Carry-over effects of soil inoculation on plant growth and health under sequential exposure to soil-borne diseases

Aim

To investigate the effects of biochar on biological and chemical phosphorus (P) processes and identify potential interactive effects between P fertilizer and biochar on P bioavailability in the rhizosphere of maize.

Methods

We conducted a pot-experiment with maize in a sandy loam soil with two fertilizer levels (0 and 100 mg P kg ^?1) and three biochars produced from soft wood (SW), rice husk (RH) and oil seed rape (OSR). Sequential P fractionation was performed on biochar, bulk soil, and rhizosphere soil samples. Acid and alkaline phosphatase activity and root exudates of citrate, glucose, fructose, and sucrose in the rhizosphere were determined.

Results

RH and OSR increased readily available soil P, whereas SW had no effect. However, over time available P from the biochars moved to less available P pools (Al-P and Fe-P). There were no interactive effects between P fertilizer and biochar on P bioavailability. Exudates of glucose and fructose were strongly affected by especially RH, whereas sucrose was mostly affected by P fertilizer. Alkaline phosphatase activity was positively correlated with pH, and citrate was positively correlated with readily available P.

Conclusion

Biochar effects on biological and chemical P processes in the rhizosphere are driven by biochar properties.

     

Bridge processes: a solution for LCI datasets independent of background databases

Introduction

New platforms are emerging that enable more data providers to publish life cycle inventory data.

Background

Providing datasets that are not complete LCA models results in fragments that are difficult for practitioners to integrate and use for LCA modeling. Additionally, when proxies are used to provide a technosphere input to a process that was not originally intended by the process authors, in most LCA software, this requires modifying the original process.

Results

The use of a bridge process, which is a process created to link two existing processes, is proposed as a solution.

Discussion

Benefits to bridge processes include increasing model transparency, facilitating dataset sharing and integration without compromising original dataset integrity and independence, providing a structure with which to make the data quality associated with process linkages explicit, and increasing model flexibility in the case that multiple bridges are provided. A drawback is that they add additional processes to existing LCA models which will increase their size.

Conclusions

Bridge processes can be an enabler in allowing users to integrate new datasets without modifying them to link to background databases or other processes they have available. They may not be the ideal long-term solution but provide a solution that works within the existing LCA data model.

     

How the chemical features of molecules may have addressed the settlement of metabolic steps

Introduction

While the evolutionary adaptation of enzymes to their own substrates is a well assessed and rationalized field, how molecules have been originally selected in order to initiate and assemble convenient metabolic pathways is a fascinating, but still debated argument.

Objectives

Aim of the present study is to give a rationale for the preferential selection of specific molecules to generate metabolic pathways.

Methods

The comparison of structural features of molecules, through an inductive methodological approach, offer a reading key to cautiously propose a determining factor for their metabolic recruitment.

Results

Starting with some commonplaces occurring in the structural representation of relevant carbohydrates, such as glucose, fructose and ribose, arguments are presented in associating stable structural determinants of these molecules and their peculiar occurrence in metabolic pathways.

Conclusions

Among other possible factors, the reliability of the structural asset of a molecule may be relevant or its selection among structurally and, a priori, functionally similar molecules.

     

Longitudinal data methods for evaluating genome-by-epigenome interactions in families

Background

Longitudinal measurement is commonly employed in health research and provides numerous benefits for understanding disease and trait progression over time. More broadly, it allows for proper treatment of correlated responses within clusters. We evaluated 3 methods for analyzing genome-by-epigenome interactions with longitudinal outcomes from family data.

Results

Linear mixed-effect models, generalized estimating equations, and quadratic inference functions were used to test a pharmacoepigenetic effect in 200 simulated posttreatment replicates. Adjustment for baseline outcome provided greater power and more accurate control of Type I error rates than computation of a pre-to-post change score.

Conclusions

Comparison of all modeling approaches indicated a need for bias correction in marginal models and similar power for each method, with quadratic inference functions providing a minor decrement in power compared to generalized estimating equations and linear mixed-effects models.

     

Metabolomic signatures of increases in temperature and ocean acidification from the reef-building coral, <Emphasis Type="Italic">Pocillopora damicornis</Emphasis>

Introduction

As a changing climate threatens the persistence of terrestrial and marine ecosystems by altering community composition and function, differential performance of taxa highlights the need for predictive metrics and mechanistic understanding of the factors underlying positive performance in the face of environmental disturbances. Biochemical reactions within cells provide a snapshot of molecular regulation and flexibility during exposure to environmental stressors. However, because the organism is the unit of selection there is a need for the integration of metabolite data with organism physiology to understand mechanisms responsible for individual success under a changing climate.

