US Dataset Research Articles

Multi-scale contrast approach for stock index prediction with adaptive stock fusion

The stock index summarizes the overall movement of a group of stocks, typically calculated as a weighted average of constituent stock prices. Stock Index Prediction (SIP) aids investors in assessing economic performance and making informed decisions. Traditional SIP methods rely heavily on historical index data, neglecting valuable insights from macro-financial indicators and stock prices. However, challenges arise when incorporating these data: (1) Including numerous unfiltered macro-financial indicators introduces noise. (2) Using non-correlated stock prices can be counterproductive. (3) Supervised training on stochastic time series can lead to overfitting. To overcome these issues, we propose a multi-scale contrast approach for stock index prediction with adaptive stock fusion (MCSIP). This method identifies highly correlated macro-financial indicators and stocks, optimizing the model through self-supervised multi-scale contrastive learning. MCSIP relies on contrastive learning in the time series domain to extract robust contextual information from stock index time series, mitigating stochastic data backpropagation and ensuring reliable representation. Experiments on US and Chinese stock datasets demonstrate superior performance compared to existing methods.

SimLVSeg: Simplifying Left Ventricular Segmentation in 2-D+Time Echocardiograms With Self- and Weakly Supervised Learning

ObjectiveAchieving reliable automatic left ventricle (LV) segmentation from echocardiograms is challenging due to the inherent sparsity of annotations in the dataset, as clinicians typically only annotate two specific frames for diagnostic purposes. Here we aim to address this challenge by introducing simplified LV segmentation (SimLVSeg), a novel paradigm that enables video-based networks for consistent LV segmentation from sparsely annotated echocardiogram videos. MethodsSimLVSeg consists of two training stages: (i) self-supervised pre-training with temporal masking, which involves pre-training a video segmentation network by capturing the cyclic patterns of echocardiograms from largely unannotated echocardiogram frames, and (ii) weakly supervised learning tailored for LV segmentation from sparse annotations. ResultsWe extensively evaluated SimLVSeg using EchoNet-Dynamic, the largest echocardiography dataset. SimLVSeg outperformed state-of-the-art solutions by achieving a 93.32% (95% confidence interval: 93.21–93.43%) dice score while being more efficient. We further conducted an out-of-distribution test to showcase SimLVSeg's generalizability on distribution shifts (CAM US dataset). ConclusionOur findings show that SimLVSeg exhibits excellent performance on LV segmentation with a relatively cheaper computational cost. This suggests that adopting video-based networks for LV segmentation is a promising research direction to achieve reliable LV segmentation. Our code is publicly available at https://github.com/BioMedIA-MBZUAI/SimLVSeg.

An open-source fine-tuned large language model for radiological impression generation: a multi-reader performance study

BackgroundThe impression section integrates key findings of a radiology report but can be subjective and variable. We sought to fine-tune and evaluate an open-source Large Language Model (LLM) in automatically generating impressions from the remainder of a radiology report across different imaging modalities and hospitals.MethodsIn this institutional review board-approved retrospective study, we collated a dataset of CT, US, and MRI radiology reports from the University of California San Francisco Medical Center (UCSFMC) (n = 372,716) and the Zuckerberg San Francisco General (ZSFG) Hospital and Trauma Center (n = 60,049), both under a single institution. The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score, an automatic natural language evaluation metric that measures word overlap, was used for automatic natural language evaluation. A reader study with five cardiothoracic radiologists was performed to more strictly evaluate the model’s performance on a specific modality (CT chest exams) with a radiologist subspecialist baseline. We stratified the results of the reader performance study based on the diagnosis category and the original impression length to gauge case complexity.ResultsThe LLM achieved ROUGE-L scores of 46.51, 44.2, and 50.96 on UCSFMC and upon external validation, ROUGE-L scores of 40.74, 37.89, and 24.61 on ZSFG across the CT, US, and MRI modalities respectively, implying a substantial degree of overlap between the model-generated impressions and impressions written by the subspecialist attending radiologists, but with a degree of degradation upon external validation. In our reader study, the model-generated impressions achieved overall mean scores of 3.56/4, 3.92/4, 3.37/4, 18.29 s,12.32 words, and 84 while the original impression written by a subspecialist radiologist achieved overall mean scores of 3.75/4, 3.87/4, 3.54/4, 12.2 s, 5.74 words, and 89 for clinical accuracy, grammatical accuracy, stylistic quality, edit time, edit distance, and ROUGE-L score respectively. The LLM achieved the highest clinical accuracy ratings for acute/emergent findings and on shorter impressions.ConclusionsAn open-source fine-tuned LLM can generate impressions to a satisfactory level of clinical accuracy, grammatical accuracy, and stylistic quality. Our reader performance study demonstrates the potential of large language models in drafting radiology report impressions that can aid in streamlining radiologists’ workflows.

