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Purpose: Despite the emergence of alternative models to traditional Batterer Intervention Programs (BIPs) to address domestic violence (DV), little research has explored stakeholders' perceptions and experiences of various treatment approaches. Therefore, this study, guided by the stakeholder engagement framework, explored the experiences, attitudes, and beliefs of court personnel and clinicians involved in mandated treatment for those convicted of DV crimes. Method: Two focus groups and nine semi-structured individual interviews were conducted with court personnel and clinicians and were analyzed using an interpretative phenomenological approach. Findings: Five themes emerged: (1) "Capturing a true batterer": The discrepancy between common perceptions of DV batterers, the legal definition of DV offenses, and the standard treatment approach for DV crimes; (2) A "cookie-cutter" approach: The controversy over the one-size-fits-all approach of court-mandated treatment; (3) "I don't have the tools": Professionals' frustration with policy and practice limitations; (4) The need for more inclusive treatment: Broadening the range of participants involved in the DV treatment process, and (5) A call for a shift in pedagogy: A new paradigm for DV and DV treatment. Overall, various stakeholders (i.e., court personnel and clinicians) expressed a sense of helplessness while attempting to navigate current DV statutes, court-mandated BIPs/DV treatment, and the realities of DV. Conclusion: The narratives of stakeholders point to the desire and the need for a new treatment paradigm to address the various types of DV crimes, the needs of those convicted of misdemeanor DV crimes, and the broader impact of DV. [ABSTRACT FROM AUTHOR]

We examine a controversy involving the politics of bilingualism as it pertains to Jejueo (ISO 639–3 jje), the critically endangered variety of speech indigenous to Jeju, Korea’s island province. Although Jejueo is officially classified as a non-standard dialect of Korean, members of the community are now calling for its recognition as a distinct language and for a corresponding change in the country’s language policy. In order to shed new light on this controversy, we adopt an experimental methodology that was initially developed for measuring cross-linguistic intelligibility in three European language families. The results of two experiments establish that the intelligibility of Jejueo to speakers of Korean is remarkably low – roughly at the same level as the intelligibility of Italian to speakers of French. We conclude that Jejueo deserves to be recognised as a distinct language. [ABSTRACT FROM AUTHOR]

BACKGROUND: Robot-assisted gait training (RAGT) was initially developed based on the passive controlled (PC) mode, where the target or ideal locomotor kinematic trajectory is predefined and a patient basically 'rides' the robot instead of actively participating in the actual locomotor relearning process. A new insightful contemporary neuroscience and mechatronic evidence suggest that robotic-based locomotor relearning can be best achieved through active interactive (AI) mode rather than PC mode. OBJECTIVE: The purpose of this study was to compare the pattern of gait-related cortical activity, specifically gait event-related spectral perturbations (ERSPs), and muscle activity from the tibialis anterior (TA) and clinical functional tests in subacute and chronic stroke patients during robot-assisted gait training (RAGT) in passive controlled (PC) and active interactive (AI) modes. METHODS: The present study involves a two-group pretest-posttest design in which two groups (i.e., PC-RAGT group and AI-RAGT group) of 14 stroke subjects were measured to assess changes in ERSPs, the muscle activation of TA, and the clinical functional tests, following 15– 18 sessions of intervention according to the protocol of each group. RESULTS: Our preliminary results demonstrated that the power in the μ band (8– 12 Hz) was increased in the leg area of sensorimotor cortex (SMC) and supplementary motor area (SMA) at post-intervention as compared to pre-intervention in both groups. Such cortical neuroplasticity change was associated with TA muscle activity during gait and functional independence in functional ambulation category (FAC) and motor coordination in Fugl– Meyer Assessment for lower extremity (FMA-LE) test as well as spasticity in the modified Ashworth scale (MAS) measures. CONCLUSIONS: We have first developed a novel neuroimaging experimental paradigm which distinguished gait event related cortical involvement between pre- and post-intervention with PC-RAGT and AI-RAGT in individuals with subacute and chronic hemiparetic stroke. [ABSTRACT FROM AUTHOR]

This study examined the role of parental aggravation in the pathway from maternal depression to child depression in different family structures. While studies have extensively examined the transmission of maternal depressive symptoms to children, there is still a limited emphasis on potential contributors, such as parental aggravation. Meanwhile, cohabiting and single‐parent families are more vulnerable to multiple risk factors than married families. Using a large national longitudinal dataset, this study examined the indirect effect of parental aggravation on the association between maternal depression and child depression among married, cohabiting and single‐mother families. Secondary data analysis was performed using multigroup mediation analysis on three waves from the Future of Families and Child Wellbeing Studies (n = 3 117). The study results show that only in cohabiting households did maternal depression have a direct association with child depression. Furthermore, in married, cohabiting and single‐mother families, parental aggravation had indirect effects on the association between maternal depression and child depression. Implications for social work professionals to address the mental health of parents and children from non‐traditional families are discussed. [ABSTRACT FROM AUTHOR]

