Inaccuracies in plasma oxytocin extraction and enzyme immunoassay techniques

Numerous studies have reported extensive associations between plasma oxytocin (OXT) concentrations and various human physiological and neurobehavioral processes. Measurement of OXT is fraught with difficulty due to its low molecular weight and plasma concentrations, with no consensus as to the optimal conditions for pre-analytical sample extraction, standards for immunoassay validation or the ideal protease inhibitors to prevent OXT degradation. Previous attempts at determining the efficacy of various purification techniques such as solid phase extraction (SPE) or ultrafiltration have only utilized human plasma samples, making it difficult to dissect out whether the effect of interference comes from the extraction process itself or cross-reactivity with other proteins. By testing these on pure OXT solutions, we demonstrate poor recovery efficacy and reliability of reversed phase SPE (maximum 58.1%) and ultrafiltration (<1%) techniques, and the potential for the former to introduce interference into enzyme immunoassay (EIA) measurements. The clonality of antibodies used in EIA kits also potentially contributes to the differences in the readings obtained, and we validate an EIA kit which did not require pre-analytical sample extraction with low cross-reactivity and high reliability (intraclass correlation coefficient 0.980 (95% CI 0.896–0.999). Biochemical techniques used for measuring plasma OXT concentrations must therefore be internally validated prior to translation into clinical studies

Gypsophile chemistry unveiled: Fourier transform infrared (FTIR) spectroscopy provides new insight into plant adaptations to gypsum soils

Gypsum soils are among the most restrictive and widespread substrates for plant life. Plants living on gypsum are classified as gypsophiles (exclusive to gypsum) and gypsovags (non-exclusive to gypsum). The former have been separated into wide and narrow gypsophiles, each with a putative different ecological strategy. Mechanisms displayed by gypsum plants to compete and survive on gypsum are still not fully understood. The aim of this study was to compare the main chemical groups in the leaves of plants with different specificity to gypsum soils and to explore the ability of Fourier transform infrared (FTIR) spectra analyzed with neural network (NN) modelling to discriminate groups of gypsum plants. Leaf samples of 14 species with different specificity to gypsum soils were analysed with FTIR spectroscopy coupled to neural network (NN) modelling. Spectral data were further related to the N, C, S, P, K, Na, Ca, Mg and ash concentrations of samples. The FTIR spectra of the three groups analyzed showed distinct features that enabled their discrimination through NN models. Wide gypsophiles stood out for the strong presence of inorganic compounds in their leaves, particularly gypsum and, in some species, also calcium oxalate crystals. The spectra of gypsovags had less inorganic chemical species, while those of narrow gypsum endemisms had low inorganics but shared with wide gypsophiles the presence of oxalate. Gypsum and calcium oxalate crystals seem to be widespread amongst gypsum specialist plants, possibly as a way to tolerate excess Ca and sulphate. However, other mechanisms such as the accumulation of sulphates in organic molecules are also compatible with plant specialization to gypsum. While gypsovags seem to be stress tolerant plants that tightly regulate the uptake of S and Ca, the ability of narrow gypsum endemisms to accumulate excess Ca as oxalate may indicate their incipient specialization to gypsum

Measuring heterogeneity in soil networks:a network analysis and simulation-based approach

Quantifying soil structural and ecological heterogeneity is crucial for understanding their interactions and their relationships to the resilience and health of the wider ecosystem. However, a clear understanding of how structural heterogeneity affects soil biodiversity is still emerging. Previous work has primarily used expensive, often laboratory-based methods to quantify soil pore network structure, and typically separated study of structural and biological dimensions. Here, we test whether standard network metrics can be used to quantify structural heterogeneity in soil pore networks, and how this network structure, along with characteristics of the consumer and resource populations, affects the heterogeneity of a population of consumers. Specifically, we extract simplified soil pore networks from digital photographs of soil profiles and apply established metrics from network science and transport geography to quantify and compare the networks. The networks are also used as the medium for an agent-based model of generalised consumers, to analyse the effects of consumer and resource parameterisations and network structure. Combining network analysis and simulation modelling in this way can provide insights on the structure, function, and diversity possible in the soil, as well as avenues for exploring the impact of future structural or environmental changes

Caffeine ingestion compromises thermoregulation and does not improve cycling time to exhaustion in the heat among males

Purpose Caffeine is a commonly used ergogenic aid for endurance events; however, its efficacy and safety have been questioned in hot environmental conditions. The aim of this study was to investigate the effects of acute caffeine supplementation on cycling time to exhaustion and thermoregulation in the heat. Methods In a double-blind, randomised, cross-over trial, 12 healthy caffeine-habituated and unacclimatised males cycled to exhaustion in the heat (35 \ub0C, 40% RH) at an intensity associated with the thermoneutral gas exchange threshold, on two separate occasions, 60 min after ingesting caffeine (5 mg/kg) or placebo (5 mg/kg). Results There was no effect of caffeine supplementation on cycling time to exhaustion (caffeine; 28.5 \ub1 8.3 min vs. placebo; 29.9 \ub1 8.8 min, P = 0.251). Caffeine increased pulmonary oxygen uptake by 7.4% (P = 0.003), heat production by 7.9% (P = 0.004), whole-body sweat rate by 21% (P = 0.008), evaporative heat transfer by 16.5% (P = 0.006) and decreased estimated skin blood flow by 14.1% (P &lt; 0.001) compared to placebo. Core temperature was higher by 0.6% (P = 0.013) but thermal comfort decreased by -18.3% (P = 0.040), in the caffeine condition, with no changes in rate of perceived exertion (P &gt; 0.05). Conclusion The greater heat production and storage, as indicated by a sustained increase in core temperature, corroborate previous research showing a thermogenic effect of caffeine ingestion. When exercising at the pre-determined gas exchange threshold in the heat, 5 mg/kg of caffeine did not provide a performance benefit and increased the thermal strain of participants

