Overview of recent developments in organic thin-film transistor sensor technology

Bio and chemical sensing represents one of the most attractive applications of organic electronics and of Organic Thin Film Transistors (OTFTs) in particular. The implementation of miniaturized portable systems for the detection of chemical analytes as well as of biological species, is still a challenge for the sensors’ community. In this respect OTFTs appear as a new class of sensors able, in principle, to overcome some of the commercial sensors drawbacks. As far as volatile analytes are concerned, commercially available sensing systems, such as metal oxide based chemi-resistors, offer great stability but rather poor selectivity. In spite of the improved selectivity offered by organic chemi-resistors the reliability of such devices is not yet satisfactorily proven. On the other hand, complex odors recognition, but also explosives or pathogen bacteria detection are currently being addressed by sensor array systems, called “e-noses”, that try to mimic the mammalian olfactory system. Even though potentially very effective, this technology has not yet reached the performance level required by the market mostly because miniaturization and cost effective production issues. OTFT sensors can offer the advantage of room temperature operation and deliver high repeatable responses. Beside, they show very good selectivity properties. In fact, they implement organic active layers, which behave as sensing layers as well. This improves OTFTs sensitivity towards different chemical and biological analytes as organic materials can be properly chemically tailored to achieve differential detection and potentially even discrimination of biological species. In addition to this, OTFTs are also able to offer the unique advantages of a multi-parametric response and a gate bias enhanced sensitivity. Recently thin dielectric low-voltage OTFTs have also been demonstrated. Their implementation in low power consumption devices has attracted the attention of the organic electronic community. But such low power transistors have also a great potential in sensing applications specifically those performed in a liquid environment. In fact, low-voltage OTFTs have been recently demonstrated to deliver reliable responses even when operated in water for hundreds of measurement cycles. This open new perspectives in the field of cheap, low-power and mass-produced aqueous sensors

Making life difficult for Clostridium difficile: augmenting the pathogen's metabolic model with transcriptomic and codon usage data for better therapeutic target characterization.

BACKGROUND: Clostridium difficile is a bacterium which can infect various animal species, including humans. Infection with this bacterium is a leading healthcare-associated illness. A better understanding of this organism and the relationship between its genotype and phenotype is essential to the search for an effective treatment. Genome-scale metabolic models contain all known biochemical reactions of a microorganism and can be used to investigate this relationship. RESULTS: We present icdf834, an updated metabolic network of C. difficile that builds on iMLTC806cdf and features 1227 reactions, 834 genes, and 807 metabolites. We used this metabolic network to reconstruct the metabolic landscape of this bacterium. The standard metabolic model cannot account for changes in the bacterial metabolism in response to different environmental conditions. To account for this limitation, we also integrated transcriptomic data, which details the gene expression of the bacterium in a wide array of environments. Importantly, to bridge the gap between gene expression levels and protein abundance, we accounted for the synonymous codon usage bias of the bacterium in the model. To our knowledge, this is the first time codon usage has been quantified and integrated into a metabolic model. The metabolic fluxes were defined as a function of protein abundance. To determine potential therapeutic targets using the model, we conducted gene essentiality and metabolic pathway sensitivity analyses and calculated flux control coefficients. We obtained 92.3% accuracy in predicting gene essentiality when compared to experimental data for C. difficile R20291 (ribotype 027) homologs. We validated our context-specific metabolic models using sensitivity and robustness analyses and compared model predictions with literature on C. difficile. The model predicts interesting facets of the bacterium's metabolism, such as changes in the bacterium's growth in response to different environmental conditions. CONCLUSIONS: After an extensive validation process, we used icdf834 to obtain state-of-the-art predictions of therapeutic targets for C. difficile. We show how context-specific metabolic models augmented with codon usage information can be a beneficial resource for better understanding C. difficile and for identifying novel therapeutic targets. We remark that our approach can be applied to investigate and treat against other pathogens.Publication charges for this article have been funded by EpiHealthNet, FP7-PEOPLE-2012-ITN and EU project H2020 FET Open CIRCLE (Coordinating European Research on Molecular Communications) No. 665564

Multi-omic data integration elucidates Synechococcus adaptation mechanisms to fluctuations in light intensity and salinity