Objectives

Our study aims to characterize the molecular response of reef corals to simulated global climate change stressors. Furthermore, we seek to relate changes in the molecular physiology to observations in overall colony response.

Methods

To this end, we applied a non-targeted metabolomic approach to describe lipid and primary metabolite composition after exposure of the reef-building coral Pocillopora damicornis to ambient and elevated experimental climate change conditions. We compared these metabolite data to organism physiology, specifically the key processes of photosynthesis, respiration, and calcification.

Results

Corals significantly altered their lipid and primary metabolite profiles in response to experimental treatments. Primary metabolite profiles predicted organisms’ net photosynthesis, but not calcification or respiration measures. Despite challenges in metabolome annotation, our data indicated corals alter carbohydrate composition, cell structural lipids, and signaling compounds in response to elevated treatment conditions.

Conclusions

The integration of metabolite and physiological data highlights the predictive power of metabolomics in defining organism performance and provides biomarkers for future studies. Here, we present a multivariate biomarker approach to assess climate change impacts and advance our mechanistic understanding of stress response in this keystone species.

     

Random walk of passive tracers among randomly moving obstacles

Background

This study is mainly motivated by the need of understanding how the diffusion behavior of a biomolecule (or even of a larger object) is affected by other moving macromolecules, organelles, and so on, inside a living cell, whence the possibility of understanding whether or not a randomly walking biomolecule is also subject to a long-range force field driving it to its target.

Method

By means of the Continuous Time Random Walk (CTRW) technique the topic of random walk in random environment is here considered in the case of a passively diffusing particle among randomly moving and interacting obstacles.

Results

The relevant physical quantity which is worked out is the diffusion coefficient of the passive tracer which is computed as a function of the average inter-obstacles distance.

Conclusions

The results reported here suggest that if a biomolecule, let us call it a test molecule, moves towards its target in the presence of other independently interacting molecules, its motion can be considerably slowed down.

     

MIDAS-G: a computational platform for investigating fragmentation rules of tandem mass spectrometry in metabolomics

Introduction

Tandem mass spectrometry (MS/MS) has been widely used for identifying metabolites in many areas. However, computationally identifying metabolites from MS/MS data is challenging due to the unknown of fragmentation rules, which determine the precedence of chemical bond dissociation. Although this problem has been tackled by different ways, the lack of computational tools to flexibly represent adjacent structures of chemical bonds is still a long-term bottleneck for studying fragmentation rules.

Objectives

This study aimed to develop computational methods for investigating fragmentation rules by analyzing annotated MS/MS data.

Methods

We implemented a computational platform, MIDAS-G, for investigating fragmentation rules. MIDAS-G processes a metabolite as a simple graph and uses graph grammars to recognize specific chemical bonds and their adjacent structures. We can apply MIDAS-G to investigate fragmentation rules by adjusting bond weights in the scoring model of the metabolite identification tool and comparing metabolite identification performances.

Results

We used MIDAS-G to investigate four bond types on real annotated MS/MS data in experiments. The experimental results matched data collected from wet labs and literature. The effectiveness of MIDAS-G was confirmed.

Conclusion

We developed a computational platform for investigating fragmentation rules of tandem mass spectrometry. This platform is freely available for download.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

Histology,imaging and new diagnostic work-flows in pathology

Introduction

Since their introduction in 1999, fully automated, high speed, high-resolution whole slide imaging devices have become increasing more reliable, fast and capable. While by no means perfect, these devices have evolved to a point where one can consider placing them in a pre-diagnostic role in a clinical histology lab.

Methods

At the Massachusetts General Hospital, we are running a pilot study placing high end WSI devices in our main clinical histology lab (after the cover slipper and before slides are sent to the pathologist) to examine the requirement for both the machine and the laboratory.

Results

Placing WSI systems in the clinical lab stresses the system in terms of reliability and throughput. Significantly however, success requires significant modification to the lab workflow. It is likely laboratories need to move from manual, large batch processes to increasingly automated, continuous flow (or mini-batch) processes orchestrated by the LIS using bar coding to track and direct slides, and incorporating the decision to image into the specimen type and the histology orders. Furthermore, image quality, capture speed and reliability are functions of the quality of the histology presented to the WSI devices.

