COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge.

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.

Reevaluation of Hemoparasites in the Black Spiny-Tailed Iguana (Ctenosaura similis) with the First Pathological and Molecular Characterizations of Lankesterella desseri n. sp. and Redescription of Hepatozoon gamezi.

Hemoprotozoa are microorganisms that parasitize the blood and possess intricate life cycles. Despite the complexity of their nature, little is known about the biology of hemoprotozoa in reptilian hosts. In this study, we conducted disease surveillance on blood samples collected from six black spiny-tailed iguanas (Ctenosaura similis) exhibiting clinical signs. We found two different types of hemoparasites in the blood films and further confirmed they belong to the genera Lakesterella and Hepatozoon through molecular methods. In the tissue section from a dead iguana infected only with Lakesterella sp., parasites were also found in melanomacrophages of the liver and kidney. Since Lakesterella sp. infection has not been reported in C. similis, we propose this hemococcidian as a new species, Lankesterella desseri n. sp. The Hepatozoon parasites discovered in this study were classified as Hepatozoon gamezi based on their morphological characteristics, particularly the notable deformation of all infected erythrocytes, and this classification was further corroborated through molecular biological and phylogenetic analyses. This is the first hemoprotozoa investigation in C. similis with pathological and molecular characterization of these pathogens. We suggest that more studies are needed to understand the epidemiology, transmission, and impact of these parasites on their hosts and ecosystems.

Developing an International Classification of Functioning, Disability and Health Core Set for Pediatric Brain Tumor Survivors in Chinese Clinical Settings.

BACKGROUND: The International Classification of Functioning, Disability and Health (ICF) core set (CS) facilitates the standardization of functioning and impairment assessment for integration of holistic care. OBJECTIVE: This study developed an ICF CS for interviewing pediatric brain tumor survivors in Taiwan to help healthcare professionals in implementing disability assessment and management measures. METHODS: A group of 29 experts in 10 relevant fields with at least 5 years of experience working with children with brain tumors participated in this study. The first questionnaire contained 247 second-level ICF categories. The experts rated the significance of each category by using a 5-point Likert scale. Correlations between individual and group scores were calculated to determine consensus. Categories with an average rating of higher than 4 and for which greater than or equal to 80% (23) of the participants provided a rating of 4 or higher were included in the final CS. RESULTS: The final CS contained a total of 57 ICF categories: 20 from the Body Functions and Structures component, 36 from the Activities and Participation component, and 1 from the Environmental Factors component. CONCLUSION: The ICF CS for pediatric brain tumor survivors provides a framework for relevant healthcare professionals to deliver patient-centered care, ensuring that services focus on all areas of development. IMPLICATIONS FOR PRACTICE: Patient ratings for this ICF CS may serve as a new practical and effective patient-reported information tool for acquiring patient input and for the systematic monitoring of pediatric brain tumor survivors in clinical practice. Further research should be conducted on this CS to verify our findings.

Prevalence of sleep disorders among first responders for medical emergencies: A meta-analysis.

Background: Shift work and irregular work schedules among first responders have been associated with physical and psychological problems such as sleep disorders. We conducted the first meta-analysis to explore and estimate the prevalence of sleep disorders among first responders for medical emergencies. Methods: We searched four databases: Web of Science, Psych Info, CINAHL, and PubMed. The Generalized Linear Mixed model (GLMM) was used to estimate the prevalence estimates of sleep disorders in R software and the DerSimonian-Lard random-effects model in Comprehensive Meta-Analysis was used to explore associated comorbidities for OSA and insomnia, presented as odds ratios (ORs) and confidence intervals (CIs). The Cochran's Q, τ2, and the statistics were used to assess heterogeneity and the moderator analysis was conducted to identify moderator variables. Results: Twenty-eight studies with 100 080 first responders were included from the total of 1119 studies retrieved from the databases. The prevalence rates for sleep disorders were 31% (95% CI = 15%-53%) for shift work disorder (SWD), 30% (95% CI = 18%-46%) for obstructive sleep apnea (OSA), 28% (95% CI = 19%-39%) for insomnia, 28% (95% CI = 24%-33%) for excessive daytime sleepiness (EDS), 2% (95% CI = 1%-4%) for restless leg syndrome, and 1% (95% CI = 0%-5%) for narcolepsy. Anxiety (OR = 2.46; 95% CI = 1.99%-3.03%), cardiovascular disease (CVD) (OR = 2.03; 95% CI = 1.43-2.88), diabetes mellitus (DM) (OR = 1.93; 95% CI = 1.41-2.65), depression (OR = 1.89; 95% CI = 1.01-3.56), gastroesophageal reflux disease (GERD) (OR = 1.83; 95% CI = 150-2.22), and post-traumatic stress disorder (PTSD) (OR = 1.78; 95% CI = 1.33-2.39) were associated with OSA. Depression (OR = 9.74; 95% CI = 4.67-20.3), anxiety (OR = 9.22; 95% CI = 3.81-22.3), and PTSD (OR = 7.13; 95% CI = 6.27-8.10) were associated with insomnia. Age, gender, first responders, continent, study quality, study design, and assessment tool were significant moderator variables for OSA, insomnia, and EDS. Conclusions: This meta-analysis found a substantially high prevalence of sleep disorders including SWD, OSA, insomnia, and EDS among first responders for medical emergencies. Early assessment and management of sleep disorders among first responders is necessary to promote good, quality sleep to help prevent anxiety, depression, CVD, DM, GERD, and PTSD.

