Male germ cell-specific protein Trs4 binds to multiple proteins

Temperature-related sequence 4 (Trs4) has been identified as a testis-specific gene with expression sensitive to the abdominal temperature changes induced by artificial cryptorchidism. In murine testes, Trs4 mRNA was detected in round spermatids and its protein was localized mainly in the elongating spermatids as well as in the acrosomes and tails of mature spermatozoa. Using a yeast two-hybrid screening system, we identified Rshl-2, Gstmu1, and Ddc8 as putative binding partners of the Trs4 protein in mouse testes. Their interactions were confirmed by in vivo and in vitro binding assays. Further studies demonstrated that Ddc8, a newly identified gene with unknown functions, displayed a similar expression pattern with Trs4 in mouse testes. In particular, Trs4, Ddc8, and Rshl-2 proteins were co-localized to the tails of mature spermatozoa. These results suggested that Trs4 might be involved in diverse processes of spermiogenesis and/or fertilization through interactions with its multiple binding partners. © 2009 Elsevier Inc.postprin

Properties of small molecular drug loading and diffusion in a fluorinated PEG hydrogel studied by ^1H molecular diffusion NMR and ^(19)F spin diffusion NMR

R_f-PEG (fluoroalkyl double-ended poly(ethylene glycol)) hydrogel is potentially useful as a drug delivery depot due to its advanced properties of sol–gel two-phase coexistence and low surface erosion. In this study, ^1H molecular diffusion nuclear magnetic resonance (NMR) and ^(19)F spin diffusion NMR were used to probe the drug loading and diffusion properties of the R_f-PEG hydrogel for small anticancer drugs, 5-fluorouracil (FU) and its hydrophobic analog, 1,3-dimethyl-5-fluorouracil (DMFU). It was found that FU has a larger apparent diffusion coefficient than that of DMFU, and the diffusion of the latter was more hindered. The result of ^(19)F spin diffusion NMR for the corresponding freeze-dried samples indicates that a larger portion of DMFU resided in the R_f core/IPDU intermediate-layer region (where IPDU refers to isophorone diurethane, as a linker to interconnect the R_f group and the PEG chain) than that of FU while the opposite is true in the PEG–water phase. To understand the experimental data, a diffusion model was proposed to include: (1) hindered diffusion of the drug molecules in the R_f core/IPDU-intermediate-layer region; (2) relatively free diffusion of the drug molecules in the PEG-water phase (or region); and (3) diffusive exchange of the probe molecules between the above two regions. This study also shows that molecular diffusion NMR combined with spin diffusion NMR is useful in studying the drug loading and diffusion properties in hydrogels for the purpose of drug delivery applications

Cancer and systemic inflammation: treat the tumour and treat the host

Determinants of cancer progression and survival are multifactorial and host responses are increasingly appreciated to have a major role. Indeed, the development and maintenance of a systemic inflammatory response has been consistently observed to confer poorer outcome, in both early and advanced stage disease. For patients, cancer-associated symptoms are of particular importance resulting in a marked impact on day-to-day quality of life and are also associated with poorer outcome. These symptoms are now recognised to cluster with one another with anorexia, weight loss and physical function forming a recognised cluster whereas fatigue, pain and depression forming another. Importantly, it has become apparent that these symptom clusters are associated with presence of a systemic inflammatory response in the patient with cancer. Given the understanding of the above, there is now a need to intervene to moderate systemic inflammatory responses, where present. In this context the rationale for therapeutic intervention using nonselective anti-inflammatory agents is clear and compelling and likely to become a part of routine clinical practice in the near future. The published literature on therapeutic intervention using anti-inflammatory agents for cancer-associated symptoms was reviewed. There are important parallels with the development of useful treatments for the systemic inflammatory response in patients with rheumatological disease and cardiovascular disease

Functional renormalization group with a compactly supported smooth regulator function

The functional renormalization group equation with a compactly supported smooth (CSS) regulator function is considered. It is demonstrated that in an appropriate limit the CSS regulator recovers the optimized one and it has derivatives of all orders. The more generalized form of the CSS regulator is shown to reduce to all major type of regulator functions (exponential, power-law) in appropriate limits. The CSS regulator function is tested by studying the critical behavior of the bosonized two-dimensional quantum electrodynamics in the local potential approximation and the sine-Gordon scalar theory for d<2 dimensions beyond the local potential approximation. It is shown that a similar smoothing problem in nuclear physics has already been solved by introducing the so called Salamon-Vertse potential which can be related to the CSS regulator.Comment: JHEP style, 11 pages, 2 figures, proofs corrected, accepted for publication by JHE

The galaxy size to halo spin relation of disc galaxies in cosmological hydrodynamical simulations

