Subsequent Principal Component Analysis Research Articles

Patients admitted to more research-active hospitals have more confidence in staff and are better informed about their condition and medication: Results from a retrospective cross-sectional study.

Clinical research activity in hospitals is associated with reduced mortality and improved overall care quality. In England, the latter is a compound score of several elements and both staff and inpatient feedback form part of the Care Quality Commission (CQC) ratings. The objective of this study was to determine if NHS Trusts' National Institute for Health Research (NIHR) study activity data correlates with specific outcomes from national NHS staff and patient surveys. Retrospective cohort design involving data for 129 English NHS hospital Trusts, including scores from recent national NHS staff and inpatient surveys and NIHR data. Statistical approach involved Spearman correlation analyses, with cut-off P value≤0.01 for qualification for subsequent principal component analysis (correlation coefficient cut-off value 0.20). Outcomes of one staff survey question (staff recommendation of the organization as a place to work or receive treatment) and multiple outcomes of inpatient survey questions were positively associated with increased NIHR-adopted clinical research activity. Better quality of information provision to patients was the dominant theme, though a higher degree of observed staff teamwork, more confidence in the treating doctors, and a better overall inpatient experience also correlated significantly. The number of different studies contributed more to positive associations with survey outcomes compared with the number of recruited participants into research. Survey elements of the CQC appraisal of English NHS Hospital Trusts are significantly associated with increased clinical research activity levels; it appears to drive better information provision to inpatients-particularly around medicine management-and contribute to a better inpatient experience overall, whilst staff are more likely to recommend their own organization. Despite clinical research activity forming a very small fraction of overall NHS activity, it has an indirect positive effect on staff and Trust performance that is measurable at patient level.

Data Independent Acquisition Mass Spectrometry Can Identify Circulating Proteins That Predict Future Weight Loss with a Diet and Exercise Programme.

We investigated biological determinants that would associate with the response to a diet and weight loss programme in impaired glucose regulation (IGR) people using sequential window acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry (MS), a data acquisition method which complement traditional mass spectrometry-based proteomics techniques. Ten women and 10 men with IGR underwent anthropometric measurements and fasting blood tests. SWATH MS was carried out with subsequent immunoassay of specific peptide levels. After a six-month intervention, 40% of participants lost 3% or more in weight, 45% of patients remained within 3% of their starting weight and 15% increased their weight by 3% or more. Hemoglobin A1c (HbA1C) level was reduced with weight loss with improvements in insulin sensitivity. SWATH MS on pre-intervention samples and subsequent principal component analysis identified a cluster of proteins associated with future weight loss, including insulin-like growth factor-II (IGF-II) and Vitamin D binding protein. Individuals who lost 3% in weight had significantly higher baseline IGF-II levels than those who did not lose weight. SWATH MS successfully discriminated between individuals who were more likely to lose weight and potentially improve their sensitivity to insulin. A higher IGF-II baseline was predictive of success with weight reduction, suggesting that biological determinants are important in response to weight loss and exercise regimes. This may permit better targeting of interventions to prevent diabetes in the future.

Psychological trauma, somatization, dissociation, and psychiatric comorbidities in patients with psychogenic nonepileptic seizures compared with those in patients with intractable partial epilepsy

