Multiple models for outbreak decision support in the face of uncertainty.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Dopamine depletion and subcortical dysfunction disrupt cortical synchronization and metastability affecting cognitive function in Parkinson's disease.

Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic cells and atrophy in subcortical regions. However, the impact of these pathological changes on large-scale dynamic integration and segregation of the cortex are not well understood. In this study, we investigated the effect of subcortical dysfunction on cortical dynamics and cognition in PD. Spatiotemporal dynamics of the phase interactions of resting-state blood-oxygen-level-dependent signals in 159 PD patients and 152 normal control (NC) individuals were estimated. The relationships between subcortical atrophy, subcortical-cortical fiber connectivity impairment, cortical synchronization/metastability, and cognitive performance were then assessed. We found that cortical synchronization and metastability in PD patients were significantly decreased. To examine whether this is an effect of dopamine depletion, we investigated 45 PD patients both ON and OFF dopamine replacement therapy, and found that cortical synchronization and metastability are significantly increased in the ON state. The extent of cortical synchronization and metastability in the OFF state reflected cognitive performance and mediates the difference in cognitive performance between the PD and NC groups. Furthermore, both the thalamic volume and thalamocortical fiber connectivity had positive relationships with cortical synchronization and metastability in the dopaminergic OFF state, and mediate the difference in cortical synchronization between the PD and NC groups. In addition, thalamic volume also reflected cognitive performance, and cortical synchronization/metastability mediated the relationship between thalamic volume and cognitive performance in PD patients. Together, these results highlight that subcortical dysfunction and reduced dopamine levels are responsible for decreased cortical synchronization and metastability, further affecting cognitive performance in PD. This might lead to biomarkers being identified that can predict if a patient is at risk of developing dementia.

Serum Ceruloplasmin Depletion is Associated With Magnetic Resonance Evidence of Widespread Accumulation of Brain Iron in Parkinson's Disease.

BACKGROUND: Excessive iron accumulation is one of the main pathogeneses of Parkinson's disease (PD). Ceruloplasmin plays an important role in keeping the iron homoeostasis. PURPOSE: To explore the association between serum ceruloplasmin depletion and subcortical iron distribution in PD. STUDY TYPE: Prospective. POPULATION: One hundred and twenty-one normal controls, 34 PD patients with low serum ceruloplasmin (PD-LC), and 28 patients with normal serum ceruloplasmin (PD-NC). SEQUENCE: Enhanced susceptibility-weighted angiography (ESWAN) on a 3 T scanner. ASSESSMENT: Quantitative susceptibility mapping was employed to quantify the regional iron content by using a semi-automatic method. Serum ceruloplasmin concentration was measured from peripheral blood sample. Clinical assessments were conducted by a neurologist. STATISTICAL TESTS: General linear model was used to compare the intergroup difference of region iron distribution among groups, and the statistics was adjusted by Bonferroni method (P < 0.01). Partial correlation analysis was used to detect the association between regional iron distribution and serum ceruloplasmin concentration (P < 0.05). RESULTS: Compared with normal controls, significant iron accumulation in substantia nigra, putamen, and red nucleus was observed in PD-LC, while the only region showing significant iron accumulation was SN in PD-NC. Between PD-NC and PD-LC, the iron accumulation in putamen remained significantly different, which had a negative correlation with serum ceruloplasmin in whole PD patients (r = -0.338, P = 0.008). DATA CONCLUSION: Nigral iron accumulation characterizes PD patients without significant association with serum ceruloplasmin. Differentially, when PD patients appear with reduced serum ceruloplasmin, more widespread iron accumulation would be expected with additionally involving putamen and red nucleus. All these findings provide insightful evidence for the abnormal iron metabolism behind the ceruloplasmin depletion in PD. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: 2.

Symptomology following mRNA vaccination against SARS-CoV-2.

