Whole-body integration of gene expression and single-cell morphology.

Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets.

Standardizing macromolecular structure files: further efforts are needed.

Investigating large datasets of biological information by automatic procedures may offer chances of progress in knowledge. Recently, tremendous improvements in structural biology have allowed the number of structures in the Protein Data Bank (PDB) archive to increase rapidly, in particular those for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-associated proteins. However, their automatic analysis can be hampered by the nonuniform descriptors used by authors in some records of the PDB and PDBx/mmCIF files. In this opinion article we highlight the difficulties encountered in automating the analysis of hundreds of structures, suggesting that further standardization of the description of these molecular entities and of their attributes, generalized to the macromolecular structures contained in the PDB, might generate files more suitable for automatized analyses of a large number of structures.

Programming patchy particles for materials assembly design.

Direct design of complex functional materials would revolutionize technologies ranging from printable organs to novel clean energy devices. However, even incremental steps toward designing functional materials have proven challenging. If the material is constructed from highly complex components, the design space of materials properties rapidly becomes too computationally expensive to search. On the other hand, very simple components such as uniform spherical particles are not powerful enough to capture rich functional behavior. Here, we introduce a differentiable materials design model with components that are simple enough to design yet powerful enough to capture complex materials properties: rigid bodies composed of spherical particles with directional interactions (patchy particles). We showcase the method with self-assembly designs ranging from open lattices to self-limiting clusters, all of which are notoriously challenging design goals to achieve using purely isotropic particles. By directly optimizing over the location and interaction of the patches on patchy particles using gradient descent, we dramatically reduce the computation time for finding the optimal building blocks.

Interpreting chemisorption strength with AutoML-based feature deletion experiments.

The chemisorption energy of reactants on a catalyst surface, [Formula: see text], is among the most informative characteristics of understanding and pinpointing the optimal catalyst. The intrinsic complexity of catalyst surfaces and chemisorption reactions presents significant difficulties in identifying the pivotal physical quantities determining [Formula: see text]. In response to this, the study proposes a methodology, the feature deletion experiment, based on Automatic Machine Learning (AutoML) for knowledge extraction from a high-throughput density functional theory (DFT) database. The study reveals that, for binary alloy surfaces, the local adsorption site geometric information is the primary physical quantity determining [Formula: see text], compared to the electronic and physiochemical properties of the catalyst alloys. By integrating the feature deletion experiment with instance-wise variable selection (INVASE), a neural network-based explainable AI (XAI) tool, we established the best-performing feature set containing 21 intrinsic, non-DFT computed properties, achieving an MAE of 0.23 eV across a periodic table-wide chemical space involving more than 1,600 types of alloys surfaces and 8,400 chemisorption reactions. This study demonstrates the stability, consistency, and potential of AutoML-based feature deletion experiment in developing concise, predictive, and theoretically meaningful models for complex chemical problems with minimal human intervention.

MyoV: a deep learning-based tool for the automated quantification of muscle fibers.

Accurate approaches for quantifying muscle fibers are essential in biomedical research and meat production. In this study, we address the limitations of existing approaches for hematoxylin and eosin-stained muscle fibers by manually and semiautomatically labeling over 660 000 muscle fibers to create a large dataset. Subsequently, an automated image segmentation and quantification tool named MyoV is designed using mask regions with convolutional neural networks and a residual network and feature pyramid network as the backbone network. This design enables the tool to allow muscle fiber processing with different sizes and ages. MyoV, which achieves impressive detection rates of 0.93-0.96 and precision levels of 0.91-0.97, exhibits a superior performance in quantification, surpassing both manual methods and commonly employed algorithms and software, particularly for whole slide images (WSIs). Moreover, MyoV is proven as a powerful and suitable tool for various species with different muscle development, including mice, which are a crucial model for muscle disease diagnosis, and agricultural animals, which are a significant meat source for humans. Finally, we integrate this tool into visualization software with functions, such as segmentation, area determination and automatic labeling, allowing seamless processing for over 400 000 muscle fibers within a WSI, eliminating the model adjustment and providing researchers with an easy-to-use visual interface to browse functional options and realize muscle fiber quantification from WSIs.

Lactylation prediction models based on protein sequence and structural feature fusion.

Lysine lactylation (Kla) is a newly discovered posttranslational modification that is involved in important life activities, such as glycolysis-related cell function, macrophage polarization and nervous system regulation, and has received widespread attention due to the Warburg effect in tumor cells. In this work, we first design a natural language processing method to automatically extract the 3D structural features of Kla sites, avoiding potential biases caused by manually designed structural features. Then, we establish two Kla prediction frameworks, Attention-based feature fusion Kla model (ABFF-Kla) and EBFF-Kla, to integrate the sequence features and the structure features based on the attention layer and embedding layer, respectively. The results indicate that ABFF-Kla and Embedding-based feature fusion Kla model (EBFF-Kla), which fuse features from protein sequences and spatial structures, have better predictive performance than that of models that use only sequence features. Our work provides an approach for the automatic extraction of protein structural features, as well as a flexible framework for Kla prediction. The source code and the training data of the ABFF-Kla and the EBFF-Kla are publicly deposited at: https://github.com/ispotato/Lactylation_model.

Batteryless implantable device with built-in mechanical clock for automated and precisely timed drug administration.

Adherence to medication plays a crucial role in the effective management of chronic diseases. However, patients often miss their scheduled drug administrations, resulting in suboptimal disease control. Therefore, we propose an implantable device enabled with automated and precisely timed drug administration. Our device incorporates a built-in mechanical clock movement to utilize a clockwork mechanism, i.e., a periodic turn of the hour axis, enabling automatic drug infusion at precise 12-h intervals. The actuation principle relies on the sophisticated design of the device, where the rotational movement of the hour axis is converted into potential mechanical energy and is abruptly released at the exact moment for drug administration. The clock movement can be charged either automatically by mechanical agitations or manually by winding the crown, while the device remains implanted, thereby enabling the device to be used permanently without the need for batteries. When tested using metoprolol, an antihypertensive drug, in a spontaneously hypertensive animal model, the implanted device can deliver drug automatically at precise 12-h intervals without the need for further attention, leading to similarly effective blood pressure control and ultimately, prevention of ventricular hypertrophy as compared with scheduled drug administrations. These findings suggest that our device is a promising alternative to conventional methods for complex drug administration.

From contigs towards chromosomes: automatic improvement of long read assemblies (ILRA).

Recent advances in long read technologies not only enable large consortia to aim to sequence all eukaryotes on Earth, but they also allow individual laboratories to sequence their species of interest with relatively low investment. Long read technologies embody the promise of overcoming scaffolding problems associated with repeats and low complexity sequences, but the number of contigs often far exceeds the number of chromosomes and they may contain many insertion and deletion errors around homopolymer tracts. To overcome these issues, we have implemented the ILRA pipeline to correct long read-based assemblies. Contigs are first reordered, renamed, merged, circularized, or filtered if erroneous or contaminated. Illumina short reads are used subsequently to correct homopolymer errors. We successfully tested our approach by improving the genome sequences of Homo sapiens, Trypanosoma brucei, and Leptosphaeria spp., and by generating four novel Plasmodium falciparum assemblies from field samples. We found that correcting homopolymer tracts reduced the number of genes incorrectly annotated as pseudogenes, but an iterative approach seems to be required to correct more sequencing errors. In summary, we describe and benchmark the performance of our new tool, which improved the quality of novel long read assemblies up to 1 Gbp. The pipeline is available at GitHub: https://github.com/ThomasDOtto/ILRA.

SmartGate is a spatial metabolomics tool for resolving tissue structures.