ObjectiveThe objective of this study was to compare patients with intractable epilepsy with patients with psychogenic nonepileptic seizures (PNES) on the presence of psychological traumas, clinical factors, and psychological measures of somatization and dissociation. BackgroundSeveral studies have reported a high prevalence of psychological trauma in patients with PNES, while less have examined the prevalence of psychological trauma in patients with epilepsy and compared both groups. Reports have been somewhat divergent with some describing significantly higher prevalence in physical abuse, others, in emotional abuse/neglect, and others, in sexual abuse in patients with PNES compared with those in patients with epilepsy. MethodsThis is a retrospective study of 96 patients (61 women, 35 men) with intractable epilepsy (2009 to 2017) and 161 patients (107 women, 54 men) with PNES (2008 to 2018).Demographic and clinical (psychological trauma, depression, anxiety, seizure frequency, and number of antiepileptic drugs) data were collected. The Trauma Symptom Inventory II and the Minnesota Multiphasic Personality Inventory 2RF were administered. ResultsPatients with PNES differed significantly from those with intractable epilepsy on sexual trauma (χ2 (5df, N = 257) =9.787, p < .002) and “other” trauma (χ2 (5df, N = 257) = 17.9076, p < .000). On psychological measures, there was a significant difference on Somatization scores in patients with PNES (M = 59.63, SD = 11.47) and patients with intractable epilepsy (M = 53.98, SD = 11.31); t(173) = 2.8396, p = .0051, but no difference was noted on a measure of Dissociation. Subsequent principal components analysis revealed that the first 3 principal components (sexual, physical, and other trauma) explained 74.19% of the variability, and that one principal component (dissociation, somatization, demoralization) explained 61.57% of the variability. However, after adjusting for the effects of covariates, only the presence of trauma discriminated between epilepsy and PNES. ConclusionsPatients with PNES diagnoses differed from those with epilepsy on a Somatization scale but not on Dissociation or Intrusive Experiences and exhibited significantly higher rates of sexual and “other” trauma compared with those with intractable epilepsy. However, subsequent analyses revealed that a history of psychological trauma was the only condition found to discriminate between patients with PNES and those with epilepsy. These findings suggest that during initial workup and diagnosis, when patients report a history of psychological trauma (sexual or otherwise) a psychogenic nonepileptic etiology should be strongly considered in the differential diagnosis.

Mapping and Quantitating Penetrating Vessels in Cortical Brain Using Eigen-Decomposition of OCT Signals and Subsequent Principal Component Analysis

Penetrating vessels bridge the mesh of communicating vessels on the surface of the cortex with the subsurface microvascular beds that feed the underlying neural tissue. Their accurate identification in vivo is important in the investigations of neural degenerative diseases, e.g., Alzheimer's disease and stroke. Here, we propose an efficient method to automatically map cortical penetrating vessels based on an eigen decompensation analysis of the optical coherence tomography (OCT) and OCT angiographic signals. We first project the ensemble of repeated OCT signals into a feature space that represents the power spectral components of eigenvectors through a well-known eigen-decomposition method. A principal component analysis is then applied to the spectral components to identify penetrating vessels. We find that the spectral components of OCT signals captured from a cortical brain tissue follow a subtle logistic distribution, which is, however, broken down if there are penetrating vessels. Such feature allows for an automatic mapping of penetrating arterioles and ascending venules from large volume OCT-scan datasets, accordingly contributing to the topological and morphological analyses of cortical microvasculature in the functioning brains. To demonstrate the utility of the proposed method, an OCT angiography imaging platform is used to show the functional behavior of penetrating blood flows before and after an ischemic insult in an established middle cerebral artery occlusion (MCAO) model of rodent, where an average of 41% reduction in penetrating vessel density ( n = 5 animals) was observed in the ischemic region post-MCAO.

RPPAs for Cell Subpopulation Analysis.

Understanding cellular heterogeneity is a challenge for cell biology, particularly in the fields of stem cell and cancer biology. We recently established a reverse phase protein array (RPPA)-modified method, colony lysate array (CoLA), which enables proteomic profiling of individual subpopulations (i.e., colonies) derived from various cell proliferative conditions. Here we describe two independent CoLA assays for a functional subpopulation, drug-tolerant persisters (DTPs), which are considered to mimic the origin of chemotherapeutic cancer relapse. In the first study, we analyzed individual DTPs derived from different cell types grown in the presence of different drugs. A hierarchical clustering analysis of DTPs using CoLA revealed two types of clonal populations, including those that expressed stem cell-associated proteins as well as epithelium-associated proteins. Subsequent principal component analyses demonstrated that the DTPs clustered on the basis of their proteomic profiles, which could change in response to drugs and doses. Another study was designed to identify signaling pathways that may be associated with drug resistance using a pair of 5-fluorouracil-tolerant and parental gastric cancer cell lines. Although a hierarchical clustering analysis did not show any specific clusters for the tolerant line, a drug dose-response analysis revealed that phosphorylation of PI3K and AKT increased and decreased, respectively, specifically in the tolerant cell line. These frameworks that readily profile small subpopulations isolated from an external stresses may be applicable to a wide variety of heterogeneous cellular samples.