Despite demonstrated efficacy of vaccines against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative agent of coronavirus disease-2019 (COVID-19), widespread hesitancy to vaccination persists. Improved knowledge regarding frequency, severity, and duration of vaccine-associated symptoms may help reduce hesitancy. In this prospective observational study, we studied 1032 healthcare workers who received both doses of the Pfizer-BioNTech SARS-CoV-2 mRNA vaccine and completed post-vaccine symptom surveys both after dose 1 and after dose 2. We defined appreciable post-vaccine symptoms as those of at least moderate severity and lasting at least 2 days. We found that symptoms were more frequent following the second vaccine dose than the first (74% vs. 60%, P < 0.001), with >80% of all symptoms resolving within 2 days. The most common symptom was injection site pain, followed by fatigue and malaise. Overall, 20% of participants experienced appreciable symptoms after dose 1 and 30% after dose 2. In multivariable analyses, female sex was associated with greater odds of appreciable symptoms after both dose 1 (OR, 95% CI 1.73, 1.19-2.51) and dose 2 (1.76, 1.28-2.42). Prior COVID-19 was also associated with appreciable symptoms following dose 1, while younger age and history of hypertension were associated with appreciable symptoms after dose 2. We conclude that most post-vaccine symptoms are reportedly mild and last <2 days. Appreciable post-vaccine symptoms are associated with female sex, prior COVID-19, younger age, and hypertension. This information can aid clinicians in advising patients on the safety and expected symptomatology associated with vaccination.

Clinically relevant connectivity features define three subtypes of Parkinson's disease patients.

Parkinson's disease (PD) is characterized by complex clinical symptoms, including classic motor and nonmotor disturbances. Patients with PD vary in clinical manifestations and prognosis, which point to the existence of subtypes. This study aimed to find the fiber connectivity correlations with several crucial clinical symptoms and identify PD subtypes using unsupervised clustering analysis. One hundred and thirty-four PD patients and 77 normal controls were enrolled. Canonical correlation analysis (CCA) was performed to define the clinically relevant connectivity features, which were then used in the hierarchical clustering analysis to identify the distinct subtypes of PD patients. Multimodal neuroimaging analyses were further used to explore the neurophysiological basis of these subtypes. The methodology was validated in an independent data set. CCA revealed two significant clinically relevant patterns (motor-related pattern and depression-related pattern; r = .94, p < .001 and r = .926, p = .001, respectively) among PD patients, and hierarchical clustering analysis identified three neurophysiological subtypes ("mild" subtype, "severe depression-dominant" subtype and "severe motor-dominant" subtype). Multimodal neuroimaging analyses suggested that the patients in the "severe depression-dominant" subtype exhibited widespread disruptions both in function and structure, while the other two subtypes exhibited relatively mild abnormalities in brain function. In the independent validation, three similar subtypes were identified. In conclusion, we revealed heterogeneous subtypes of PD patients according to their distinct clinically relevant connectivity features. Importantly, depression symptoms have a considerable impact on brain damage in patients with PD.

Aberrant Fiber Coherence of Amygdala-Accumbens-Pallidum Pathway Is Associated With Disorganized Nigrostriatal-Nigropallidal Pathway in Parkinson's Disease.

BACKGROUND: Motor disturbances in Parkinson's disease (PD) mainly result from the degeneration of classic motor pathways. Given that the specific limbic pathway participates in movements, it is reasonable to consider that limbic pathway have the pathologic potential of motor disturbance in PD. PURPOSE: To explore the white matter changes of limbic and motor pathways and their relations in PD patients. STUDY TYPE: Prospective. POPULATION: 39 PD patients and 55 normal controls. SEQUENCE: Sagittal 3D T1 -weighted fast spoiled gradient recalled sequence, diffusion-weighted spin echo-echo planar imaging sequence on a 3T scanner. ASSESSMENT: Probabilistic tractography was used to reconstruct the motor pathways (nigrostriatal-nigropallidal and basal ganglia-motor cortex pathways) and limbic pathway (amygdala-accumbens-pallidum pathway). White matter alterations of these pathways were evaluated by fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), neurite density (NDI), and orientation dispersion (ODI). Clinical assessment was conducted by a neurologist. STATISTICAL TESTS: Group comparisons were performed using unpaired t-tests. Pearson or Spearman correlation was used to explore the relationships between variables. RESULTS: Compared with normal controls, PD patients showed decreased ODI as well as increased MD and AD in the bilateral nigrostriatal-nigropallidal pathway (P < 0.05), decreased FA in left basal ganglia-motor cortex pathway (P < 0.05), and decreased ODI in left limbic pathway (P < 0.05). MD and AD in the left nigrostriatal-nigropallidal pathway was negatively correlated with FA in left basal ganglia-motor cortex pathway (r = -0.597, P < 0.05 and r = -0.433, P < 0.05, respectively). MD in the left nigrostriatal-nigropallidal pathway was significantly correlated with ODI in the left limbic pathway (r = -0.404, P < 0.05). ODI was associated with AD within each hemisphere of the nigrostriatal-nigropallidal pathway (r = -0.591, P < 0.05 for left; r = -0.589, P < 0.05 for right). DATA CONCLUSION: The relationship between the degenerated motor pathways and aberrant limbic pathway suggest the existence of neuronal modulation between motor and limbic pathways, providing novel evidence of the neuromechanism for motor disruption in PD patients. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1 J. MAGN. RESON. IMAGING 2020;52:1799-1808.

Interactions between monoamine oxidase A rs1137070 and smoking on brain structure and function in male smokers.

The monoamine oxidase A (MAOA) enzyme metabolizes monoamine neurotransmitters such as dopamine, serotonin and norepinephrine, and its genetic polymorphism (rs1137070) influences its activity level and is associated with smoking behaviors. However, the underlying neural mechanisms of the gene × environment interactions remain largely unknown. In this study, we aimed to explore the interactive effects of the rs1137070 and cigarette smoking on gray matter volume (GMV) and functional connectivity strength (FCS). A total of 81 smokers and 42 nonsmokers were enrolled in the present study. Voxel-based morphometry analysis showed a significant rs1137070 genotype × smoking effect on the GMV of the left orbitofrontal cortex (OFC), such that individuals with risk allele had greater GMV among nonsmokers but not smokers. Meanwhile, rs1137070 variant and nicotine dependence interactively altered the FCS of the right hippocampus, the left inferior parietal lobule (IPL), the left dorsolateral prefrontal cortex and bilateral OFC. In addition, the FCS in the left IPL was correlated with smoking initiation and smoking years in smokers with the risk allele. These findings suggest that MAOA rs1137070 contributes to the susceptibility to nicotine dependence through its influence on brain circuits involved in reward and attention, and interacts with smoking in the progression.

Alteration of Brain Functional Connectivity in Parkinson's Disease Patients with Dysphagia.

Dysphagia is a common non-primary symptom of patients with Parkinson's disease. The aim of this study is to investigate the underlying alterations of brain functional connectivity in Parkinson's disease patients with dysphagia by resting-state functional magnetic resonance imaging. We recruited 13 Parkinson's disease patients with dysphagia and ten patients without dysphagia, diagnosed by videofluoroscopic study of swallowing. Another 13 age and sex-matched healthy subjects were recruited. Eigenvector centrality mapping was computed to identify functional connectivity alterations among these groups. Parkinson's disease patients with dysphagia had significantly increased functional connectivity in the cerebellum, left premotor cortex, the supplementary motor area, the primary motor cortex, right temporal pole of superior temporal gyrus, inferior frontal gyrus, anterior cingulate cortex and insula, compared with patients without dysphagia. This study suggests that functional connectivity changes in swallowing-related cortexes might contribute to the occurrence of dysphagia in Parkinson's disease patients.

Identifying gene regulatory network rewiring using latent differential graphical models.

Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizing much smaller sample size to achieve reliable differential network estimation. Our simulation results demonstrated that LDGM consistently outperforms other Gaussian graphical model based methods. We further evaluated LDGM by applying to the brain and blood gene expression data from the GTEx consortium. We also applied LDGM to identify network rewiring between cancer subtypes using the TCGA breast cancer samples. Our results suggest that LDGM is an effective method to infer differential network using high-throughput gene expression data to identify GRN dynamics among different cellular conditions.

