Ion channels as insecticide targets.

Published onlineJournal ArticleIon channels remain the primary target of most of the small molecule insecticides. This review examines how the subunit composition of heterologously expressed receptors determines their insecticide-specific pharmacology and how the pharmacology of expressed receptors differs from those found in the insect nervous system. We find that the insecticide-specific pharmacology of some receptors, like that containing subunits of the Rdl encoded GABA receptor, can be reconstituted with very few of the naturally occurring subunits expressed. In contrast, workers have struggled even to express functional insect nicotinic acetylcholine receptors (nAChRs), and work has therefore often relied upon the expression of vertebrate receptor subunits in their place. We also examine the extent to which insecticide-resistance-associated mutations, such as those in the para encoded voltage-gated sodium channel, can reveal details of insecticide-binding sites and mode of action. In particular, we examine whether mutations are present in the insecticide-binding site and/or at sites that allosterically affect the drug preferred conformation of the receptor. We also discuss the ryanodine receptor as a target for the recently developed diamides. Finally, we examine the lethality of the genes encoding these receptor subunits and discuss how this might determine the degree of conservation of the resistance-associated mutations found

Landmark Detection in Cardiac MRI Using a Convolutional Neural Network

Purpose: To develop a convolutional neural network (CNN) solution for landmark detection in cardiac MRI. / Materials and Methods: This retrospective study included cine, late-gadolinium enhancement (LGE), and T1 mapping scans from two hospitals. The training set included 2329 patients (34019 images; mean age 54.1 years; 1471 men; December 2017-March 2020). A hold-out test set included 531 patients (7723 images; mean age 51.5 years, 323 men; May 2020-July 2020). CNN models were developed to detect two mitral valve plane and apical points on long-axis images. On short-axis images, anterior and posterior right ventricular insertion points and left ventricle center were detected. Model outputs were compared with manual labels by two readers. The trained model was deployed to MR scanners. / Results: For the long-axis images, successful detection of cardiac landmarks ranged from 99.7% to 100% for cine images and from 99.2% to 99.5% for LGE images. For the short-axis, detection rates was 96.6% for cine, 97.6% for LGE, and 98.9% for T1-mapping. The Euclidean distances between model and manual labels ranged from 2 to 3.5 mm for different landmarks, indicating close agreement between model landmarks to manual labels. No differences were found for the anterior right ventricular insertion angle and left ventricle length by the models and readers for all views and imaging sequences. Model inference on MR scanner took 610 msec on the graphics processing unit and 5.6 sec on central processing unit, respectively, for a typical cardiac cine series. / Conclusion: A CNN was developed for landmark detection in both long and short-axis cardiac MR images for cine, LGE and T1 mapping sequences, with the accuracy comparable to the interreader variation

A multi-beam HI survey of the Virgo Cluster - two isolated HI clouds ?

We have carried out a fully sampled large area ($4^{\circ} \times 8^{\circ}$) 21cm \HI line survey of part of the Virgo cluster using the Jodrell Bank multi-beam instrument. The survey has a sensitivity some 3 times better than the standard HIJASS and HIPASS surveys. We detect 31 galaxies, 27 of which are well known cluster members. The four new detections have been confirmed in the HIPASS data and by follow up Jodrell Bank pointed observations. One object lies behind M86, but the other 3 have no obvious optical counter parts upon inspection of the digital sky survey fields. These 3 objects were mapped at Arecibo with a smaller \am{3}{6} HPBW and a 4 times better sensitivity than the Jodrell Bank data, which allow an improved determination of the dimensions and location of two of the objects, but surprisingly failed to detect the third. The two objects are resolved by the Arecibo beam giving them a size far larger than any optical images in the nearby field. To our mass limit of $5 \times 10^{7}$ $\frac{\Delta v}{50 km s^{-1}}$ $M_{\odot}$ and column density limit of $3 \times 10^{18}$ $\frac{\Delta v}{50 km s^{-1}}$ atoms cm$^{-2}$ these new detections represent only about 2% of the cluster atomic hydrogen mass. Our observations indicate that the \HI mass function of the cluster turns down at the low mass end making it very different to the field galaxy \HI mass function. This is quite different to the Virgo cluster optical luminosity function which is much steeper than that in the general field. Many of the sample galaxies are relatively gas poor compared to \HI selected samples of field galaxies, confirming the 'anaemic spirals' view of Virgo cluster late type galaxies.Comment: Accepted for publication in MNRA

Genetic diversity of Campylobacter jejuni and Campylobacter coli isolates from conventional broiler flocks and the impacts of sampling strategy and laboratory method

