Biology subjects

Hwang, G.

Publications and source records attributed to Hwang, G..

7 recordsLinked to original sources

bioRxiv2026

A physics-informed neural network framework for inferring infinitesimal phase response curves from finite-pulse circadian recordings

Biological clocks reset and entrain to external cues in a phase-dependent manner. The infinitesimal phase response curve (iPRC) provides the local dynamical rule underlying this process by quantifying how an input changes the instantaneous phase velocity of an oscillator. Inferring iPRCs would make it possible to predict how rhythms respond to new stimulus timings and durations, but existing approaches often require detailed mechanistic models, long perturbation recordings, or specialized population-level resetting experiments. Here we introduce Physics-informed neural network for iPRC inference (PINN-iPRC), a physics-informed neural network framework for inferring iPRCs from standard finite-pulse stimulus-response recordings. PINN-iPRC represents the unknown iPRC as a neural network and embeds the phase equation into the loss function, identifying the iPRC that leads to the observed transient phase trajectory under the known stimulus. In synthetic oscillators, PINN-iPRC accurately recovered iPRCs from finite-pulse recordings, including complex ones that are difficult to capture with simpler parametric reconstructions. Applied to existing experimental Arabidopsis circadian recordings, PINN-iPRC recovered iPRCs comparable to those obtained from specialized population-level resetting experiments, while requiring only conventional stimulus-response trajectories. The inferred iPRCs were further used to predict finite-duration PRCs and circadian peak phases across photoperiods. In particular, the predicted near-invariance of the prr7 peak phase across 8-16 h of darkness was consistent with independently published prr7-11 root recordings. These results support PINN-iPRC as a practical physics-informed approach for extracting interpretable phase-sensitivity functions from routine biological oscillator recordings, with potential applications in experimental design, entrainment prediction, and chronotherapeutic or pharmacological schedule optimization.

bioinformatics↗

bioRxiv2026

FERONIA limits jasmonic acid overaccumulation and oxidative stress to enable plant survival at elevated temperatures

Plants, as sessile organisms, must continually adapt to fluctuating temperatures to ensure survival. The plasma membrane-localized receptor-like kinase FERONIA (FER) coordinates diverse physiological processes and responses to various biotic and abiotic stresses. However, the role of FER in plant adaptation to elevated temperatures remains largely unexplored. Here, we report that FER is indispensable for plant thermotolerance. We found that fer loss-of-function mutants exhibit impaired thermomorphogenic growth and are hypersensitive to mild heat stress, displaying extensive oxidative stress-mediated cell death at elevated temperatures. Combined genetic and molecular analyses revealed that these temperature-sensitive defects in fer mutants are caused by an overaccumulation of jasmonic acid (JA), which subsequently triggers excessive production of reactive oxygen species. Furthermore, we show that this aberrant JA accumulation and oxidative stress are attributable to impaired FER-mediated regulation of turgor-dependent cell wall tensile stress. Taken together, our results suggest that FER-mediated cell wall tensile stress regulation serves as a critical mechanism to prevent aberrant JA accumulation and oxidative stress at elevated temperatures, thereby enabling plants to adapt to and survive under high-temperature conditions.

plant biology↗

bioRxiv2022

Genetic, clinical underpinnings of subtle early brain change along Alzheimer's dimensions

Alzheimers disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised clustering technique known as Surreal-GAN, through which we identified two dominant dimensions of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the "diffuse-AD" (R1) dimension shows widespread brain atrophy, and the "MTL-AD" (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE {varepsilon}4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were "druggable genes" for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE {varepsilon}4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction - driven by genes different from APOE - which may collectively contribute to the early pathogenesis of AD.

bioengineering↗

bioRxiv2021

Multi-scale semi-supervised clustering of brain images: deriving disease subtypes

Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity for stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by anatomical and functional variations not related to a disease or pathology of interest. Semi-supervised clustering techniques have been proposed to overcome this and, therefore, capture disease-specific patterns more effectively. An additional limitation of both unsupervised and semi-supervised conventional machine learning methods is that they typically model, learn and infer from data using a basis of feature sets pre-defined at a fixed anatomical or functional scale (e.g., atlas-based regions of interest). Herein we propose a novel method, "Multi-scAle heteroGeneity analysIs and Clustering" (MAGIC), to depict the multi-scale presentation of disease heterogeneity, which builds on a previously proposed semi-supervised clustering method, HYDRA. It derives multi-scale and clinically interpretable feature representations and exploits a double-cyclic optimization procedure to effectively drive identification of inter-scale-consistent disease subtypes. More importantly, to understand the conditions under which the clustering model can estimate true heterogeneity related to diseases, we conducted extensive and systematic semi-simulated experiments to evaluate the proposed method on a sizeable healthy control sample from the UK Biobank (N=4403). We then applied MAGIC to imaging data from Alzheimers disease (ADNI, N=1728) and schizophrenia (PHENOM, N=1166) patients to demonstrate its potential and challenges in dissecting the neuroanatomical heterogeneity of common brain diseases. Taken together, we aim to provide guidance regarding when such analyses can succeed or should be taken with caution. The code of the proposed method is publicly available at https://github.com/anbai106/MAGIC. HighlightsO_LIWe propose a novel multi-scale semi-supervised clustering method, termed MAGIC, to disentangle the heterogeneity of brain diseases. C_LIO_LIWe perform extensive semi-simulated experiments on large control samples (UK Biobank, N=4403) to precisely quantify performance under various conditions, including varying degrees of brain atrophy, different levels of heterogeneity, overlapping disease subtypes, class imbalance, and varying sample sizes. C_LIO_LIWe apply MAGIC to MCI and Alzheimers disease (ADNI, N=1728) and schizophrenia (PHENOM, N=1166) patients to dissect their neuroanatomical heterogeneity, providing guidance regarding the use of the semi-simulated experiments to validate the subtypes found in actual clinical applications. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC="FIGDIR/small/440501v2_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@2769e9org.highwire.dtl.DTLVardef@19a691borg.highwire.dtl.DTLVardef@6a9264org.highwire.dtl.DTLVardef@b0e686_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Genetic influence on resting state networks in young male and female adults

Determining genetic versus environmental influences on the human brain is of crucial importance to understand the healthy brain as well as in a variety of disease and disorder states. Here we propose a unique, minimal assumption, approach to investigate genetic influence on the functional connectivity of the brain using 260 subjects" (65 monozygotic (MZ) and 65 dizygotic (DZ) healthy young adult twin pairs) resting state fMRI (rsfMRI) data from the Human Connectome Project (HCP). For any given resting state connection between twin pairs, the connection strengths across pairs were subtracted from each other in both directions. By applying the F-Test for equality of variances per connection, we found that there were a number of significant connections that demonstrated greater variance among dizygotic pairs in comparison to monozygotic pairs, implying these connections were under significant genetic influence. These population (DZ-MZ) results remained true irrespective of gender, with the caveat that certain connections were significant on a gender-specific basis. This is the first study to our knowledge to assess the heritability across young healthy adults both in general and specific to gender. Population Results & DiscussionAt the population level, there appears to be a posterior to anterior gradient of more to less genetic influence on brain connections and networks with visual > temporal, parietal > frontal. There was a high density of genetically-influenced functional connections predominantly involving posterior regions or networks of the brain: Visual Networks (VNs - primary visual, early visual, dorsal stream and ventral stream visual cortices, MT+ complex). These posterior regions of the brain with greater genetic influence are implicated for example in visual, perceptual, dorsal ("where") and ventral ("what") visuospatial processing streams (VNs). There was a low-density or paucity of genetically-influenced functional connections predominantly involving anterior regions or networks of the brain comprising Task Positive Networks (TPNs): FrontoParietal Networks (FPNs - dorsolateral prefrontal, orbital and polar frontal, midcingulate, insular and frontal opercular, superior and inferior parietal cortices); FrontoTemporal Networks (FTNs - inferior frontal, posterior opercular, early auditory, auditory association cortices); Sensorimotor Networks (SMNs - premotor, somatosensory, paralobular, and motor cortices); These anterior regions of the brain with lesser genetic influence are implicated in various TPN processes; for example in high-level cognitive and affective processes such as working memory, executive function, reasoning, attentional and impulse control, emotional judgement and decision making (FPNs); language and auditory processes (FTNs); action-planning and movement processes (SMN). There was a mix of high (posterior) and low (anterior) density of genetically influenced functional connections involving the extended Default Mode Network (eDMN). Specifically, there was a high density of genetically-influenced functional connections involving predominantly posterior-medial regions of eDMN - hippocampus and precuneus/posterior cingulate cortices; There was a low density of genetically influenced connections involving anterior regions (anterior cingulate and medial prefrontal) and lateral (inferior parietal, temporoparietooccipital) regions of the eDMN. The eDMN is involved in low-level cognitive and affective processes such as those involved in episodic memory retrieval, mental imagery, introspection, rumination, evaluation of self and others. These differences in genetic influence on posterior (more) vs. anterior (less) brain regions may have implications in terms of the environmental influence (e.g., education, school and work environment, family and home environment, social interaction with friends and peers, medications, nutrition, sports and physical exercise) on posterior (less) vs. anterior (more) portions of the brain during development and later in life. Gender-Specific Results & DiscussionAs noted at the population level, both males and females were under extensive genetic influence in terms of network interactions involving visual cortices. In addition, males were more genetically influenced in terms of network interactions involving auditory-language related cortices compared to females. This finding suggests that males may be more functionally "hard-wired" and females may be more environmentally influenced and shaped in terms of auditory-language systems than males. As noted at the population level, both males and females were under extensive genetic influence in terms of interactions involving the eDMN which is considered a central hub of the brain for various processes such as internal monitoring, rumination and evaluation of self and others, as noted previously. In addition, males also were more genetically influenced compared to females in terms of intranetwork and internetwork interactions of eDMN and other brain regions (occipital, temporal, parietal, and frontal regions) involved in various task-oriented processes and attending to and interacting with the environment which comprise part of the Task Positive Networks (TPNs). There were also nearly five times more genetically influenced functional connections in males (310) than females (64) suggesting that male brains are more genetically influenced, i.e. functionally "hard-wired", than females. This result suggests differences in genetic predisposition in males (more) vs. females (less) in terms of interplay of attending to task-oriented interactions with the environment (TPNs) vs. internal and external interactions with self and others (eDMN). This finding may also have implications in terms of brain plasticity differences in males (less) versus females (more) in terms of ability to react or adapt/maladapt to environmental influences (e.g. task completion demands, psychosocial stressors, positive and negative feedback, meditation, cognitive behavioral therapy, pharmacotherapy) and their overall malleability. These results reveal the similarities and differences of genetics and environmental influences on different connections, areas, and networks of the resting state functional brain in young healthy males and females with implications in development and later in life. This unique method can be applied in healthy as well as in patient populations to reveal the genetic and environmental influences on the brain. SignificanceThere were high vs. low genetic influences on posterior vs. anterior brain regions involved in low-level visuospatial processes vs. high-level cognitive processes such as reasoning and language respectively. This finding may have implications in terms of the brain to be environmentally influenced (e.g., school, work and home environment) during development and later in life. There were nearly five times more genetically influenced functional connections in males than females in brain regions involved in task-oriented interactions with environment vs. interactions with self and others. This finding may have implications in terms of brain plasticity differences in males (less) versus females (more) in terms of ability to adapt/maladapt to environmental influences (e.g. task completion demands, psychosocial stressors, various therapies) and their overall malleability. This is the first study to our knowledge to assess the heritability across young healthy adults both in general and specific to gender.

neuroscience↗

bioRxiv2021

Online learning for orientation estimation during translation in an insect ring attractor network

Insect neural systems are a promising source of inspiration for new algorithms for navigation, especially on low size, weight, and power platforms. There have been unprecedented recent neuroscience breakthroughs with Drosophila in behavioral and neural imaging experiments as well as the mapping of detailed connectivity of neural structures. General mechanisms for learning orientation in the central complex (CX) of Drosophila have been investigated previously; however, it is unclear how these underlying mechanisms extend to cases where there is translation through an environment (beyond only rotation), which is critical for navigation in robotic systems. Here, we develop a CX neural connectivity-constrained model that performs sensor fusion, as well as unsupervised learning of visual features for path integration; we demonstrate the viability of this circuit for use in robotic systems in simulated and physical environments. Furthermore, we propose a theoretical understanding of how distributed online unsupervised network weight modification can be leveraged for learning in a trajectory through an environment by minimizing of orientation estimation error. Overall, our results here may enable a new class of CX-derived low power robotic navigation algorithms and lead to testable predictions to inform future neuroscience experiments. SummaryAn insect neural connectivity-constrained model performs sensor fusion and online learning for orientation estimation.

neuroscience↗

bioRxiv2020

Comparison of everting sutures and the lateral tarsal strip with or without everting sutures for involutional lower eyelid entropion: A meta-analysis

There are three pathophysiologies of involutional entropion, vertical laxity (VL), horizontal laxity (HL), and overriding of the preseptal orbicularis. The effects of methods to correct VL only, HL only, or both VL and HL in patients with involutional entropion were compared using the published results of randomized controlled trials (RCTs). To find RCT studies that investigated methods to correct involutional entropion, a systematic search was performed from database inception to April 2020 in the Medline, EMBASE, and Cochrane databases. Two independent researchers conducted the literature selection and data extraction. Evaluation of the quality of the reports was performed using the Cochrane Collaboration tool for assessing the risk of bias (ROB 2.0). The data analysis was conducted according to the PRISMA guidelines using Review Manager 5.3. Two RCT studies were included in this meta-analysis. Surgery for involutional entropion was performed on a total of 109 eyes. Everting sutures (ES) were used on 57 eyes and lateral tarsal strips (LTS) or combined procedures (LTS + ES) were performed on 52 eyes. At the end of the follow-up periods, involutional entropion recurred in 18 eyes (31.6%) in the ES group and three eyes (5.8%) in the LTS +/- ES group. Analysis of the risk ratio showed that the LTS +/- ES method significantly lowered the recurrence rate compared to using ES only (P = 0.007). Performing LTS +/- ES effectively lowered the recurrence rate of involutional entropion compared to ES alone. However, some patients cannot tolerate more invasive corrections such as LTS. Therefore, sequential procedures, in which ES is performed first and then when entropion recurs LTS +/- ES is performed, or another methods depending upon the degree of HL may be used.

biophysics↗