In the standard disc galaxy formation model, the sizes of galactic discs are tightly related to the spin parameters λ of their dark matter haloes. The model has been wildly adopted by various semi-analytical galaxy formation models which have been extremely successful to interpret a large body of observational data. However, the size–λ correlation was rarely seen in most modern hydrodynamical simulations of galaxy formation. In this short paper, we make use of 4 sets of large hydrodynamical simulations to explore the size–spin parameter relation with a large sample of simulated disc galaxies and compare it with a popular disc galaxy formation model of Mo et al. (1998). Intriguingly, galactic sizes correlate with spin parameters of their dark matter haloes in the simulations developed by the IllustrisTNG collaborations, albeit the relation does not always agree with prediction of MMW98 model overall stellar mass range we examined. There is also a size–spin correlation for the Milky Way analogies in the EAGLE simulations, while it is relatively weaker than that of the IllustrisTNG counterparts. For the dwarfs in the simulations from the EAGLE collaboration, there is NULL correlation. We conclude that either the detailed subgrid physics or hydrodynamics solvers account for the size-spin parameter relation, which will be explored in our future work

Promoting influenza prevention for elderly people in Hong Kong using health action process approach: Study protocol

Background: People 65 years or older are at greater risk of serious complications from the seasonal influenza compared with young. To promote elderly people's behavioral compliance toward influenza prevention, the aim of the current project is to develop, implement, and evaluate a theory-based low-administration-cost intervention building on a leading psychological theory, the Health Action Process Approach (HAPA). Methods: The target group is Hong Kong Chinese elderly people aged 65 or older who rarely or never adopt any preventive actions. This project will be conducted in three phases over 24 months. In phase 1, intervention program will be developed building on the HAPA theoretical framework which comprises both the initiation and maintenance of influenza prevention behaviors. In phase 2, intervention will be implemented and evaluated using a randomized controlled trial, including: (a) behavior initiation only, (b) behavior initiation + behavior maintenance, and (c) control group. Both the initiation and maintenance components will comprise weekly-delivered telephone-based individual intervention sessions in 3 months. In phase 3, outcome evaluation of behavioral and psychological variables and process evaluation will be conducted. The effectiveness of the intervention will be analyzed using a series of linear mixed models on each behavioral and psychological outcome variable. Structural equation modelling will be used to test the hypothesized theoretical sequence in the HAPA model. Discussion: The proposed project is expected to design theory-based intervention materials to promote the influenza prevention behaviors in Hong Kong elderly people and provide information on its effectiveness and the potential changing mechanism of behavior initiation and maintenance. Trial registration: This randomized controlled trial was funded by the Health and Medical Research Fund (HMRF), Food and Health Bureau of the Government of the Hong Kong Special Administrative Region (Ref: 16151222) and was registered on 13/10/2017 at CCRB Clinical Trials Registry of the Chinese University of Hong Kong, a Partner Registry of a WHO Primary Registry (Ref: CUHK-CCRB00567)

Self-healing in fractured GaAs nanowires

Molecular dynamics simulations are performed to investigate a spontaneous self-healing process in fractured GaAs nanowires with a zinc blende structure. The results show that such self-healing can indeed occur via rebonding of Ga and As atoms across the fracture surfaces, but it can be strongly influenced by several factors, including wire size, number of healing cycles, temperature, fracture morphology, oriented attachment and atomic diffusion. For example, it is found that the self-healing capacity is reduced by 46% as the lateral dimension of the wire increases from 2.3 to 9.2 nm, and by 64% after 24 repeated cycles of fracture and healing. Other factors influencing the self-healing behavior are also discussed

Ensemble Modeling for Aromatic Production in Escherichia coli

Ensemble Modeling (EM) is a recently developed method for metabolic modeling, particularly for utilizing the effect of enzyme tuning data on the production of a specific compound to refine the model. This approach is used here to investigate the production of aromatic products in Escherichia coli. Instead of using dynamic metabolite data to fit a model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate) to screen possible models. These data are routinely generated during strain design. An ensemble of models is constructed that all reach the same steady state and are based on the same mechanistic framework at the elementary reaction level. The behavior of the models spans the kinetics allowable by thermodynamics. Then by using existing data from the literature for the overexpression of genes coding for transketolase (Tkt), transaldolase (Tal), and phosphoenolpyruvate synthase (Pps) to screen the ensemble, we arrive at a set of models that properly describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the characteristic of the cell that Tkt is the first rate controlling step, and correctly predicts that only after Tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that EM is able to capture the result of enzyme overexpression on aromatic producing bacteria by successfully utilizing routinely generated enzyme tuning data to guide model learning

Word correlation matrices for protein sequence analysis and remote homology detection

<p>Abstract</p> <p>Background</p> <p>Classification of protein sequences is a central problem in computational biology. Currently, among computational methods discriminative kernel-based approaches provide the most accurate results. However, kernel-based methods often lack an interpretable model for analysis of discriminative sequence features, and predictions on new sequences usually are computationally expensive.</p> <p>Results</p> <p>In this work we present a novel kernel for protein sequences based on average word similarity between two sequences. We show that this kernel gives rise to a feature space that allows analysis of discriminative features and fast classification of new sequences. We demonstrate the performance of our approach on a widely-used benchmark setup for protein remote homology detection.</p> <p>Conclusion</p> <p>Our word correlation approach provides highly competitive performance as compared with state-of-the-art methods for protein remote homology detection. The learned model is interpretable in terms of biologically meaningful features. In particular, analysis of discriminative words allows the identification of characteristic regions in biological sequences. Because of its high computational efficiency, our method can be applied to ranking of potential homologs in large databases.</p