Growth-regulated co-occupancy of Mediator and Lsm3 at intronic ribosomal protein genes.

Mediator is a well-known transcriptional co-regulator and serves as an adaptor between gene-specific regulatory proteins and RNA polymerase II. Studies on the chromatin-bound form of Mediator revealed interactions with additional protein complexes involved in various transcription-related processes, such as the Lsm2-8 complex that is part of the spliceosomal U6 small nuclear ribonucleoprotein complex. Here, we employ Chromatin Immunoprecipitation sequencing (ChIP-seq) of chromatin associated with the Lsm3 protein and the Med1 or Med15 Mediator subunits. We identify 86 genes co-occupied by both Lsm3 and Mediator, of which 73 were intron-containing ribosomal protein genes. In logarithmically growing cells, Mediator primarily binds to their promoter regions but also shows a second, less pronounced occupancy at their 3'-exons. During the late exponential phase, we observe a near-complete transition of Mediator from these promoters to a position in their 3'-ends, overlapping the Lsm3 binding sites ∼250 bp downstream of their last intron-exon boundaries. Using an unbiased RNA sequencing approach, we show that transition of Mediator from promoters to the last exon of these genes correlates to reduction of both their messenger RNA levels and splicing ratios, indicating that the Mediator and Lsm complexes cooperate to control growth-regulated expression of intron-containing ribosomal protein genes at the levels of transcription and splicing.

Pseudomonas syringae infectivity correlates to altered transcript and metabolite levels of Arabidopsis mediator mutants.

Rapid metabolic responses to pathogens are essential for plant survival and depend on numerous transcription factors. Mediator is the major transcriptional co-regulator for integration and transmission of signals from transcriptional regulators to RNA polymerase II. Using four Arabidopsis Mediator mutants, med16, med18, med25 and cdk8, we studied how differences in regulation of their transcript and metabolite levels correlate to their responses to Pseudomonas syringae infection. We found that med16 and cdk8 were susceptible, while med25 showed increased resistance. Glucosinolate, phytoalexin and carbohydrate levels were reduced already before infection in med16 and cdk8, but increased in med25, which also displayed increased benzenoids levels. Early after infection, wild type plants showed reduced glucosinolate and nucleoside levels, but increases in amino acids, benzenoids, oxylipins and the phytoalexin camalexin. The Mediator mutants showed altered levels of these metabolites and in regulation of genes encoding key enzymes for their metabolism. At later stage, mutants displayed defective levels of specific amino acids, carbohydrates, lipids and jasmonates which correlated to their infection response phenotypes. Our results reveal that MED16, MED25 and CDK8 are required for a proper, coordinated transcriptional response of genes which encode enzymes involved in important metabolic pathways for Arabidopsis responses to Pseudomonas syringae infections.

SeedTransNet: a directional translational network revealing regulatory patterns during seed maturation and germination.

Seed maturation is the developmental process that prepares the embryo for the desiccated waiting period before germination. It is associated with a series of physiological changes leading to the establishment of seed dormancy, seed longevity, and desiccation tolerance. We studied translational changes during seed maturation and observed a gradual reduction in global translation during seed maturation. Transcriptome and translatome profiling revealed specific reduction in the translation of thousands of genes. By including previously published data on germination and seedling establishment, a regulatory network based on polysome occupancy data was constructed: SeedTransNet. Network analysis predicted translational regulatory pathways involving hundreds of genes with distinct functions. The network identified specific transcript sequence features suggesting separate translational regulatory circuits. The network revealed several seed maturation-associated genes as central nodes, and this was confirmed by specific seed phenotypes of the respective mutants. One of the regulators identified, an AWPM19 family protein, PM19-Like1 (PM19L1), was shown to regulate seed dormancy and longevity. This putative RNA-binding protein also affects the translational regulation of its target mRNA, as identified by SeedTransNet. Our data show the usefulness of SeedTransNet in identifying regulatory pathways during seed phase transitions.

GENOMES UNCOUPLED1 plays a key role during the de-etiolation process in Arabidopsis.

