Secondary relaxation dynamics in rigid glass-forming molecular liquids with related structures

The dielectric relaxation in three glass-forming molecular liquids, 1-methylindole (1MID), 5H-5-Methyl-6,7-dihydrocyclopentapyrazine (MDCP), and Quinaldine (QN) is studied focusing on the secondary relaxation and its relation to the structural α-relaxation. All three glass-formers are rigid and more or less planar molecules with related chemical structures but have dipoles of different strengths at different locations. A strong and fast secondary relaxation is detected in the dielectric spectra of 1MID, while no resolved β-relaxation is observed in MDCP and QN. If the observed secondary relaxation in 1MID is identified with the Johari-Goldstein (JG) β-relaxation, then apparently the relation between the α- and β-relaxation frequencies of 1MID is not in accord with the Coupling Model (CM). The possibility of the violation of the prediction in 1MID as due to either the formation of hydrogen-bond induced clusters or the involvement of intramolecular degree of freedom is ruled out. The violation is explained by the secondary relaxation originating from the in-plane rotation of the dipole located on the plane of the rigid molecule, contributing to dielectric loss at higher frequencies and more intense than the JG β-relaxation generated by the out-of-plane rotation. MDCP has smaller dipole moment located in the plane of the molecule; however, presence of the change of curvature of dielectric loss, ε″(f), at some frequency on the high-frequency flank of the α-relaxation reveals the JG β-relaxation in MDCP and which is in accord with the CM prediction. QN has as large an in-plane dipole moment as 1MID, and the absence of the resolved secondary relaxation is explained by the smaller coupling parameter than the latter in the framework of the C

Bound States and Critical Behavior of the Yukawa Potential

We investigate the bound states of the Yukawa potential $V(r)=-\lambda \exp(-\alpha r)/ r$, using different algorithms: solving the Schr\"odinger equation numerically and our Monte Carlo Hamiltonian approach. There is a critical $\alpha=\alpha_C$, above which no bound state exists. We study the relation between $\alpha_C$ and $\lambda$ for various angular momentum quantum number $l$, and find in atomic units, $\alpha_{C}(l)= \lambda [A_{1} \exp(-l/ B_{1})+ A_{2} \exp(-l/ B_{2})]$, with $A_1=1.020(18)$, $B_1=0.443(14)$, $A_2=0.170(17)$, and $B_2=2.490(180)$.Comment: 15 pages, 12 figures, 5 tables. Version to appear in Sciences in China

Transcriptional profiling of long non-coding RNAs and novel transcribed regions across a diverse panel of archived human cancers

Background: Molecular characterization of tumors has been critical for identifying important genes in cancer biology and for improving tumor classification and diagnosis. Long non-coding RNAs, as a new, relatively unstudied class of transcripts, provide a rich opportunity to identify both functional drivers and cancer-type-specific biomarkers. However, despite the potential importance of long non-coding RNAs to the cancer field, no comprehensive survey of long non-coding RNA expression across various cancers has been reported. Results: We performed a sequencing-based transcriptional survey of both known long non-coding RNAs and novel intergenic transcripts across a panel of 64 archival tumor samples comprising 17 diagnostic subtypes of adenocarcinomas, squamous cell carcinomas and sarcomas. We identified hundreds of transcripts from among the known 1,065 long non-coding RNAs surveyed that showed variability in transcript levels between the tumor types and are therefore potential biomarker candidates. We discovered 1,071 novel intergenic transcribed regions and demonstrate that these show similar patterns of variability between tumor types. We found that many of these differentially expressed cancer transcripts are also expressed in normal tissues. One such novel transcript specifically expressed in breast tissue was further evaluated using RNA in situ hybridization on a panel of breast tumors. It was shown to correlate with low tumor grade and estrogen receptor expression, thereby representing a potentially important new breast cancer biomarker. Conclusions: This study provides the first large survey of long non-coding RNA expression within a panel of solid cancers and also identifies a number of novel transcribed regions differentially expressed across distinct cancer types that represent candidate biomarkers for future research

Investigation of the Acetylation Mechanism by GCN5 Histone Acetyltransferase

The histone acetylation of post-translational modification can be highly dynamic and play a crucial role in regulating cellular proliferation, survival, differentiation and motility. Of the enzymes that mediate post-translation modifications, the GCN5 of the histone acetyltransferase (HAT) proteins family that add acetyl groups to target lysine residues within histones, has been most extensively studied. According to the mechanism studies of GCN5 related proteins, two key processes, deprotonation and acetylation, must be involved. However, as a fundamental issue, the structure of hGCN5/AcCoA/pH3 remains elusive. Although biological experiments have proved that GCN5 mediates the acetylation process through the sequential mechanism pathway, a dynamic view of the catalytic process and the molecular basis for hGCN5/AcCoA/pH3 are still not available and none of theoretical studies has been reported to other related enzymes in HAT family. To explore the molecular basis for the catalytic mechanism, computational approaches including molecular modeling, molecular dynamic (MD) simulation and quantum mechanics/molecular mechanics (QM/MM) simulation were carried out. The initial hGCN5/AcCoA/pH3 complex structure was modeled and a reasonable snapshot was extracted from the trajectory of a 20 ns MD simulation, with considering post-MD analysis and reported experimental results. Those residues playing crucial roles in binding affinity and acetylation reaction were comprehensively investigated. It demonstrated Glu80 acted as the general base for deprotonation of Lys171 from H3. Furthermore, the two-dimensional QM/MM potential energy surface was employed to study the sequential pathway acetylation mechanism. Energy barriers of addition-elimination reaction in acetylation obtained from QM/MM calculation indicated the point of the intermediate ternary complex. Our study may provide insights into the detailed mechanism for acetylation reaction of GCN5, and has important implications for the discovery of regulators against GCN5 enzymes and related HAT family enzymes