Firms' organisation capital: Do peers matter?

This paper explores the role of industry-level factor in determining organisation capital. Using US dataset, we show evidence that peers' organisation capital matters, and firms mimic their peers in formulating organisation capital. We also find that mimicking behaviour is more pronounced for firms operating in competitive product markets and environments with more information asymmetry, as is consistent with theoretical explanations for why firms imitate each other. Our results are robust to alternative measures of organisation capital and endogeneity concerns.

Mapping the Hidden Terrain of Hepatocellular Carcinoma: Exploring Regional Differences in Incidence and Mortality across Two Decades by Using the Largest US Datasets.

Background: There is an observed variation in the burden of hepatocellular carcinoma (HCC) across different US populations. Our study aims to comprehensively assess variations in HCC incidence and mortality rates across different regions of the US. Understanding these geographical differences is crucial, given prior evidence indicating variations in the incidence of viral hepatitis and metabolic dysfunction-associated steatotic liver disease and varying access to curative HCC treatment among states. Methods: HCC age-adjusted incidence rates between 2001 and 2021 were obtained from the United States Cancer Statistics (USCS) database (which covers approximately 98% of the US population). HCC age-adjusted mortality rates between 2000 and 2022 were obtained from the National Center of Health Statistics (NCHS) database (covering approximately 100% of the US population). The rates were categorized by US geographical region into West, Midwest, Northeast, and South. Incidence rates were also categorized by race/ethnicity. Time trends [annual percentage change (APC) and average APC (AAPC)] were estimated by using Joinpoint Regression via the weighted Bayesian Information Criteria (p < 0.05). Results: Between 2001 and 2021, there were 491,039 patients diagnosed with HCC in the US (74.2% males). The highest incidence rate per 100,000 population was noted in the West (7.38), followed by the South (6.85). Overall incidence rates increased between 2001 and 2015 and then significantly decreased until 2021 (APC = -2.29). Most cases were in the South (38.8%), which also had the greatest increase in incidence (AAPC = 2.74). All four geographical regions exhibited an overall similar trend with an increase in incidence over the first 10-15 years followed by stable or decreasing rates. While stratification of the trends by race/ethnicity showed slight variations among the regions and groups, the findings are largely similar to all race/ethnic groups combined. Between 2000 and 2022, there were 370,450 patients whose death was attributed to HCC in the US (71.6% males). The highest mortality rate per 100,000 population was noted in the South (5.02), followed by the West (4.99). Overall mortality rates significantly increased between 2000 and 2013 (APC = 1.90), then stabilized between 2013 and 2016, and then significantly decreased till 2022 (APC = -1.59). Most deaths occurred in the South (35.8%), which also had the greatest increase in mortality (AAPC = 1.33). All four geographical regions followed an overall similar trend, with an increase in mortality over the first 10-15 years, followed by stable or decreasing rates. Conclusions: Our analysis, capturing about 98% of the US population, demonstrates an increase in HCC incidence and mortality rates in all geographical regions from 2000 to around 2014-2016, followed by stabilizing and decreasing incidence and mortality rates. We observed regional variations, with the highest incidence and mortality rates noted in the West and South regions and the fastest increase in both incidence and mortality noted in the South. Our findings are likely attributable to the introduction of antiviral therapy. Furthermore, demographic, socioeconomic, and comorbid variability across geographical regions in the US might also play a role in the observed trends. We provide important epidemiologic data for HCC in the US, prompting further studies to investigate the underlying factors responsible for the observed regional variations in HCC incidence and mortality.

