Some Thoughts on the Dynamics of Federal Trademark Legislation and the Trademark Dilution Act of 1995

Basic transcription factor 3 (BTF3) is a general RNA polymerase II transcription factor and is also involved in apoptosis regulation. Increasing evidence shows that BTF3 is aberrantly expressed in several kinds of malignancies, but there is no study to analyze BTF3 expression in colorectal cancer (CRC) patients. Applying immunohistochemistry, we detected BTF3 in CRCs (n = 156), the corresponding distant (n = 42), adjacent normal mucosa (n = 96), lymph node metastases (n  = 35), and analyzed its relationships with clinicopathological and biological variables. Our results showed that BTF3 staining significantly increased from distant or adjacent normal mucosa to primary CRCs (p < 0.0001) or metastases (p = 0.002 and p < 0.0001). BTF3 was higher in distal cancers than in proximal cancers (57 % vs. 39 %, p = 0.041). It also showed stronger staining in primary CRCs stage I and II than that in stage III and IV (64 % vs. 35 %, p = 0.0004), or metastases (64 % vs. 29 %, p = 0.004). Cancers with better differentiation had a higher expression than those with worse differentiation (56 % vs. 37 %, p  = 0.031). There were positive correlations of BTF3 expression with nuclear factor kappa B (NF-κB), RAD50, MRE11, NBS1, and AEG-1 (p  < 0.05). In conclusion, BTF3 overexpression may be an early event in CRC development and could be useful biomarker for the early stage of CRCs. BTF3 has positive correlations with NF-κB, RAD50, MRE11, NBS1 and AEG-1, and might influence complex signal pathways in CRC

A Comprehensive Analysis of Fermi Gamma-Ray Burst Data. IV. Spectral Lag and its Relation to E p Evolution

The spectral evolution and spectral lag behavior of 92 bright pulses from 84 gamma-ray bursts observed by the Fermi Gamma-ray Burst Monitor (GBM) telescope are studied. These pulses can be classified into hard-to-soft pulses (H2S; 64/92), H2S-dominated-tracking pulses (21/92), and other tracking pulses (7/92). We focus on the relationship between spectral evolution and spectral lags of H2S and H2S-dominated-tracking pulses. The main trend of spectral evolution (lag behavior) is estimated with ( ), where E p is the peak photon energy in the radiation spectrum, t + t 0 is the observer time relative to the beginning of pulse −t 0, and is the spectral lag of photons with energy E with respect to the energy band 8–25 keV. For H2S and H2S-dominated-tracking pulses, a weak correlation between and k E is found, where W is the pulse width. We also study the spectral lag behavior with peak time of pulses for 30 well-shaped pulses and estimate the main trend of the spectral lag behavior with . It is found that is correlated with k E . We perform simulations under a phenomenological model of spectral evolution, and find that these correlations are reproduced. We then conclude that spectral lags are closely related to spectral evolution within the pulse. The most natural explanation of these observations is that the emission is from the electrons in the same fluid unit at an emission site moving away from the central engine, as expected in the models invoking magnetic dissipation in a moderately high-σ outflow

A comprehensive analysis of Fermi Gamma-Ray Burst Data: IV. Spectral lag and Its Relation to Ep Evolution

The spectral evolution and spectral lag behavior of 92 bright pulses from 84 gamma-ray bursts (GRBs) observed by the Fermi GBM telescope are studied. These pulses can be classified into hard-to-soft pulses (H2S, 64/92), H2S-dominated-tracking pulses (21/92), and other tracking pulses (7/92). We focus on the relationship between spectral evolution and spectral lags of H2S and H2S-dominated-tracking pulses. %in hard-to-soft pulses (H2S, 64/92) and H2S-dominating-tracking (21/92) pulses. The main trend of spectral evolution (lag behavior) is estimated with $\log E_p\propto k_E\log(t+t_0)$ (${\hat{\tau}} \propto k_{\hat{\tau}}\log E$), where $E_p$ is the peak photon energy in the radiation spectrum, $t+t_0$ is the observer time relative to the beginning of pulse $-t_0$, and ${\hat{\tau}}$ is the spectral lag of photons with energy $E$ with respect to the energy band $8$-$25$ keV. For H2S and H2S-dominated-tracking pulses, a weak correlation between $k_{{\hat{\tau}}}/W$ and $k_E$ is found, where $W$ is the pulse width. We also study the spectral lag behavior with peak time $t_{\rm p_E}$ of pulses for 30 well-shaped pulses and estimate the main trend of the spectral lag behavior with $\log t_{\rm p_E}\propto k_{t_p}\log E$. It is found that $k_{t_p}$ is correlated with $k_E$. We perform simulations under a phenomenological model of spectral evolution, and find that these correlations are reproduced. We then conclude that spectral lags are closely related to spectral evolution within the pulse. The most natural explanation of these observations is that the emission is from the electrons in the same fluid unit at an emission site moving away from the central engine, as expected in the models invoking magnetic dissipation in a moderately-high-$\sigma$ outflow.Comment: 58 pages, 11 figures, 3 tables. ApJ in pres

Differentiation of Glial Cells From hiPSCs: Potential Applications in Neurological Diseases and Cell Replacement Therapy.

Glial cells are the most abundant cell type in the central nervous system (CNS) and play essential roles in maintaining brain homeostasis, forming myelin, and providing support and protection for neurons, etc. Over the past decade, significant progress has been made in the reprogramming field. Given the limited accessibility of human glial cells, in vitro differentiation of human induced pluripotent stem cells (hiPSCs) into glia may provide not only a valuable research tool for a better understanding of the functions of glia in the CNS but also a potential cellular source for clinical therapeutic purposes. In this review, we will summarize up-to-date novel strategies for the committed differentiation into the three major glial cell types, i.e., astrocyte, oligodendrocyte, and microglia, from hiPSCs, focusing on the non-neuronal cell effects on the pathology of some representative neurological diseases. Furthermore, the application of hiPSC-derived glial cells in neurological disease modeling will be discussed, so as to gain further insights into the development of new therapeutic targets for treatment of neurological disorders