Plio-Quaternary exhumation history of the central Nepalese Himalaya: 1. Apatite and zircon fission track and apatite [U-Th]/He analyses

New apatite and zircon fission track and (U-Th)/He analyses serve to document the bedrock cooling history of the central Nepalese Himalaya near the Annapurna Range. We have obtained 82 apatite fission track (AFT), 7 zircon fission track (ZFT), and 7 apatite (U-Th)/He (AHe) ages from samples collected along the Marsyandi drainage, including eight vertical relief profiles from ridges on either side of the river averaging more than 2 km in elevation range. In addition, three profiles were sampled along ridge crests that also lie ∼2 km above the adjacent valleys, and a transect of >20 valley bottom samples spans from the Lesser Himalaya across the Greater Himalaya and into the Tethyan strata. As a consequence, these data provide one of the more comprehensive low-temperature thermochronologic studies within the Himalaya. Conversely, the youthfulness of this orogen is pushing the limits of these dating techniques. AFT ages range from >3.8 to 0 Ma, ZFT ages from 1.9 to 0.8 Ma, and AHe ages from 0.9 to 0.3 Ma. Most ridges have maximum ages of 1.3–0.8 Ma at 2 km above the valley bottom. Only one ridge crest (in the south central zone of the field area) yielded significantly older ZFT and AFT ages of ∼2 Ma; we infer that a splay of the Main Central Thrust separates this ridge from the rest of the Greater Himalaya. ZFT and AFT ages from a vertical transect along this ridge indicate exhumation rates of ∼1.5 km Myr−1 (r2 > 0.7) from ∼2 to 0.6–0.8 Ma, whereas AHe ages indicate a faster exhumation rate of ∼2.6 km Myr−1 (r2 = 0.9) over the last 0.8 Myr. Exhumation rates calculated for six of the remaining seven vertical profiles ranged from 1.5 to 12 km Myr−1 (all with low r2 values of <0.6) for the time period from ∼1.2 to 0.3 Ma, with no discernible patterns in south to north exhumation rates evident. The absence of a trend in exhumation rates, despite a strong spatial gradient in rainfall, argues against a correlation of long-term exhumation rates with modern patterns of rainfall. AFT ages in the Tethyan strata are, on average, older than in the Greater Himalaya and may be a response to a drier climate, slip on the South Tibetan Detachment, or a gentler dip of the underlying thrust ramp. These data are further evaluated with thermokinematic modeling in the companion paper by Whipp et al

Antimicrobial resistance in rural rivers: Comparative study of the Coquet (Northumberland) and Eden (Cumbria) River catchments

\ua9 2024 The Author(s)Many studies have characterised resistomes in river microbial communities. However, few have compared resistomes in parallel rural catchments that have few point-source inputs of antimicrobial genes (ARGs) and organisms (i.e., AMR) – catchments where one can contrast more nebulous drivers of AMR in rural rivers. Here, we used quantitative microbial profiling (QMP) to compare resistomes and microbiomes in two rural river catchments in Northern England, the Coquet and Eden in Northumberland and Cumbria, respectively, with different hydrological and geographical conditions. The Eden has higher flow rates, higher annual surface runoff, and longer periods of soil saturation, whereas the Coquet is drier and has lower flowrates. QMP analysis showed the Eden contained significantly more abundant microbes associated with soil sources, animal faeces, and wastewater than the Coquet, which had microbiomes like less polluted rivers (Wilcoxon test, p &lt; 0.01). The Eden also had greater ARG abundances and resistome diversity (Kruskal Wallis, p &lt; 0.05), and higher levels of potentially clinically relevant ARGs. The Eden catchment had greater and flashier runoff and more extensive agricultural land use in its middle reach, which explains higher levels of AMR in the river. Hydrological and geographic factors drive AMR in rural rivers, which must be considered in environmental monitoring programmes

The bright optical afterglow of the nearby gamma-ray burst of 29 March 2003

Many past studies of cosmological gamma-ray bursts (GRBs) have been limited because of the large distance to typical GRBs, resulting in faint afterglows. There has long been a recognition that a nearby GRB would shed light on the origin of these mysterious cosmic explosions, as well as the physics of their fireballs. However, GRBs nearer than z=0.2 are extremely rare, with an estimated rate of localisation of one every decade. Here, we report the discovery of bright optical afterglow emission from GRB 030329. Our prompt dissemination and the brilliance of the afterglow resulted in extensive followup (more than 65 telescopes) from radio through X-ray bands, as well as measurement of the redshift, z=0.169. The gamma-ray and afterglow properties of GRB 030329 are similar to those of cosmological GRBs (after accounting for the small distance), making this the nearest known cosmological GRB. Observations have already securely identified the progenitor as a massive star that exploded as a supernova, and we anticipate futher revelations of the GRB phenomenon from studies of this source.Comment: 13 pages, 4 figures. Original tex