The genetic diversity of Campylobacter jejuni and Campylobacter coli isolates from commercial broiler farms was examined by multilocus sequence typing (MLST), with an assessment of the impact of the sample type and laboratory method on the genotypes of Campylobacter isolated. A total of 645 C. jejuni and 106 C. coli isolates were obtained from 32 flocks and 17 farms, with 47 sequence types (STs) identified. The Campylobacter jejuni isolates obtained by different sampling approaches and laboratory methods were very similar, with the same STs identified at similar frequencies, and had no major effect on the genetic profile of Campylobacter population in broiler flocks at the farm level. For C. coli, the results were more equivocal. While some STs were widely distributed within and among farms and flocks, analysis of molecular variance (AMOVA) revealed a high degree of genetic diversity among farms for C. jejuni, where farm effects accounted for 70.5% of variance, and among flocks from the same farm (9.9% of variance for C. jejuni and 64.1% for C. coli). These results show the complexity of the population structure of Campylobacter in broiler production and that commercial broiler farms provide an ecological niche for a wide diversity of genotypes. The genetic diversity of C. jejuni isolates among broiler farms should be taken into account when designing studies to understand Campylobacter populations in broiler production and the impact of interventions. We provide evidence that supports synthesis of studies on C. jejuni populations even when laboratory and sampling methods are not identical

Self-Supervised Discovery of Anatomical Shape Landmarks

Statistical shape analysis is a very useful tool in a wide range of medical and biological applications. However, it typically relies on the ability to produce a relatively small number of features that can capture the relevant variability in a population. State-of-the-art methods for obtaining such anatomical features rely on either extensive preprocessing or segmentation and/or significant tuning and post-processing. These shortcomings limit the widespread use of shape statistics. We propose that effective shape representations should provide sufficient information to align/register images. Using this assumption we propose a self-supervised, neural network approach for automatically positioning and detecting landmarks in images that can be used for subsequent analysis. The network discovers the landmarks corresponding to anatomical shape features that promote good image registration in the context of a particular class of transformations. In addition, we also propose a regularization for the proposed network which allows for a uniform distribution of these discovered landmarks. In this paper, we present a complete framework, which only takes a set of input images and produces landmarks that are immediately usable for statistical shape analysis. We evaluate the performance on a phantom dataset as well as 2D and 3D images.Comment: Early accept at MICCAI 202

Daily emollient during infancy for prevention of eczema: the BEEP randomised controlled trial.

BACKGROUND: Skin barrier dysfunction precedes eczema development. We tested whether daily use of emollient in the first year could prevent eczema in high-risk children. METHODS: We did a multicentre, pragmatic, parallel-group, randomised controlled trial in 12 hospitals and four primary care sites across the UK. Families were approached via antenatal or postnatal services for recruitment of term infants (at least 37 weeks' gestation) at high risk of developing eczema (ie, at least one first-degree relative with parent-reported eczema, allergic rhinitis, or asthma, diagnosed by a doctor). Term newborns with a family history of atopic disease were randomly assigned (1:1) to application of emollient daily (either Diprobase cream or DoubleBase gel) for the first year plus standard skin-care advice (emollient group) or standard skin-care advice only (control group). The randomisation schedule was created using computer-generated code (stratified by recruiting centre and number of first-degree relatives with atopic disease) and participants were assigned to groups using an internet-based randomisation system. The primary outcome was eczema at age 2 years (defined by UK working party criteria) with analysis as randomised regardless of adherence to allocation for participants with outcome data collected, and adjusting for stratification variables. This trial is registered with ISRCTN, ISRCTN21528841. Data collection for long-term follow-up is ongoing, but the trial is closed to recruitment. FINDINGS: 1394 newborns were randomly assigned to study groups between Nov 19, 2014, and Nov 18, 2016; 693 were assigned to the emollient group and 701 to the control group. Adherence in the emollient group was 88% (466 of 532) at 3 months, 82% (427 of 519) at 6 months, and 74% (375 of 506) at 12 months in those with complete questionnaire data. At age 2 years, eczema was present in 139 (23%) of 598 infants with outcome data collected in the emollient group and 150 (25%) of 612 infants in the control group (adjusted relative risk 0·95 [95% CI 0·78 to 1·16], p=0·61; adjusted risk difference -1·2% [-5·9 to 3·6]). Other eczema definitions supported the results of the primary analysis. Mean number of skin infections per child in year 1 was 0·23 (SD 0·68) in the emollient group versus 0·15 (0·46) in the control group; adjusted incidence rate ratio 1·55 (95% CI 1·15 to 2·09). INTERPRETATION: We found no evidence that daily emollient during the first year of life prevents eczema in high-risk children and some evidence to suggest an increased risk of skin infections. Our study shows that families with eczema, asthma, or allergic rhinitis should not use daily emollients to try and prevent eczema in their newborn. FUNDING: National Institute for Health Research Health Technology Assessment

Daily emollient during infancy for prevention of eczema: the BEEP randomised controlled trial.

