Pitfalls in aminoacid and organic acid analysis: 3-hydroxypropionic aciduria.

Pitfalls in organic acid analysis can originate from inadequate methodology, analytical interferences, in vivo interactions and from pre-analytical conditions which often are unknown to the specialized analytical laboratory. Among the latter, ingested food and additives, metabolites of food processing or medications have to be considered. Bacterial metabolites from the gastrointestinal or urogenital system or formed after sample collection can lead to pitfalls as well. An example of such a patient whose urinary metabolites mimic at first glance inherited propionic aciduria is described.

An evaluation of the inorganic and organic geochemistry of the San Vicente Mississippi Valley-type zinc-lead district, central Peru: Implications for ore fluid composition, mixing processes, and sulfate reduction

Mississippi Tialley-type zinc-lead deposits and ore occurrences in the San Vicente belt are hosted in dolostones of the eastern Upper Triassic to Lower Jurassic Pucara basin, central Peru. Combined inorganic and organic geochemical data from 22 sites, including the main San Vicente deposit, minor ore occurrences, and barren localities, provide better understanding of fluid pathways and composition, ore precipitation mechanisms, Eh-pH changes during mineralization, and relationships between organic matter and ore formation. Ore-stage dark replacement dolomite and white sparry dolomite are Fe and rare earth element (REE) depleted, and Mn enriched, compared to the host dolomite. In the main deposit, they display significant negative Ce and probably Eu anomalies. Mixing of an incoming hot, slightly oxidizing, acidic brine (H2CO3 being the dominant dissolved carbon species), probably poor in REE and Fe, with local intraformational, alkaline, reducing waters explains the overall carbon and oxygen isotope variation and the distributions of REE and other trace elements in the different hydrothermal carbonate generations. The incoming ore fluid flowed through major aquifers, probably basal basin detrital units, with limited interaction with the carbonate host rocks. The hydrothermal carbonates show a strong regional chemical homogeneity, indicating access of the ore fluids by interconnected channelways near the ore occurrences. Negative Ce anomalies in the main deposit, that are absent at the district scale, indicate local ore-fluid chemical differences. Oxidation of both migrated and indigenous hydrocarbons by the incoming fluid provided the local reducing conditions necessary for sulfate reduction to H2S, pyrobitumen precipitation, and reduction of Eu3+ to Eu2+. Fe-Mn covariations, combined with the REE contents of the hydrothermal carbonates, are consistent with the mineralizing system shifting from reducing/rock-dominated to oxidizing/fluid-dominated conditions following ore deposition. Sulfate and sulfide sulfur isotopes support sulfide origin from evaporite-derived sulfate by thermochemical organic reduction; further evidence includes the presence of C-13-depleted calcite cements (similar to-12 parts per thousand delta(13)C) as sulfate pseudomorphs, elemental sulfur, altered organic matter in the host dolomite, and isotopically heavier, late, solid bitumen. Significant alteration of the indigenous and extrinsic hydrocarbons, with absent bacterial membrane biomarkers (hopanes) is observed. The light delta(34)S of sulfides from small mines and occurrences compared to the main deposit reflect a local contribution of isotopically light sulfur, evidence of local differences in the ore-fluid chemistry.

Gene Ontology Function prediction in Mollicutes using Protein-Protein Association Networks

Abstract Background: Many complex systems can be represented and analysed as networks. The recent availability of large-scale datasets, has made it possible to elucidate some of the organisational principles and rules that govern their function, robustness and evolution. However, one of the main limitations in using protein-protein interactions for function prediction is the availability of interaction data, especially for Mollicutes. If we could harness predicted interactions, such as those from a Protein-Protein Association Networks (PPAN), combining several protein-protein network function-inference methods with semantic similarity calculations, the use of protein-protein interactions for functional inference in this species would become more potentially useful. Results: In this work we show that using PPAN data combined with other approximations, such as functional module detection, orthology exploitation methods and Gene Ontology (GO)-based information measures helps to predict protein function in Mycoplasma genitalium. Conclusions: To our knowledge, the proposed method is the first that combines functional module detection among species, exploiting an orthology procedure and using information theory-based GO semantic similarity in PPAN of the Mycoplasma species. The results of an evaluation show a higher recall than previously reported methods that focused on only one organism network.

