Segmental Level Research Articles

Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression Approach

Abstract. This study investigated the probability of road closure due to flooding. Logistic regression model was developed using the road closure data and the daily rainfall data for Houston, TX, USA during 2017 and 2018. The road network was further divided into flood prone zones. The spatial analysis revealed that the rainfall at the road segment level could be sufficiently represented by that recorded by nearest sensors. Within a 4 d window, the rainfall in the current day and 3 d prior played a more influential role in predicting road closure. The differential outcomes due to distinct regional features were explained. Finally, a watershed delineation approach substantially improved the model's predictive power and sensitivity.

Validation of Inter-Reader Agreement/Consistency for Quantification of Ellipsoid Zone Integrity and Sub-RPE Compartmental Features Across Retinal Diseases

Background: An unmet need exists when clinically assessing retinal and layer-based features of retinal diseases. Therefore, quantification of retinal-layer-thicknesses/fluid volumes using deep-learning-augmented platforms to reproduce human-obtained clinical measurements is needed. Methods: In this analysis, 210 spectral-domain optical coherence tomography (SD-OCT) scans (30 without pathology, 60 dry age-related macular degeneration [AMD], 60 wet AMD, and 60 diabetic macular edema [total 23,625 B-scans]) were included. A fully automated segmentation platform segmented four retinal layers for compartmental assessment (internal limiting membrane, ellipsoid zone [EZ], retinal pigment epithelium [RPE], and Bruch’s membrane). Two certified OCT readers independently completed manual segmentation and B-scan level validation of automated segmentation, with segmentation correction when needed (semi-automated). Certified reader metrics were compared to gold standard metrics using intraclass correlation coefficients (ICCs) to assess overall agreement. Across different diseases, several metrics generated from automated segmentations approached or matched human readers performance. Results: Absolute ICCs for retinal mean thickness measurements showed excellent agreement (range 0.980–0.999) across four cohorts. EZ-RPE thickness values and sub-RPE compartment ICCs demonstrated excellent agreement (ranges of 0.953–0.987 and 0.944–0.997, respectively) for full dataset, dry-AMD, and wet-AMD cohorts. Conclusions: Analyses demonstrated high reliability and consistency of segmentation of outer retinal compartmental features using a completely human/manual approach or a semi-automated approach to segmentation. These results support the critical role that measuring features, such as photoreceptor preservation through EZ integrity, in future clinical trials may optimize clinical care.

Surgical Management of Thoracic Dorsal Arachnoid Webs: A Ten-Year Single-Institution Experience

ObjectiveThoracic dorsal arachnoid webs are intradural membranes that may cause obstruction of CSF flow and spinal cord compression. While well-recognized, they are rare and there is a paucity of long-term data on their natural history and prognosis. We reviewed radiographic features, surgical indications, and pathologic specimens of patients diagnosed with focal thoracic dorsal arachnoid webs. MethodsA radiology database and surgical case logs were queried for thoracic arachnoid webs at a single hospital system for a ten-year period. A retrospective chart review was performed on identified cases. ResultsWe identified 127 patients with dorsal thoracic arachnoid webs. Arachnoid webs were radiographically classified into three morphologic types: Type 1 (54%) causing spinal cord deformity only, Type 2 (32%) producing cord deformity with myelomalacia, and Type 3 (14%) with cord deformity, myelomalacia, and syringomyelia. These arachnoid webs were commonly centered at the upper thoracic T4 segmental level. Forty-one cases (32%) required surgery, generally for thoracic myelopathy with gait instability (46%) and lower extremity numbness and pain (39%). In patients who underwent surgery, 79% experienced symptomatic improvement and 21% remained stable, after an average of 21 months follow-up evaluation. Surgical pathology revealed fibrous connective tissue (100%) with calcifications (26%) or inflammation (7%). ConclusionsThe majority in a large series of patients with dorsal arachnoid webs did not undergo surgical intervention, but those with myelomalacia and syrinx experienced radiographic and clinical deterioration without surgery. Surgery to treat symptomatic arachnoid webs results in significant clinical improvement with low surgical morbidity.

