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

Comfort-driven disparity adjustment for stereoscopic video

Pixel disparity—the offset of corresponding pixels between left and right views—is a crucial parameter in stereoscopic three-dimensional (S3D) video, as it determines the depth perceived by the human visual system (HVS). Unsuitable pixel disparity distribution throughout an S3D video may lead to visual discomfort. We present a unified and extensible stereoscopic video disparity adjustment framework which improves the viewing experience for an S3D video by keeping the perceived 3D appearance as unchanged as possible while minimizing discomfort. We first analyse disparity and motion attributes of S3D video in general, then derive a wide-ranging visual discomfort metric from existing perceptual comfort models. An objective function based on this metric is used as the basis of a hierarchical optimisation method to find a disparity mapping function for each input video frame. Warping-based disparity manipulation is then applied to the input video to generate the output video, using the desired disparity mappings as constraints. Our comfort metric takes into account disparity range, motion, and stereoscopic window violation; the framework could easily be extended to use further visual comfort models. We demonstrate the power of our approach using both animated cartoons and real S3D videos

{meso-Tetra­kis[p-(hept­yloxy)phen­yl]­porphyrinato}silver(II)

The title compound, [Ag(C72H84N4O4)], crystallizes with the AgII cation on a centre of symmetry. The macrocyclic 24-membered ring core is planar with a mean deviation of 0.0311 (15) Å and the four-coordinate AgII cation fits into its center, at 2.0814 (19) and 2.0872 (19) Å, from the surrounding pyrrole-N atoms, in agreement with what is found in related compounds. The p-heptyl­oxyphenyl groups are rotated 75.51 (5) and 84.45 (8)° with respect to the porphyrin mean plane, due to steric hindrance with the pyrrole-H atoms of the macrocycle

{5,10,15,20-Tetra­kis[4-(oct­yloxy)phen­yl]porphyrinato}copper(II)

In the title compound, [Cu(C76H92N4O4)], the central Cu(II) ion is situated on an inversion centre. The porphyrinate core exhibits a nearly planar conformation [maximum deviation = 0.027 (3) Å], with Cu—N distances of 1.997 (2) and 2.001 (2) Å. The benzene rings of the 4-octyloxyphenyl groups are rotated at angles of 84.02 (8) and 77.02 (6)° with respect to the mean plane of the porphyrin fragment. The two terminal C atoms in the octyl group are disordered over two positions of equal occupancy

Skp2 expression unfavorably impacts survival in resectable esophageal squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>The correlation of S-phase kinase–associated protein 2 (Skp2) with metastasis and prognosis in esophageal squamous cell carcinoma (ESCC) is controversial. The purpose of this study was to explore whether there was a correlation between the expression of Skp2 evaluated by immunohistochemistry and the clinical outcome of patients with operable ESCC, and to further determine the possible mechanism of the impact of Skp2 on survival.</p> <p>Methods</p> <p>Tissue microarrays that included 157 surgically resected ESCC specimens was successfully generated for immunohistochemical evaluation. The clinical/prognostic significance of Skp2 expression was analyzed. Kaplan-Meier analysis was used to compare the postoperative survival between groups. The prognostic impact of clinicopathologic variables and Skp2 expression was evaluated using a Cox proportional hazards model. A cell proliferation assay and a colony formation assay were performed in ESCC cell lines to determine the function of Skp2 on the progression of ESCC <it>in vitro</it>.</p> <p>Results</p> <p>Skp2 expression correlated closely with the T category (<it>p</it> = 0.035) and the pathological tumor-node-metastasis (TNM) stage (<it>p</it> = 0.027). High expression of Skp2 was associated with poor overall survival in resectable ESCC (<it>p</it> = 0.01). The multivariate Cox regression analysis demonstrated that pathological T category, pathological N category, cell differentiation, and negative Skp2 expression were independent factors for better overall survival. <it>In vitro</it> assays of ESCC cell lines demonstrated that Skp2 promoted the proliferative and colony-forming capacity of ESCCs.</p> <p>Conclusions</p> <p>Negative Skp2 expression in primary resected ESCC is an independent factor for better survival. Skp2 may play a pro-proliferative role in ESCC cells.</p

Sleep behavior and depression: Findings from the China Kadoorie Biobank of 0.5 million Chinese adults.

BACKGROUND: Mixed results have shown the association between sleep behavior and depression, but evidence relating the joint effect of sleep duration and sleep disturbances is limited, especially in Chinese population. METHODS: A total of 512,891 adults aged 30-79 years from China Kadoorie Biobank (CKB) were included. Depression was defined by Composite International Diagnostic Inventory-short form (CIDI-SF). Sleep duration and sleep disturbances, including difficulty initiating and maintaining sleep (DIMS), early morning awakening (EMA), daytime dysfunction (DDF) and any sleep disturbances (ASD), were obtained by a self-reported questionnaire. Logistic regression was applied to examine the association between sleep behavior and depression. RESULTS: About 23.1% of participants reported short sleep duration (≤ 6h), and 5.1% reported long sleep duration (> 9h). Compared with normal sleep duration (7-9h), both groups were associated greater likelihood of having depression (short sleep: OR = 2.32, 95%CI: 2.14-2.51; long sleep: OR = 1.56, 96%CI: 1.34-1.81). Participants reported sleep disturbances were significantly associated with depression (odds ratios ranged from 3.31 to 4.17). Moreover, the associations tended to be stronger for those who reported both abnormal sleep duration and sleep disturbances (p for interactions < 0.05), especially for those who slept long. LIMITATIONS: The cross-sectional nature of the study design limits the interpretation of the results. CONCLUSIONS: Abnormal sleep duration and sleep disturbances were associated with depression. The associations were stronger for abnormal sleep duration accompanied with sleep disturbances, especially for a long duration. More attention should be paid on these persons in clinical practice

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts