Functional Data Research Articles

NORDIC denoising on VASO data.

The use of submillimeter resolution functional magnetic resonance imaging (fMRI) is increasing in popularity due to the prospect of studying human brain activation non-invasively at the scale of cortical layers and columns. This method, known as laminar fMRI, is inherently signal-to-noise ratio (SNR)-limited, especially at lower field strengths, with the dominant noise source being of thermal origin. Furthermore, laminar fMRI is challenged with signal displacements due to draining vein effects in conventional gradient-echo blood oxygen level-dependent (BOLD) imaging contrasts. fMRI contrasts such as cerebral blood volume (CBV)-sensitive vascular space occupancy (VASO) sequences have the potential to mitigate draining vein effects. However, VASO comes along with another reduction in detection sensitivity. NOise Reduction with DIstribution Corrected (NORDIC) PCA (principal component analysis) is a denoising technique specifically aimed at suppressing thermal noise, which has proven useful for increasing the SNR of high-resolution functional data. While NORDIC has been examined for BOLD acquisitions, its application to VASO data has been limited, which was the focus of the present study. We present a preliminary analysis to evaluate NORDIC's capability to suppress thermal noise while preserving the VASO signal across a wide parameter space at 3T. For the data presented here, with a proper set of parameters, NORDIC reduced thermal noise with minimal bias on the underlying signal and preserved spatial resolution. Denoising performance was found to vary with different implementation strategies and parameter choices, for which we provide recommendations. We conclude that when applied properly, NORDIC has the potential to overcome the sensitivity limitations of laminar-specific VASO fMRI. Since very few groups currently have 3T VASO data, by sharing our analysis and code, we can compile and compare the effects of NORDIC across a broader range of acquisition parameters and study designs. Such a communal effort will help develop robust recommendations that will increase the utility of laminar fMRI at lower field strengths.

Item Classification by Difficulty Using Functional Principal Component Clustering and Neural Networks.

Maintaining consistent item difficulty across test forms is crucial for accurately and fairly classifying examinees into pass or fail categories. This article presents a practical procedure for classifying items based on difficulty levels using functional data analysis (FDA). Methodologically, we clustered item characteristic curves (ICCs) into difficulty groups by analyzing their functional principal components (FPCs) and then employed a neural network to predict difficulty for ICCs. Given the degree of similarity between many ICCs, categorizing items by difficulty can be challenging. The strength of this method lies in its ability to provide an empirical and consistent process for item classification, as opposed to relying solely on visual inspection. The findings reveal that most discrepancies between visual classification and FDA results differed by only one adjacent difficulty level. Approximately 67% of these discrepancies involved items in the medium to hard range being categorized into higher difficulty levels by FDA, while the remaining third involved very easy to easy items being classified into lower levels. The neural network, trained on these data, achieved an accuracy of 79.6%, with misclassifications also differing by only one adjacent difficulty level compared to FDA clustering. The method demonstrates an efficient and practical procedure for classifying test items, especially beneficial in testing programs where smaller volumes of examinees tested at various times throughout the year.

Refining breast cancer genetic risk and biology through multi-ancestry fine-mapping analyses of 192 risk regions.

Genome-wide association studies have identified approximately 200 genetic risk loci for breast cancer, but the causal variants and target genes are mostly unknown. We sought to fine-map all known breast cancer risk loci using genome-wide association study data from 172,737 female breast cancer cases and 242,009 controls of African, Asian and European ancestry. We identified 332 independent association signals for breast cancer risk, including 131 signals not reported previously, and for 50 of them, we narrowed the credible causal variants down to a single variant. Analyses integrating functional genomics data identified 195 putative susceptibility genes, enriched in PI3K/AKT, TNF/NF-κB, p53 and Wnt/β-catenin pathways. Single-cell RNA sequencing or in vitro experiment data provided additional functional evidence for 105 genes. Our study uncovered large numbers of association signals and candidate susceptibility genes for breast cancer, uncovered breast cancer genetics and biology, and supported the value of including multi-ancestry data in fine-mapping analyses.