Automated segment-level coronary artery calcium scoring on non-contrast CT: a multi-task deep-learning approach

ObjectivesTo develop and evaluate a multi-task deep-learning (DL) model for automated segment-level coronary artery calcium (CAC) scoring on non-contrast computed tomography (CT) for precise localization and quantification of calcifications in the coronary artery tree.MethodsThis study included 1514 patients (mean age, 60.0 ± 10.2 years; 56.0% female) with stable chest pain from 26 centers participating in the multicenter DISCHARGE trial (NCT02400229). The patients were randomly assigned to a training/validation set (1059) and a test set (455). We developed a multi-task neural network for performing the segmentation of calcifications on the segment level as the main task and the segmentation of coronary artery segment regions with weak annotations as an auxiliary task. Model performance was evaluated using (micro-average) sensitivity, specificity, F1-score, and weighted Cohen’s κ for segment-level agreement based on the Agatston score and performing interobserver variability analysis.ResultsIn the test set of 455 patients with 1797 calcifications, the model assigned 73.2% (1316/1797) to the correct coronary artery segment. The model achieved a micro-average sensitivity of 0.732 (95% CI: 0.710–0.754), a micro-average specificity of 0.978 (95% CI: 0.976–0.980), and a micro-average F1-score of 0.717 (95% CI: 0.695–0.739). The segment-level agreement was good with a weighted Cohen’s κ of 0.808 (95% CI: 0.790–0.824), which was only slightly lower than the agreement between the first and second observer (0.809 (95% CI: 0.798–0.845)).ConclusionAutomated segment-level CAC scoring using a multi-task neural network approach showed good agreement on the segment level, indicating that DL has the potential for automated coronary artery calcification classification.Critical relevance statementMulti-task deep learning can perform automated coronary calcium scoring on the segment level with good agreement and may contribute to the development of new and improved calcium scoring methods.Key PointsSegment-level coronary artery calcium scoring is a tedious and error-prone task.The proposed multi-task model achieved good agreement with a human observer on the segment level.Deep learning can contribute to the automation of segment-level coronary artery calcium scoring.Graphical

Research of the dynamics of wholesale segment concentration level in the regional pharmaceutical market

Introduction: The study of the competitive environment in the pharmaceutical market is an element of the analysis of the drug supply system. The purpose of the study is to analyze the dynamics of concentration levels in the wholesale segment of the regional pharmaceutical market. Materials and methods: Data on market shares of key players were collected and summarized based on the executed contracts (461 contracts in total) for the supply of pharmaceutical products in the model region. For the competitive analysis of pharmaceutical distributors, the Herfindahl-Hirschman Index (HHIn) calculation method was used. We also used the method of constructing the SV matrix, which has the form of a graph consisting of four quadrants divided on the abscissa scale by the obtained values of market concentration ratios (CRSV) and on the ordinate scale by the values of Hall-Teidman indices (HTSV). Results: Market concentration indicators in the wholesale segment of the regional pharmaceutical market indicate the formation of oligopoly. The constructed SV matrix demonstrated the dynamics of market concentration levels corresponding to the changing economic situation. Based on the results of the analysis, proposals to improve the competitiveness of regional pharmaceutical distributors were formed. Conclusion: The study of the dynamics of market concentration indicators allowed to analyze their state, establish the vectors of their drift, and propose measures to optimize the wholesale supply of pharmaceutical goods.

Dynamic Multi-Granularity Translation System: DAG-Structured Multi-Granularity Representation and Self-Attention

In neural machine translation (NMT), the sophistication of word embeddings plays a pivotal role in the model’s ability to render accurate and contextually relevant translations. However, conventional models with single granularity of word segmentation cannot fully embed complex languages like Chinese, where the granularity of segmentation significantly impacts understanding and translation fidelity. Addressing these challenges, our study introduces the Dynamic Multi-Granularity Translation System (DMGTS), an innovative approach that enhances the Transformer model by incorporating multi-granularity position encoding and multi-granularity self-attention mechanisms. Leveraging a Directed Acyclic Graph (DAG), the DMGTS utilizes four levels of word segmentation for multi-granularity position encoding. Dynamic word embeddings are also introduced to enhance the lexical representation by incorporating multi-granularity features. Multi-granularity self-attention mechanisms are applied to replace the conventional self-attention layers. We evaluate the DMGTS on multiple datasets, where our system demonstrates marked improvements. Notably, it achieves significant enhancements in translation quality, evidenced by increases of 1.16 and 1.55 in Bilingual Evaluation Understudy (BLEU) scores over traditional static embedding methods. These results underscore the efficacy of the DMGTS in refining NMT performance.

