International Society of Chronobiology: Membership Information

Most variables of interest in laboratory medicine show predictable changes with several frequencies in the span of time investigated. The waveform of such nonsinusoidal rhythms can be well described by the use of multiple components rhythmometry, a method that allows fitting a linear model with several cosine functions. The method, originally described for analysis of longitudinal time series, is here extended to allow analysis of hybrid data (time series sampled from a group of subjects, each represented by an individual series). Given k individual series, we can fit the same linear model with m different frequencies (harmonics or not from one fundamental period) to each series. This fit will provide estimations for 2m + 1 parameters, namely, the amplitude and acrophase of each component, as well as the rhythm-adjusted mean. Assuming that the set of parameters obtained for each individual is a random sample from a multivariate normal population, the corresponding population parameter estimates can be based on the means of estimates obtained from individuals in the sample. Their confidence intervals depend on the variability among individual parameter estimates. The vari-ance-covariance matrix can then be estimated on the basis of the sample covariances. Confidence intervals for the rhythm-adjusted mean, as well as for the amplitude-acrophase pair, of each component can then be computed using the estimated covariance matrix. The p-values for testing the zero-amplitude assumption for each component, as well as for the global model, can finally be derived using those confidence intervals and the t and F distributions. The method, validated by a simulation study and illustrated by an example of modeling the circadian variation of heart rate, represents a new step in the development of statistical procedures in chronobiology.     

A 2,6,9-hetero-trisubstituted purine inhibitor exhibits potent biological effects against multiple myeloma cells

A focused library of hetero-trisubstituted purines was developed for improving the cell penetrating and biological efficacy of a series of anti-Stat3 protein inhibitors. From this SAR study, lead agent 22e was identified as being a promising inhibitor of MM tumour cells (IC₅₀’s <5 μM). Surprisingly, biophysical and biochemical characterization proved that 22e was not a Stat3 inhibitor. Initial screening against the kinome, prompted by the purine scaffold’s history for targeting ATP binding pockets, suggests possible targeting of the JAK family kinases, as well for ABL1 (nonphosphorylated F317L) and AAK1.     

蓖麻毒蛋白A链基因RNAi转化研究

通过基因沉默技术调控蓖麻毒蛋白A链基因的表达,以期获得低毒蓖麻新材料.利用基因克隆技术获得蓖麻毒蛋白A链基因762 bp片段,命名为RTA基因.进一步利用该基因构建了植物RNAi表达载体pBI-RTA-S-AS,通过农杆菌介导法转化蓖麻子叶节,用卡那抗性筛选转化再生植株,PCR进一步鉴定转基因植株.结果表明:克隆得到目的基因长762 bp,与预期结果一致;卡那抗性筛选和PCR鉴定结果显示,获得了3株转基因阳性植株.     

Increased Activity of Liver Xanthine Oxidase in Rats from Increasing Dietary Calcium

A new binary vector, pZT4B, containing the UDP-N-acetylglucosamine: dolichol phosphate N-acetylglucosamine-1-P transferase (GPT) gene as a selection marker, was constructed. The green fluorescent protein (GFP) gene was inserted into pZT4B, and the resulting plasmid was used in the transformation of Arabidopsis. All of six independent transformants obtained after selection with 0.3 mg/l tunicamycin contained the transgene and showed GFP fluorescence.     

Development of R4 Gateway Binary Vectors (R4pGWB) Enabling High-Throughput Promoter Swapping for Plant Research

We developed a new series of Gateway binary vectors, R4pGWBs, that are plant transformation vectors designed for one-step construction of chimeric genes between any promoter and any cDNA. The structure of R4pGWBs is almost the same as the promoterless type of improved pGWBs (ImpGWBs), except that the attR1 site is replaced with attR4, which enables tripartite recombination of these vectors with promoter- and cDNA-entry clones. While ImpGWBs are suitable for promoter analysis and constitutive expression of cDNAs in higher plants, R4pGWBs have a great advantage in expressing a cDNA under the regulation of desired promoters.     