Major ion and dissolved heavy metal geochemistry, distribution, and relationship in the overlying water of Dongting Lake, China.

Deteriorating lake water quality has become a serious environmental issue around the globe. Heavy metals dissolved in the overlying water of lakes are notably more toxic than those found in lake sediment. Given this, we sought to better understand the characteristics of particular major ion and dissolved heavy metal in Dongting Lake-the second largest freshwater lake in China. Overlying water samples were collected from Dongting Lake to investigate the major ion geochemistry and to examine the relationship between the major ions and dissolved heavy metals. Chemical analysis of the overlying water showed that the average cation concentration was the highest for Ca2+, followed by Mg2+, Na+, and K+. Similarly, the highest anion concentration was SO42-, followed by Cl-, NO3-, and HCO3-. Total dissolved solids in the overlying water of Dongting Lake ranged from 66.19 to 159.20mg/L, with an average value of 93.13mg/L. The predominant hydrochemical type was Ca-SO4. The mean concentrations of dissolved heavy metal in both surface and deep waters decreased in the following order: Zn > Cr > Ni > Cu > Pb > Cd. Importantly, all of the selected heavy metals tested in the overlying water were lower than the corresponding toxicity reference values. Co-occurrence network analyses were performed and compared the correlations between all measured major ions and heavy metals. Results of the subsequent principal component analyses revealed that heavy metal levels in the aquatic environment primarily originated from natural processes and were enhanced by anthropogenic activities.

Profiling neurotransmitters in a crustacean neural circuit for locomotion.

Locomotor systems are widely used to study rhythmically active neural networks. These networks have to be coordinated in order to produce meaningful behavior. The crayfish swimmeret system is well suited to investigate such coordination of distributed neural oscillators because the neurons and their connectivity for generating and especially for coordinating the motor output are identified. The system maintains a fixed phase lag between the segmental oscillators, independent of cycle period. To further the understanding of the system’s plasticity for keeping the phase lag fixed, we profiled the neurotransmitters used by the Coordinating Neurons, which are necessary and sufficient for coordination of the segmental oscillators. We used a combination of electrophysiological, immunohistochemical, and mass spectrometric methods. This arrangement of methods ensured that we could screen for several specific neurotransmitters, since a single method is often not suitable for all neurotransmitters of interest. In a first step, to preselect neurotransmitter candidates, we investigated the effect of substances known to be present in some swimmeret system neurons on the motor output and coordination. Subsequently, we demonstrated electrophysiologically that the identified synapse between the Coordinating Neurons and their target is mainly chemical, but neither glutamate antagonist nor γ-aminobutyric acid antagonist application affected this synapse. With immunohistochemical experiments, we provide strong evidence that the Coordinating Neurons are not serotonergic. Single-cell MALDI-TOF mass spectrometry with subsequent principal component analysis identified acetylcholine as the putative neurotransmitter for both types of Coordinating Neurons.

Chemical composition, genetic diversity, antibacterial, antifungal and antioxidant activities of camphor-basil (Ocimum kilimandscharicum Guerke)

This study investigates the variations in the chemical, genetic, and biological activity (antibacterial, antifungal, and antioxidant) profiles of thirteen populations of Ocimum kilimandscharicum Guerke (OK1-OK13) from India. Correlations between chemical composition and the biological activities of O. kilimandscharicum were assessed. The essential oils (EOs) of different populations, extracted by hydrodistillation process were characterised by gas chromatography-flame ionization detector (GC-FID) and gas chromatography-mass spectrometry (GC–MS). The antimicrobial activities of the EOs were evaluated by disc diffusion and micro dilution broth assays. The antioxidant activity of the EOs was determined by ferric reducing antioxidant power (FRAP) assay. The essential oil (EO) content varied significantly among the studied populations (0.15–0.93%). The EO analysis, and subsequent cluster and principal component analyses classified the populations in three distinct chemotypes, namely camphor (52.0–57.2%), linalool (65.2–91.0%), and phenylpropanoid/sesquiterpene. Amplification of genomic DNA using 20 inter simple sequence repeat (ISSR) primers yielded a total of 224 loci, out of which 210 loci were polymorphic in nature, representing 93.75% polymorphism. Pearson coefficient correlation (r = 0.32) suggesting low correlation between the distances obtained by molecular markers and EO compositions. The EO of population OK12 showed significant activity against Staphylococcus epidermidis, Enterococcus faecalis, and Staphylococcus aureus. However, EOs of populations OK11 and OK13 showed significant activity against Candida albicans (clinical isolate) and Candida albicans (ATCC), respectively. The EO of OK13 exhibited significant antioxidant activity. In-vitro safety evaluation study revealed that the EOs of most of the populations showed no significant toxicity against peritoneal macrophages cells. It is concluded that chemical and genetic profiles of O. kilimandscharicum varied considerably and these variations determined changes in its biological activities. The observations of this study could pave the way to optimize the use of O. kilimandscharicum populations in relation with its biological properties.

