Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. RESULTS: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. CONCLUSION: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features

Disruption of Higher Order DNA Structures in Friedreich's Ataxia (GAA)n Repeats by PNA or LNA Targeting

Expansion of (GAA)n repeats in the first intron of the Frataxin gene is associated with reduced mRNA and protein levels and the development of Friedreich’s ataxia. (GAA)n expansions form non-canonical structures, including intramolecular triplex (H-DNA), and R-loops and are associated with epigenetic modifications. With the aim of interfering with higher order H-DNA (like) DNA structures within pathological (GAA)n expansions, we examined sequence-specific interaction of peptide nucleic acid (PNA) with (GAA)n repeats of different lengths (short: n=9, medium: n=75 or long: n=115) by chemical probing of triple helical and single stranded regions. We found that a triplex structure (H-DNA) forms at GAA repeats of different lengths; however, single stranded regions were not detected within the medium size pathological repeat, suggesting the presence of a more complex structure. Furthermore, (GAA)4-PNA binding of the repeat abolished all detectable triplex DNA structures, whereas (CTT)5-PNA did not. We present evidence that (GAA)4-PNA can invade the DNA at the repeat region by binding the DNA CTT strand, thereby preventing non-canonical-DNA formation, and that triplex invasion complexes by (CTT)5-PNA form at the GAA repeats. Locked nucleic acid (LNA) oligonucleotides also inhibited triplex formation at GAA repeat expansions, and atomic force microscopy analysis showed significant relaxation of plasmid morphology in the presence of GAA-LNA. Thus, by inhibiting disease related higher order DNA structures in the Frataxin gene, such PNA and LNA oligomers may have potential for discovery of drugs aiming at recovering Frataxin expression

MIR-99a and MIR-99b Modulate TGF-β Induced Epithelial to Mesenchymal Plasticity in Normal Murine Mammary Gland Cells

Epithelial to mesenchymal transition (EMT) is a key process during embryonic development and disease development and progression. During EMT, epithelial cells lose epithelial features and express mesenchymal cell markers, which correlate with increased cell migration and invasion. Transforming growth factor-β (TGF-β) is a multifunctional cytokine that induces EMT in multiple cell types. The TGF-β pathway is regulated by microRNAs (miRNAs), which are small non-coding RNAs regulating the translation of specific messenger RNAs

Socio-demographic determinants of Toxoplasma gondii seroprevalence in migrant workers of Peninsular Malaysia

Background The number of migrants working in Malaysia has increased sharply since the 1970’s and there is concern that infectious diseases endemic in other (e.g. neighbouring) countries may be inadvertently imported. Compulsory medical screening prior to entering the workforce does not include parasitic infections such as toxoplasmosis. Therefore, this study aimed to evaluate the seroprevalence of T. gondii infection among migrant workers in Peninsular Malaysia by means of serosurveys conducted on a voluntary basis among low-skilled and semi-skilled workers from five working sectors, namely, manufacturing, food service, agriculture and plantation, construction and domestic work. Methods A total of 484 migrant workers originating from rural locations in neighbouring countries, namely, Indonesia (n = 247, 51.0%), Nepal (n = 99, 20.5%), Bangladesh (n = 72, 14.9%), India (n = 52, 10.7%) and Myanmar (n = 14, 2.9%) were included in this study. Results The overall seroprevalence of T. gondii was 57.4% (n = 278; 95% CI: 52.7–61.8%) with 52.9% (n = 256; 95% CI: 48.4–57.2%) seropositive for anti-Toxoplasma IgG only, 0.8% (n = 4; 95% CI: 0.2–1.7%) seropositive for anti-Toxoplasma IgM only and 3.7% (n = 18; 95% CI: 2.1–5.4%) seropositive with both IgG and IgM antibodies. All positive samples with both IgG and IgM antibodies showed high avidity (> 40%), suggesting latent infection. Age (being older than 45 years), Nepalese nationality, manufacturing occupation, and being a newcomer in Malaysia (excepting domestic work) were positively and statistically significantly associated with seroprevalence (P < 0.05). Conclusions The results of this study suggest that better promotion of knowledge about parasite transmission is required for both migrant workers and permanent residents in Malaysia. Efforts should be made to encourage improved personal hygiene before consumption of food and fluids, thorough cooking of meat and better disposal of feline excreta from domestic pets

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation