Improvements and simplifications in in-gel fluorescent detection of proteins using ruthenium II tris-(bathophenanthroline disulfonate): the poor man's fluorescent detection method

Fluorescent detection of proteins is a popular method of detection allying sensitivity, linearity and compatibility with mass spectrometry. Among the numerous methods described in the literature, staining with ruthenium II tris(bathophenanthroline disulfonate) is particularly cost-effective, but slightly cumbersome owing to difficulties in the preparation of the complex and complexity of staining protocols. We describe here the modifications on both aspects that allow to perform a higher contrast staining and offer a more robust method of complex preparation, thereby maximizing the advantages of the method.     

Conditionally autoregressive models improve occupancy analyses of autocorrelated data: An example with environmental DNA

Site occupancy‐detection models (SODMs) are statistical models widely used for biodiversity surveys where imperfect detection of species occurs. For instance, SODMs are increasingly used to analyse environmental DNA (eDNA) data, taking into account the occurrence of both false‐positive and false‐negative errors. However, species occurrence data are often characterized by spatial and temporal autocorrelation, which might challenge the use of standard SODMs. Here we reviewed the literature of eDNA biodiversity surveys and found that most of studies do not take into account spatial or temporal autocorrelation. We then demonstrated how the analysis of data with spatial or temporal autocorrelation can be improved by using a conditionally autoregressive SODM, and show its application to environmental DNA data. We tested the autoregressive model on both simulated and real data sets, including chronosequences with different degrees of autocorrelation, and a spatial data set on a virtual landscape. Analyses of simulated data showed that autoregressive SODMs perform better than traditional SODMs in the estimation of key parameters such as true‐/false‐positive rates and show a better discrimination capacity (e.g., higher true skill statistics). The usefulness of autoregressive SODMs was particularly high in data sets with strong autocorrelation. When applied to real eDNA data sets (eDNA from lake sediment cores and freshwater), autoregressive SODM provided more precise estimation of true‐/false‐positive rates, resulting in more reasonable inference of occupancy states. Our results suggest that analyses of occurrence data, such as many applications of eDNA, can be largely improved by applying conditionally autoregressive specifications to SODMs.     

Is my species distribution model fit for purpose? Matching data and models to applications

Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end‐use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose.     

Detection limits of quantitative and digital PCR assays and their influence in presence–absence surveys of environmental DNA

A set of universal guidelines is needed to determine the limit of detection (LOD) in PCR‐based analyses of low‐concentration DNA. In particular, environmental DNA (eDNA) studies require sensitive and reliable methods to detect rare and cryptic species through shed genetic material in environmental samples. Current strategies for assessing detection limits of eDNA are either too stringent or subjective, possibly resulting in biased estimates of species’ presence. Here, a conservative LOD analysis grounded in analytical chemistry is proposed to correct for overestimated DNA concentrations predominantly caused by the concentration plateau, a nonlinear relationship between expected and measured DNA concentrations. We have used statistical criteria to establish formal mathematical models for both quantitative and droplet digital PCR. To assess the method, a new Grass Carp (Ctenopharyngodon idella) TaqMan assay was developed and tested on both PCR platforms using eDNA in water samples. The LOD adjustment reduced Grass Carp occupancy and detection estimates while increasing uncertainty—indicating that caution needs to be applied to eDNA data without LOD correction. Compared to quantitative PCR, digital PCR had higher occurrence estimates due to increased sensitivity and dilution of inhibitors at low concentrations. Without accurate LOD correction, species occurrence and detection probabilities based on eDNA estimates are prone to a source of bias that cannot be reduced by an increase in sample size or PCR replicates. Other applications also could benefit from a standardized LOD such as GMO food analysis and forensic and clinical diagnostics.     

Cloning of the Glyceraldehyde 3‐phosphate Dehydrogenase Gene of Flavescence dorée Phytoplasma and Development of Serological and Molecular Tools for Studying its Expression

The glyceraldehyde 3‐phosphate dehydrogenase (gapA) gene codes for a protein involved in the glycolytic pathway and is commonly used in Real‐Time RT‐PCR quantification studies as housekeeping gene. In this work we cloned and sequenced the full‐length gapA gene from Flavescence dorée phytoplasma (FDp). A ～35 kDa recombinant GapA protein was over‐expressed in Escherichia coli, purified and used as antigen to raise anti‐GapA rabbit polyclonal antibodies. The antiserum detected the GapA protein by western blot analysis of total protein extracts of FDp‐infected experimental host (Catharanthus roseus) and grapevine plants collected in the field. We also developed an FDp‐specific gapA Taqman Real‐Time RT‐PCR assay suitable for quantification overtime of gapA mRNA in infected plants.     

