Dimensionality reduction and visualization in principal component analysis

Low adsorption affinity of athabasca oil sands naphthenic acid fraction compounds to a peat-mineral mixture

Much of the toxicity in oil sands process-affected water in Athabasca oil sands tailings has been attributed to naphthenic acids (NAs) and associated naphthenic acid fraction compounds (NAFCs). Previous work has characterized the environmental behaviour and fate of these compounds, particularly in the context of constructed treatment wetlands. There is evidence that wetlands can attenuate NAFCs in natural and engineered contexts, but relative contributions of chemical, biotic, and physical adsorption with sequestration require deconvolution. In this work, the objective was to evaluate the extent to which prospective wetland substrate material may adsorb NAFCs using a peat-mineral mix (PMM) sourced from the Athabasca Oil Sands Region (AOSR). The PMM and NAFCs were first mixed and then equilibrated across a range of NAFC concentrations (5-500 mg/L) with moderate ionic strength and hardness (∼200 ppm combined Ca2+ and Mg2+) that approximate wetland water chemistry. Under these experimental conditions, low sorption of NAFCs to PMM was observed, where sorbed concentrations of NAFCs were approximately zero mg/kg at equilibrium. When NAFCs and PMM were mixed and equilibrated together at environmentally relevant concentrations, formula diversity increased more than could be explained by combining constituent spectra. The TOC present in this PMM was largely cellulose-derived, with low levels of thermally recalcitrant carbon (e.g., lignin, black carbon). The apparent enhancement of the concentration and diversity of components in PMM/NAFCs mixtures are likely related to aqueous solubility of some PMM-derived organic materials, as post-hoc combination of dissolved components from PMM and NAFCs cannot replicate enhanced complexity observed when the two components are agitated and equilibrated together.

A machine-learning method isolating changes in wrist kinematics that identify age-related changes in arm movement

Normal aging often results in an increase in physiological tremors and slowing of the movement of the hands, which can impair daily activities and quality of life. This study, using lightweight wearable non-invasive sensors, aimed to detect and identify age-related changes in wrist kinematics and response latency. Eighteen young (ages 18-20) and nine older (ages 49-57) adults performed two standard tasks with wearable inertial measurement units on their wrists. Frequency analysis revealed 5 kinematic variables distinguishing older from younger adults in a postural task, with best discrimination occurring in the 9-13 Hz range, agreeing with previously identified frequency range of age-related tremors, and achieving excellent classifier performance (0.86 AUROC score and 89% accuracy). In a second pronation-supination task, analysis of angular velocity in the roll axis identified a 71 ms delay in initiating arm movement in the older adults. This study demonstrates that an analysis of simple kinematic variables sampled at 100 Hz frequency with commercially available sensors is reliable, sensitive, and accurate at detecting age-related increases in physiological tremor and motor slowing. It remains to be seen if such sensitive methods may be accurate in distinguishing physiological tremors from tremors that occur in neurological diseases, such as Parkinson's Disease.

Modern machine-learning applications in ambient ionization mass spectrometry

This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.

Proteomic Characteristics of the Prefrontal Cortex and Hippocampus in Mice with Chronic Ketamine-Induced Anxiety and Cognitive Impairment

Schizophrenia, a complex psychiatric disorder with diverse symptoms, has been linked to ketamine, known for its N-methyl-D-aspartate (NMDA) receptor antagonistic properties. Understanding the distinct roles and mechanisms of ketamine is crucial, especially regarding its induction of schizophrenia-like symptoms. Recent research highlights the impact of ketamine on key brain regions associated with schizophrenia, specifically the prefrontal cortex (PFC) and hippocampus (Hip). This study focused on these regions to explore proteomic changes related to anxiety and cognitive impairment in a chronic ketamine-induced mouse model of schizophrenia. After twelve consecutive days of ketamine administration, brain tissues from these regions were dissected and analyzed. Using tandem mass tag (TMT) labeling quantitative proteomics techniques, 34,797 and 46,740 peptides were identified in PFC and Hip, corresponding to 5,668 and 6,463 proteins, respectively. In the PFC, a total of 113 proteins showed differential expression, primarily associated with the immuno-inflammatory process, calmodulin, postsynaptic density protein, and mitochondrial function. In the Hip, 129 differentially expressed proteins were screened, mainly related to synaptic plasticity proteins and mitochondrial respiratory chain complex-associated proteins. Additionally, we investigated key proteins within the glutamatergic synapse pathway and observed decreased expression levels of phosphorylated CaMKII and CREB. Overall, the study unveiled a significant proteomic signature in the chronic ketamine-induced schizophrenia mouse model, characterized by anxiety and cognitive impairment in both the PFC and Hip, and this comprehensive proteomic dataset may not only enhance our understanding of the molecular mechanisms underlying ketamine-related mental disorders but also offer valuable insights for future disease treatments.

Endothelial IL17RD promotes Western diet-induced aortic myeloid cell infiltration

The Interleukin-17 (IL17) family is a group of cytokines implicated in the etiology of several inflammatory diseases. Interleukin-17 receptor D (IL17RD), also known as Sef (similar expression to fibroblast growth factor) belonging to the family of IL17 receptors, has been shown to modulate IL17A-associated inflammatory phenotypes. The objective of this study was to test the hypothesis that IL17RD promotes endothelial cell activation and consequent leukocyte adhesion. We utilized primary human aortic endothelial cells and demonstrated that RNAi targeting of IL17RD suppressed transcript levels by 83 % compared to non-targeted controls. Further, RNAi knockdown of IL17RD decreased the adhesion of THP-1 monocytic cells onto a monolayer of aortic endothelial cells in response to IL17A. Additionally, we determined that IL17A did not significantly enhance the activation of canonical MAPK and NFκB pathways in endothelial cells, and further did not significantly affect the expression of VCAM-1 and ICAM-1 in aortic endothelial cells, which is contrary to previous findings. We also determined the functional relevance of our findings in vivo by comparing the expression of endothelial VCAM-1 and ICAM-1 and leukocyte infiltration in the aorta in Western diet-fed Il17rd null versus wild-type mice. Our results showed that although Il17rd null mice do not have significant alteration in aortic expression of VCAM-1 and ICAM-1 in endothelial cells, they exhibit decreased accumulation of proinflammatory monocytes and neutrophils, suggesting that endothelial IL17RD induced in vivo myeloid cell accumulation is not dependent on upregulation of VCAM-1 and ICAM-1 expression. We further performed proteomics analysis to identify potential molecular mediators of the IL17A/IL17RD signaling axis. Collectively, our results underscore a critical role for Il17rd in the regulation of aortic myeloid cell infiltration in the context of Western diet feeding.

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows

Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a "user guide" for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure-property relationships in soft materials.