Impact of mandibular overdentures on maxillary resorption and its association with masticatory function: A prospective longitudinal study with 3 years of follow-up.

This longitudinal clinical study monitored annually the maxillary and mandibular bone remodeling and masticatory function in complete denture (CD) wearers rehabilitated with implant-retained mandibular overdentures (MO) over three years and combined radiographic and masticatory function data to assess the correlation between bone remodeling and masticatory function. Thirty-nine MO wearers were monitored annually to assess changes in: i) residual ridge in the anterior and posterior maxillary region; ii) posterior height and posterior area index (PAI) in the mandible; and iii) masticatory function. Bone remodeling was measured through linear and angular measurements using panoramic radiographs. Masticatory function was evaluated by swallowing threshold test, to determine the median particle size (X50) and homogenization index (B). A mixed-effects multilevel regression model estimated differences between radiological and masticatory parameters over time, with associations verified using Pearson's correlation test. Significant changes in mandibular bone variables (PAI and L1/R1, L2/R2, L3/R3; p ≤ 0.01) occurred when transitioning from CD to MO, showing a reduction in PAI values and slight reduction in mandibular linear measurements. Improvements in masticatory parameters were also observed, highlighting the benefits of transitioning to MO. Over three years of MO usage, significant changes in all radiographic bone parameters were found, with increased PAI and maxillary bone values in the first year. The correlations between masticatory function and radiological parameters indicated that mandibular morphology can significantly influence masticatory function especially in patients wearing CDs. Following the transition to MO, only one consistent significant correlation was observed between X50 and R3/L3 at the 1-year and 3-year intervals. Continuous changes in bone parameters were observed in MO wearers, with stabilization of maxillary ridge resorption and ongoing mandibular remodeling, alongside slight changes in masticatory function, suggesting transitory correlations between linear changes in the posterior region of mandible and masticatory function over time.

The microbiome's influence on the neurobiology of opioid addiction and brain connectivity.

Opioids are the most effective and potent analgesics available for acute pain management. With no viable alternative for treating chronic or post operative pain, it is not surprising that over 10 million people misuse opioids. This study explores the developmental influence of the microbiome on resistance to opioid addictive behavior and functional connectivity. Female germ free reared (GFR) mice were compared to wild-type (WT) mice, before and after conventionalization using conditioned place preference (CPP) with oxycodone (OXY) exposure. Functional connectivity data were collected providing site-specific analysis for over 140 different brain areas. GFR mice showed significant reduction in CPP after OXY exposure. When GFR mice are conventionalized CPP reward behavior mirrors WT mice. Functional connectivity data shows significant differences across several brain regions e.g., thalamus, hippocampus, and sensory cortices between GFR and WT before and after conventionalization. Prior to conventionalization GFR mice showed hyperconnectivity that became less organized and more global after conventionalization. Sequencing of the fecal microbiome of the GFR mice before conventionalization showed an absence of normal murine gut microbiome members, but the presence of Corynebacterium, Staphylococcus, Paenibacillus, and Turicibacter. The implications suggest the microbiome has a direct impact on the development of reward seeking behavior. With the widespread number of opioid receptors found in the gut, studying the interaction between the microbiota and substance use disorder may lead to a better understanding of the mechanisms that lead to the development of addiction as well as potential treatments.

Baseline characteristics and 2-year functional outcome data of patients undergoing an arthroscopic rotator cuff repair in Switzerland, results of the ARCR_Pred study.

