Influences of Momentum Ratio on Transverse Dispersion for Intermediate-Field Mixing Downstream of Channel Confluence.

This study aims to analyze the influences of momentum ratio (Mr) and confluence angle (α) on the transverse dispersion in an urban scale confluence channel from the numerical simulation results using the Environmental Fluid Dynamics Code model. By changing the momentum flux and confluence angle from the simulation results, the analysis focused on the relations between the vertical variations of transverse velocity and transverse dispersion. The high momentum tributary aligned the mixing interface toward the outer bank and created a strong helical motion, which transported the contaminated water along the channel bed and inflows into the recirculation zone. The high momentum ratio induced the large vertical shear in transverse velocity with a strong helical motion and increased the transverse dispersion. However, the helical motion persistence rapidly decreased as the flow reached downstream and led to a decrease in the transverse dispersion for the large confluence angle. Thus, the transverse dispersion coefficient increased with a high momentum ratio and low confluence angle, and the dimensionless transverse dispersion coefficient was in the range of 0.39-0.67, which is observed in meandering channels, for Mr > 1 and α = 45°.

Fabrication of Substrate-Integrated Waveguide Using Micromachining of Photoetchable Glass Substrate for 5G Millimeter-Wave Applications.

A millimeter-wave substrate-integrated waveguide (SIW) was firstly demonstrated using the micromachining of photoetchable glass (PEG) for 5G applications. A PEG substrate was used as a dielectric material of the SIW, and its photoetchable properties were used to fabricate through glass via (TGV) holes. Instead of the conventional metallic through glass via (TGV) array structures that are typically used for the SIW, two continuous empty TGV holes with metallized sidewalls connecting the top metal layer to the bottom ground plane were used as waveguide walls. The proposed TGV walls were fabricated by using optical exposure, heat development and anisotropic HF (hydrofluoric acid) etching of the PEG substrate, followed by a metal sputtering technique. The SIW was fed by microstrip lines connected to the waveguide through tapered microstrip-to-waveguide transitions. The top metal layer, including these feedlines and transitions, was fabricated by selective metal sputtering through a silicon shadow mask, which was prefabricated by a silicon deep-reactive ion-etching (DRIE) technique. The developed PEG-based process provides a relatively simple, wafer-level manufacturing method to fabricate the SIW in a low-cost glass dielectric substrate, without the formation of individual of TGV holes, complex time-consuming TGV filling processes and repeated photolithographic steps. The fabricated SIW had a dimension of 6 × 10 × 0.42 mm3 and showed an average insertion loss of 2.53 ± 0.55 dB in the Ka-band frequency range from 26.5 GHz to 40 GHz, with a return loss better than 13.86 dB. The proposed process could be used not only for SIW-based devices, but also for various millimeter-wave applications where a glass substrate with TGV structures is required.

Brief Adjustment Scale-6 for Measurement-Based Care: Psychometric Properties, Measurement Invariance, and Clinical Utility.

The Brief Adjustment Scale-6 (BASE-6) was recently developed for measuring general psychological functioning within measurement-based care (MBC). The present study further evaluated psychometric properties, generalizability to race/ethnic populations, and clinical utility of the BASE-6. Three adult samples, Sample 1: online community participants (n = 394); Sample 2: college students (n = 249); Sample 3: outpatient clinic clients (n = 80), were included. The results demonstrated a high level of internal consistency, good test-retest reliability, and convergent validity in all samples. The unidimensional structure of BASE-6 was confirmed and factorial invariance was established across groups. Finally, the BASE-6 captured change over time by demonstrating a large effect size of pre-post treatment changes and significant linear change in multilevel growth modeling. These results support the BASE-6 as a reliable and valid measure regardless of race/ethnicity and can sensitively detect clinical change over the course of the treatment. Thus, the BASE-6 appears to accurately monitor overall psychological adjustment.

Cognitive profiles in bipolar I disorder and associated risk factors: Using Wechsler adult intelligence scale-IV.