One of the most dramatic challenges in the life of a plant occurs when the seedling emerges from the soil and exposure to light triggers expression of genes required for establishment of photosynthesis. This process needs to be tightly regulated, as premature accumulation of light-harvesting proteins and photoreactive Chl precursors causes oxidative damage when the seedling is first exposed to light. Photosynthesis genes are encoded by both nuclear and plastid genomes, and to establish the required level of control, plastid-to-nucleus (retrograde) signalling is necessary to ensure correct gene expression. We herein show that a negative GENOMES UNCOUPLED1 (GUN1)-mediated retrograde signal restricts chloroplast development in darkness and during early light response by regulating the transcription of several critical transcription factors linked to light response, photomorphogenesis, and chloroplast development, and consequently their downstream target genes in Arabidopsis. Thus, the plastids play an essential role during skotomorphogenesis and the early light response, and GUN1 acts as a safeguard during the critical step of seedling emergence from darkness.

Norway spruce deploys tissue-specific responses during acclimation to cold.

Climate change in the conifer-dominated boreal forest is expected to lead to warmer but more dynamic winter air temperatures, reducing the depth and duration of snow cover and lowering winter soil temperatures. To gain insight into the mechanisms that have enabled conifers to dominate extreme cold environments, we performed genome-wide RNA-Seq analysis from needles and roots of non-dormant two-year Norway spruce (Picea abies (L.) H. Karst), and contrasted these response to herbaceous model Arabidopsis We show that the main transcriptional response of Norway spruce needles exposed to cold was delayed relative to Arabidopsis, and this delay was associated with slower development of freezing tolerance. Despite this difference in timing, Norway spruce principally utilizes early response transcription factors (TFs) belonging to the same gene families as Arabidopsis, indicating broad evolutionary conservation of cold response networks. In keeping with their different metabolic and developmental states, needles and root of Norway spruce showed contrasting results. Regulatory network analysis identified both conserved TFs with known roles in cold acclimation (e.g. homologs of ICE1, AKS3, and of the NAC and AP2/ERF superfamilies), but also a root-specific bHLH101 homolog, providing functional insights into cold stress response strategies in Norway spruce.

Candidate regulators and target genes of drought stress in needles and roots of Norway spruce.

Drought stress impacts seedling establishment, survival and whole-plant productivity. Molecular responses to drought stress have been most extensively studied in herbaceous species, mostly considering only aboveground tissues. Coniferous tree species dominate boreal forests, which are predicted to be exposed to more frequent and acute drought as a result of ongoing climate change. The associated impact at all stages of the forest tree life cycle is expected to have large-scale ecological and economic impacts. However, the molecular response to drought has not been comprehensively profiled for coniferous species. We assayed the physiological and transcriptional response of Picea abies (L.) H. Karst seedling needles and roots after exposure to mild and severe drought. Shoots and needles showed an extensive reversible plasticity for physiological measures indicative of drought-response mechanisms, including changes in stomatal conductance (gs), shoot water potential and abscisic acid (ABA). In both tissues, the most commonly observed expression profiles in response to drought were highly correlated with the ABA levels. Still, root and needle transcriptional responses contrasted, with extensive root-specific down-regulation of growth. Comparison between previously characterized Arabidopsis thaliana L. drought-response genes and P. abies revealed both conservation and divergence of transcriptional response to drought. In P. abies, transcription factors belonging to the ABA responsive element(ABRE) binding/ABRE binding factors ABA-dependent pathway had a more limited role. These results highlight the importance of profiling both above- and belowground tissues, and provide a comprehensive framework to advance the understanding of the drought response of P. abies. The results demonstrate that a short-term, severe drought induces severe physiological responses coupled to extensive transcriptome modulation and highlight the susceptibility of Norway spruce seedlings to such drought events.

Seed characterization and early nitrogen metabolism performance of seedlings from Altiplano and coastal ecotypes of Quinoa.