Sagittal morphometry of intervertebral spaces in subaxial cervical region of asymptomatic Chinese.

This study aimed to quantify and evaluate morphology of intervertebral space between neighboring cervical units using radiographic imaging indices, to help spine surgeons when performing anterior cervical discectomy and fusion (ACDF) surgery on the Chinese population. The background and imaging parameters of the subjects were assessed. Cervical lateral radiographs were employed to measure the intervertebral height (IH), intervertebral height index (IHI), and segmental lordosis (SL). Endplate parameters measurements were conducted on sagittal T2-weighted magnetic resonance imaging (MRI), including endplate sagittal diameter (ESD), and endplate concavity depth (ECD). All individuals were divided into three age groups: individuals aged 20-35 were in group A, individuals aged 36-50 were in group B, and individuals aged over 50 were in group C. A comparison of the variables was conducted among the three groups. Additionally, these radiographic parameters were also compared between males and females. A total of 102 individuals were included in this study. IH was greater at C6/7 than those at other segmental levels (p < 0.001). The largest SL values were found at C6/7, while the least were found at C3/4. The superior ESD (ESDs) and ECD (ECDs) of the intervertebral space were significantly greater than those of the inferior endplates (p < 0.05). The ESD and ECD values were the largest at C6/7, while the least at C3/4. Additionally, age and gender had an influence on several parameters. IH was significantly lower in group A compared to group B (p < 0.05) and group C (p < 0.05) from C3/4 level to C6/7 level. ECDs were lower in group A compared to group B (p < 0.05) and group C (p < 0.05) at each level. IH and ESD in males were generally significantly greater than those in females at all levels (p < 0.05). The current study found that C6/7 had the greatest IH, SL, ESD, and ECD values in asymptomatic Chinese. SL gradually increased from C3/4 to C6/7 levels. IH and ECD were significantly associated with age. Males had greater IH and ESD values than females. These findings provide baseline information for planning for selection of anterior screws and intervertebral implants.

Cycle route uptake and scenario estimation (CRUSE): an approach for developing strategic cycle network planning tools

This paper describes an approach for developing strategic cycle network planning tools. Based on our experience developing and deploying the Cycle Route Uptake and Scenario Estimation (CRUSE) Tool for Ireland, we outline the underlying methods, including disaggregation of origin–destination data with the open source ‘odjitter’ software, incorporation of additional trip purposes, routing, scenario generation, and development of an intuitive user interface that is tested and used by practitioners. Commissioned by the national infrastructure agency Transport Infrastructure Ireland, CRUSE provides estimates of current and potential future cycling levels under ‘snapshot’ scenarios to inform investment decisions. The publicly available results at https://cruse.bike/ enable planners, engineers, and other stakeholders to make more evidence-based decisions. CRUSE goes beyond previous work by: modeling networks at high spatial resolution; simulating multiple trip purposes (social, shopping, personal utility, recreational, and cycle touring), supplementing official origin–destination datasets on travel for work and education; and providing estimates of ‘quietness’ (a proxy for cyclist comfort and route preference) at the route segment level. Three network types—‘Fastest’, ‘Balanced’, and ‘Quietest’—help plan both arterial and residential cycle networks. Workshops with stakeholders were used to inform the development of the tool. Feedback shows that the tool has a wide range of uses and is already being used in practice to inform urban, inter-urban, and rural cycle network designs. The approach is flexible and open source, allowing the underlying ideas and code to be adapted, supporting more evidence-based and effective cycling policies and interventions internationally.

