Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data

Current methods for hyperpolarized [1-13C]pyruvate MRI human studies

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of HP agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate-by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation; (2) MRI system setup and calibrations; (3) data acquisition and image reconstruction; and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the "HP 13C MRI Consensus Group" as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods and Equipment study groups. It further aims to provide a comprehensive reference for future consensus, building as the field continues to advance human studies with this metabolic imaging modality.

Navigating color integrity in data visualization

Color is crucial in scientific visualization, yet it is often misused. Addressing this, we think accessible and accurate techniques, such as color-blind friendly palettes and perceptually even gradients, are vital. Accountability and basic knowledge in data visualization are key in fostering a culture of color integrity, ensuring accurate and inclusive data representation.

Stated-Preference Survey Design and Testing in Health Applications

Following the conceptualization of a well-formulated and relevant research question, selection of an appropriate stated-preference method, and related methodological issues, researchers are tasked with developing a survey instrument. A major goal of designing a stated-preference survey for health applications is to elicit high-quality data that reflect thoughtful responses from well-informed respondents. Achieving this goal requires researchers to design engaging surveys that maximize response rates, minimize hypothetical bias, and collect all the necessary information needed to answer the research question. Designing such a survey requires researchers to make numerous interrelated decisions that build upon the decision context, selection of attributes, and experimental design. Such decisions include considering the setting(s) and study population in which the survey will be administered, the format and mode of administration, and types of contextual information to collect. Development of a survey is an interactive process in which feedback from respondents should be collected and documented through qualitative pre-test interviews and pilot testing. This paper describes important issues to consider across all major steps required to design and test a stated-choice survey to elicit patient preferences for health preference research.

Current Methods for Hyperpolarized [1-13C]pyruvate MRI Human Studies

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of hyperpolarized agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate - by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the HP 13C MRI Consensus Group as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods & Equipment study groups. It further aims to provide a comprehensive reference for future consensus building as the field continues to advance human studies with this metabolic imaging modality.

Direct observation of glycans bonded to proteins and lipids at the single-molecule level

Proteins and lipids decorated with glycans are found throughout biological entities, playing roles in biological functions and dysfunctions. Current analytical strategies for these glycan-decorated biomolecules, termed glycoconjugates, rely on ensemble-averaged methods that do not provide a full view of positions and structures of glycans attached at individual sites in a given molecule, especially for glycoproteins. We show single-molecule analysis of glycoconjugates by direct imaging of individual glycoconjugate molecules using low-temperature scanning tunneling microscopy. Intact glycoconjugate ions from electrospray are soft-landed on a surface for their direct single-molecule imaging. The submolecular imaging resolution corroborated by quantum mechanical modeling unveils whole structures and attachment sites of glycans in glycopeptides, glycolipids, N-glycoproteins, and O-glycoproteins densely decorated with glycans.

MYC disrupts transcriptional and metabolic circadian oscillations in cancer and promotes enhanced biosynthesis

The molecular circadian clock, which controls rhythmic 24-hour oscillation of genes, proteins, and metabolites in healthy tissues, is disrupted across many human cancers. Deregulated expression of the MYC oncoprotein has been shown to alter expression of molecular clock genes, leading to a disruption of molecular clock oscillation across cancer types. It remains unclear what benefit cancer cells gain from suppressing clock oscillation, and how this loss of molecular clock oscillation impacts global gene expression and metabolism in cancer. We hypothesized that MYC or its paralog N-MYC (collectively termed MYC herein) suppress oscillation of gene expression and metabolism to upregulate pathways involved in biosynthesis in a static, non-oscillatory fashion. To test this, cells from distinct cancer types with inducible MYC were examined, using time-series RNA-sequencing and metabolomics, to determine the extent to which MYC activation disrupts global oscillation of genes, gene expression pathways, and metabolites. We focused our analyses on genes, pathways, and metabolites that changed in common across multiple cancer cell line models. We report here that MYC disrupted over 85% of oscillating genes, while instead promoting enhanced ribosomal and mitochondrial biogenesis and suppressed cell attachment pathways. Notably, when MYC is activated, biosynthetic programs that were formerly circadian flipped to being upregulated in an oscillation-free manner. Further, activation of MYC ablates the oscillation of nutrient transporter proteins while greatly upregulating transporter expression, cell surface localization, and intracellular amino acid pools. Finally, we report that MYC disrupts metabolite oscillations and the temporal segregation of amino acid metabolism from nucleotide metabolism. Our results demonstrate that MYC disruption of the molecular circadian clock releases metabolic and biosynthetic processes from circadian control, which may provide a distinct advantage to cancer cells.

MYC disrupts transcriptional and metabolic circadian oscillations in cancer and promotes enhanced biosynthesis

The molecular circadian clock, which controls rhythmic 24-hour oscillation of genes, proteins, and metabolites in healthy tissues, is disrupted across many human cancers. Deregulated expression of the MYC oncoprotein has been shown to alter expression of molecular clock genes, leading to a disruption of molecular clock oscillation across cancer types. It remains unclear what benefit cancer cells gain from suppressing clock oscillation, and how this loss of molecular clock oscillation impacts global gene expression and metabolism in cancer. We hypothesized that MYC or its paralog N-MYC (collectively termed MYC herein) suppress oscillation of gene expression and metabolism to upregulate pathways involved in biosynthesis in a static, non-oscillatory fashion. To test this, cells from distinct cancer types with inducible MYC were examined, using time-series RNA-sequencing and metabolomics, to determine the extent to which MYC activation disrupts global oscillation of genes, gene expression pathways, and metabolites. We focused our analyses on genes, pathways, and metabolites that changed in common across multiple cancer cell line models. We report here that MYC disrupted over 85% of oscillating genes, while instead promoting enhanced ribosomal and mitochondrial biogenesis and suppressed cell attachment pathways. Notably, when MYC is activated, biosynthetic programs that were formerly circadian flipped to being upregulated in an oscillation-free manner. Further, activation of MYC ablates the oscillation of nutrient transporter proteins while greatly upregulating transporter expression, cell surface localization, and intracellular amino acid pools. Finally, we report that MYC disrupts metabolite oscillations and the temporal segregation of amino acid metabolism from nucleotide metabolism. Our results demonstrate that MYC disruption of the molecular circadian clock releases metabolic and biosynthetic processes from circadian control, which may provide a distinct advantage to cancer cells.