BACKGROUND: Early seed germination and a functional root system development during establishment are crucial attributes contributing to nutrient competence under marginal nutrient soil conditions. Chenopodium quinoa Willd (Chenopodiaceae) is a rustic crop, able to grow in marginal areas. Altiplano and Coastal/Lowlands are two representative zones of quinoa cultivation in South America with contrasting soil fertility and edaphoclimatic conditions. In the present work, we hypothesize that the ecotypes of Quinoa from Altiplano (landrace Socaire) and from Coastal/Lowland (landrace Faro) have developed differential adaptive responses in order to survive under conditions of low availability of N in their respective climatic zones of Altiplano and Lowlands. In order to understand intrinsic differences for N competence between landraces, seed metabolite profile and germinative capacity were studied. Additionally, in order to elucidate the mechanisms of N uptake and assimilation at limiting N conditions during establishment, germinated seeds of both landraces were grown at either sufficient nitrate (HN) or low nitrate (LN) supply. We studied the photosynthetic performance, protein storage, root morphometrical parameters, activity and expression of N-assimilating enzymes, and the expression of nitrate transporters of roots in plants submitted to the different treatments. RESULTS: Seeds from Socaire landrace presented higher content of free N-related metabolites and faster seed germination rate compared to Faro landrace. Seedlings of both ecotypes presented similar physiological performance at HN supply, but at LN supply their differences were exalted. At LN, Socaire plants showed an increased root biomass (including a higher number and total length of lateral roots), a differential regulation of a nitrate transporter (a NPF6.3-like homologue) belonging to the Low Affinity Transport System (LATS), and an upregulation of a nitrate transporter (a NRT2.1-like homologue) belonging to the High Affinity nitrate Transport System (HATS) compared to Faro. These responses as a whole could be linked to a higher amount of stored proteins in leaves, associated to an enhanced photochemical performance in Altiplano plants, in comparison to Lowland quinoa plants. CONCLUSIONS: These differential characteristics of Socaire over Faro plants could involve an adaptation to enhanced nitrate uptake under the brutal unfavorable climate conditions of Altiplano.

Stable odor recognition by a neuro-adaptive electronic nose.

Sensitivity, selectivity and stability are decisive properties of sensors. In chemical gas sensors odor recognition can be severely compromised by poor signal stability, particularly in real life applications where the sensors are exposed to unpredictable sequences of odors under changing external conditions. Although olfactory receptor neurons in the nose face similar stimulus sequences under likewise changing conditions, odor recognition is very stable and odorants can be reliably identified independently from past odor perception. We postulate that appropriate pre-processing of the output signals of chemical sensors substantially contributes to the stability of odor recognition, in spite of marked sensor instabilities. To investigate this hypothesis, we use an adaptive, unsupervised neural network inspired by the glomerular input circuitry of the olfactory bulb. Essentially the model reduces the effect of the sensors' instabilities by utilizing them via an adaptive multicompartment feed-forward inhibition. We collected and analyzed responses of a 4 × 4 gas sensor array to a number of volatile compounds applied over a period of 18 months, whereby every sensor was sampled episodically. The network conferred excellent stability to the compounds' identification and was clearly superior over standard classifiers, even when one of the sensors exhibited random fluctuations or stopped working at all.

Chemical discrimination in turbulent gas mixtures with MOX sensors validated by gas chromatography-mass spectrometry.

Chemical detection systems based on chemo-resistive sensors usually include a gas chamber to control the sample air flow and to minimize turbulence. However, such a kind of experimental setup does not reproduce the gas concentration fluctuations observed in natural environments and destroys the spatio-temporal information contained in gas plumes. Aiming at reproducing more realistic environments, we utilize a wind tunnel with two independent gas sources that get naturally mixed along a turbulent flow. For the first time, chemo-resistive gas sensors are exposed to dynamic gas mixtures generated with several concentration levels at the sources. Moreover, the ground truth of gas concentrations at the sensor location was estimated by means of gas chromatography-mass spectrometry. We used a support vector machine as a tool to show that chemo-resistive transduction can be utilized to reliably identify chemical components in dynamic turbulent mixtures, as long as sufficient gas concentration coverage is used. We show that in open sampling systems, training the classifiers only on high concentrations of gases produces less effective classification and that it is important to calibrate the classification method with data at low gas concentrations to achieve optimal performance.

Demonstration of fast and accurate discrimination and quantification of chemically similar species utilizing a single cross-selective chemiresistor.

Performance characteristics of gas-phase microsensors will determine the ultimate utility of these devices for a wide range of chemical monitoring applications. Commonly employed chemiresistor elements are quite sensitive to selected analytes, and relatively new methods have increased the selectivity to specific compounds, even in the presence of interfering species. Here, we have focused on determining whether purposefully driven temperature modulation can produce faster sensor-response characteristics, which could enable measurements for a broader range of applications involving dynamic compositional analysis. We investigated the response speed of a single chemiresitive In2O3 microhotplate sensor to four analytes (methanol, ethanol, acetone, 2-butanone) by systematically varying the oscillating frequency (semicycle periods of 20-120 ms) of a bilevel temperature cycle applied to the sensing element. It was determined that the fastest response (≈ 9 s), as indicated by a 98% signal-change metric, occurred for a period of 30 ms and that responses under such modulation were dramatically faster than for isothermal operation of the same device (>300 s). Rapid modulation between 150 and 450 °C exerts kinetic control over transient processes, including adsorption, desorption, diffusion, and reaction phenomena, which are important for charge transfer occurring in transduction processes and the observed response times. We also demonstrate that the fastest operation is accompanied by excellent discrimination within a challenging 16-category recognition problem (consisting of the four analytes at four separate concentrations). This critical finding demonstrates that both speed and high discriminatory capabilities can be realized through temperature modulation.

