Explore the vast range of titles available.

Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2⋆, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research. [Display omitted]

The availability of citizen science data has resulted in growing applications in biodiversity science. One widely used platform, iNaturalist, provides millions of digitally vouchered observations submitted by a global user base. These observation records include a date and a location but otherwise do not contain any information about the sampling process. As a result, sampling biases must be inferred from the data themselves. In the present article, we examine spatial and temporal biases in iNaturalist observations from the platform’s launch in 2008 through the end of 2019. We also characterize user behavior on the platform in terms of individual activity level and taxonomic specialization. We found that, at the level of taxonomic class, the users typically specialized on a particular group, especially plants or insects, and rarely made observations of the same species twice. Biodiversity scientists should consider whether user behavior results in systematic biases in their analyses before using iNaturalist data.

Existing evidence on the beneficial effects of nature on mental health comes from studies using cross-sectional designs. We developed a smartphone-based tool (Urban Mind; www.urbanmind.info) to examine how exposure to natural features within the built environment affects mental well-being in real time. The tool was used to monitor 108 individuals who completed 3013 assessments over a 1-week period. Significant immediate and lagged associations with mental well-being were found for several natural features. These associations were stronger in people with higher trait impulsivity, a psychological measure of one’s tendency to behave with little forethought or consideration of the consequences, which is indicative of a higher risk of developing mental-health issues. Our investigation suggests that the benefits of nature on mental well-being are time-lasting and interact with an individual’s vulnerability to mental illness. These findings have potential implications from the perspectives of global mental health as well as urban planning and design.

The demand for remote assessment tools has increased, yet there is a lack of standardized adaptations for remote administration. This pilot study investigates the equivalency of in-person and remote cognitive assessments using the NIH Toolbox Cognition Battery (NIHTB-CB) among children aged 7 to 17 years. Forty-seven children (51.1% female; M age = 12.26, SD age = 3.23) were assessed in two formats: in-person at a study site and remotely from home, with the order of assessments counterbalanced. The NIHTB-CB was used for in-person evaluations, while a newly developed version, the NIH Toolbox Participant/Examiner (NIHTB-P/E) App , was used for remote assessments through built-in teleconferencing features. The results showed considerable consistency between in-person and remote scores across all NIHTB-CB tests. Certain differences were noted, including longer test durations for remote assessments and potential practice effects on some measures. Overall, preliminary findings from this pilot study support thefeasibility of administering the NIHTB-CB remotely using the NIHTB-P/E App, providing a viable option to traditional in-person cognitive assessments in pediatric populations.

An improved architecture and enthusiastic user base are driving uptake of the open-source web tool. An improved architecture and enthusiastic user base are driving uptake of the open-source web tool.

IntroductionTo reduce the use of animals in basic cardiovascular research, the applications of in vitro 3D cardiac organoids (COs) in heart disease modelling and drug screenings are increasing incrementally. However, the electrophysiological recording of 3D COs is still challenging and very expensive. We, therefore, developed a Generic deep learning-based Edge detection Tracking (GET) toolbox for motion tracking of COs’ electrophysiological activities from any video record source.MethodsOur lab has recently established a novel CO model (WT CO) with inside chamber-like cavities and vascularised networks, which can recapture the key structures of early-developing hearts. We also generated the STX18-AS1 (CHD-associated risk lncRNA gene identified from GWAS) knockout CO (KO CO) for modelling congenital heart disease (CHD). Videos of live COs on their beating activities are recorded with a microscope with or without calcium imaging. Using the ‘GET’ toolbox developed in MATLAB environment, the COs in videos are firstly extracted using the deep-learning-based Segment Anything Model (SAM); the AI tool can detect and segmentise designated objects with pre-defined prompt points. The extracted objects are then tracked using the Kanade-Lucas-Tomasi (KLT) method. The motion speeds and magnitudes of the tracking edges are then determined in each video frame to quantify the dynamic movement of the segmented COs.ResultsThere is a high variety of brightness, contrasts, and debris in the background of CO videos captured from different days or with different microscopes. The GET toolbox can successfully extract the targets and detect their edge (figure 1A). The edge tracking points are moving dynamically along with the electrophysiological beating of objects. The pixel variation speed shows the excitation and relaxation rate of CO contraction (figure 1B). The contraction amplitudes and beating rates are indicated by pixel variations (figure 1C). The KO CO is detected to have an irregular beating rate. We quantified 134 COs and found that KO COs (26/66, 39.39%) had a higher frequency of irregular beating than WT COs (4/68, 5.88%; χ2=21.65, p<0.01). Figure 1D shows one single contraction of both WT and KO COs, indicating a reduction in contraction excitation and amplitude of KO CO. Additionally, the GET toolbox can detect the contractions from videos of 2D cardiomyocytes at either monolayer or single cell levels.ConclusionsOverall, we developed an AI-based motion tracking toolbox which can be widely applied to 3D, 2D, and single-cell levels for contraction analyses from various qualities of video records. GET is a versatile tool that benefits electrophysiological studies on in vitro cardiac models for genetic investigations.Abstract BS35 Figure 1GET toolbox detects the altered contraction activities in CHD-associated gene knockout cardiac organoids (CO). A, the schema of video frame segmentation, edge tracking and analyses using the GET toolbox. Example videos of wild-type (WT) CO and STX18-AS1 knockout (KO) CO are shown on their object extraction with Segment Anything Model (SAM), Edge detection and tracking with Kanade-Lucas-Tomasi (KLT). The green dots in the pictures are the KLT trackers. B, the real-time pixel-moving speeds for both WT CO and KO CO videos. C, the dynamic pixel movement of WT CO and KO CO. D, the focused one single contraction peak of WT CO and KO COConflict of InterestNo

ERPLAB toolbox is a freely available, open-source toolbox for processing and analyzing event-related potential (ERP) data in the MATLAB environment. ERPLAB is closely integrated with EEGLAB, a popular open-source toolbox that provides many EEG preprocessing steps and an excellent user interface design. ERPLAB adds to EEGLAB's EEG processing functions, providing additional tools for filtering, artifact detection, re-referencing, and sorting of events, among others. ERPLAB also provides robust tools for averaging EEG segments together to create averaged ERPs, for creating difference waves and other recombinations of ERP waveforms through algebraic expressions, for filtering and re-referencing the averaged ERPs, for plotting ERP waveforms and scalp maps, and for quantifying several types of amplitudes and latencies. ERPLAB's tools can be accessed either from an easy-to-learn graphical user interface or from MATLAB scripts, and a command history function makes it easy for users with no programming experience to write scripts. Consequently, ERPLAB provides both ease of use and virtually unlimited power and flexibility, making it appropriate for the analysis of both simple and complex ERP experiments. Several forms of documentation are available, including a detailed user's guide, a step-by-step tutorial, a scripting guide, and a set of video-based demonstrations.

Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.