Age-Friendly Urban Design for Older Pedestrian Road Safety: A Street Segment Level Analysis in Madrid

Walking benefits older pedestrians but exposes them to traffic crashes. With an aging population, designing age-friendly cities is crucial, yet research on older pedestrian safety at a micro-level is limited. This study aims to reduce older pedestrian–vehicle collisions and create more livable environments through infrastructure policies derived from statistical data analysis. Special attention is focused on collecting a holistic set of infrastructure variables to reflect most of the street built environment elements, which helps policymakers implement short-term safety measures. Using Bayesian Poisson regression, this study analyzes factors contributing to the occurrence of crashes involving older and non-older pedestrians on road segments in Madrid, Spain. The results indicate that different factors affect the occurrence of crashes for all pedestrians versus older pedestrians specifically. Traffic crashes involving all pedestrians are affected by leisure points of interest, bus stops, and crosswalk density. Older pedestrian traffic crashes are influenced by population density, the presence of trees and trash containers, and contour complexity. Proposed measures include relocating trees and trash containers, modifying bus stops, and adding crosswalks and traffic lights. This paper also shows that these countermeasures, aimed at creating age-friendly streets for older pedestrians, are not expected to worsen the road safety of other pedestrians.

Mild cognitive impairment prediction based on multi-stream convolutional neural networks

BackgroundMild cognitive impairment (MCI) is the transition stage between the cognitive decline expected in normal aging and more severe cognitive decline such as dementia. The early diagnosis of MCI plays an important role in human healthcare. Current methods of MCI detection include cognitive tests to screen for executive function impairments, possibly followed by neuroimaging tests. However, these methods are expensive and time-consuming. Several studies have demonstrated that MCI and dementia can be detected by machine learning technologies from different modality data. This study proposes a multi-stream convolutional neural network (MCNN) model to predict MCI from face videos.ResultsThe total effective data are 48 facial videos from 45 participants, including 35 videos from normal cognitive participants and 13 videos from MCI participants. The videos are divided into several segments. Then, the MCNN captures the latent facial spatial features and facial dynamic features of each segment and classifies the segment as MCI or normal. Finally, the aggregation stage produces the final detection results of the input video. We evaluate 27 MCNN model combinations including three ResNet architectures, three optimizers, and three activation functions. The experimental results showed that the ResNet-50 backbone with Swish activation function and Ranger optimizer produces the best results with an F1-score of 89% at the segment level. However, the ResNet-18 backbone with Swish and Ranger achieves the F1-score of 100% at the participant level.ConclusionsThis study presents an efficient new method for predicting MCI from facial videos. Studies have shown that MCI can be detected from facial videos, and facial data can be used as a biomarker for MCI. This approach is very promising for developing accurate models for screening MCI through facial data. It demonstrates that automated, non-invasive, and inexpensive MCI screening methods are feasible and do not require highly subjective paper-and-pencil questionnaires. Evaluation of 27 model combinations also found that ResNet-50 with Swish is more stable for different optimizers. Such results provide directions for hyperparameter tuning to further improve MCI predictions.

UT-1 Transporter Expression in the Spiny Dogfish (Squalus acanthias): UT-1 Protein Shows a Different Localization in Comparison to That of Other Sharks.