Tests of vocabulary and verbal patterns in Jejueo and English reveal that the acquisition of the former language is in sharp decline, while progress in the latter language reaches a developmental plateau after high school. This dissertation investigates the acquisition of Jejueo and English on Jeju Island, a province of Korea located approximately 45 miles off the southern coast of the Korean peninsula. Jejueo, which was recognized as an endangered language by UNESCO in 2010, is primarily spoken between family members and close friends, while Korean is used in other contexts, including school. This is the first study of the acquisition of Jejueo and also the first attempt to compare fluency in that language with fluency in Korean and with proficiency in English, which is taught as a compulsory foreign language in schools from Grade 3 to college for two to five hours per week but is rarely heard outside that setting. [ABSTRACT FROM AUTHOR]

Background/Purpose: Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. Methods: A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist’s and non-expert’s evaluation. Results: The accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert’s evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden’s index like 0.6795, 0.6073, which were similar score with the expert. Conclusion: Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet. [ABSTRACT FROM AUTHOR]

Background: Photographs of skin wounds have the most important information during the secondary intention healing (SIH). However, there is no standard method for handling those images and analyzing them efficiently and conveniently. Objective: To investigate the sequential changes of SIH depending on the body sites using a color patch method Methods: We performed retrospective reviews of 30 patients (11 facial and 19 non-facial areas) who underwent SIH for the restoration of skin defects and captured sequential photographs with a color patch which is specially designed for automatically calculating defect and scar sizes. Results: Using a novel image analysis method with a color patch, skin defects were calculated more accurately (range of error rate: -3.39% ~ + 3.05%). All patients had smaller scar size than the original defect size after SIH treatment (rates of decrease: 18.8% ~ 86.1%), and facial area showed significantly higher decrease rate compared with the non-facial area such as scalp and extremities (67.05 ± 12.48 vs. 53.29 ± 18.11, P < 0.05). From the result of estimating the date corresponding to the half of the final decrement, all of the facial area showed improvements within two weeks (8.45 ± 3.91), and non-facial area needed 14.33 ± 9.78 days. Conclusion: From the results of sequential changes of skin defects, SIH can be recommended as an alternative treatment method for restoration with more careful dressing for initial two weeks. [ABSTRACT FROM AUTHOR]

This study aimed to examine pathways from child abuse to school adjustment and the roles of self-control and academic stress on the link among North Korean adolescent refugees living in South Korea and native South Korean adolescents. A total of 610 students (adolescents from South Korea = 325 and adolescents from North Korea = 285) living in South Korea, from juniors in middle schools to seniors in high schools, were interviewed in 2017. Multigroup structural equation modeling was used to examine differences in the country of origin on the pathways from abuse to school adjustment via self-control and academic stress. North Korean adolescent refugees were less likely to adjust to their school life than South Korean adolescents. Academic stress was found as a significant mediator between self-control and school adjustment in both South Korean and North Korean adolescents. Child abuse was associated with self-control of South Korean adolescents. Childhood abuse from parents can have an overall influence on individual characteristics and school life for adolescents. By paying attention to this process, comprehensive solutions are urgently required not only to intervene in the problem of abusive parenting behaviors but also to block the path of the expanding negative consequences among both groups of adolescents. [ABSTRACT FROM AUTHOR]

In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: A subject-specific local in vivo passive shimming method, focusing on the prefrontal and temporal regions, is proposed. The aim of the investigation is to show that subject variability exists in optimal passive shimming and that the proposed method can be effectively used to overcome these differences. A shimming structure capable of adjusting the position of the passive shims to within a millimeter resolution is built. The optimal shim positions for each individual subject are computed from obtained field map using a convex optimization algorithm. Passive shim experiments at predetermined fixed shim positions vs. individually adjusted shim positions were performed and compared. The results show that intersubject variability exists in the optimal shim positions and that the location-sensitive method proposed can be useful for improving main field homogeneity in vivo. [Copyright &y& Elsevier]

The majority of color histogram equalization methods do not yield uniform histogram in gray scale. After converting a color histogram equalized image into gray scale, the contrast of the converted image is worse than that of an 1-D gray scale histogram equalized image. We propose a novel 3-D color histogram equalization method that produces uniform distribution in gray scale histogram by defining a new cumulative probability density function in 3-D color space. Test results with natural and synthetic images are presented to compare and analyze various color histogram equalization algorithms based upon 3-D color histograms. We also present theoretical analysis for nonideal performance of existing methods. [ABSTRACT FROM AUTHOR]

