14-3-3epsilon contributes to tumour suppression in laryngeal carcinoma by affecting apoptosis and invasion

<p>Abstract</p> <p>Background</p> <p>14-3-3epsilon regulates a wide range of biological processes, including cell cycle control, proliferation, and apoptosis, and plays a significant role in neurogenesis and the formation of malignant tumours. However, the exact function and regulatory mechanism of 14-3-3epsilon in carcinogenesis have not been elucidated.</p> <p>Methods</p> <p>The expression of <it>14-3-3epsilon </it>was assessed by RT-PCR and western blotting. The invasiveness and viability of Hep-2 cells were determined by the transwell migration assay and MTT assay, respectively. Cell cycle and apoptosis of Hep-2 cells were detected by flow cytometry.</p> <p>Results</p> <p>The mRNA and protein expression of <it>14-3-3epsilon </it>in larynx squamous cell carcinoma (LSCC) tissues were significantly lower than those in clear surgical margin tissues. Statistical analysis showed that the 14-3-3epsilon protein level in metastatic lymph nodes was lower than that in paired tumour tissues. In addition, the protein level of 14-3-3epsilon in stage III or IV tumours was significantly lower than that in stage I or II tumours. Compared with control Hep-2 cells, the percentages of viable cells in the 14-3-3epsilon-GFP and negative control GFP groups were 36.68 ± 14.09% and 71.68 ± 12.10%, respectively. The proportions of S phase were 22.47 ± 3.36%, 28.17 ± 3.97% and 46.15 ± 6.82%, and the apoptotic sub-G1 populations were 1.23 ± 1.02%, 2.92 ± 1.59% and 13.72 ± 3.89% in the control, negative control GFP and 14-3-3epsilon-GFP groups, respectively. The percentages of the apoptotic cells were 0.84 ± 0.25%, 1.08 ± 0.24% and 2.93 ± 0.13% in the control, negative control GFP and 14-3-3epsilon-GFP groups, respectively. The numbers of cells that penetrated the filter membrane in the control, negative control GFP and 14-3-3epsilon-GFP groups were 20.65 ± 1.94, 17.63 ± 1.04 and 9.1 ± 0.24, respectively, indicating significant differences among the different groups.</p> <p>Conclusions</p> <p>Decreased expression of <it>14-3-3epsilon </it>in LSCC tissues contributes to the initiation and progression of LSCC. <it>14-3-3epsilon </it>can promote apoptosis and inhibit the invasiveness of LSCC.</p

Observing the temperature dependent transition of the GP2 peptide using terahertz spectroscopy

The GP2 peptide is derived from the Human Epidermal growth factor Receptor 2 (HER2/nue), a marker protein for breast cancer present in saliva. In this paper we study the temperature dependent behavior of hydrated GP2 at terahertz frequencies and find that the peptide undergoes a dynamic transition between 200 and 220 K. By fitting suitable molecular models to the frequency response we determine the molecular processes involved above and below the transition temperature (TD). In particular, we show that below TD the dynamic transition is dominated by a simple harmonic vibration with a slow and temperature dependent relaxation time constant and that above TD, the dynamic behavior is governed by two oscillators, one of which has a fast and temperature independent relaxation time constant and the other of which is a heavily damped oscillator with a slow and temperature dependent time constant. Furthermore a red shifting of the characteristic frequency of the damped oscillator was observed, confirming the presence of a non-harmonic vibration potential. Our measurements and modeling of GP2 highlight the unique capabilities of THz spectroscopy for protein characterization.Yiwen Sun, Zexuan Zhu, Siping Chen, Jega Balakrishnan, Derek Abbott, Anil T. Ahuja and Emma Pickwell-MacPherso

Time variance and defect prediction in software projects

It is crucial for a software manager to know whether or not one can rely on a bug prediction model. A wrong prediction of the number or the location of future bugs can lead to problems in the achievement of a project’s goals. In this paper we first verify the existence of variability in a bug prediction model’s accuracy over time both visually and statistically. Furthermore, we explore the reasons for such a highvariability over time, which includes periods of stability and variability of prediction quality, and formulate a decision procedure for evaluating prediction models before applying them. To exemplify our findings we use data from four open source projects and empirically identify various project features that influence the defect prediction quality. Specifically, we observed that a change in the number of authors editing a file and the number of defects fixed by them influence the prediction quality. Finally, we introduce an approach to estimate the accuracy of prediction models that helps a project manager decide when to rely on a prediction model. Our findings suggest that one should be aware of the periods of stability and variability of prediction quality and should use approaches such as ours to assess their models’ accuracy in advance