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Ultrafast, dual common cathode, epitaxial rectifier diode in a SOT186A (TO-220F)) plastic package

Targeting BRCA1-BER deficient breast cancer by ATM or DNA-PKcs blockade either alone or in combination with cisplatin for personalized therapy

BRCA1, a key factor in homologous recombination repair may also regulate base excision repair (BER). Targeting BRCA1-BER deficient cells by blockade of ATM and DNA-PKcs could be a promising strategy in breast cancer. We investigated BRCA1, XRCC1 and pol β protein expression in two cohorts (n=1602 sporadic and n=50 germ-line BRCA1 mutated) and mRNA expression in two cohorts (n=1952 and n=249). Artificial neural network analysis for BRCA1-DNA repair interacting genes was conducted in 249 tumours. Pre-clinically, BRCA1 proficient and deficient cells were DNA repair expression profiled and evaluated for synthetic lethality using ATM and DNA-PKcs inhibitors either alone or in combination with cisplatin. In human tumours, BRCA1 negativity was strongly associated with low XRCC1, and low pol β at mRNA and protein levels (p<0.0001). In patients with BRCA1 negative tumours, low XRCC1 or low pol β expression was significantly associated with poor survival in univariate and multivariate analysis compared to high XRCC1 or high pol β expressing BRCA1 negative tumours (ps<0.05). Pre-clinically, BRCA1 negative cancer cells exhibit low mRNA and low protein expression of XRCC1 and pol β. BRCA1-BER deficient cells were sensitive to ATM and DNA-PKcs inhibitor treatment either alone or in combination with cisplatin and synthetic lethality was evidenced by DNA double strand breaks accumulation, cell cycle arrest and apoptosis. We conclude that XRCC1 and pol β expression status in BRCA1 negative tumours may have prognostic significance. BRCA1-BER deficient cells could be targeted by ATM or DNA-PKcs inhibitors for personalized therapy

Evaluation of advanced artificial neural network classification and feature extraction techniques for detecting preterm births using ehg records

Globally, the rate of preterm births is increasing and this is resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. However, there has been some evidence to suggest that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect when preterm delivery is about to occur. Using advanced machine learning algorithms, in conjunction with electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. In this paper however, the electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset that contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilized, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. Seven artificial neural network algorithms are considered with the results showing that the Radial Basis Function Neural Network classifier performs the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate. © 2014 Springer International Publishing Switzerland

Primordial magnetic fields at preheating

Using lattice techniques we investigate the generation of long range cosmological magnetic fields during a cold electroweak transition. We will show how magnetic fields arise, during bubble collisions, in the form of magnetic strings. We conjecture that these magnetic strings originate from the alignment of magnetic dipoles associated with EW sphaleron-like configurations. We also discuss the early thermalisation of photons and the turbulent behaviour of the scalar fields after tachyonic preheating.Comment: 7 pages. Talk presented at Lattice200

Artificial Intelligence for Detecting Preterm Uterine Activity in Gynacology and Obstertric Care

Preterm birth brings considerable emotional and economic costs to families and society. However, despite extensive research into understanding the risk factors, the prediction of patient mechanisms and improvements to obstetrical practice, the UK National Health Service still annually spends more than £2.95 billion on this issue. Diagnosis of labour in normal pregnancies is important for minimizing unnecessary hospitalisations, interventions and expenses. Moreover, accurate identification of spontaneous preterm labour would also allow clinicians to start necessary treatments early in women with true labour and avert unnecessary treatment and hospitalisation for women who are simply having preterm contractions, but who are not in true labour. In this research, the Electrohysterography signals have been used to detect preterm births, because Electrohysterography signals provide a strong basis for objective prediction and diagnosis of preterm birth. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Three different machine learning algorithm were used to identify these records. The results illustrate that the Random Forest performed the best of sensitivity 97%, specificity of 85%, Area under the Receiver Operator curve (AUROC) of 94% and mean square error rate of 14%

Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

Worldwide the rate of preterm birth is increasing, which presents significant health, developmental and economic problems. Current methods for predicting preterm births at an early stage are inadequate. Yet, there has been increasing evidence that the analysis of uterine electrical signals, from the abdominal surface, could provide an independent and easy way to diagnose true labour and predict preterm delivery. This analysis provides a heavy focus on the use of advanced machine learning techniques and Electrohysterography (EHG) signal processing. Most EHG studies have focused on true labour detection, in the window of around seven days before labour. However, this paper focuses on using such EHG signals to detect preterm births. In achieving this, the study uses an open dataset containing 262 records for women who delivered at term and 38 who delivered prematurely. The synthetic minority oversampling technique is utilized to overcome the issue with imbalanced datasets to produce a dataset containing 262 term records and 262 preterm records. Six different artificial neural networks were used to detect term and preterm records. The results show that the best performing classifier was the LMNC with 96% sensitivity, 92% specificity, 95% AUC and 6% mean error

