Autocrine, paracrine and necrotic NMDA receptor signalling in mouse pancreatic neuroendocrine tumour cells.

N-Methyl-d-aspartate receptor (NMDAR) activation is implicated in the malignant progression of many cancer types, as previously shown by the growth-inhibitory effects of NMDAR antagonists. NMDAR-mediated calcium influx and its downstream signalling depend critically, however, on the dynamics of membrane potential and ambient glutamate concentration, which are poorly characterized in cancer cells. Here, we have used low-noise whole-cell patch-clamp recording to investigate the electrophysiology of glutamate signalling in pancreatic neuroendocrine tumour (PanNET) cells derived from a genetically-engineered mouse model (GEMM) of PanNET, in which NMDAR signalling is known to promote cancer progression. Activating NMDARs caused excitation and intracellular calcium elevation, and intracellular perfusion with physiological levels of glutamate led to VGLUT-dependent autocrine NMDAR activation. Necrotic cells, which are often present in rapidly-growing tumours, were shown to release endogenous cytoplasmic glutamate, and necrosis induced by mechanical rupture of the plasma membrane produced intense NMDAR activation in nearby cells. Computational modelling, based on these results, predicts that NMDARs in cancer cells can be strongly activated in the tumour microenvironment by both autocrine glutamate release and necrosis

Rainfall data simulation by hidden Markov model and discrete wavelet transformation

In many regions, monthly (or bimonthly) rainfall data can be considered as deterministic while daily rainfall data may be treated as random. As a result, deterministic models may not sufficiently fit the daily data because of the strong stochastic nature, while stochastic models may also not reliably fit into daily rainfall time series because of the deterministic nature at the large scale (i.e. coarse scale). Although there are different approaches for simulating daily rainfall, mixing of deterministic and stochastic models (towards possible representation of both deterministic and stochastic properties) has not hitherto been proposed. An attempt is made in this study to simulate daily rainfall data by utilizing discrete wavelet transformation and hidden Markov model. We use a deterministic model to obtain large-scale data, and a stochastic model to simulate the wavelet tree coefficients. The simulated daily rainfall is obtained by inverse transformation. We then compare the accumulated simulated and accumulated observed data from the Chao Phraya Basin in Thailand. Because of the stochastic nature at the small scale, the simulated daily rainfall on a point to point comparison show deviations with the observed data. However the accumulated simulated data do show some level of agreement with the observed data. © Springer-Verlag 2008.postprin

Rainfall data simulation by hidden Markov model and discrete wavelet transformation

In many regions, monthly (or bimonthly) rainfall data can be considered as deterministic while daily rainfall data may be treated as random. As a result, deterministic models may not sufficiently fit the daily data because of the strong stochastic nature, while stochastic models may also not reliably fit into daily rainfall time series because of the deterministic nature at the large scale (i.e. coarse scale). Although there are different approaches for simulating daily rainfall, mixing of deterministic and stochastic models (towards possible representation of both deterministic and stochastic properties) has not hitherto been proposed. An attempt is made in this study to simulate daily rainfall data by utilizing discrete wavelet transformation and hidden Markov model. We use a deterministic model to obtain large-scale data, and a stochastic model to simulate the wavelet tree coefficients. The simulated daily rainfall is obtained by inverse transformation. We then compare the accumulated simulated and accumulated observed data from the Chao Phraya Basin in Thailand. Because of the stochastic nature at the small scale, the simulated daily rainfall on a point to point comparison show deviations with the observed data. However the accumulated simulated data do show some level of agreement with the observed data. © Springer-Verlag 2008.postprin

A knowledge-based weighting framework to boost the power of genome-wide association studies

Background: We are moving to second-wave analysis of genome-wide association studies (GWAS), characterized by comprehensive bioinformatical and statistical evaluation of genetic associations. Existing biological knowledge is very valuable for GWAS, which may help improve their detection power particularly for disease susceptibility loci of moderate effect size. However, a challenging question is how to utilize available resources that are very heterogeneous to quantitatively evaluate the statistic significances. Methodology/Principal Findings: We present a novel knowledge-based weighting framework to boost power of the GWAS and insightfully strengthen their explorative performance for follow-up replication and deep sequencing. Built upon diverse integrated biological knowledge, this framework directly models both the prior functional information and the association significances emerging from GWAS to optimally highlight single nucleotide polymorphisms (SNPs) for subsequent replication. In the theoretical calculation and computer simulation, it shows great potential to achieve extra over 15% power to identify an association signal of moderate strength or to use hundreds of whole-genome subjects fewer to approach similar power. In a case study on late-onset Alzheimer disease (LOAD) for a proof of principle, it highlighted some genes, which showed positive association with LOAD in previous independent studies, and two important LOAD related pathways. These genes and pathways could be originally ignored due to involved SNPs only having moderate association significance. Conclusions/Significance: With user-friendly implementation in an open-source Java package, this powerful framework will provide an important complementary solution to identify more true susceptibility loci with modest or even small effect size in current GWAS for complex diseases. © 2010 Li et al.published_or_final_versio

