Combined Analysis Research Articles

Combined analysis of cross-population healthy adult human microbiome reveals consistent differences in gut microbial characteristics between Western and non-Western countries

Despite extensive research on the gut microbiome of healthy individuals from a single country, there are still a limited number of population-level comparative studies. Moreover, the sequencing approach used in most related studies involves 16S ribosomal RNA (rRNA) sequencing with a limited resolution, which cannot provide detailed functional profiles. In the present study, we applied a combined analysis approach to analyze whole metagenomic shotgun sequencing data from 2035 healthy adult samples from six countries across four continents. Analysis of core species revealed that 13 species were present in more than 90% of all investigated individuals, the majority of which produced short-chain fatty acids (SCFA)-producing bacteria. Our analysis revealed consistently significant differences in gut microbial species and pathways between Western and non-Western countries, such as Escherichia coli and the relation of MetaCyc pathways to the TCA cycle. Specific changes in microbial species and pathways are potentially related to lifestyle and diet. Furthermore, we identified several noteworthy microbial species and pathways that exhibit distinct characteristics specific to China. Interestingly, we observed that China (CHN) was more similar to the United States (USA) and United Kingdom (GBR) in terms of the taxonomic and functional composition of the gut microbiome than India (IND) and Madagascar (MDG), which were more similar to the China (CHN) diet. The current study identified consistent microbial features associated with population and geography, which will inspire further clinical translations that consider paying attention to differences in microbiota backgrounds and confounding factors.

Chromatin remodelling in damaged intestinal crypts orchestrates redundant TGFβ and Hippo signalling to drive regeneration.

Cell state dynamics underlying successful tissue regeneration are undercharacterized. In the intestine, damage prompts epithelial reprogramming into revival stem cells (revSCs) that reconstitute Lgr5+ intestinal stem cells (ISCs). Here single-nuclear multi-omics of mouse crypts regenerating from irradiation shows revSC chromatin accessibility overlaps with ISCs and differentiated lineages. While revSC genes themselves are accessible throughout homeostatic epithelia, damage-induced remodelling of chromatin in the crypt converges on Hippo and the transforming growth factor-beta (TGFβ) signalling pathway, which we show is transiently activated and directly induces functional revSCs. Combinatorial gene expression analysis further suggests multiple sources of revSCs, and we demonstrate TGFβ can reprogramme enterocytes, goblet and paneth cells into revSCs and show individual revSCs form organoids. Despite this, loss of TGFβ signalling yields mild regenerative defects, whereas interference in both Hippo and TGFβ leads to profound defects and death. Intestinal regeneration is thus poised for activation by a compensatory system of crypt-localized, transient morphogen cues that support epithelial reprogramming and robust intestinal repair.

Soluble Urokinase-Type Plasminogen Activator Receptor and Inflammatory Biomarker Response with Prognostic Significance after Acute Neuronal Injury - a Prospective Cohort Study.

Aneurysmal subarachnoid hemorrhage (aSAH), ischemic stroke (IS), and traumatic brain injury (TBI) are severe conditions impacting individuals and society. Identifying reliable prognostic biomarkers for predicting survival or recovery remains a challenge. Soluble urokinase type plasminogen activator receptor (suPAR) has gained attention as a potential prognostic biomarker in acute sepsis. This study evaluates suPAR and related neuroinflammatory biomarkers in serum for brain injury prognosis. This prospective study included 31 aSAH, 30 IS, 13 TBI, and three healthy controls (n = 77). Serum samples were collected on average 5.9days post-injury, analyzing suPAR, IL-1β, cyclophilin A, and TNFα levels using ELISA. Outcomes were assessed 90days post-injury with the modified Rankin Scale (mRS), categorized as favorable (mRS 0-2) or unfavorable (mRS 3-6). Statistical analyses included 2-tailed t-tests, Pearson's correlations, and machine learning linear discriminant analysis (LDA) for biomarker combinations. Elevated suPAR levels were found in brain injury patients compared to controls (p = 0.017). Increased suPAR correlated with unfavorable outcomes (p = 0.0018) and showed prognostic value (AUC = 0.66, p = 0.03). IL-1β levels were higher in the unfavorable group (p = 0.0015). LDA combinatory analysis resulted a fair prognostic accuracy with canonical equation = 0.775[suPAR] + 0.667[IL1-β] (AUC = 0.77, OR 0.296, sensitivity 93.1%, specificity 53.1%, p = 0.0007). No correlation was found between suPAR and CRP or infection status. Elevated suPAR levels in acute brain injury patients were associated with poorer outcomes, highlighting suPAR's potential as a prognostic biomarker across different brain injury types. Combining IL-1β with suPAR improved prognostic accuracy, supporting a multimodal biomarker approach for predicting outcomes.

EPCO-01. FROM HIERARCHY TO GEOGRAPHY: MULTI-CLONES DRIVE THE CELLULAR DIVERSITY AND EVOLUTION OF HUMAN MEDULLOBLASTOMA

Abstract We’ve curated a comprehensive multi-omics database of medulloblastoma (MB) serving as a resource to understand spatiotemporal organization and guide therapeutic strategies. Herein, our analysis encompasses single-nucleus transcriptome (n = 51), chromatin accessibility (n = 46), and spatial transcriptomic profiles (n = 31) from human MB samples and patient-derived xenograft lines spanning four subgroups, augmented by bulk RNA (n = 322), whole-genome short-sequencing (n = 279), and bisulfite sequencing (n = 300) from matched samples. We delineate the malignant cellular hierarchy, identifying MB cancer stem-like cells (MB-CSCs), cycling cells, and more differentiated populations. Gene signatures of different cell subpopulations strongly correlate to clinical outcomes, while spatial character of patient-derived materials emphasizes the geographically heterogeneous nature of MB. Examination of somatic copy number variants (CNVs) and transcriptional signatures at single-nucleus resolution reveals geographically defined subclones harboring distinct CNVs, suggesting a competitive agglomeration of co-existing multi-clones for MB. Alongside the trajectorial transition, we observe distinct chromosomal alterations in apical MB-CSCs compared to more differentiated cells, indicative of extensive subclone-primed spatiotemporal evolution. Further analysis of reagent-gene interactions of subclonal lineages contributes to predicting the druggable candidates for treatment optimization. Integration of spatial architecture data highlights the recapitulation of subclone-determined niches through colocation with identified malignant lineages and their own inflammatory or fibrotic adaptions. Taken together, this comprehensive database, comprising five or six kinds of datatypes on the same MB cases, facilitates combinatorial analyses across different genomic modalities, offering genetic and geographic insights into therapeutic advancements.

Aging and Pathological Conditions Similarity Revealed by Meta-Analysis of Metabolomics Studies Suggests the Existence of the Health and Age-Related Metapathway

Background: The incidence of many diseases increases with age and leads to multimorbidity, characterized by the presence of multiple diseases in old age. This phenomenon is closely related to systemic metabolic changes; the most suitable way to study it is through metabolomics. The use of accumulated metabolomic data to characterize this phenomenon at the system level may provide additional insight into the nature and strength of aging–disease relationships. Methods: For this purpose, metabolic changes associated with human aging and metabolic alterations under different pathological conditions were compared. To do this, the published results of metabolomic studies on human aging were compared with data on metabolite alterations collected in the human metabolome database through metabolite set enrichment analysis (MSEA) and combinatorial analysis. Results: It was found that human aging and pathological conditions involve the set of the same metabolic pathways with a probability of 99.96%. These data show the high identity of the aging process and the development of diseases at the metabolic level and allow to identify the set of metabolic pathways reflecting age-related changes closely associated with health. Based on these pathways, a metapathway was compiled, changes in which are simultaneously associated with health and age. Conclusions: The knowledge about the strength of the convergence of aging and pathological conditions has been supplemented by the rigor evidence at the metabolome level, which also made it possible to outline the age and health-relevant place in the human metabolism.