[Metamorphosis of Nurses: The Development and Breakthroughs of Advanced Practice Registered Nurses].

BACKGROUND: The introduction and development of the advanced practice registered nurse (APRN) is a global trend in nursing. However, the development of APRNs in Taiwan remains uncertain and lacks necessary consensus. PURPOSE: This research study aimed to explore the views and suggestions of nursing experts in industry, government, and academia regarding the development of APRNs (clinical nurse specialists, case managers, certified clinical registered nurse anesthetists, and certified nurse-midwives) in Taiwan. METHODS: Data were collected from March to August 2017. Sixty-four experts participated in one of six focus group discussions held in northern, central, and southern Taiwan. These group discussions were recorded and transcribed verbatim with the consent of the participants. Content analysis was used to analyze the transcribed data. RESULTS: The comments and suggestions raised during the discussions were categorized into four major themes: professional development of necessity, core competencies, accreditation, and future promotion-related issues. Each theme was further divided into several subthemes. CONCLUSIONS / IMPLICATIONS FOR PRACTICE: The opinions of relevant experts regarding the current status of development of the roles, practical scope, and management and suggestions for APRNs were summarized to facilitate the future development of APRNs in Taiwan in terms of education, core competencies, certification, and practical scope. Furthermore, the results may be referenced in the establishment of a nursing consensus model and as a basis for promoting APRNs.

Effectiveness of prone position in acute respiratory distress syndrome and moderating factors of obesity class and treatment durations for COVID-19 patients: A meta-analysis.

OBJECTIVES: To examine the effectiveness of prone positioning on COVID-19 patients with acute respiratory distress syndrome with moderating factors in both traditional prone positioning (invasive mechanical ventilation) and awake self-prone positioning patients (non-invasive ventilation). RESEARCH METHODOLOGY: A comprehensive search was conducted in CINAHL, Cochrane library, Embase, Medline-OVID, NCBI SARS-CoV-2 Resources, ProQuest, Scopus, and Web of Science without language restrictions. All studies with prospective and experimental designs evaluating the effect of prone position patients with COVID-19 related to acute respiratory distress syndrome were included. Pooled standardised mean differences were calculated after prone position for primary (PaO2/FiO2) and secondary outcomes (SpO2 and PaO2) RESULTS: A total of 15 articles were eligible and included in the final analysis. Prone position had a statistically significant effect in improving PaO2/FiO2 with standardised mean difference of 1.10 (95%CI 0.60-1.59), SpO2 with standardised mean difference of 3.39 (95% CI 1.30-5.48), and PaO2 with standardised mean difference of 0.77 (95% CI 0.19-1.35). Patients with higher body mass index and longer duration/day are associated with larger standardised mean difference effect sizes for prone positioning. CONCLUSIONS: Our findings demonstrate that prone position significantly improved oxygen saturation in COVID-19 patients with acute respiratory distress syndrome in both traditional prone positioning and awake self-prone positioning patients. Prone position should be recommended for patients with higher body mass index and longer durations to obtain the maximum effect.

Prevalence of dysphagia and risk of pneumonia and mortality in acute stroke patients: a meta-analysis.

