Key Factors for Activated Carbon Adsorption of Pharmaceutical Compounds from Wastewaters: A Multivariate Modelling Approach

Projection to Latent Structures (PLS) regression, a generalization of multiple linear regression, is used to model two datasets (40 observed data points each) of adsorption removal of three pharmaceutical compounds (PhCs), of different therapeutic classes and physical–chemical properties (carbamazepine, diclofenac, and sulfamethoxazole), from six real secondary effluents collected from wastewater treatment plants onto different powdered activated carbons (PACs). For the PLS regression, 25 descriptors were considered: 7 descriptors related to the PhCs properties, 10 descriptors related to the wastewaters properties (8 related to the organic matrix and 2 to the inorganic matrix), and 8 descriptors related to the PACs properties. This modelling approach showed good descriptive capability, showing that hydrophobic PhC-PAC interactions play the major role in the adsorption process, with the solvation energy and log Kow being the most suitable descriptors. The results also stress the importance of the competition effects of water dissolved organic matter (DOM), namely of its slightly hydrophobic compounds impacting the adsorption capacity or its charged hydrophilic compounds impacting the short-term adsorption, while the water inorganic matrix only appears to impact PAC adsorption capacity and not the short-term adsorption. For the pool of PACs tested, the results point to the BET area as a good descriptor of the PAC capacity, while the short-term adsorption kinetics appears to be better related to its supermicropore volume and density. The improvement in these PAC properties should be regarded as a way of refining their performance. The correlations obtained, involving the impact of water, PhC and PAC-related descriptors, show the existence of complex interactions that a univariate analysis is not sufficient to describe.

Global Plus Local Projection to Latent Structures

Owing to the raised demands on process operation and product quality, the modern industrial process becomes more complicated when accompanied by the large number of process and quality variables produced. Therefore, quality-related fault detection and diagnosis are extremely necessary for complex industrial processes. Data-driven statistical process monitoring plays an important role in this topic for digging out the useful information from these highly correlated process and quality variables, because the quality variables are measured at a much lower frequency and usually have a significant time delay (Ding 2014; Aumi et al. 2013; Peng et al. 2015; Zhang et al. 2016; Yin et al. 2014). Monitoring the process variables related to the quality variables is significant for finding potential harm that may lead to system shutdown with possible enormous economic loss.

Isolating salient variations of interest in single-cell transcriptomic data with contrastiveVI

Single-cell RNA sequencing (scRNA-seq) technologies enable a better understanding of previously unexplored biological diversity. Oftentimes, researchers are specifically interested in modeling the latent structures and variations enriched in one target scRNA-seq dataset as compared to another background dataset generated from sources of variation irrelevant to the task at hand. For example, we may wish to isolate factors of variation only present in measurements from patients with a given disease as opposed to those shared with data from healthy control subjects. Here we introduce Contrastive Variational Inference (contrastiveVI; https://github.com/suinleelab/contrastiveVI), a framework for end-to-end analysis of target scRNA-seq datasets that decomposes the variations into shared and target-specific factors of variation. On three target-background dataset pairs we demonstrate that contrastiveVI learns latent representations that recover known subgroups of target data points better than previous methods and finds differentially expressed genes that agree with known ground truths.

PCR, PLS, or OPLS Evaluation of different regression techniques for hypothesis generation

In the current era of ‘big data’, scientists are able to quickly amass enormous amount of data in a limited number of experiments. The investigators then try to hypothesize about the root cause based on the observed trends for the predictors and the response variable. This involves identifying the discriminatory predictors that are most responsible for explaining variation in the response variable. In the current work, we investigated three related multivariate techniques: Principal Component Regression (PCR), Partial Least Squares or Projections to Latent Structures (PLS), and Orthogonal Partial Least Squares (OPLS). To perform a comparative analysis, we used a publicly available dataset for Parkinson’ disease patien ts. We first performed the analysis using a cross-validated number of principal components for the aforementioned techniques. Our results demonstrated that PLS and OPLS were better suited than PCR for identifying the discriminatory predictors. Since the X data did not exhibit a strong correlation, we also performed Multiple Linear Regression (MLR) on the dataset. A comparison of the top five discriminatory predictors identified by the four techniques showed a substantial overlap between the results obtained by PLS, OPLS, and MLR, and the three techniques exhibited a significant divergence from the variables identified by PCR. A further investigation of the data revealed that PCR could be used to identify the discriminatory variables successfully if the number of principal components in the regression model were increased. In summary, we recommend using PLS or OPLS for hypothesis generation and systemizing the selection process for principal components when using PCR.rewordexplain later why MLR can be used on a dataset with no correlation