The original UT-1 transporter gene was initially identified in the spiny dogfish (Squalus acanthias), but localization of the UT-1 protein was not determined. Subsequent UT-1 expression was shown to localize to the collecting tubule (CT) of the shark nephron in other shark species, with expression in a closely related chimaera species also located additionally at a lower level in the intermediate-I segment (IS-I) of the nephron. In spiny dogfish, two UT-1 splice variants are known (UT-1 long and short), and there was also a second UT-1 gene described (here termed Brain UT). In this study, a second splice variant of the second Brain UT gene was discovered. Expression profiles (mRNA) of UT-1 long and short and Brain UT were determined in a number of spiny dogfish tissues. Quantitative PCR in kidney samples showed that the level of the short variant of UT-1 was around 100 times higher than the long variant, which was itself expressed around 10 times higher than Brain UT cDNA/mRNA (in kidney). For the long variant, there was a significantly higher level of mRNA abundance in fish acclimatized to 75% seawater. Ultimately, three UT-1 antibodies were made that could bind to both the UT-1 short and long variant proteins. The first two of these showed bands of appropriate sizes on Western blots of around 52.5 and 46 kDa. The second antibody had some additional lower molecular weight bands. The third antibody was mainly bound to the 46 kDa band with faint 52.5 kDa staining. Both the 52.5 and 46 kDa bands were absent when the antibodies were pre-blocked with the peptide antigens used to make them. Across the three antibodies, there were many similarities in localization but differences in subcellular localization. Predominantly, antibody staining was greatest in the intermediate segment 1 (IS-I) and proximal (PIb) segments of the first sinus zone loop of the nephron, with reasonably strong expression also found at the start and middle of the late distal tubule (LDT; second sinus zone loop). While some expression in the collecting tubule (CT) could not be ruled out, the level of staining seemed to be low or non-existent in convoluted bundle zone nephron segments such as the CT. Hence, this suggests that spiny dogfish have a fundamentally different mode of urea absorption in comparison to that found in other shark species, potentially focused more on the nephron sinus zone loops than the CT.

Geothermal resources of Hungary: a play-based review

In this paper we review the geothermal resources of Hungary using the geothermal play concept and its scale-dependent hierarchical categorization. The Pannonian Basin belongs to an extensional domain play type, with two main plays: the porous Neogene basin fill sediments and the fractured-karstified Palaeozoic and Mesozoic basement rocks. Both plays can be divided into hydrostatic and overpressured play segments. The most widely utilized play level in the hydrostatic segment of the porous Neogene sediment play comprises laterally extended highly permeable sandstone layers storing low salinity thermal water with average flow rate around 900 l/min and temperatures between 50 °C and 100 °C in the depth range of 1–2 km. The other prosperous play level is represented by the fractured-karstified Mesozoic carbonates in the hydrostatic segment of the basement play having elevated temperatures (90–120 °C) and several thousand l/min flow rates. The play level related to fractured Palaeozoic rocks is less important due to its limited extent. The overpressured segment of the basement play may have high flow rates, but still an untapped resource, where exploitation is limited by high overpressure, high temperature (170–200 °C), and highly saline hot waters.

Classification methods of pulmonary contusion based on chest CT and the association with in-hospital outcomes: a systematic review of literature.

Patients sustaining pulmonary contusion (PC) have a higher risk of complications and long-term respiratory difficulty. Computed tomography (CT) scans have a high sensitivity for PC. However, since PC develops over time, CT scans made directly post-trauma may underestimate the full extent of PC. This creates a need to better define in which PC-patients complications are more likely. The aim of this systematic review was to identify different classification systems of PC, and investigate the association between amount of PC and in-hospital outcomes. A systematic review was conducted in accordance with PRISMA guidelines. Studies reporting a classification system for PC after blunt thoracic trauma based on a CT scan were included. Outcomes were classification method of PC and the relation between classification and pulmonary complications and in-hospital outcomes. Twenty studies were included. Total number of patients ranged from 49 to 148,140 patients. The most common classification system used was calculating the percentage of contused lung volume. Other classification methods were based on Blunt Pulmonary Contusion score-6 and -18, Abbreviated Injury Score and Thoracic Trauma Severity scores. Worse outcomes were generally associated with between > 18 to > 24% contusion volume. The heterogeneity of currently available literature makes comparing classification methods challenging. The most common classification of PC was based on volumetric analysis. Calculating a percentage of PC as part of the total volume allows for the highest level of segmentation of lung parenchyma as compared to using BPC-6, BPC-18, or AIS. Contusion volume exceeding 18-24% was generally associated with worse outcomes.