The ARCR_Pred study was initiated to document and predict the safety and effectiveness of arthroscopic rotator cuff repair (ARCR) in a representative Swiss patient cohort. In the present manuscript, we aimed to describe the overall and baseline characteristics of the study, report on functional outcome data and explore case-mix adjustment and differences between public and private hospitals. Between June 2020 and November 2021, primary ARCR patients were prospectively enrolled in a multicenter cohort across 18 Swiss and one German orthopedic center. Baseline characteristics, including sociodemographic and diagnostic variables, were reported. Clinical scores and patient-reported outcome measures were assessed up to 24-month follow-up. After screening 2350 individuals, 973 patients with ARCR were included. Follow-up rates reached 99%, 95%, 89% and 88% at 6 weeks, 6, 12, and 24 months, respectively. While the proportion of massive tears was higher in the study population (44% vs. 20%, Std. Diff. = 0.56), there were no other major differences in key characteristics between enrolled and non-enrolled patients or in patients lost to follow-up. Functional scores improved over time, with positive changes rates ranging from 83% to 92% at 6-month, reaching 91% to 97% at 12- and 24-month follow-up. In linear mixed models, used to estimate the associations between baseline factors, hospital type and standardized 0-100 scores, marginal effects for time ranged from 20 to 30, 28 to 39 and 34 to 41 points at the 6-, 12- and 24-month follow-up, respectively. Except at the 12-month follow-up, where marginal effects for the interaction terms ranged from -5 to -4 points in the standardized scores, there were no consistent outcome differences between public and private hospitals. Increasing number of years of education was consistently associated with better scores, greater feelings of depression and anxiety, smoking and ASA group III-IV were consistently associated with worse scores. Tear severity showed a consistent negative association solely for the Constant-Score. The ARCR_Pred study shows high potential for generalizability to the population of patients undergoing an ARCR in Switzerland. Further analyses are needed to establish relevant clinimetrics for the Swiss population and to compare outcomes for surgical techniques, surgeon experiences profiles and post-operative management.

The cognitive critical brain: modulation of criticality in perception-related cortical regions

The constantly evolving world necessitates a brain that can swiftly adapt and respond to rapid changes. The brain, conceptualized as a system performing cognitive functions through collective neural activity, has been shown to maintain a resting state characterized by near-critical neural dynamics, positioning it to effectively respond to external stimuli. However, how near-criticality is dynamically modulated during task performance remains insufficiently understood. In this study, we utilized the prototypical Ising Hamiltonian model to investigate the modulation of near-criticality in neural activity at the cortical subsystem level during perceptual tasks. Specifically, we simulated 2D-Ising models in silico using structural MRI data and empirically estimated the system's state in vivo using functional MRI data. We first replicated previous findings that the resting state is typically near-critical as captured by the Ising model. Importantly, we observed heterogeneous changes in criticality across cortical subsystems during a naturalistic movie-watching task, with visual and auditory regions fine-tuned closer to criticality. A more fine-grained analysis of the ventral temporal cortex during an object recognition task further revealed that only regions selectively responsive to a specific object category were tuned closer to criticality when processing that object category. In conclusion, our study provides empirical evidence from the domain of perception supporting the cognitive critical brain hypothesis that modulating the criticality of subsystems within the brain's hierarchical and modular organization may be a fundamental mechanism for achieving diverse cognitive functions.

Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI.

Goal: Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brain mask as nodes of high-resolution, subject-specific graphs. Methods: We define the strength of local voxel-to-voxel connections using diffusion tensors and orientation distribution functions derived from diffusion MRI data. We study the graphs' Laplacian spectral properties on data from the Human Connectome Project. We then assess the extent of inter-subject variability of the Laplacian eigenmodes via a procrustes validation scheme. Finally, we demonstrate the extent to which functional MRI data are shaped by the underlying anatomical structure via graph signal processing. Results: The graph Laplacian eigenmodes manifest highly resolved spatial profiles, reflecting distributed patterns that correspond to major white matter pathways. We show that the intrinsic dimensionality of the eigenspace of such high-resolution graphs is only a mere fraction of the graph dimensions. By projecting task and resting-state data on low-frequency graph Laplacian eigenmodes, we show that brain activity can be well approximated by a small subset of low-frequency components. Conclusions: The proposed graphs open new avenues in studying the brain, be it, by exploring their organisational properties via graph or spectral graph theory, or by treating them as the scaffold on which brain function is observed at the individual level.