Geometric transformation of cognitive maps for generalization across hippocampal-prefrontal circuits

The ability to abstract information to guide decisions during navigation across changing environments is essential for adaptation and requires the integrity of the hippocampal-prefrontal circuitry. The hippocampus encodes navigational information in a cognitive map, but it remains unclear how cognitive maps are transformed across hippocampal-prefrontal circuits to support abstraction and generalization. Here, we simultaneously record hippocampal-prefrontal ensembles as rats generalize navigational rules across distinct environments. We find that, whereas hippocampal representational maps maintain specificity of separate environments, prefrontal maps generalize across environments. Furthermore, while both maps are structured within a neural manifold of population activity, they have distinct representational geometries. Prefrontal geometry enables abstraction of rule-informative variables, a representational format that generalizes to novel conditions of existing variable classes. Hippocampal geometry lacks such abstraction. Together, these findings elucidate how cognitive maps are structured into distinct geometric representations to support abstraction and generalization while maintaining memory specificity.

SMART: A data reporting standard for mass spectrometry imaging

Mass spectrometry imaging (MSI) is an important analytical technique that simultaneously reports the spatial location and abundance of detected ions in biological, chemical, clinical, and pharmaceutical studies. As MSI grows in popularity, it has become evident that data reporting varies among different research groups and between techniques. The lack of consistency in data reporting inherently creates additional challenges in comparing intra- and inter-laboratory MSI data. In this tutorial, we propose a unified data reporting system, SMART, based on the common features shared between techniques. While there are limitations to any reporting system, SMART was decided upon after significant discussion to more easily understand and benchmark MSI data. SMART is not intended to be comprehensive but rather capture essential baseline information for a given MSI study; this could be within a study (e.g., effect of spot size on the measured ion signals) or between two studies (e.g., different MSI platform technologies applied to the same tissue type). This tutorial does not attempt to address the confidence with which annotations are made nor does it deny the importance of other parameters that are not included in the current SMART format. Ultimately, the goal of this tutorial is to discuss the necessity of establishing a uniform reporting system to communicate MSI data in publications and presentations in a simple format to readily interpret the parameters and baseline outcomes of the data.

Texture formation in W-type hexaferrite by cold compaction of non-magnetic interacting anisotropic shaped precursor crystallites

Crystallites of the W-type hexaferrites, Sr(Ni_1-xZn_x)₂Fe₁₆O₂₇ (x = 0, 0.5, 1) have been aligned without applying magnetic field nor hot compaction, but through a simple synthesis process taking advantage of easy alignment of non-magnetic interacting, anisotropic-shaped precursor crystallites of goethite. The goethite precursor was prepared through a simple hydrothermal synthesis route, forming lathlike crystallites with apparent dimensions of 23.3 × 40.1 × 11.0 nm³ as extracted from powder X-ray diffraction along the a-, b- and c-axis, respectively. The calcined pellets consisted of almost phase pure W-type hexaferrites with relative small impurities of spinel ferrite (≤9.02(3) wt%). The high synthesis temperature resulted in large crystallites, which in turn caused low coercivities (H_c ≤ 5.4(1) kA m^-1) and a squareness ration (M_r/M_s, remanence (M_r) over saturation magnetisation (M_s)) close to zero for all samples. The vanishing coercivity makes M_r/M_s an unsatisfying measure of preferred orientation. Quantitative texture analysis of the samples was carried out based on 2D transmission synchrotron diffraction data collected at different orientations of the samples. The texture investigations revealed alignment of the crystallites with the c-axis normal to the pressing surface of the pellets. The SrNi₂Fe₁₆O₂₇ sample showed the highest texture index of 7.5 m.r.d.².

On the importance of color in mass spectrometry imaging

Mass spectrometry imaging (MSI) data visualization relies on heatmaps to show the spatial distribution and measured abundances of molecules within a sample. Nonuniform color gradients such as jet are still commonly used to visualize MSI data, increasing the probability of data misinterpretation and false conclusions. Also, the use of nonuniform color gradients and the combination of hues used in common colormaps make it challenging for people with color vision deficiencies (CVDs) to visualize and accurately interpret data. Here we present best practices for choosing a colormap to accurately display MSI data, improve readability, and accommodate all CVDs. We also provide other resources on the misuse of color in the scientific field and resources on scientifically derived colormaps presented herein.

Sample size requirement for achieving multisite harmonization using structural brain MRI features

When data is pooled across multiple sites, the extracted features are confounded by site effects. Harmonization methods attempt to correct these site effects while preserving the biological variability within the features. However, little is known about the sample size requirement for effectively learning the harmonization parameters and their relationship with the increasing number of sites. In this study, we performed experiments to find the minimum sample size required to achieve multisite harmonization (using neuroHarmonize) using volumetric and surface features by leveraging the concept of learning curves. Our first two experiments show that site-effects are effectively removed in a univariate and multivariate manner; however, it is essential to regress the effect of covariates from the harmonized data additionally. Our following two experiments with actual and simulated data showed that the minimum sample size required for achieving harmonization grows with the increasing average Mahalanobis distances between the sites and their reference distribution. We conclude by positing a general framework to understand the site effects using the Mahalanobis distance. Further, we provide insights on the various factors in a cross-validation design to achieve optimal inter-site harmonization.

Data-Driven Colormap Adjustment for Exploring Spatial Variations in Scalar Fields

Colormapping is an effective and popular visualization technique for analyzing patterns in scalar fields. Scientists usually adjust a default colormap to show hidden patterns by shifting the colors in a trial-and-error process. To improve efficiency, efforts have been made to automate the colormap adjustment process based on data properties (e.g., statistical data value or histogram distribution). However, as the data properties have no direct correlation to the spatial variations, previous methods may be insufficient to reveal the dynamic range of spatial variations hidden in the data. To address the above issues, we conduct a pilot analysis with domain experts and summarize three requirements for the colormap adjustment process. Based on the requirements, we formulate colormap adjustment as an objective function, composed of a boundary term and a fidelity term, which is flexible enough to support interactive functionalities. We compare our approach with alternative methods under a quantitative measure and a qualitative user study (25 participants), based on a set of data with broad distribution diversity. We further evaluate our approach via three case studies with six domain experts. Our method is not necessarily more optimal than alternative methods of revealing patterns, but rather is an additional color adjustment option for exploring data with a dynamic range of spatial variations.