Multi-scale freeform surface texture filtering using a mesh relaxation scheme

Surface filtering algorithms using Fourier, Gaussian, wavelets, etc, are well-established for simple Euclidean geometries. However, these filtration techniques cannot be applied to today's complex freeform surfaces, which have non-Euclidean geometries, without distortion of the results. This paper proposes a new multi-scale filtering algorithm for freeform surfaces that are represented by triangular meshes based on a mesh relaxation scheme. The proposed algorithm is capable of decomposing a freeform surface into different scales and separating surface roughness, waviness and form from each other, as will be demonstrated throughout the paper. Results of applying the proposed algorithm to computer-generated as well as real surfaces are represented and compared with a lifting wavelet filtering algorithm

Leisure-time physical activity mitigated the cognitive effect of PM2.5 and PM2.5 components exposure: Evidence from a nationwide longitudinal study

Background: Exposure to fine particulate matter (PM2.5) impairs cognition, while physical activity (PA) improves cognitive function. However, whether taking PA with PM2.5 exposure is still beneficial to cognition remains unknown. Methods: We utilized national representative longitudinal data from the China Family Panel Study (CFPS), comprising a total sample of 108,099 from 2010 to 2018 in three waves. Cognitive performance and leisure-time PA were measured using the standard cognitive module and Godin-Shephard Leisure-Time Physical Activity Questionnaire. Gridded overall PM2.5 and major chemical components of PM2.5 were estimated using a two-stage machine learning model and matched to each participant based on their residential location. Mixed-effect models and difference-in-difference models were employed to investigate the individual and joint effects of total PM2.5, PM2.5 components, and leisure-time PA on cognition. Results: Every 1 μg/m3 increase in PM2.5 was associated with a −0.035 (95% confidence interval [CI] = −0.052, −0.018) point change in cognitive score. All PM2.5 components exhibited negative associations with cognitive change, with black carbon (BC) contributing the most significant cognitive decline (β = −1.025, 95% CI = −1.367, −0.683). Every one-time (or one-hour) increase in leisure-time PA frequency (or PA time) per week was associated with an increase in cognitive score by 0.576 (0.270) points (PA frequency: 95% CI = 0.544, 0.608, PA time: 95% CI = 0.248, 0.293). PA frequency (β = −0.005, 95% CI = −0.006, −0.003) and PA time (β = −0.002, 95% CI = −0.003, −0.001) exhibited interactive effects with PM2.5. Increased PA frequency and time were more beneficial to cognitive function in the low PM2.5 exposure group compared to those exposed to high PM2.5 levels. Moreover, relative to lower PM2.5 exposure, the cognitive benefits of physically active individuals with higher PM2.5 exposure were attenuated but still improved cognition when compared to those with no PA. Conclusion: Engaging in leisure-time PA provides cognitive benefits even under PM2.5 exposure, although PM2.5 exposure attenuates these benefits. Among all PM2.5 components, BC demonstrated the most significant cognitive hazard and interaction with leisure-time PA. Promoting PA as a preventive measure may offer a cost-effective and convenient strategy to mitigate the negative impact of PM2.5 exposure on cognition. There is no excuse to avoid PA under PM2.5 exposure, as its cognitive benefits persist even in polluted environments

Identification of stable QTLs causing chalk in rice grains in nine environments

Key message: A novel QTL cluster for chalkiness on Chr04 was identified using single environment analysis and joint mapping across 9 environments in Asia and South American. QTL NILs showed that each had a significant effect on chalk. Abstract: Chalk in rice grains leads to a significant loss in the proportion of marketable grains in a harvested crop, leading to a significant financial loss to rice farmers and traders. To identify the genetic basis of chalkiness, two sets of recombinant inbred lines (RILs) derived from reciprocal crosses between Lemont and Teqing were used to find stable QTLs for chalkiness. The RILs were grown in seven locations in Asia and Latin American and in two controlled environments in phytotrons. A total of 32 (21) and 46 (22) QTLs for DEC and PGWC, most of them explaining more than 10\ua0% of phenotypic variation, were detected based on single environment analysis in T/L (L/T) population, respectively. Seven (2) and 7 (3) QTLs for DEC and PGWC were identified in the T/L (L/T) population using joined analysis across all environments, respectively. Six major QTLs clusters were found on five chromosomes: 1, 2, 4, 5 and 11. The biggest cluster at id4007289-RM252 on Chr04 was a novelty, including 16 and 4 QTLs detected by single environment analysis and joint mapping across all environments, respectively. The detected digenic epistatic QTLs explained up to 13\ua0% of phenotypic variation, suggesting that epistasis play an important role in the genetic control of chalkiness in rice. QTL NILs showed that each QTL cluster had a significant effect on chalk. These chromosomal regions could be targets for MAS, fine mapping and map-based cloning for low chalkiness breeding