Development of resources for training machine learning systems for the eye

Regulation of distinct molecular pathways in coordinately developing tissues drives eye development. This process involves factors in signaling, transcription, translation and post‐transcriptional control that together determine the proteome of individual cells. Characterizing these regulatory events and their relationships with each other in distinct ocular tissues will lead to the derivation of gene regulatory networks (GRNs) for eye development, which in turn will allow building of the “developmental oculome” (Lachke, Maas (2010) Wiley Interdiscip Rev Syst Biol Med 2(3):305‐323 doi: 10.1002/wsbm.59). Toward this goal, we generated and analyzed various omics datasets on developing eye tissues, performed functional characterization of key molecules, and developed a web resource called iSyTE (Anand, Lachke (2017) Exp Eye Res 156:22‐33 PMC5026553; Kakrana et al (2018) Nucleic Acids Res 46(D1): D875‐D885 doi: 10.1093/nar/gkx837; Lachke (2022) Exp Eye Res 214:108889 PMC8792301). To extract further insights from these omics data, it is necessary to analyze them in the context of experimentally verified data on the eye. These data can be used for training machine learning systems to develop models for eye development. The data used for training represents a an important factor in machine learning systems, i.e. the quality of these data is a key contributor that determines the robustness of the resulting models. There is a wealth of molecular functional (i.e. experimental evidence‐based) data on the eye that has been generated by researchers worldwide over the past several decades. These individual regulatory data “nodes” (regulators and targets) – currently “static” in the literature with limited connectivity – hold rich potential to define detailed, and importantly, experimentally validated GRNs that can be used for training machine learning systems. As proof of principle, we have focused on ocular lens development and curated mechanistic data from individual gene perturbation evidences in the mouse from >100 original research articles. This has led to definition of thousands of experimentally validated relationships (edges) between key factors and their targets (nodes) in lens development. These resources serve as strong foundational data for training machine learning systems and represent the essential first step toward advancing our understanding of the natural computing governing eye development, and how its perturbation results in ocular developmental disorders.

Disrupted Cross-Scale Network Associated With Cognitive-Emotional Disorders in Sudden Sensorineural Hearing Loss.

Sudden sensorineural hearing loss (SSNHL) is associated with abnormal changes in the brain's central nervous system. Previous studies on the brain networks of SSNHL have primarily focused on functional connectivity within the brain. However, in addition to functional connectivity, structural connectivity also plays a crucial role in brain networks. Moreover, traditional functional connectivity analyses often overlook the spatial and temporal characteristics of connectivity changes and fail to provide directional information and causal relationships. This study utilized Structural Covariance Network (SCN), multilayer network analysis, and Dynamic Causal Modeling (DCM) to investigate the cross-scale changes in neural network structure and function in SSNHL patients with accompanying cognitive and emotional disorders. We collected 3D-T1 structural magnetic resonance image data and functional magnetic resonance image data from 70 SSNHL patients and 81 healthy controls (HCs). SCN analysis was performed based on gray matter volume, and multilayer network analysis was used to calculate node switching rates. Based on the results of multilayer network analysis, six nodes exhibiting significant inter-group differences in node switching rates were selected as regions of interest (ROIs). DCM was then conducted to explore the causal relationships of functional connectivity between these nodes. Based on SCN, there were no significant inter-group differences in global network properties between SSNHL and HCs. At the node level, the left precentral gyrus in SSNHL showed a significant decrease in node efficiency. In the multilayer network analysis, SSNHL showed a significantly increased node switching rate at the level of the Left Superior Frontal Gyrus (L.SFG), Left Supplementary Motor Area (L.SMA), Left Superior Parietal Gyrus (L.SPG), Right Superior Parietal Gyrus (R.SPG), Right Inferior Parietal Lobe(R.IPL), and Left Thalamus (L.THA). Furthermore, the node switching rate of L.SFG showed a significant negative correlation with the Self-Rating Anxiety Scale (SAS) scores. DCM analysis of these six nodes revealed differences in the functional effective connectivity between the left superior parietal gyrus (L.SPG) and the left supplementary motor area (L.SMA), which were positively correlated with the AVLT-delay scores. These findings suggest that SSNHL patients experience structural and functional remodeling of the cerebral cortex, with hearing loss leading to the reallocation of cognitive resources. This provides new insights into understanding the potential mechanisms between cross-scale networks and cognitive-emotional disorders in SSNHL.