Fatty acidomics: Evaluation of the effects of thermal treatments on commercial mussels through an extended characterization of their free fatty acids by liquid chromatography – Fourier transform mass spectrometry

An unprecedented characterization of free fatty acids (FFA) in the lipid extracts of fresh or thermally treated mussels of sp. Mytilus galloprovincialis, including up to 128 saturated, mono- or poly-unsaturated and 63 oxidized (i.e., modified by hydroxylic, carbonylic and/or epoxylic groups) compounds, was achieved using reverse phase chromatography coupled to electrospray ionization-Fourier transform single and tandem mass spectrometry (RPC-ESI-FTMS,MS/MS). Subsequent Principal Components Analysis (PCA) evidenced several effects of thermal treatments on the mussel FFA profiles. In particular, death-inducing low temperature treatments (freezing at −16 °C or refrigeration at 4 °C for several days) induced a peculiar increase in the incidence of FFA, whereas the effect was absent in mussels undergoing death upon prolonged storage at room temperature (25 °C, 6 h) or fast cooking (100 °C, 5 min). Alive mussels, either fresh or resulting from short term (up to 48 h) refrigeration were actually indistinguishable by PCA, although subtle seasonal effects were observed.

Inter-joint coordination changes during and after muscle fatigue

People produce multi-joint movements by organizing many degrees of freedom into a few major covarying relationships, indicating a high level of inter-joint coordination. These relationships can be identified using data decomposition analyses (e.g. principal components analysis, non-negative matrix factorization). The purpose of this study was to determine how movement coordination changes during muscle fatigue by analyzing the covariance structure of multi-joint movements. Sixteen (16) healthy adults completed a continuous, timed ratcheting task with the right arm for three 1-min intervals before, during, and after an intermittent shoulder fatigue protocol. Joint angles from the right arm and trunk were tracked for subsequent principal components analysis. Principal component waveforms were constructed from the original joint angles, and changes in the waveforms during fatigue were assessed using cross-correlations. The variance explained by the first four principal components reached a maximum of 90.5% in the second minute of the pre-test and decreased to a minimum of 86.0% in the last minute of fatigue (p = .033). In the last minute of the post-test, explained variance (87.1%) did not differ from any other pre, fatigue, or post-test time point (p > .23). These results suggest that inter-joint coordination decreased during fatigue. Changes in the movement patterns and principal component waveforms suggest that subjects adopted a more rigid movement strategy when fatigued. However, the rigid movement strategy was not observed during the post-test. The results suggest that people adopted a new pattern of inter-joint coordination while using novel kinematics.

Classification of Chinese wine varieties using 1H NMR spectroscopy combined with multivariate statistical analysis

In this study, the feasibility of discriminating grape varieties of Chinese red and white wines was investigated using 1H NMR spectroscopy in combination with a multivariate statistical procedure consisting of two steps: principal component analysis (PCA) plus linear discriminant analysis (LDA). Three grape varieties of red wines (Cabernet Sauvignon, Rose Honey, Cabernet Gernischt) and white wines (Ugni Blanc, Long Yan, Chardonnay) were examined, respectively. A segment-wise peak alignment was employed to handle peak misalignments of recorded 1H NMR spectra. Binning of the aligned 1H NMR spectra was performed for data reduction. The resulting bins were employed as input variables for the subsequent PCA and LDA analyses. The combination of PCA and LDA yielded in a sufficient discrimination of the examined grape varieties. The validity of the PCA/LDA model was confirmed by internal leave-one-out cross validation (LOOCV) as well as by external repeated double random cross validation (RDRCV). LOOCV and RDRCV led to average correct classification rates of 82% and 83% for red wine varieties, respectively, and 94% and 90% for white wine varieties, respectively. The results demonstrate that 1H NMR spectroscopy combined with multivariate analysis is an effective tool for verifying the authenticity of Chinese wines.