Estimating the maximum interval between repeat surveys

Abstract Surveying species that are present in low numbers is difficult because often the survey fails to locate any individuals. One strategy to improve the sample design is to survey the site repeatedly. With repeat surveys the abundance of the target species, and hence likelihood of detection, may change between visits. We present a model for deciding on the maximum surveillance interval between repeat surveys so that there is a high probability of detecting the species. We use as an example surveys for new weed infestations and model the chance of detecting the weed before control costs reach a threshold. The maximum surveillance interval depends on the rate of weed growth, the ability to detect the weed, and the cost of controlling the weed. Typically weed growth rates are high. Fast growing plants need to be detected early before they spread, but often weeds are difficult to detect when young and still comparatively cheap to control. Results from the model to determine maximum surveillance intervals are given for five broad habitat types and seven weed types. Surveillance intervals ranged from 1 to 10 years. Longer intervals are appropriate when searching for weeds with slower growth rates, that are easier to detect, and those that can be controlled cheaply.     

Cytokine detection in HIV-1/HHV-8 co-infected subjects

In a previous work we have evaluated some immunologic and haematologic parameters of HIV-1 positive subjects co-infected with HHV-8. A worsening of these values were generally described in these patients as compared with those HIV-1 positive, but negative for HHV-8. Now we have studied the influence of HHV-8 co-infection of HIV-1 positive subjects on the production of some cytokines to make clear the question of its role in the immuno-deregulation of the above-mentioned subjects. In particular we have analysed serum levels of IL-4 and IL-10, Th2 type T cells cytokines, IFN-gamma, an indirect marker of Th1 cells activation and IL-18, a cytokine produced by monocytic-macrophagic cells, which is able to induce IFN-gamma production and Th1 T lymphocytes activation. No significant differences were found as regards the IFN-gamma serum levels (92.1 +/- 24.3 pg ml(-1) in the case of HIV-1 positive/HHV-8 negative subjects and 96.0 +/- 17.4 pg ml(-1) in those HIV-1 positive/HHV-8 positive). In healthy subjects the mean level of this cytokine was 17.6 +/- 5.2 pg ml(-1) (significant difference with both the former values at p < 0.001). Moreover IL-4 and IL-10, which were undetectable in healthy individuals, showed the following values in HIV-1 positive/HHV-8 negative subjects: 31.9 +/- 2.7 pg ml(-1) and 119.8 +/- 85.1 pg ml(-1) respectively and in HIV-1 positive/HHV-8 positive subjects: 30.4 +/- 4.8 pg ml(-1) and 69.4 +/- 65.3 pg ml(-1) (not significant differences). In contrast IL-18 reached a mean level of 1001.2 +/- 360.5 pg ml(-1) in HIV-1 positive/HHV-8 negative subjects, but showed a significant reduction in HIV-1 positive/HHV-8 positive subjects (737.6 +/- 284.3 pg ml(-1) --> p < 0.05) and presented very low levels in healthy individuals (21.3 +/- 30.3 pg ml(-1)). Moreover a significant correlation (-0.984 --> p < 0.001) was noticed between IL-18 reduction in HIV-1 positive subjects co-infected with HHV-8 and the degree of positivity of HHV-8. These data suggest that HHV-8 co-infection has no influence on the switch Th1 --> Th2 in HIV-1 positive subjects, but is able to reduce IL-18 production, useful for Th1 subset restoration.     

Raman spectroscopy can differentiate malignant tumors from normal breast tissue and detect early neoplastic changes in a mouse model

Raman spectroscopy shows potential in differentiating tumors from normal tissue. We used Raman spectroscopy with near-infrared light excitation to study normal breast tissue and tumors from 11 mice injected with a cancer cell line. Spectra were collected from 17 tumors, 18 samples of adjacent breast tissue and lymph nodes, and 17 tissue samples from the contralateral breast and its adjacent lymph nodes. Discriminant function analysis was used for classification with principal component analysis scores as input data. Tissues were examined by light microscopy following formalin fixation and hematoxylin and eosin staining. Discriminant function analysis and histology agreed on the diagnosis of all contralateral normal, tumor, and mastitis samples, except one tumor which was found to be more similar to normal tissue. Normal tissue adjacent to each tumor was examined as a separate data group called tumor bed. Scattered morphologically suspicious atypical cells not definite for tumor were present in the tumor bed samples. Classification of tumor bed tissue showed that some tumor bed tissues are diagnostically different from normal, tumor, and mastitis tissue. This may reflect malignant molecular alterations prior to morphologic changes, as expected in preneoplastic processes. Raman spectroscopy not only distinguishes tumor from normal breast tissue, but also detects early neoplastic changes prior to definite morphologic alteration.     

Label‐free optical detection of cyclosporine in biological fluids

In this paper, a spectroscopic method for determination of cyclosporine concentrations in biological fluids is presented. Blood plasma and hemoglobin solutions are chosen for the experiment. For various cyclosporine concentrations in blood plasma and hemoglobin, absorbance measurements in spectra range from 600 to 1100 nm are performed. The measurement results are analyzed by the use of a dedicated algorithm. The obtained data are characterized by a high coefficient of correlation R², which is equal to 0.9461 and 0.9808 for blood plasma and hemoglobin, respectively. The proposed method enables the selective detection of cyclosporine level and could be applied in medicine and laboratory diagnostics. The obtained result can be the base to build the point‐of‐care CsA level detection optical sensor.      

Apartment electrical wiring: a cause of extremely low frequency magnetic field exposure in residential areas

Extremely low frequency (ELF) magnetic fields (MFs) were measured at 696 points in a room of a Japanese apartment building. The building had 124 rooms with layouts and wiring identical to those of the studied room. ELF-MFs exceeded 0.4 microT in 24% of the living space, and the maximum value, 1.8 microT, was detected at floor level. Analysis of the MF distribution revealed that 60 Hz 100 V electrical wiring for room lights within the floor and ceiling had been laid out in large rectangles, equivalent to 1 turn coils. Further plotting of the vertical components every 0.01 m on the floor indicated that the depth of the cable was 0.23 m. Further studies should be conducted in order to confirm that the building investigated in this pilot study is typical of Japanese apartment buildings in terms of ELF-MFs.     

A first insight into the scanning behaviour of the presocial blow fly larvae

Aggregation of necrophagous larvae has several benefits: the sharing of salivary enzymes (exodigestion), temperature regulation, protection from predators and parasites, etc., and is well developed in blow flies (Diptera: Calliphoridae). The present study focuses on the aggregation mechanism used by the necrophagous larvae of Lucilia sericata Meigen, the common green bottle fly. The ability of single larva to detect and follow a signal (trail) created by conspecifics is investigated initially. A circular ring is drawn in a Petri dish where 20 starved larvae have crawled for a period of 30 min. A naïve (test) larva is then placed in the dish and video‐tracked. Naïve larvae are able to detect the boundaries of the larvae‐crawled area and stay preferentially within this conspecific‐marked zone. In a second step, the orientation of larvae by scanning, a dedicated, ground‐signal detection behaviour of dipteran larvae, is analyzed. Four experimental conditions are tested: control, presence of food, conspecifics, and food + conspecifics. When conspecifics have been previously present in a given area, the scanning behaviour by naïve larvae in this area decreases (both in number and frequency of scans). Accordingly, scanning by necrophagous larvae of L. sericata should be considered not only as locomotion behaviour, but also as a potential way to detect signals from conspecifics for the purpose of aggregation. The chemical composition of the attractant(s), the behavioural effects (attraction or retention) and the implication of larval signalling in the aggregation process are new fields to explore.     

Broad-spectrum protein biosensors for class-specific detection of antibiotics

The dramatically increasing prevalence of multi-drug-resistant human pathogenic bacteria and related mortality requires two key actions: (i) decisive initiatives for the detection of novel antibiotics and (ii) a global ban for use of antibiotics as growth promotants in stock farming. Both key actions entail technology for precise, high-sensitive detection of antibiotic substances either to detect and validate novel anti-infective structures or to enforce the non-use of clinically relevant antibiotics. We have engineered prokaryotic antibiotic response regulators into a molecular biosensor configuration able to detect tetracycline, streptogramin, and macrolide antibiotics in spiked liquids including milk and serum at ng/mL concentrations and up to 2 orders of magnitude below current Swiss and EC threshold values. This broad-spectrum, class-specific, biosensor-based assay has been optimized for use in a storable ready-to-use and high-throughput-compatible ELISA-type format. At the center of the assay is an antibiotic sensor protein whose interaction with specific DNA fragments is responsive to a particular class of antibiotics. Binding of biosensor protein to the cognate DNA chemically linked to a solid surface is converted into an immuno-based colorimetric readout correlating with specific antibiotics concentrations.     