Role of Virtual Monoenergetic Images in the Assessment of Vessel Enhancement in Segmental Level in Third-Generation Dual-Source Dual-Energy CT Pulmonary Angiography—A Prospective Study

Abstract Introduction Pulmonary embolism is the third most common cause of cardiovascular death worldwide and imaging plays a pivotal role in establishing the diagnosis. Computed tomography pulmonary angiography (CTPA) scores over other modalities and is the current diagnostic investigation of choice. In this study, we assessed the main pulmonary artery and its corresponding segmental artery attenuation in reconstructed virtual monoenergetic (mono plus) images (VMI-MP) and linear blended images (spectral post processing, SPP) obtained from dual-energy CTPA. The values were compared using contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). Materials and Methods Forty patients (mean age group, 53.6 years; 26 females and 14 males) with segmental pulmonary thromboembolism were included in this prospective study. The patients underwent CTPA study using bolus tracking in the dual-source CT-SOMATOM Force, Siemens. Postcontrast datasets (90 kV, 150 kV, and SPP) were used to reconstruct the monoenergetic images using syngo.via software virtually. Comparison was done between bivariate samples using the paired sample t-test. Results The mean Hounsfield unit (HU) artery in the left lung for VMI-MP and SPP images were 886.9 ± 242 and 356.8 ± 121.3 HU, respectively. Similarly, for the right lung, it was 868.3 ± 243.5 and 336.1 ± 105.5 HU, respectively. The mean attenuation of the arteries in MP images was higher and statistically significant (p-value &lt;0.005). Likewise, the CNR) and SNR were found to have a statistically significant p-value (&lt;0.005). An acceptable increase in image noise was seen in VMI as compared with SPP images. Conclusion Low-keV VMIs perform more effectively than the conventional polyenergetic spectrum to evaluate vessel attenuation, which in turn increases thrombus detectability. The increased CNR in VMI enables improved lesion conspicuity.

Analyzing Risk Factors for Delayed Extubation Following Posterior Approach Surgery for Congenital Scoliosis: A Retrospective Cohort Study.

Retrospective cohort study. Investigate the risk factors for delayed extubation after posterior approach orthopedic surgery in patients with congenital scoliosis. The clinical data of patients who received surgery for congenital scoliosis at the First Affiliated Hospital of Xinjiang Medical University between January 2021 and July 2023 have been gathered. Patients are categorized into the usual and the delayed extubation groups, depending on the duration of tracheal intubation after surgery. The study employs univariate and multivariate logistic regression models to examine the clinical characteristics of the two cohorts and discover potential risk factors linked to delayed extubation. In addition, a prediction model is created to visually depict the significance of each risk factor in terms of weight according to the nomogram. A total of 119 patients (74.8% females), with a median age of 15 years, are included. A total of 32 patients, accounting for 26.9% of the sample, encountered delayed extubation. Additionally, 13 patients (10.9%) suffered perioperative complications, with pneumonia being the most prevalent. The multivariate regression analysis revealed that the number of osteotomy segments, postoperative hematocrit, postoperative Interleukin-6 levels, and weight are predictive risk factors for delayed extubation. Postoperative hematocrit and Interleukin-6 level, weight, and number of osteotomy segments can serve as independent risk factors for predicting delayed extubation, with combined value to assist clinicians in evaluating the risk of delayed extubation of postoperative congenital scoliosis patients, improving the success rate of extubation, and reducing postoperative treatment time in the intensive care unit.

Multiphoton Lithography of Interpenetrating Polymer Networks for Tailored Microstructure Thermal and Micromechanical Properties.

Multiphoton lithography (MPL), an emerging truly 3D microfabrication technique, exhibits substantial potential in biomedical applications, including drug delivery and tissue engineering. Fabricated micro-objects are often expected to undergo shape morphing or bending of the entire structure or its parts. Furthermore, ensuring precise property tuning is detrimental to the realization of the functionality of MPL microstructures. Herein, novel MPL materials based on interpenetrating polymer networks (IPNs) are presented that effectively combine the advantages of acrylate and epoxy systems. IPNs with varying component ratios are investigated for their microfabrication performance and structural integrity with respect to thermal and micromechanical properties. A variety of high-resolution techniques is applied to comprehensively evaluate IPN properties at the bulk, micron, and segmental levels. This study shows that the MPL laser scanning velocity and power, photoinitiator content, and multi-step exposure can be used to tune the morphology and properties of the IPN. As a result, a library of 3D MPL IPN microstructures with high 3D structural stability and tailored thermal and micromechanical properties is achieved. New IPN microstructures with Young's moduli of 3-4MPa demonstrate high-to-fully elastic responses to deformations, making them promising for applications in morphable microsystems, soft micro-robotics, and cell engineering.

Towards development of a roadway flood severity index

Flooding is among the costliest and deadliest weather disasters. Moreover, different types of flooding have significant impacts on the transportation network and infrastructure including flash, riverine, urban, coastal, and storm surge. The variety of flooding scenarios makes it challenging to quantify the impacts of flooding on transportation across spatial scales; however, such metrics would be beneficial both prior to and after the event. Pre-flood metrics can promote enhanced impact-based decision-support guidance and hazard communication, while post-flood metrics may include larger regional disruptions located away from the most inundated areas and their associated secondary societal impacts. This study developed a retrospective Roadway Flood-Severity Index (RFSI) from 1982 to 2020 capable of integrating geo-located hydrometeorological data and transportation mobility information across localized and multi-state, sub-national regions to (1) categorize larger-scale, flood-related transportation disruptions, (2) understand the origins of those disruptions, and (3) identify severity risk levels of individual road segments and broader regions of transportation disruption during flood events. The fundamental question is, as flooding events unfold, can past hydrometeorological inundation information be coupled with transportation system network and mobility data to identify the most vulnerable roadway segments and regions? The overall mobility impacts of flooding on transportation were highly variable and relatively uncommon throughout the study period. Given this variability in other mobility data (e.g., vehicle speeds), hydrometeorological parameters were used exclusively as model inputs and crowdsourced Waze flood reports were used as the target response variable. A logistic regression based RFSI was found to best align with the dataset providing a “no flood” or “flood” classification. Eventually, this retrospective analysis will be extended to provide predictive capability as well. The RFSI is intended to provide transportation agencies with a quantitative metric to classify, categorize, and communicate the potential impacts of flood events throughout the transportation network.

Characteristics of electrocardiogram in fulminant myocarditis

Objective: To investugate the unique electrocardiogram (ECG) characteristics of fulminant myocarditis (FM) patients and provide important clues for the diagnosis of FM. Methods: This was a retrospective study. Patients diagnosed with acute myocarditis at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from February 2017 to April 2022 were enrolled and divided into fulminant myocarditis group (FM) and non-fulminant myocarditis group (NFM) according to clinical diagnosis. A total of 246 healthy people who underwent physical examination in the Health examination Center of Tongji Hospital at the same period were selected as the control group. The clinical data and ECG characteristics of the above 3 groups were analyzed and compared. Logistic regression model was used to analyze the influence of ECG parameters on left ventricular ejection fraction in FM patients. Receiver operating curves were constructed to evaluate the predictive value of different ECG parameters for FM. Results: A total of 180 patients were included in this study (FM group: n=123; NFM group: n=57), with an age of (35.0±16.2) years and 106 males (58.89%). Compared with NFM group, ECG was significantly abnormal in FM group, with a higher incidence of sinus tachycardia, ventricular tachycardia or ventricular fibrillation, escape rhythm, right bundle branch block, third degree atrioventricular block, ST-segment elevation, low voltage, prolonged QTc interval, and widened QRS wave in the FM group (all P<0.05). The ECG parameters showed that the amplitude of the full lead QRS wave in FM group was lower than that in NFM group (P<0.01). The average heart rate and QTc interval of FM group were significantly higher than those of NFM and control groups (all P<0.05). Although ST-segment elevation had a higher incidence in the FM group, ECG parameters showed that except for leads Ⅲ and aVF, the ST segment levels in all leads in the FM group were lower than those in the control group (all P<0.05). There was a statistically significant difference in some ST segment changes between FM and NFM groups, while there was no statistical difference between the NFM and control groups. Multivariate regression analysis showed that widened QRS wave and increased heart rate were the influencing factors for left ventricular systolic dysfunction in FM patients (OR=16.914, 95%CI: 1.367-209.224, P=0.028; OR=1.026, 95%CI: 1.010-1.042, P=0.001). Receiver operating curve analysis showed that heart rate>86.90 beat/min, QTc>431.50 ms, and RV5+SV1<1.72 mV had certain predictive value for FM diagnosis. Conclusions: FM patients displayed marked and severe ECG abnormalities, and characteristic changes in ECG can provide important first clues for the diagnosis of FM.