Developing transmission mode for infrared matrix-assisted laser desorption electrospray ionization mass spectrometry imaging

RATIONALE

The development and characterization of the novel NextGen infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source catalyzed new advancements in IR-MALDESI instrumentation, including the development of a new analysis geometry.

METHODS

A vertically oriented transmission mode (tm)-IR-MALDESI setup was developed and optimized on thawed mouse tissue. In addition, glycerol was introduced as an alternative energy-absorbing matrix for tm-IR-MALDESI because the new geometry does not currently allow for the formation of an ice matrix. The tm geom was evaluated against the optimized standard geometry for the NextGen source in reflection mode (rm).

RESULTS

It was found that tm-IR-MALDESI produces comparable results to rm-IR-MALDESI after optimization. The attempt to incorporate glycerol as an alternative matrix provided little improvement to tm-IR-MALDESI ion abundances.

CONCLUSIONS

This work has successfully demonstrated the adaptation of the NextGen IR-MALDESI source through the feasibility of tm-IR-MALDESI mass spectrometry imaging on mammalian tissue, expanding future biological applications of the method.

Software tool for visualization of a probabilistic map of the epileptogenic zone from seizure semiologies

Around one third of epilepsies are drug-resistant. For these patients, seizures may be reduced or cured by surgically removing the epileptogenic zone (EZ), which is the portion of the brain giving rise to seizures. If noninvasive data are not sufficiently lateralizing or localizing, the EZ may need to be localized by precise implantation of intracranial electroencephalography (iEEG) electrodes. The choice of iEEG targets is influenced by clinicians' experience and personal knowledge of the literature, which leads to substantial variations in implantation strategies across different epilepsy centers. The clinical diagnostic pathway for surgical planning could be supported and standardized by an objective tool to suggest EZ locations, based on the outcomes of retrospective clinical cases reported in the literature. We present an open-source software tool that presents clinicians with an intuitive and data-driven visualization to infer the location of the symptomatogenic zone, that may overlap with the EZ. The likely EZ is represented as a probabilistic map overlaid on the patient's images, given a list of seizure semiologies observed in that specific patient. We demonstrate a case study on retrospective data from a patient treated in our unit, who underwent resective epilepsy surgery and achieved 1-year seizure freedom after surgery. The resected brain structures identified as EZ location overlapped with the regions highlighted by our tool, demonstrating its potential utility.

Color Vision Deficiency Survey in Anatomic Pathology

OBJECTIVES

To learn what color vision-deficient pathologists and cytotechnologists consider their most significant problems and advantages as well as any accommodations.

METHODS

An anonymous online survey developed for practicing pathologists and cytotechnologists regarding their experiences with stains was sent to the members of 4 national societies.

RESULTS

We received 377 responses. Twenty-three people, all men, identified themselves as color vision deficient, with 22 reporting red-green color vision deficiency and 1 reporting uncertain type. Eight pathologists and cytotechnologists indicated that they thought that their color vision deficiency conferred advantages to them, including a greater appreciation of morphology, with less confusion resulting from variations in stain quality or intensity. Nineteen pathologists and cytotechnologists thought that their color vision deficiency conferred disadvantages; the most common disadvantages stated were the identification of eosinophils and acid-fast bacilli. Other difficulties included interpretation of RBCs and nucleoli and sometimes Alcian blue, Brown and Brenn, Congo red, crystal violet, Fite, Giemsa, mucicarmine, periodic acid-Schiff, and fluorescence in situ hybridization stains. Only 2 of the color vision-deficient pathologists and cytotechnologists found digital slides more difficult than glass slides.

CONCLUSIONS

Color vision-deficient pathologists and cytotechnologists report that they have developed approaches to viewing slides that do not compromise their interpretations. Digital pathology may provide several approaches for aiding color vision-deficient pathologists with the interpretation of certain stains.

Novel matrix strategies for improved ionization and spatial resolution using IR-MALDESI mass spectrometry imaging

In mass spectrometry imaging (MSI) applications of infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI), an exogenous ice layer is the gold standard for an energy-absorbing matrix. However, the formation of the ice matrix requires additional time and instrument hardware, so glycerol was investigated herein as an alternative to the ice matrix to potentially improve spatial resolution and ionization, while decreasing experiment time. Glycerol solutions of varying concentrations were sprayed over top of rat liver tissue sections for analysis by IR-MALDESI and compared to the typical ice matrix condition. Additionally, we tested if combining the ice matrix and glycerol matrix would further improve analyses. Matrix conditions were evaluated by comparing ion abundance of six lipid species, the laser ablation spot diameter, and number of METASPACE annotations. The ion abundances were also normalized to the volume of tissue ablated to correct for lower abundance values due to less ablated tissue. It was observed that utilizing a 50% glycerol matrix without ice provides improved spatial resolution with lipid abundances and annotations comparable to the ice matrix standard, while decreasing the time required to complete an IR-MALDESI tissue imaging experiment.

[18F]-Labeled PARP-1 PET imaging of PSMA targeted alpha particle radiotherapy response

The growing interest and clinical translation of alpha particle (α) therapies brings with it new challenges to assess target cell engagement and to monitor therapeutic effect. Noninvasive imaging has great potential to guide α-treatment and to harness the potential of these agents in the complex environment of disseminated disease. Poly(ADP) ribose polymerase 1 (PARP-1) is among the most abundantly expressed DNA repair enzymes with key roles in multiple repair pathways-such as those induced by irradiation. Here, we used a third-generation PARP1-specific radiotracer, [18F]-PARPZ, to delineate castrate resistant prostate cancer xenografts. Following treatment with the clinically applied [225Ac]-PSMA-617, positron emission tomography was performed and correlative autoradiography and histology acquired. [18F]-PARPZ was able to distinguish treated from control (saline) xenografts by increased uptake. Kinetic analysis of tracer accumulation also suggests that the localization of the agent to sites of increased PARP-1 expression is a consequence of DNA damage response. Together, these data support expanded investigation of [18F]-PARPZ to facilitate clinical translation in the ⍺-therapy space.

Acute behavioral and Neurochemical Effects of Novel N-Benzyl-2-Phenylethylamine Derivatives in Adult Zebrafish

Hallucinogenic drugs potently affect brain and behavior and have also recently emerged as potentially promising agents in pharmacotherapy. Complementing laboratory rodents, the zebrafish (Danio rerio) is a powerful animal model organism for screening neuroactive drugs, including hallucinogens. Here, we test a battery of ten novel N-benzyl-2-phenylethylamine (NBPEA) derivatives with the 2,4- and 3,4-dimethoxy substitutions in the phenethylamine moiety and the -OCH₃, -OCF₃, -F, -Cl, and -Br substitutions in the ortho position of the phenyl ring of the N-benzyl moiety, assessing their acute behavioral and neurochemical effects in the adult zebrafish. Overall, substitutions in the Overall, substitutions in the N-benzyl moiety modulate locomotion, and substitutions in the phenethylamine moiety alter zebrafish anxiety-like behavior, also affecting the brain serotonin and/or dopamine turnover. The 24H-NBOMe(F) and 34H-NBOMe(F) treatment also reduced zebrafish despair-like behavior. Computational analyses of zebrafish behavioral data by artificial intelligence identified several distinct clusters for these agents, including anxiogenic/hypolocomotor (24H-NBF, 24H-NBOMe, and 34H-NBF), behaviorally inert (34H-NBBr, 34H-NBCl, and 34H-NBOMe), anxiogenic/hallucinogenic-like (24H-NBBr, 24H-NBCl, and 24H-NBOMe(F)), and anxiolytic/hallucinogenic-like (34H-NBOMe(F)) drugs. Our computational analyses also revealed phenotypic similarity of the behavioral activity of some NBPEAs to that of selected conventional serotonergic and antiglutamatergic hallucinogens. In silico functional molecular activity modeling further supported the overlap of the drug targets for NBPEAs tested here and the conventional serotonergic and antiglutamatergic hallucinogens. Overall, these findings suggest potent neuroactive properties of several novel synthetic NBPEAs, detected in a sensitive in vivo vertebrate model system, the zebrafish, raising the possibility of their potential clinical use and abuse.

Diagnosis of colour vision deficits using eye movements

We set out to develop a simple objective test of functional colour vision based on eye movements made in response to moving patterns. We exploit the finding that while the motion of a colour-defined stimulus can be cancelled by adding a low-contrast luminance-defined stimulus moving in the opposite direction, the "equivalent luminance contrast" required for such cancellation is reduced when colour vision is abnormal. We used a consumer-grade infrared eye-tracker to measure eye movements made in response to coloured patterns drifting at different speeds. An automated analysis of these movements estimated individuals' red-green equiluminant point and their equivalent luminance contrast. We tested 34 participants: 23 colour vision normal controls, 9 deuteranomalous and 2 protanomalous individuals. We obtained reliable estimates of strength of directed eye movements (i.e. combined optokinetic and voluntary tracking) for stimuli moving at 16 deg/s and could use these data to classify participants' colour vision status with a sensitivity rate of 90.9% and a specificity rate of 91.3%. We conclude that an objective test of functional colour vision combining a motion-nulling technique with an automated analysis of eye movements can diagnose and assess the severity of protanopia and deuteranopia. The test places minimal demands on patients (who simply view a series of moving patterns for less than 90 s), requires modest operator expertise, and can be run on affordable hardware.

IRimage: open source software for processing images from infrared thermal cameras

IRimage aims at increasing throughput, accuracy and reproducibility of results obtained from thermal images, especially those produced with affordable, consumer-oriented cameras. IRimage processes thermal images, extracting raw data and calculating temperature values with an open and fully documented algorithm, making this data available for further processing using image analysis software. It also allows the making of reproducible measurements of the temperature of objects in a series of images, and produce visual outputs (images and videos) suitable for scientific reporting. IRimage is implemented in a scripting language of the scientific image analysis software ImageJ, allowing its use through a graphical user interface and also allowing for an easy modification or expansion of its functionality. IRimage's results were consistent with those of standard software for 15 camera models of the most widely used brand. An example use case is also presented, in which IRimage was used to efficiently process hundreds of thermal images to reveal subtle differences in the daily pattern of leaf temperature of plants subjected to different soil water contents. IRimage's functionalities make it better suited for research purposes than many currently available alternatives, and could contribute to making affordable consumer-grade thermal cameras useful for reproducible research.

Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series

Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.

CDFA method: a way to assess assembly and installation performance of aircraft system architectures at the conceptual design

This paper describes an engineering design methodology, called conceptual design for assembly (CDFA) in the context of aircraft development, to assess aircraft systems' installation during conceptual phase, in relation to industrial performance objectives. The methodology is based on a given framework (hierarchical structure) which includes a set of attributes, collected in recognized domains that characterize the aircraft systems installation. The framework of the CDFA methodology enables to analyze product architectures at different levels of granularity, splitting the global analysis into sub-problems (problem discretization) with the aim to help architects and designers to identify product architecture weaknesses in terms of fit for assembly performances. The CDFA methodology was applied on a complex system (the nose-fuselage of a commercial aircraft) presenting a high number of criticalities both for the product and its assembly operations. Results identified the architectural components leading to the less efficient assembly operations and the rationales enabling to elaborate alternative architectures for an improved product industrial efficiency.

Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses

Zebrafish (Danio rerio) are rapidly emerging in biomedicine as promising tools for disease modelling and drug discovery. The use of zebrafish for neuroscience research is also growing rapidly, necessitating novel reliable and unbiased methods of neurophenotypic data collection and analyses. Here, we applied the artificial intelligence (AI) neural network-based algorithms to a large dataset of adult zebrafish locomotor tracks collected previously in a series of in vivo experiments with multiple established psychotropic drugs. We first trained AI to recognize various drugs from a wide range of psychotropic agents tested, and then confirmed prediction accuracy of trained AI by comparing several agents with known similar behavioral and pharmacological profiles. Presenting a framework for innovative neurophenotyping, this proof-of-concept study aims to improve AI-driven movement pattern classification in zebrafish, thereby fostering drug discovery and development utilizing this key model organism.

Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content

Natural language descriptions sometimes accompany visualizations to better communicate and contextualize their insights, and to improve their accessibility for readers with disabilities. However, it is difficult to evaluate the usefulness of these descriptions, and how effectively they improve access to meaningful information, because we have little understanding of the semantic content they convey, and how different readers receive this content. In response, we introduce a conceptual model for the semantic content conveyed by natural language descriptions of visualizations. Developed through a grounded theory analysis of 2,147 sentences, our model spans four levels of semantic content: enumerating visualization construction properties (e.g., marks and encodings); reporting statistical concepts and relations (e.g., extrema and correlations); identifying perceptual and cognitive phenomena (e.g., complex trends and patterns); and elucidating domain-specific insights (e.g., social and political context). To demonstrate how our model can be applied to evaluate the effectiveness of visualization descriptions, we conduct a mixed-methods evaluation with 30 blind and 90 sighted readers, and find that these reader groups differ significantly on which semantic content they rank as most useful. Together, our model and findings suggest that access to meaningful information is strongly reader-specific, and that research in automatic visualization captioning should orient toward descriptions that more richly communicate overall trends and statistics, sensitive to reader preferences. Our work further opens a space of research on natural language as a data interface coequal with visualization.

Over the rainbow: Guidelines for meaningful use of colour maps in neurophysiology

Visualization of complex data is commonplace in neurophysiology research. Here, we highlight specific perceptual issues related to the ongoing misuse of variations of the rainbow colour scheme, with a particular emphasis on time-frequency decompositions in electrophysiology as an illustrative example. We review the risks of biased interpretation of neurophysiological data in this context, and provide guidelines to improve the use of colour maps to visualise complex, multidimensional data in neurophysiology research.

Contrast optimization of mass spectrometry imaging (MSI) data visualization by threshold intensity quantization (TrIQ)

Mass spectrometry imaging (MSI) enables the unbiased characterization of surfaces with respect to their chemical composition. In biological MSI, zones with differential mass profiles hint towards localized physiological processes, such as the tissue-specific accumulation of secondary metabolites, or diseases, such as cancer. Thus, the efficient discovery of 'regions of interest' (ROI) is of utmost importance in MSI. However, often the discovery of ROIs is hampered by high background noise and artifact signals. Especially in ambient ionization MSI, unmasking biologically relevant information from crude data sets is challenging. Therefore, we implemented a Threshold Intensity Quantization (TrIQ) algorithm for augmenting the contrast in MSI data visualizations. The simple algorithm reduces the impact of extreme values ('outliers') and rescales the dynamic range of mass signals. We provide an R script for post-processing MSI data in the imzML community format (https://bitbucket.org/lababi/msi.r) and implemented the TrIQ in our open-source imaging software RmsiGUI (https://bitbucket.org/lababi/rmsigui/). Applying these programs to different biological MSI data sets demonstrated the universal applicability of TrIQ for improving the contrast in the MSI data visualization. We show that TrIQ improves a subsequent detection of ROIs by sectioning. In addition, the adjustment of the dynamic signal intensity range makes MSI data sets comparable.

Systematic evaluation of the impact of defacing on quality and volumetric assessments on T1-weighted MR-images

BACKGROUND AND PURPOSE

Facial features can be potentially reconstructed from structural magnetic resonance images, thereby compromising the confidentiality of study participants. Defacing methods can be applied to MRI images to ensure privacy of study participants. These methods remove facial features, thereby rendering the image unidentifiable. It is commonly assumed that defacing would not have any impact on quantitative assessments of the brain. In this study, we have assessed the impact of different defacing methods on quality and volumetric estimates.

MATERIALS AND METHODS

We performed SPM-, Freesurfer-, pydeface, and FSL-based defacing on 30 T1-weighted images. We statistically compared the change in quality measurements (from MRIQC) and volumes (from SPM, CAT, and Freesurfer) between non-defaced and defaced images. We also calculated the Dice coefficient of each tissue class between non-defaced and defaced images.

RESULTS

Almost all quality measurements and tissue volumes changed after defacing, irrespective of the method used. All tissue volumes decreased post-defacing for CAT, but no such consistent trend was seen for SPM and Freesurfer. Dice coefficients indicated that segmentations are relatively robust; however, partial volumes might be affected leading to changed volumetric estimates.

CONCLUSION

In this study, we demonstrated that volumes and quality measurements get affected differently by defacing methods. It is likely that this will have a significant impact on the reproducibility of experiments. We provide suggestions on ways to minimize the impact of defacing on outcome measurements. Our results warrant the need for robust handling of defaced images at different steps of image processing.

A Practical Guide to Metabolomics Software Development

A growing number of software tools have been developed for metabolomics data processing and analysis. Many new tools are contributed by metabolomics practitioners who have limited prior experience with software development, and the tools are subsequently implemented by users with expertise that ranges from basic point-and-click data analysis to advanced coding. This Perspective is intended to introduce metabolomics software users and developers to important considerations that determine the overall impact of a publicly available tool within the scientific community. The recommendations reflect the collective experience of an NIH-sponsored Metabolomics Consortium working group that was formed with the goal of researching guidelines and best practices for metabolomics tool development. The recommendations are aimed at metabolomics researchers with little formal background in programming and are organized into three stages: (i) preparation, (ii) tool development, and (iii) distribution and maintenance.

The misuse of colour in science communication

The accurate representation of data is essential in science communication. However, colour maps that visually distort data through uneven colour gradients or are unreadable to those with colour-vision deficiency remain prevalent in science. These include, but are not limited to, rainbow-like and red-green colour maps. Here, we present a simple guide for the scientific use of colour. We show how scientifically derived colour maps report true data variations, reduce complexity, and are accessible for people with colour-vision deficiencies. We highlight ways for the scientific community to identify and prevent the misuse of colour in science, and call for a proactive step away from colour misuse among the community, publishers, and the press.