BACKGROUND: Post-stroke dysphagia (PSD) has been associated with high risk of aspiration pneumonia and mortality. However, limited evidence on pooled prevalence of post-stroke dysphagia and influence of individual, disease and methodological factors reveals knowledge gap. Therefore, to extend previous evidence from systematic reviews, we performed the first meta-analysis to examine the pooled prevalence, risk of pneumonia and mortality and influence of prognostic factors for PSD in acute stroke. METHODS: Our search was conducted in CINAHL, Cochrane Library, EMBASE, Ovid-Medline, PubMed, and Web of Science an initial search in October 2020 and a follow-up search in May 2021. Data synthesis was conducted using the Freeman-Tukey double-arcsine transformation model for the pooled prevalence rate and the DerSimonian-Lard random-effects model for prognostic factors and outcomes of PSD. RESULTS: The pooled prevalence of PSD was 42% in 42 studies with 26,366 participants. PSD was associated with higher pooled odds ratio (OR) for risk of pneumonia 4.08 (95% CI, 2.13-7.79) and mortality 4.07 (95% CI, 2.17-7.63). Haemorrhagic stroke 1.52 (95% CI, 1.13-2.07), previous stroke 1.40 (95% CI, 1.18-1.67), severe stroke 1.38 (95% CI, 1.17-1.61), females 1.25 (95% CI, 1.09-1.43), and diabetes mellitus 1.24 (95% CI, 1.02-1.51) were associated with higher risk of PSD. Males 0.82 (95% CI, 0.70-0.95) and ischaemic stroke 0.54 (95% CI, 0.46-0.65) were associated with lower risk of PSD. Haemorrhagic stroke, use of instrumental assessment method, and high quality studies demonstrated to have higher prevalence of PSD in the moderator analysis. CONCLUSIONS: Assessment of PSD in acute stroke with standardized valid and reliable instruments should take into account stroke type, previous stroke, severe stroke, diabetes mellitus and gender to aid in prevention and management of pneumonia and thereby, reduce the mortality rate. TRIAL REGISTRATION: https://osf.io/58bjk/?view_only=26c7c8df8b55418d9a414f6d6df68bdb .

Humoral Immunogenicity and Reactogenicity of the Standard ChAdOx1 nCoV-19 Vaccination in Taiwan.

BACKGROUND: The ChAdOx1 nCoV-19 vaccine has been widely administered against SARS-CoV-2 infection; however, data regarding its immunogenicity, reactogenicity, and potential differences in responses among Asian populations remain scarce. METHODS: 270 participants without prior COVID-19 were enrolled to receive ChAdOx1 nCoV-19 vaccination with a prime-boost interval of 8-9 weeks. Their specific SARS-CoV-2 antibodies, neutralizing antibody titers (NT50), platelet counts, and D-dimer levels were analyzed before and after vaccination. RESULTS: The seroconversion rates of anti-RBD and anti-spike IgG at day 28 after a boost vaccination (BD28) were 100% and 95.19%, respectively. Anti-RBD and anti-spike IgG levels were highly correlated (r = 0.7891), which were 172.9 ± 170.4 and 179.3 ± 76.88 BAU/mL at BD28, respectively. The geometric mean concentrations (GMCs) of NT50 for all participants increased to 132.9 IU/mL (95% CI 120.0-147.1) at BD28 and were highly correlated with anti-RBD and anti-spike IgG levels (r = 0.8248 and 0.7474, respectively). Body weight index was statistically significantly associated with anti-RBD IgG levels (p = 0.035), while female recipients had higher anti-spike IgG levels (p = 0.038). The GMCs of NT50 declined with age (p = 0.0163) and were significantly different across age groups (159.7 IU/mL for 20-29 years, 99.4 IU/mL for ≥50 years, p = 0.0026). Injection-site pain, fever, and fatigue were the major reactogenicity, which were more pronounced after prime vaccination and in younger participants (<50 years). Platelet counts decreased and D-dimer levels increased after vaccination but were not clinically relevant. No serious adverse events or deaths were observed. CONCLUSION: The vaccine is well-tolerated and elicited robust humoral immunity against SARS-CoV-2 after standard prime-boost vaccination in Taiwanese recipients.

Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning Techniques.