Background: Despite the growing evidence of cognitive impairments in bipolar disorder (BD), little work has evaluated cognitive performances utilizing the latest version of the Wechsler Intelligence Scale-IV (WAIS-IV), which is one of the most widely used neurocognitive assessments in clinical settings. Furthermore, clinical characteristics or demographic features that negatively affect the cognitive functioning of BD were not systematically compared or evaluated. Accordingly, the present study aimed to examine the cognitive profile of bipolar I disorder (BD-I) patients and associated risk factors. Methods: Participants included 45 patients, diagnosed with BD-I, current or most recent episode manic, and matching 46 healthy controls (HC). Cognitive performance was evaluated via WAIS-IV, and clinical characteristics of the BD-I group were examined via multiple self- and clinician-report questionnaires. Results: Multivariate analysis of covariance (MANCOVA) results indicated that the BD-I group demonstrated significantly poorer performance compared to the HC group in subtests and indexes that reflect working memory and processing speed abilities. Redundancy analysis revealed that overall symptom severity, manic symptom severity, and anxiety were significant predictors of cognitive performance in BD-I, while age of onset, past mood disorder history, depression severity, and impulsiveness showed comparatively smaller predictive values. Conclusion: The current study suggests cognitive deterioration in the cognitive proficiency area while generalized ability, including verbal comprehension and most of the perceptual reasoning skills, remain intact in BD-I. The identified risk factors of cognitive performance provide specific clinical recommendations for intervention and clinical decision-making.

Artificial intelligence assessment for early detection and prediction of renal impairment using electrocardiography.

PURPOSE: Although renal failure is a major healthcare burden globally and the cornerstone for preventing its irreversible progression is an early diagnosis, an adequate and noninvasive tool to screen renal impairment (RI) reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance. METHODS: This retrospective cohort study included two hospitals. We included 115,361 patients who had at least one ECG taken with an estimated glomerular filtration rate measurement within 30 min of the index ECG. A DLM was developed using 96,549 ECGs of 55,222 patients. The internal validation included 22,949 ECGs of 22,949 patients. Furthermore, we conducted an external validation with 37,190 ECGs of 37,190 patients from another hospital. The endpoint was to detect a moderate to severe RI (estimated glomerular filtration rate < 45 ml/min/1.73m2). RESULTS: The area under the receiver operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting RI during the internal and external validation was 0.858 (95% confidence interval 0.851-0.866) and 0.906 (0.900-0.912), respectively. In the initial evaluation of 25,536 individuals without RI patients whose DLM was defined as having a higher risk had a significantly higher chance of developing RI than those in the low-risk group (17.2% vs. 2.4%, p < 0.001). The sensitivity map indicated that the DLM focused on the QRS complex and T-wave for detecting RI. CONCLUSION: The DLM demonstrated high performance for RI detection and prediction using 12-, 6-, single-lead ECGs.

Deep-learning model for screening sepsis using electrocardiography.

BACKGROUND: Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG). METHODS: This retrospective cohort study included 46,017 patients who were admitted to two hospitals. A total of 1,548 and 639 patients had sepsis and septic shock, respectively. The DLM was developed using 73,727 ECGs from 18,142 patients, and internal validation was conducted using 7774 ECGs from 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs from 20,101 patients from another hospital to verify the applicability of the DLM across centers. RESULTS: During the internal and external validations, the area under the receiver operating characteristic curve (AUC) of the DLM using 12-lead ECG was 0.901 (95% confidence interval, 0.882-0.920) and 0.863 (0.846-0.879), respectively, for screening sepsis and 0.906 (95% confidence interval (CI), 0.877-0.936) and 0.899 (95% CI, 0.872-0.925), respectively, for detecting septic shock. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs was 0.845-0.882. A sensitivity map revealed that the QRS complex and T waves were associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who were admitted with an infectious disease, and the AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793-0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs. 0.574, p < 0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs. 0.725, p = 0.018). CONCLUSIONS: The DLM delivered reasonable performance for sepsis screening using 12-, 6-, and single-lead ECGs. The results suggest that sepsis can be screened using not only conventional ECG devices but also diverse life-type ECG machines employing the DLM, thereby preventing irreversible disease progression and mortality.

Detection and classification of arrhythmia using an explainable deep learning model.

BACKGROUND: Early detection and intervention is the cornerstone for appropriate treatment of arrhythmia and prevention of complications and mortality. Although diverse deep learning models have been developed to detect arrhythmia, they have been criticized due to their unexplainable nature. In this study, we developed an explainable deep learning model (XDM) to classify arrhythmia, and validated its performance using diverse external validation data. METHODS: In this retrospective study, the Sejong dataset comprising 86,802 electrocardiograms (ECGs) was used to develop and internally variate the XDM. The XDM based on a neural network-backed ensemble tree was developed with six feature modules that are able to explain the reasons for its decisions. The model was externally validated using data from 36,961 ECGs from four non-restricted datasets. RESULTS: During internal and external validation of the XDM, the average area under the receiver operating characteristic curves (AUCs) using a 12­lead ECG for arrhythmia classification were 0.976 and 0.966, respectively. The XDM outperformed a previous simple multi-classification deep learning model that used the same method. During internal and external validation, the AUCs of explainability were 0.925-0.991. CONCLUSION: Our XDM successfully classified arrhythmia using diverse formats of ECGs and could effectively describe the reason for the decisions. Therefore, an explainable deep learning methodology could improve accuracy compared to conventional deep learning methods, and that the transparency of XDM can be enhanced for its application in clinical practice.