Deficits across multiple behavioral domains align with susceptibility to stress in 129S1/SvImJ mice

Acute physical or psychological stress can elicit adaptive behaviors that allow an organism maintain homeostasis. However, intense and/or prolonged stressors often have the opposite effect, resulting in maladaptive behaviors and curbing goal-directed action; in the extreme, this may contribute to the development of psychiatric conditions like generalized anxiety disorder, major depressive disorder, or post-traumatic stress disorder. While treatment of these disorders generally focuses on reducing reactivity to potentially threatening stimuli, there are in fact impairments across multiple domains including valence, arousal, and cognition. Here, we use the genetically stress-susceptible 129S1 mouse strain to explore the effects of stress across multiple domains. We find that 129S1 mice exhibit a potentiated neuroendocrine response across many environments and paradigms, and that this is associated with reduced exploration, neophobia, decreased novelty- and reward-seeking, and spatial learning and memory impairments. Taken together, our results suggest that the 129S1 strain may provide a useful model for elucidating mechanisms underlying myriad aspects of stress-linked psychiatric disorders as well as potential treatments that may ameliorate symptoms.

Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals

This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemented to reduce the features generated by continuous wavelet transform (CWT). Performance comparisons were conducted among several machine learning (ML) algorithms, including decision tree, random forest, multi-layer perceptron neural network, and extreme gradient boosting. In addition, a two-dimensional convolutional neural network (2D-CNN) was developed using the CWT coefficients as images. The 2D-CNN was made with a custom-built architecture and a CNN based on Mobile Net via transfer learning. After the reduction of features by 95.47%, the results obtained report 0.87 on accuracy by decision tree, 0.96 by random forest, 0.91 by simple neural network, and 0.95 by XGBoost. Via the 2D-CNN framework, the transfer learning of Mobile Net shows an accuracy of 0.91, while the custom-constructed classifier reveals an accuracy of 0.89. Our results validate the effectiveness of the feature selection and classification framework. They also show a promising potential for the implementation of deep learning tools on the classification of AS.

Intracellular Parasites Toxoplasma gondii and Besnoitia besnoiti, Unveiled in Single Host Cells Using AP-SMALDI MS Imaging

The obligate intracellular apicomplexan parasites Toxoplasma gondii and Besnoitia besnoiti are important causes of disease in both humans and cattle. To date, effective specific treatments are lacking for both infections. To counteract severe symptoms leading to, e.g., disabilities and even abortion in the case of human toxoplasmosis and bovine besnoitiosis, novel targets are required for development of drugs and vaccines. A promising emerging technique for molecular characterization of organisms is high-resolution atmospheric-pressure scanning microprobe matrix-assisted laser desorption/ionization (AP-SMALDI) mass spectrometry imaging (MSI) which enables semiquantitative visualization of metabolite distributions. MSI was here used to trace and characterize lipid metabolites in primary bovine umbilical vein endothelial cells (BUVECs) upon infection with tachyzoites, an early and pathogenic fast-replicating life stage of T. gondii and B. besnoiti. A cell bulk, derived from noninfected controls and parasite-infected cell pellets, was analyzed by AP-SMALDI MSI in technical and biological triplicates. Multivariate statistical analysis including hierarchical clustering and principle component analysis revealed infection-specific metabolites in both positive- and negative-ion mode, identified by combining database search and LC-MS² experiments. MSI analyses of host cell monolayers were conducted at 5 μm lateral resolution, allowing single apicomplexan-infected cells to be allocated. This is the first mass spectrometry imaging study on intracellular T. gondii and B. besnoiti infections and the first detailed metabolomic characterization of B. besnoiti tachyzoites. MSI was used here as an efficient tool to discriminate infected from noninfected cells at the single-cell level in vitro.

Considerations for MALDI-Based Quantitative Mass Spectrometry Imaging Studies

Significant advances in mass spectrometry imaging (MSI) have pushed the boundaries in obtaining spatial information and quantification in biological samples. Quantitative MSI (qMSI) has typically been challenging to achieve because of matrix and tissue heterogeneity, inefficient analyte extraction, and ion suppression effects, but recent studies have demonstrated approaches to obtain highly robust methods and reproducible results. In this perspective, we share our insights into sample preparation, how the choice of matrix influences sensitivity, construction of calibration curves, signal normalization, and visualization of MSI data. We hope that by articulating these guidelines that qMSI can be routinely conducted while retaining the analytical merits of other mass spectrometry modalities.

Nitrogen Source Governs Community Carbon Metabolism in a Model Hypersaline Benthic Phototrophic Biofilm

Increasing anthropogenic inputs of fixed nitrogen are leading to greater eutrophication of aquatic environments, but it is unclear how this impacts the flux and fate of carbon in lacustrine and riverine systems. Here, we present evidence that the form of nitrogen governs the partitioning of carbon among members in a genome-sequenced, model phototrophic biofilm of 20 members. Consumption of NO₃ ^- as the sole nitrogen source unexpectedly resulted in more rapid transfer of carbon to heterotrophs than when NH₄ ⁺ was also provided, suggesting alterations in the form of carbon exchanged. The form of nitrogen dramatically impacted net community nitrogen, but not carbon, uptake rates. Furthermore, this alteration in nitrogen form caused very large but focused alterations to community structure, strongly impacting the abundance of only two species within the biofilm and modestly impacting a third member species. Our data suggest that nitrogen metabolism may coordinate coupled carbon-nitrogen biogeochemical cycling in benthic biofilms and, potentially, in phototroph-heterotroph consortia more broadly. It further indicates that the form of nitrogen inputs may significantly impact the contribution of these communities to carbon partitioning across the terrestrial-aquatic interface.IMPORTANCE Anthropogenic inputs of nitrogen into aquatic ecosystems, and especially those of agricultural origin, involve a mix of chemical species. Although it is well-known in general that nitrogen eutrophication markedly influences the metabolism of aquatic phototrophic communities, relatively little is known regarding whether the specific chemical form of nitrogen inputs matter. Our data suggest that the nitrogen form alters the rate of nitrogen uptake significantly, whereas corresponding alterations in carbon uptake were minor. However, differences imposed by uptake of divergent nitrogen forms may result in alterations among phototroph-heterotroph interactions that rewire community metabolism. Furthermore, our data hint that availability of other nutrients (i.e., iron) might mediate the linkage between carbon and nitrogen cycling in these communities. Taken together, our data suggest that different nitrogen forms should be examined for divergent impacts on phototrophic communities in fluvial systems and that these anthropogenic nitrogen inputs may significantly differ in their ultimate biogeochemical impacts.