When do firms benefit from overconfident CEOs? The role of board expertise and power for technological breakthrough innovation

AbstractResearch SummaryWhile prior upper echelon research has shown that overconfident CEOs are beneficial for innovation, less is known about how firms can harness the benefits of these CEOs for breakthrough innovations. To extend this stream of research, we identify crucial board characteristics that enable firms to benefit from overconfident CEOs in the context of promoting breakthrough innovations. Using longitudinal data of US high‐tech firms, our results emphasize that overconfident CEOs guided by boards with expertise and power strongly outperform fellow CEOs who are monitored by boards lacking either or both of these characteristics. By theorizing and empirically demonstrating how powerful expert boards are important for firms to profit from their CEO's overconfidence, our study provides important contributions to the CEO overconfidence, corporate board, and breakthrough innovation literatures.Managerial SummaryCan boards harness the benefits of overconfident CEOs to increase breakthrough innovation for their firms? We examine this question using a longitudinal dataset of US high‐tech firms and find that board expertise and power enable firms to benefit from CEO overconfidence in the context of breakthrough innovations. Our results suggest that boards need both power and expertise to effectively channel an overconfident CEO's drive toward breakthrough innovations. Interestingly, powerful boards that lack expertise may be detrimental to harnessing CEO overconfidence for breakthrough innovations. For governance practitioners, ensuring that a board is composed of directors with knowledge and understanding of breakthrough innovations and a balance of power with their CEOs will create a beneficial context for firms led by CEO overconfidence to produce breakthrough innovations.

Automatic high-detailed building reconstruction workflow for urban microscale simulations

Reconstructing urban scenarios for computational fluid dynamics simulations typically requires significant manual effort, especially when higher geometrical details are required. To address this issue, we present a workflow to automatically reconstruct buildings in three levels of detail (LoDs): LoD1.2, LoD1.3, and LoD2.2, tailored to urban microscale simulations. The workflow uses a combination of building footprints and a point cloud to segment roof planes, create partitions, optimise planes, and finally assemble roof planes into 3D building models. Reconstructed buildings are seamlessly integrated into the terrain together with different surface layers such as water, low vegetation, and paved surfaces. Apart from three general LoDs, building footprints can be simplified as a part of the 2D generalisation; additionally, smaller surfaces such as chimneys and ventilation shafts can be removed using a graph-cut optimisation. The integrated geometry validator can report on validity of building models, such as watertightness, manifoldness, or occurrences of self-intersections. In the case of invalid geometries, we can generate an approximation: geometry repair the with alpha wrapping algorithm, or reconstruction in lower LoD. We tested our implementation on two different real-world datasets — one in The Netherlands, and another one in the USA. The results showed that 95% (Dutch dataset) and 90% (US dataset) buildings were valid according to the ISO 19107 standard. Generated grids showed satisfactory quality as we observed monotonous convergence in simulations with grid convergence indices up to 3.8% for pressure and velocity variables. These results indicate that the workflow is suitable for typical urban microscale simulations.

A real-world study on biomarker retesting among patients with non-small cell lung cancer and disparity in outcomes.

