Use of anchorchip-time-of-flight spectrometry technology to screen tumor biomarker proteins in serum for small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>The purpose of this study is to discover potential biomarkers in serum for the detection of small cell lung cancer (SCLC).</p> <p>Methods</p> <p>74 serum samples including 30 from SCLC patients and 44 from healthy controls were analyzed using ClinProt system combined with matrix-assisted laser desorption/ionization time-of-flight masss spectrometry (MALDI-TOF-MS). ClinProt software and genetic algorithm analysis selected a panel of serum markers that most efficiently predicted which patients had SCLC.</p> <p>Results</p> <p>The diagnostic pattern combined with 5 potential biomarkers could differentiate SCLC patients from healthy persons, with a sensitivity of 90%, specificity of 97.73%. Remarkably, 88.89% of stage I/II patients were accurately assigned to SCLC.</p> <p>Conclusions</p> <p>Anchorchip-time-of-flight spectrometry technology will provide a highly accurate approach for discovering new biomarkers for the detection of SCLC.</p

A Recurrent Log-Linearized Gaussian Mixture Network

Context in time series is one of the most useful andinteresting characteristics for machine learning. In some cases, thedynamic characteristic would be the only basis for achieving a possibleclassification. A novel neural network, which is named “a recurrentlog-linearized Gaussian mixture network (R-LLGMN)," isproposed in this paper for classification of time series. The structureof this network is based on a hidden Markov model (HMM),which has been well developed in the area of speech recognition.R-LLGMN can as well be interpreted as an extension of a probabilisticneural network using a log-linearized Gaussian mixturemodel, in which recurrent connections have been incorporated tomake temporal information in use. Some simulation experimentsare carried out to compare R-LLGMN with the traditional estimatorof HMM as classifiers, and finally, pattern classification experimentsfor EEG signals are conducted. It is indicated from theseexperiments that R-LLGMN can successfully classify not only artificialdata but real biological data such as EEG signals

On-Line Learning of Non-Contact Impedance and its Appliation to Contact Tasks

Impedance control is one of the most effective control methods for interaction between a manipulator and task environments. The force resulted by the interaction, however, does not occur until the end-effector of the manipulator touches its environment. A non-contact impedance control method has been proposed in order to achieve the impedance control before the contact. The method can regulate not only the end-point impedance but also the virtual impedance such that the end-effector is surrounded by a virtual object which can touch the environment before a real contact. This paper proposes a learning method using neural networks to regulate the virtual impedance parameters for given tasks. The validity of the method is verified through computer simulations and experiments with a conventional robot manipulator

tRNASer(CGA) differentially regulates expression of wild-type and codon-modified papillomavirus L1 genes

Exogenous transfer RNAs (tRNAs) favor translation of bovine papillomavirus 1 wild-type (wt) L1 mRNA in in vitro translation systems (Zhou et al. 1999, J. Virol., 73, 4972-4982). We, therefore, investigated whether papillomavirus (PV) wt L1 protein expression could be enhanced in eukaryotic cells following exogenous tRNA supplementation. Both Chinese hamster ovary (CHO) and Cos1 cells, transfected with PV1 wt L1 genes, effectively transcribed the genes but did not translate them. However, L1 protein translation was demonstrated following co-transfection with the L1 gene and a gene expressing tRNA(Ser)(CGA). Cell lines, stably transfected with a bovine papillomavirus 1 (BPV1) wt L1 expression construct, produced L1 protein after the transfection of the tRNA(Ser)(CGA) gene, but not following the transfection with basal vectors, suggesting that tRNA(Ser)(CGA) gene enhanced wt L1 translation as a result of endogenous tRNA alterations and phosphorylation of translation initiation factors elF4E and elF2alpha in the tRNA(Ser)(CGA) transfected L1 cell lines. The tRNA(Ser)(CGA) gene expression significantly reduced translation of L1 proteins expressed from codon-modified (HB) PV L1 genes utilizing mammalian preferred codons, but had variable effects on translation of green fluorescent proteins (GFPs) expressed from six serine GFP variants. The changes of tRNA pools appear to match the codon composition of PV wt and HB L1 genes and serine GFP variants to regulate translation of their mRNAs. These findings demonstrate for the first time in eukaryotic cells that translation of the target genes can be differentially influenced by the provision of a single tRNA expression construct

Coping Strategies for HIV-Related Stigma in Liuzhou, China

This study explores the feelings, experiences, and coping strategies of people living with HIV (PLHIV) in Liuzhou, China. In a southwestern Chinese city with high HIV prevalence, we conducted semi-structured in-depth interviews with 47 PLHIV selected to represent individuals who had acquired HIV via different acquisition routes. Many participants felt severely stigmatized; they commonly reported having very low self-esteem and feelings of despair. Based on style of coping and whether it occurred at the interpersonal or intrapersonal level, four types of coping that participants used to deal with HIV-associated stigma were identified: (1) Compassion (Passive/Avoidant-Interpersonal); (2) Hiding HIV status (Passive/Avoidant-Intrapersonal); (3) Social support (Active/Problem-focused-Interpersonal; and (4) Self-care (Active/Problem-focused-Intrapersonal). Educational and stigma-reduction interventions targeting potential social support networks for PLHIV (e.g., family, close friends, and peers) could strengthen active interpersonal PLHIV coping strategies. Interventions teaching self-care to PLHIV would encourage active intrapersonal coping, both of which may enhance PLHIV quality of life in Liuzhou, China

Hilbert-Huang Transform analysis of quasi-periodic oscillations in MAXI J1820+070

We present time-frequency analysis, based on the Hilbert-Huang transform (HHT), of the evolution on the low-frequency quasi-periodic oscillations (LFQPOs) observed in the black hole X-ray binary MAXI J1820+070. Through the empirical mode decomposition (EMD) method, we decompose the light curve of the QPO component and measure its intrinsic phase lag between photons from different energy bands. We find that the QPO phase lag is negative (low energy photons lag behind high energy photons), meanwhile the absolute value of the lag increases with energy. By applying the Hilbert transform to the light curve of the QPO, we further extract the instantaneous frequency and amplitude of the QPO. Compared these results with those from the Fourier analysis, we find that the broadening of the QPO peak is mainly caused by the frequency modulation. Through further analysis, we find that these modulations could share a common physical origin with the broad-band noise, and can be well explained by the internal shock model of the jet