Fragmented perspective of self-organized criticality and disorder in log gravity

We use a statistical model to discuss nonequilibrium fragmentation phenomena taking place in the stochastic dynamics of the log sector in log gravity. From the canonical Gibbs model, a combinatorial analysis reveals an important aspect of the n-particle evolution previously shown to generate a collection of random partitions according to the Ewens distribution realized in a disconnected double Hurwitz number in genus zero. By treating each possible partition as a member of an ensemble of fragmentations, and ensemble averaging over all partitions with the Hurwitz number as a special case of the Gibbs distribution, a resulting distribution of cluster sizes appears to fall as a power of the size of the cluster. Dynamical systems that exhibit a distribution of sizes giving rise to a scale-invariant power-law behavior at a critical point possess an important property called self-organized criticality. As a corollary, the log sector of log gravity is a self-organized critical system at the critical point μl = 1. A similarity between self-organized critical systems, spin glass models and the dynamics of the log sector which exhibits aging behavior reminiscent of glassy systems is pointed out by means of the Pòlya distribution, also known to classify various models of (randomly fragmented) disordered systems, and by presenting the cluster distribution in the log sector of log gravity as a distinguished member of this probability distribution. We bring arguments from a probabilistic perspective to discuss the disorder in log gravity, largely anticipated through the conjectured AdS3/LCFT2 correspondence.

GENERALIZED PASCAL’S PYRAMIDS AND COMBINATORIAL INFORMATION RETRIEVAL PROBLEMS

The work studies combinatorial objects of pyramidal structure. It proposes fundamental relations for generalizations of Pascal’s triangle and pyramid. It considers problems of finding new ways of constructing information relevance indices and offers a way of constructing such an index with the help of Pascal’s generalized pyramids. Using structures of a type of Pascal’s generalized pyramids with weight factors allow construction of an index that reflects a degree of relevance of keywords, as well as makes it possible to allocate subsets of keywords with specific properties. The offered methods of the combinatorial analysis of hierarchical structures in information retrieval problems can be used in creating and analyzing knowledge databases.

Supersolvable posets and fiber-type abelian arrangements

We present a combinatorial analysis of fiber bundles of generalized configuration spaces on connected abelian Lie groups. These bundles are akin to those of Fadell–Neuwirth for configuration spaces, and their existence is detected by a combinatorial property of an associated finite partially ordered set. This is consistent with Terao’s fibration theorem connecting bundles of hyperplane arrangements to Stanley’s lattice supersolvability. We obtain a combinatorially determined class of K(π,1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K(\\pi ,1)$$\\end{document} toric and elliptic arrangements. Under a stronger combinatorial condition, we prove a factorization of the Poincaré polynomial when the Lie group is noncompact. In the case of toric arrangements, this provides an analogue of Falk–Randell’s formula relating the Poincaré polynomial to the lower central series of the fundamental group.

Valved Microwell Array Platforms for Stepwise Liquid Dispensing.

The fabrication of microarray chips and the precise dispensing of nanoliter to microliter liquids are fundamental for high-throughput parallel biochemical testing. Conventional microwells, typically featuring a uniform cross section, fill completely in a single operation, complicating the introduction of multiple reagents for stepwise and combinatorial analyses. To overcome this limitation, we developed an innovative valved microwell array. Using ultraviolet (UV)-curing resin three-dimensional (3D) printing, these multilayer configurations can be rapidly fabricated through direct template printing and polydimethylsiloxane (PDMS) casting. Each microwell incorporates a microvalve structure, truncating fluids within the upper metering well and allowing transfer to the bottom reservoir well under centrifugal force. Sequential operations enable the introduction of multiple reagents, facilitating orthogonal combinations for complex assays. We explored four types of valving methods: DeepWell, Expansion, Bottleneck, and Membrane valve, each offering varying degrees of design complexity, operational efficiency, robustness, and precision. These methods constitute a versatile toolkit to accommodate a broad spectrum of analytical requirements. Our innovative approach redefines microwell architecture, direct manufacturing techniques, and stepwise fluid dispensation in microarrays.