Regionally progressive accumulation of iron in Parkinson's disease as measured by quantitative susceptibility mapping.

The progression of Parkinson's disease (PD) seems to vary according to the disease stage, which greatly influences the management of PD patients. However, the underlying mechanism of progression in PD remains unclear. This study was designed to explore the progressive pattern of iron accumulation at different stages in PD patients. Sixty right-handed PD patients and 40 normal controls were recruited. According to the disease stage, 45 patients with Hoehn-Yahr stage ≤ 2.5 and 15 patients with Hoehn-Yahr stage ≥ 3 were grouped into early-stage PD (EPD) and late-stage PD (LPD) groups, respectively. The iron content in the cardinal subcortical nuclei covering the cerebrum, cerebellum and midbrain was measured using quantitative susceptibility mapping (QSM). The substantia nigra pars compacta (SNc) showed significantly increased QSM values in the EPD patients compared with the controls. In the LPD patients, while the SNc continued to show increased QSM values compared with the controls and EPD patients, the regions showing increased QSM values spread to include the substantia nigra pars reticulata (SNr), red nucleus (RN) and globus pallidus (GP). Our data also indicated that iron deposition was more significant in the GP internal segment (GPi) than in the GP external segment. No other regions showed significant changes in QSM values among the groups. Therefore, we were able to confirm a regionally progressive pattern of iron accumulation in the different stages of PD, indicating that iron deposition in the SNc is affected exclusively in the early stages of the disease, while the SNr, RN and GP, and particularly the GPi segment, become involved in advanced stages of the disease. This is a preliminary study providing objective evidence of the iron-related progression in PD. Copyright © 2016 John Wiley & Sons, Ltd.

Influence of regional iron on the motor impairments of Parkinson's disease: A quantitative susceptibility mapping study.

PURPOSE: Because the roles of striatal-thalamo-cortical and cerebello-thalamo-cortical circuits in the heterogeneous motor impairments of Parkinson's disease (PD) are becoming recognized, this study was designed to investigate the relationships between regional iron in the cardinal subcortical nuclei in these circuits and the different motor impairments. MATERIALS AND METHODS: Sixty-two PD patients and 40 normal subjects were included and accepted for Enhanced T2 -Star Weighted Angiography Scanning (3.0T). According to the Unified Parkinson's Disease Rating Scale, patients were divided into tremor-dominant (PD-TD) and akinetic/rigid-dominant groups (PD-AR). The intergroup differences of magnetic susceptibility in those cardinal nuclei were measured. Correlation analyses between magnetic susceptibility and motor impairments were performed in all patients. RESULTS: Nigral magnetic susceptibility significantly increased for each PD group compared with controls (P < 0.001 for PD-TD; P = 0.001 for PD-AR). Magnetic susceptibility in the dentate nucleus (DN) and red nucleus (RN) for the PD-TD patients were significantly increased compared with controls (P < 0.001 and P = 0.004, respectively). Magnetic susceptibility in these regions was also significantly correlated with tremor severity (r = 0.444, P = 0.001 for DN; r = 0.418, P = 0.001 for RN). Significant correlation between caudate magnetic susceptibility and akinetic/rigid severity were observed (r = -0.322, P = 0.015). CONCLUSION: This study provides evidence that nigral iron accumulation is a common characteristic in PD, while iron accumulation in the DN and RN is correlated with tremor symptoms. Our data also indicate that caudate iron content may be a potential marker for akinetic/rigid progression. LEVEL OF EVIDENCE: 3 J. MAGN. RESON. IMAGING 2017;45:1335-1342.

Abnormal baseline brain activity in Parkinson's disease with and without REM sleep behavior disorder: A resting-state functional MRI study.

PURPOSE: To investigate the differences in spontaneous brain activity between Parkinson's disease (PD) patients with rapid eye movement sleep behavior disorder (RBD), PD patients without RBD, and normal controls, which may shed new light on the neural mechanism of RBD. MATERIALS AND METHODS: Eighteen PD patients with RBD, 16 patients without RBD, and 19 age- and gender-matched normal controls underwent clinical assessment and functional magnetic resonance imaging (fMRI) with a 3.0T scanner. Resting-state fMRI scans were collected using an echo planar imaging sequence. Amplitude of low-frequency fluctuations (ALFF) were calculated to measure spontaneous brain activity in each subject. RESULTS: Compared with PD patients without RBD, patients with RBD exhibited significantly decreased ALFF values (P < 0.001, cluster level) in primary motor cortex extending to premotor cortex. Compared with normal controls, PD patients exhibited decreased ALFF values (P < 0.001, cluster level) in caudate and putamen (P < 0.001, cluster level), and increased ALFF values (P = 0.03, cluster level) in prefrontal cortex. CONCLUSION: The altered spontaneous brain activity in motor cortex may contribute to the pathogenesis of RBD in PD patients, which further supports the idea that the pathophysiology of RBD involves not only midbrain dysfunction but also cerebral cortex abnormalities. Our findings provide additional insight into the neural mechanism of RBD and may drive future research to develop better treatment. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;46:697-703.

A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression.

BACKGROUND: Cancer subtype information is critically important for understanding tumor heterogeneity. Existing methods to identify cancer subtypes have primarily focused on utilizing generic clustering algorithms (such as hierarchical clustering) to identify subtypes based on gene expression data. The network-level interaction among genes, which is key to understanding the molecular perturbations in cancer, has been rarely considered during the clustering process. The motivation of our work is to develop a method that effectively incorporates molecular interaction networks into the clustering process to improve cancer subtype identification. RESULTS: We have developed a new clustering algorithm for cancer subtype identification, called "network-assisted co-clustering for the identification of cancer subtypes" (NCIS). NCIS combines gene network information to simultaneously group samples and genes into biologically meaningful clusters. Prior to clustering, we assign weights to genes based on their impact in the network. Then a new weighted co-clustering algorithm based on a semi-nonnegative matrix tri-factorization is applied. We evaluated the effectiveness of NCIS on simulated datasets as well as large-scale Breast Cancer and Glioblastoma Multiforme patient samples from The Cancer Genome Atlas (TCGA) project. NCIS was shown to better separate the patient samples into clinically distinct subtypes and achieve higher accuracy on the simulated datasets to tolerate noise, as compared to consensus hierarchical clustering. CONCLUSIONS: The weighted co-clustering approach in NCIS provides a unique solution to incorporate gene network information into the clustering process. Our tool will be useful to comprehensively identify cancer subtypes that would otherwise be obscured by cancer heterogeneity, using high-throughput and high-dimensional gene expression data.

Greater loss of white matter integrity in postural instability and gait difficulty subtype of Parkinson's disease.

BACKGROUND: Patients with the postural instability and gait difficulty (PIGD) subtype of Parkinson disease (PD) are at a higher risk of dysfunction and are less responsive to dopamine replacement therapy. The PIGD subtype was found to largely associate with white matter lesions, but details of the diffusion changes within these lesions have not been fully investigated. Voxel-based analysis for diffusion tensor imaging data is one of the preferred measures to compare diffusion changes in each voxel in any part of the brain. METHODS: PD patients with the PIGD (n=12) and non-PIGD subtypes (n=12) were recruited to compare diffusion differences in fractional anisotropy, axial diffusivity, and radial diffusivity with voxel-based analysis. RESULTS: Significantly reduced fractional anisotropy in bilateral superior longitudinal fasciculus, bilateral anterior corona radiata, and the left genu of the corpus callosum were shown in the PIGD subtype compared with the non-PIGD subtype. Increased radial diffusivity in the left superior longitudinal fasciculus was found in the PIGD subtype with no statistical differences in axial diffusivity found. CONCLUSIONS: Our study confirms previous findings that white matter abnormalities were greater in the PIGD subtype than in the non-PIGD subtype. Additionally, our findings suggested: (1) compared with the non-PIGD subtype, loss of white matter integrity was greater in the PIGD subtype; (2) bilateral superior longitudinal fasciculus may play a critical role in microstructural white matter abnormalities in the PIGD subtype; and (3) reduced white matter integrity in the PIGD subtype could be mainly attributed to demyelination rather than axonal loss.

Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI).

Background: Magnetic resonance imaging (MRI) is a primary non-invasive imaging modality for tumor segmentation, leveraging its exceptional soft tissue contrast and high resolution. Current segmentation methods typically focus on structural MRI, such as T1-weighted post-contrast-enhanced or fluid-attenuated inversion recovery (FLAIR) sequences. However, these methods overlook the blood perfusion and hemodynamic properties of tumors, readily derived from dynamic susceptibility contrast (DSC) enhanced MRI. This study introduces a novel hybrid method combining density-based analysis of hemodynamic properties in time-dependent perfusion imaging with deep learning spatial segmentation techniques to enhance tumor segmentation. Methods: First, a U-Net convolutional neural network (CNN) is employed on structural images to delineate a region of interest (ROI). Subsequently, Hierarchical Density-Based Scans (HDBScan) are employed within the ROI to augment segmentation by exploring intratumoral hemodynamic heterogeneity through the investigation of tumor time course profiles unveiled in DSC MRI. Results: The approach was tested and evaluated using a cohort of 513 patients from the open-source University of Pennsylvania glioblastoma database (UPENN-GBM) dataset, achieving a 74.83% Intersection over Union (IoU) score when compared to structural-only segmentation. The algorithm also exhibited increased precision and localized predictions of heightened segmentation boundary complexity, resulting in a 146.92% increase in contour complexity (ICC) compared to the reference standard provided by the UPENN-GBM dataset. Importantly, segmenting tumors with the developed new approach uncovered a negative correlation of the tumor volume with the scores in the Karnofsky Performance Scale (KPS) clinically used for assessing the functional status of patients (-0.309), which is not observed with the prevailing segmentation standard. Conclusions: This work demonstrated that including hemodynamic properties of tissues from DSC MRI can improve existing structural or morphological feature-based tumor segmentation techniques with additional information on tumor biology and physiology. This approach can also be applied to other clinical indications that use perfusion MRI for diagnosis or treatment monitoring.

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation.

Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism's presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.

PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning.

Hypergraphs are powerful tools for modeling complex interactions across various domains, including biomedicine. However, learning meaningful node representations from hypergraphs remains a challenge. Existing supervised methods often lack generalizability, thereby limiting their real-world applications. We propose a new method, Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning (PhyGCN), which leverages hypergraph structure for self-supervision to enhance node representations. PhyGCN introduces a unique training strategy that integrates variable hyperedge sizes with self-supervised learning, enabling improved generalization to unseen data. Applications on multi-way chromatin interactions and polypharmacy side-effects demonstrate the effectiveness of PhyGCN. As a generic framework for high-order interaction datasets with abundant unlabeled data, PhyGCN holds strong potential for enhancing hypergraph node representations across various domains.

Genetic impacts on nigral iron deposition in Parkinson's disease: A preliminary quantitative susceptibility mapping study.

BACKGROUND: Dysfunction of iron metabolism, especially in substantia nigra (SN), is widely acknowledged in Parkinson's disease (PD), but the genetic influence on iron deposition remains largely unknown. Thus, in this study, we aimed to investigate potential genetic impacts on iron deposition in PD. METHODS: Seventy-four subjects, including 38 patients with PD and 36 age-matched normal controls, participated in this study. Imaging genetic association analysis was used to identify the specific influence of single nucleotide polymorphism (SNP) on iron-related quantitative traits (QT). Genetic effects on iron deposition at the disease level, SNP level, and their interactive effect were highlighted. RESULTS: Four strong SNP-QT associations were detected: rs602201-susceptibility of bilateral SN, rs198440-susceptibility of left SN, and rs7895403-susceptibility of left caudate head. Detailed analyses showed that: (1) significant iron deposition was exclusively found in bilateral SN in PD; (2) altered polymorphisms of the A allele/A- genotype of rs602201 and G allele/G- genotype of rs198440 and rs7895403 were more frequently observed in PD; (3) for rs602201, among all subjects, A- genotype carriers showed significantly increased iron content than TT genotype in bilateral SN; for rs198440 and rs7895403, G- carriers showed increased iron content than AA genotype in left SN and left caudate head, respectively; and (4) rs602201 exhibited significant SNP-by-disease interaction in bilateral SN. CONCLUSIONS: This study shows that rs602201 and rs198440 have a stimulative impact on nigral iron deposition in PD, which provides improved understanding of iron-related pathogenesis in PD, and specifically, that vulnerability to iron deposition in SN is genetic-based.

The protective role of cigarette smoking against Parkinson's disease via moderation of the interaction between iron deposition in the nigrostriatal pathway and clinical symptoms.

Background: Although cigarette smoking is a risk factor for multiple disorders, it has long been thought to protect against Parkinson's disease (PD). Quantitative susceptibility mapping (QSM) is a novel magnetic resonance imaging (MRI)-based technique for assessing iron accumulation in vivo that has been widely applied in PD studies. This study aimed to investigate how cigarette smoking affects clinical performance of PD using quantified iron deposition as a proxy for PD pathology. Methods: In this observational study, we enrolled 35 male PD patients and 47 male healthy controls (HCs) and divided them into four groups. We performed an enhanced T2 star-weighted angiography (ESWAN) MRI sequence to measure the iron content of the nuclei within the nigrostriatal pathway. With the age and total intracranial volume (TIV) controlled as covariates, we performed inter-group comparisons of QSM values and moderation analyses for PD patients using smoking status and the smoking index (SI), respectively, as moderator variables. Results: The 2-way multivariate analysis of covariance (MANCOVA) results showed higher QSM values in the left red nucleus (P=0.024) in PD patients compared with those in HCs, and in the bilateral globi pallidi [left/right (L/R): P=0.009/0.003], substantia nigra pars compacta (SNc; L/R: P=0.001/0.037), and right substantia nigra pars reticulata (SNr; P=0.002) in non-smokers compared with smokers, with no marked interaction effect between PD and smoking status observed when applying the Bonferroni adjustment for multiple comparisons. Using cigarette smoking status and the SI as separate moderator variables, the moderation was shown up by a significant interaction effect in a disordinal and double-edged form. In our results, smoking-moderated protection for PD movement deficits emerged when PD was progressed. Among the affected deep brain nuclei, the nuclei most moderated by the impact of cigarette smoking on the interaction between brain iron and PD symptoms were the thalamus [smoking status associated with the Unified Parkinson's Disease Rating Scale (UPDRS) total score, P=0.04 (L); rigidity, P=0.03 (L); SI associated with UPDRS-III, P (L/R) =0.049/0.0497; rigidity, P (L/R) =0.01/0.02; bradykinesia, P (L/R) =0.048/0.04], the right red nucleus (SI associated with rigidity, P=0.04; bradykinesia, P=0.02), and the left SNc [smoking status associated with the Hoehn and Yahr (H&Y) stage, P=0.01]. Conclusions: This was the first study investigating the impacts of current cigarette smoking on PD using quantified iron deposition. Our study confirmed the protective role of cigarette smoking against PD, consistent with the findings of previous studies. Furthermore, neuroprotection was present only when the PD pathology had progressed to a certain extent. In the interaction between iron deposition and clinical PD symptoms, our findings suggest that the thalamus, red nucleus, and SNc are likely to be the most affected nuclei moderated by cigarette smoking.

Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning.

For decades, employing cyclic voltammetry for mechanistic investigation has demanded manual inspection of voltammograms. Here, we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a probable electrochemical mechanism among five of the most common ones in homogeneous molecular electrochemistry. The reported algorithm will aid researchers' mechanistic analyses, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry. An automated voltammogram analysis will aid the analysis of complex electrochemical systems and promise autonomous high-throughput research in electrochemistry with minimal human interference.