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

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

PHYSIOCHEMICAL, PROXIMATE, AND SENSORY PROPERTIES OF UNFERMENTED AND FERMENTED SOY-CARROT BEVERAGES SWEETENED WITH SUGAR, DATE, AND HONEY

Objective: Physiochemical, proximate, and sensory properties of unfermented and fermented soy-carrot beverage sweetened with sugar, date, and honey were evaluated. Phytochemical content of soymilk, carrot juice, and their blend was also analyzed. Methods: Three sets of soy-carrot beverages were produced by homogenizing soy milk and carrot juice in a ratio of 2:1 and sweetened to 12% Brix. Each set was sweetened with sugar, date, and honey, respectively. A fourth set was unsweetened and served as control. After pasteurization, one part was fermented with pure culture of Lactobacillus acidophilus at 42°C for 24 h. Results: Fermentation significantly (p≤0.05) decreased pH (≥5.40–≤3.90), increased titratable acidity (≤0.55–≥0.90% lactic acid), and viscosity (≤0.65–≥0.87 Pa.S) of the soy-carrot beverages. Moisture, protein, fat, ash, carbohydrate, and energy content of unfermented beverages were 82.95– 93.95%, 2.15–2.87%, 0.42–1.21%, 0.10–0.20%, 3.21–12.55%, and 25.46–73.53 Kcal/g, respectively, while fermented beverages had 90.00–93.00%, 2.06–2.20%, 0.88–1.08%, 0.11–10.20%, 4.85–8.75%, and 36.76–52.20 Kcal/g, respectively. Total carotenoid, phenol, and DPPH radical scavenging activity varied, respectively, from 2.40–7.90, 14.81–26.59 mg tannic acid/ml, and 4.02–27.83% and were significantly (p≤0.05) highest in soy-carrot blend with carrot as major contributor. Degree of likeness of the sensory attributes for the sweetened and unfermented beverages was significantly (p≤0.05) higher than the fermented. Conclusion: Date and honey (12% Brix) can be used as sucrose alternatives in producing acceptable nutritious beverage from soymilk and carrot juice

PHYSICO-CHEMICAL, PROXIMATE COMPOSITION, ASCORBIC ACID, SENSORY, AND MICROBIOLOGICAL QUALITY OF MINIMALLY PROCESSED CARICA PAPAYA CONSUMED IN RIVERS STATE, NIGERIA

Objective: This study evaluated the physico-chemical, proximate, ascorbic acid, sensory, and microbiological properties of minimally processed Carica papaya consumed in Rivers State Nigeria. Methods: Minimally processed papaya in transparent polyethylene bags were purchased from four different locations: Nwinpi, Mile III, Rumuokuta, and Rumuokoro Junctions in Port Harcourt, Rivers State, Nigeria. Control sample was prepared in the laboratory. Standard analytical methods were used for analysis. Results: pH and titratable acidity ranged from 4.90–5.20 to 1.00–1.04% citric acid, respectively. Moisture, fat, ash, crude fiber, and carbohydrate ranged, respectively, from 85.80–89.60, 0.64–0.69, 0.55–0.96, 1.71–1.93, and 7.20–10.97%. Energy value was 35.31–50.07 kcal/g while protein was 0.09% for all samples. Ascorbic acid varied significantly (p<0.05) from 17.81 to 44.91 mg/100 g. Sensory results showed that 75% of the assessors’ degree of likeness for aroma, appearance/color, texture (smoothness), sweetness, and overall acceptability was that of moderate to extreme likeness. Total aerobic, coliform, Escherichia coli, Salmonella, and Staphylococcus aureus counts varied from 3.85–5.76, 3.74–5.68, 3.95–5.57, 3.82–5.58, and 3.30–5.45 Log10CFU/g, respectively. The control had significantly (p<0.05) the least bacterial count. Fungi count varied from 3.65 to 4.62 Log10CFU/g. Conclusion: The minimally processed papaya was low in acidity, rich in ascorbic acid and a good source of the nutrient. Sensory attributes of the products were acceptable to the assessors. Microbial counts were unsatisfactory and can pose a risk factor to public health

Forensically informative nucleotide sequencing (FINS) for the authentication of Chinese medicinal materials

Chinese medicinal materials may be authenticated by molecular identification. As a definitive approach to molecular identification of medicinal materials, forensically informative nucleotide sequencing (FINS) comprises four steps, namely (1) DNA extraction from biological samples, (2) selection and amplification of a specific DNA fragment, (3) determination of the sequence of the amplified DNA fragment and (4) cladistic analysis of the sample DNA sequence against a DNA database. Success of the FINS identification depends on the selection of DNA region and reference species. This article describes the techniques and applications of FINS for authenticating Chinese medicinal materials. © 2011 Li et al; licensee BioMed Central Ltd.published_or_final_versio

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