The brain, rapid eye movement sleep, and major depressive disorder: A multimodal neuroimaging study

BackgroundEvidence has established the prominent involvement of rapid eye movement (REM) sleep disturbance in major depressive disorder (MDD). However, the neural correlates of REM sleep in MDD and their clinical significance are less clear. MethodsCross-sectional and longitudinal polysomnography and resting-state functional MRI data were collected from 131 MDD patients and 71 healthy controls to measure REM sleep and voxel-mirrored homotopic connectivity (VMHC). Correlation and mediation analyses were performed to examine the associations between REM sleep, VMHC, and clinical variables. Moreover, we conducted spatial correlations between the neural correlates of REM sleep and a multimodal collection of reference brain maps to facilitate genetic, structural and functional annotations. ResultsMDD patients exhibited REM sleep abnormalities manifesting as higher REM sleep latency and lower REM sleep duration, which were correlated with decreased VMHC of the precentral gyrus and inferior parietal lobe and mediated their associations with more severe anxiety symptoms. Longitudinal data showed that VMHC increase of the inferior parietal lobe was related to improvement of depression symptoms in MDD patients. Spatial correlation analyses revealed that the neural correlates of REM sleep in MDD were linked to gene categories primarily involving cellular metabolic process, signal pathway, and ion channel activity as well as linked to cortical microstructure, metabolism, electrophysiology, and cannabinoid receptor. ConclusionThese findings may add important context to the growing literature on the complex interplay between sleep and MDD, and more broadly may inform future treatment for depression via regulating sleep.

Technological Advances in Radiation Oncology

Abstract One of the most technologically advanced treatment modalities for cancer is radiotherapy (RT). Because of advancements in computer and engineering over the past few decades, RT approaches have undergone significant modification. Our goal is to draw attention to the latest advancements in radiation oncology, with an emphasis on both biological and technological innovations. Modern treatment methods using photon beams, including intensity-modulated radiotherapy, volumetric-modulated arc therapy, stereotactic body radiotherapy, and adaptive radiotherapy, will be demonstrated. A highly customized dosage distribution with the greatest amount of normal tissue preservation is feasible. Imaging, defining the tumor and the organs at risk, treatment planning, and, lastly, image-guidance for precise tumor localization both before and during treatment delivery will all be examined. The critical function that imaging plays throughout the entire process will receive special attention. In the case of adaptive radiotherapy, multimodality imaging that combines morphological, functional, and metabolic data is primarily responsible for the accurate identification of target volumes and the tracking of tumor response or change over the course of treatment. Furthermore, breathing adaptive approaches that correct for tumor movements caused by respiration require real-time imaging of the tumor. The recent popularity of particle beam therapy—specifically, the use of protons—as well as the relatively new experience with heavy particles like carbon ions will be briefly discussed. Lastly, we’ll examine the most recent biological developments in tumor targeting. In fact, the integration of radiobiological information to create more effective and individualized treatment plans has increased the efficacy of RT in addition to technological advancements.

A Functional Data Analysis Framework Incorporating Derivative Information and Mixed-Frequency Data for Predictive Modeling of Crude Oil Price

A Functional Data Analysis Framework Incorporating Derivative Information and Mixed-Frequency Data for Predictive Modeling of Crude Oil Price

Safety and efficacy of different doses of erythromycin for noncystic fibrosis bronchiectasis in the stable phase: A randomized controlled trial