Anatomical and Chemical Characterization of Ulmus Species from South Korea

Ulmus species (Ulmaceae) are large deciduous trees distributed throughout Korea. Although their root and stem bark have been used to treat gastrointestinal diseases and wounds in folk medicine, commercial products are consumed without any standardization. Therefore, we examined anatomical and chemical differences among five Ulmus species in South Korea. Transverse sections of leaf, stem, and root barks were examined under a microscope to elucidate anatomical differences. Stem and root bark exhibited characteristic medullary ray and secretary canal size. Leaf surface, petiole, and midrib exhibited characteristic inner morphologies including stomatal size, parenchyma, and epidermal cell diameter, as well as ratio of vascular bundle thickness to diameter among the samples. Orthogonal projections to latent structures discriminant analysis of anatomical data efficiently differentiated the five species. To evaluate chemical differences among the five species, we quantified (-)-catechin, (-)-catechin-7-O-β-D-apiofuranoside, (-)-catechin-7-O-α-L-rhamnopyranoside, (-)-catechin-7-O-β-D-xylopyranoside, (-)-catechin-7-O-β-D-glucopyranoside, and (-)-catechin-5-O-β-D-apiofuranoside using high-performance liquid chromatography with a diode-array detector. (-)-Catechin-7-O-β-D-apiofuranoside content was the highest among all compounds in all species, and (-)-catechin-7-O-α-L-rhamnopyranoside content was characteristically the highest in Ulmus parvifolia among the five species. Overall, the Ulmus species tested was able to be clearly distinguished on the basis of anatomy and chemical composition, which may be used as scientific criteria for appropriate identification and standard establishment for commercialization of these species

What Explains Successful or Unsuccessful Postural Adaptations to Repeated Surface Perturbations among Older Adults?

As we age there are natural physiological deteriorations that decrease the accuracy and flexibility of the postural control system, which increases the risk of falling. Studies have found that there are individual differences in the ability to learn to manage repeated postural threats. The aim of this study was to investigate which factors explain why some individuals are less proficient at adapting to recurrent postural perturbations. Thirty-five community dwelling older adults performed substantial sensory and motor testing and answered surveys regarding fall-related concerns and cognitive function. They were also subjected to three identical surface perturbations where both kinematics and electromyography was captured. Those that were able to adapt to the third perturbation were assigned to the group “Non-fallers” whereas those that fell during all perturbations were assigned to the group “Fallers”. The group designation dichotomized the sample in a hierarchical orthogonal projection of latent structures— the discriminant analysis model. We found that those who fell were older, had poorer physical performance, poorer strength and longer reaction times. The Fallers’ postural control strategies were more reliant on the stiffening strategy along with a more extended posture and they were less skillful at making appropriate feedforward adaptations prior to the third perturbation.

BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation

Existing topic modeling and text segmentation methodologies generally require large datasets for training, limiting their capabilities when only small collections of text are available. In this work, we reexamine the inter-related problems of “topic identification” and “text segmentation” for sparse document learning, when there is a single new text of interest. In developing a methodology to handle single documents, we face two major challenges. First is sparse information : with access to only one document, we cannot train traditional topic models or deep learning algorithms. Second is significant noise : a considerable portion of words in any single document will produce only noise and not help discern topics or segments. To tackle these issues, we design an unsupervised, computationally efficient methodology called Biclustering Approach to Topic modeling and Segmentation (BATS). BATS leverages three key ideas to simultaneously identify topics and segment text: (i) a new mechanism that uses word order information to reduce sample complexity, (ii) a statistically sound graph-based biclustering technique that identifies latent structures of words and sentences, and (iii) a collection of effective heuristics that remove noise words and award important words to further improve performance. Experiments on six datasets show that our approach outperforms several state-of-the-art baselines when considering topic coherence, topic diversity, segmentation, and runtime comparison metrics.