Estimation of the limit of detection using information theory measures.

Definitions of the limit of detection (LOD) based on the probability of false positive and/or false negative errors have been proposed over the past years. Although such definitions are straightforward and valid for any kind of analytical system, proposed methodologies to estimate the LOD are usually simplified to signals with Gaussian noise. Additionally, there is a general misconception that two systems with the same LOD provide the same amount of information on the source regardless of the prior probability of presenting a blank/analyte sample. Based upon an analogy between an analytical system and a binary communication channel, in this paper we show that the amount of information that can be extracted from an analytical system depends on the probability of presenting the two different possible states. We propose a new definition of LOD utilizing information theory tools that deals with noise of any kind and allows the introduction of prior knowledge easily. Unlike most traditional LOD estimation approaches, the proposed definition is based on the amount of information that the chemical instrumentation system provides on the chemical information source. Our findings indicate that the benchmark of analytical systems based on the ability to provide information about the presence/absence of the analyte (our proposed approach) is a more general and proper framework, while converging to the usual values when dealing with Gaussian noise.

Two-dimensional wavelet transform feature extraction for porous silicon chemical sensors.

Designing reliable, fast responding, highly sensitive, and low-power consuming chemo-sensory systems has long been a major goal in chemo-sensing. This goal, however, presents a difficult challenge because having a set of chemo-sensory detectors exhibiting all these aforementioned ideal conditions are still largely un-realizable to-date. This paper presents a unique perspective on capturing more in-depth insights into the physicochemical interactions of two distinct, selectively chemically modified porous silicon (pSi) film-based optical gas sensors by implementing an innovative, based on signal processing methodology, namely the two-dimensional discrete wavelet transform. Specifically, the method consists of using the two-dimensional discrete wavelet transform as a feature extraction method to capture the non-stationary behavior from the bi-dimensional pSi rugate sensor response. Utilizing a comprehensive set of measurements collected from each of the aforementioned optically based chemical sensors, we evaluate the significance of our approach on a complex, six-dimensional chemical analyte discrimination/quantification task problem. Due to the bi-dimensional aspects naturally governing the optical sensor response to chemical analytes, our findings provide evidence that the proposed feature extractor strategy may be a valuable tool to deepen our understanding of the performance of optically based chemical sensors as well as an important step toward attaining their implementation in more realistic chemo-sensing applications.

Multifrequency interrogation of nanostructured gas sensor arrays: a tool for analyzing response kinetics.

This paper presents a unique perspective on enhancing the physicochemical mechanisms of two distinct highly sensitive nanostructured metal oxide micro hot plate gas sensors by utilizing an innovative multifrequency interrogation method. The two types of sensors evaluated here employ an identical silicon transducer geometry but with a different morphological structure of the sensitive film. While the first sensing film consists of self-ordered tungsten oxide nanodots, limiting the response kinetics of the sensor-chemical species pair only to the reaction phenomena occurring at the sensitive film surface, the second modality is a three-dimensional array of tungsten oxide nanotubes, which in turn involves both the diffusion and adsorption of the gas during its reaction kinetics with the sensitive film itself. By utilizing the proposed multifrequency interrogation methodology, we demonstrate that the optimal temperature modulation frequencies employed for the nanotubes-based sensors to selectively detect hydrogen, carbon monoxide, ethanol, and dimethyl methyl phosphonate (DMMP) are significantly higher than those utilized for the nanodot-based sensors. This finding helps understand better the amelioration in selectivity that temperature modulation of metal oxides brings about, and, most importantly, it sets the grounds for the nanoengineering of gas-sensitive films to better exploit their practical usage.

Feature selection versus feature compression in the building of calibration models from FTIR-spectrophotometry datasets.