Conclusion

Imaging in pathology does not begin in a WSI robot but in the grossing room and in the histology lab. As more and more imaging devices are placed in histology lab, the inter-relationships histology and pathology imaging will become increasing understood.

     

Statistical relationship between metabolic decomposition and chemical uptake predicts bioconcentration factor data for diverse chemical exposures

Background

A challenge of in vitro to in vivo extrapolation (IVIVE) is to predict the physical state of organisms exposed to chemicals in the environment from in vitro exposure assay data. Although toxicokinetic modeling approaches promise to bridge in vitro screening data with in vivo effects, they are often encumbered by a need for redesign or re-parameterization when applied to different tissues or chemicals.

Results

We demonstrate a parameterization of reverse toxicokinetic (rTK) models developed for the adult zebrafish (Danio rerio) based upon particle swarm optimizations (PSO) of the chemical uptake and degradation rates that predict bioconcentration factors (BCF) for a broad range of chemicals. PSO reveals a relationship between chemical uptake and decomposition parameter values that predicts chemical-specific BCF values with moderate statistical agreement to a limited yet diverse chemical dataset, and all without a need to retrain the model to new data.

Conclusions

The presented model requires only the octanol-water partitioning ratio to predict BCFs to a fidelity consistent with existing QSAR models. This success begs re-evaluation of the modeling assumptions; specifically, it suggests that chemical uptake into arterial blood may be limited by transport across gill membranes (diffusion) rather than by counter-current flow between gill lamellae (convection). Therefore, more detailed molecular modeling of aquatic respiration may further improve predictive accuracy of the rTK approach.

     

On consciousness,resting state fMRI,and neurodynamics

Background

During the last years, functional magnetic resonance imaging (fMRI) of the brain has been introduced as a new tool to measure consciousness, both in a clinical setting and in a basic neurocognitive research. Moreover, advanced mathematical methods and theories have arrived the field of fMRI (e.g. computational neuroimaging), and functional and structural brain connectivity can now be assessed non-invasively.

Results

The present work deals with a pluralistic approach to "consciousness'', where we connect theory and tools from three quite different disciplines: (1) philosophy of mind (emergentism and global workspace theory), (2) functional neuroimaging acquisitions, and (3) theory of deterministic and statistical neurodynamics – in particular the Wilson-Cowan model and stochastic resonance.

Conclusions

Based on recent experimental and theoretical work, we believe that the study of large-scale neuronal processes (activity fluctuations, state transitions) that goes on in the living human brain while examined with functional MRI during "resting state", can deepen our understanding of graded consciousness in a clinical setting, and clarify the concept of "consiousness" in neurocognitive and neurophilosophy research.

     

Diel plant water use and competitive soil cation exchange interact to enhance NH<Subscript>4</Subscript><Superscript>+</Superscript> and K<Superscript>+</Superscript> availability in the rhizosphere

Aims

Hydro-biogeochemical processes in the rhizosphere regulate nutrient and water availability, and thus ecosystem productivity. We hypothesized that two such processes often neglected in rhizosphere models — diel plant water use and competitive cation exchange — could interact to enhance availability of K⁺ and NH₄ ⁺, both high-demand nutrients.

Methods

A rhizosphere model with competitive cation exchange was used to investigate how diel plant water use (i.e., daytime transpiration coupled with no nighttime water use, with nighttime root water release, and with nighttime transpiration) affects competitive ion interactions and availability of K⁺ and NH₄ ⁺.

Results

Competitive cation exchange enabled low-demand cations that accumulate against roots (Ca²⁺, Mg²⁺, Na⁺) to desorb NH₄ ⁺ and K⁺ from soil, generating non-monotonic dissolved concentration profiles (i.e. ‘hotspots’ 0.1–1 cm from the root). Cation accumulation and competitive desorption increased with net root water uptake. Daytime transpiration rate controlled diel variation in NH₄ ⁺ and K⁺ aqueous mass, nighttime water use controlled spatial locations of ‘hotspots’, and day-to-night differences in water use controlled diel differences in ‘hotspot’ concentrations.

Conclusions

Diel plant water use and competitive cation exchange enhanced NH₄ ⁺ and K⁺ availability and influenced rhizosphere concentration dynamics. Demonstrated responses have implications for understanding rhizosphere nutrient cycling and plant nutrient uptake.