Automatic detection of intact tomatoes on plants is highly expected for low-cost and optimal management in tomato farming. Mature tomato detection has been wildly studied, while immature tomato detection, especially when occluded with leaves, is difficult to perform using traditional image analysis, which is more important for long-term yield prediction. Therefore, tomato detection that can generalize well in real tomato cultivation scenes and is robust to issues such as fruit occlusion and variable lighting conditions is highly desired. In this study, we build a tomato detection model to automatically detect intact green tomatoes regardless of occlusions or fruit growth stage using deep learning approaches. The tomato detection model used faster region-based convolutional neural network (R-CNN) with Resnet-101 and transfer learned from the Common Objects in Context (COCO) dataset. The detection on test dataset achieved high average precision of 87.83% (intersection over union ≥ 0.5) and showed a high accuracy of tomato counting (R2 = 0.87). In addition, all the detected boxes were merged into one image to compile the tomato location map and estimate their size along one row in the greenhouse. By tomato detection, counting, location and size estimation, this method shows great potential for ripeness and yield prediction.

Predicting Rice Heading Date Using an Integrated Approach Combining a Machine Learning Method and a Crop Growth Model.

Accurate prediction of heading date under various environmental conditions is expected to facilitate the decision-making process in cultivation management and the breeding process of new cultivars adaptable to the environment. Days to heading (DTH) is a complex trait known to be controlled by multiple genes and genotype-by-environment interactions. Crop growth models (CGMs) have been widely used to predict the phenological development of a plant in an environment; however, they usually require substantial experimental data to calibrate the parameters of the model. The parameters are mostly genotype-specific and are thus usually estimated separately for each cultivar. We propose an integrated approach that links genotype marker data with the developmental genotype-specific parameters of CGMs with a machine learning model, and allows heading date prediction of a new genotype in a new environment. To estimate the parameters, we implemented a Bayesian approach with the advanced Markov chain Monte-Carlo algorithm called the differential evolution adaptive metropolis and conducted the estimation using a large amount of data on heading date and environmental variables. The data comprised sowing and heading dates of 112 cultivars/lines tested at 7 locations for 14 years and the corresponding environmental variables (day length and daily temperature). We compared the predictive accuracy of DTH between the proposed approach, a CGM, and a single machine learning model. The results showed that the extreme learning machine (one of the implemented machine learning models) was superior to the CGM for the prediction of a tested genotype in a tested location. The proposed approach outperformed the machine learning method in the prediction of an untested genotype in an untested location. We also evaluated the potential of the proposed approach in the prediction of the distribution of DTH in 103 F2 segregation populations derived from crosses between a common parent, Koshihikari, and 103 cultivars/lines. The results showed a high correlation coefficient (ca. 0.8) of the 10, 50, and 90th percentiles of the observed and predicted distribution of DTH. In this study, the integration of a machine learning model and a CGM was better able to predict the heading date of a new rice cultivar in an untested potential environment.

An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise.

In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants' muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising.

A network perspective of engaging patients in specialist and chronic illness care: The 2014 International Health Policy Survey.

BACKGROUND: Patient engagement helps to improve health outcomes and health care quality. However, the overall relationships among patient engagement measures and health outcomes remain unclear. This study aims to integrate expert knowledge and survey data for the identification of measures that have extensive associations with other variables and can be prioritized to engage patients. METHODS: We used the 2014 International Health Policy Survey (IHPS), which provided information on elder adults in 11 countries with details in patient characteristics, healthcare experiences, and patient-physician communication. Patient engagement or support was measured with eight variables including patients' treatment choices, involvement, and treatment priority setting. Three types of care were identified: primary, specialist and chronic illness care. Specialists were doctors specializing in one area of health care. Chronic illness included eight chronic conditions surveyed. Expert knowledge was used to assist variable selection. We used Bayesian network models consisting of nodes that represented variables of interest and arcs that represented their relationships. RESULTS: Among 25,530 participants, the mean age was 68.51 years and 57.40% were females. The distributions of age, sex, education, and patient engagement were significantly different across countries. For chronic illness care, written plans provided by professionals were linked to treatment feasibility and helpfulness. Whether professionals contacted patients was associated with the availability of professionals they could reach for chronic illness care. For specialist care, if specialists provided treatment choices, patients were more likely to be involved and discuss about what mattered to them. CONCLUSION: The strategies to engage patients may depend on the types of care, specialist or chronic illness care. For the study on the observational IHPS data, network modeling is useful to integrate expert knowledge. We suggest considering other theory-based patient engagement in major surveys, as well as engaging patients in their healthcare by providing written plans and actively communicating with patients for chronic illnesses, and encouraging specialists to discuss and provide treatment options.