Evolution of Coordination in Social Networks: A Numerical Study

Coordination games are important to explain efficient and desirable social behavior. Here we study these games by extensive numerical simulation on networked social structures using an evolutionary approach. We show that local network effects may promote selection of efficient equilibria in both pure and general coordination games and may explain social polarization. These results are put into perspective with respect to known theoretical results. The main insight we obtain is that clustering, and especially community structure in social networks has a positive role in promoting socially efficient outcomes.

Learning about Bayesian networks for forensic interpretation : An example based on the "the problem of multiple propositions"

Both, Bayesian networks and probabilistic evaluation are gaining more and more widespread use within many professional branches, including forensic science. Notwithstanding, they constitute subtle topics with definitional details that require careful study. While many sophisticated developments of probabilistic approaches to evaluation of forensic findings may readily be found in published literature, there remains a gap with respect to writings that focus on foundational aspects and on how these may be acquired by interested scientists new to these topics. This paper takes this as a starting point to report on the learning about Bayesian networks for likelihood ratio based, probabilistic inference procedures in a class of master students in forensic science. The presentation uses an example that relies on a casework scenario drawn from published literature, involving a questioned signature. A complicating aspect of that case study - proposed to students in a teaching scenario - is due to the need of considering multiple competing propositions, which is an outset that may not readily be approached within a likelihood ratio based framework without drawing attention to some additional technical details. Using generic Bayesian networks fragments from existing literature on the topic, course participants were able to track the probabilistic underpinnings of the proposed scenario correctly both in terms of likelihood ratios and of posterior probabilities. In addition, further study of the example by students allowed them to derive an alternative Bayesian network structure with a computational output that is equivalent to existing probabilistic solutions. This practical experience underlines the potential of Bayesian networks to support and clarify foundational principles of probabilistic procedures for forensic evaluation.

EEG-based functional networks in schizophrenia.

Schizophrenia is often considered as a dysconnection syndrome in which, abnormal interactions between large-scale functional brain networks result in cognitive and perceptual deficits. In this article we apply the graph theoretic measures to brain functional networks based on the resting EEGs of fourteen schizophrenic patients in comparison with those of fourteen matched control subjects. The networks were extracted from common-average-referenced EEG time-series through partial and unpartial cross-correlation methods. Unpartial correlation detects functional connectivity based on direct and/or indirect links, while partial correlation allows one to ignore indirect links. We quantified the network properties with the graph metrics, including mall-worldness, vulnerability, modularity, assortativity, and synchronizability. The schizophrenic patients showed method-specific and frequency-specific changes especially pronounced for modularity, assortativity, and synchronizability measures. However, the differences between schizophrenia patients and normal controls in terms of graph theory metrics were stronger for the unpartial correlation method.

Cognate microRNA-Gene Regulatory Networks Mediating Glioblastoma Oncogenic Properties

Malignant gliomas, including the most common and fatal form glioblastoma (GBM, WHO grade IV astrocytoma), remain a challenge to treat. In the United States and Europe, more than 30,000 patients per year are newly diagnosed with GBM. Despite ongoing trials, the best currently available multimodal treatment approaches include surgical resection followed by concomitant and adjuvant radiation (RT) and temozolomide (TMZ) therapy, resulting in a low median overall survival (OS) rate ranging from 12.2 - 15.9 months. The important role of genetic and epigenetic changes in DNA, RNA, and protein alteration as well as epigenetic changes secondary to the tumor microenvironment and outside selection pressure (therapeutic interventions), are increasingly being recognized. In GBM treatment, the focus is shifting toward a more patient-centered (personalized) therapy. In this regard, in particular, microRNAs are being increasingly studied. MicroRNAs are non¬protein coding small RNAs that serve as negative gene regulators by binding to a specific sequence in the promoter region of a target gene, thus regulating gene expression. A single microRNA potentially targets hundreds of genes; thus, microRNAs and their cognate target genes have important roles as tumor suppressors and oncogenes as well as regulators of various cancer- specific cellular features, such as proliferation, apoptosis, invasion, and metastasis. The identification of distinct microRNA-gene regulatory networks in GBM patients can be expected to provide novel therapeutic insights by identifying candidate patients for targeted therapies. To this end, in this work we identified and validated clinically relevant and meaningful novel gene- microRNA regulatory networks that correlated with MR tumor phenotypes, histopathology, and patient survival and response rates to therapy. - Le traitement des gliomes malins, y compris sous leur forme la plus commune et meurtrière, le glioblastome (GBM, ou astrocytome de grade IV selon l'OMS), demeure à ce jour un défi. Aux États-Unis et en Europe, un nouveau diagnostic de GBM est prononcé dans plus de 30Ό00 cas par an. En dépit de tests en cours, les meilleures approches thérapeutiques combinées actuellement disponibles comprennent la résection chirurgicale de la tumeur, suivie d'une radiothérapie adjuvante ainsi que d'un traitement au temozolomide (RT/TMZ), thérapies dont résulte une médiane de survie globale basse (overall survival, OS), comprise entre 12.2 et 15.9 mois. On reconnaît de plus en plus le rôle majeur de l'ADN, de l'ARN et de l'altération des protéines ainsi que des modifications épigénétiques, secondaires par rapport au microenvironnement de la tumeur et à la pression de sélection extérieure (les interventions thérapeutiques). Dans le traitement du GBM, le centre d'intérêt se déplace vers une thérapie centrée sur le cas individuel du patient. Dans ce but, en particulier les microARN sont de plus en plus analysés. Les microARN sont de petits ARN non-codants (les protéines) qui servent de régulateurs négatifs de gènes en s'attachant à une séquence spécifique dans la région promotrice d'un gène-cible, régulant ainsi l'expression du gène. Un seul microARN cible potentiellement des centaines de gènes; on a ainsi découvert que les microARN et leurs gènes-cibles apparentés ont une fonction importante en tant que suppresseurs de tumeurs et d'oncogènes, ainsi que comme régulateurs de diverses caractéristiques cellulaires spécifiques du cancer, comme la prolifération, l'apoptose, l'invasion et la métastase. On peut s'attendre à ce que l'identification de réseaux microARN régulateurs de gènes, distincts selon les patients de GBM, fournisse une approche thérapeutique inédite par la détermination des patients susceptibles de réagir favorablement à des thérapies ciblées.