Sexual somatic dimorphism in connection with ecological factors

Introduction. Analysis of correlations of sexual somatic dimorphism (SD) with the degree of urbanization (quantity of population) and geographic latitude of the residence place is carried out using the material of monitoring of Russian children and adolescents. Material and methods. The study embraces the wide specter of ethno-territorial samples of children and adolescents from Russia and neighboring countries (literary data), examined through the historic interval 1930-2010 years. To estimate the direction and degree of associations of anthropometric traits (height, weight, chest girth) and ecological factors the classic correlation analysis for pairwise combinations of variables was implemented for age groups of 9-year-old children and 13-year-old adolescents. The quantitative estimation of SD was carried out using Kullback divergence, the analogue of Makhalanobis distance. Results. Significant correlations of SD of height with both ecological factors for 13-year-old adolescents and SD of weight for 9-year-old children with the quantity of population were fixed on base of the whole data massive. The more homogeneous data base (only Slavonic groups examined in 1960s-1970s) shows significant correlations of latitude with SD of height for 13-year-old adolescents only in combination with the quantity of population of the residence place up to 500 thousands of people; and latitude with SD of weight for 9-year-old children only for million-plus cities. Significant correlations of quantity of population were fixed for SD of height of 13-year-old adolescents in more southern ranges of latitude (37-50degrees), for SD of weight for 9-year-old children in more northern ranges of latitude (50 degrees and more). Conclusion. The study shows complex superposition of ecological factors, which influence somatic variability of children through growth process. High degree of urbanization conceal anthropoecological correlations SD-latitude for ecosensitive age 13 years, but reveal them for “neutral” age, 9 years, with the opposite vector. The most southern range of latitudes (about 40 degrees) reveal positive associations of SD with the degree of urbanization for 13-year-olds, which are not fixed in more northern latitudes. More frequent anthropoecological correlations of SD of height, as genetic marker, of 13-year-olds, and SD of weight as the marker of nutrition status and life style of 9-year-olds reflect the physiological essence of age periods - increase of intersex differences for adolescents and decrease of intersex differences for second childhood children

Predicting genome-wide tissue-specific enhancers via combinatorial transcription factor genomic occupancy analysis.

Enhancers are non-coding cis-regulatory elements crucial for transcriptional regulation. Mutations in enhancers can disrupt gene regulation, leading to disease phenotypes. Identifying enhancers and their tissue-specific activity is challenging due to their lack of stereotyped sequences. This study presents a sequence-based computational model that uses combinatorial transcription factor (TF) genomic occupancy to predict tissue-specific enhancers. Trained on diverse datasets, including ENCODE and Vista enhancer browser data, the model predicted 25 000 forebrain-specific cis-regulatory modules (CRMs) in the human genome. Validation using biochemical features, disease-associated SNPs, and in vivo zebrafish analysis confirmed its effectiveness. This model aids in predicting enhancers lacking well-characterized chromatin features, complementing experimental approaches in tissue-specific enhancer discovery.

B-122 Improving the Diagnostic Utility of Multiplexed Assays in Laboratory Medicine with Machine Learning

Abstract Background The application of machine learning (ML) and artificial intelligence (AI) in precision medicine is of great interest and continues to grow. ML approaches offer a flexible and data-centric methodology for modeling diagnostic and prognostic trends, capable of identifying complex non-linear patterns and relationships between predictor variables and outcomes within large data sets. Although ML holds great promise, there are several common pitfalls that must be avoided for ML models to provide clinical utility. In the current study, we provide a rigorous and systematic evaluation of the utility of ML algorithms to predict disease in several representative data sets generated via multiplexed biomarker assays. We compare five major classes of ML algorithms compared to more traditional combinatorial analysis (logistic regression) to offer perspective against the current gold standard. Methods We obtained five data sets from published literature that include measurements of multiplexed biomarkers in patients with obstructive sleep apnea, sepsis, and breast and colon cancers in comparison to healthy controls. These datasets were generated by bead-based and planar multiplexed immunoassays. We chose representative ML algorithms from the classes of support vector machines (SVM), random forests (RF), neural networks (NNet), and extreme gradient boosting (xgBoost) in comparison to logistic regression (LR). Datasets were randomly split into internal validation and external validation sets via 10 iterations of permutation analysis. We tuned and validated the models using 5-fold cross validation, then tested those on external subsets. We report the average of several model classification metrics and associated variances across all resampled data sets. Results For the classification of sepsis using inflammatory cytokines measured on the Luminex bead-based immunoassay, RF outperformed LR in external validation sets by a 9.8% increase in AUROC. For classification of breast cancer patients, xgBoost and RF performed best with 20.0% and 16.9% increases in AUROC, respectively, whereas for classification of colon cancer patients, NNet outperformed LR by 11.5%. Across all datasets, on average, all ML algorithms outperformed LR in the internal validation sets by a 7.9% (6.3% - 9.9%) increase in AUROC. However, in external validation sets, only xgBoost and SVM outperformed LR, where RF and NNet showed signs of overfitting. Conclusions Overall, certain ML algorithms show improvement over LR for diagnostic applications using multiplexed assays, by up to 20% increase in AUROC. Interpretation of ML results must be performed rigorously to prevent overly optimistic and unrealistic conclusions. In the context of laboratory medicine, our results showcase the utility of ML in clinical applications, while highlighting potential disadvantages of ML. This analysis underscores the need for establishing rigorous guidelines to support data analytics during the development of novel multiplexed assays, which is needed to advance precision medicine.