Per- and Poly-fluoroalkyl Substances and Bile Acid Profiles in Pregnant Women

Alterations in bile acid (BA) profiles are closely associated with adverse outcomes in pregnant women and their offspring and may be one potential pathway underlying the related metabolic effects of per- and poly-fluoroalkyl substances (PFAS) exposure. However, evidence of associations between PFAS exposure and BA profiles in pregnant women is scarce. This study examined the associations of individual PFAS and PFAS mixture with BA profiles of pregnant women. We obtained quantitative data on the plasma concentrations of 13 PFAS and 15 BAs in 645 pregnant women from the Jiashan birth cohort. In Bayesian kernel machine regression models, the PFAS mixture was associated with increased plasma CA, TCA, TCDCA, and GLCA levels but with decreased GCA and LCA concentrations. Furthermore, the PFAS mixture was associated with increased concentrations of total BAs and the secondary/primary BA ratio but with decreased conjugated/unconjugated and glycine/taurine-conjugated BA ratios. PFHxS, PFUdA, PFOS, PFNA, and PFDA were the dominant contributors. The results of the linear regression analysis of individual PFAS were generally similar. Our findings provide the first epidemiological evidence for the associations of a PFAS mixture with BA profiles in pregnant women and may provide explanatory insights into the biological pathways underlying the related metabolic effects of PFAS exposure.

Purification of functional mouse skeletal muscle mitochondria using percoll density gradient centrifugation

OBJECTIVE

Our goal was to isolate purified mitochondria from mouse skeletal muscle using a Percoll density gradient and to assess bioenergetic function and purity via Seahorse Extracellular Flux (XF) Analyses and mass spectrometry.

RESULTS

Mitochondria isolated from murine quadriceps femoris skeletal muscle using a Percoll density gradient method allowed for minimally contaminated preparations with time from tissue harvest to mitochondrial isolation and quantification in about 3-4 h. Percoll purification from 100 to 200 mg fresh tissue yielded ~ 200-400 ug protein. Mitochondrial bioenergetics evaluated using the Seahorse XFe96 analyzer, a high-throughput respirometry platform, showed optimum mitochondrial input at 500 ng with respiratory control ratio ranging from 3.9 to 7.1 using various substrates demonstrating a high degree of functionality. Furthermore, proteomic analysis of Percoll-enriched mitochondria isolated from skeletal muscle using this method showed significant enrichment of mitochondrial proteins indicating high sample purity. This study established a methodology that ensures sufficient high quality mitochondria for downstream analyses such as mitochondrial bioenergetics and proteomics.

Evaluating the attenuation of naphthenic acids in constructed wetland mesocosms planted with Carex aquatilis

Surface oil sands mining and extraction in northern Alberta's Athabasca oil sands region produce large volumes of oil sands process-affected water (OSPW). OSPW is a complex mixture containing major contaminant classes including trace metals, polycyclic aromatic hydrocarbons, and naphthenic acid fraction compounds (NAFCs). Naphthenic acids (NAs) are the primary organic toxicants in OSPW, and reducing their concentrations is a priority for oil sands companies. Previous evidence has shown that constructed wetland treatment systems (CWTSs) are capable of reducing the concentration of NAs and the toxicity of OSPW through bioremediation. In this study, we constructed greenhouse mesocosms with OSPW or lab process water (LPW) (i.e., water designed to mimic OSPW minus the NAFC content) with three treatments: (1) OSPW planted with Carex aquatilis; (2) OSPW, no plants; and (3) LPW, no plants. The OSPW-C. aquatilis treatment saw a significant reduction in NAFC concentrations in comparison to OSPW, no plant treatments, but both changed the distribution of the NAFCs in similar ways. Upon completion of the study, treatments with OSPW saw fewer high-molecular-weight NAs and an increase in the abundance of O3- and O4-containing formulae. Results from this study provide invaluable information on how constructed wetlands can be used in future remediation of OSPW in a way that previous studies were unable to achieve due to uncontrollable environmental factors in field experiments and the active, high-energy processes used in CWTSs pilot studies.

Site-specific spatiotemporal occurrence and molecular congener distributions of naphthenic acids in Athabasca oil sands wetlands of Alberta, Canada

The Athabasca oil sands region (AOSR) of Alberta, Canada is notable for its considerable unconventional petroleum extraction projects, where bitumen is extracted from naturally-occurring oil sands ore. The large scale of these heavy crude oil developments raises concerns because of their potential to distribute and/or otherwise influence the occurrence, behaviour, and fate of environmental contaminants. Naphthenic acids (NAs) are one such contaminant class of concern in the AOSR, so studies have examined the occurrence and molecular profiles of NAs in the region. We catalogued the spatiotemporal occurrence and characteristics of NAs in boreal wetlands in the AOSR over a 7-year period, using derivatized liquid chromatography-tandem mass spectrometry (LC-MS/MS). Comparing median concentrations of NAs across these wetlands revealed a pattern of NAs suggesting NAs in surface waters derived from oil sands deposits. Opportunistic wetlands that formed adjacent to reclaimed overburden and other reclamation activities had the highest concentrations of NAs and consistent patterns suggestive of bitumen-derived inputs. However, similar patterns in the occurrence of NAs were also observed in undeveloped natural wetlands located above the known surface-mineable oil sands deposit that underlies the region. Intra-annual sampling results along with inter-annual comparisons across wetlands demonstrated that differences in the spatial and temporal NA concentrations were dependent on local factors, particularly when naturally occurring oil sands ores were observed in the wetland or drainage catchment.

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence

A novel approach is presented in this study for the classification of lower limb disorders, with a specific emphasis on the knee, hip, and ankle. The research employs gait analysis and the extraction of PoseNet features from video data in order to effectively identify and categorize these disorders. The PoseNet algorithm facilitates the extraction of key body joint movements and positions from videos in a non-invasive and user-friendly manner, thereby offering a comprehensive representation of lower limb movements. The features that are extracted are subsequently standardized and employed as inputs for a range of machine learning algorithms, such as Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks, and Convolutional Neural Networks. The models undergo training and testing processes using a dataset consisting of 174 real patients and normal individuals collected at the Tehsil Headquarter Hospital Sadiq Abad. The evaluation of their performance is conducted through the utilization of K-fold cross-validation. The findings exhibit a notable level of accuracy and precision in the classification of various lower limb disorders. Notably, the Artificial Neural Networks model achieves the highest accuracy rate of 98.84%. The proposed methodology exhibits potential in enhancing the diagnosis and treatment planning of lower limb disorders. It presents a non-invasive and efficient method of analyzing gait patterns and identifying particular conditions.

Leaf functional diversity and environmental filtering in a tropical dry forest: Comparison between two geological substrates

The role of geological substrate in shaping plant community functional diversity remains poorly understood. Considering the involvement of leaves in the energy, water, and nutrient economics of plants, we hypothesized that leaves experience geology-related filtering, which in turn shapes their functional attributes and community leaf functional diversity on different substrates. We studied tropical dry forest communities on limestone and siliciclastic phyllite-derived soils, comparing their functional diversity and soil physico-chemical properties. We predicted the most benign habitat (less severe filter) to be associated with higher leaf functional diversity and an acquisitive strategy prevalence, while the more stressful habitat should show conservative leaf traits and lower leaf functional diversity. We measured six traits in 31 common tree species (representing ~80% of community crown cover): leaf area, specific leaf area, leaf thickness, leaf dry matter content, petiole length, and leaf blade narrowness. Leaf functional diversity was assessed through the functional trait dispersion metric. Intraspecific functional variation was examined in 25 species shared between substrates. The limestone substrate was more fertile (higher phosphorous) with higher water retention, while phyllite had higher nitrogen and lower humidity. Principal component analysis segregated plots by substrate, with limestone plots being more clustered. Community leaf functional diversity was higher in the limestone forest. Most species examined showed inter-substrate trait differences in at least one leaf functional trait. The two substrates constituted distinct growth environments, with the more benign substrate associated with higher community leaf functional diversity. The intraspecific analysis revealed the prevalence of acquisitive traits in the more benign and more conservative traits in the more stressful habitat. This study advances our understanding of the role of geological substrate as an environmental filter in tropical dry forests, influencing leaf functional responses and emphasizing the importance of intraspecific functional variation.