Statistical mechanics of multi-edge networks

Statistical properties of binary complex networks are well understood and recently many attempts have been made to extend this knowledge to weighted ones. There are, however, subtle yet important considerations to be made regarding the nature of the weights used in this generalization. Weights can be either continuous or discrete magnitudes, and in the latter case, they can additionally have undistinguishable or distinguishable nature. This fact has not been addressed in the literature insofar and has deep implications on the network statistics. In this work we face this problem introducing multiedge networks as graphs where multiple (distinguishable) connections between nodes are considered. We develop a statistical mechanics framework where it is possible to get information about the most relevant observables given a large spectrum of linear and nonlinear constraints including those depending both on the number of multiedges per link and their binary projection. The latter case is particularly interesting as we show that binary projections can be understood from multiedge processes. The implications of these results are important as many real-agent-based problems mapped onto graphs require this treatment for a proper characterization of their collective behavior.

Organic acids in the rhizosphere and phytoavailability of sewage sludge-borne trace elements

The aim of this work was to quantify low molecular weight organic acids in the rhizosphere of plants grown in a sewage sludge-treated media, and to assess the correlation between the release of the acids and the concentrations of trace-elements in the shoots of the plants. The species utilized in the experiment were cultivated in sand and sewage sludge-treated sand. The acetic, citric, lactic, and oxalic acids, were identified and quantified by high performance liquid chromatography in samples collected from a hydroponics system. Averages obtained from each treatment, concentration of trace elements in shoots and concentration of organic acids in the rhizosphere, were compared by Tukey test, at 5% of probability. Linear correlation analysis was applied to verify an association between the concentrations of organic acids and of trace elements. The average composition of organic acids for all plants was: 43.2, 31.1, 20.4 and 5.3% for acetic, citric, lactic, and oxalic acids, respectively. All organic acids evaluated, except for the citric acid, showed a close statistical agreement with the concentrations of Cd, Cu, Ni, and Zn found in the shoots. There is a positive relationship between organic acids present in the rhizosphere and trace element phytoavailability.

Reconstruction of the recent history of a large deep prealpine lake (Lake Bourget, France) using subfossil chironomids, diatoms, and organic matter analysis: towards the definition of a lake-specific reference state