Color transforms are important methods in the analysis and processing of images. Image color transform and its inverse transform should be reversible for lossless image processing applications. However, color conversions are not reversible due to finite precision of the conversion coefficients. To overcome this limitation, reversible color transforms have been developed. Color integer transform requires multiplications of coefficients, which are implemented with shift and add operations in most cases. We propose to use canonical signed digit (CSD) representation of reversible color transform coefficients and exploitation of their common subexpressions to reduce the complexity of the hardware implementation significantly. We demonstrate roughly 50 reduction in computation with the proposed method. [ABSTRACT FROM AUTHOR]

• This study proposed a novel stitching method capable of automatically stitching field images, even with a narrow overlapping region. • Regardless of the presence of cells in the overlapping region, the method performed well. • The method was rigorously tested in various experimental conditions (e.g., various microscope images and grid structures). • The results are a first attempt at morphologically analyzing brain tumor cells in stitched microscopy images from a live human brain. The morphology of tumor cells is highly related to their phenotype and activity. To verify the drug response of a brain tumor patient, fluorescence microscope images of drug-treated patient-derived cells in each well are analyzed. Due to the limitation of the field of view (FOV), a large number of small FOVs are acquired to compose one complete microscope well. Here, we propose an automated method for accurately stitching tile-scanned fluorescence microscope images, even with noise and a narrow overlapping region between adjacent fields. The proposed method is based on intensity-based normalized cross-correlation (NCC) and a triangular method-based threshold. The proposed method's quantitative accuracy and the sensitivity of the input was compared to other existing stitching tools, MIST and FijiIS, setting manually stitched images as the ground truth. The test images were 20 samples of 3 × 3 grid images in three versions of the fluorescence channel. The distance between the location of each field and number of cells was determined for different input field overlap ranges (1%, 3%, 5%, and 10%), while the actual value was about 1.15%. The proposed method had a distance error of 1.5 pixels at an input overlap of 1%, showing the lowest minimum error at all channels. Regarding the difference in cell numbers, although the number of overlapping cells was always small because of the narrow overlapping range, the proposed method was able to generate the resultant image with the smallest difference. In addition, to confirm the size limitation of the proposed algorithm, the accuracy of stitching images of grid structures 3 × 3, 5 × 5, 10 × 10–20 × 20 was tested, showing consistent results. In conclusion, quantitative evaluation of the performance of the method proved its improved accuracy compared to other current state-of-art techniques, and it showed robust performance even with noise and a narrow overlapping region between adjacent fields. [ABSTRACT FROM AUTHOR]

Goblet cells (GCs) in the conjunctiva are specialized epithelial cells secreting mucins for the mucus layer of protective tear film and playing immune tolerance functions for ocular surface health. Because GC loss is observed in various ocular surface diseases, GC examination is important for precision diagnosis. Moxifloxacin-based fluorescence microscopy (MBFM) was recently developed for non-invasive high-contrast GC visualization. MBFM showed promise for GC examination by high-speed large-area imaging and a robust analysis method is needed to provide GC information. In this study, we developed a deep learning framework for GC image analysis, named dual-channel attention U-Net (DCAU-Net). Dual-channel convolution was used both to extract the overall image texture and to acquire the GC morphological characteristics. A global channel attention module was adopted by combining attention algorithms and channel-wise pooling. DCAU-Net showed 93.1% GC segmentation accuracy and 94.3% GC density estimation accuracy. Further application to both normal and ocular surface damage rabbit models revealed the spatial variations of both GC density and size in normal rabbits and the decreases of both GC density and size in damage rabbit models during recovery after acute damage. The GC analysis results were consistent with histology. Together with the non-invasive high-contrast imaging method, DCAU-Net would provide GC information for the diagnosis of ocular surface diseases. [ABSTRACT FROM AUTHOR]

Benign paroxysmal positional vertigo (BPPV), the most common vestibular disorder, is diagnosed by an examiner changing the posture of the examinee and inducing nystagmus. Among the diagnostic methods used to observe nystagmus, video-nystagmography has been widely used recently because it is non-invasive. A specialist with professional knowledge and training in vertigo diagnosis is needed to diagnose BPPV accurately, but the ratio of vertigo patients to specialists is too high, thus necessitating the need for automated diagnosis of BPPV. In this paper, a convolutional neural network-based nystagmus extraction system, ANyEye, optimized for video-nystagmography data is proposed. A pupil was segmented to track the exact pupil trajectory from real-world data obtained during field inspection. A deep convolutional neural network model was trained with the new video-nystagmography dataset for the pupil segmentation task, and a compensation algorithm was designed to correct pupil position. In addition, a slippage detection algorithm based on moving averages was designed to eliminate the motion artifacts induced by goggle slippage. ANyEye outperformed other eye-tracking methods including learning and non-learning-based algorithms with five-pixel error detection rate of 91.26%. [ABSTRACT FROM AUTHOR]

Benign paroxysmal positional vertigo (BPPV), the most common vestibular disorder, is diagnosed by an examiner changing the posture of the examinee and inducing nystagmus. Among the diagnostic methods used to observe nystagmus, video-nystagmography has been widely used recently because it is non-invasive. A specialist with professional knowledge and training in vertigo diagnosis is needed to diagnose BPPV accurately, but the ratio of vertigo patients to specialists is too high, thus necessitating the need for automated diagnosis of BPPV. In this paper, a convolutional neural network-based nystagmus extraction system, ANyEye, optimized for video-nystagmography data is proposed. A pupil was segmented to track the exact pupil trajectory from real-world data obtained during field inspection. A deep convolutional neural network model was trained with the new video-nystagmography dataset for the pupil segmentation task, and a compensation algorithm was designed to correct pupil position. In addition, a slippage detection algorithm based on moving averages was designed to eliminate the motion artifacts induced by goggle slippage. ANyEye outperformed other eye-tracking methods including learning and non-learning-based algorithms with five-pixel error detection rate of 91.26%. [ABSTRACT FROM AUTHOR]

In this study, a combined convolutional neural network for the diagnosis of three benign skin tumors was designed, and its effectiveness was verified through quantitative and statistical analysis. To this end, 698 sonographic images were taken and diagnosed at the Department of Dermatology at Severance Hospital in Seoul, Korea, between 10 November 2017 and 17 January 2020. Through an empirical process, a convolutional neural network combining two structures, which consist of a residual structure and an attention-gated structure, was designed. Five-fold cross-validation was applied, and the train set for each fold was augmented by the Fast AutoAugment technique. As a result of training, for three benign skin tumors, an average accuracy of 95.87%, an average sensitivity of 90.10%, and an average specificity of 96.23% were derived. Also, through statistical analysis using a class activation map and physicians' findings, it was found that the judgment criteria of physicians and the trained combined convolutional neural network were similar. This study suggests that the model designed and trained in this study can be a diagnostic aid to assist physicians and enable more efficient and accurate diagnoses. [ABSTRACT FROM AUTHOR]

Benign paroxysmal positional vertigo (BPPV), the most common vestibular disorder, is diagnosed by an examiner changing the posture of the examinee and inducing nystagmus. Among the diagnostic methods used to observe nystagmus, video-nystagmography has been widely used recently because it is non-invasive. A specialist with professional knowledge and training in vertigo diagnosis is needed to diagnose BPPV accurately, but the ratio of vertigo patients to specialists is too high, thus necessitating the need for automated diagnosis of BPPV. In this paper, a convolutional neural network-based nystagmus extraction system, ANyEye, optimized for video-nystagmography data is proposed. A pupil was segmented to track the exact pupil trajectory from real-world data obtained during field inspection. A deep convolutional neural network model was trained with the new video-nystagmography dataset for the pupil segmentation task, and a compensation algorithm was designed to correct pupil position. In addition, a slippage detection algorithm based on moving averages was designed to eliminate the motion artifacts induced by goggle slippage. ANyEye outperformed other eye-tracking methods including learning and non-learning-based algorithms with five-pixel error detection rate of 91.26%. [ABSTRACT FROM AUTHOR]

Left ventricular hypertrophy is a significant independent risk factor for all-cause mortality and morbidity, and an accurate diagnosis at an early stage of heart change is clinically significant. Electrocardiography is the most convenient, economical, and non-invasive method for screening in primary care. However, the coincidence rate of the actual left ventricular hypertrophy and diagnostic findings was low, consequently increasing the interest in algorithms using big data and deep learning. We attempted to diagnose left ventricular hypertrophy using big data and deep learning algorithms, and aimed to confirm its diagnostic power according to the differences between males and females. This retrospective study used electrocardiographs obtained at Yonsei University Wonju Severance Christian Hospital, Wonju, Korea, from October 2010 to February 2020. Binary classification was performed for primary screening for left ventricular hypertrophy. Three datasets were used for the experiment: the male, female, and entire dataset. A cutoff for binary classification was defined as the meaningful as a screening test (<132 g/m2 vs. ≥132 g/m2, <109 g/m2 vs. ≥109 g/m2). Six types of input were used for the classification tasks. We attempted to determine whether electrocardiography had predictive power for left ventricular hypertrophy diagnosis. For the entire dataset, the model achieved an area under the receiver operating characteristic (AUROC) curve of 0.836 (95% CI, 0.833–838) with a sensitivity of 78.37% (95% CI, 76.79–79.95). For the male dataset, the AUROC was 0.826 (95% CI, 0.822–830) with a sensitivity of 76.73% (95% CI, 75.14–78.33). For the female dataset, the AUROC was 0.772 (95% CI, 0.769–775) with a sensitivity of 72.90% (95% CI, 70.33–75.46). Our model confirmed that left ventricular hypertrophy can be classified to some extent using electrocardiography, demographics, and electrocardiography features. In particular, a learning environment that considered gender differences was constructed. Consequently, the difference in diagnostic power between men and women was confirmed. Our model will help patients with suspected left ventricular hypertrophy to undergo screening tests at a low cost. In addition, our research and attempts will show the expected effect that gender-consideration approaches can help with various currently proposed diagnostic methods. [ABSTRACT FROM AUTHOR]

There is a paucity of research that systematically examines how food behaviors play a role in intimate partner violence (IPV). Therefore, this qualitative study aims to answer the broad question, what role do food behaviors play in intimate relationships? Food behavior narratives emerging from participants of court-mandated domestic violence (DV) offender treatment programs were analyzed using grounded theory methods. Five themes emerged. Two described inflammatory/harmful roles: (1) food as a trigger for anger and violence and (2) food as a mechanism of "othering." One theme described the role of food behaviors in promoting unequal and equal relationships: (3) food as an embodiment of gender roles. Two themes described reconciliatory/beneficial roles: (4) food as a mechanism of recognition and (5) and food as a representation of group rapport. Food behaviors can escalate into conflicts but can also be used as a tool to resolve conflicts. Limitations and the need for future research are further discussed. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) has been known to be affected by demographic and anthropometric factors. This study aimed to develop deep learning models to predict the subject's age, sex, ABO blood type, and body mass index (BMI) based on ECGs. This retrospective study included individuals aged 18 years or older who visited a tertiary referral center with ECGs acquired from October 2010 to February 2020. Using convolutional neural networks (CNNs) with three convolutional layers, five kernel sizes, and two pooling sizes, we developed both classification and regression models. We verified a classification model to be applicable for age (<40 years vs. ≥40 years), sex (male vs. female), BMI (<25 kg/m2 vs. ≥25 kg/m2), and ABO blood type. A regression model was also developed and validated for age and BMI estimation. A total of 124,415 ECGs (1 ECG per subject) were included. The dataset was constructed by dividing the entire set of ECGs at a ratio of 4:3:3. In the classification task, the area under the receiver operating characteristic (AUROC), which represents a quantitative indicator of the judgment threshold, was used as the primary outcome. The mean absolute error (MAE), which represents the difference between the observed and estimated values, was used in the regression task. For age estimation, the CNN achieved an AUROC of 0.923 with an accuracy of 82.97%, and a MAE of 8.410. For sex estimation, the AUROC was 0.947 with an accuracy of 86.82%. For BMI estimation, the AUROC was 0.765 with an accuracy of 69.89%, and a MAE of 2.332. For ABO blood type estimation, the CNN showed an inferior performance, with a top-1 accuracy of 31.98%. For the ABO blood type estimation, the CNN showed an inferior performance, with a top-1 accuracy of 31.98% (95% CI, 31.98–31.98%). Our model could be adapted to estimate individuals' demographic and anthropometric features from their ECGs; this would enable the development of physiologic biomarkers that can better reflect their health status than chronological age. [ABSTRACT FROM AUTHOR]

Purpose: There has been little progress in research on the best anatomical position for effective chest compressions and cardiac function during cardiopulmonary resuscitation (CPR). This study aimed to divide the left ventricle (LV) into segments to determine the best position for effective chest compressions using the LV systolic function seen during CPR. Methods: We used transesophageal echocardiography images acquired during CPR. A deep neural network with an attention mechanism and a residual feature aggregation module were applied to the images to segment the LV. The results were compared between the proposed model and U-Net. Results: The results of the proposed model showed higher performance in most metrics when compared to U-Net: dice coefficient (0.899±0.017 vs. 0.792±0.027, p<0.05); intersection of union (0.822±0.026 vs. 0.668±0.034, p<0.05); recall (0.904±0.023 vs. 0.757±0.037, p<0.05); precision (0.901±0.021 vs. 0.859±0.034, p>0.05). There was a significant difference between the proposed model and U-Net. Conclusion: Compared to U-Net, the proposed model showed better performance for all metrics. This model would allow us to evaluate the systolic function of the heart during CPR in greater detail by segmenting the LV more accurately. [ABSTRACT FROM AUTHOR]