Artificial intelligence for detecting electrolyte imbalance using electrocardiography.

INTRODUCTION: The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study. METHODS AND RESULTS: This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance. CONCLUSION: The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.

Artificial intelligence to diagnose paroxysmal supraventricular tachycardia using electrocardiography during normal sinus rhythm.

Aims: Paroxysmal supraventricular tachycardia (PSVT) is not detected owing to its paroxysmal nature, but it is associated with the risk of cardiovascular disease and worsens the patient quality of life. A deep learning model (DLM) was developed and validated to identify patients with PSVT during normal sinus rhythm in this multicentre retrospective study. Methods and results: This study included 12 955 patients with normal sinus rhythm, confirmed by a cardiologist. A DLM was developed using 31 147 electrocardiograms (ECGs) of 9069 patients from one hospital. We conducted an accuracy test with 13 753 ECGs of 3886 patients from another hospital. The DLM was developed based on residual neural network. Digitally stored ECG were used as predictor variables and the outcome of the study was ability of the DLM to identify patients with PSVT using an ECG during sinus rhythm. We employed a sensitivity map method to identify an ECG region that had a significant effect on developing PSVT. During accuracy test, the area under the receiver operating characteristic curve of a DLM using a 12-lead ECG for identifying PSVT patients during sinus rhythm was 0.966 (0.948-0.984). The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of DLM were 0.970, 0.868, 0.972, 0.255, and 0.998, respectively. The DLM showed delta wave and QT interval were important to identify the PSVT. Conclusion: The proposed DLM demonstrated a high performance in identifying PSVT during normal sinus rhythm. Thus, it can be used as a rapid, inexpensive, point-of-care means of identifying PSVT in patients.

Relations between CBM (oral reading and maze) and reading comprehension on state achievement tests: A meta-analysis.

The purpose of this study was to examine the validity of two widely used Curriculum-Based Measures (CBM) in reading-oral reading and maze-in relation to reading comprehension on state tests using a meta-analysis. A total of 61 studies (132 correlations) were identified across Grades 1 to 10. A random-effects meta-analysis was conducted to estimate the average correlations between the two CBM tasks and reading comprehension on state tests, and to analyze the effects of potential moderating variables (characteristics of study, students, CBM, and state tests). Results revealed that the average correlation for oral reading was significantly larger than that for maze when all grade levels were included together in the analysis. When grade levels were separated, the difference between average correlations was only at the higher grades (Grades 4-10), favoring oral reading. In terms of correlations by grade level, oral reading and maze showed a similar pattern; that is, correlations were comparable across elementary grades, but decreased for secondary grades. In addition to the type of CBM and grade level differences, type of publication, type of state tests (commercial versus state-developed), and time interval between CBM and state tests were significant sources of variance in correlations. Implications for research and educational practice are discussed highlighting the somewhat different conclusions from previous literature, especially regarding the use of CBM for older students.

Random Weighting, Strong Tracking, and Unscented Kalman Filter for Soft Tissue Characterization.

This paper presents a new nonlinear filtering method based on the Hunt-Crossley model for online nonlinear soft tissue characterization. This method overcomes the problem of performance degradation in the unscented Kalman filter due to contact model error. It adopts the concept of Mahalanobis distance to identify contact model error, and further incorporates a scaling factor in predicted state covariance to compensate identified model error. This scaling factor is determined according to the principle of innovation orthogonality to avoid the cumbersome computation of Jacobian matrix, where the random weighting concept is adopted to improve the estimation accuracy of innovation covariance. A master-slave robotic indentation system is developed to validate the performance of the proposed method. Simulation and experimental results as well as comparison analyses demonstrate that the efficacy of the proposed method for online characterization of soft tissue parameters in the presence of contact model error.

Soft tissue deformation estimation by spatio-temporal Kalman filter finite element method.

