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 relative ease, speed, and biological scope of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated Protein9 (Cas9)-based reagents for genomic manipulations are revolutionizing virtually all areas of molecular biosciences, including functional genomics, genetics, applied biomedical research, and agricultural biotechnology. In plant systems, however, a number of hurdles currently exist that limit this technology from reaching its full potential. For example, significant plant molecular biology expertise and effort is still required to generate functional expression constructs that allow simultaneous editing, and especially transcriptional regulation, of multiple different genomic loci or multiplexing, which is a significant advantage of CRISPR/Cas9 versus other genome-editing systems. To streamline and facilitate rapid and wide-scale use of CRISPR/Cas9-based technologies for plant research, we developed and implemented a comprehensive molecular toolbox for multifaceted CRISPR/Cas9 applications in plants. This toolbox provides researchers with a protocol and reagents to quickly and efficiently assemble functional CRISPR/Cas9 transfer DNA constructs for monocots and dicots using Golden Gate and Gateway cloning methods. It comes with a full suite of capabilities, including multiplexed gene editing and transcriptional activation or repression of plant endogenous genes. We report the functionality and effectiveness of this toolbox in model plants such as tobacco (Nicotiana benthamiana), Arabidopsis (Arabidopsis thaliana), and rice (Oryza sativa), demonstrating its utility for basic and applied plant research.

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.

Based on technical advances in the sequencing and synthesis of genetic components as well as the genome, significant progress has recently been made in developing synthetic biology toolboxes and chassis for the model Gram-positive bacterium Bacillus subtilis. In this review, we discuss recently developed synthetic biology toolboxes, including gene expression toolsets and genome editing tools. Next, advances in the B. subtilis chassis and its applications are discussed in comparison to those of other model microorganisms. Finally, future directions for the integrative use of B. subtilis synthetic biology tools and the development of an advanced chassis for efficient biomanufacturing are discussed. These factors are expected to become a major driving force for facilitating biotechnological applications of B. subtilis. Recent development of synthetic biology toolboxes for B. subtilis, including gene expression regulatory toolboxes and genome-wide editing tools, provides powerful tools for precise gene expression control and efficient genome editing. Advances of B. subtilis chassis and their applications help to understand fundamental cellular processes and techniques for improving production of biomolecules or heterologous enzymes. Comparing B. subtilis chassis development with E. coli and S. cerevisiae chassis may provide potential directions for B. subtilis chassis construction.

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes. When applied carefully to MEG data with a typical SNR (3–15 ​dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization. •Different beamformer implementations are reported to sometimes yield differing source estimates for the same MEG data.•We compared beamformers in four major open-source MEG analysis toolboxes.•All toolboxes provide consistent and accurate results with 3–15-dB input SNR.•However, localization errors are high at very high input SNR for the tested scalar beamformers.•We discuss the critical differences between the implementations.