Objective: To evaluate the efficacy and side effects of different doses of erythromycin in patients with stable non-cystic fibrosis bronchiectasis (NCFB). Method: Altogether, 148 patients diagnosed with stable NCFB were enrolled in this study and randomly assigned to one of the following four treatment groups: Group A received erythromycin 250 mg/day; Group B received erythromycin 375 mg/day; Group C received erythromycin 500 mg/day; and Group D received a placebo. Each group consisted of 35 patients. The dataset comprised lung function data and computed tomography(CT), St. George’s Respiratory Questionnaire (SGRQ), modified Medical Research Council, bronchiectasis severity index(BSI), and E-FACED scores, which were collected before and at 6 months after treatment. These were subjected to a univariate analysis. Result: Groups C and D exhibited significant differences in forced expiratory volume in 1 second, forced vital capacity (FVC), quality of life, and CT Bhalla, BSI, and E-FACED scores (P < 0.05). Additionally, Groups B and D demonstrated significant differences in FVC and SGRQ, BSI, and EFACED scores (P < 0.05). Contrarily, Group A exhibited non-significant differences. Conclusion: The oral erythromycin regimen at a dose of 500 mg/day can improve the lung function and quality of life of stable NCFB patients.

Forensically useful mid-term and short-term temperature reconstruction for quasi-indoor death scenes.

While estimating postmortem interval (PMI) ambient temperature plays a pivotal role, so its reconstruction is crucial for forensic scientists. The recommended procedure is to correct temperatures from the nearest meteorological station based on measurements from the death scene; typically applying linear regression. Recently, there have been attempts to use different algorithms, that can improve that correction, for example GAM algorithm. Unfortunately, the improvements are usually a consequence of using more dependent variables than just the temperature from the death scene (e.g. humidity), which is impractical. This study develops practical new methods to accurately reconstruct ambient temperatures at a death scene, using just temperature measurements. Since the main difficulty preventing practitioners from using the correction protocol more frequently is likely the need to record temperatures on-site for at least several days, we searched for possibilities to shorten the measurement period. For this purpose, we tested two less popular algorithms to achieve this goal. The concurrent regression model (the model from the functional data analysis field) for the mid-term reconstruction (measurements lasting several days) and the functional model based on Fourier expansion for the short-term reconstruction (measurements lasting a few hours). The algorithms' performance was tested using data collected in six places: a roof and an attic of a heated building, an unheated garage inside the heated building, an unheated wooden shack, an uninhabited building, and an underground (the data logger was buried about 30 cm below the ground level). We classified these locations as quasi-indoor conditions, contrasting them with typical indoor conditions, where temperatures are nearly constant, and typical outdoor conditions, where there is no heat insulation. The mid-term model reduced error compared to the linear regression, providing nearly perfect reconstruction for measurement periods longer than six days. More importantly, however, the accuracy of short-term reconstruction was also high. The short-term model closely matched the concurrent regression model's performance after only four to five hours of measurements. In practice, both methods are very similar to the standard procedure. The main difference is the change in the algorithm and its implementation. In conclusion, this study demonstrates that correction of weather station temperatures can provide fairly accurate temperature data for use in estimating PMI after only 4-5 h of measurements on a death scene.

Structure dependent optical and conductive properties of HPMC:CoCl2 composites: A functional data analysis study

This study rigorously investigates cobalt chloride doped hydroxypropyl methylcellulose composite films, taking a significant step toward understanding structure property relations. The aim is to develop biodegradable yet conductive polymer composites by meticulously examining the effects of incorporating a metal salt like cobalt chloride. The x-ray diffraction results obtained for these samples are analyzed to relate structure property dependencies. Conductivity tests provide valuable insights into variations in electrical conductivity, impedance, and dielectric properties. The optical bandgap obtained from UV/visible spectroscopic studies aligns with other studied physical parameters, supporting dopant-driven changes in these composites. In addition, employing the tightly bound electron model, we detail the bandgap variation in these composites concerning the dopant concentration. This study reveals that cobalt chloride is an effective dopant for modifying conductivity in a non-conductive polymer. Furthermore, we observe that the addition of CoCl2 in the polymer matrix forms packets of ordered regions, referred to as domains or crystalline grains. These domains act as sources of free ions and electrons, enhancing electrical and optical properties. Alongside experimental evidence, we employ a robust calculation method using functional data analysis to draw clear relationships among the parameters under study, further emphasizing the scientific rigor and significance of our findings.

Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis.

Condition monitoring and fault classification in engineering systems is a critical challenge within the scope of Prognostics and Health Management (PHM). The fault diagnosis of complex nonlinear systems, such as hydraulic systems, has become increasingly important due to advancements in big data analytics, machine learning (ML), Industry 4.0, and Internet of Things (IoT) applications. Multi-sensor data provides opportunities to predict component conditions; however, environments characterized by multiple sensors and diverse fault states across various components complicate the fault classification process. To address these challenges, this study introduces a novel multivariate Functional Data Analysis (FDA) framework based on Multivariate Functional Principal Component Analysis (MFPCA) for classifying failure conditions in hydraulic systems. The proposed method systematically tackles condition-based diagnostics and addresses fundamental issues in multi-fault classification. Experimental results demonstrate that this approach achieves high classification accuracy using raw multi-sensor data, establishing multivariate FDA as a powerful tool for fault diagnosis in complex systems.

Wavelet thresholding on independent subspace factorizations of spatially indexed wide functional data for robust estimation of cortical activity

Wavelet thresholding on independent subspace factorizations of spatially indexed wide functional data for robust estimation of cortical activity

SRCES: a simulation program for collective effects in electron storage rings

Collective effects are important in the physics design of electron storage rings, especially for the fourth-generation synchrotron light source, where the collective effects are more severe than before and limit maximum stored beam current. To estimate beam collective effects, besides theoretical analysis, numerical tracking methods are necessary for obtaining more accurate and comprehensive results. To systematically study the comprehensive influence of wakefields and various damping mechanisms, we developed a new code Storage Ring Collective Effects Simulator (SRCES), which is a tracking code based on macroparticle model in 6-dimensional phase space. Several models are employed to handle various processes of particle motion. The modified transfer matrix and high order Taylor transfer map are used to solve particle motion under external electro-magnetic fields. A simplified equivalent damping and excitation model is used to represent the effects of synchrotron radiation on particle motion. The kick model is used to represent the influence of wakefields and RF cavity. Furthermore, the wake function data can be input as numerical arrays directly or through specific impedance models. In this paper, we describe the basic theory foundation, functions and benchmarked results of the code SRCES. Then, SRCES is used to conduct preliminary research on the collective effects of the Hefei Advanced Light Facility storage ring.

From Text to Translation: Using Language Models to Prioritize Variants for Clinical Review.

Despite rapid advances in genomic sequencing, most rare genetic variants remain insufficiently characterized for clinical use, limiting the potential of personalized medicine. When classifying whether a variant is pathogenic, clinical labs adhere to diagnostic guidelines that comprehensively evaluate many forms of evidence including case data, computational predictions, and functional screening. While a substantial amount of clinical evidence has been developed for these variants, the majority cannot be definitively classified as 'pathogenic' or 'benign', and thus persist as 'Variants of Uncertain Significance' (VUS). We processed over 2.4 million plaintext variant summaries from ClinVar, employing sentence-level classification to remove content that does not contain evidence and removing uninformative summaries. We developed ClinVar-BERT to discern clinical evidence within these summaries by fine-tuning a BioBERT-based model with labeled records. When validated classifications from this model against orthogonal functional screening data, ClinVar-BERT significantly separated estimates of functional impact in clinically actionable genes, including BRCA1 (p = 1.90 × 10 - 20 ), TP53 (p = 1.14 × 10 - 47 ), and PTEN (p = 3.82 × 10 - 7 ). Additionally, ClinVar-BERT achieved an AUROC of 0.927 in classifying ClinVar VUS against this functional screening data. This suggests that ClinVar-BERT is capable of discerning evidence from diagnostic reports and can be used to prioritize variants for re-assessment by diagnostic labs and expert curation panels.