Imaging mass spectrometry (IMS) is one of the powerful tools in spatial metabolomics for obtaining metabolite data and probing the internal microenvironment of organisms. It has dramatically advanced the understanding of the structure of biological tissues and the drug treatment of diseases. However, the complexity of IMS data hinders the further acquisition of biomarkers and the study of certain specific activities of organisms. To this end, we introduce an artificial intelligence tool, SmartGate, to enable automatic peak selection and spatial structure identification in an iterative manner. SmartGate selects discriminative m/z features from the previous iteration by differential analysis and employs a graph attention autoencoder model to perform spatial clustering for tissue segmentation using the selected features. We applied SmartGate to diverse IMS data at multicellular or subcellular spatial resolutions and compared it with four competing methods to demonstrate its effectiveness. SmartGate can significantly improve the accuracy of spatial segmentation and identify biomarker metabolites based on tissue structure-guided differential analysis. For multiple consecutive IMS data, SmartGate can effectively identify structures with spatial heterogeneity by introducing three-dimensional spatial neighbor information.

An automatic immunofluorescence pattern classification framework for HEp-2 image based on supervised learning.

Immunofluorescence patterns of anti-nuclear antibodies (ANAs) on human epithelial cell (HEp-2) substrates are important biomarkers for the diagnosis of autoimmune diseases. There are growing clinical requirements for an automatic readout and classification of ANA immunofluorescence patterns for HEp-2 images following the taxonomy recommended by the International Consensus on Antinuclear Antibody Patterns (ICAP). In this study, a comprehensive collection of HEp-2 specimen images covering a broad range of ANA patterns was established and manually annotated by experienced laboratory experts. By utilizing a supervised learning methodology, an automatic immunofluorescence pattern classification framework for HEp-2 specimen images was developed. The framework consists of a module for HEp-2 cell detection and cell-level feature extraction, followed by an image-level classifier that is capable of recognizing all 14 classes of ANA immunofluorescence patterns as recommended by ICAP. Performance analysis indicated an accuracy of 92.05% on the validation dataset and 87% on an independent test dataset, which has surpassed the performance of human examiners on the same test dataset. The proposed framework is expected to contribute to the automatic ANA pattern recognition in clinical laboratories to facilitate efficient and precise diagnosis of autoimmune diseases.

Automatic ICD-10-CM coding via Lambda-Scaled attention based deep learning model.

The International Classification of Diseases (ICD) serves as a global healthcare administration standard, with one of its editions being ICD-10-CM, an enhanced diagnostic classification system featuring numerous new codes for specific anatomic sites, co-morbidities, and causes. These additions facilitate conveying the complexities of various diseases. Currently, ICD-10 coding is widely adopted worldwide. However, public hospitals in Pakistan have yet to implement it and automate the coding process. In this research, we implemented ICD-10-CM coding for a private database and named it Clinical Pool of Liver Transplant (CPLT). Additionally, we proposed a novel deep learning model called Deep Recurrent-Convolution Neural Network with a lambda-scaled Attention module (DRCNN-ATT) using the CPLT database to achieve automatic ICD-10-CM coding. DRCNN-ATT combines a bi-directional long short-term memory network (bi-LSTM), a multi-scale convolutional neural network (MS-CNN), and a lambda-scaled attention module. Experimental results demonstrate that deep recurrent convolutional neural network (DRCNN) faces attention score vanishing problem with a standard attention module for automatic ICD coding. However, adding a lambda-scaled attention module resolves this issue. We evaluated DRCNN-ATT model using two distinct datasets: a private CPLT dataset and a public MIMIC III top 50 dataset. The results indicate that the DRCNN-ATT model outperformed various baselines by generating 0.862 micro F1 and 0.25 macro F1 scores on CPLT dataset and 0.705 micro F1 and 0.655 macro F1 scores on MIMIC III top 50 dataset. Furthermore, we also deployed our model for automatic ICD-10-CM coding using ngrok and the Flask APIs, which receives input, processes it, and then returns the results.

Induced emotion counter-regulation affects attentional inhibition of emotional information: ERP evidence from a randomized manipulation approach.