Unevenly Sampled Dynamic Data Modeling and Monitoring With an Industrial Application

In this paper, a dynamic modeling method for unevenly sampled data is proposed for the monitoring of bi-layer (i.e., a process layer and a quality layer) dynamic processes. First, a novel uneven data dynamic canonical correlation analysis method with an integrated dynamic time window is proposed for interlayer latent structure modeling, which captures the dynamic relations between regularly sampled process data and quality data with slow and irregular sampling. The new model is a step toward big data modeling to deal with data irregularity and diversity. Second, after extracting covariations using an interlayer model, intralayer variations are extracted using subsequent principal component analysis on the residual subspaces of the original process data and quality data, respectively. Third, a concurrent monitoring method for unevenly sampled bi-layer data is proposed. Finally, the proposed method is demonstrated using an illustrative simulation example and applied successfully to a real blast furnace iron-making process.

Streptococcus pneumoniae in the heart subvert the host response through biofilm-mediated resident macrophage killing.

For over 130 years, invasive pneumococcal disease has been associated with the presence of extracellular planktonic pneumococci, i.e. diplococci or short chains in affected tissues. Herein, we show that Streptococcus pneumoniae that invade the myocardium instead replicate within cellular vesicles and transition into non-purulent biofilms. Pneumococci within mature cardiac microlesions exhibited salient biofilm features including intrinsic resistance to antibiotic killing and the presence of an extracellular matrix. Dual RNA-seq and subsequent principal component analyses of heart- and blood-isolated pneumococci confirmed the biofilm phenotype in vivo and revealed stark anatomical site-specific differences in virulence gene expression; the latter having major implications on future vaccine antigen selection. Our RNA-seq approach also identified three genomic islands as exclusively expressed in vivo. Deletion of one such island, Region of Diversity 12, resulted in a biofilm-deficient and highly inflammogenic phenotype within the heart; indicating a possible link between the biofilm phenotype and a dampened host-response. We subsequently determined that biofilm pneumococci released greater amounts of the toxin pneumolysin than did planktonic or RD12 deficient pneumococci. This allowed heart-invaded wildtype pneumococci to kill resident cardiac macrophages and subsequently subvert cytokine/chemokine production and neutrophil infiltration into the myocardium. This is the first report for pneumococcal biofilm formation in an invasive disease setting. We show that biofilm pneumococci actively suppress the host response through pneumolysin-mediated immune cell killing. As such, our findings contradict the emerging notion that biofilm pneumococci are passively immunoquiescent.

Multi-angle backscatter classification and sub-bottom profiling for improved seafloor characterization

This study applies three classification methods exploiting the angular dependence of acoustic seafloor backscatter along with high resolution sub-bottom profiling for seafloor sediment characterization in the Eckernforde Bay, Baltic Sea Germany. This area is well suited for acoustic backscatter studies due to its shallowness, its smooth bathymetry and the presence of a wide range of sediment types. Backscatter data were acquired using a Seabeam1180 (180 kHz) multibeam echosounder and sub-bottom profiler data were recorded using a SES-2000 parametric sonar transmitting 6 and 12 kHz. The high density of seafloor soundings allowed extracting backscatter layers for five beam angles over a large part of the surveyed area. A Bayesian probability method was employed for sediment classification based on the backscatter variability at a single incidence angle, whereas Maximum Likelihood Classification (MLC) and Principal Components Analysis (PCA) were applied to the multi-angle layers. The Bayesian approach was used for identifying the optimum number of acoustic classes because cluster validation is carried out prior to class assignment and class outputs are ordinal categorical values. The method is based on the principle that backscatter values from a single incidence angle express a normal distribution for a particular sediment type. The resulting Bayesian classes were well correlated to median grain sizes and the percentage of coarse material. The MLC method uses angular response information from five layers of training areas extracted from the Bayesian classification map. The subsequent PCA analysis is based on the transformation of these five layers into two principal components that comprise most of the data variability. These principal components were clustered in five classes after running an external cluster validation test. In general both methods MLC and PCA, separated the various sediment types effectively, showing good agreement (kappa >0.7) with the Bayesian approach which also correlates well with ground truth data (r2 > 0.7). In addition, sub-bottom data were used in conjunction with the Bayesian classification results to characterize acoustic classes with respect to their geological and stratigraphic interpretation. The joined interpretation of seafloor and sub-seafloor data sets proved to be an efficient approach for a better understanding of seafloor backscatter patchiness and to discriminate acoustically similar classes in different geological/bathymetric settings.