BACKGROUND: Skin barrier dysfunction precedes eczema development. We tested whether daily use of emollient in the first year could prevent eczema in high-risk children. METHODS: We did a multicentre, pragmatic, parallel-group, randomised controlled trial in 12 hospitals and four primary care sites across the UK. Families were approached via antenatal or postnatal services for recruitment of term infants (at least 37 weeks' gestation) at high risk of developing eczema (ie, at least one first-degree relative with parent-reported eczema, allergic rhinitis, or asthma, diagnosed by a doctor). Term newborns with a family history of atopic disease were randomly assigned (1:1) to application of emollient daily (either Diprobase cream or DoubleBase gel) for the first year plus standard skin-care advice (emollient group) or standard skin-care advice only (control group). The randomisation schedule was created using computer-generated code (stratified by recruiting centre and number of first-degree relatives with atopic disease) and participants were assigned to groups using an internet-based randomisation system. The primary outcome was eczema at age 2 years (defined by UK working party criteria) with analysis as randomised regardless of adherence to allocation for participants with outcome data collected, and adjusting for stratification variables. This trial is registered with ISRCTN, ISRCTN21528841. Data collection for long-term follow-up is ongoing, but the trial is closed to recruitment. FINDINGS: 1394 newborns were randomly assigned to study groups between Nov 19, 2014, and Nov 18, 2016; 693 were assigned to the emollient group and 701 to the control group. Adherence in the emollient group was 88% (466 of 532) at 3 months, 82% (427 of 519) at 6 months, and 74% (375 of 506) at 12 months in those with complete questionnaire data. At age 2 years, eczema was present in 139 (23%) of 598 infants with outcome data collected in the emollient group and 150 (25%) of 612 infants in the control group (adjusted relative risk 0·95 [95% CI 0·78 to 1·16], p=0·61; adjusted risk difference -1·2% [-5·9 to 3·6]). Other eczema definitions supported the results of the primary analysis. Mean number of skin infections per child in year 1 was 0·23 (SD 0·68) in the emollient group versus 0·15 (0·46) in the control group; adjusted incidence rate ratio 1·55 (95% CI 1·15 to 2·09). INTERPRETATION: We found no evidence that daily emollient during the first year of life prevents eczema in high-risk children and some evidence to suggest an increased risk of skin infections. Our study shows that families with eczema, asthma, or allergic rhinitis should not use daily emollients to try and prevent eczema in their newborn. FUNDING: National Institute for Health Research Health Technology Assessment

Evaluating semi-supervision methods for medical image segmentation: applications in cardiac magnetic resonance imaging

PURPOSE: Purpose Neural networks have potential to automate medical image segmentation but require expensive labeling efforts. While methods have been proposed to reduce the labeling burden, most have not been thoroughly evaluated on large, clinical datasets or clinical tasks. We propose a method to train segmentation networks with limited labeled data and focus on thorough network evaluation. APPROACH: We propose a semi-supervised method that leverages data augmentation, consistency regularization, and pseudolabeling and train four cardiac magnetic resonance (MR) segmentation networks. We evaluate the models on multiinstitutional, multiscanner, multidisease cardiac MR datasets using five cardiac functional biomarkers, which are compared to an expert’s measurements using Lin’s concordance correlation coefficient (CCC), the within-subject coefficient of variation (CV), and the Dice coefficient. RESULTS: The semi-supervised networks achieve strong agreement using Lin’s CCC (>0.8), CV similar to an expert, and strong generalization performance. We compare the error modes of the semi-supervised networks against fully supervised networks. We evaluate semi-supervised model performance as a function of labeled training data and with different types of model supervision, showing that a model trained with 100 labeled image slices can achieve a Dice coefficient within 1.10% of a network trained with 16,000+ labeled image slices. CONCLUSION: We evaluate semi-supervision for medical image segmentation using heterogeneous datasets and clinical metrics. As methods for training models with little labeled data become more common, knowledge about how they perform on clinical tasks, how they fail, and how they perform with different amounts of labeled data is useful to model developers and users