     

<Emphasis Type="Italic">massPix</Emphasis>: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics

Introduction

Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.

Objectives

We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.

Methods

massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.

Results

Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.

Conclusion

massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.

     

Attributional and consequential LCA in the ILCD handbook

Purpose

This discussion article aims to highlight two problematic aspects in the International Reference Life Cycle Data System (ILCD) Handbook: its guidance to the choice between attributional and consequential modeling and to the choice between average and marginal data as input to the life cycle inventory (LCI) analysis.

Methods

We analyze the ILCD guidance by comparing different statements in the handbook with each other and with previous research in this area.

Results and discussion

We find that the ILCD handbook is internally inconsistent when it comes to recommendations on how to choose between attributional and consequential modeling. We also find that the handbook is inconsistent with much of previous research in this matter, and also in the recommendations on how to choose between average and marginal data in the LCI.

Conclusions

Because of the inconsistencies in the ILCD handbook, we recommend that the handbook be revised.

     

Development of a brain MRI-based hidden Markov model for dementia recognition

Background

Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition.

Methods

Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range.

Results

The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia.

Conclusion

The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Intracellular organelle networks: Understanding their organization and communication through systems-level modeling and analysis

Background

Membrane-bound intracellular organelles are biochemically distinct compartments used by eukaryotic cells for serving specialized physiological functions and organizing their internal environment. Recent studies revealed surprisingly extensive communication between these organelles and highlighted the network nature of their organization and communication. Since organization and communication of the organelles are carried out at the systems level through their networks, systems-level studies are essential for understanding the underlying mechanisms.

Methods

We reviewed recent studies that used systems-level quantitative modeling and analysis to understand organization and communication of intracellular organelle networks.

Results

We first review modeling and analysis studies on how fusion/fission and degradation/biogenesis, two essential and closely related classes of activities of individual organelles, collectively mediate the dynamic organization of their networks. We then turn to another important aspect of the dynamic organization of the organelle networks, namely how organelles are physically connected within their networks, a property referred to as the topology of the networks in mathematics, and summarize some of their distinct properties. Lastly, we briefly review modeling and analysis studies that aim to understand communication between different organelle networks, focusing on cellular calcium homeostasis as an example. We conclude with a brief discussion of future directions for research in this area.

Conclusion

Together, the reviewed studies provide critical insights into how diverse activities of individual organelles collectively mediate the organization and communication of their networks. They demonstrate the essential role of systemslevel modeling and analysis in understanding complex behavior of such networks.

     

Distribution and accumulation of trace elements in rhizosphere and non-rhizosphere soils on a karst plateau after vegetation restoration

Aims

The restoration of vegetation in the rocky desertified areas of karst plateaus is a major problem for present-day ecological studies. The aim of this study was to determine the effects of vegetation restoration on the distribution and accumulation of trace elements in rhizosphere and non-rhizosphere soils.

Methods

Four representative areas containing the plants Coriaria nepalensis Wall., Pinus armandii Franch., Elaeagnus pungens Thunb., and Cotoneaster hissaricus Pojark. were selected within a vegetation restoration area in the Karst Plateau of Caohai County, Guizhou Province, China. Soils were sampled using a grid method to measure the total contents of the trace elements iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn) in rhizosphere and non-rhizosphere soils.

Results

The representative area containing Elaeagnus displayed the greatest amount of accumulation in the rhizosphere of both total and available trace elements, except for total Zn. Representative areas of the rhizosphere with other types of vegetation showed accumulation of only some of the trace elements studied. All types of vegetation were associated with the bioenrichment of available trace elements in both rhizosphere and non-rhizosphere soils, except for available Cu in areas associated with Cotoneaster.

Conclusions

Representative areas containing Pinus displayed the greatest degree of bioenrichment for both total and available trace elements in both rhizosphere and non-rhizosphere soils.

     

PROPER: Performance visualization for optimizing and comparing ranking classifiers in MATLAB

Background

One of the recent challenges of computational biology is development of new algorithms, tools and software to facilitate predictive modeling of big data generated by high-throughput technologies in biomedical research.

Results

To meet these demands we developed PROPER - a package for visual evaluation of ranking classifiers for biological big data mining studies in the MATLAB environment.

Conclusion

PROPER is an efficient tool for optimization and comparison of ranking classifiers, providing over 20 different two- and three-dimensional performance curves.