Cyclopropane-Based Peptidomimetics Mimicking Wide-Ranging Secondary Structures of Peptides: Conformational Analysis and Their Use in Rational Ligand Optimization.

Detailed conformational analyses of our previously reported cyclopropane-based peptidomimetics and conformational analysis-driven ligand optimization are described. Computational calculations and X-ray crystallography showed that the characteristic features of cyclopropane function effectively to constrain the molecular conformation in a three-dimensionally diverse manner. Subsequent principal component analysis revealed that the diversity covers the broad chemical space filled by peptide secondary structures in terms of both main-chain and side-chain conformations. Based on these analyses, a lead stereoisomer targeting melanocortin receptors was identified, and its potency and subtype selectivity were improved by further derivatization. The presented strategy is effective not only for designing non-peptidic ligands from a peptide ligand but also for the rational optimization of these ligands based on the plausible target-binding conformation without requiring the three- dimensional structural information of the target and its peptide ligands.

Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.’69 to Dec.’94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.’95–Dec.’05) and forecast (Jan.’06–Dec.’35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90–95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

The rate of change in declining steroid hormones: a new parameter of healthy aging in men?

Research on healthy aging in men has increasingly focused on age-related hormonal changes. Testosterone (T) decline is primarily investigated, while age-related changes in other sex steroids (dehydroepiandrosterone [DHEA], estradiol [E2], progesterone [P]) are mostly neglected. An integrated hormone parameter reflecting aging processes in men has yet to be identified. 271 self-reporting healthy men between 40 and 75 provided both psychometric data and saliva samples for hormone analysis. Correlation analysis between age and sex steroids revealed negative associations for the four sex steroids (T, DHEA, E2, and P). Principal component analysis including ten salivary analytes identified a principal component mainly unifying the variance of the four sex steroid hormones. Subsequent principal component analysis including the four sex steroids extracted the principal component of declining steroid hormones (DSH). Moderation analysis of the association between age and DSH revealed significant moderation effects for psychosocial factors such as depression, chronic stress and perceived general health. In conclusion, these results provide further evidence that sex steroids decline in aging men and that the integrated hormone parameter DSH and its rate of change can be used as biomarkers for healthy aging in men. Furthermore, the negative association of age and DSH is moderated by psychosocial factors.

Variation in Multitrack Mixes: Analysis of Low-level Audio Signal Features

To further the development of intelligent music production tools, towards generating mixes that would realistically be created by a human mix-engineer, it is important to understand what kind of mixes can be created, and are typically created, by human mix-engineers. This paper presents an analysis of 1501 mixes, over 10 different songs, created by mix-engineers. The primary dimensions of variation in the full dataset of mixes were ‘amplitude’, ‘brightness’, ‘bass’ and ‘width’, as determined by feature-extraction and subsequent principal component analysis. The distribution of representative features approximated a normal distribution and this is then used to obtain general trends and tolerance bounds for these features. The results presented here are useful as parametric guidance for intelligent music production systems.

Proteotyping of biogas plant microbiomes separates biogas plants according to process temperature and reactor type.