Metabolomics: A useful tool for ischemic stroke research

Ischemic stroke (IS) is a multifactorial and heterogeneous disease. Despite years of studies, effective strategies for the diagnosis, management and treatment of stroke are still lacking in clinical practice. Metabolomics is a growing field in systems biology. It is starting to show promise in the identification of biomarkers and in the use of pharmacometabolomics to help patients with certain disorders choose their course of treatment. The development of metabolomics has enabled further and more biological applications. Particularly, metabolomics is increasingly being used to diagnose diseases, discover new drug targets, elucidate mechanisms, and monitor therapeutic outcomes and its potential effect on precision medicine. In this review, we reviewed some recent advances in the study of metabolomics as well as how metabolomics might be used to identify novel biomarkers and understand the mechanisms of IS. Then, the use of metabolomics approaches to investigate the molecular processes and active ingredients of Chinese herbal formulations with anti-IS capabilities is summarized. We finally summarized recent developments in single cell metabolomics for exploring the metabolic profiles of single cells. Although the field is relatively young, the development of single cell metabolomics promises to provide a powerful tool for unraveling the pathogenesis of IS.

Deoxyhypusine synthase mutations alter the post-translational modification of eukaryotic initiation factor 5A resulting in impaired human and mouse neural homeostasis

DHPS deficiency is a rare genetic disease caused by biallelic hypomorphic variants in the Deoxyhypusine synthase (DHPS) gene. The DHPS enzyme functions in mRNA translation by catalyzing the post-translational modification, and therefore activation, of eukaryotic initiation factor 5A (eIF5A). The observed clinical outcomes associated with human mutations in DHPS include developmental delay, intellectual disability, and seizures. Therefore, to increase our understanding of this rare disease, it is critical to determine the mechanisms by which mutations in DHPS alter neurodevelopment. In this study, we have generated patient-derived lymphoblast cell lines and demonstrated that human DHPS variants alter DHPS protein abundance and impair enzyme function. Moreover, we observe a shift in the abundance of the post-translationally modified forms of eIF5A; specifically, an increase in the nuclear localized acetylated form (eIF5A^AcK47) and concomitant decrease in the cytoplasmic localized hypusinated form (eIF5A^HYP). Generation and characterization of a mouse model with a genetic deletion of Dhps in the brain at birth shows that loss of hypusine biosynthesis impacts neuronal function due to impaired eIF5A^HYP-dependent mRNA translation; this translation defect results in altered expression of proteins required for proper neuronal development and function. This study reveals new insight into the biological consequences and molecular impact of human DHPS deficiency and provides valuable information toward the goal of developing treatment strategies for this rare disease.

Purification of functional mouse skeletal muscle mitochondria using Percoll density gradient centrifugation

Objective

Our goal was to isolate purified mitochondria from mouse skeletal muscle using a Percoll density gradient and to assess bioenergetic function and purity via Seahorse Extracellular Flux (XF) Analyses and mass spectrometry.

Results

Mitochondria isolated from murine quadriceps femoris skeletal muscle using a Percoll density gradient method allowed for minimally contaminated preparations with time from tissue harvest to mitochondrial isolation and quantification in about 3-4 hours. Percoll purification from 100-200 mg fresh tissue yielded ∼200-400 ug protein. Mitochondrial bioenergetics evaluated using the Seahorse XFe96 analyzer, a high-throughput respirometry platform, showed optimum mitochondrial input at 500 ng with respiratory control ratio ranging from 3.9-7.1 using various substrates demonstrating a high degree of functionality. Furthermore, proteomic analysis of Percoll-enriched mitochondria isolated from skeletal muscle using this method showed significant enrichment of mitochondrial proteins indicating high sample purity. This study established a methodology that ensures sufficient high quality mitochondria for downstream analyses such as mitochondrial bioenergetics and proteomics.

Effects of Nightshift Work on Blood Metabolites in Female Nurses and Paramedic Staff: A Cross-sectional Study

Nightshift work disturbs the circadian rhythm, which might contribute to the development of cardio-metabolic disorders. In this cross-sectional study, we aimed to gain insight into perturbations of disease relevant metabolic pathways due to nightshift work. We characterized the metabolic profiles of 237 female nurses and paramedic staff participating in the Klokwerk study using the Nightingale Health platform. We performed analyses on plasma levels of 225 metabolites, including cholesterol, triglycerides, fatty acids, and amino acids. Using both principal component- and univariate-regression, we compared metabolic profiles of nightshift workers to metabolic profiles from workers that did not work night shifts (defined as day workers). We also assessed whether differential effects were observed between recently started versus more experienced workers. Within the group of nightshift workers, we compared metabolic profiles measured right after a nightshift with metabolic profiles measured on a day when no nightshift work was conducted. We observed evidence for an impact of nightshift work on the presence of unfavorable fatty acid profiles in blood. Amongst the fatty acids, effects were most prominent for PUFA/FA ratios (consistently decreased) and SFA/FA ratios (consistently elevated). This pattern of less favorable fatty acid profiles was also observed in samples collected directly after a night shift. Amino acid levels (histidine, glutamine, isoleucine, and leucine) and lipoproteins (especially HDL-cholesterol, VLDL-cholesterol, and triglycerides) were elevated when comparing nightshift workers with day workers. Amino acid levels were decreased in the samples that were collected directly after working a nightshift (compared to levels in samples that were collected during a non-nightshift period). The observed effects were generally more pronounced in samples collected directly after the nightshift and among recently started compared to more experienced nightshift workers. Our finding of a suggested impact of shift work on impaired lipid metabolism is in line with evidence that links disruption of circadian rhythmicity to obesity and metabolic disorders.

Simple preparation of broadband UV filters based on TiO2 coated with aqueous extracts of native trees from the Chaco region of Argentina

The native forest of northwestern Argentina, as part of the Chaco region, is a rich and unexploited source of phytochemical compounds for medicinal/cosmetic applications. In the present study, fruit, leaf, branch, and bark organs of the native trees Sarcomphalus mistol (Mistol, M) and Schinopsis lorentzii (Quebracho Colorado santiagueño, QC) were harvested, and aqueous plant extracts (PE) were prepared. The spectroscopic (UV-Vis absorbance, diffuse reflectance, ATR-FTIR) and antioxidant (TEAC, Folin-Ciocalteu) properties of PE were characterized and used as TiO₂ coating material to obtain a series of TiO₂@PE nanocomposites. These materials showed almost null photocatalytic activity compared to aqueous suspensions of bare TiO₂, displaying yellowish to brownish coloration and high long-term stability in both freshwater and seawater model solutions. The loss of photocatalytic activity in TiO₂@PE was associated with the combination of the internal filter effect and the antioxidant/radical capacity exerted by the phytochemicals of the PE coating, with higher broadband photoprotection for the nanocomposites prepared with QC extracts. Thus, this study shows the potential capacity of the forest resources of the Chaco region of Argentina for the development of new cosmetic and/or sun protection formulations.

Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA

Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.

Neural manifold analysis of brain circuit dynamics in health and disease

Recent developments in experimental neuroscience make it possible to simultaneously record the activity of thousands of neurons. However, the development of analysis approaches for such large-scale neural recordings have been slower than those applicable to single-cell experiments. One approach that has gained recent popularity is neural manifold learning. This approach takes advantage of the fact that often, even though neural datasets may be very high dimensional, the dynamics of neural activity tends to traverse a much lower-dimensional space. The topological structures formed by these low-dimensional neural subspaces are referred to as "neural manifolds", and may potentially provide insight linking neural circuit dynamics with cognitive function and behavioral performance. In this paper we review a number of linear and non-linear approaches to neural manifold learning, including principal component analysis (PCA), multi-dimensional scaling (MDS), Isomap, locally linear embedding (LLE), Laplacian eigenmaps (LEM), t-SNE, and uniform manifold approximation and projection (UMAP). We outline these methods under a common mathematical nomenclature, and compare their advantages and disadvantages with respect to their use for neural data analysis. We apply them to a number of datasets from published literature, comparing the manifolds that result from their application to hippocampal place cells, motor cortical neurons during a reaching task, and prefrontal cortical neurons during a multi-behavior task. We find that in many circumstances linear algorithms produce similar results to non-linear methods, although in particular cases where the behavioral complexity is greater, non-linear methods tend to find lower-dimensional manifolds, at the possible expense of interpretability. We demonstrate that these methods are applicable to the study of neurological disorders through simulation of a mouse model of Alzheimer's Disease, and speculate that neural manifold analysis may help us to understand the circuit-level consequences of molecular and cellular neuropathology.

Charcot-Marie-Tooth-1A and sciatic nerve crush rat models: insights from proteomics

The sensorimotor and histological aspects of peripheral neuropathies were already studied by our team in two rat models: the sciatic nerve crush and the Charcot-Marie-Tooth-1A disease. In this study, we sought to highlight and compare the protein signature of these two pathological situations. Indeed, the identification of protein profiles in diseases can play an important role in the development of pharmacological targets. In fact, Charcot-Marie-Tooth-1A rats develop motor impairments that are more severe in the hind limbs. Therefore, for the first time, protein expression in sciatic nerve of Charcot-Marie-Tooth-1A rats was examined. First, distal sciatic nerves were collected from Charcot-Marie-Tooth-1A and uninjured wild-type rats aged 3 months. After protein extraction, sequential window acquisition of all theoretical fragment ion spectra liquid chromatography and mass spectrometry was employed. 445 proteins mapped to Swiss-Prot or trEMBL Uniprot databases were identified and quantified. Of these, 153 proteins showed statistically significant differences between Charcot-Marie-Tooth-1A and wild-type groups. The majority of these proteins were overexpressed in Charcot-Marie-Tooth-1A. Hierarchical clustering and functional enrichment using Gene Ontology were used to group these proteins based on their biological effects concerning Charcot-Marie-Tooth-1A pathophysiology. Second, proteomic characterization of wild-type rats subjected to sciatic nerve crush was performed sequential window acquisition of all theoretical fragment ion spectra liquid chromatography and mass spectrometry. One month after injury, distal sciatic nerves were collected and analyzed as described above. Out of 459 identified proteins, 92 showed significant differences between sciatic nerve crush and the uninjured wild-type rats used in the first study. The results suggest that young adult Charcot-Marie-Tooth-1A rats (3 months old) develop compensatory mechanisms at the level of redox balance, protein folding, myelination, and axonogenesis. These mechanisms seem insufficient to hurdle the progress of the disease. Notably, response to oxidative stress appears to be a significant feature of Charcot-Marie-Tooth-1A, potentially playing a role in the pathological process. In contrast to the first experiment, the majority of the proteins that differed from wild-type were downregulated in the sciatic nerve crush group. Functional enrichment suggested that neurogenesis, response to axon injury, and oxidative stress were important biological processes. Protein analysis revealed an imperfect repair at this time point after injury and identified several distinguishable proteins. In conclusion, we suggest that peripheral neuropathies, whether of a genetic or traumatic cause, share some common pathological pathways. This study may provide directions for better characterization of these models and/or identifying new specific therapeutic targets.

Revealing the Hidden Diagnostic Clues of Male Infertility from Human Seminal Plasma by Dispersive Solid Phase Extraction and MALDI-TOF MS

Seminal plasma (SP) mirrors the local pathophysiology of the male reproductive system and represents a non-invasive fluid for the study of infertility. Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight Mass Spectrometry (MALDI-TOF-MS) provides a high-throughput platform to rapidly extrapolate the diagnostic profiles of information-rich patterns. In this study, dispersive solid phase extraction (d-SPE) combined with MALDI-TOF-MS was applied for the first time to the human SP, with the aim of revealing a diagnostic signature for male infertility. Commercially available octadecyl (C₁₈)-, octyl (C₈)-bonded silica sorbents and hexagonal mesoporous silica (HMS) were tested and the robustness of MALDI-TOF peptide profiling was evaluated. Best performances were obtained for C₁₈-bonded silica with the highest detection of peaks and the lowest variation of spectral features. To assess the diagnostic potential of the method, C₁₈-bonded silica d-SPE and MALDI-TOF-MS were used to generate enriched endogenous peptide profiles of SP from 15 fertile and 15 non-fertile donors. Principal component analysis (PCA) successfully separated fertile from non-fertile men into two different clusters. An array of seven semenogelin-derived peptides was found to distinguish the two groups, with high statistical significance. These findings, while providing a rapid and convenient route to selectively enrich native components of SP peptidome, strongly reinforce the prominent role of semenogelins in male infertility.

Mitochondrial hyperfusion via metabolic sensing of regulatory amino acids

The relationship between nutrient starvation and mitochondrial dynamics is poorly understood. We find that cells facing amino acid starvation display clear mitochondrial fusion as a means to evade mitophagy. Surprisingly, further supplementation of glutamine (Q), leucine (L), and arginine (R) did not reverse, but produced stronger mitochondrial hyperfusion. Interestingly, the hyperfusion response to Q + L + R was dependent upon mitochondrial fusion proteins Mfn1 and Opa1 but was independent of MTORC1. Metabolite profiling indicates that Q + L + R addback replenishes amino acid and nucleotide pools. Inhibition of fumarate hydratase, glutaminolysis, or inosine monophosphate dehydrogenase all block Q + L + R-dependent mitochondrial hyperfusion, which suggests critical roles for the tricarboxylic acid (TCA) cycle and purine biosynthesis in this response. Metabolic tracer analyses further support the idea that supplemented Q promotes purine biosynthesis by serving as a donor of amine groups. We thus describe a metabolic mechanism for direct sensing of cellular amino acids to control mitochondrial fusion and cell fate.