Undoubtedly, FTIR-spectrophotometry has become a standard in chemical industry for monitoring, on-the-fly, the different concentrations of reagents and by-products. However, representing chemical samples by FTIR spectra, which spectra are characterized by hundreds if not thousands of variables, conveys their own set of particular challenges because they necessitate to be analyzed in a high-dimensional feature space, where many of these features are likely to be highly correlated and many others surely affected by noise. Therefore, identifying a subset of features that preserves the classifier/regressor performance seems imperative prior any attempt to build an appropriate pattern recognition method. In this context, we investigate the benefit of utilizing two different dimensionality reduction methods, namely the minimum Redundancy-Maximum Relevance (mRMR) feature selection scheme and a new self-organized map (SOM) based feature compression, coupled to regression methods to quantitatively analyze two-component liquid samples utilizing FTIR spectrophotometry. Since these methods give us the possibility of selecting a small subset of relevant features from FTIR spectra preserving the statistical characteristics of the target variable being analyzed, we claim that expressing the FTIR spectra by these dimensionality-reduced set of features may be beneficial. We demonstrate the utility of these novel feature selection schemes in quantifying the distinct analytes within their binary mixtures utilizing a FTIR-spectrophotometer.

Sensor selection and chemo-sensory optimization: toward an adaptable chemo-sensory system.

Over the past two decades, despite the tremendous research on chemical sensors and machine olfaction to develop micro-sensory systems that will accomplish the growing existent needs in personal health (implantable sensors), environment monitoring (widely distributed sensor networks), and security/threat detection (chemo/bio warfare agents), simple, low-cost molecular sensing platforms capable of long-term autonomous operation remain beyond the current state-of-the-art of chemical sensing. A fundamental issue within this context is that most of the chemical sensors depend on interactions between the targeted species and the surfaces functionalized with receptors that bind the target species selectively, and that these binding events are coupled with transduction processes that begin to change when they are exposed to the messy world of real samples. With the advent of fundamental breakthroughs at the intersection of materials science, micro- and nano-technology, and signal processing, hybrid chemo-sensory systems have incorporated tunable, optimizable operating parameters, through which changes in the response characteristics can be modeled and compensated as the environmental conditions or application needs change. The objective of this article, in this context, is to bring together the key advances at the device, data processing, and system levels that enable chemo-sensory systems to "adapt" in response to their environments. Accordingly, in this review we will feature the research effort made by selected experts on chemical sensing and information theory, whose work has been devoted to develop strategies that provide tunability and adaptability to single sensor devices or sensory array systems. Particularly, we consider sensor-array selection, modulation of internal sensing parameters, and active sensing. The article ends with some conclusions drawn from the results presented and a visionary look toward the future in terms of how the field may evolve.

DC information preservation for cardiopulmonary monitor utilizing CW Doppler radar.

Direct conversion RF receivers introduce large DC offsets, reducing the dynamic range of the baseband signal. Coupled with the relatively small time varying signals in human vital sign monitoring using CW Doppler radar, extraction of cardio-pulmonary information becomes difficult. Previous DC offset compensation techniques utilizing AC coupling have proven detrimental to the performance of the system and the integrity of the low-frequency cardiopulmonary signals. A proposed system utilizing digitally controlled voltage feedback and center finding preserves the important DC information for optimal extraction of phase information in the quadrature system.

Data acquisition system for Doppler radar vital-sign monitor.

Automatic gain control (AGC) units increase the dynamic range of a system to compensate for the limited dynamic range of analog to digital converters. This problem is compounded in wireless systems in which large changes in signal strength are effects of a changing environment. These issues are evident in the direct-conversion Doppler radar vital-sign monitor. Utilizing microwave radar signals reflecting off a human subject, a two-channel quadrature receiver can detect periodic movement resulting from cardio-pulmonary activity. The quadrature signal is analyzed using an arctangent demodulation that extracts vital phase information. A data acquisition (DAQ) system is proposed to deal with issues inherent in arctangent demodulation of a quadrature radar signal.

Modelo de perfil de morbilidad, características sociodemográficas y de saneamiento ambiental: Barrio El Rodeo de la localidad de San Cristóbal, Bogotá D. C / Model of the morbidity profile, sociodemographics characteristics of environmental cleaning: District El Rodeo of the locality of San Cristóbal, Bogota

Para la implementación del programa "Barrio Saludable" era importante determinar el perfil de morbilidad de los habitantes del barrio El Rodeo, sus necesidades fundamentales de saneamiento y sus características sociodemográficas, con el fin de establecer programas de salud, generar conductas de autoestima y autocuidado, promoviendo así la salud y evitando la aparición y el desenlace de patologías modificables, intervenibles y prevenibles, utilizando al máximo los escasos recursos de la salud.