Implications of coronary calcification on the assessment of plaque pathology: a comparison of computed tomography and multimodality intravascular imaging.

This study aimed to investigate the impact of calcific (Ca) on the efficacy of coronary computed coronary angiography (CTA) in evaluating plaque burden (PB) and composition with near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) serving as the reference standard. Sixty-four patients (186 vessels) were recruited and underwent CTA and 3-vessel NIRS-IVUS imaging (NCT03556644). Expert analysts matched and annotated NIRS-IVUS and CTA frames, identifying lumen and vessel wall borders. Tissue distribution was estimated using NIRS chemograms and the arc of Ca on IVUS, while in CTA Hounsfield unit cut-offs were utilized to establish plaque composition. Plaque distribution plots were compared at segment-, lesion-, and cross-sectional-levels. Segment- and lesion-level analysis showed no effect of Ca on the correlation of NIRS-IVUS and CTA estimations. However, at the cross-sectional level, Ca influenced the agreement between NIRS-IVUS and CTA for the lipid and Ca components (p-heterogeneity < 0.001). Proportional odds model analysis revealed that Ca had an impact on the per cent atheroma volume quantification on CTA compared to NIRS-IVUS at the segment level (p-interaction < 0.001). At lesion level, Ca affected differences between the modalities for maximum PB, remodelling index, and Ca burden (p-interaction < 0.001, 0.029, and 0.002, respectively). Cross-sectional-level modelling demonstrated Ca's effect on differences between modalities for all studied variables (p-interaction ≤ 0.002). Ca burden influences agreement between NIRS-IVUS and CTA at the cross-sectional level and causes discrepancies between the predictions for per cent atheroma volume at the segment level and maximum PB, remodelling index, and Ca burden at lesion-level analysis. Coronary calcification affects the quantification of lumen and plaque dimensions and the characterization of plaque composition coronary CTA. This should be considered in the analysis and interpretation of CTAs performed in patients with extensive Ca burden. Coronary CT Angiography is limited in assessing coronary plaques by resolution and blooming artefacts. Agreement between dual-source CT angiography and NIRS-IVUS is affected by a Ca burden for the per cent atheroma volume. Advanced CT imaging systems that eliminate blooming artefacts enable more accurate quantification of coronary artery disease and characterisation of plaque morphology.

Prosody matters: Preserved prominence marking strategies in people with Parkinson's disease independent of motor status.

While many studies focus on segmental variation in Parkinsonian speech, little is known about prosodic modulations reflecting the ability to adapt to communicative demands in people with Parkinson's disease (PwPD). This type of prosodic modulation is important for social interaction, and it involves modifications in speech melody (intonational level) and articulation of consonants and vowels (segmental level). The present study investigates phonetic cues of prosodic modulations with respect to different focus structures in mild dysarthric PwPD as a function of levodopa. Acoustic and kinematic speech parameters of 25 PwPD were assessed in two motor conditions. Speech production data from PwPD were collected before (medication-OFF) and after levodopa intake (medication-ON) by means of 3-D electromagnetic articulography. On the acoustic level, intensity, pitch, and syllable durations were analyzed. On the kinematic level, movement duration and amplitude were investigated. Spatio-temporal modulations of speech parameters were examined and compared across three different prosodic focus structures (out-of-focus, broad focus, contrastive focus) to display varying speech demands. Overall, levodopa had beneficial effects on motor performance, speech loudness, and pitch modulation. Acoustic syllable durations and kinematic movement durations did not change, revealing no systematic effects of motor status on the temporal domain. In contrast, there were spatial modulations of the oral articulators: tongue tip movements were smaller and lower lip movements were larger in amplitude under levodopa, reflecting a more agile and efficient articulatory movement under levodopa. Thus, respiratory-phonatory functions and consonant production improved, while syllable duration and tongue body kinematics did not change. Interestingly, prominence marking strategies were comparable between the medication conditions under investigation, and in fact, appear to be preserved in mild dysarthric PwPD.