Monitoring Fish Freshness in Real Time under Realistic Conditions through a Single Metal Oxide Gas Sensor

The realization of an unobtrusive and effective technology able to track fish freshness in real time and inform on its edibility is highly demanded, but still unachieved. In the present paper, we address this issue through a single metal oxide gas sensor working in temperature modulation mode. The system can work without an external reference air source, which is an appealing feature for its possible integration in domestic refrigerators. Tests were carried out using fresh sea bream fillets as case study and working both inside the refrigerator and at room temperature. Parallel gas chromatography-mass spectrometry and microbiological characterization indicated the marked dependence of both the microbiological condition and the gas-phase composition from the individual sample and from the storage temperature. Despite such a large variability, which may be expected in real applications, the proposed system provided similar responses whenever the total bacterial population approached and exceeded the edibility threshold of 10⁷ CFU/g.

Establishing a High-Throughput Locomotion Tracking Method for Multiple Biological Assessments in Tetrahymena

Protozoa are eukaryotic, unicellular microorganisms that have an important ecological role, are easy to handle, and grow rapidly, which makes them suitable for ecotoxicity assessment. Previous methods for locomotion tracking in protozoa are largely based on software with the drawback of high cost and/or low operation throughput. This study aimed to develop an automated pipeline to measure the locomotion activity of the ciliated protozoan Tetrahymena thermophila using a machine learning-based software, TRex, to conduct tracking. Behavioral endpoints, including the total distance, velocity, burst movement, angular velocity, meandering, and rotation movement, were derived from the coordinates of individual cells. To validate the utility, we measured the locomotor activity in either the knockout mutant of the dynein subunit DYH7 or under starvation. Significant reduction of locomotion and alteration of behavior was detected in either the dynein mutant or in the starvation condition. We also analyzed how Tetrahymena locomotion was affected by the exposure to copper sulfate and showed that our method indeed can be used to conduct a toxicity assessment in a high-throughput manner. Finally, we performed a principal component analysis and hierarchy clustering to demonstrate that our analysis could potentially differentiate altered behaviors affected by different factors. Taken together, this study offers a robust methodology for Tetrahymena locomotion tracking in a high-throughput manner for the first time.

Metabolomics Analysis of Bacterial Pathogen Burkholderia thailandensis and Mammalian Host Cells in Co-culture

The Tier 1 HHS/USDA Select Agent Burkholderia pseudomallei is a bacterial pathogen that is highly virulent when introduced into the respiratory tract and intrinsically resistant to many antibiotics. Transcriptomic- and proteomic-based methodologies have been used to investigate mechanisms of virulence employed by B. pseudomallei and Burkholderia thailandensis, a convenient surrogate; however, analysis of the pathogen and host metabolomes during infection is lacking. Changes in the metabolites produced can be a result of altered gene expression and/or post-transcriptional processes. Thus, metabolomics complements transcriptomics and proteomics by providing a chemical readout of a biological phenotype, which serves as a snapshot of an organism's physiological state. However, the poor signal from bacterial metabolites in the context of infection poses a challenge in their detection and robust annotation. In this study, we coupled mammalian cell culture-based metabolomics with feature-based molecular networking of mono- and co-cultures to annotate the pathogen's secondary metabolome during infection of mammalian cells. These methods enabled us to identify several key secondary metabolites produced by B. thailandensis during infection of airway epithelial and macrophage cell lines. Additionally, the use of in silico approaches provided insights into shifts in host biochemical pathways relevant to defense against infection. Using chemical class enrichment analysis, for example, we identified changes in a number of host-derived compounds including immune lipids such as prostaglandins, which were detected exclusively upon pathogen challenge. Taken together, our findings indicate that co-culture of B. thailandensis with mammalian cells alters the metabolome of both pathogen and host and provides a new dimension of information for in-depth analysis of the host-pathogen interactions underlying Burkholderia infection.

Surviving in the Brine: A Multi-Omics Approach for Understanding the Physiology of the Halophile Fungus Aspergillus sydowii at Saturated NaCl Concentration

Although various studies have investigated osmoadaptations of halophilic fungi to saline conditions, only few analyzed the fungal mechanisms occurring at saturated NaCl concentrations. Halophilic Aspergillus sydowii is a model organism for the study of molecular adaptations of filamentous fungi to hyperosmolarity. For the first time a multi-omics approach (i.e., transcriptomics and metabolomics) was used to compare A. sydowii at saturated concentration (5.13 M NaCl) to optimal salinity (1 M NaCl). Analysis revealed 1,842 genes differentially expressed of which 704 were overexpressed. Most differentially expressed genes were involved in metabolism and signal transduction. A gene ontology multi-scale network showed that ATP binding constituted the main network node with direct interactions to phosphorelay signal transduction, polysaccharide metabolism, and transferase activity. Free amino acids significantly decreased and amino acid metabolism was reprogrammed at 5.13 M NaCl. mRNA transcriptional analysis revealed upregulation of genes involved in methionine and cysteine biosynthesis at extreme water deprivation by NaCl. No modifications of membrane fatty acid composition occurred. Upregulated genes were involved in high-osmolarity glycerol signal transduction pathways, biosynthesis of β-1,3-glucans, and cross-membrane ion transporters. Downregulated genes were related to the synthesis of chitin, mannose, cell wall proteins, starvation, pheromone synthesis, and cell cycle. Non-coding RNAs represented the 20% of the total transcripts with 7% classified as long non-coding RNAs (lncRNAs). The 42% and 69% of the total lncRNAs and RNAs encoding transcription factors, respectively, were differentially expressed. A network analysis showed that differentially expressed lncRNAs and RNAs coding transcriptional factors were mainly related to the regulation of metabolic processes, protein phosphorylation, protein kinase activity, and plasma membrane composition. Metabolomic analyses revealed more complex and unknown metabolites at saturated NaCl concentration than at optimal salinity. This study is the first attempt to unravel the molecular ecology of an ascomycetous fungus at extreme water deprivation by NaCl (5.13 M). This work also represents a pioneer study to investigate the importance of lncRNAs and transcriptional factors in the transcriptomic response to high NaCl stress in halophilic fungi.

Untargeted profiling and differentiation of geographical variants of wine samples using headspace solid-phase microextraction flow-modulated comprehensive two-dimensional gas chromatography with the support of tile-based Fisher ratio analysis

The volatile fraction of food, also called the food volatilome, is increasingly used to develop new fingerprinting approaches. The characterization of the food volatilome is important to achieve desired flavor profiles in food production processes, or to differentiate different products, with winemaking being one popular area of interest. In the present research, headspace solid-phase microextraction (HS SPME) coupled to flow-modulated comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (FM GC×GC-TOFMS) was used to characterize geographical-based differences in the volatilome of five white "Grillo" wines (of Sicilian origin), comprising the five sample classes. All wines were produced with the same vinification method in 2019. To minimize the influence of minor bottle-to-bottle differences, three bottles of the same wine were randomly selected, and three samples were collected per bottle, resulting in nine sample replicates per wine. Particular emphasis was devoted to the operational conditions of a novel low duty cycle flow modulator. A fast FM GC×GC-TOFMS method with a modulation period of 700 ms and a re-injection period of 80 ms was developed. Following, the instrumental software was exploited to identify class-distinguishing analytes in the dataset via tile-based Fisher ratio analysis (i.e., ChromaTOF Tile). A tile size of 10 modulations (7 s) on the first dimension and 45 spectra (300 ms) on the second dimension was used to encompass average peak widths and to account for minor retention time shifting. Off-line software was used to apply an ANOVA test. A p-value of 0.01 was applied in order to select the most important class-distinguishing analytes, which were input to principal component analysis (PCA). The PCA scores plot showed distinct clustering of the wines according to geographical origin, although the loadings revealed that only a few analytes were necessary to differentiate the wines. However, a comprehensive flavor profile assessment underscored the importance of all the information output by the ChromaTOF Tile software.

