Sequence analysis, homology modeling, tissue expression, and potential functions of seven putative acetylcholinesterases in the spider Cupiennius salei.

Acetylcholine esterases (AChEs) are essential enzymes in cholinergic synapses, terminating neurotransmission by hydrolysing acetylcholine. While membrane bound AChEs at synaptic clefts efficiently perform this task, soluble AChEs are less stable and effective, but function over broader areas. In vertebrates, a single gene produces alternatively spliced forms of AChE, whereas invertebrates often have multiple genes, producing both enzyme types.

Research in methodologies for modelling the oral cavity.

The paper aims to explore the current state of understanding surrounding in silico oral modelling. This involves exploring methodologies, technologies and approaches pertaining to the modelling of the whole oral cavity; both internally and externally visible structures that may be relevant or appropriate to oral actions. Such a model could be referred to as a 'complete model' which includes consideration of a full set of facial features (i.

Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning.

This study evaluated deep learning algorithms for semantic segmentation and quantification of intracerebral hemorrhage (ICH), perihematomal edema (PHE), and intraventricular hemorrhage (IVH) on noncontrast CT scans of patients with spontaneous ICH. Models were assessed on 1732 annotated baseline noncontrast CT scans obtained from the Tranexamic Acid for Hyperacute Primary Intracerebral Haemorrhage (ie, TICH-2) international multicenter trial (ISRCTN93732214), and different loss functions using a three-dimensional no-new-U-Net (nnU-Net) were examined to address class imbalance (30% of participants with IVH in dataset). On the test cohort ( = 174, 10% of dataset), the top-performing models achieved median Dice similarity coefficients of 0.

Opsin knockdown specifically slows phototransduction in broadband and UV-sensitive photoreceptors in Periplaneta americana.

Photoreceptors with different spectral sensitivities serve different physiological and behavioral roles. We hypothesized that such functional evolutionary optimization could also include differences in phototransduction dynamics. We recorded elementary responses to light, quantum bumps (QBs), of broadband green-sensitive and ultraviolet (UV)-sensitive photoreceptors in the cockroach, Periplaneta americana, compound eyes using intracellular recordings.

Domain Adaptation of Synthetic Images for Wheat Head Detection.

Wheat head detection is a core computer vision problem related to plant phenotyping that in recent years has seen increased interest as large-scale datasets have been made available for use in research. In deep learning problems with limited training data, synthetic data have been shown to improve performance by increasing the number of training examples available but have had limited effectiveness due to . To overcome this, many adversarial approaches such as Generative Adversarial Networks (GANs) have been proposed as a solution by better aligning the distribution of synthetic data to that of real images through domain augmentation.

Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models.

Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is much more strenuous than in 2D images. For 4D time-series tomograms, this is usually handled by segmenting the constituent tomograms independently through time with 3D convolutional neural networks.

GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit.

3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines. Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available training data in most domains, with methods typically depending on large datasets of high-quality image-model pairs. In this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images exist in our source synthetic domain, and training is supplemented with different datasets from the target real domain.

Forecasting water temperature in lakes and reservoirs using seasonal climate prediction.

Seasonal climate forecasts produce probabilistic predictions of meteorological variables for subsequent months. This provides a potential resource to predict the influence of seasonal climate anomalies on surface water balance in catchments and hydro-thermodynamics in related water bodies (e.g.

Gene transcription changes in a locust model of noise-induced deafness.

Locusts have auditory structures called Müller's organs attached to tympanic membranes on either side of the abdomen. We measured the normalized abundances of 500 different mRNA transcripts in 320 Müller's organs obtained from 160 locusts () that had been subjected to a loud continuous 3-kHz tone for 24 h. Abundance ratios were then measured relative to transcripts from 360 control organs.

Improving Atraumatic Chest Pain Evaluation in an Urban, Safety-net Hospital Through Incorporation of a Modified HEART Score.

Atraumatic chest pain is a common emergency department (ED) presentation and the American College of Cardiology and American Heart Association recommends stress testing within 72 hours. The HEART score predicts major adverse cardiac events (MACE) in ED populations and does not require universal stress testing. An evaluation based solely on history, electrocardiography, and biomarkers, therefore, is an attractive approach to risk stratification in resource-limited settings.

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning.

Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development-a process referred to as plant phenotyping-is increasingly important in the plant sciences, and is often a bottleneck. Automated tools are required to analyze the data in microscopy images depicting plant growth, either locating or counting regions of cellular features in images.

Suppression of Gq and PLC gene expression has a small effect on quantum bumps in vivo in Periplaneta americana.

Visual signal transmission by Drosophila melanogaster photoreceptors is mediated by a Gq protein that activates a phospholipase C (PLC). Mutations and deficiencies in expression of either of these proteins cause severe defects in phototransduction. Here we investigated whether these proteins are also involved in the cockroach, Periplaneta americana, phototransduction by silencing Gq α-subunit (Gqα) and phosphoinositide-specific phospholipase C (PLC) by RNA interference and observing responses to single photons (quantum bumps, QB).

Deep convolutional neural networks for image-based detection in sugar beet fields.

Background: (hedge bindweed) detection in sugar beet fields remains a challenging problem due to variation in appearance of plants, illumination changes, foliage occlusions, and different growth stages under field conditions. Current approaches for weed and crop recognition, segmentation and detection rely predominantly on conventional machine-learning techniques that require a large set of hand-crafted features for modelling. These might fail to generalize over different fields and environments.

Physiological Basis of Noise-Induced Hearing Loss in a Tympanal Ear.

Acoustic overexposure, such as listening to loud music too often, results in noise-induced hearing loss. The pathologies of this prevalent sensory disorder begin within the ear at synapses of the primary auditory receptors, their postsynaptic partners and their supporting cells. The extent of noise-induced damage, however, is determined by overstimulation of primary auditory receptors, upstream of where the pathologies manifest.

RNA interference supports a role for Nanchung-Inactive in mechanotransduction by the cockroach, Periplaneta americana, tactile spine.

Proteins encoded by nanchung, inactive, nompC and piezo genes have been shown to play crucial roles in the initial detection of mechanical force by various insect auditory neurons, nociceptors and touch receptors. Most of this previous research has been performed on the larval and adult fruit fly, Drosophila melanogaster. We identified and assembled all four homologous genes in transcriptomes from the cockroach, Periplaneta americana.

Towards infield, live plant phenotyping using a reduced-parameter CNN.

There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes.

Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images.

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots are usually done manually, or semi-automatically by first segmenting cassava root images.

Deployment of Lidar from a Ground Platform: Customizing a Low-Cost, Information-Rich and User-Friendly Application for Field Phenomics Research.

Using sensors and electronic systems for characterization of plant traits provides valuable digital inputs to support complex analytical modeling in genetics research. In field applications, frequent sensor deployment enables the study of the dynamics of these traits and their interaction with the environment. This study focused on implementing lidar (light detection and ranging) technology to generate 2D displacement data at high spatial resolution and extract plant architectural parameters, namely canopy height and cover, in a diverse population of 252 maize ( L.

A low-cost aeroponic phenotyping system for storage root development: unravelling the below-ground secrets of cassava ().

Background: Root and tuber crops are becoming more important for their high source of carbohydrates, next to cereals. Despite their commercial impact, there are significant knowledge gaps about the environmental and inherent regulation of storage root (SR) differentiation, due in part to the innate problems of studying storage roots and the lack of a suitable model system for monitoring storage root growth. The research presented here aimed to develop a reliable, low-cost effective system that enables the study of the factors influencing cassava storage root initiation and development.

Habitat and host factors associated with liver fluke (Fasciola hepatica) diagnoses in wild red deer (Cervus elaphus) in the Scottish Highlands.

Background: Red deer (Cervus elaphus) are a common wild definitive host for liver fluke (Fasciola hepatica) that have been the subject of limited diagnostic surveillance. This study aimed to explore the extent to which coprological diagnoses for F. hepatica in red deer in the Scottish Highlands, Scotland, are associated with variability among hosts and habitats.

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures.

Background: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions.

A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram.

X-ray computed tomography and, specifically, time-resolved volumetric tomography data collections (4D datasets) routinely produce terabytes of data, which need to be effectively processed after capture. This is often complicated due to the high rate of data collection required to capture at sufficient time-resolution events of interest in a time-series, compelling the researchers to perform data collection with a low number of projections for each tomogram in order to achieve the desired `frame rate'. It is common practice to collect a representative tomogram with many projections, after or before the time-critical portion of the experiment without detrimentally affecting the time-series to aid the analysis process.