Mapping Function Research Articles

Combining Hyperpolarized 129Xe MR Imaging and Spectroscopy to Non-Invasively Estimate Pulmonary Vascular Resistance.

Hyperpolarized 129Xe MRI/MRS enables quantitative mapping of function in lung airspaces, membrane tissue, and red blood cells (RBCs) within the pulmonary capillaries. The RBC signal also exhibits cardiogenic oscillations that are reduced in pre-capillary pulmonary hypertension (PH). This effect is obscured in patients with concomitant defects in transfer from airspaces to RBCs, which increase RBC oscillation amplitudes. Here, we provide a framework for interpreting RBC oscillations and show their relationship to pulsatile blood flow, capillary blood volume, capillary compliance, and impedance of the capillary and venous circulation. This framework was first applied to characterize RBC oscillations in a cohort of subjects with pulmonary disease but no known PH (n=129). 129Xe MRI of RBC transfer was used to estimate capillary blood volume, and as it decreased, RBC oscillations sharply increased (r2adj=0.53), consistent with model predictions. Model-derived fit parameters were then used to estimate the distribution of pulmonary vascular resistance (PVR) across arterial, capillary, and venous circulation, and to correct oscillations for RBC transfer defects. 70% of PVR was estimated to arise from pulmonary arteries, 11% from capillaries, and 19% from veins. When tested in a second cohort of subjects who underwent 129Xe MRI/MRS and right heart catheterization (n=40), oscillations corrected for capillary blood volume correlated moderately with PVR (r2=0.27, p=0.0014). For every 1.96 WU increase in PVR, corrected oscillations decreased by 1 absolute percentage point. This work demonstrates that, although 129Xe-RBC oscillations are only indirectly sensitive to pre-capillary obstruction, corrected oscillations below 7.5% were 100% specific for elevated PVR.

First-in-human experience performing high-resolution cortical mapping using a novel microelectrode array containing 1,024 electrodes.

Localization of function within the brain and central nervous system is an essential aspect of clinical neuroscience. Classical descriptions of functional neuroanatomy provide a foundation for understanding the functional significance of identifiable anatomic structures. However, individuals exhibit substantial variation, particularly in the presence of disorders that alter tissue structure or impact function. Furthermore, functional regions do not always correspond to identifiable structural features. Understanding function at the level of individual patients-and diagnosing and treating such patients-often requires techniques capable of correlating neural activity with cognition, behavior, and experience in anatomically precise ways. 

Approach: Recent advances in brain-computer interface technology have given rise to a new generation of electrophysiologic tools for scalable, nondestructive functional mapping with spatial precision in the range of tens to hundreds of micrometers, and temporal resolutions in the range of tens to hundreds of microseconds. Here we describe our initial intraoperative experience with novel, thin-film arrays containing 1024 surface microelectrodes for electrocorticographic mapping in a first-in-human study. 

Main results: Six patients undergoing standard electrophysiologic cortical mapping during resection of eloquent-region brain tumors consented to brief sessions of concurrent mapping (micro-electrocorticography) using the novel arrays. Three patients underwent motor mapping using somatosensory evoked potentials while under general anesthesia, and three underwent awake language mapping, using both standard paradigms and the novel microelectrode array. Somatosensory evoked potential phase reversal was identified in the region predicted by conventional mapping, but at higher resolution (0.4 mm) and as a contour rather than as a point. In Broca's area (confirmed by direct cortical stimulation), speech planning was apparent in the micro-electrocorticogram as high-amplitude beta-band activity immediately prior to the articulatory event.

Significance: These findings support the feasibility and potential clinical utility of incorporating micro-electrocorticography into the intraoperative workflow for systematic cortical mapping of functional brain regions.

Causal Associations Between Remnant Cholesterol Levels and Atherosclerosis-Related Cardiometabolic Risk Factors: A Bidirectional Mendelian Randomization Analysis

Background: Despite the widespread use of lipid-lowering agents, the risk of atherosclerotic cardiovascular disease (ASCVD) remains; this residual risk has been attributed to remnant cholesterol (RC) levels. However, the causal associations between RC levels and various atherosclerosis-related cardiometabolic and vascular risk factors for ASCVD remain unclear. Methods: Using genetic and biochemical data of 108,876 Taiwan Biobank study participants, follow-up data of 31,790 participants, and follow-up imaging data of 18,614 participants, we conducted a genome-wide association study, a Functional Mapping and Annotation analysis, and bidirectional Mendelian randomization analyses to identify the genetic determinants of RC levels and the causal associations between RC levels and various cardiometabolic and vascular risk factors. Results: We found that higher RC levels were associated with higher prevalence or incidence of the analyzed risk factors. The genome-wide association study unveiled 61 lead genetic variants determining RC levels. The Functional Mapping and Annotation analysis revealed 21 gene sets exhibiting strong enrichment signals associated with lipid metabolism. Standard Mendelian randomization models adjusted for nonlipid variables and low-density lipoprotein cholesterol levels unraveled forward causal associations of RC levels with the prevalence of diabetes mellitus, hypertension, microalbuminuria, and metabolic liver disease. Reverse Mendelian randomization analysis revealed the causal association of diabetes mellitus with RC levels. Conclusions: RC levels, mainly influenced by genes associated with lipid metabolism, exhibit causal associations with various cardiometabolic risk factors, including diabetes mellitus, hypertension, microalbuminuria, and metabolic liver disease. This study provides further insights into the role of RC levels in predicting the residual risk of ASCVD.

Neural Density Functional Theory of Liquid-Gas Phase Coexistence

We use supervised machine learning together with the concepts of classical density functional theory to investigate the effects of interparticle attraction on the pair structure, thermodynamics, bulk liquid-gas coexistence, and associated interfacial phenomena in many-body systems. Local learning of the one-body direct correlation functional is based on Monte Carlo simulations of inhomogeneous systems with randomized thermodynamic conditions, randomized planar shapes of the external potential, and randomized box sizes. Focusing on the prototypical Lennard-Jones system, we test predictions of the resulting neural attractive density functional across a broad spectrum of physical behavior associated with liquid-gas phase coexistence in bulk and at interfaces. We analyze the bulk radial distribution function g(r) obtained from automatic differentiation and the Ornstein-Zernike route and determine (i) the Fisher-Widom line, i.e., the crossover of the asymptotic (large distance) decay of g(r) from monotonic to oscillatory, (ii) the (Widom) line of maximal correlation length, (iii) the line of maximal isothermal compressibility, and (iv) the spinodal by calculating the poles of the structure factor in the complex plane. The bulk binodal and the density profile of the free liquid-gas interface are obtained from density functional minimization and the corresponding surface tension from functional line integration. We also show that the neural functional describes accurately the phenomena of drying at a hard wall and of capillary evaporation for a liquid confined in a slit pore. Our neural framework yields results that improve significantly upon standard mean-field treatments of interparticle attraction. Comparison with independent simulation results demonstrates a consistent picture of phase separation even when restricting the training to supercritical states only. We argue that phase coexistence and its associated signatures can be discovered as emerging phenomena via functional mappings and educated extrapolation. Published by the American Physical Society 2025

A Minimum Potential Energy-Based Nonlocal Physics-Informed Deep Learning Method for Solid Mechanics

Abstract Although success is achieved by physics-informed neural networks (PINNs) as a deep learning solver in many fields, they face some challenges when solving solid mechanics problems. The most notable challenges include the neural network mapping of discontinuous field functions and the time-consuming of training PINNs. To tackle these challenges, this article proposes a minimum potential energy-based nonlocal physics-informed deep learning method (MPE-nPINNs), instead of relying on physical constraints expressed in strong form partial differential equations (PDEs). Additionally, we redesign the neural network structure by integrating peridynamic damage features as additional inputs, which can enhance the ability of the networks to describe the discontinuous field and reduce the size of the networks. We evaluate the training efficiency of the proposed method in problems of solid mechanics through comparative examples, and we verify the effectiveness of incorporating peridynamic damage features into optimizing the network structure. The numerical results indicate that the MPE-nPINNs method exhibits superior convergence speed and effectively characterizes discontinuous field functions with fewer number of hyperparameters of neural networks. This study has significant importance in enhancing the generalization ability of physics-informed neural networks and expediting optimization processes.

FldtMatch: Improving Unbalanced Data Classification via Deep Semi-Supervised Learning with Self-Adaptive Dynamic Threshold

Among the many methods of deep semi-supervised learning (DSSL), the holistic method combines ideas from other methods, such as consistency regularization and pseudo-labeling, with great success. This method typically introduces a threshold to utilize unlabeled data. If the highest predictive value from unlabeled data exceeds the threshold, the associated class is designated as the data’s pseudo-label. However, current methods utilize fixed or dynamic thresholds, disregarding the varying learning difficulties across categories in unbalanced datasets. To overcome these issues, in this paper, we first designed Cumulative Effective Labeling (CEL) to reflect a particular class’s learning difficulty. This approach differs from previous methods because it uses effective pseudo-labels and ground truth, collectively influencing the model’s capacity to acquire category knowledge. In addition, based on CEL, we propose a simple but effective way to compute the threshold, Self-adaptive Dynamic Threshold (SDT). It requires a single hyperparameter to adjust to various scenarios, eliminating the necessity for a unique threshold modification approach for each case. SDT utilizes a clever mapping function that can solve the problem of differential learning difficulty of various categories in an unbalanced image dataset that adversely affects dynamic thresholding. Finally, we propose a deep semi-supervised method with SDT called FldtMatch. Through theoretical analysis and extensive experiments, we have fully proven that FldtMatch can overcome the negative impact of unbalanced data. Regardless of the choice of the backbone network, our method achieves the best results on multiple datasets. The maximum improvement of the macro F1-Score metric is about 5.6% in DFUC2021 and 2.2% in ISIC2018.

Optimized genetic tools for neuroanatomical and functional mapping of the Aedes aegypti olfactory system.

The mosquito Aedes aegypti is an emerging model insect for invertebrate neurobiology. We detail the application of a dual transgenesis marker system that reports the nature of transgene integration with circular donor template for CRISPR-Cas9-mediated homology-directed repair at target mosquito chemoreceptor genes. Employing this approach, we demonstrate the establishment of cell-type-specific T2A-QF2 driver lines for the A. aegypti olfactory co-receptor genes Ir8a and orco via canonical homology-directed repair and the CO2 receptor complex gene Gr1 via noncanonical homology-directed repair involving duplication of the intended T2A-QF2 integration cassette separated by intervening donor plasmid sequence. Using Gr1+ olfactory sensory neurons as an example, we show that introgression of such T2A-QF2 driver and QUAS responder transgenes into a yellow cuticular pigmentation mutant strain facilitates transcuticular calcium imaging of CO2-evoked neural activity on the maxillary palps with enhanced sensitivity relative to wild-type mosquitoes enveloped by dark melanized cuticle. We further apply Cre-loxP excision to derive marker-free T2A-QF2 in-frame fusions to clearly map axonal projection patterns from olfactory sensory neurons expressing these 3 chemoreceptors into the A. aegypti antennal lobe devoid of background interference from 3xP3-based fluorescent transgenesis markers. The marker-free Gr1 T2A-QF2 driver facilitates clear recording of CO2-evoked responses in this central brain region using the genetically encoded calcium indicators GCaMP6s and CaMPARI2. Systematic application of these optimized methods to different chemoreceptors stands to enable mapping A. aegypti olfactory circuits at peripheral and central levels of olfactory coding at high resolution.

A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology

A major goal of cancer biology is to understand the mechanisms driven by somatically acquired mutations. Two distinct methodologies—one analyzing mutation clustering within protein sequences and 3D structures, the other leveraging protein-protein interaction network topology—offer complementary strengths. We present NetFlow3D, a unified, end-to-end 3D structurally-informed protein interaction network propagation framework that maps the multiscale mechanistic effects of mutations. Built upon the Human Protein Structurome, which incorporates the 3D structures of every protein and the binding interfaces of all known protein interactions, NetFlow3D integrates atomic, residue, protein and network-level information: It clusters mutations on 3D protein structures to identify driver mutations and propagates their impacts anisotropically across the protein interaction network, guided by the involved interaction interfaces, to reveal systems-level impacts. Applied to 33 cancer types, NetFlow3D identifies 2 times more 3D clusters and incorporates 8 times more proteins in significantly interconnected network modules compared to traditional methods.

Zero-Shot Traffic Identification with Attribute and Graph-Based Representations for Edge Computing.