Sex-Specific Pharmacotherapy for Back Pain: A Proof-of-Concept Randomized Trial

BACKGROUND

Preventing transition to chronic back pain (CBP) is a long-sought strategy that could rescue patients from prolonged suffering. Recent rodent and human brain imaging studies suggest involvement of sexually dimorphic, dopaminergic-motivational, mesolimbic circuits in the transition to chronic pain (tCBP), and hint that the combination of carbidopa/levodopa and naproxen (LDP + NPX) may block tCBP. Here we evaluated, in people with recent-onset back pain, whether a 3-month treatment with LDP + NPX is safe, blocks tCBP, and whether its efficacy is sex-dependent.

METHODS

A total of 72 participants were enrolled and stratified by risk for tCBP using brain-imaging biomarkers. Low-risk participants entered a no-treatment arm. Others were randomized to placebo + naproxen or LDP + NPX for 3 months.

RESULTS

Both treatments resulted in more than 50% pain relief for approximately 75% of participants. A strong sex by treatment interaction was observed for daily pain intensity (phone NRS, P = 0.007), replicated on 4-week average pain (Pain/4w, P = 0.00001), and in intent-to-treat analysis (Pain/4w, P = 0.000004). Nucleus accumbens functional connectivity with medial prefrontal cortex, a predefined objective biomarker, showed sex dependence at baseline (P = 0.03) and sex-by-treatment interaction effect 3 months after treatment cessation (P = 0.031). Treatment modified the psychological profile of participants, and disrupted brain modeling-based predicted back pain intensity trajectories. Forty participants were queried 3.3 years from trial start; back pain ratings were similar between end of treatment and at 3.3 years (P = 0.62), indicating persistence of relief for this duration.

CONCLUSIONS

These results provide the first evidence for preventing transition to chronic back pain using sex-specific pharmacotherapy. These provocative observations require confirmation in a larger study. ClinicalTrials.gov identifier: NCT01951105.

The Extracellular Matrix Environment of Clear Cell Renal Cell Carcinoma Determines Cancer Associated Fibroblast Growth

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and is often caused by mutations in the oxygen-sensing machinery of kidney epithelial cells. Due to its pseudo-hypoxic state, ccRCC recruits extensive vasculature and other stromal components. Conventional cell culture methods provide poor representation of stromal cell types in primary cultures of ccRCC, and we hypothesized that mimicking the extracellular environment of the tumor would promote growth of both tumor and stromal cells. We employed proteomics to identify the components of ccRCC extracellular matrix (ECM) and found that in contrast to healthy kidney cortex, laminin, collagen IV, and entactin/nidogen are minor contributors. Instead, the ccRCC ECM is composed largely of collagen VI, fibronectin, and tenascin C. Analysis of single cell expression data indicates that cancer-associated fibroblasts are a major source of tumor ECM production. Tumor cells as well as stromal cells bind efficiently to a nine-component ECM blend characteristic of ccRCC. Primary patient-derived tumor cells bind the nine-component blend efficiently, allowing to us to establish mixed primary cultures of tumor cells and stromal cells. These miniature patient-specific replicas are conducive to microscopy and can be used to analyze interactions between cells in a model tumor microenvironment.

Principle component analysis for nonlinear optical properties of thiophene-based metal complexes

The simulation of molecular descriptors of thiophene-based metal complexes has been performed using Gaussian 03 and Atomistic toolkit Virtual Nanolab (ATK-VNL) software. It is found that with respect to the obtained molecular descriptors, the molecules show distinct properties. The dimensions of the data set being large, the principal components (PC1 and PC2) have been obtained using principal component analysis (PCA). Analysis has been done for the Linear regression of principal components with first hyperpolarizability and second hyperpolarizability of the molecules. The results indicate that, of all the calculated molecular descriptors of thiophene-based metal complexes, the molecular energy (E), ionization energy (E_I), and molecular dipole moment (D) plays a dominant role in determining their nonlinear optical properties i.e., the hyperpolarizability value, of the studied molecules. Also, the molecular descriptors, polarizability (P) and molar refractivity (MR), show considerable impact on the nonlinear optical properties of the studied molecules.

Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management

Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.

Discovery of benthol A and its challenging stereochemical assignment: opening up a new window for skeletal diversity of super-carbon-chain compounds

Super-carbon-chain compounds (SCCCs) are marine organic molecules featuring long polyol carbon chains with numerous stereocenters. Polyol-polyene compounds (PPCs) and ladder-frame polyethers (LFPs) are two major families. It is highly challenging to establish the absolute configurations of SCCCs. In this century, few new SCCC families have been reported. Benthol A, an aberrant SCCC, was obtained from a South China Sea benthic dinoflagellate that should belong to a new taxon. Its planar structure and absolute configuration, containing thirty-five carbon stereocenters, were unambiguously established by a combination of extensive NMR spectroscopic investigations, periodate degradation of the 1,2-diol groups, ozonolysis of the carbon-carbon double bonds, J-based configurational analysis, NOE interactions, modified Mosher's MTPA ester method, and DFT-NMR ¹³C chemical-shift calculations aided by DP4+ statistical analysis. Benthol A displayed potent antimalarial activity against Plasmodium falciparum 3D7 parasites. This new molecule combines extraordinary structural features, particularly eight scattered ether rings on a C₇₂ backbone chain, which places it within a new SCCC family between PPCs and LFPs, herein termed polyol-polyether compounds. This suggestion was strongly supported by principal component analysis. The discovery of benthol A does not only provide new insights into the untapped biosynthetic potential of marine dinoflagellates, but also opens up a new window for skeletal diversity of SCCCs.

Quality Assessment of Wild and Cultivated Green Tea from Different Regions of China

Natural products have always enjoyed great popularity among consumers. Wild tea is an interesting alternative to tea from intensive plantations. The term "wild tea" is applied to many different varieties of tea, the most desirable and valued of which are native or indigenous tea plants. Special pro-health properties of wild tea are attributed to the natural conditions in which it grows. However, there are no complex studies that describe quality and health indicators of wild tea. The aim of this research was to evaluate the quality of wild and cultivated green tea from different regions of China: Wuzhishan, Baisha, Kunlushan, and Pu'Er. The assessment was carried out by verifying the concentration of selected chemical components in tea and relating it to the health risks they may pose, as well as to the nutritional requirements of adults. Wild tea was characterized by higher micronutrient concentration. The analyzed teas can constitute a valuable source of Mn in the diet. A higher concentration of nitrates and oxalates in cultivated tea can be associated with fertilizer use. The analyzed cultivated tea was a better source of antioxidants with a higher concentration of caffeine. There were no indications of health risks for wild or cultivated teas.