Quantifying Fatigue in the Rugby Codes: The Interplay Between Collision Characteristics and Neuromuscular Performance, Biochemical Measures, and Self-Reported Assessments of Fatigue

Locomotor and collision actions that rugby players complete during match-play often lead to substantial fatigue, and in turn, delays in recovery. The methods used to quantify post-match fatigue and recovery can be categorised as subjective and objective, with match-related collision characteristics thought to have a primary role in modulating these recovery measures. The aim of this review was to (1) evaluate how post-match recovery has been quantified in the rugby football codes (i.e., rugby league, rugby union, and rugby sevens), (2) to explore the time-course of commonly used measures of fatigue post-match, and (3) to investigate the relationships between game-related collisions and fatigue metrics. The available evidence suggests that upper-, and lower-body neuromuscular performance are negatively affected, and biomarkers of muscular damage and inflammation increase in the hours and days following match-play, with the largest differences being at 12–36 h post-match. The magnitude of such responses varies within and between neuromuscular performance (Δ ≤ 36%, n = 13 studies) and tissue biomarker (Δ ≤ 585%, n = 18 studies) measures, but nevertheless appears strongly related to collision frequency and intensity. Likewise, the increase in perceived soreness in the hours and days post-match strongly correlate to collision characteristics across the rugby football codes. Within these findings, there are specific differences in positional groups and recovery trajectories between the codes which relate to athlete characteristics, and/or locomotor and collision characteristics. Finally, based on these findings, we offer a conceptual model of fatigue which details the multidimensional latent structure of the load to fatigue relationship contextualised to rugby. Research to date has been limited to univariate associations to explore relationships between collision characteristics and recovery, and multivariate methods are necessary and recommended to account for the latent structures of match-play external load and post-match fatigue constructs. Practitioners should be aware of the typical time windows of fatigue recovery and utilise both subjective and objective metrics to holistically quantify post-match recovery in rugby.

Developmental Programming: Prenatal Testosterone Excess on Liver and Muscle Coding and Non-Coding RNA in Female Sheep

Prenatal testosterone (T)-treated female sheep manifest peripheral insulin resistance, ectopic lipid accumulation and insulin signaling disruption in liver and muscle. This study investigated transcriptional changes and transcriptome signature of prenatal T excess-induced hepatic and muscle-specific metabolic disruptions. Genome-wide coding and non-coding (nc) RNA expression in liver and muscle from 21-month-old prenatal T-treated (T propionate 100mg intramuscular twice weekly from days 30 to 90 of gestation; Term: 147 days) and control females were compared. Prenatal T (1) induced differential expression of mRNAs in liver (15 down, 17 up) and muscle (66 down, 176 up) (FDR<0.05, absolute log2 fold change>0.5); (2) downregulated mitochondrial pathway genes in liver and muscle; (3) downregulated hepatic lipid catabolism and PPAR signaling gene pathways; (4) modulated ncRNA metabolic processes gene pathway in muscle and (5) downregulated 5 uncharacterized long ncRNA (lncRNA) in the muscle but no ncRNA changes in the liver. Correlation analysis showed downregulation of lncRNAs LOC114112974 and LOC105607806 was associated with decreased TPK1, and LOC114113790 with increased ZNF470 expression. Orthogonal Projections to Latent Structures Discriminant Analysis identified mRNAs HADHA and SLC25A45, and miRNAs MIR154A, MIR25 and MIR487B in liver and ARIH1 and ITCH and miRNAs MIR369, MIR10A and MIR10B in muscle as potential biomarkers of prenatal T-excess. These findings suggest downregulation of mitochondria, lipid catabolism, and PPAR signaling genes in liver and dysregulation of mitochondrial and ncRNA gene pathways in muscle are contributors of lipotoxic and insulin resistant hepatic and muscle phenotype. Gestational T excess programming of metabolic dysfunctions involve tissue-specific ncRNA modulated transcriptional changes.