8590 Background: Prior studies revealed sociodemographic disparities in biomarker testing among patients (pts) with advanced/metastatic non-small cell lung cancer (a/mNSCLC). However, the association of testing disparities with clinical outcomes among real-world pts requires further characterization. Methods: This retrospective study used ConcertAI Patient360™ US dataset to assess biomarker testing disparities and their association with median overall survival (mOS) among adults with a/mNSCLC (2017–2022) who received ≥1 line of therapy with ≥90-day follow-up. Analyses were conducted in pts who initiated first-line (1L) treatment and stratified by setting (community vs academic) and race. Biomarker testing is a curated data element in this database, leveraging structured and unstructured electronic medical record data and includes the following biomarkers ALK, BRAF, EGFR, KRAS, MET amp, MET exon 14, NTRK, PD-L1, RET, and ROS1. Biomarker testing to inform 1L decision was defined as testing before 1L start. Testing to inform second-line (2L) decision was defined as testing after 1L start and before 2L start. Variables were compared using chi-square or ANOVA tests. Unadjusted and adjusted OS using multivariate Cox proportional hazards are reported. Results: Of 3678 pts included in the 1L cohort, 3127 (85%) received 1L testing. In community settings, 22% of Black pts vs 15% of White pts did not receive 1L testing (P=0.002), whereas 1L testing rates between races were similar in academic settings (Table). In 2L community setting among pts who received 1L but not 2L testing, mOS was 5.9 vs 9.9 months in Black (n=54) vs White (n=529) pts, respectively (P=0.35). In 2L community setting among pts who were and were not tested prior to 1L and not tested at 2L, mOS was 5.9 vs 10.1 months in Black (n=62) vs White (n=613) pts, respectively (P=0.27). Accounting for recorded sociodemographic and clinical characteristics at baseline, differences in OS by testing setting and status start to normalize to the reference group. Disparities within groups appear to potentially exist despite no statistical significance (except for ECOG score of 2+). Conclusions: Despite small 2L sample sizes and lack of significance, disparities in testing rates and outcomes were observed, which may be overcome by normalizing testing across race and treatment setting to potentially improve pt outcomes. Further analysis can be performed across census regions. Further research is needed to confirm the impact of lack of biomarker testing on pt outcomes on a broader scale, particularly around characteristics that may impact outcomes but were undetected in this dataset. [Table: see text]

Scale effects and implications of the stochastic structure of customer water demands

ABSTRACT The effect of different temporal (from seconds to months) and spatial aggregation scales (from individual users to full urban areas) on water demand behavior has been explored to a limited degree. The effort described here extends those works by evaluating the scale effects of residential water consumption in a unique US data set that covers 10,000 households with a 1-gallon (3.79 L) hourly resolution over 2 years. A preliminary data analysis and a sequential Principal Component Analysis (PCA) is carried out to assess the effect of different temporal (weekly, daily, hourly) and spatial aggregation (individual meters and groups every 10, 100 and 1,000 meters) levels on demand. Results show that individual users act very differently from each other, and individual consumer variability is only canceled out when a significant number of households are aggregated. The implications of this finding are assessed from a hydraulic modeling perspective as the spatiotemporal scale of measurements may condition the type of analysis that can be carried out in practice. However, additional work is needed to explore the point at which it may be worth embracing a micro (per fixture/household) or a macro (per node/network) approach for different purposes.

Do customers matter in currency hedging policies? Evidence from product warranties

AbstractUsing a data set of US manufacturing firms with sales from foreign operations, we find that firms' currency hedging activities vary with their warranty obligations. The positive link of warranty obligations to currency hedging policies prevails in financially more constrained firms, companies facing fiercer product market competition and corporations producing more unique products. Our results suggest that firms are likely to incorporate their contractual commitments to customers into their currency hedging activities, especially when their failures to honor these commitments are more likely or costly.

Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan.

Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening on multinational clinical workflows. Materials and Methods An AI assistant for lung cancer screening was evaluated on two retrospective randomized multireader multicase studies where 627 (141 cancer-positive cases) low-dose chest CT cases were each read twice (with and without AI assistance) by experienced thoracic radiologists (six U.S.-based or six Japan-based radiologists), resulting in a total of 7524 interpretations. Positive cases were defined as those within 2 years before a pathology-confirmed lung cancer diagnosis. Negative cases were defined as those without any subsequent cancer diagnosis for at least 2 years and were enriched for a spectrum of diverse nodules. The studies measured the readers' level of suspicion (on a 0-100 scale), country-specific screening system scoring categories, and management recommendations. Evaluation metrics included the area under the receiver operating characteristic curve (AUC) for level of suspicion and sensitivity and specificity of recall recommendations. Results With AI assistance, the radiologists' AUC increased by 0.023 (0.70 to 0.72; P = .02) for the U.S. study and by 0.023 (0.93 to 0.96; P = .18) for the Japan study. Scoring system specificity for actionable findings increased 5.5% (57% to 63%; P < .001) for the U.S. study and 6.7% (23% to 30%; P < .001) for the Japan study. There was no evidence of a difference in corresponding sensitivity between unassisted and AI-assisted reads for the U.S. (67.3% to 67.5%; P = .88) and Japan (98% to 100%; P > .99) studies. Corresponding stand-alone AI AUC system performance was 0.75 (95% CI: 0.70, 0.81) and 0.88 (95% CI: 0.78, 0.97) for the U.S.- and Japan-based datasets, respectively. Conclusion The concurrent AI interface improved lung cancer screening specificity in both U.S.- and Japan-based reader studies, meriting further study in additional international screening environments. Keywords: Assistive Artificial Intelligence, Lung Cancer Screening, CT Supplemental material is available for this article. Published under a CC BY 4.0 license.

Early Diagnosis of Hereditary Angioedema in Japan Based on a US Medical Dataset: Algorithm Development and Validation.

Hereditary angioedema (HAE), a rare genetic disease, induces acute attacks of swelling in various regions of the body. Its prevalence is estimated to be 1 in 50,000 people, with no reported bias among different ethnic groups. However, considering the estimated prevalence, the number of patients in Japan diagnosed with HAE remains approximately 1 in 250,000, which means that only 20% of potential HAE cases are identified. This study aimed to develop an artificial intelligence (AI) model that can detect patients with suspected HAE using medical history data (medical claims, prescriptions, and electronic medical records [EMRs]) in the United States. We also aimed to validate the detection performance of the model for HAE cases using the Japanese dataset. The HAE patient and control groups were identified using the US claims and EMR datasets. We analyzed the characteristics of the diagnostic history of patients with HAE and developed an AI model to predict the probability of HAE based on a generalized linear model and bootstrap method. The model was then applied to the EMR data of the Kyoto University Hospital to verify its applicability to the Japanese dataset. Precision and sensitivity were measured to validate the model performance. Using the comprehensive US dataset, the precision score was 2% in the initial model development step. Our model can screen out suspected patients, where 1 in 50 of these patients have HAE. In addition, in the validation step with Japanese EMR data, the precision score was 23.6%, which exceeded our expectations. We achieved a sensitivity score of 61.5% for the US dataset and 37.6% for the validation exercise using data from a single Japanese hospital. Overall, our model could predict patients with typical HAE symptoms. This study indicates that our AI model can detect HAE in patients with typical symptoms and is effective in Japanese data. However, further prospective clinical studies are required to investigate whether this model can be used to diagnose HAE.

Beneath the surface: The asymmetric effects of unconventional monetary policy on corporate investment

This paper explores the asymmetric effects of unconventional monetary policy on corporate investment in the aftermath of the Great Recession. Using a comprehensive dataset of US listed firms, our research unveils a compelling insight: firms with higher investment irreversibility and market power are less responsive to fluctuations in corporate bond yields following an unconventional monetary policy announcement. These results emphasize the critical importance of taking into consideration firm-specific characteristics when formulating monetary policy, providing valuable insights for central banks aiming to enhance the effectiveness of their policies.

A long-short dual-mode knowledge distillation framework for empirical asset pricing models in digital financial networks

The continuous combination of digital network technology and traditional financial services has given birth to digital financial networks, which explore massive economic data under the AI-driven models to achieve intelligent connections among financial institutions, markets, transactions, and instruments. Empirical asset pricing is a challenging task in financial analysis, which has attracted research attention. However, existing studies only focus on tackling the challenges of equity risk premium in the single stock market. Considering multiple economic linkages between the two countries, the transaction history of the US stock market as empirical knowledge is a powerful supplement to improve the prediction of equity risk premium in the China market. In this paper, we aim to fully leverage the prior information in two stock markets for empirical asset pricing models. Due to the rich financial domain knowledge, there may be various characteristic signals that partially overlap in different periods. To address these issues, we propose a framework based on long-short dual-mode knowledge distillation, termed as LSDM-KD, which incorporates US and China stock market models, and a shared characteristic signals model. The method effectively understands the relationships between assets and market behaviour, reducing reliance on expensive correlation databases and professional knowledge. Extensive experiments conducted on US and China stock market datasets demonstrate that our LSDM-KD can significantly improve the performance of empirical asset pricing.

Patient Demographics and Major Adverse Cardiovascular Events after Androgen Deprivation Therapy for Prostate Cancer.

The association between patient demographics and CV events after ADT using real-world data was evaluated. In addition to encompassing >30 times more patients than all previous MACE studies, this is the first study, to the best of our knowledge, to include a comprehensive listing of many demographic factors from one large, recent US dataset over a long period of time. The retrospective analysis of data in the Decision Resources Group (now Clarivate) Real World Evidence repository, representing >300M US patients from 1991 to 2020 across all US regions, was performed. Patients with PCa receiving ≥1 ADT injection were included. MACE risk after ADT initiation was evaluated for demographic and potential PCa-related risk factors. Kaplan-Meier survival curves were constructed, and Cox regression was used to evaluate the association between MACE risk and demographic/PCa-related risk factors. Overall, MACE risk was slightly lower in the first year after ADT initiation (3.9%) vs. years 2-4 (∼5.2%). In a multivariate Cox model, MACE risk after ADT initiation was significantly higher for older vs. younger patients (adjusted HR per increasing year = 1.08, 95% CI: 1.07-1.09), men with a history of MACE vs. without (HR = 2.22, 95% CI: 1.72-2.88), men with very low BMI vs. normal or high BMI (HR for decreasing BMI per kg/m2 = 1.02, 95% CI: 1.01-1.03), White vs. Black patients (HR = 1.30, 95% CI: 1.08-1.55), and patients who did not use statins vs. those who did (HR = 1.13, 95% CI: 1.00-1.27). Of the PCa-related risk factors, MACE risk after ADT initiation was significantly higher for oncology vs. urology treatment setting (HR = 2.47, 95% CI: 2.12-2.88), patients with baseline metastasis vs. those without (HR = 2.30, 95% CI: 1.72-3.07), and patients treated with antagonists vs. agonists (HR = 1.62, 95% CI: 1.25-2.10). Demographic factors are important contributors to increased MACE risk for men with PCa on ADT. Clinicians should monitor risk factors and modify if possible.

Transfer learning for the generalization of artificial intelligence in breast cancer detection: a case-control study.

Some researchers have questioned whether artificial intelligence (AI) systems maintain their performance when used for women from populations not considered during the development of the system. To evaluate the impact of transfer learning as a way of improving the generalization of AI systems in the detection of breast cancer. This retrospective case-control Finnish study involved 191 women diagnosed with breast cancer and 191 matched healthy controls. We selected a state-of-the-art AI system for breast cancer detection trained using a large US dataset. The selected baseline system was evaluated in two experimental settings. First, we examined our private Finnish sample as an independent test set that had not been considered in the development of the system (unseen population). Second, the baseline system was retrained to attempt to improve its performance in the unseen population by means of transfer learning. To analyze performance, we used areas under the receiver operating characteristic curve (AUCs) with DeLong's test. Two versions of the baseline system were considered: ImageOnly and Heatmaps. The ImageOnly and Heatmaps versions yielded mean AUC values of 0.82±0.008 and 0.88±0.003 in the US dataset and 0.56 (95% CI=0.50-0.62) and 0.72 (95% CI=0.67-0.77) when evaluated in the unseen population, respectively. The retrained systems achieved AUC values of 0.61 (95% CI=0.55-0.66) and 0.69 (95% CI=0.64-0.75), respectively. There was no statistical difference between the baseline system and the retrained system. Transfer learning with a small study sample did not yield a significant improvement in the generalization of the system.

Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting

The problem of air pollution threatens public health. Air quality forecasting can provide the air quality index hours or even days later, which can help the public to prevent air pollution in advance. Previous works focus on citywide air quality forecasting and cannot solve nationwide city forecasting problems, whose difficulties lie in capturing the latent dependencies between geographically distant but highly correlated cities. In this article, we propose the group-aware graph neural network (GAGNN), a hierarchical model for nationwide city air quality forecasting. The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively. GAGNN introduces a differentiable grouping network to discover the latent dependencies among cities and generate city groups. Based on the generated city groups, a group correlation encoding module is introduced to learn the correlations between them, which can effectively capture the dependencies between city groups. After the graph construction, GAGNN implements message passing mechanism to model the dependencies between cities and city groups. The evaluation experiments on two real-world nationwide city air quality datasets, including the China dataset and the US dataset, indicate that our GAGNN outperforms existing forecasting models.

Watershed sediment cannot offset sea level rise in most US tidal wetlands

Watershed sediment can increase elevation of tidal wetlands struggling against rising seas, but where and how much watershed sediment helps is unknown. By combining contiguous US datasets on sediment loads and tidal wetland areas for 4972 rivers and their estuaries, we calculated that river sediment accretion will be insufficient to match sea level rise in 72% of cases because most watersheds are too small (median 21 square kilometers) to generate adequate sediment. Nearly half the tidal wetlands would require 10 times more river sediment to match sea level, a magnitude not generally achievable by dam removal in some regions. The realization that watershed sediment has little effect on most tidal wetland elevations shifts research priorities toward biological processes and coastal sediment dynamics that most influence elevation change.

Acceleration-Based Kalman Tracking for Super-Resolution Ultrasound Imaging In Vivo.

Super-resolution ultrasound (SRUS) can image microvascular structure and flow at subwave-diffraction resolution based on localizing and tracking microbubbles (MBs). Currently, tracking MBs accurately under limited imaging frame rates and high MB concentrations remains a challenge, especially under the effect of cardiac pulsatility and in highly curved vessels. In this study, an acceleration-incorporated MB motion model is introduced into a Kalman tracking framework. The tracking performance was evaluated using simulated microvasculature with different MB motion parameters, concentrations, and acquisition frame rates, and in vivo human breast tumor US datasets. The simulation results show that the acceleration-based method outperformed the nonacceleration-based method at different levels of acceleration and acquisition frame rates and achieved significant improvement in true positive rate (TPR; up to 11.3%) and false negative rate (FNR; up to 13.2%). The proposed method can also reduce errors in vasculature reconstruction via the acceleration-based nonlinear interpolation, compared with linear interpolation (up to [Formula: see text]). The tracking results from temporally downsampled low frame rate in vivo datasets from human breast tumors show that the proposed method has better MB tracking performance than the baseline method, if using results from the initial high frame data as a reference. Finally, the acceleration estimated from tracking results also provides a spatial speed gradient map that may contain extra valuable diagnostic information.

Analyzing the demographic coherence of selected US, Australian and Chinese biometric data sets used to price long-term care insurance and life care annuities

This paper examines the implicit healthy life expectancy used for actuarial calculations in some selected biometric data sets from the US, Australia and China. We are interested in checking the demographic/epidemiological coherence of these data sets because this health indicator is rarely presented when authors build their biometric data sets, nor when they are used to calculate long-term care insurance (LTCI) and life care annuity (LCAs) premiums, nor when they are employed in research articles to estimate the future demand for LTC services. We follow a methodology based on multistate life table methods that enables us to obtain a life expectancy matrix for individuals on the basis of their initial health state. We also present some additional indicators of longevity, mortality and morbidity, these being the median age at death, the interquartile range, the weighted modal age at death, the mortality ratio and the implicit LTC prevalence rates broken down by health state. We find several weaknesses that highlight the difficulty involved in building the biometric data sets needed to make an actuarially fair valuation of the premiums for LTCI and LCAs. We also verify the existence of the so-called “male–female health-survival paradox”. From the perspective of a potential purchaser of this type of insurance products, disclosing and explaining the summary measures of health and longevity would make it easier for them to understand the need to protect themselves against the cost of possible LTC services and also make the computation of the premiums more transparent.