With the proliferation of mobile terminals and the rapid growth of network applications, fine-grained traffic identification has become increasingly challenging. Methods based on machine learning and deep learning have achieved remarkable results, but they heavily rely on the distribution of training data, which makes them ineffective in handling unseen samples. In this paper, we propose AG-ZSL, a zero-shot learning framework based on traffic behavior and attribute representations for general encrypted traffic classification. AG-ZSL primarily learns two mapping functions: one that captures traffic behavior embeddings from burst-based traffic interaction graphs, and the other that learns attribute embeddings from traffic attribute descriptions. Then, the framework minimizes the distance between these embeddings within the shared feature space. The gradient rejection algorithm and K-Nearest Neighbors are introduced to implement a two-stage method for general traffic classification. Experimental results on IoT datasets demonstrate that AG-ZSL achieves exceptional performance in classifying both known and unknown traffic, highlighting its potential for enhancing secure and efficient traffic management at the network edge.

Toward a physics-guided machine learning approach for predicting chaotic systems dynamics

Predicting the dynamics of chaotic systems is crucial across various practical domains, including the control of infectious diseases and responses to extreme weather events. Such predictions provide quantitative insights into the future behaviors of these complex systems, thereby guiding the decision-making and planning within the respective fields. Recently, data-driven approaches, renowned for their capacity to learn from empirical data, have been widely used to predict chaotic system dynamics. However, these methods rely solely on historical observations while ignoring the underlying mechanisms that govern the systems' behaviors. Consequently, they may perform well in short-term predictions by effectively fitting the data, but their ability to make accurate long-term predictions is limited. A critical challenge in modeling chaotic systems lies in their sensitivity to initial conditions; even a slight variation can lead to significant divergence in actual and predicted trajectories over a finite number of time steps. In this paper, we propose a novel Physics-Guided Learning (PGL) method, aiming at extending the scope of accurate forecasting as much as possible. The proposed method aims to synergize observational data with the governing physical laws of chaotic systems to predict the systems' future dynamics. Specifically, our method consists of three key elements: a data-driven component (DDC) that captures dynamic patterns and mapping functions from historical data; a physics-guided component (PGC) that leverages the governing principles of the system to inform and constrain the learning process; and a nonlinear learning component (NLC) that effectively synthesizes the outputs of both the data-driven and physics-guided components. Empirical validation on six dynamical systems, each exhibiting unique chaotic behaviors, demonstrates that PGL achieves lower prediction errors than existing benchmark predictive models. The results highlight the efficacy of our design of data-physics integration in improving the precision of chaotic system dynamics forecasts.

Seizure network characterization by functional connectivity mapping and manipulation.

Despite the availability of various anti-seizure medications, nearly 1/3 of epilepsy patients experience drug-resistant seizures. These patients are left with invasive surgical options that do not guarantee seizure remission. The development of novel treatment options depends on elucidating the complex biology of seizures and brain networks. We aimed to develop an experimental paradigm that uses anatomical network information, functional connectivity, and in vivo seizure models to determine how brain networks, and their manipulation, affect seizure propagation. Guided by a known anatomical network, we applied widefield calcium imaging to determine how neural activity and seizures spread through the network regions, focusing on the primary somatosensory cortex and secondary motor cortex. We used in vivo microstimulation to induce suprathreshold excitatory activation and compared this reproducible stimulus with acute pharmacologically induced spontaneous seizure propagation. In a proof-of-concept experiment, we ablated a single node within this bilateral network and measured the effect on propagation and recruitment. Similar preliminary experiments were repeated in a chronic seizure model. The microstimulation of the somatosensory cortex propagated in a distinct pattern throughout the bilateral network with sequential reproducible node recruitment. Seizures recapitulated this same pattern, indicating a hijacking of existing pathways. Ablation of a key node in the network in the secondary motor cortex changed contralateral spread. Early chronic cobalt seizure data are presented. Here, we demonstrate a paradigm for combining widefield calcium imaging with microstimulation, cortical ablation, and seizure mapping to determine how anatomical networks inform the propagation patterns of cortical seizures. These experiments can be extended to long-term tracking of epilepsy to study epileptogenesis in other cortical networks. Our proof-of-concept findings suggest that this paradigm may be useful in the development of novel therapies for drug-resistant epilepsy patients and can be extended to the study of other disorders involving brain networks.

Leveraging descriptor learning and functional map-based shape matching for automated anatomical Landmarking in mouse mandibles.

Geometric morphometrics is used in the biological sciences to quantify morphological traits. However, the need for manual landmark placement hampers scalability, which is both time-consuming, labor-intensive, and open to human error. The selected landmarks embody a specific hypothesis regarding the critical geometry relevant to the biological question. Any adjustment to this hypothesis necessitates acquiring a new set of landmarks or revising them significantly, which can be impractical for large datasets. There is a pressing need for more efficient and flexible methods for landmark placement that can adapt to different hypotheses without requiring extensive human effort. This study investigates the precision and accuracy of landmarks derived from functional correspondences obtained through the functional map framework of geometry processing. We utilize a deep functional map network to learn shape descriptors, which enable us to achieve functional map-based and point-to-point correspondences between specimens in our dataset. Our methodology involves automating the landmarking process by interrogating these maps to identify corresponding landmarks, using manually placed landmarks from the entire dataset as a reference. We apply our method to a dataset of rodent mandibles and compare its performance to MALPACA's, a standard tool for automatic landmark placement. Our model demonstrates a speed improvement compared to MALPACA while maintaining a competitive level of accuracy. Although MALPACA typically shows the lowest RMSE, our models perform comparably well, particularly with smaller training datasets, indicating strong generalizability. Visual assessments confirm the precision of our automated landmark placements, with deviations consistently falling within an acceptable range for MALPACA estimates. Our results underscore the potential of unsupervised learning models in anatomical landmark placement, presenting a practical and efficient alternative to traditional methods. Our approach saves significant time and effort and provides the flexibility to adapt to different hypotheses about critical geometrical features without the need for manual re-acquisition of landmarks. This advancement can significantly enhance the scalability and applicability of geometric morphometrics, making it more feasible for large datasets and diverse biological studies.

Neuroimaging model of visceral manipulation in awake rat.

Reciprocal neuronal connections exist between the internal organs of the body and the nervous system. These projections to and from the viscera play an essential role in maintaining and finetuning organ responses in order to sustain homeostasis and allostasis. Functional maps of brain regions participating in this bidirectional communication have been previously studied in awake humans and anesthetized rodents. To further refine the mechanistic understanding of visceral influence on brain states, however, new paradigms that allow for more invasive, and ultimately more informative, measurements and perturbations must be explored. Further, such paradigms should prioritize human translatability. In the current paper, we address these issues by demonstrating the feasibility of non-anesthetized animal imaging during visceral manipulation. More specifically, we used a barostat interfaced with an implanted gastric balloon to cyclically induce distension of a non-anesthetized male rat's stomach during simultaneous BOLD fMRI. General linear modeling and spatial independent component analysis revealed several regions with BOLD activation temporally coincident with the gastric distension stimulus. The ON-OFF (20 mmHg - 0 mmHg) barostat-balloon pressure cycle resulted in widespread BOLD activation of the inferior colliculus, cerebellum, ventral midbrain, and a variety of hippocampal structures. These results suggest that neuroimaging models of gastric manipulation in the non-anesthetized rat are achievable and provide an avenue for more comprehensive studies involving the integration of other neuroscience techniques like electrophysiology.Significance Statement It is unclear to what extent measurements of brain activity are affected by interoceptive processes. To advance our understanding of ongoing visceral activity's influence on brain states, here we provide a proof of concept, anesthesia-free animal model of visceral manipulation during simultaneous BOLD fMRI. We successfully demonstrated BOLD activation during gastric distension of the unanesthetized rat in both classically reported (cerebellum, hippocampus) and novel (inferior colliculus) regions. This paradigm establishes an important foundation for further interrogation of viscera-brain interactions.

Fantastic Discovery: Guidelines for Cataloging Fictional Maps

Geographic depictions of fictional spaces present a unique combination of features relevant to an array of disciplines. Scholars interested in cartography, the narrative functions of maps in literature, the specific universe which the materials depict, and the relationships between fictional and extant geography all have cause to seek out these resources. However, standard map cataloging practices can create records that do not prioritize elements most relevant to users searching for them. This paper compares different types of fictional maps, examines which aspects might be most important to users, and suggests how to best ensure their discovery as MARC records.