Basal tolerance but not plasticity gives invasive springtails the advantage in an assemblage setting

As global climates change, alien species are anticipated to have a growing advantage relative to their indigenous counterparts, mediated through consistent trait differences between the groups. These insights have largely been developed based on interspecific comparisons using multiple species examined from different locations. Whether such consistent physiological trait differences are present within assemblages is not well understood, especially for animals. Yet, it is at the assemblage level that interactions play out. Here, we examine whether physiological trait differences observed at the interspecific level are also applicable to assemblages. We focus on the Collembola, an important component of the soil fauna characterized by invasions globally, and five traits related to fitness: critical thermal maximum, minimum and range, desiccation resistance and egg development rate. We test the predictions that the alien component of a local assemblage has greater basal physiological tolerances or higher rates, and more pronounced phenotypic plasticity than the indigenous component. Basal critical thermal maximum, thermal tolerance range, desiccation resistance, optimum temperature for egg development, the rate of development at that optimum and the upper temperature limiting egg hatching success are all significantly higher, on average, for the alien than the indigenous components of the assemblage. Outcomes for critical thermal minimum are variable. No significant differences in phenotypic plasticity exist between the alien and indigenous components of the assemblage. These results are consistent with previous interspecific studies investigating basal thermal tolerance limits and development rates and their phenotypic plasticity, in arthropods, but are inconsistent with results from previous work on desiccation resistance. Thus, for the Collembola, the anticipated advantage of alien over indigenous species under warming and drying is likely to be manifest in local assemblages, globally

A bioinformatic and transcriptomic approach to identifying positional candidate genes without fine mapping: an example using rice root-growth QTLs

Fine mapping can accurately identify positional candidate genes for quantitative trait loci (QTLs) but can be time consuming, costly, and, for small-effect QTLs with low heritability, difficult in practice. We propose an alternative approach, which uses meta-analysis of original mapping data to produce a relatively small confidence interval for target QTLs, lists the underlying positional candidates, and then eliminates them using whole-genome transcriptomics. Finally, sequencing is conducted on the remaining candidate genes allowing identification of allelic variation in either expression or protein sequence. We demonstrate the approach using root-growth QTLs on chromosomes 2, 5, and 9 of the Bala × Azucena rice mapping population. Confidence intervals of 10.5, 9.6, and 5.4 cM containing 189, 322, and 81 genes, respectively, were produced. Transcriptomics eliminated 40% of candidate genes and identified nine expression polymorphisms. Sequencing of 30 genes revealed that 57% of the predicted proteins were polymorphic. The limitations of this approach are discussed

OPTIma:a tracking solution for proton computed tomography in high proton flux environments

Currently there is a large discrepancy between the currents that are used for treatments in proton beam therapy facilities and the ultra low beam currents required for many proton CT imaging systems. Here we provide details of the OPTIma silicon strip based tracking system, which has been designed for performing proton CT imaging in conditions closer to the high proton flux environments of modern spot scanning treatment facilities. Details on the physical design, sensor testing, modelling, and track reconstruction are provided along with Monte-Carlo simulation studies of the expected performance for proton beam currents of up to 50 pA at the nozzle when using a σ = ∼10 mm spot scanning cyclotron system. Using a detailed simulation of the proposed OPTIma system, a discrepancy of less than 1% on the Relative Stopping Power is found for various tissues when embedded within a 150 mm diameter Perspex sphere. It is found that by accepting up to 7 protons per bunch it is possible to operate at cyclotron beam currents up to 5 times higher than would be possible with a single proton based readout, significantly reducing the total beam time required to produce an image, while also reducing the discrepancy between the beam currents required for treatment and those used for proton CT

Measuring the vulnerability of Scottish soils to a changing climate

The second Scottish Climate Change Adaptation Programme (SCCAP) identifies soil health as a priority research area to support sustainable soil management and ecosystem services. This follows concerns over a perceived lack of data or gaps in understanding that have been raised in both independent assessments of the first SCCAP by the Committee on Climate Change. The aim of this study is to summarise previous work on Scottish soils, explore existing datasets, and identify those metrics which could support the monitoring of Scotland’s soil health and measure the vulnerability of Scottish soils to climate change in future

A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions

Abstract Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400–2500 nm in this case, giving detailed information about the spectral reflectance of the plant. Although this technology has been used in laboratory-based controlled lighting conditions for early detection of plant disease, the transfer of such technology to imaging plants in field conditions presents a number of challenges. These include problems caused by varying light levels and difficulties of separating the target plant from its background. Here we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection. Graph theory was used to minimise a cost function to detect the continuous boundary between uninteresting plants and the area of interest. The method includes automatic detection of a known reflectance tile which was kept constantly within the field of view for all image scans. A method to split images containing rows of multiple raspberry plants into individual plants was also developed. Validation was carried out by comparison of plant height and density measurements with manually scored values. A reasonable correlation was found between these manual scores and measurements taken from the images (r2 = 0.75 for plant height). These preliminary steps are an essential requirement before detailed spectral analysis of the plants can be achieved