Simple Summary: With advances in radiotherapy (RT) technique and more frequent use of stereotactic ablative radiotherapy (SABR), precise segmentation of all brain metastases (BM) including a small volume of BM is essential to choose an appropriate treatment modality. However, the process of detecting and manually delineating BM with small volumes often results in missing delineation and requires a great amount of labor. To address this issue, we present a useful deep learning (DL) model for the detection and segmentation of BMwith contrast-enhanced magnetic resonance images. Specifically, we applied effective training techniques to detect and segment a BM of less than 0.04 cc, which is relatively small compared to previous studies. The results of our DL model demonstrated that the proposed methods provide considerable benefit for BM, even small-volume BM, detection, and segmentation for SABR. Recently, several efforts have been made to develop the deep learning (DL) algorithms for automatic detection and segmentation of brain metastases (BM). In this study, we developed an advanced DL model to BM detection and segmentation, especially for small-volume BM. From the institutional cancer registry, contrast-enhanced magnetic resonance images of 65 patients and 603 BM were collected to train and evaluate our DL model. Of the 65 patients, 12 patients with 58 BM were assigned to test-set for performance evaluation. Ground-truth for BM was assigned to one radiation oncologist to manually delineate BM and another one to cross-check. Unlike other previous studies, our study dealt with relatively small BM, so the area occupied by the BM in the high-resolution images were small. Our study applied training techniques such as the overlapping patch technique and 2.5-dimensional (2.5D) training to the well-known U-Net architecture to learn better in smaller BM. As a DL architecture, 2D U-Net was utilized by 2.5D training. For better efficacy and accuracy of a two-dimensional U-Net, we applied effective preprocessing include 2.5D overlapping patch technique. The sensitivity and average false positive rate were measured as detection performance, and their values were 97% and 1.25 per patient, respectively. The dice coefficient with dilation and 95% Hausdorff distance were measured as segmentation performance, and their values were 75% and 2.057 mm, respectively. Our DL model can detect and segment BM with small volume with good performance. Our model provides considerable benefit for clinicians with automatic detection and segmentation of BM for stereotactic ablative radiotherapy. [ABSTRACT FROM AUTHOR]

" now reads: "This research was supported by 'Regional Innovation Strategy (RIS)' through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2022RIS-005) (Y.J.S.) and a National Research Foundation of Korea grant provided by the Korean government (Ministry of Science and ICT) (NRF-2022R1A2C2091160) (S.Y.). Consequently, "The acquired data were observed to analyze slippage-induced motion artifacts, with the section where the slippage occurred indicated by two otolaryngologists (Supplementary Fig. [Extracted from the article]

Background: Intraoperative hypotension is associated with a risk of postoperative organ dysfunction. In this study, we aimed to present deep learning algorithms for real-time predictions 5, 10, and 15 min before a hypotensive event.Methods: In this retrospective observational study, deep learning algorithms were developed and validated using biosignal waveforms acquired from patient monitoring of noncardiac surgery. The classification model was a binary classifier of a hypotensive event (MAP <65 mm Hg) or a non-hypotensive event by analysing biosignal waveforms. The regression model was developed to directly estimate the MAP. The primary outcome was area under the receiver operating characteristic (AUROC) curve and the mean absolute error (MAE).Results: In total, 3301 patients were included. For invasive models, the multichannel model with an arterial pressure waveform, electrocardiography, photoplethysmography, and capnography showed greater AUROC than the arterial-pressure-only models (AUROC15-min, 0.897 [95% confidence interval {CI}: 0.894-0.900] vs 0.891 [95% CI: 0.888-0.894]) and lesser MAE (MAE15-min, 7.76 mm Hg [95% CI: 7.64-7.87 mm Hg] vs 8.12 mm Hg [95% CI: 8.02-8.21 mm Hg]). For the noninvasive models, the multichannel model showed greater AUROCs than that of the photoplethysmography-only models (AUROC15-min, 0.762 [95% CI: 0.756-0.767] vs 0.694 [95% CI: 0.686-0.702]) and lesser MAEs (MAE15-min, 11.68 mm Hg [95% CI: 11.57-11.80 mm Hg] vs 12.67 [95% CI: 12.56-12.79 mm Hg]).Conclusions: Deep learning models can predict hypotensive events based on biosignals acquired using invasive and noninvasive patient monitoring. In addition, the model shows better performance when using combined rather than single signals. [ABSTRACT FROM AUTHOR]