Hybrid modeling and prediction of oyster norovirus outbreaks

This paper presents a hybrid model for predicting oyster norovirus outbreaks by combining the Artificial Neural Networks (ANNs) and Principal Component Analysis (PCA) methods and using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote-sensing data. Specifically, 10 years (2007-2016) of cloud-free MODIS Aqua data for water leaving reflectance and environmental data were extracted from the center of each oyster harvest area. Then, the PCA was utilized to compress the size of the MODIS Aqua data. An ANN model was trained using the first 4 years of the data from 2007 to 2010 and validated using the additional 6 years of independent datasets collected from 2011 to 2016. Results indicated that the hybrid PCA-ANN model was capable of reproducing the 10 years of historical oyster norovirus outbreaks along the Northern Gulf of Mexico coast with a sensitivity of 72.7% and specificity of 99.9%, respectively, demonstrating the efficacy of the hybrid model.

Probing cell metabolism on insulin like growth factor(IGF)-1/tumor necrosis factor(TNF)-α and chargeable polymers co-immobilized conjugates

Cell culturing on different synthetic biomaterials would reprogram cell metabolism for adaption to their living conditions because such alterations in cell metabolism were necessary for cellular functions on them. Here we used metabolomics to uncover metabolic changes when liver cells were cultured on insulin-like growth factor (IGF)/tumor necrosis factor-α (TNF-α) and chargeable polymers co-modified biomaterials with the aim to explain their modulating effects on cell metabolism. The results showed that cell metabolism on IGF-1/TNF-α co-immobilized conjugates was significantly regulated according to their scatterings on the score plot of principal component analysis. Specifically, cell metabolisms were reprogrammed to the higher level of pyrimidine metabolism, β-alanine metabolism, and pantothenate and CoA biosynthesis, and the lower level of methionine salvage pathway in order to promote cell growth on IGF/TNF-α co-modified surface. Furthermore, cell senescence on PSt-PAAm-IGF/TNF-α surface was delayed through the regulation of branch amino acid metabolism and AMPK signal pathway. The research showed that metabolomics had great potential to uncover the molecular interaction between biomaterials and seeded cells, and provide the insights about cell metabolic reprogramming on IGF/TNF-α co-modified conjugates for cell growth.

Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.

Regional-level risk factors for severe hand-foot-and-mouth disease: an ecological study from mainland China

Background

Severe hand-foot-and-mouth disease (HFMD) is a life-threatening contagious disease among young children and infants. Although enterovirus A71 has been well acknowledged to be the dominant cause of severe HFMD, there still remain other unidentified risk factors for severe HFMD. Previous studies mainly focused on identifying the individual-level risk factors from a clinical perspective, while rare studies aimed to clarify the association between regional-level risk factors and severe HFMD, which may be more important from a public health perspective.

Methods

We retrieved the clinical HFMD counts between 2008 and 2014 from the Chinese Center for Disease Control and Prevention, which were used to calculated the case-severity rate in 143 prefectural-level cities in mainland China. For each of those 143 cities, we further obtained city-specific characteristics from the China City Statistical Yearbook (social and economic variables) and the national meteorological monitoring system (meteorological variables). A Poisson regression model was then used to estimate the associations between city-specific characteristics (reduced by the principal component analysis to avoid multicollinearity) and the case-severity rate of HFMD. The above analysis was further stratified by age and gender to examine potential modifying effects and vulnerable sub-populations.

Results

We found that the case-severity rate of HFMD varied dramatically between cities, ranging from 0 to 8.09%. Cities with high case-severity rates were mainly clustered in Central China. By relating the case-severity rate to city-specific characteristics, we found that both the principal component characterized by a high level of social and economic development (RR = 0.823, 95%CI 0.739, 0.916) and another that characterized by warm and humid climate (RR = 0.771, 95%CI 0.619, 0.960) were negatively associated with the case-severity rate of HFMD. These estimations were consistent across age and gender sub-populations.

Conclusion

Except for the type of infected pathogen, the case-severity rate of HFMD was closely related to city development and meteorological factor. These findings suggest that social and environmental factors may also play an important role in the progress of severe HFMD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12199-020-00927-9.

Characteristics and clinical subtypes of cancer patients in the intensive care unit: a retrospective observational study for two large databases

Background

Previous studies have reported very different mortality rates among cancer patients in the intensive care unit (ICU), implying different clinical subtypes. We aimed to reveal the clinical subtypes and demonstrate the importance of segregating the patients in clinical research, and to report the ICD-level mortality of cancer patients in the ICU.

Methods

Two ICU databases (MIMIC-III and eICU) were utilized to identify cancer patients. Mortality based on ICD-level diagnoses were calculated, and K-means clustering was used to identify different clinical subtypes in the MIMIC database. Clinical characteristics and outcomes were compared among subtypes, and the calibration of SAPS II and APACHE IV among different subtypes was evaluated.

Results

In total, 6,505 (13.8%) cancer patients of the MIMIC database and 7,351 (4.9%) ones in eICU database, were enrolled in the study. Metastasis involving pleura, metastasis involving the liver, and acute myeloid leukemia were in the top 5 diagnoses with the highest mortality in both databases. Clinical subtypes identified by K-means clustering were closely associated with admission type (elective or emergency) and clinical service provider (surgical or medical). In a four-cluster pattern, nearly all patients in the first cluster were elective admissions (99.1%), whereas in the rest of the clusters, most were emergency admissions (93.7%). Most surgical patients were in the 1+2 clusters (92.0%) and most medical patients were in the 3+4 clusters (93.5%). Most characteristics and outcomes as well as the calibration of SAPS II and APACHE IV scoring systems were significantly different among clinical subtypes.

Conclusions

Different clinical subtypes can be well identified by admission type and clinical service provider among ICU patients with cancer. Caution should be exercised when considering these patients as a whole population both in clinical practice and research. Moreover, APACHE IV has better calibration than SAPS II for cancer patients at low risk of mortality in the ICU.

Targeting Bone Cells During Sexual Maturation Reveals Sexually Dimorphic Regulation of Endochondral Ossification

In endochondral ossification, chondroblasts become embedded in their matrix and become chondrocytes, which are mature cells that continue to proliferate, eventually becoming hypertrophic. Hypertrophic chondrocytes produce cartilage that is then resorbed by osteoclasts prior to bone matrix replacement via osteoblasts. Although sexually dimorphic bone phenotypes have long been characterized, specific modulation of the growth plate during a critical window in sexual maturation has not been evaluated. Here we report that specific depletion of osteocalcin‐ (OCN‐) expressing cells in vivo during sexual maturation leads to dimorphic bone phenotypes in males and females. At 6 to 8 weeks of age, OCN‐Cre;iDTR (inducible diphtheria toxin receptor‐expressing) mice were treated with diphtheria toxin (DT) for 2 weeks to deplete OCN+ cells. At the end of the study, long bones were collected for μCT and histomorphometry, and serum was collected for proteomic and lipidomic analyses. Ablation of OCN+ cells in mice leads to consistent trends for weight loss after 2 weeks of treatment. Females exhibited decreased skeletal parameters in response to OCN+ cell ablation treatment, as expected. However, OCN+ cell ablation in males uniquely displayed an expansion of hypertrophic chondrocytes, a widening of the growth plate, and an abnormal “clubbing” anatomy of the distal femur. Following DT treatment, mice from both sexes also underwent metabolic cage analysis, in which both sexes exhibited decreased energy expenditure. We conclude that skewing endochondral bone formation during longitudinal growth has a profound effect on body weight and energy expenditure with sex‐specific effects on developing bone. © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Pathological Conversion of Mouse Perivascular Adipose Tissue by Notch Activation