Automatic emotion counter-regulation refers to an unintentional attentional shift away from the current emotional state and toward information of the opposite valence. It is a useful emotion regulation skill that prevents the escalation of current emotional state. However, the cognitive mechanisms of emotion counter-regulation are not fully understood. Using a randomization approach, this study investigated how automatic emotion counter-regulation impacted attentional inhibition of emotional stimuli, an important aspect of emotion processing closely associated with emotion regulation and mental health. Forty-six university students were randomly assigned to an emotion counter-regulation group and a control group. The former group watched an anger-inducing video to evoke automatic emotion counter-regulation of anger, while the latter group watched an emotionally neutral video. Next, both groups completed a negative priming task of facial expressions with EEG recorded. In the emotion counter-regulation group, we observed an enhanced attentional inhibition of the angry, but not happy, faces, as indicated by a prolonger response time, a larger N2, and a smaller P3 in response to angry versus happy stimuli. These patterns were not observed in the control group, supporting the role of elicited emotion counter-regulation of anger in causing these modulation patterns in responses.

Charged Gold Nanoparticles for Target Identification-Alignment and Automatic Segmentation of CT Image-Guided Adaptive Radiotherapy in Small Hepatocellular Carcinoma.

Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification-alignment and automatic segmentation. The current study aims to improve sHCC imaging for ART using a gold nanoparticle (Au NP)-based CT contrast agent to enhance AI-driven automated image processing. The synthesized charged Au NPs demonstrated notable in vitro aggregation, low cytotoxicity, and minimal organ toxicity. Over time, an in situ sHCC mouse model was established for in vivo CT imaging at multiple time points. The enhanced CT images processed using 3D U-Net and 3D Trans U-Net AI models demonstrated high geometric and dosimetric accuracy. Therefore, charged Au NPs enable accurate and automatic sHCC segmentation in CT images using classical AI models, potentially addressing the technical challenges related to tumor identification, alignment, and automatic segmentation in CT-guided online ART.

Unveiling success determinants for AMB-assisted phase expansion of fusion proteins in ARP/wARP.

Fusion proteins (FPs) are frequently utilized as a biotechnological tool in the determination of macromolecular structures using X-ray methods. Here, we explore the use of different protein tags in various FP, to obtain initial phases by using them in a partial molecular replacement (MR) and constructing the remaining FP structure with ARP/wARP. Usually, the tag is removed prior to crystallization, however leaving the tag on may facilitate crystal formation, and structural determination by expanding phases from known to unknown segments of the complex. In this study, the Protein Data Bank was mined for an up-to-date list of FPs with the most used protein tags, Maltose Binding Protein (MBP), Green Fluorescent Protein (GFP), Thioredoxin (TRX), Glutathione transferase (GST) and the Small Ubiquitin-like Modifier Protein (SUMO). Partial MR using the protein tag, followed by automatic model building, was tested on a subset of 116 FP. The efficiency of this method was analyzed and factors that influence the coordinate construction of a substantial portions of the fused protein were identified. Using MBP, GFP, and SUMO as phase generators it was possible to build at least 75 % of the protein of interest in 36 of the 116 cases tested. Our results reveal that tag selection has a significant impact; tags with greater structural stability, such as GFP, increase the success rate. Further statistical analysis identifies that resolution, Wilson B factor, solvent percentage, completeness, multiplicity, protein tag percentage in the FP (considering amino acids), and the linker length play pivotal roles using our approach. In cases where a structural homologous is absent, this method merits inclusion in the toolkit of protein crystallographers.

Systematic review and meta-analysis of automated methods for quantifying enlarged perivascular spaces in the brain.