This paper presents the recent history of a large prealpine lake (Lake Bourget) using chironomids, diatoms and organic matter analysis, and deals with the ability of paleolimnological approach to define an ecological reference state for the lake in the sense of the European Framework Directive. The study at low resolution of subfossil chironomids in a 4-m-long core shows the remarkable stability over the last 2.5 kyrs of the profundal community dominated by a Micropsectra-association until the beginning of the twentieth century, when oxyphilous taxa disappeared. Focusing on this key recent period, a high resolution and multiproxy study of two short cores reveals a progressive evolution of the lake's ecological state. Until AD 1880, Lake Bourget showed low organic matter content in the deep sediments (TOC less than 1%) and a well-oxygenated hypolimnion that allowed the development of a profundal oxyphilous chironomid fauna (Micropsectra-association). Diatom communities were characteristic of oligotrophic conditions. Around AD 1880, a slight increase in the TOC was the first sign of changes in lake conditions. This was followed by a first limited decline in oligotrophic diatom taxa and the disappearance of two oxyphilous chironomid taxa at the beginning of the twentieth century. The 1940s were a major turning point in recent lake history. Diatom assemblages and accumulation of well preserved planktonic organic matter in the sediment provide evidence of strong eutrophication. The absence of profundal chironomid communities reveals permanent hypolimnetic anoxia. From AD 1995 to 2006, the diatom assemblages suggest a reduction in nutrients, and a return to mesotrophic conditions, a result of improved wastewater management. However, no change in hypolimnion benthic conditions has been shown by either the organic matter or the subfossil chironomid profundal community. Our results emphasize the relevance of the paleolimnological approach for the assessment of reference conditions for modern lakes. Before AD 1900, the profundal Micropsectra-association and the Cyclotella dominated diatom community can be considered as the Lake Bourget reference community, which reflects the reference ecological state of the lake.

Organic matter processing and soil evolution in a braided river system

Traditionally, braided river research has considered flow, sediment transport processes and, recently, vegetation dynamics in relation to river morphodynamics. However, if considering the development of woody vegetated patches over a time scale of decades, we must consider the extent to which soil forming processes, particularly related to soil organic matter, impact the alluvial geomorphic-vegetation system. Here we quantify the soil organic matter processing (humification) that occurs on young alluvial landforms. We sampled different geomorphic units, ranging from the active river channel to established river terraces in a braided river system. For each geomorphic unit, soil pits were used to sample sediment/soil layers that were analysed in terms of grain size (<2mm) and organic matter quantity and quality (RockEval method). A principal components analysis was used to identify patterns in the dataset. Results suggest that during the succession from bare river gravels to a terrace soil, there is a transition from small amounts of external organic matter supply provided by sedimentation processes (e.g. organic matter transported in suspension and deposited on bars), to large amounts of autogenic in situ organic matter production due to plant colonisation. This appears to change the time scale and pathways of alluvial succession (bio-geomorphic succession). However, this process is complicated by: the ongoing possibility of local sedimentation, which can serve to isolate surface layers via aggradation from the exogenic supply; and erosion which tends to create fresh deposits upon which organic matter processing must re-start. The result is a complex pattern of organic matter states as well as a general lack of any clear chronosequence within the active river corridor. This state reflects the continual battle between deposition events that can isolate organic matter from the surface, erosion events that can destroy accumulating organic matter and the early ecosystem processes necessary to assist the co-evolution of soil and vegetation. A key question emerges over the extent to which the fresh organic matter deposited in the active zone is capable of significantly transforming the local geochemical environment sufficiently to accelerate soil development.

Wireless Sensor Networks for Infrastructure Monitoring, TR-611, 2010

A good system of preventive bridge maintenance enhances the ability of engineers to manage and monitor bridge conditions, and take proper action at the right time. Traditionally infrastructure inspection is performed via infrequent periodical visual inspection in the field. Wireless sensor technology provides an alternative cost-effective approach for constant monitoring of infrastructures. Scientific data-acquisition systems make reliable structural measurements, even in inaccessible and harsh environments by using wireless sensors. With advances in sensor technology and availability of low cost integrated circuits, a wireless monitoring sensor network has been considered to be the new generation technology for structural health monitoring. The main goal of this project was to implement a wireless sensor network for monitoring the behavior and integrity of highway bridges. At the core of the system is a low-cost, low power wireless strain sensor node whose hardware design is optimized for structural monitoring applications. The key components of the systems are the control unit, sensors, software and communication capability. The extensive information developed for each of these areas has been used to design the system. The performance and reliability of the proposed wireless monitoring system is validated on a 34 feet span composite beam in slab bridge in Black Hawk County, Iowa. The micro strain data is successfully extracted from output-only response collected by the wireless monitoring system. The energy efficiency of the system was investigated to estimate the battery lifetime of the wireless sensor nodes. This report also documents system design, the method used for data acquisition, and system validation and field testing. Recommendations on further implementation of wireless sensor networks for long term monitoring are provided.