Simultaneous Determination of Levo-tetrahydropalmatine and Naltrexone in Rat Plasma by LC-MS/MS and its Application in a Pharmacokinetic Study.

Levo-tetrahydropalmatine and low-dose naltrexone are used in association with reducing cocaine-related cravings, but there are no analytical methods for the quantitative simultaneous analysis of this drug combination. A highly selective and sensitive LC-MS/MS assay was developed and validated to simultaneously quantify l-THP and naltrexone. The analytical method for l-THP offers improved sensitivity compared to previously published methods. The product ion transitions of l-THP and naltrexone were 357.0→193.0 and 342.2→324.1, respectively. Chromatographic separations were performed using a BEH-C18 column by an isocratic elution mode with acetonitrile and 0.1% formic acid in water containing 3 mM ammonium acetate. L-THP and naltrexone were extracted from rat plasma using a liquidliquid extraction method. For l-THP and naltrexone, the assay displayed good linear response over a concentration range of 0.5-1000 ng/mL and 0.25-500 ng/mL, respectively. The intra-day accuracy of the method for l-THP and naltrexone was 93.8-101% with a precision (%CV) of 2.43-8.15% and 93.4-108% with a precision of 3.47-8.22%. The inter-day accuracy for l-THP and naltrexone was 91.2-102% with a CV of 2.46-8.06% and 91.5-97.8% with a CV of 3.29-8.92%, respectively. The assay has been used for pharmacokinetic studies of l-THP and naltrexone in the rat.

Combinatorial Reliability Analysis of Multicore Processors

Combinatorial Reliability Analysis of Multicore Processors

Assessment of rainfall trend over Periyar Vaigai Command area of Tamil Nadu

Rainfall trend analysis is critical for studying the effects of climate change. The most crucial aspect of understanding climate change at the basin level is, how it will affect water usage, planning, and development. The present study has been attempted to analyze the trend of annual and seasonal rainfall data over Periyar Vaigai Command area from 1982 – 2021 (40 years) employing different trend analysis methods i.e., Mann-Kendall test for trend significance, Sequential Mann-Kendall test for assessment of start and end of trend, Sen’s slope test for trend magnitude, Innovative Trend Analysis for combination of trends and linear regression analysis for trend detection. Statistically positive trend was detected in summer season using all methods whereas, annual and SWM rainfall showed a statistically significant decreasing trend. Winter and NEM rainfall resulted with no significant trend. The results from this study helps to adopt various water management practices and useful for policy makers to prepare appropriate mitigation strategies.

Galectin-3 Predicts Long-Term Risk of Cerebral Disability and Mortality in Out-of-Hospital Cardiac Arrest Survivors.