Research into magnetic resonance imaging (MRI)-visible perivascular spaces (PVS) has recently increased, as results from studies in different diseases and populations are cementing their association with sleep, disease phenotypes, and overall health indicators. With the establishment of worldwide consortia and the availability of large databases, computational methods that allow to automatically process all this wealth of information are becoming increasingly relevant. Several computational approaches have been proposed to assess PVS from MRI, and efforts have been made to summarise and appraise the most widely applied ones. We systematically reviewed and meta-analysed all publications available up to September 2023 describing the development, improvement, or application of computational PVS quantification methods from MRI. We analysed 67 approaches and 60 applications of their implementation, from 112 publications. The two most widely applied were the use of a morphological filter to enhance PVS-like structures, with Frangi being the choice preferred by most, and the use of a U-Net configuration with or without residual connections. Older adults or population studies comprising adults from 18 years old onwards were, overall, more frequent than studies using clinical samples. PVS were mainly assessed from T2-weighted MRI acquired in 1.5T and/or 3T scanners, although combinations using it with T1-weighted and FLAIR images were also abundant. Common associations researched included age, sex, hypertension, diabetes, white matter hyperintensities, sleep and cognition, with occupation-related, ethnicity, and genetic/hereditable traits being also explored. Despite promising improvements to overcome barriers such as noise and differentiation from other confounds, a need for joined efforts for a wider testing and increasing availability of the most promising methods is now paramount.

Medial positioning of the hippocampus and hippocampal fissure volume in developmental topographical disorientation.

Developmental topographical disorientation (DTD) refers to the lifelong inability to orient by means of cognitive maps in familiar surroundings despite otherwise well-preserved general cognitive functions, and the absence of any acquired brain injury or neurological condition. While reduced functional connectivity between the hippocampus and other brain regions has been reported in DTD individuals, no structural differences in gray matter tissue for the whole brain neither for the hippocampus were detected. Considering that the human hippocampus is the main structure associated with cognitive map-based navigation, here, we investigated differences in morphological and morphometric hippocampal features between individuals affected by DTD (N = 20) and healthy controls (N = 238). Specifically, we focused on a developmental anomaly of the hippocampus that is characterized by the incomplete infolding of hippocampal subfields during fetal development, giving the hippocampus a more round or pyramidal shape, called incomplete hippocampal inversion (IHI). We rated IHI according to standard criteria and extracted hippocampal subfield volumes after FreeSurfer's automatic segmentation. We observed similar IHI prevalence in the group of individuals with DTD with respect to the control population. Neither differences in whole hippocampal nor major hippocampal subfield volumes have been observed between groups. However, when assessing the IHI independent criteria, we observed that the hippocampus in the DTD group is more medially positioned comparing to the control group. In addition, we observed bigger hippocampal fissure volume for the DTD comparing to the control group. Both of these findings were stronger for the right hippocampus comparing to the left. Our results provide new insights regarding the hippocampal morphology of individuals affected by DTD, highlighting the role of structural anomalies during early prenatal development in line with the developmental nature of the spatial disorientation deficit.

Early detection of white matter hyperintensities using SHIVA-WMH detector.

White matter hyperintensities (WMHs) are well-established markers of cerebral small vessel disease, and are associated with an increased risk of stroke, dementia, and mortality. Although their prevalence increases with age, small and punctate WMHs have been reported with surprisingly high frequency even in young, neurologically asymptomatic adults. However, most automated methods to segment WMH published to date are not optimized for detecting small and sparse WMH. Here we present the SHIVA-WMH tool, a deep-learning (DL)-based automatic WMH segmentation tool that has been trained with manual segmentations of WMH in a wide range of WMH severity. We show that it is able to detect WMH with high efficiency in subjects with only small punctate WMH as well as in subjects with large WMHs (i.e., with confluency) in evaluation datasets from three distinct databases: magnetic resonance imaging-Share consisting of young university students, MICCAI 2017 WMH challenge dataset consisting of older patients from memory clinics, and UK Biobank with community-dwelling middle-aged and older adults. Across these three cohorts with a wide-ranging WMH load, our tool achieved voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than for three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LPA: Schmidt), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al.), and HyperMapper tool (Mojiri Forooshani et al.), another DL-based method with high reported performance in subjects with mild WMH burden. Our tool is publicly and openly available to the research community to facilitate investigations of WMH across a wide range of severity in other cohorts, and to contribute to our understanding of the emergence and progression of WMH.

