In this paper, a penalized maximal t test (PMT) is proposed for detecting undocumented mean shifts in climate data series. PMT takes the relative position of each candidate changepoint into account, to diminish the effect of unequal sample sizes on the power of detection. Monte Carlo simulation studies are conducted to evaluate the performance of PMT, in comparison with the most popularly used method, the standard normal homogeneity test (SNHT). An application of the two methods to atmospheric pressure series recorded at a Canadian site is also presented. It is shown that the false-alarm rate of PMT is very close to the specified level of significance and is evenly distributed across all candidate changepoints, whereas that of SNHT can be up to 10 times the specified level for points near the ends of series and much lower for the middle points. In comparison with SNHT, therefore, PMT has higher power for detecting all changepoints that are not too close to the ends of series and lower power for detecting changepoints that are near the ends of series. On average, however, PMT has significantly higher power of detection. The smaller the shift magnitude Δ is relative to the noise standard deviation σ, the greater is the improvement of PMT over SNHT. The improvement in hit rate can be as much as 14%–25% for detecting small shifts (Δ < σ) regardless of time series length and up to 5% for detecting medium shifts (Δ = σ–1.5σ) in time series of length N < 100. For all detectable shift sizes, the largest improvement is always obtained when N < 100, which is of great practical importance, because most annual climate data series are of length N < 100.

This study integrates a Box–Cox power transformation procedure into a common trend two-phase regression-model-based test (the extended version of the penalized maximal F test, or “PMFred,” algorithm) for detecting changepoints to make the test applicable to non-Gaussian data series, such as nonzero daily precipitation amounts or wind speeds. The detection-power aspects of the transformed method (transPMFred) are assessed by a simulation study that shows that this new algorithm is much better than the corresponding untransformed method for non-Gaussian data; the transformation procedure can increase the hit rate by up to ∼70%. Examples of application of this new transPMFred algorithm to detect shifts in real daily precipitation series are provided using nonzero daily precipitation series recorded at a few stations across Canada that represent very different precipitation regimes. The detected changepoints are in good agreement with documented times of changes for all of the example series. This study clarifies that it is essential for homogenization of daily precipitation data series to test the nonzero precipitation amount series and the frequency series of precipitation occurrence (or nonoccurrence), separately. The new transPMFred can be used to test the series of nonzero daily precipitation (which are non Gaussian and positive), and the existing PMFred algorithm can be used to test the frequency series. A software package for using the transPMFred algorithm to detect shifts in nonzero daily precipitation amounts has been developed and made freely available online, along with a quantile-matching (QM) algorithm for adjusting shifts in nonzero daily precipitation series, which is applicable to all positive data. In addition, a similar QM algorithm has also been developed for adjusting Gaussian data such as temperatures. It is noticed that frequency discontinuities are often inevitable because of changes in the measuring precision of precipitation, and that they could complicate the detection of shifts in nonzero daily precipitation data series and void any attempt to homogenize the series. In this case, one must account for all frequency discontinuities before attempting to adjust the measured amounts. This study also proposes approaches to account for detected frequency discontinuities, for example, to fill in the missed measurements of small precipitation or the missed reports of trace precipitation. It stresses the importance of testing the homogeneity of the frequency series of reported zero precipitation and of various small precipitation events, along with testing the series of daily precipitation amounts that are larger than a small threshold value, varying the threshold over a set of small values that reflect changes in measuring precision over time.

Carvacrol is one of the members of monoterpene phenol and is present in the volatile oils of Thymus vulgaris, Carum copticum, origanum and oregano. It is a safe food additive commonly used in our daily life, and few studies have indicated that carvacrol has anti-hepatocarcinogenic activities. The rationale of the study was to examine whether carvacrol affects apoptosis of human hepatoma HepG2 cells. In this study, we showed that carvacrol inhibited HepG2 cell growth by inducing apoptosis as evidenced by Hoechst 33258 stain and Flow cytometric (FCM) analysis. Incubation of HepG2 cells with carvacrol for 24 h induced apoptosis by the activation of caspase-3, cleavage of PARP and decreased Bcl-2 gene expression. These results demonstrated that a significant fraction of carvacrol treated cells died by an apoptotic pathway in HepG2 cells. Moreover, carvacrol selectively altered the phosphorylation state of members of the MAPK superfamily, decreasing phosphorylation of ERK1/2 significantly in a dosedependent manner, and activated phosphorylation of p38 but not affecting JNK MAPK phosphorylation. These results suggest that carvacrol may induce apoptosis by direct activation of the mitochondrial pathway, and the mitogen-activated protein kinase pathway may play an important role in the antitumor effect of carvacrol. These results have identified, for the first time, the biological activity of carvacrol in HepG2 cells and should lead to further development of carvacrol for liver disease therapy.

Diallyl disulfide (DADS) has been shown to have multi-targeted antitumor activities. We have previously discovered that it has a repressive effect on LIM kinase-1 (LIMK1) expression in gastric cancer MGC803 cells. This suggests that DADS may inhibit epithelial-mesenchymal transition (EMT) by downregulating LIMK1, resulting in the inhibition of invasion and growth in gastric cancer. In this study, we reveal that LIMK1 expression is correlated with tumor differentiation, invasion depth, clinical stage, lymph node metastasis, and poor prognosis. DADS downregulated the Rac1-Pak1/Rock1-LIMK1 pathway in MGC803 cells, as shown by decreased p-LIMK1 and p-cofilin1 levels, and suppressed cell migration and invasion. Knockdown and overexpression experiments performed in vitro demonstrated that downregulating LIMK1 with DADS resulted in restrained EMT that was coupled with decreased matrix metalloproteinase-9 (MMP-9) and increased tissue inhibitor of metalloproteinase-3 (TIMP-3) expression. In in vitro and in vivo experiments, the DADS-induced suppression of cell proliferation was enhanced and antagonized by the knockdown and overexpression of LIMK1, respectively. Similar results were observed for DADS-induced changes in the expression of vimentin, CD34, Ki-67, and E-cadherin in xenografted tumors. These results indicate that downregulation of LIMK1 by DADS could explain the inhibition of EMT, invasion and proliferation in gastric cancer cells.

miR-185 has been identified as an important factor in several cancers such as breast cancer, ovarial cancer, and prostate cancer. However, its effect and prognostic value in gastric cancer are still poorly known. In this study, we found that the expression levels of miR-185 were strongly downregulated in gastric cancer and associated with clinical stage and the presence of lymph node metastases. Moreover, miR-185 might independently predict OS and RFS in gastric cancer. We further found that upregulation of miR-185 inhibited the proliferation and metastasis of gastric cancer cells in vitro and in vivo. Taken together, our findings demonstrate that the miR-185 is important for gastric cancer initiation and progression and holds promise as a prognostic biomarker to predict survival and relapse in gastric cancer. It is also a potential therapeutic tool to improve clinical outcomes in the above disease.