A Filtered Embedded Weighted Compact Non‐Linear Scheme for Hyperbolic Conservation Law

ABSTRACTIn situations where a wide range of flow scales are involved, the non‐linear scheme should be capable of both shock capturing and low‐dissipation. Most of the existing Weighted Compact Non‐linear Schemes (WCNS) are too dissipative and incapable of achieving fourth‐order for the two smooth stencils located on the same side of a discontinuity due to the weight deviations and the defect of the weighting strategy. In this paper, a novel filtered embedded WCNS is introduced for complex flow simulations involving both shock and small‐scale structures. To overcome the above deficiency of existing WCNS, a pre‐discrete mapping function is proposed to filter the weight deviation out and amend the inappropriate weights to ideal weights in smooth regions. Meanwhile, the embedded process is also implemented by this function, which is utilized to improve the resolution of shock capturing in certain discontinuity distributions. The pre‐discrete mapping function is also extended to the WENO framework. The approximate‐dispersion‐relation analysis indicates that the scheme with the mapping function has lower dispersion and dissipation error than the WCNS‐JS, WCNS‐Z, and WCNS‐T schemes. Numerical results show that WCNS with the new non‐linear weights captures discontinuities sharply without obvious oscillation, has a higher resolution than other non‐linear schemes, and has an obvious advantage in capturing small‐scale structures.

Multi-Scale Long- and Short-Range Structure Aggregation Learning for Low-Illumination Remote Sensing Imagery Enhancement

Profiting from the surprising non-linear expressive capacity, deep convolutional neural networks have inspired lots of progress in low illumination (LI) remote sensing image enhancement. The key lies in sufficiently exploiting both the specific long-range (e.g., non-local similarity) and short-range (e.g., local continuity) structures distributed across different scales of each input LI image to build an appropriate deep mapping function from the LI images to their corresponding high-quality counterparts. However, most existing methods can only individually exploit the general long-range or short-range structures shared across most images at a single scale, thus limiting their generalization performance in challenging cases. We propose a multi-scale long–short range structure aggregation learning network for remote sensing imagery enhancement. It features flexible architecture for exploiting features at different scales of the input low illumination (LI) image, with branches including a short-range structure learning module and a long-range structure learning module. These modules extract and combine structural details from the input image at different scales and cast them into pixel-wise scale factors to enhance the image at a finer granularity. The network sufficiently leverages the specific long-range and short-range structures of the input LI image for superior enhancement performance, as demonstrated by extensive experiments on both synthetic and real datasets.

Possible linking and treatment between Parkinson’s disease and inflammatory bowel disease: a study of Mendelian randomization based on gut–brain axis

BackgroundMounting evidence suggests that Parkinson’s disease (PD) and inflammatory bowel disease (IBD) are closely associated and becoming global health burdens. However, the causal relationships and common pathogeneses between them are uncertain. Furthermore, they are uncurable. Thus, we aimed to identify the causal relationships and novel therapeutic targets shared between them based on their common pathophysiological mechanisms in gut–brain-axis (GBA).MethodsA meta-analysis on bidirectional Mendelian randomization (MR) utilizing various datasets was performed to estimate their causal relationship. Then, pleiotropic analysis under the composite null hypothesis (PLACO) with functional mapping combined with annotation of genetic associations (FUMA) analysis were conducted to identify pleiotropic genes. Next, blood, brain and intestine expression quantitative trait locus (eQTL) were taken to perform drug-target MR finding common causal genes in two diseases. Colocalization analysis ensured the eQTLs of corresponding gene colocalized with disease. Enrichment analysis and protein‒protein interaction (PPI) network were done to explore common pathogenesis pathways. Genes passed all analysis were regarded as drug targets.ResultsOur MR meta-analysis revealed the bidirectional causal relationship between diseases, with combined ORs for PD on IBD, CD, UC (1.050 [95% CI 1.014–1.086], 1.044 [95% CI 0.995–1.095], 1.063 [95% CI 1.016–1.120]); for IBD, CD, UC on PD (1.003 [95% CI 0.973–1.034], 1.035 [95% CI 1.004–1.067], 1.008 [95% CI 0.977–1.040]). Overall, 277, 216 and 201 genes were identified as pleiotropic genes between PD and IBD, CD, UC. Total of 733 genes were classified as tier 3 (found in only one tissue) druggable targets, 57 as tier 2 (found in two tissues, 51 protein-coding genes) and 9 as tier 3 (found in three tissues). Among 60 protein-coding druggable targets over tier 2, 18 overlapped with pleiotropic genes and enriched in mitochondria, antigen presentation, processing and immune cell regulation pathways. Three druggable genes (LRRK2, RAB29 and HLA-DQA2) passed colocalization analysis. LRRK2 and RAB29 were reported to be pleiotropic genes, and RAB29 and HLA-DQA2 were reported for the first time as potential drug targets.ConclusionsThis study established a reliable causal relationship, possible shared drug targets and common pathogenesis pathways of two diseases, which had important implications for intervention and treatment of two diseases simultaneously.