Development of low-cost cardiac and skeletal muscle laboratory activities to teach physiology concepts and the scientific method

Anatomy and Physiology courses taught at community colleges tend to focus laboratory hours primarily on anatomy as opposed to physiology. However, research demonstrates that, when instructors utilize active learning approaches (such as in laboratory settings) where students participate in their own learning, students have improved outcomes, such as higher test scores and better retention of material. To provide community college students with opportunities for active learning in physiology, we developed two laboratory exercises to engage students in cardiac and skeletal muscle physiology. We utilized low-cost SpikerBox devices to measure electrical activity during cardiac (electrocardiogram) and skeletal muscle (electromyogram) contraction. Laboratory activities were employed in Anatomy and Physiology courses at two community colleges in southeast Michigan. A 2-h laboratory period was structured with a 20-min slide presentation covering background material on the subject and experiments to examine the effects of environmental variables on nervous system control of cardiac and skeletal muscle contraction. Students were asked to provide hypotheses and proposed mechanisms, complete a results section, and provide conclusions for the experiments based on their results. Our laboratory exercises improved student learning in physiology and knowledge of the scientific method and were well-received by community college students enrolled in Anatomy and Physiology. Our results demonstrate that the use of a SpikerBox for cardiac and skeletal muscle physiology concepts is a low-cost and effective approach to integrate physiology activities into an Anatomy and Physiology course.

Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning

Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several shopfloor management (SM) systems can aid this conversion. They form two major factions. The first includes methodologies such as Balanced Scorecard (BSC). A defining feature is rigid structures to fixate on pre-defined goals. Other SM strategies instead concentrate on continuous improvement by giving directions. An example of this group is the "HOSHIN KANRI TREE" (HKT). One way of analyzing the dissimilarities, the advantages and disadvantages of these groups, is to examine the neurological patterns of workers as they are applying these. This paper aims to achieve this evaluation through non-invasive electroencephalography (EEG) sensors, which capture the electrical activity of the brain. A deep learning (DL) soft sensor is used to classify the recorded data with an accuracy of 96.5%. Through this result and an analysis using the correlations of the EEG signals, it has been possible to detect relevant characteristics and differences in the brain's activity. In conclusion, these findings are expected to help assess SM systems and give guidance to Industry 4.0 leaders.

Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence

Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology-imaging mass spectrometry-generate big hyper-spectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.

An approach for visualizing the spatial metabolome of an entire plant root system inspired by the Swiss-rolling technique

The spatial configuration and morphology of roots are commonly monitored for a better understanding of plant health and development. However, this approach provides minimal details about the biochemistry regulating the observable traits. Therefore, the ability to metabolically map the entire root structure would be of major value. Here, we developed a sample preparation approach that enables imaging of the entire root within a restricted space (width of microscope slide), which was influenced by the Swiss-rolling technique. We were able to image and confidently identify molecules along the entire root structure from rolled-root tissue sections using multiple spatially resolved mass spectrometry approaches.

Chemometric Strategies for Sensitive Annotation and Validation of Anatomical Regions of Interest in Complex Imaging Mass Spectrometry Data

Imaging mass spectrometry (IMS) is a promising new chemical imaging modality that generates a large body of complex imaging data, which in turn can be approached using multivariate analysis approaches for image analysis and segmentation. Processing IMS raw data is critically important for proper data interpretation and has significant effects on the outcome of data analysis, in particular statistical modeling. Commonly, data processing methods are chosen based on rational motivations rather than comparative metrics, though no quantitative measures to assess and compare processing options have been suggested. We here present a data processing and analysis pipeline for IMS data interrogation, processing and ROI annotation, segmentation, and validation. This workflow includes (1) objective evaluation of processing methods for IMS datasets based on multivariate analysis using PCA. This was then followed by (2) ROI annotation and classification through region-based active contours (AC) segmentation based on the PCA component scores matrix. This provided class information for subsequent (3) OPLS-DA modeling to evaluate IMS data processing based on the quality metrics of their respective multivariate models and for robust quantification of ROI-specific signal localization. This workflow provides an unbiased strategy for sensitive annotation of anatomical regions of interest combined with quantitative comparison of processing procedures for multivariate analysis allowing robust ROI annotation and quantification of the associated molecular histology.

Laboratory setup for rapid in situ powder X-ray diffraction elucidating Ni particle formation in supercritical methanol

The design and function of a custom-made Soller slit for a laboratory 2D area detector is presented through a series of demonstration images and an in situ experiment following the formation of nickel particles in supercritical methanol. The in situ experiment is performed in a capillary sample environment, modified for a laboratory scale Rigaku Smartlab diffractometer, and with a temperature range of 300-1050 K. The formation of nickel particles was followed successfully using laboratory in situ X-ray powder diffraction with a time resolution in the order of 27 s. Observations from the area detector images showed the appearance of three distinct phases during the reaction: Ni₃(NO₃)₂(OH)₄, NiO, and Ni. The images were linearly integrated and analyzed using Rietveld refinement. A reaction mechanism is proposed based on an evaluation of the weight fractions and scattering factors as a function of reaction time.

Spatiotemporal Transformation in the Alkaloid Profile of Pinus Roots in Response to Mycorrhization

Root alkaloids remain highly unexplored in ectomycorrhizae development studies. By employing ultrahigh mass resolution mass spectrometry imaging techniques, we showed substantial relocation and transformation of piperidine alkaloids in pine root tips in response to Suillus mycorrhization. We imaged, in the time frame of ectomycorrhizae formation, a completely different alkaloid profile in Pinus strobus, where basidiospores of Suillus spraguei induce morphogenesis of symbiotic tissues, than in Pinus taeda, where such interaction fails to induce morphogenesis. On the basis of spatial colocalization studies, we proposed some alternative routes for biosynthesis of these alkaloids that supplement existing literature data.

