Epigenetics and chromatin remodeling play a role in lung disease

Epigenetics is defined as heritable changes that affect gene expression without altering the DNA sequence. Epigenetic regulation of gene expression is facilitated through different mechanisms such as DNA methylation, histone modifications and RNA-associated silencing by small non-coding RNAs. All these mechanisms are crucial for normal development, differentiation and tissue-specific gene expression. These three systems interact and stabilize one another and can initiate and sustain epigenetic silencing, thus determining heritable changes in gene expression. Histone acetylation regulates diverse cellular functions including inflammatory gene expression, DNA repair and cell proliferation. Transcriptional coactivators possess intrinsic histone acetyltransferase activity and this activity drives inflammatory gene expression. Eleven classical histone deacetylases (HDACs) act to regulate the expression of distinct subsets of inflammatory/immune genes. Thus, loss of HDAC activity or the presence of HDAC inhibitors can further enhance inflammatory gene expression by producing a gene-specific change in HAT activity. For example, HDAC2 expression and activity are reduced in lung macrophages, biopsy specimens, and blood cells from patients with severe asthma and smoking asthmatics, as well as in patients with chronic obstructive pulmonary disease (COPD). This may account, at least in part, for the enhanced inflammation and reduced steroid responsiveness seen in these patients. Other proteins, particularly transcription factors, are also acetylated and are targets for deacetylation by HDACs and sirtuins, a related family of 7 predominantly protein deacetylases. Thus the acetylation/deacetylation status of NF-κB and the glucocorticoid receptor can also affect the overall expression pattern of inflammatory genes and regulate the inflammatory response. Understanding and targeting specific enzymes involved in this process might lead to new therapeutic agents, particularly in situations in which current anti-inflammatory therapies are suboptimal

Technical note: Guide to groundwater monitoring for the coal industry

It is well established in literature that the environmental impacts associated with the coal industry are numerous. In respect of South Africa’s groundwater resources the major impact of the coal industry is a reduction in groundwater quantity and quality. There is therefore a need to proactively prevent or minimise these potential impacts through long-term protection and improved water management practices. One such initiative is to implement monitoring programmes in various sectors of the coal industry for groundwater quality and quantity. Groundwater monitoring requires sophisticated interlinked stages which are often overlooked or not fully understood. Consequently a methodical approach must be undertaken in order to have an effective and economical groundwater monitoring system. This paper provides a comprehensive guide to the establishment of a groundwater monitoring programme for environmental practitioners in the coal industry. An inclusive 7-stage methodology is presented describing the different stages of establishing a groundwater monitoring programme, focusing on the ‘why’, ‘how’, and ‘who’ of groundwater monitoring

Pharmacogenetic GWAS meta-analysis of LDL cholesterol response to statins

Cardiolog

Tapering practices of New Zealand’s elite raw powerlifters

The major aim of this study was to determine tapering strategies of elite powerlifters. Eleven New Zealand powerlifters (28.4 ± 7.0 years, best Wilks score of 431.9 ± 43.9 points) classified as elite were interviewed, using semi-structured interviews, about their tapering strategies. Interviews were transcribed verbatim and content analyzed. Total training volume peaked 5.2 ± 1.7 weeks from competition while average training intensity (of 1RM) peaked 1.9 ± 0.8 weeks from competition. During tapering volume was reduced by 58.9 ± 8.4% while intensity was maintained (or slightly reduced) and the final weight training session was performed 3.7 ± 1.6 days out from competition. Participants generally stated that tapering was performed to achieve full recovery; that accessory work was removed around two weeks out from competition; and, deadlifting takes longer to recover from than other lifts. Typically participants stated that trial and error, and changes based on ‘feel’ were the sources of tapering strategies; equipment used and movements performed during tapering are the same as in competition; nutrition was manipulated during the taper (for weight cutting and/or performance aims); and, poor tapering occurred when too long (one week or more) was taken off training. These results suggest that athletes may benefit from continuing to strength train prior to important events with reduced volume and maintained intensity. Only exercises that directly assist sports performance should remain in the strength program during tapering, to assist with reductions in fatigue while maintaining/improving strength expression and performance

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.

Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS - the ability to detect genetic association by linkage disequilibrium (LD) - is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact

Which feedback mechanisms dominate in the high-pressure environment of the Central Molecular Zone?

Supernovae (SNe) dominate the energy and momentum budget of stellar feedback, but the efficiency with which they couple to the interstellar medium (ISM) depends strongly on how effectively early, pre-SN feedback clears dense gas from star-forming regions. There are observational constraints on the magnitudes and timescales of early stellar feedback in low ISM pressure environments, yet no such constraints exist for more cosmologically typical high ISM pressure environments. In this paper, we determine the mechanisms dominating the expansion of HII regions as a function of size-scale and evolutionary time within the high-pressure (P/k_\rm{B}~$10^{7-8}$K cm$^{-3}$) environment in the inner 100pc of the Milky Way. We calculate the thermal pressure from the warm ionised (P_\rm{HII}; 10$^{4}$K) gas, direct radiation pressure (P_\rm{dir}), and dust processed radiation pressure (P_\rm{IR}). We find that (1) P_\rm{dir} dominates the expansion on small scales and at early times (0.01-0.1pc; $0.1$pc; $>1$Myr); (3) during the first ~1Myr of growth, but not thereafter, either $P_{\rm IR}$ or stellar wind pressure likely make a comparable contribution. Despite the high confining pressure of the environment, natal star-forming gas is efficiently cleared to radii of several pc within ~2Myr, i.e. before the first SNe explode. This `pre-processing' means that subsequent SNe will explode into low density gas, so their energy and momentum will efficiently couple to the ISM. We find the HII regions expand to a radius of 3pc, at which point they have internal pressures equal with the surrounding external pressure. A comparison with HII regions in lower pressure environments shows that the maximum size of all HII regions is set by pressure equilibrium with the ambient ISM

Temporal Network Analysis of Email Communication Patterns in a Long Standing Hierarchy

An important concept in organisational behaviour is how hierarchy affects the voice of individuals, whereby members of a given organisation exhibit differing power relations based on their hierarchical position. Although there have been prior studies of the relationship between hierarchy and voice, they tend to focus on more qualitative small-scale methods and do not account for structural aspects of the organisation. This paper develops large-scale computational techniques utilising temporal network analysis to measure the effect that organisational hierarchy has on communication patterns within an organisation, focusing on the structure of pairwise interactions between individuals. We focus on one major organisation as a case study - the Internet Engineering Task Force (IETF) - a major technical standards development organisation for the Internet. A particularly useful feature of the IETF is a transparent hierarchy, where participants take on explicit roles (e.g. Area Directors, Working Group Chairs). Its processes are also open, so we have visibility into the communication of people at different hierarchy levels over a long time period. We utilise a temporal network dataset of 989,911 email interactions among 23,741 participants to study how hierarchy impacts communication patterns. We show that the middle levels of the IETF are growing in terms of their dominance in communications. Higher levels consistently experience a higher proportion of incoming communication than lower levels, with higher levels initiating more communications too. We find that communication tends to flow "up" the hierarchy more than "down". Finally, we find that communication with higher-levels is associated with future communication more than for lower-levels, which we interpret as "facilitation". We conclude by discussing the implications this has on patterns within the wider IETF and for other organisations

Energy-dependent tunneling from few-electron dynamic quantum dots

We measure the electron escape rate from surface-acoustic-wave dynamic quantum dots (QDs) through a tunnel barrier. Rate equations are used to extract the tunneling rates, which change by an order of magnitude with tunnel-barrier-gate voltage. We find that the tunneling rates depend on the number of electrons in each dynamic QD because of Coulomb energy. By comparing this dependence to a saddle-point-potential model, the addition energies of the second and third electron in each dynamic QD are estimated. The scale (similar to a few meV) is comparable to those in static QDs as expected