BACKGROUND: Soft tissue modeling plays an important role in the development of surgical training simulators as well as in robot-assisted minimally invasive surgeries. It has been known that while the traditional Finite Element Method (FEM) promises the accurate modeling of soft tissue deformation, it still suffers from a slow computational process. OBJECTIVE: This paper presents a Kalman filter finite element method to model soft tissue deformation in real time without sacrificing the traditional FEM accuracy. METHODS: The proposed method employs the FEM equilibrium equation and formulates it as a filtering process to estimate soft tissue behavior using real-time measurement data. The model is temporally discretized using the Newmark method and further formulated as the system state equation. RESULTS: Simulation results demonstrate that the computational time of KF-FEM is approximately 10 times shorter than the traditional FEM and it is still as accurate as the traditional FEM. The normalized root-mean-square error of the proposed KF-FEM in reference to the traditional FEM is computed as 0.0116. CONCLUSIONS: It is concluded that the proposed method significantly improves the computational performance of the traditional FEM without sacrificing FEM accuracy. The proposed method also filters noises involved in system state and measurement data.

Early Writing Intervention: A Best Evidence Synthesis.

The purpose of this best evidence synthesis was to identify promising interventions that align with a theoretical model of early writing development, targeting three components of early writing: transcription, text generation, and self-regulation. We determined the extent to which these interventions are effective for children who struggle with early writing skills, by calculating effect sizes for group and single-subject designs, and we examined the overall quality of the research. Twenty-five studies met inclusion criteria. Among group design studies, mean effects (Hedge's g) ranged from 0.19 to 1.17 for measures of writing quantity and from 0.17 to 0.85 for measures of writing quality. Percentage of all nonoverlapping data for single-subject designs ranged from 83% to 100% for measures of writing quantity. Interventions with the strongest evidence of effects and highest methodological quality are described in detail. Recommendations for research and practice are provided.

Master-slave robotic system for needle indentation and insertion.

Bilateral control of a master-slave robotic system is a challenging issue in robotic-assisted minimally invasive surgery. It requires the knowledge on contact interaction between a surgical (slave) robot and soft tissues. This paper presents a master-slave robotic system for needle indentation and insertion. This master-slave robotic system is able to characterize the contact interaction between the robotic needle and soft tissues. A bilateral controller is implemented using a linear motor for robotic needle indentation and insertion. A new nonlinear state observer is developed to online monitor the contact interaction with soft tissues. Experimental results demonstrate the efficacy of the proposed master-slave robotic system for robotic needle indentation and needle insertion.

Activity monitoring using a mHealth device and correlations with psychopathology in patients with chronic schizophrenia.

There are few studies of mobile-Health (mHealth) device application with schizophrenic patients. We aimed to quantitatively assess patient's activity and the relationship between their physical activity and the severity of their psychopathologies. Then we attempted to identify the patients who required intervention and evaluated the feasibility of using the mHealth device. A total of 61 of the 76 available hospitalized patients with chronic schizophrenia who participated in the activity programs were enrolled. They wore a mHealth device for a week to assess their activity (steps/day). The Positive and Negative Syndrome Scale (PANSS) was completed by the subjects. As a result, the positive subscale of the PANSS and the positive and negative factors of the PANSS 5-factor structure showed a predictive value for low levels of physical activity. The group of subjects with a high total PANSS score had a significantly lower level of physical activity than the other groups. In conclusion, physical activity showed a significant association with positive symptoms as well as negative symptoms. The mHealth device showed relatively good feasibility for schizophrenic patients. We should pay more attention to the activity of patients with high PANSS scores.

Fluoroquinolone resistance in uncomplicated acute pyelonephritis: epidemiology and clinical impact.

The objectives of this study were to investigate antibiotic resistance in urinary pathogens from Korean patients with uncomplicated acute pyelonephritis (UAPN), and to determine the effect of fluoroquinolone (FQ) resistance on clinical outcome in those patients with UAPN initially treated with FQ. Clinical and microbiologic data for all the APN patients attending 14 hospitals in South Korea in 2008 were collected retrospectively. Urinary pathogens were identified in 719 cases, and Escherichia coli was the most common pathogen (661/719, 91.9%). Antibiotic susceptibilities to several E. coli antibiotics were as follows: ciprofloxacin, 84.1%; trimethoprim-sulfamethoxazola (TMP-SMX), 67.2%; and extended-spectrum beta-lactamase-negative, 92.4%. FQ was the most frequent antibiotic prescribed for UAPN (45.3% intravenously and 53.9% by mouth). We compared clinical outcomes and hospital days in patients with FQ-resistant (32) and FQ-sensitive E. coli (173) who received FQ as initial empirical therapy. Clinical cure was higher in the FQ-sensitive group (78% vs. 91%, p=0.027), and hospital days were longer in the FQ-resistant group (9.6±5.5 days vs. 7±3.5 days, p=0.001). In conclusion, FQ-sensitivity of E. coli from UAPN was 84.1%. FQ treatment of UAPN caused by FQ-resistant E. coli has a lower cure rate and involves longer hospital stay than FQ treatment of cases caused by FQ-sensitive E. coli.