Environmental variables that ameliorate extinction learning deficits in the 129S1/SvlmJ mouse strain

Fear conditioning is an associative learning process by which organisms learn to avoid environmental stimuli that are predictive of aversive outcomes. Fear extinction learning is a process by which avoidance of fear-conditioned stimuli is attenuated when the environmental stimuli is no longer predictive of the aversive outcome. Aberrant fear conditioning and extinction learning are key elements in the development of several anxiety disorders. The 129S1 inbred strain of mice is used as an animal model for maladaptive fear learning because this strain has been shown to generalize fear to other nonaversive stimuli and is less capable of extinguishing fear responses relative to other mouse strains, such as the C57BL/6. Here we report new environmental manipulations that enhance fear and extinction learning, including the ability to discriminate between an aversively paired tone and a neutral tone, in both the 129S1 and C57BL/6 strains of mice. Specifically, we show that discontinuous ("pipped") tone stimuli significantly enhance within-session extinction learning and the discrimination between neutral and aversively paired stimuli in both strains. Furthermore, we find that extinction training in novel contexts significantly enhances the consolidation and recall of extinction learning for both strains. Cumulatively, these results underscore how environmental changes can be leveraged to ameliorate maladaptive learning in animal models and may advance cognitive and behavioral therapeutic strategies.

Macrophages restrict the nephrogenic field and promote endothelial connections during kidney development

The origins and functions of kidney macrophages in the adult have been explored, but their roles during development remain largely unknown. Here we characterise macrophage arrival, localisation, heterogeneity, and functions during kidney organogenesis. Using genetic approaches to ablate macrophages, we identify a role for macrophages in nephron progenitor cell clearance as mouse kidney development begins. Throughout renal organogenesis, most kidney macrophages are perivascular and express F4/80 and CD206. These macrophages are enriched for mRNAs linked to developmental processes, such as blood vessel morphogenesis. Using antibody-mediated macrophage-depletion, we show macrophages support vascular anastomoses in cultured kidney explants. We also characterise a subpopulation of galectin-3⁺ (Gal3⁺) myeloid cells within the developing kidney. Our findings may stimulate research into macrophage-based therapies for renal developmental abnormalities and have implications for the generation of bioengineered kidney tissues.

The kidneys clean our blood by filtering out waste products while ensuring that useful components, like nutrients, remain in the bloodstream. Blood enters the kidneys through a network of intricately arranged blood vessels, which associate closely with the ‘cleaning tubes’ that carry out filtration. Human kidneys start developing during the early phases of embryonic development. During this process, the newly forming blood vessels and cleaning tubes must grow in the right places for the adult kidney to work properly.

Macrophages are cells of the immune system that clear away foreign, diseased, or damaged cells. They are also thought to encourage growth of the developing kidney, but how exactly they do this has remained unknown. Munro et al. therefore wanted to find out when macrophages first appeared in the embryonic kidney and how they might help control their development.

Experiments using mice revealed that the first macrophages arrived in the kidney early during its development, alongside newly forming blood vessels. Further investigation using genetically modified mice that did not have macrophages revealed that these immune cells were needed at this stage to clear away misplaced kidney cells and help ‘set the scene’ for future development.

At later stages, macrophages in the kidney interacted closely with growing blood vessels. As well as producing molecules linked with blood vessel formation, the macrophages wrapped around the vessels themselves, sometimes even eating cells lining the vessels and the blood cells carried within them. These observations suggested that macrophages actively shaped the network of blood vessels developing within the kidneys. Experiments removing macrophages from kidney tissue confirmed this: in normal kidneys, the blood vessels grew into a continuous network, but in kidneys lacking macrophages, far fewer connections formed between the vessels.

This work sheds new light on how the complex structures in the adult kidney first arise and could be useful in future research. For example, adding macrophages to simplified, laboratory-grown ‘mini-kidneys’ could make them better models to study kidney growth, while patients suffering from kidney diseases might benefit from new drugs targeting macrophages.

Automated Nuclear Cartography Reveals Conserved Sperm Chromosome Territory Localization across 2 Million Years of Mouse Evolution

Measurements of nuclear organization in asymmetric nuclei in 2D images have traditionally been manual. This is exemplified by attempts to measure chromosome position in sperm samples, typically by dividing the nucleus into zones, and manually scoring which zone a fluorescence in-situ hybridisation (FISH) signal lies in. This is time consuming, limiting the number of nuclei that can be analyzed, and prone to subjectivity. We have developed a new approach for automated mapping of FISH signals in asymmetric nuclei, integrated into an existing image analysis tool for nuclear morphology. Automatic landmark detection defines equivalent structural regions in each nucleus, then dynamic warping of the FISH images to a common shape allows us to generate a composite of the signal within the entire cell population. Using this approach, we mapped the positions of the sex chromosomes and two autosomes in three mouse lineages (Mus musculus domesticus, Mus musculus musculus and Mus spretus). We found that in all three, chromosomes 11 and 19 tend to interact with each other, but are shielded from interactions with the sex chromosomes. This organization is conserved across 2 million years of mouse evolution.

Hidden Rice Diversity in the Guianas

Traditional crop varieties are an important source of genetic diversity for crop adaptation and modern breeding. Landraces of Asian (Oryza sativa) and African (Oryza glaberrima) rice have been well studied on the continents where they were domesticated. However, their history of cultivation in northern South America is poorly understood. Here, we reveal the rice diversity that is maintained by Maroons, descendants of enslaved Africans who fled to the interior forests of the Guianas ca. 300 years ago. We interviewed subsistence farmers who practice shifting cultivation along the Maroni and Lawa rivers that form the natural border between French Guiana and Suriname, and used ethnobotanical and morphological methods to identify around 50 varieties, of which 15 were previously undocumented. The genetic origin of these varieties was explored using the Angiosperms353 universal probe set. Despite the large distances between sites and relative inaccessibility of the area, phenotypic and genetic diversity did not display any geographic structure, which is consistent with knowledge of seed exchange among members of the same ethnolinguistic group. Although improved US cultivars were introduced in Maroon villages in the 1940s, these have not displaced the traditional landraces, which are cherished for their taste and nutritious qualities and for their importance in Maroon spiritual life. The unique agricultural and ritual practices of Maroons confirm their role as custodians of rice diversity, a role that is currently under threat from external pressures and encroaching globalization. We expect that the rice diversity uncovered in this study represents only a fraction of the total diversity in the Guianas and may constitute a large untapped resource that holds promise for future rice improvement. Further efforts to inventory and preserve these landraces will help to protect a precious cultural heritage and local food security.