Bayesian analysis of wildlife age-at-harvest data

SUMMARY: State and federal natural resource management agencies often collect age-structured harvest data. These data represent finite realizations of stochastic demographic and sampling processes and have long been used by biologists to infer population trends. However, different sources of data have been combined in ad hoc ways and these methods usually failed to incorporate sampling error. In this article, we propose a "hidden process" (or state-space) model for estimating abundance, survival, recovery rate, and recruitment from age-at-harvest data that incorporate both demographic and sampling stochasticity. To this end, a likelihood for age-at-harvest data is developed by embedding a population dynamics model within a model for the sampling process. Under this framework, the identification of abundance parameters can be achieved by conducting a joint analysis with an auxiliary data set. We illustrate this approach by conducting a Bayesian analysis of age-at-harvest and mark-recovery data from black bears (Ursus americanus) in Pennsylvania. Using a set of reasonable prior distributions, we demonstrate a substantial increase in precision when posterior summaries of abundance are compared to a bias-corrected Lincoln-Petersen estimator. Because demographic processes link consecutive abundance estimates, we also obtain a more realistic biological picture of annual changes in abundance. Because age-at-harvest data are often readily obtained, we argue that this type of analysis provides a valuable strategy for wildlife population monitoring.     

Taking the bait: The influence of attractants and microhabitat on detections of fauna by remote‐sensing cameras

Autonomously triggered cameras are a common wildlife survey technique. The use of attractants and surrounding microhabitats is likely to influence detection probabilities and survey outcomes; however, few studies consider these factors. We compared three attractants (peanut butter‐based, tuna‐based and a control) in a Latin square design through a coastal shrubland with high microhabitat variability at Cape Otway, Victoria, Australia (38º50?S, 143º30?E). Deployments involved 36 cameras for four days in each of five years. The percentage cover of each vegetation structural type (low [no or sparse cover], moderate [grass] or high [shrubs]) within 20 m of each camera was calculated and reduced to a single variable using PCA. Dynamic occupancy modelling, with lure type and vegetation structure as covariates of detection probability, found that peanut butter attracted the greatest diversity of species (24 of 35 species, 69%) and yielded the greatest number of detections (50% of 319) when compared with tuna oil (66% and 24%, respectively) and the control (43% and 26%, respectively). Peanut butter attracted more Macropodidae (wallabies) and Muridae (rats and mice); however, vegetation structural variables were the greatest influence on Corvidae/Artamidae (raven/currawong) detections with higher detectability in more open areas. Vegetation structure also influenced Muridae detections. This study reinforces the critical choice of appropriate attractants and camera placement when investigating vertebrate groups and highlights the role of microhabitat in the detection of small mammals and birds. We suggest future large‐scale camera surveys consider different bait types and microhabitats in their designs, to control for any biases and enable future advice on ‘optimal’ methods.     

Mathematical models of cochlear nucleus onset neurons: I. Point neuron with many weak synaptic inputs

The cochlear nucleus (CN) presents a unique opportunity for quantitatively studying input-output transformations by neurons because it gives rise to a variety of different response types from a relatively homogeneous input source, the auditory nerve (AN). Particularly interesting among CN neurons are Onset (On) neurons, which have a prominent response to the onset of sustained sounds followed by little or no response in the steady-state. On neurons contrast sharply with their AN inputs, which respond vigorously throughout stimuli. On neurons can entrain to stimuli (firing once per cycle of a periodic stimulus) at up to 1000 Hz, unlike their AN inputs. To understand the mechanisms underlying these response patterns, we tested whether an integrate-to-threshold point-neuron model with a fixed refractory period can account for On discharge patterns for tones, systematically examining the effect of membrane time constant and the number and strength of the exclusively excitatory AN synaptic inputs. To produce both onset responses to high-frequency tone bursts and entrainment to a broad range of low-frequency tones, the model must have a short time constant (0.125 ms) and a large number (>100) of weak synaptic inputs, properties that are consistent with the electrical properties and anatomy of On-responding cells. With these parameters, the model acts like a coincidence detector with a threshold-like relationship between the instantaneous discharge rates of the output and the inputs. Onset responses to high-frequency tone bursts result because the threshold effect enhances the initial response of the AN inputs and suppresses their relatively lower sustained response. However, when the model entrains across a broad range of frequencies, it also produces short interspike intervals at the onset of high-frequency tone bursts, a response pattern not found in all types of On neurons. These results show a tradeoff, that may be a general property of many neurons, between following rapid stimulus fluctuations and responding without short interspike intervals at the onset of sustained stimuli.