Out-of-hospital cardiac arrest (OHCA) is associated with high mortality and cerebral disability in survivors. Current models of risk prediction and survival are mainly based on resuscitation duration. We examined the prognostic value of circulating biomarkers in predicting mortality and severe cerebral disability for OHCA survivors, alongside traditional clinical risk indicators. Biomarkers including BNP, troponin I, and galectin-3 were measured at hospital admission in resuscitated OHCA patients. Prognostic significance for mortality and cerebral disability involving circulating biomarkers, resuscitation duration, demographics, and laboratory and clinical characteristics was examined via univariate and multivariate Cox proportional hazards regression models. The incremental prognostic value of the index covariates was examined through model diagnostics, focusing on the Akaike information criterion (AIC) and Harrell's concordance statistic (c-statistic). In a combinatorial analysis of 144 OHCA survivors (median follow-up 5.7 years (IQR 2.9-6.6)), BNP, galectin-3, arterial pH, and resuscitation time were significant predictors of all-cause death and severe cerebral disability, whereas troponin I levels were not. Multivariate regression, adjusting for BNP, arterial pH, and resuscitation time, identified galectin-3 as an independent predictor of long-term mortality. Multiple linear regression models also confirmed galectin-3 as the strongest predictor of cerebral disability. The incorporation of galectin-3 into models for predicting mortality and cerebral disability enhanced fit and discrimination, demonstrating the incremental value of galectin-3 beyond traditional risk predictors. Galectin-3 is a significant, independent long-term risk predictor of cerebral disability and mortality in OHCA survivors. Incorporating galectin-3 into current risk stratification models may enhance early prognostication and guide targeted clinical interventions.

CMAA–AHP: combinatorial multicriteria acceptability analysis with the analytic hierarchy process

AbstractCombinatorial multi-criteria acceptability analysis (CMAA) is a framework for supporting multicriteria group decisions that provides both a detailed analysis of the effects of individual decision-maker inputs as well as interactive guidance for a consensus-building process. The analytical hierarchy process (AHP) is a widely-used model of decision-maker evaluations that is based on pairwise comparisons. The goal of this work is to show how CMAA can be integrated with AHP in order to make its benefits available to AHP users. We use a minimal input format for AHP which avoids a problem with inconsistency and also reduces the cognitive load on the decision-makers. We extend the CMAA method by introducing new judgement and preference sensitivity variables, which provide helpful insights for the facilitator of the group decision. An example illustrates the combined CMAA–AHP method and its ability to deliver consensus in a very small number of iterations. Monte Carlo simulation is used to study the convergence behavior of the method for a range of problem dimensions. It was found that the mean number of steps to reach consensus grows linearly with the number of alternatives and criteria. We consider two previously published group decisions that use the standard AHP approach of averaging decision-maker judgements and preferences. In both cases, CMAA–AHP delivers the same rankings based on the original input. However, the new method also provides insight into each decision and would have been able to guide each group to consensus within a small number of resolution steps.

Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.

Purpose To determine whether time-dependent deep learning models can outperform single time point models in predicting preoperative upgrade of ductal carcinoma in situ (DCIS) to invasive malignancy at dynamic contrast-enhanced (DCE) breast MRI without a lesion segmentation prerequisite. Materials and Methods In this exploratory study, 154 cases of biopsy-proven DCIS (25 upgraded at surgery and 129 not upgraded) were selected consecutively from a retrospective cohort of preoperative DCE MRI in women with a mean age of 59 years at time of diagnosis from 2012 to 2022. Binary classification was implemented with convolutional neural network (CNN)-long short-term memory (LSTM) architectures benchmarked against traditional CNNs without manual segmentation of the lesions. Combinatorial performance analysis of ResNet50 versus VGG16-based models was performed with each contrast phase. Binary classification area under the receiver operating characteristic curve (AUC) was reported. Results VGG16-based models consistently provided better holdout test AUCs than did ResNet50 in CNN and CNN-LSTM studies (multiphase test AUC, 0.67 vs 0.59, respectively, for CNN models [P = .04] and 0.73 vs 0.62 for CNN-LSTM models [P = .008]). The time-dependent model (CNN-LSTM) provided a better multiphase test AUC over single time point (CNN) models (0.73 vs 0.67; P = .04). Conclusion Compared with single time point architectures, sequential deep learning algorithms using preoperative DCE MRI improved prediction of DCIS lesions upgraded to invasive malignancy without the need for lesion segmentation. Keywords: MRI, Dynamic Contrast-enhanced, Breast, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.

Decomposer faecal food web and C sequestration in soil. Can near infrared spectroscopy describe transfers and transformations from fresh organic inputs to protected forms in soil aggregates?