BackgroundMethane yield and biogas productivity of biogas plants (BGPs) depend on microbial community structure and function, substrate supply, and general biogas process parameters. So far, however, relatively little is known about correlations between microbial community function and process parameters. To close this knowledge gap, microbial communities of 40 samples from 35 different industrial biogas plants were evaluated by a metaproteomics approach in this study.ResultsLiquid chromatography coupled to tandem mass spectrometry (Orbitrap Elite™ Hybrid Ion Trap-Orbitrap Mass Spectrometer) of all 40 samples as triplicate enabled the identification of 3138 different metaproteins belonging to 162 biological processes and 75 different taxonomic orders. The respective database searches were performed against UniProtKB/Swiss-Prot and seven metagenome databases. Subsequent clustering and principal component analysis of these data allowed for the identification of four main clusters associated with mesophile and thermophile process conditions, the use of upflow anaerobic sludge blanket reactors and BGP feeding with sewage sludge. Observations confirm a previous phylogenetic study of the same BGP samples that was based on 16S rRNA gene sequencing by De Vrieze et al. (Water Res 75:312–323, 2015). In particular, we identified similar microbial key players of biogas processes, namely Bacillales, Enterobacteriales, Bacteriodales, Clostridiales, Rhizobiales and Thermoanaerobacteriales as well as Methanobacteriales, Methanosarcinales and Methanococcales. For the elucidation of the main biomass degradation pathways, the most abundant 1 % of metaproteins was assigned to the KEGG map 1200 representing the central carbon metabolism. Additionally, the effect of the process parameters (i) temperature, (ii) organic loading rate (OLR), (iii) total ammonia nitrogen (TAN), and (iv) sludge retention time (SRT) on these pathways was investigated. For example, high TAN correlated with hydrogenotrophic methanogens and bacterial one-carbon metabolism, indicating syntrophic acetate oxidation.ConclusionsThis is the first large-scale metaproteome study of BGPs. Proteotyping of BGPs reveals general correlations between the microbial community structure and its function with process parameters. The monitoring of changes on the level of microbial key functions or even of the microbial community represents a well-directed tool for the identification of process problems and disturbances.Graphical abstractCorrelation between the different orders and process parameter, as well as principle component analysis of all investigated biogas plants based on the identified metaproteins.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-016-0572-4) contains supplementary material, which is available to authorized users.

Deep transcriptomic profiling reveals the similarity between endothelial cells cultured under static and oscillatory shear stress conditions.

Atherosclerosis is a multifactorial disease that preferentially develops in specific regions in the arterial tree. This characteristic is mainly attributed to the unique pattern of hemodynamic shear stress in vivo. High laminar shear stress (LS) found in straight lumen exerts athero-protective effects. Low or oscillatory shear stress (OS) present in regions of lesser curvature and arterial bifurcations predisposes arterial intima to atherosclerosis. Shear stress-regulated endothelial function plays an important role in the process of atherosclerosis. Most in vitro research studies focusing on the molecular mechanisms of endothelial function are performed in endothelial cells (ECs) under cultured static (ST) condition. Some findings, however, are not recapitulated in subsequent translational studies, mostly likely due to the missing biomechanical milieu. Here, we profiled the whole transcriptome of primary human coronary arterial endothelial cells (HCAECs) under different shear stress conditions with RNA sequencing. Among 16,313 well-expressed genes, we detected 8,177 that were differentially expressed in OS vs. LS conditions and 9,369 in ST vs. LS conditions. Notably, only 1,618 were differentially expressed in OS vs. ST conditions. Hierarchical clustering of ECs demonstrated a strong similarity between ECs under OS and ST conditions at the transcriptome level. Subsequent pairwise heat mapping and principal component analysis gave further weight to the similarity. At the individual gene level, expressional analysis of representative well-known genes as well as novel genes showed a comparable amount at mRNA and protein levels in ECs under ST and OS conditions. In conclusion, the present work compared the whole transcriptome of HCAECs under different shear stress conditions at the transcriptome level as well as at the individual gene level. We found that cultured ECs are significantly different from those under LS conditions. Thus using cells under ST conditions is unlikely to elucidate endothelial physiology. Given the revealed high similarities of the endothelial transcriptome under OS and ST conditions, it may be helpful to understand the underlying mechanisms of OS-induced endothelial dysfunction from static cultured endothelial studies.