Algorithms for optimizing drug therapy

BACKGROUND: Drug therapy has become increasingly efficient, with more drugs available for treatment of an ever-growing number of conditions. Yet, drug use is reported to be sub optimal in several aspects, such as dosage, patient's adherence and outcome of therapy. The aim of the current study was to investigate the possibility to optimize drug therapy using computer programs, available on the Internet. METHODS: One hundred and ten officially endorsed text documents, published between 1996 and 2004, containing guidelines for drug therapy in 246 disorders, were analyzed with regard to information about patient-, disease- and drug-related factors and relationships between these factors. This information was used to construct algorithms for identifying optimum treatment in each of the studied disorders. These algorithms were categorized in order to define as few models as possible that still could accommodate the identified factors and the relationships between them. The resulting program prototypes were implemented in HTML (user interface) and JavaScript (program logic). RESULTS: Three types of algorithms were sufficient for the intended purpose. The simplest type is a list of factors, each of which implies that the particular patient should or should not receive treatment. This is adequate in situations where only one treatment exists. The second type, a more elaborate model, is required when treatment can by provided using drugs from different pharmacological classes and the selection of drug class is dependent on patient characteristics. An easily implemented set of if-then statements was able to manage the identified information in such instances. The third type was needed in the few situations where the selection and dosage of drugs were depending on the degree to which one or more patient-specific factors were present. In these cases the implementation of an established decision model based on fuzzy sets was required. Computer programs based on one of these three models could be constructed regarding all but one of the studied disorders. The single exception was depression, where reliable relationships between patient characteristics, drug classes and outcome of therapy remain to be defined. CONCLUSION: Algorithms for optimizing drug therapy can, with presumably rare exceptions, be developed for any disorder, using standard Internet programming methods

Prediction of landing gear loads using machine learning techniques

This article investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, the results on drop test data and flight test data will be examined. This article will focus on the use of Gaussian process regression for the prediction of loads on the components of a landing gear. For the learning task, comprehensive measurement data from drop tests are available. These include measurements of strains at key locations, such as on the side-stay and torque link, as well as acceleration measurements of the drop carriage and the gear itself, measurements of shock absorber travel, tyre closure, shock absorber pressure and wheel speed. Ground-to-tyre loads are also available through measurements made with a drop test ground reaction platform. The aim is to train the Gaussian process to predict load at a particular location from other available measurements, such as accelerations, or measurements of the shock absorber. If models can be successfully trained, then future load patterns may be predicted using only these measurements. The ultimate aim is to produce an accurate model that can predict the load at a number of locations across the landing gear using measurements that are readily available or may be measured more easily than directly measuring strain on the gear itself (for example, these may be measurements already available on the aircraft, or from a small number of sensors attached to the gear). The drop test data models provide a positive feasibility test which is the basis for moving on to the critical task of prediction on flight test data. For this, a wide range of available flight test measurements are considered for potential model inputs (excluding strain measurements themselves), before attempting to refine the model or use a smaller number of measurements for the prediction

A novel approach to simulate gene-environment interactions in complex diseases

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study

Transitions/relaxations in polyester adhesive/PET system

The correlations between the transitions and the dielectric relaxation processes of the oriented poly(ethylene terephthalate) (PET) pre-impregnated of the polyester thermoplastic adhesive have been investigated by differential scanning calorimetry (DSC) and dynamic dielectric spectroscopy (DDS). The thermoplastic polyester adhesive and the oriented PET films have been studied as reference samples. This study evidences that the adhesive chain segments is responsible for the physical structure evolution in the PET-oriented film. The transitions and dielectric relaxation modes’ evolutions in the glass transition region appear characteristic of the interphase between adhesive and PET film, which is discussed in terms of molecular mobility. The storage at room temperature of the adhesive tape involves the heterogeneity of the physical structure, characterized by glass transition dissociation. Thus, the correlation between the transitions and the dielectric relaxation processes evidences a segregation of the amorphous phases. Therefore, the physical structure and the properties of the material have been linked to the chemical characteristics

The Effective Fragment Molecular Orbital Method for Fragments Connected by Covalent Bonds

We extend the effective fragment molecular orbital method (EFMO) into treating fragments connected by covalent bonds. The accuracy of EFMO is compared to FMO and conventional ab initio electronic structure methods for polypeptides including proteins. Errors in energy for RHF and MP2 are within 2 kcal/mol for neutral polypeptides and 6 kcal/mol for charged polypeptides similar to FMO but obtained two to five times faster. For proteins, the errors are also within a few kcal/mol of the FMO results. We developed both the RHF and MP2 gradient for EFMO. Compared to ab initio, the EFMO optimized structures had an RMSD of 0.40 and 0.44 {\AA} for RHF and MP2, respectively.Comment: Revised manuscrip

Insight into glucocorticoid receptor signalling through interactome model analysis

Glucocorticoid hormones (GCs) are used to treat a variety of diseases because of their potent anti-inflammatory effect and their ability to induce apoptosis in lymphoid malignancies through the glucocorticoid receptor (GR). Despite ongoing research, high glucocorticoid efficacy and widespread usage in medicine, resistance, disease relapse and toxicity remain factors that need addressing. Understanding the mechanisms of glucocorticoid signalling and how resistance may arise is highly important towards improving therapy. To gain insight into this we undertook a systems biology approach, aiming to generate a Boolean model of the glucocorticoid receptor protein interaction network that encapsulates functional relationships between the GR, its target genes or genes that target GR, and the interactions between the genes that interact with the GR. This model named GEB052 consists of 52 nodes representing genes or proteins, the model input (GC) and model outputs (cell death and inflammation), connected by 241 logical interactions of activation or inhibition. 323 changes in the relationships between model constituents following in silico knockouts were uncovered, and steady-state analysis followed by cell-based microarray genome-wide model validation led to an average of 57% correct predictions, which was taken further by assessment of model predictions against patient microarray data. Lastly, semi-quantitative model analysis via microarray data superimposed onto the model with a score flow algorithm has also been performed, which demonstrated significantly higher correct prediction ratios (average of 80%), and the model has been assessed as a predictive clinical tool using published patient microarray data. In summary we present an in silico simulation of the glucocorticoid receptor interaction network, linked to downstream biological processes that can be analysed to uncover relationships between GR and its interactants. Ultimately the model provides a platform for future development both by directing laboratory research and allowing for incorporation of further components, encapsulating more interactions/genes involved in glucocorticoid receptor signalling