As members of a research group at New York University's Center on Violence and Recovery, we, the authors, strive to promote social justice through our research and awareness in relationships between members of the research team as we conduct studies on domestic violence (DV) and restorative justice (RJ). Our diverse group members have become a "research family." We have learned through this experience that group work within research is one way to enact social justice. Like a wave, our group dynamics have and will continue to influence our individual and collective potentials as well as our contribution to the fields of DV and RJ. In this narrative we share our experience and the multiple factors that are necessary for our group to sustain its impact. [ABSTRACT FROM AUTHOR]

Highlights • We provide a conceptual advance of the method to produce stable Parkinsonian models. • Individual MPTP dose adjustments according to the severity of symptoms are needed. • Global activity using a video tracking system is useful in evaluating Parkinsonism. • Global activity provides an objective and accurate evaluation of symptom severity. • Global activity provides the objective criteria for adjustments of MPTP treatment. Abstract Background The guidelines for applying individual adjustments to macaques according to the severity of behavioral symptoms during 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) treatment were provided to reproduce stable chronic Parkinsonism in a recent study (Potts et al., 2014). But, since there are insufficient guidelines regarding objective severity criteria of individual symptoms for adjustments of MPTP treatment, it is difficult to develop MPTP-induced chronic non-human primate (NHP) models with stable symptoms. New method The individual adjustments of MPTP administration based on results of automatic quantification of global activity (GA) using a video-based tracking system were applied to develop MPTP-PD model. Low-dose (0.2 mg/kg) intramuscular injection was repeated continuously until GA was lower than 8% of baseline Parkinsonian behavior scores. The positron emission tomography imaging were used to follow the longitudinal course of Parkinson's disease (PD). Results Significant reductions in GA and dopamine transporter activity, along with significant increases in Parkinsonian behavior scores were found from 4 to 48 weeks following the first administration. GA was correlated with the Parkinsonian behavior score. The dopamine transporter activity was correlated with GA and the Parkinsonian behavior score. However, it was not correlated with the total dose of MPTP. Damage of dopaminergic neuronal systems in the basal ganglia was confirmed by immunohistochemistry and Western blot. Comparison with existing method This study reinforces previous guidelines regarding production of NHP models with stable Parkinsonian symptoms. Conclusions This novel strategy of MPTP administration based on global activity evaluations provides an important conceptual advance for the development of chronic NHP Parkinsonian models. [ABSTRACT FROM AUTHOR]

In animal models of Parkinson's disease (PD), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is one of the most widely used agents that damages the nigrostriatal dopaminergic pathway. However, brain structural changes in response to MPTP remain unclear. This study aimed to investigate in vivo longitudinal changes in gray matter (GM) volume and white matter (WM) microstructure in primate models administered with MPTP. In six cynomolgus monkeys, high-resolution magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) scans were acquired 7 times over 32 weeks, and assessments of motor symptoms were conducted over 15 months, before and after the MPTP injection. Changes in GM volume and WM microstructure were estimated on a voxel-by-voxel basis. Mixed-effects regression models were used to examine the trajectories of these structural changes. GM volume initially increased after the MPTP injection and gradually decreased in the striatum, midbrain, and other dopaminergic areas. The cerebellar volume temporarily decreased and returned to its baseline level. The rate of midbrain volume increase was positively correlated with the increase rate of motor symptom severity (Spearman rho = 0.93, p = 0.008). Mean, axial, and radial diffusivity in the striatum and frontal areas demonstrated initial increases and subsequent decreases. The current multi-modal imaging study of MPTP-administered monkeys revealed widespread and dynamic structural changes not only in the nigrostriatal pathway but also in other cortical, subcortical, and cerebellar areas. Our findings may suggest the need to further investigate the roles of inflammatory reactions and glial activation as potential underlying mechanisms of these structural changes. [ABSTRACT FROM AUTHOR]

The non-invasive examination of conjunctival goblet cells using a microscope is a novel procedure for the diagnosis of ocular surface diseases. However, it is difficult to generate an all-in-focus image due to the curvature of the eyes and the limited focal depth of the microscope. The microscope acquires multiple images with the axial translation of focus, and the image stack must be processed. Thus, we propose a multi-focus image fusion method to generate an all-in-focus image from multiple microscopic images. First, a bandpass filter is applied to the source images and the focus areas are extracted using Laplacian transformation and thresholding with a morphological operation. Next, a self-adjusting guided filter is applied for the natural connections between local focus images. A window-size-updating method is adopted in the guided filter to reduce the number of parameters. This paper presents a novel algorithm that can operate for a large quantity of images (10 or more) and obtain an all-in-focus image. To quantitatively evaluate the proposed method, two different types of evaluation metrics are used: "full-reference" and "no-reference". The experimental results demonstrate that this algorithm is robust to noise and capable of preserving local focus information through focal area extraction. Additionally, the proposed method outperforms state-of-the-art approaches in terms of both visual effects and image quality assessments. [ABSTRACT FROM AUTHOR]