Synechococcus sp. PCC 7002 is a fast-growing cyanobacterium which flourishes in freshwater and marine environments, owing to its ability to tolerate high light intensity and a wide range of salinities. Harnessing the properties of cyanobacteria and understanding their metabolic efficiency has become an imperative goal in recent years owing to their potential to serve as biocatalysts for the production of renewable biofuels. To improve characterisation of metabolic networks, genome-scale models of metabolism can be integrated with multi-omic data to provide a more accurate representation of metabolic capability and refine phenotypic predictions. In this work, a heuristic pipeline is constructed for analysing a genome-scale metabolic model of Synechococcus sp. PCC 7002, which utilises flux balance analysis across multiple layers to observe flux response between conditions across four key pathways. Across various conditions, the detection of significant patterns and mechanisms to cope with fluctuations in light intensity and salinity provides insights into the maintenance of metabolic efficiency

Bioinformatics challenges and potentialities in studying extreme environments

Cold environments are populated by organisms able to contravene deleterious effects of low temperature by diverse adaptive strategies, including the production of ice binding proteins (IBPs) that inhibit the growth of ice crystals inside and outside cells. We describe the properties of such a protein (EfcIBP) identified in the metagenome of an Antarctic biological consortium composed of the ciliate Euplotes focardii and psychrophilic non-cultured bacteria. Recombinant EfcIBP can resist freezing without any conformational damage and is moderately heat stable, with a midpoint temperature of 66.4 degrees C. Tested for its effects on ice, EfcIBP shows an unusual combination of properties not reported in other bacterial IBPs. First, it is one of the best-performing IBPs described to date in the inhibition of ice recrystallization, with effective concentrations in the nanomolar range. Moreover, EfcIBP has thermal hysteresis activity (0.53 degrees C at 50 mu M) and it can stop a crystal from growing when held at a constant temperature within the thermal hysteresis gap. EfcIBP protects purified proteins and bacterial cells from freezing damage when exposed to challenging temperatures. EfcIBP also possesses a potential N-terminal signal sequence for protein transport and a DUF3494 domain that is common to secreted IBPs. These features lead us to hypothesize that the protein is either anchored at the outer cell surface or concentrated around cells to provide survival advantage to the whole cell consortium

Loss of full-length dystrophin expression results in major cell-autonomous abnormalities in proliferating myoblasts

Duchenne muscular dystrophy (DMD) affects myofibers and muscle stem cells, causing progressive muscle degeneration and repair defects. It was unknown whether dystrophic myoblasts—the effector cells of muscle growth and regeneration—are affected. Using transcriptomic, genome-scale metabolic modelling and functional analyses, we demonstrate, for the first time, convergent abnormalities in primary mouse and human dystrophic myoblasts. In Dmd(mdx) myoblasts lacking full-length dystrophin, the expression of 170 genes was significantly altered. Myod1 and key genes controlled by MyoD (Myog, Mymk, Mymx, epigenetic regulators, ECM interactors, calcium signalling and fibrosis genes) were significantly downregulated. Gene ontology analysis indicated enrichment in genes involved in muscle development and function. Functionally, we found increased myoblast proliferation, reduced chemotaxis and accelerated differentiation, which are all essential for myoregeneration. The defects were caused by the loss of expression of full-length dystrophin, as similar and not exacerbated alterations were observed in dystrophin-null Dmd(mdx-βgeo) myoblasts. Corresponding abnormalities were identified in human DMD primary myoblasts and a dystrophic mouse muscle cell line, confirming the cross-species and cell-autonomous nature of these defects. The genome-scale metabolic analysis in human DMD myoblasts showed alterations in the rate of glycolysis/gluconeogenesis, leukotriene metabolism, and mitochondrial beta-oxidation of various fatty acids. These results reveal the disease continuum: DMD defects in satellite cells, the myoblast dysfunction affecting muscle regeneration, which is insufficient to counteract muscle loss due to myofiber instability. Contrary to the established belief, our data demonstrate that DMD abnormalities occur in myoblasts, making these cells a novel therapeutic target for the treatment of this lethal disease