OBJECTIVE

Perivascular adipose tissue (PVAT) surrounding arteries supports healthy vascular function. During obesity, PVAT loses its vasoprotective effect. We study pathological conversion of PVAT, which involves molecular changes in protein profiles and functional changes in adipocytes. Approach and Results: C57BL6/J mice were fed a 60% high-fat diet for 12 weeks or a cardioprotective 30% calorie-restricted diet for 5 weeks. Proteomic analysis identified PVAT as a molecularly distinct adipose depot, and novel markers for thermogenic adipocytes, such as GRP75 (stress-70 protein, mitochondrial), were identified. High-fat diet increased the similarity of protein signatures in PVAT and brown adipose, suggesting activation of a conserved whitening pathway. The whitening phenotype was characterized by suppression of UCP1 (uncoupling protein 1) and increased lipid deposition, leptin, and inflammation, and specifically in PVAT, elevated Notch signaling. Conversely, PVAT from calorie-restricted mice had decreased Notch signaling and less lipid. Using the Adipoq-Cre strain, we constitutively activated Notch1 signaling in adipocytes, which phenocopied the changes in PVAT caused by a high-fat diet, even on a standard diet. Preadipocytes from mouse PVAT expressed Sca1, CD140a, Notch1, and Notch2, but not CD105, showing differences compared with preadipocytes from other depots. Inhibition of Notch signaling during differentiation of PVAT-derived preadipocytes reduced lipid deposition and adipocyte marker expression.

CONCLUSIONS

PVAT shares features with other adipose depots, but has a unique protein signature that is regulated by dietary stress. Increased Notch signaling in PVAT is sufficient to initiate the pathological conversion of PVAT by promoting adipogenesis and lipid accumulation and may thus prime the microenvironment for vascular disease.

Metabolome Changes in Cerebral Ischemia

Cerebral ischemia is caused by perturbations in blood flow to the brain that trigger sequential and complex metabolic and cellular pathologies. This leads to brain tissue damage, including neuronal cell death and cerebral infarction, manifesting clinically as ischemic stroke, which is the cause of considerable morbidity and mortality worldwide. To analyze the underlying biological mechanisms and identify potential biomarkers of ischemic stroke, various in vitro and in vivo experimental models have been established investigating different molecular aspects, such as genes, microRNAs, and proteins. Yet, the metabolic and cellular pathologies of ischemic brain injury remain not fully elucidated, and the relationships among various pathological mechanisms are difficult to establish due to the heterogeneity and complexity of the disease. Metabolome-based techniques can provide clues about the cellular pathologic status of a condition as metabolic disturbances can represent an endpoint in biological phenomena. A number of investigations have analyzed metabolic changes in samples from cerebral ischemia patients and from various in vivo and in vitro models. We previously analyzed levels of amino acids and organic acids, as well as polyamine distribution in an in vivo rat model, and identified relationships between metabolic changes and cellular functions through bioinformatics tools. This review focuses on the metabolic and cellular changes in cerebral ischemia that offer a deeper understanding of the pathology underlying ischemic strokes and contribute to the development of new diagnostic and therapeutic approaches.

Verification of Chromatographic Profile of Primary Essential Oil of Pinus sylvestris L. Combined with Chemometric Analysis

Chromatographic profiles of primary essential oils (EO) deliver valuable authentic information about composition and compound pattern. Primary EOs obtained from Pinus sylvestris L. (PS) from different global origins were analyzed using gas chromatography coupled to a flame ionization detector (GC-FID) and identified by GC hyphenated to mass spectrometer (GC-MS). A primary EO of PS was characterized by a distinct sesquiterpene pattern followed by a diterpene profile containing diterpenoids of the labdane, pimarane or abietane type. Based on their sesquiterpene compound patterns, primary EOs of PS were separated into their geographical origin using component analysis. Furthermore, differentiation of closely related pine EOs by partial least square discriminant analysis proved the existence of a primary EO of PS. The developed and validated PLS-DA model is suitable as a screening tool to assess the correct chemotaxonomic identification of a primary pine EOs as it classified all pine EOs correctly.

Partially overlapping sequential window acquisition of all theoretical mass spectra: A methodology to improve the spectra quality of veterinary drugs present at low concentrations in highly complex biological matrices

RATIONALE

Residues of veterinary drugs in food matrices have to be detected, identified and confirmed at low concentrations. Data-independent acquisition (DIA) methods such as the sequential window acquisition of all theoretical spectra (SWATH) permit the extraction of relatively clean spectra out of complex matrices. Such spectra can be significantly improved by using a modified SWATH algorithm which provides several times narrower mass isolation windows without affecting the cycle time.

METHODS

A quadrupole-time-of-flight mass spectrometer was operated in a partially overlapping SWATH mode. Unlike in conventional SWATH, acquisition sequences are not identically repeated, but each sequence is initiated with a mass intercept (mass shift). Pearson correlation is used to assign HRMS-resolved product ions to the precursor mass of interest. Trace analytes (veterinary drugs) in a complex matrix extract (bovine liver) were investigated.

RESULTS

Utilizing identical cycle times, the partially overlapping SWATH mode produced for the investigated small molecules a significantly higher selectivity than the conventional SWATH acquisition mode. The acquisition strategy enables long ion accumulation times and therefore the required high sensitivity of detection. The study investigates the quality of the obtained product ion spectra and compares it with that in conventional acquisition modes.

CONCLUSIONS

The modified SWATH mode permits high duty cycles in combination with narrow virtual mass windows. The technique is a further step toward harvesting a HRMS product ion spectrum out of a data-independent acquisition which moves closer towards the quality of a dedicated precursor unit isolation product ion spectrum.

Insights into the function of NADPH thioredoxin reductase C (NTRC) based on identification of NTRC-interacting proteins in vivo

Redox regulation in heterotrophic organisms relies on NADPH, thioredoxins (TRXs), and an NADPH-dependent TRX reductase (NTR). In contrast, chloroplasts harbor two redox systems, one that uses photoreduced ferredoxin (Fd), an Fd-dependent TRX reductase (FTR), and TRXs, which links redox regulation to light, and NTRC, which allows the use of NADPH for redox regulation. It has been shown that NTRC-dependent regulation of 2-Cys peroxiredoxin (PRX) is critical for optimal function of the photosynthetic apparatus. Thus, the objective of the present study was the analysis of the interaction of NTRC and 2-Cys PRX in vivo and the identification of proteins interacting with them with the aim of identifying chloroplast processes regulated by this redox system. To assess this objective, we generated Arabidopsis thaliana plants expressing either an NTRC-tandem affinity purification (TAP)-Tag or a green fluorescent protein (GFP)-TAP-Tag, which served as a negative control. The presence of 2-Cys PRX and NTRC in complexes isolated from NTRC-TAP-Tag-expressing plants confirmed the interaction of these proteins in vivo. The identification of proteins co-purified in these complexes by MS revealed the relevance of the NTRC-2-Cys PRX system in the redox regulation of multiple chloroplast processes. The interaction of NTRC with selected targets was confirmed in vivo by bimolecular fluorescence complementation (BiFC) assays.