A Neural-Network-Based Mapping and Optimization Framework for High-Precision Coarse-Grained Simulation.

The accuracy and efficiency of a coarse-grained (CG) force field are pivotal for high-precision molecular simulations of large systems with complex molecules. We present an automated mapping and optimization framework for molecular simulation (AMOFMS), which is designed to streamline and improve the force field optimization process. It features a neural-network-based mapping function, DSGPM-TP (deep supervised graph partitioning model with type prediction). This model can accurately and efficiently convert atomistic structures to CG mappings, reducing the need for manual intervention. By integrating bottom-up and top-down methodologies, AMOFMS allows users to freely combine these approaches or use them independently as optimization targets. Moreover, users can select and combine different optimizers to meet their specific mission. With its parallel optimizer, AMOFMS significantly accelerates the optimization process, reducing the time required to achieve optimal results. Successful applications of AMOFMS include parameter optimizations for systems such as POPC and PEO, demonstrating its robustness and effectiveness. Overall, AMOFMS provides a general and flexible framework for the automated development of high-precision CG force fields.

Mapping mitochondrial morphology and function: COX-SBFSEM reveals patterns in mitochondrial disease

Mitochondria play a crucial role in maintaining cellular health. It is interesting that the shape of mitochondria can vary depending on the type of cell, mitochondrial function, and other cellular conditions. However, there are limited studies that link functional assessment with mitochondrial morphology evaluation at high magnification, even fewer that do so in situ and none in human muscle biopsies. Therefore, we have developed a method which combines functional assessment of mitochondria through Cytochrome c Oxidase (COX) histochemistry, with a 3D electron microscopy (EM) technique, serial block-face scanning electron microscopy (SBFSEM). Here we apply COX-SBFSEM to muscle samples from patients with single, large-scale mtDNA deletions, a cause of mitochondrial disease. These deletions cause oxidative phosphorylation deficiency, which can be observed through changes in COX activity. One of the main advantages of combining 3D-EM with the COX reaction is the ability to look at how per-mitochondrion oxidative phosphorylation status is spatially distributed within muscle fibres. Here we show a robust spatial pattern in COX-positive and intermediate-fibres and that the spatial pattern is less clear in COX-deficient fibres.

High-resolution awake mouse fMRI at 14 tesla.

High-resolution awake mouse functional magnetic resonance imaging (fMRI) remains challenging despite extensive efforts to address motion-induced artifacts and stress. This study introduces an implantable radio frequency (RF) surface coil design that minimizes image distortion caused by the air/tissue interface of mouse brains while simultaneously serving as a headpost for fixation during scanning. Furthermore, this study provides a thorough acclimation method used to accustom animals to the MRI environment minimizing motion-induced artifacts. Using a 14 T scanner, high-resolution fMRI enabled brain-wide functional mapping of visual and vibrissa stimulation at 100 µm×100 µm×200 µm resolution with a 2 s per frame sampling rate. Besides activated ascending visual and vibrissa pathways, robust blood oxygen level-dependent (BOLD) responses were detected in the anterior cingulate cortex upon visual stimulation and spread through the ventral retrosplenial area (VRA) with vibrissa air-puff stimulation, demonstrating higher-order sensory processing in association cortices of awake mice. In particular, the rapid hemodynamic responses in VRA upon vibrissa stimulation showed a strong correlation with the hippocampus, thalamus, and prefrontal cortical areas. Cross-correlation analysis with designated VRA responses revealed early positive BOLD signals at the contralateral barrel cortex (BC) occurring 2 s prior to the air-puff in awake mice with repetitive stimulation, which was not detected using a randomized stimulation paradigm. This early BC activation indicated a learned anticipation through the vibrissa system and association cortices in awake mice under continuous exposure of repetitive air-puff stimulation. This work establishes a high-resolution awake mouse fMRI platform, enabling brain-wide functional mapping of sensory signal processing in higher association cortical areas.