ASIC1a contributes to the symptom of pain in a rat model of chronic prostatitis.

This study aims to validate our hypothesis that acid-sensing ion channels (ASICs) may contribute to the symptom of pain in patients with chronic prostatitis (CP). We first established a CP rat model, then isolated the L5-S2 spinal dorsal horn neurons for further studies. ASIC1a was knocked down and its effects on the expression of neurogenic inflammation-related factors in the dorsal horn neurons of rat spinal cord were evaluated. The effect of ASIC1a on the Ca2+ ion concentration in the dorsal horn neurons of rat spinal cord was measured by the intracellular calcium ([Ca2+]i) intensity. The effect of ASIC1a on the p38/mitogen-activated protein kinase (MAPK) signaling pathway was also determined. ASIC1a was significantly upregulated in the CP rat model as compared with control rats. Acid-induced ASIC1a expression increased [Ca2+]i intensity in the dorsal horn neurons of rat spinal cord. ASIC1a also increased the levels of neurogenic inflammation-related factors and p-p38 expression in the acid-treated dorsal horn neurons. Notably, ASIC1a knockdown significantly decreased the expression of pro-inflammatory cytokines. Furthermore, the levels of p-p38 and pro-inflammatory cytokines in acid-treated dorsal horn neurons were significantly decreased in the presence of PcTx-1, BAPTA-AM, or SB203580. Our results showed that ASIC1a may contribute to the symptom of pain in patients with CP, at least partially, by regulating the p38/MAPK signaling pathway.

A network perspective on patient experiences and health status: the Medical Expenditure Panel Survey 2004 to 2011.

BACKGROUND: There is a growing emphasis on the need to engage patients in order to improve the quality of health care and improve health outcomes. However, we are still lacking a comprehensive understanding on how different measures of patient experiences interact with one another or relate to health status. This study takes a network perspective to 1) study the associations between patient characteristics and patient experience in health care and 2) identify factors that could be prioritized to improve health status. METHODS: This study uses data from the two-year panels from the Medical Expenditure Panel Survey (MEPS) initiated between 2004 and 2011 in the United States. The 88 variables regarding patient health and experience with health care were identified through the MEPS documentation. Sex, age, race/ethnicity, and years of education were also included for analysis. The bnlearn package within R (v3.20) was used to 1) identify the structure of the network of variables, 2) assess the model fit of candidate algorithms, 3) cross-validate the network, and 4) fit conditional probabilities with the given structure. RESULTS: There were 51,023 MEPS interviewees aged 18 to 85 years (mean = 44, 95% CI = 43.9 to 44.2), with years of education ranging from 1 to 19 (mean = 7.4, 95% CI = 7.40 to 7.46). Among all, 55% and 74% were female and white, respectively. There were nine networks identified and 17 variables not linked to others, including death in the second years, sex, entry years to the MEPS, and relations of proxies. The health status in the second years was directly linked to that in the first years. The health care ratings were associated with how often professionals listened to them and whether professionals' explanation was understandable. CONCLUSIONS: It is feasible to construct Bayesian networks with information on patient characteristics and experiences in health care. Network models help to identify significant predictors of health care quality ratings. With temporal relationships established, the structure of the variables can be meaningful for health policy researchers, who search for one or a few key priorities to initiate interventions or health care quality improvement programs.

Protein quantitation by complexation of fluorescent tetraphenylthiophene cation to anion-terminated poly(N-isopropylacrylamide): aggregation-enhanced emission and electrostatic interaction.

A fluorescent biological sensor utilizing aggregation-enhanced emission (AEE) property was developed in our laboratory. First, the AEE-active fluorescent tetraphenylthiophene (TP) unit was synthetically connected to poly(N-isopropylacrylamide) by covalent and ionic bonds, resulting in the respective c- and i-TP-PNIPAM for the detection and quantification of the bovine serum albumin (BSA) model protein. When bind to BSA, the ionic i-TP-PNIPAM shows much better fluorescence (FL) sensitivity compared to c-PNIPAM. The fluorescence (FL) intensity of i-TP-PNIAPM displays a good linear dependence on concentration of BSA (0-1 mg/mL), indicating quantitative fluorimetric protein detection can be achieved. Further addition of anionic surfactant of sodium dodecylsulfate (SDS) considerably raised the FL intensity of the complex solution. All the FL response was discussed in term of conformational freedom of the TP unit under different environmental constraints.

