious for improving glycaemic control. This study investigated the effects of an oat-enriched diet on glycaemic control, postprandial glycaemia, inflammation and oxidative stress compared with standard dietary advice.

METHODS

In a randomized crossover design, 27 volunteers with Type 2 diabetes, managed on diet and lifestyle only, were observed for two consecutive 8-week periods following either the oat-enriched diet or re-enforced standard dietary advice. Volunteers attended at baseline (habitual intake) and 8 and 16 weeks. Measurements included basic clinical measurements and fasted and postprandial (3-h) glucose and insulin in response to a healthy test meal. Markers of inflammation and oxidative stress, including high-sensitivity C-reactive protein, interleukin 6, interleukin 18, tumour necrosis factor-alpha, adiponectin, thiobarbituric acid reactive substances, oxygen radical antioxidant capacity, oxidized LDL and urinary isoprostanes, were also measured at fasting and in the postprandial period.

RESULTS

There were no diet-related effects on glycaemic control or glycaemic or insulinaemic responses to the test meal. Total cholesterol (5.1 ± 1.0 vs. 4.9 ± 0.8 mmol/l, P = 0.019) concentrations declined following the oat-enriched diet compared with standard dietary advice. There was a postprandial decline in adiponectin concentration (P = 0.009), but no effect of dietary intervention. None of the measures of oxidative stress or inflammation were altered by the oat-enriched diet compared with standard dietary advice.

CONCLUSION

The oat-enriched diet had a modest impact on lipid lowering, but did not impact on oxidative stress or inflammation in these volunteers with Type 2 diabetes.

Introducing asymmetry into interneuron learning

A review is given of a new artificial neural network architecture in which the weights converge to the principal component subspace. The weights learn by only simple Hebbian learning yet require no clipping, normalization or weight decay. The net self-organizes using negative feedback of activation from a set of "interneurons" to the input neurons. By allowing this negative feedback from the interneurons to act on other interneurons we can introduce the necessary asymmetry to cause convergence to the actual principal components. Simulations and analysis confirm such convergence.

Stochastic ICA contrast maximisation using OJA's nonlinear PCA algorithm

Independent Component Analysis (ICA) is an important extension of linear Principal Component Analysis (PCA). PCA performs a data transformation to provide independence to second order, that is, decorrelation. ICA transforms data to provide approximate independence up to and beyond second order yielding transformed data with fully factorable probability densities. The linear ICA transformation has been applied to the classical statistical signal-processing problem of Blind Separation of Sources (BSS), that is, separating unknown original source signals from a mixture whose mode of mixing is undetermined. In this paper it is shown that Oja's Nonlinear PCA algorithm performs a general stochastic online adaptive ICA. This analysis is corroborated with three simulations. The first separates unknown mixtures of original natural images, which have sub-Gaussian densities, the second separates linear mixtures of natural speech whose densities are super-Gaussian. Finally unknown mixtures of original images, which have both sub- and super-Gaussian densities are separated.

Dexamethasone effects on cerebral protein synthesis prior to and following hypoxia-ischemia in immature rat

We hypothesized that the neuroprotection against cerebral hypoxic-ischemic damage observed with dexamethasone treatment in immature rats is related to a change in cerebral protein synthesis. Six-day-old Wistar rats were injected with either vehicle (10 ml/kg) or dexamethasone (0.1 mg/kg) 24 h prior to cerebral hypoxia-ischemia. Local cerebral protein synthesis (incorporation of 14C-leucine into proteins) was measured in 7-day-old rats during normoxia, during hypoxia-ischemia, and after hypoxia-ischemia which was produced with right carotid artery ligation and 2-h exposure to 8% O2. In normoxic controls, cerebral protein synthesis was similar in dexamethasone and vehicle-treated animals. During hypoxia-ischemia, local cerebral protein synthesis decreased markedly (p < 0.0001) in ischemic regions ipsilateral to the occlusion, irrespective of treatment. After hypoxia-ischemia, protein synthesis declined even further in vehicle-treated animals. Reductions in protein synthesis were substantially more severe in vehicle- than dexamethasone-treated animals, particularly after hypoxia-ischemia (p < 0.0001). Thus, neuroprotection with dexamethasone is not related to a reduction in basal levels of cerebral protein synthesis, but is associated with an improved protein synthesis during and following hypoxia-ischemia.