The Massive Progenitor of the Type II-Linear Supernova 2009kr

We present early-time photometric and spectroscopic observations of supernova (SN) 2009kr in NGC 1832. We find that its properties to date support its classification as Type II-linear (SN II-L), a relatively rare subclass of core-collapse supernovae (SNe). We have also identified a candidate for the SN progenitor star through comparison of pre-explosion, archival images taken with WFPC2 on board the Hubble Space Telescope with SN images obtained using adaptive optics plus NIRC2 on the 10 m Keck-II telescope. Although the host galaxy's substantial distance (similar to 26 Mpc) results in large uncertainties in the relative astrometry, we find that if this candidate is indeed the progenitor, it is a highly luminous (M(V)(0) = -7.8 mag) yellow supergiant with initial mass similar to 18-24 M(circle dot). This would be the first time that an SN II-L progenitor has been directly identified. Its mass may be a bridge between the upper initial mass limit for the more common Type II-plateau SNe and the inferred initial mass estimate for one Type II-narrow SN.Hungarian OTKA K76816NSF AST-0707769, AST-0908886Sylvia & Jim Katzman FoundationTABASGO FoundationNASA through STScI AR-11248, GO-10877Harvard UniversityUC BerkeleyUniversity of VirginiaNASA/Swift NNX09AQ66GDOEAstronom

Advanced Artificial Neural Network Classification for Detecting Preterm Births Using EHG Records

Globally, the rate of preterm births are increasing, thus resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. Nevertheless, there has been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect when preterm delivery is about to occur. Using advanced machine learning algorithms, in conjunction with Electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. However, in this paper, the Electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilized, as well as feature-ranking techniques. Features are ranked to determine their discriminative capabilities in detecting term and preterm records. Seven different artificial neural networks were then used to identify these records. The results illustrate that the Radial Basis Function Neural Network classifier performed the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate

Three "universal" mesoscopic Josephson effects

1. Introduction 2. Supercurrent from Excitation Spectrum 3. Excitation Spectrum from Scattering Matrix 4. Short-Junction Limit 5. Universal Josephson Effects 5.1 Quantum Point Contact 5.2 Quantum Dot 5.3 Disordered Point Contact (Average supercurrent, Supercurrent fluctuations)Comment: 21 pages, 2 figures; legacy revie

Untangling the ATR-CHEK1 network for prognostication, prediction and therapeutic target validation in breast cancer

Background: ATR-Chk1 signalling network is critical for genomic stability. ATR-Chk1 may be deregulated in breast cancer and have prognostic, predictive and therapeutic significance. Patients and methods: We investigated ATR and phosphorylated CHK1Ser345 protein (pChk1) expression in 1712 breast cancers (Nottingham Tenovus series). ATR and Chk1 mRNA were evaluated in 1950 breast cancers (METABRIC cohort). Pre-clinically, biological consequences of ATR gene knockdown or ATR inhibition by small molecule inhibitor (VE-821) were investigated in MCF-7 and MDA-MB-231 breast cancer cell lines and in non-tumorigenic breast epithelial cells (MCF10A). Results: High ATR and high cytoplasmic pChk1 expression was significantly associated with higher tumour stage, higher mitotic index, pleomorphism and lymphovascular invasion. In univariate analysis, high ATR and high cytoplasmic pChk1 protein expression was associated with shorter breast cancer specific survival (BCSS). In multivariate analysis, high ATR remains an independent predictor of adverse outcome. At the mRNA level, high Chk1 remains associated with aggressive phenotypes including lymph node positivity, high grade, Her-2 overexpression, triple-negative phenotype and molecular classes associated with aggressive behaviour and shorter survival.. Pre-clinically, Chk1 phosphorylation at serine 345 following replication stress (induced by gemcitabine or hydroxyurea treatment) was impaired in ATR knockdown and in VE-821 treated breast cancer cells. Doxycycline inducible knockdown of ATR suppressed growth, which was restored when ATR was re-expressed. Similarly, VE-821 treatment resulted in a dose dependent suppression of cancer cell growth and survival (MCF7 and MDA-MB-231) but had no effect on non-tumorigenic breast epithelial cells (MCF10A). Conclusions: We provides evidence that ATR and Chk1 are promising biomarkers and rational drug target for personalized therapy in breast cancer