Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

Well-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques

s1a 5 molecular stratification of autoimmune diseases based on epigenetic profiles

Systemic autoimmune diseases (SADs) are a group of chronic inflammatory conditions with autoimmune aetiology and many common clinical features, leading to a difficult diagnosis or deciding the appropriate treatment. Finding new treatments or applying the existing ones in a more effective way is especially hard in SADs due to the heterogeneity of molecular mechanisms within the same disease class. Based on this premise, the first step towards establishing a precision medicine strategy for SADs is to reclassify these conditions at the molecular level, which might result in a more homogenous stratification in terms of pathological molecular pathways. It is well known that the interplay of DNA methylation patterns and environmental factors, and between these, is determinant in the regulation of the immune system. This, along with the fact that the genetic contribution to disease is dependent on regulatory variants with very small effects, and the low concordance for autoimmunity in monozygotic twins suggests that epigenetic regulation may play an important role in the development of these diseases. Thus, DNA methylation information might be a valuable marker to reclassify the autoimmune disorders molecularly. We performed an unsupervised clustering analysis of genome-wide DNA methylation profiling of 437 cases distributed across 7 different clinical entities (rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, primary Sjogren´s syndrome, primary antiphospholipid antibody syndrome, mixed connective tissue disease and undifferentiated connective tissue disease) and 115 healthy individuals. In this analysis we were able to identify new groups of patients composed of the different clinical diagnoses but with common biological features

High spatial resolution analysis of ferromanganese concretions by LA-ICP-MS†

A procedure was developed for the determination of element distributions in cross-sections of ferromanganese concretions using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The effects of carrier flow rates, rf forward power, ablation energy, ablation spot size, repetition rate and number of shots per point on analyte intensity were studied. It is shown that different carrier gas flow rates are required in order to obtain maximum sensitivities for different groups of elements, thus complicating the optimisation of ICP parameters. On the contrary, LA parameters have very similar effects on almost all elements studied, thus providing a common optimum parameter set for the entire mass range. However, for selected LA parameters, the use of compromise conditions was necessary in order to compensate for relatively slow data acquisition by ICP-MS and maintain high spatial resolution without sacrificing the multielemental capabilities of the technique. Possible variations in ablation efficiency were corrected for mathematically using the sum of Fe and Mn intensities. Quantification by external calibration against matrix-matched standards was successfully used for more than 50 elements. These standards, in the form of pressed pellets (no binder), were prepared in-house using ferromanganese concentrates from a deep-sea nodule reference material as well as from shallow-marine concretions varying in size and having different proportions of three major phases: aluminosilicates, Fe- and Mn-oxyhydroxides. Element concentrations in each standard were determined by means of conventional solution nebulisation ICP-MS following acid digestion. Examples of selected inter-element correlations in distribution patterns along the cross-section of a concretion are given

Multivariate curve resolution of time course microarray data

BACKGROUND: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. RESULTS: In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data. CONCLUSION: Profiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements

Are movement disorders and sensorimotor injuries pathologic synergies? When normal multi-joint movement synergies become pathologic

The intact nervous system has an exquisite ability to modulate the activity of multiple muscles acting at one or more joints to produce an enormous range of actions. Seemingly simple tasks, such as reaching for an object or walking, in fact rely on very complex spatial and temporal patterns of muscle activations. Neurological disorders such as stroke and focal dystonia affect the ability to coordinate multi-joint movements. This article reviews the state of the art of research of muscle synergies in the intact and damaged nervous system, their implications for recovery and rehabilitation, and proposes avenues for research aimed at restoring the nervous system’s ability to control movement

Stable Isotope Tracking of Endangered Sea Turtles: Validation with Satellite Telemetry and δ15N Analysis of Amino Acids

Effective conservation strategies for highly migratory species must incorporate information about long-distance movements and locations of high-use foraging areas. However, the inherent challenges of directly monitoring these factors call for creative research approaches and innovative application of existing tools. Highly migratory marine species, such as marine turtles, regularly travel hundreds or thousands of kilometers between breeding and feeding areas, but identification of migratory routes and habitat use patterns remains elusive. Here we use satellite telemetry in combination with compound-specific isotope analysis of amino acids to confirm that insights from bulk tissue stable isotope analysis can reveal divergent migratory strategies and within-population segregation of foraging groups of critically endangered leatherback sea turtles (Dermochelys coriacea) across the Pacific Ocean. Among the 78 turtles studied, we found a distinct dichotomy in δ15N values of bulk skin, with distinct “low δ15N” and “high δ15N” groups. δ15N analysis of amino acids confirmed that this disparity resulted from isotopic differences at the base of the food chain and not from differences in trophic position between the two groups. Satellite tracking of 13 individuals indicated that their bulk skin δ15N value was linked to the particular foraging region of each turtle. These findings confirm that prevailing marine isoscapes of foraging areas can be reflected in the isotopic compositions of marine turtle body tissues sampled at nesting beaches. We use a Bayesian mixture model to show that between 82 and 100% of the 78 skin-sampled turtles could be assigned with confidence to either the eastern Pacific or western Pacific, with 33 to 66% of all turtles foraging in the eastern Pacific. Our forensic approach validates the use of stable isotopes to depict leatherback turtle movements over broad spatial ranges and is timely for establishing wise conservation efforts in light of this species’ imminent risk of extinction in the Pacific

Effects of Wearing a Surgical Mask During Moderate Exercise on Muscle and Blood Oxygen Saturation

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Gut Microbial and Metabolic Responses to Salmonella enterica Serovar Typhimurium and Candida albicans

The gut microbiota is increasingly recognized for playing a critical role in human health and disease, especially in conferring resistance to both virulent pathogens such as Salmonella, which infects 1.2 million people in the United States every year (E. Scallan, R. M. Hoekstra, F. J. Angulo, R. V. Tauxe, et al., Emerg Infect Dis 17:7–15, 2011, https://doi.org/10.3201/eid1701.P11101), and opportunistic pathogens like Candida, which causes an estimated 46,000 cases of invasive candidiasis each year in the United States (Centers for Disease Control and Prevention, Antibiotic Resistance Threats in the United States, 2013, 2013). Using a gnotobiotic mouse model, we investigate potential changes in gut microbial community structure and function during infection using metagenomics and metabolomics. We observe that changes in the community and in biosynthetic gene cluster potential occur within 3 days for the virulent Salmonella enterica serovar Typhimurium, but there are minimal changes with a poorly colonizing Candida albicans. In addition, the metabolome shifts depending on infection status, including changes in glutathione metabolites in response to Salmonella infection, potentially in response to host oxidative stress.The gut microbiota confers resistance to pathogens of the intestinal ecosystem, yet the dynamics of pathogen-microbiome interactions and the metabolites involved in this process remain largely unknown. Here, we use gnotobiotic mice infected with the virulent pathogen Salmonella enterica serovar Typhimurium or the opportunistic pathogen Candida albicans in combination with metagenomics and discovery metabolomics to identify changes in the community and metabolome during infection. To isolate the role of the microbiota in response to pathogens, we compared mice monocolonized with the pathogen, uninfected mice “humanized” with a synthetic human microbiome, or infected humanized mice. In Salmonella-infected mice, by 3 days into infection, microbiome community structure and function changed substantially, with a rise in Enterobacteriaceae strains and a reduction in biosynthetic gene cluster potential. In contrast, Candida-infected mice had few microbiome changes. The LC-MS metabolomic fingerprint of the cecum differed between mice monocolonized with either pathogen and humanized infected mice. Specifically, we identified an increase in glutathione disulfide, glutathione cysteine disulfide, inosine 5’-monophosphate, and hydroxybutyrylcarnitine in mice infected with Salmonella in contrast to uninfected mice and mice monocolonized with Salmonella. These metabolites potentially play a role in pathogen-induced oxidative stress. These results provide insight into how the microbiota community members interact with each other and with pathogens on a metabolic level