High-sensitivity optical preamplifier for WDM systems using an optical parametric amplifier

We propose and demonstrate a novel preamplifier to improve receiver sensitivity for a 10-Gb/s return-to-zero on-off keying format by using a fiber optical parametric amplifier. Receiver sensitivity can reach down to -42 dBm at bit-error rate = 10-9 This sensitivity is only 1.1 dB off the quantum limit. The crosstalk issue is also investigated for this dual-end detection scheme in a wavelength-division-multiplexing system. © 2009 IEEE.published_or_final_versio

FastPval: A fast and memory efficient program to calculate very low P-values from empirical distribution

Motivation: Resampling methods, such as permutation and bootstrap, have been widely used to generate an empirical distribution for assessing the statistical significance of a measurement. However, to obtain a very low P-value, a large size of resampling is required, where computing speed, memory and storage consumption become bottlenecks, and sometimes become impossible, even on a computer cluster. Results: We have developed a multiple stage P-value calculating program called FastPval that can efficiently calculate very low (up to 10-9) P-values from a large number of resampled measurements. With only two input files and a few parameter settings from the users, the program can compute P-values from empirical distribution very efficiently, even on a personal computer. When tested on the order of 109 resampled data, our method only uses 52.94% the time used by the conventional method, implemented by standard quicksort and binary search algorithms, and consumes only 0.11% of the memory and storage. Furthermore, our method can be applied to extra large datasets that the conventional method fails to calculate. The accuracy of the method was tested on data generated from Normal, Poison and Gumbel distributions and was found to be no different from the exact ranking approach. © The Author(s) 2010. Published by Oxford University Press.published_or_final_versio

Sperm retrieval rate and pregnancy rate in infertile couples undergoing in-vitro fertilisation and testicular sperm extraction for non-obstructive azoospermia in Hong Kong

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Evaluating the role of serum AMH in predicting suboptimal or excessive ovarian response to standard dosing regimen of ovarian stimulation in in-vitro fertilisation using GNRH agonist long protocol

Poster PresentationConference Theme: The Oocyte: from Basic Research to Clinical PracticeIntroduction: Antral follicle count (AFC) is widely used for individualising gonadotrophin dosage in in-vitro fertilisation (IVF) treatment. This retrospective study tried to determine whether baseline serum anti-Mullerian hormone (AMH) measurement would offer any additional role in predicting suboptimal or excessive ovarian response among subjects classified to have normal ovarian reserve based on AFC. Methods: We reviewed 338 women undergoing the first IVF cycle using GnRH agonist long protocol who had baseline AFC of 6 to 14. Ovarian stimulation was initiated with gonadotrophin 300IU daily for two days followed by 150IU daily. Archival serum samples taken on the day before starting gonadotrophin were assayed for AMH. High responders were defined by retrieval of 15 or more oocytes or peak serum oestradiol >20000 pmol/l. Low responders were defined by retrieval of 5 or less oocytes. Results: Among the study cohort, 201 (59.5%), 77 (22.8%) and 73 (21.6%) women had optimal, low and high ovarian response respectively, and their respective median AMH concentrations differed significantly (22.5, 15.1 and 36.1 pmol/l). The area under the ROC curves for predicting high and low response were 0.740 and 0.688 respectively. At the best cut-off of 29 pmol/l, AMH has a sensitivity of 66% and specificity of 73% for predicting high response. At the best cut-off of 15 pmol/l, it has a sensitivity of 52% and specificity of 79% for predicting low response. Conclusion: Baseline serum AMH measurement offers a modest role for individualisation of gonadotrophin dosage in women with normal ovarian reserve based on AFC.published_or_final_versio

Replication study of genetic loci influencing age at menopause in Southern Chinese women

Session - Ovarian AgeingThis journal issue contain Abstracts: 10th EMAS Congress 2015INTRODUCTION: Age at menopause is a highly heritable trait. Previous genome-wide meta-analysis in European and Northern Chinese identified 26 loci underlying age at menopause. OBJECTIVES: To validate these 26 genetic loci in Southern Chinese women. AIMS: To study genetic factors which may influence age at menopause in Southern Chinese. METHODS: This study was performed on 653 women who participated in the Hong Kong Osteoporosis Study, whose age at menopause was available. These women consented to have blood taken and archived for genotyping. DNA was extracted from the buffy coat, and genotyping was performed using Sequenom iPLEX. Age at menopause ...postprin