An automatic mass detection system in mammograms based on complex texture features.

It is difficult for radiologists to identify the masses on a mammogram because they are surrounded by complicated tissues. In current breast cancer screening, radiologists often miss approximately 10-30% of tumors because of the ambiguous margins of lesions and visual fatigue resulting from long-time diagnosis. For these reasons, many computer-aided detection (CADe) systems have been developed to aid radiologists in detecting mammographic lesions which may indicate the presence of breast cancer. This study presents an automatic CADe system that uses local and discrete texture features for mammographic mass detection. This system segments some adaptive square regions of interest (ROIs) for suspicious areas. This study also proposes two complex feature extraction methods based on cooccurrence matrix and optical density transformation to describe local texture characteristics and the discrete photometric distribution of each ROI. Finally, this study uses stepwise linear discriminant analysis to classify abnormal regions by selecting and rating the individual performance of each feature. Results show that the proposed system achieves satisfactory detection performance.

Risk adjustment and observation time: comparison between cross-sectional and 2-year panel data from the Medical Expenditure Panel Survey (MEPS).

BACKGROUND: Risk adjustment models were used to estimate health care consumption after adjusting for individual characteristics or other factors. The results of this technique were not satisfying. One reason could be that the length of time to document consumption might be associated with the mean and variance of observed health care consumption. This study aims to use a simplified mathematical model and real-world data to explore the relationship of observation time (one or two years) and predictability. METHODS: This study used cross-sectional (one-year) and 2-year panel data sets of the Medical Expenditure Panel Survey (MEPS) from 1996 to 2008. Comparisons of the health care consumption (total health expenditure, emergency room (ER) and office-based visits) included ratios of means and standard errors (SEs). Risk adjustment models for one- and two-year data used generalized linear model. RESULTS: The ratios of mean health care consumption (two-year to one-year total expenditure, ER and office-based visits) seemed to be two in most age groups and the ratios of SEs varied around or above two. The R-squared of two-year models seemed to be slightly better than that of one-year models. CONCLUSIONS: We find health expenditure and ER or office-based visits observed in two consecutive years were about twice those observed in a single year for most age, similar to the ratios predicted in mathematical examples. The ratios of mean spending and visits varied across age groups. The other finding is that the predictability of two-year consumption seems better than that of one-year slightly. The reason is not clear and we will continue studying this phenomenon.

The dependence of terahertz radiation on the built-in electric field in semiconductor microstructures.

The amplitudes of terahertz radiation are measured for a series of GaAs surface intrinsic-N(+) structures with various built-in surface electric fields as the bias. As the surface field is lower than the so-called "critical electric field" related with the energy difference between the Gamma to L valley of the semiconductor, the amplitude is proportional to the product of the surface field and the number of photo-excited carriers. As the surface field exceeds the critical field, the amplitude is independent of the surface field but proportional to the product of the critical field and the number of the photo-excited carriers.

Beat-based ECG compression using gain-shape vector quantization.

An electrocardiogram (ECG) data compression scheme is presented using the gain-shape vector quantization. The proposed approach utilizes the fact that ECG signals generally show redundancy among adjacent heartbeats and adjacent samples. An ECG signal is QRS detected and segmented according to the detected fiducial points. The segmented heartbeats are vector quantized, and the residual signals are calculated and encoded using the AREA algorithm. The experimental results show that with the proposed method both visual quality and the objective quality are excellent even in low bit rates. An average PRD of 5.97% at 127 b/s is obtained for the entire 48 records in the MIT-BIH database. The proposed method also outperforms others for the same test dataset.

A 2-D ECG compression method based on wavelet transform and modified SPIHT.

A two-dimensional (2-D) wavelet-based electrocardiogram (ECG) data compression method is presented which employs a modified set partitioning in hierarchical trees (SPIHT) algorithm. This modified SPIHT algorithm utilizes further the redundancy among medium- and high-frequency subbands of the wavelet coefficients and the proposed 2-D approach utilizes the fact that ECG signals generally show redundancy between adjacent beats and between adjacent samples. An ECG signal is cut and aligned to form a 2-D data array, and then 2-D wavelet transform and the modified SPIHT can be applied. Records selected from the MIT-BIH arrhythmia database are tested. The experimental results show that the proposed method achieves high compression ratio with relatively low distortion and is effective for various kinds of ECG morphologies.