Integrative phenotyping analyses reveal the relevance of the phyB-PIF4 pathway in Arabidopsis thaliana reproductive organs at high ambient temperature.

BACKGROUND: The increasing ambient temperature significantly impacts plant growth, development, and reproduction. Uncovering the temperature-regulating mechanisms in plants is of high importance, for increasing our fundamental understanding of plant thermomorphogenesis, for its potential in applied science, and for aiding plant breeders in improving plant thermoresilience. Thermomorphogenesis, the developmental response to warm temperatures, has been primarily studied in seedlings and in the regulation of flowering time. PHYTOCHROME B and PHYTOCHROME-INTERACTING FACTORs (PIFs), particularly PIF4, are key components of this response. However, the thermoresponse of other adult vegetative tissues and reproductive structures has not been systematically evaluated, especially concerning the involvement of phyB and PIFs. RESULTS: We screened the temperature responses of the wild type and several phyB-PIF4 pathway Arabidopsis mutant lines in combined and integrative phenotyping platforms for root growth in soil, shoot, inflorescence, and seed. Our findings demonstrate that phyB-PIF4 is generally involved in the relay of temperature signals throughout plant development, including the reproductive stage. Furthermore, we identified correlative responses to high ambient temperature between shoot and root tissues. This integrative and automated phenotyping was complemented by monitoring the changes in transcript levels in reproductive organs. Transcriptomic profiling of the pistils from plants grown under high ambient temperature identified key elements that may provide insight into the molecular mechanisms behind temperature-induced reduced fertilization rate. These include a downregulation of auxin metabolism, upregulation of genes involved auxin signalling, miRNA156 and miRNA160 pathways, and pollen tube attractants. CONCLUSIONS: Our findings demonstrate that phyB-PIF4 involvement in the interpretation of temperature signals is pervasive throughout plant development, including processes directly linked to reproduction.

"On-the-fly" Crystal : How to reliably and automatically characterize and construct potential energy surfaces.

In this work, the Crystal  code, developed previously by the authors to find "holes" as well as legitimate transition states in existing potential energy surface (PES) functions [JPC Lett. 11, 6468 (2020)], is retooled to perform on-the-fly "direct dynamics"-type PES explorations, as well as automatic construction of new PES functions. In all of these contexts, the chief advantage of Crystal  over other methods is its ability to globally map the PES, thereby determining the most relevant regions of configuration space quickly and reliably-even when the dimensionality is rather large. Here, Crystal  is used to generate a uniformly spaced grid of density functional theory (DFT) or ab initio points, truncated over the relevant regions, which can then be used to either: (a) hone in precisely on PES features such as minima and transition states, or; (b) create a new PES function automatically, via interpolation. Proof of concept is demonstrated via application to three molecular systems: water (H 2 O), (reduced-dimensional) methane (CH 4 ), and methylene imine (CH 2 NH).

Antigravity Autonomous Superwettable Pumps for Spontaneous Separation of Oil-Water Emulsions.

Oil-water separation based on superwettable materials offers a promising way for the treatment of oil-water mixtures and emulsions. Nevertheless, such separation techniques often require complex devices and external energy input. Therefore, it remains a great challenge to separate oil-water mixtures and emulsions through an energy-efficient, economical, and sustainable way. Here, a novel approach demonstrating the successful separation of oil-water emulsions using antigravity-driven autonomous superwettable pumps is presented. By transitioning from traditional gravity-driven to antigravity-driven separation, the study showcases the unprecedented success in purifying oil/water from emulsions by capillary/siphon-driven superwettable autonomous pumps. These pumps, composed of self-organized interconnected channels formed by the packing of superhydrophobic and superhydrophilic sand particles, exhibit outstanding separation flux, efficiency, and recyclability. The findings of this study not only open up a new avenue for oil-water emulsion separation but also hold promise for profound impacts in the field.