Integrated multi‑omics analysis of ovarian cancer using variational autoencoders

Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi‑omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi‑omics analysis of cancer data. However, high dimensional multi‑omics data are generally imbalanced with too many molecular features and relatively few patient samples. This imbalance makes a DL based integrated multi‑omics analysis difficult. DL‑based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi‑omics data. However, there are few VAE‑based integrated multi‑omics analyses, and they are limited to pancancer. In this work, we did an integrated multi‑omics analysis of ovarian cancer using the compressed features learned through VAE and an improved version of VAE, namely Maximum Mean Discrepancy VAE (MMD‑VAE). First, we designed and developed a DL architecture for VAE and MMD‑VAE. Then we used the architecture for mono‑omics, integrated di‑omics and tri‑omics data analysis of ovarian cancer through cancer samples identification, molecular subtypes clustering and classification, and survival analysis. The results show that MMD‑VAE and VAE‑based compressed features can respectively classify the transcriptional subtypes of the TCGA datasets with an accuracy in the range of 93.2‑95.5% and 87.1‑95.7%. Also, survival analysis results show that VAE and MMD‑VAE based compressed representation of omics data can be used in cancer prognosis. Based on the results, we can conclude that (i) VAE and MMD‑VAE outperform existing dimensionality reduction techniques, (ii) integrated multi‑omics analyses perform better or similar compared to their mono‑omics counterparts, and (iii) MMD‑VAE performs better than VAE in most omics dataset

Dissecting the transcriptome in cardiovascular disease

The human transcriptome comprises a complex network of coding and non-coding RNAs implicated in a myriad of biological functions. Non-coding RNAs exhibit highly organized spatial and temporal expression patterns and are emerging as critical regulators of differentiation, homeostasis, and pathological states, including in the cardiovascular system. This review defines the current knowledge gaps, unmet methodological needs, and describes the challenges in dissecting and understanding the role and regulation of the non-coding transcriptome in cardiovascular disease. These challenges include poor annotation of the non-coding genome, determination of the cellular distribution of transcripts, assessment of the role of RNA processing and identification of cell-type specific changes in cardiovascular physiology and disease. We highlight similarities and differences in the hurdles associated with the analysis of the non-coding and protein-coding transcriptomes. In addition, we discuss how the lack of consensus and absence of standardized methods affect reproducibility of data. These shortcomings should be defeated in order to make significant scientific progress and foster the development of clinically applicable non-coding RNA-based therapeutic strategies to lessen the burden of cardiovascular disease

Real-time wavefront control for the PALM-3000 high order adaptive optics system

We present a cost-effective scalable real-time wavefront control architecture based on off-the-shelf graphics processing units hosted in an ultra-low latency, high-bandwidth interconnect PC cluster environment composed of modules written in the component-oriented language of nesC. We demonstrate the architecture is capable of supporting the most computation and memory intensive wavefront reconstruction method (vector-matrix-multiply) at frame rates up to 2 KHz with latency under 250 &mgr;s for the PALM-3000 adaptive optics systems, a state-of-the-art upgrade on the 5.1 meter Hale Telescope that consists of a 64x64 subaperture Shack-Hartmann wavefront sensor and a 3368 active actuator high order deformable mirror in series with a 349 actuator "woofer" DM. This architecture can easily scale up to support larger AO systems at higher rates and lower latency

Know The Star, Know the Planet. IV. A Stellar Companion to the Host star of the Eccentric Exoplanet HD 8673b

HD 8673 hosts a massive exoplanet in a highly eccentric orbit (e=0.723). Based on two epochs of speckle interferometry a previous publication identified a candidate stellar companion. We observed HD 8673 multiple times with the 10 m Keck II telescope, the 5 m Hale telescope, the 3.63 m AEOS telescope and the 1.5m Palomar telescope in a variety of filters with the aim of confirming and characterizing the stellar companion. We did not detect the candidate companion, which we now conclude was a false detection, but we did detect a fainter companion. We collected astrometry and photometry of the companion on six epochs in a variety of filters. The measured differential photometry enabled us to determine that the companion is an early M dwarf with a mass estimate of 0.33-0.45 M?. The companion has a projected separation of 10 AU, which is one of the smallest projected separations of an exoplanet host binary system. Based on the limited astrometry collected, we are able to constrain the orbit of the stellar companion to a semi-major axis of 35{60 AU, an eccentricity ? 0.5 and an inclination of 75{85?. The stellar companion has likely strongly in uenced the orbit of the exoplanet and quite possibly explains its high eccentricity.Comment: Accepted to the Astronomical Journal, 6 Pages, 5 Figure