Dietary carbohydrate rather than protein intake drives colonic microbial fermentation during weight loss

Purpose

High protein weight loss diets are effective in aiding body weight management. However, high protein and low carbohydrate intakes can alter colonic fermentation profiles in humans and may impact on colonic health. This study aims to identify the most important dietary contributors to colonic fermentation during diet-controlled weight loss.

Methods

Overweight or obese male volunteers (n = 18) consumed a body weight maintenance diet (fed at 1.5× basic metabolic rate, BMR) followed by three weight loss diets (fed at 1× BMR) for 10 days each in a cross-over design. Weight loss diets were designed as normal protein (NPWL, 15% of energy from protein, 55% from carbohydrate), normal protein enriched with free amino acids and moderate amounts of carbohydrate (NPAAWL, 15% of energy from protein, 15% from free AA, 40% from carbohydrate) or high protein containing moderate amounts of carbohydrate (HPWL, 30% of energy from protein, 40% from carbohydrate). Faecal samples collected at the end of each diet period were profiled for dietary metabolites using LC–MS/MS.

Results

This study shows that the NPWL diet only induced very minor changes in the faecal metabolome, whereas NPAAWL and HPWL diets decreased carbohydrate-related metabolites (butyrate, ferulic acid) and increased protein-related metabolites. Most faecal metabolites were correlated with dietary carbohydrate and not protein intake.

Conclusion

This study demonstrates that dietary carbohydrate is the main driver of colonic fermentation in humans and that a balance between dietary carbohydrate and protein should be maintained when designing safe, effective and healthy weight loss diets.

Electronic supplementary material

The online version of this article (10.1007/s00394-018-1629-x) contains supplementary material, which is available to authorised users.

A scale-invariant feature map

We use a simple network which uses negative feedback of activation and simple Hebbian learning to self-organize in such a way as to produce a hierarchical classification network. By adding neighbourhood relations to its learning rule, we create a feature map which has the property of retaining the angular properties of the input data, i.e. vectors of similar directions are classified similarly regardless of their magnitude. We use neither re-normalization of weights nor data preprocessing in the network despite using competition based on maximizing the neuron's activation.

The combined use of self-organizing maps and Andrews' Curves

The use of self-organizing maps to analyze data often depends on finding effective methods to visualize the SOM's structure. In this paper we propose a new way to perform that visualization using a variant of Andrews' Curves. Also we show that the interaction between these two methods allows us to find sub-clusters within identified clusters. Perhaps more importantly, using the SOM to pre-process data by identifying gross features enables us to use Andrews' Curves on data sets which would have previously been too large for the methodology. Finally we show how a three way interaction between the human user and these two methods can be a valuable exploratory data analysis tool.

A simple adjustable functional brace for tibial fractures

The use of an adjustable functional brace for fractures of the tibia has several practical and theoretical advantages. The high cost of commercially available braces has limited their use until now. We describe a simple, inexpensive orthosis which has given good results in a clinical study for 32 unselected fractures. The fractures' alignment was well maintained. The brace was comfortable to wear and there were few complications. It is hoped that the low cost of this simple custom-made brace will encourage more widespread use of this technique.

Investigations of the horse conceptus via magnetic resonance imaging (MRI) and nitroxide spin labels as contrast agents

Results are presented which illustrate the usefulness of Magnetic Resonance Imaging as applied to the study of living embryos. Nitroxide spin labels were employed as contrast agents to study the structure and properties of the embryos. These spin labels offer the additional advantage that they may potentially be bound to biologically important molecules thereby imparting the ability to produce contrast in the MR images to these new molecules. The horse conceptus was chosen over other embryos due to its large size. Whereas the embryos of cattle and swine are sub-millimetre in size, the horse conceptus is on the order of 10 millimetres in diameter. The availability of microscopic imaging gradient coils will allow the techniques developed in this study to be applied to the smaller embryos of other species.

Modelling multiple-cause structure using rectification constraints

We present an artificial neural network which self-organizes in an unsupervised manner to form a sparse distributed representation of the underlying causes in data sets. This coding is achieved by introducing several rectification constraints to a PCA network, based on our prior beliefs about the data. Through experimentation we investigate the relative performance of these rectifications on the weights and/or outputs of the network. We find that use of an exponential function on the output to the network is most reliable in discovering all the causes in data sets even when the input data are strongly corrupted by random noise. Preprocessing our inputs to achieve unit variance on each is very effective in helping us to discover all underlying causes when the power on each cause is variable. Our resulting network methodologies are straightforward yet extremely robust over many trials.

A Computer Vision Approach toward Verifying CFD Models of Stirred Tank Reactors

Mixing is one of the most important nonchemical considerations in the design of scalable processes. While noninvasive imaging approaches to deliver a quantifiable understanding of mixing dynamics are well-known, the use of imaging to verify computational fluid dynamics (CFD) models remains in its infancy. Herein, we use colorimetric reactions and our kinetic imaging software, Kineticolor, to explore (i) the correlation of imaging kinetics with pH probe measurements, (ii) feed point sensitivity for Villermaux-Dushman-type competing parallel reactions, and (iii) the use of experimental imaging kinetic data to qualitatively assess CFD models. We report further evidence that the influences of the stirring rate, baffle presence, and feed position on mixing in a tank reactor can be informatively captured with a camcorder and help experimentally verify CFD models. Overall, this work advances scarce little precedent in demonstrating the use of computer vision to verify CFD models of fluid flow in tank reactors.

Parallel and High Throughput Reaction Monitoring with Computer Vision

We report the development and applications of a computer vision based reaction monitoring method for parallel and high throughput experimentation (HTE). Whereas previous efforts reported methods to extract bulk kinetics of one reaction from one video, this new approach enables one video to capture bulk kinetics of multiple reactions running in parallel. Case studies, in and beyond well-plate high throughput settings, are described. Analysis of parallel dye-quenching hydroxylations, DMAP-catalysed esterification, solid-liquid sedimentation dynamics, metal catalyst degradation, and biologically-relevant sugar-mediated nitro reduction reactions have each provided insight into the scope and limitations of camera-enabled high throughput kinetics as a means of widening known analytical bottlenecks in HTE for reaction discovery, mechanistic understanding, and optimisation. It is envisaged that the nature of the multi-reaction time-resolved datasets made available by this analytical approach will later serve a broad range of downstream efforts in machine learning approaches to exploring chemical space.

Serum lipid profile determines platelet reactivity to native and modified LDL-cholesterol in humans

The effects of thrombin (0.2 U/ml) and native (n-LDL), malondialdehyde-modified (MDA-LDL) and auto-oxidized (ox-LDL) low-density lipoproteins (20 micrograms of protein/ml) on platelet activation were evaluated in seven hyperlipidemic patients and compared to seven controls (fasting serum cholesterol 8.49 +/- 0.5 and 4.61 +/- 0.4 mM, respectively). Basal and thrombin-induced increases in platelet intracellular free calcium ion concentration ([Ca2+]i; fura-2) were similar in hyperlipidemic patients and controls (45 +/- 5 vs 42 +/- 3 and 635 +/- 51 vs 599 +/- 69 mM, respectively). n-LDL, MDA-LDL and ox-LDL increased basal [Ca2+]i (16, 36 and 81 percent, p < 0.01 between LDL-types), increases were consistently smaller in patients. There was an inverse relationship between LDL-induced responses and fasting serum LDL cholesterol as well as LDL/HDL ratio. In conclusion, modified LDL activated platelets to a greater extent than n-LDL, suggesting different types of LDL-receptors. Their agonistic effect was inversely related to the fasting serum lipid profile, suggesting that blunting of platelet responses to LDL could represent a protective mechanism in hyperlipidemic patients.

Computer vision as a new paradigm for monitoring of solution and solid phase peptide synthesis

We report a strategy for the camera-enabled non-contact colourimetric reaction monitoring and optimisation of amide bond formation, mediated by coupling reagents. For amide bond formation in solution phase, investigation of reactions mediated by HATU, PyAOP, and DIC/Oxyma evidenced correlations between colour parameters extracted from video data and conversion to amide product measured by off-line HPLC analysis of concentration. These correlations, supported by mutual information analysis, were further investigated using video recordings of solid phase peptide synthesis (SPPS), co-analysed by off-line HPLC to track remaining unreacted substrate in solution. An optimisation method of coupling time in SPPS was derived from ΔE (a measurement of colour contrast), giving comparable isolated peptide yield and purity at 65-95% reduced overall reaction time. The same colour data enabled data-rich monitoring of reaction rate attenuation, consisted with computationally-derived measures of amino acid steric bulk. These findings provide a foundation for exploring the use of camera technology and computer vision towards automated and online mechanistic profiling of SPPS.