This study sought to characterize the transfer of agricultural organic inputs by macrofauna to the biogenic aggregate compartments of the soil and test the existence of a faecal food web process based on coprophagic feeding behaviours. In an experimental plantain crop field of Colombia, we applied the agroecological FBO (Fertilisation Bio Organique ®) technology, a nucleation technique which consists in planting perennial plants in 1.0 x 0.4 × 0.4 m deep trenches where low- and high-quality organic materials are added in a specific design, and endogeic earthworms are inoculated. We evaluated the effectiveness of Near Infrared Spectroscopy (NIRS) to track this flow of organic matter through different natural stages of decomposition represented by soil macroaggregates produced by soil macroinvertebrates as faecal pellets and casts. After one year, great part of the organic inputs had been either mineralized or incorporated into macroaggregates of different sizes and origins. Using NIRS and physicochemical laboratory analyses, we assessed the quality and quantity of organic matter in the different types of aggregates separated manually according to their origin: large biogenic aggregates (LBA), medium sized biogenic aggregates (MBA) and small biogenic aggregates (SBA); physical aggregates (PA), root aggregates (RA) and residual soil (RS).Earlier studies had shown that these structures were presumably formed by the activity of macroinvertebrates of three different functional groups, Diplopoda and Isopoda litter transformers for SBA, epidendogeic earthworms for MBA and mesohumic endogeic earthworms for LBA, organized in a feeding succession.A Coinertia analysis between showed significant covariation among the two sets of physico chemical (22 variables) and NIR spectral (100 different wavelength absorbances) characterization of 135 samples representative of the three classes of biogenic macroaggregates, physical aggregates and residual non macroaggregated soil, (RV = 0.55; p < 0.001). This analysis clearly separated biogenic structures and ranked them according to their size, from small SBA to medium sized MBA and large LBA. Physical PA aggregates and RS residual soil were projected close to the LBAs in the coinertia factorial plan. Multiple combinatorial data analysis CDA, associated 5 specific wavelength absorbance ranges with aggregate types and residual soil. Along the sequence from small to large biogenic aggregates, residual soil and physical aggregates, wavelengths associated to easily decomposed substrates (in ranges 1708–1716 nm; 1796–1948 and 2164–2316 nm) had progressively decreasing absorbances. Substrates associated to slowly decomposing aromatic, alkane and phenolic substrates either increased (1420–1436) or decreased (1284–1380) along this sequence.These results are compatible with the hypothesis of a progressive transformation and transfer of organic residues first into small biogenic aggregates that are presumably faecal pellets of Isopoda and Diplopoda, then to medium sized biogenic aggregates identified as casts of epiendogeic earthworms and finally to large biogenic aggregates expected to be the casts of the large endogeic earthworms present in these soils. A definitive verification of the hypothesis and the involvement of specific macroinvertebrates will require direct experimentation in controlled laboratory conditions.

Proteome and metabolomic profile of Mongolian horse follicular fluid during follicle development

During follicular development, changes in the composition of the follicular fluid are synchronized with the development of oocytes. Our aim was to screen the key factors affecting oocyte maturation and optimize the in vitro culture protocol by understanding the changes of proteins and metabolites in follicular fluid. Follicles are divided into three groups according to their diameter (small follicle fluid (SFF): 10 mm < d < 20 mm; medium follicle fluid (MFF): 20 mm < d < 30 mm; large follicle fluid (LFF): 30 mm < d). Proteins and metabolites from the follicular fluid were analyzed by mass spectrometry. The results showed that: in LFF vs MFF, 20 differential abundant protein (DAP) and 88 differential abundant metabolites (DAM) were screened out; In SFF vs MFF, 3 DAPs and 65 DAMs were screened out; In MFF vs SFF, 24 DAPs and 35 DAMs were screened out. The analysis of differential proteins and metabolites showed that glycerophosphate hydrolysis decreased during follicular development, and proteins played a major role in metabolism and binding. In addition, DAMs and DAPs are co-enriched in the “linoleic acid metabolism” pathway. Combinatorial analysis reveals the dynamic profile of follicular fluid during follicular development and provides fundation for further exploring the function of follicular fluid in Mongolian horse.