Multi-patch deterministic and stochastic models for wildlife diseases

Spatial heterogeneity and host demography have a direct impact on the persistence or extinction of a disease. Natural or human-made landscape features such as forests, rivers, roads, and crops are important to the persistence of wildlife diseases. Rabies, hantaviruses, and plague are just a few examples of wildlife diseases where spatial patterns of infection have been observed. We formulate multi-patch deterministic and stochastic epidemic models and use these models to investigate problems related to disease persistence and extinction. We show in some special cases that a unique disease-free equilibrium exists. In these cases, a basic reproduction number ?₀ can be computed and shown to be bounded below and above by the minimum and maximum patch reproduction numbers ? _j , j=1, …, n. The basic reproduction number has a simple form when there is no movement or when all patches are identical or when the movement rate approaches infinity. Numerical examples of the deterministic and stochastic models illustrate the disease dynamics for different movement rates between three patches.     

Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines

Background

Recent advances in genome technologies and the subsequent collection of genomic information at various molecular resolutions hold promise to accelerate the discovery of new therapeutic targets. A critical step in achieving these goals is to develop efficient clinical prediction models that integrate these diverse sources of high-throughput data. This step is challenging due to the presence of high-dimensionality and complex interactions in the data. For predicting relevant clinical outcomes, we propose a flexible statistical machine learning approach that acknowledges and models the interaction between platform-specific measurements through nonlinear kernel machines and borrows information within and between platforms through a hierarchical Bayesian framework. Our model has parameters with direct interpretations in terms of the effects of platforms and data interactions within and across platforms. The parameter estimation algorithm in our model uses a computationally efficient variational Bayes approach that scales well to large high-throughput datasets.

Results

We apply our methods of integrating gene/mRNA expression and microRNA profiles for predicting patient survival times to The Cancer Genome Atlas (TCGA) based glioblastoma multiforme (GBM) dataset. In terms of prediction accuracy, we show that our non-linear and interaction-based integrative methods perform better than linear alternatives and non-integrative methods that do not account for interactions between the platforms. We also find several prognostic mRNAs and microRNAs that are related to tumor invasion and are known to drive tumor metastasis and severe inflammatory response in GBM. In addition, our analysis reveals several interesting mRNA and microRNA interactions that have known implications in the etiology of GBM.

Conclusions

Our approach gains its flexibility and power by modeling the non-linear interaction structures between and within the platforms. Our framework is a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers. We have a freely available software at: http://odin.mdacc.tmc.edu/~vbaladan.

     

Trend (1999–2009) in U.S. death rates from myelodysplastic syndromes: Utility of multiple causes of death in surveillance

BackgroundFor myelodysplastic syndromes (MDS) (formerly known as preleukemia), a diverse group of myeloid neoplasms usually involving anemia in elderly persons, trends in U.S. death rates apparently have not been reported.MethodsTrends in annual age-standardized rates per 100,000 from 1999 to 2009 were examined for MDS using multiple causes vs. underlying cause alone, coded on death certificates for U.S. residents.ResultsThe death rate (all ages combined) for MDS increased from 1999 to 2009, from 1.62 to 1.84 using underlying cause alone and from 2.89 to 3.27 using multiple causes. Rates using multiple causes were about 80% higher than those based on underlying cause alone. From 2001 to 2004 the rate for MDS using underlying cause alone (but not using multiple causes) declined, accompanied by an increase in the rate for deaths from leukemia as underlying cause with mention of MDS; this trend coincided with the advent of the 2001 World Health Organization's reclassification of certain MDS as leukemia. The MDS rate for age 65+ years increased after 2005, whereas the rate for age 25–64 years was low but declined from 2001 to 2003 and then stabilized. For deaths with MDS coded as other than underlying cause, rates did not decline for deaths from each of the two most common causes (i.e., cardiovascular diseases and leukemia).ConclusionsEvidence for decreases in MDS-related mortality rates was limited; the increase at age 65+ years is consistent with increases in incidence rates reported from cancer registries. Using multiple causes of death vs. only the underlying cause results in substantially higher MDS-related death rates, shows the impact of changes in the classification of myeloid neoplasms and emphasizes the importance of reducing cardiovascular disease mortality in MDS patients.