In this study, we aimed to develop a new automated method for kidney volume measurement in children using ultrasonography (US) with image pre-processing and hybrid learning and to formulate an equation to calculate the expected kidney volume. The volumes of 282 kidneys (141 subjects, <19 years old) with normal function and structure were measured using US. The volumes of 58 kidneys in 29 subjects who underwent US and computed tomography (CT) were determined by image segmentation and compared to those calculated by the conventional ellipsoidal method and CT using intraclass correlation coefficients (ICCs). An expected kidney volume equation was developed using multivariate regression analysis. Manual image segmentation was automated using hybrid learning to calculate the kidney volume. The ICCs for volume determined by image segmentation and ellipsoidal method were significantly different, while that for volume calculated by hybrid learning was significantly higher than that for ellipsoidal method. Volume determined by image segmentation was significantly correlated with weight, body surface area, and height. Expected kidney volume was calculated as (2.22 × weight (kg) + 0.252 × height (cm) + 5.138). This method will be valuable in establishing an age-matched normal kidney growth chart through the accumulation and analysis of large-scale data. [ABSTRACT FROM AUTHOR]

Cell migration plays an important role in the identification of various diseases and physiological phenomena in living organisms, such as cancer metastasis, nerve development, immune function, wound healing, and embryo formulation and development. The study of cell migration with a real-time microscope generally takes several hours and involves analysis of the movement characteristics by tracking the positions of cells at each time interval in the images of the observed cells. Morphological analysis considers the shapes of the cells, and a phase contrast microscope is used to observe the shape clearly. Therefore, we developed a segmentation and tracking method to perform a kinetic analysis by considering the morphological transformation of cells. The main features of the algorithm are noise reduction using a block-matching 3D filtering method, k-means clustering to mitigate the halo signal that interferes with cell segmentation, and the detection of cell boundaries via active contours, which is an excellent way to detect boundaries. The reliability of the algorithm developed in this study was verified using a comparison with the manual tracking results. In addition, the segmentation results were compared to our method with unsupervised state-of-the-art methods to verify the proposed segmentation process. As a result of the study, the proposed method had a lower error of less than 40% compared to the conventional active contour method. [ABSTRACT FROM AUTHOR]

• Automatic segmentation and tracking method to measure DEP-induced trajectories of numerous cells at the single cell level. • Evaluation of the method with cell count capability, cell trapping efficiency, and cell trapping Occupancy. • Measurement of dielectric mobility of each cell using the automatic method in a robust, efficient, and accurate manner. • Verification in comparison of the measured cellular dielectric mobility with previous results and statistical consistency. The dielectrophoresis (DEP) technique is increasingly being recognised as a potentially valuable tool for non-contact manipulation of numerous cells as well as for biological single cell analysis with non-invasive characterisation of a cell's electrical properties. Several studies have attempted to track multiple cells to characterise their cellular DEP mobility. However, they encountered difficulties in simultaneously tracking the movement of a large number of individual cells in a bright-field image sequence because of interference from the background electrode pattern. Consequently, this present study aims to develop an automatic system for imaging-based characterisation of cellular DEP mobility, which enables the simultaneous tracking of several hundred of cells inside a microfluidic device. : The proposed method for segmentation and tracking of cells consists of two main stages: pre-processing and particle centre localisation. In the pre-processing stage, background subtraction and contrast enhancement were performed to distinguish the cell region from the background image. In the particle centre localisation stage, the unmarked cell was automatically detected via graph-cut algorithm-based K-means clustering. : Our algorithm enabled segmentation and tracking of numerous Michigan Cancer Foundation-7 (MCF-7) cell trajectories while the DEP force was oscillated between positive and negative. The cell tracking accuracy and cell count capability was at least 90% of the total number of cells with the newly developed algorithm. In addition, the cross-over frequency was measured by analysing the segmented and tracked trajectory data of the cellular movements caused by the positive and negative DEP force. The measured cross-over frequency was compared with previous results. The multi-cellular movements investigation based on the measured cross-over frequency was repeated until the viability of cells was unchanged in the same environment as in a microfluidic device. The results were statistically consistent, indicating that the developed algorithm was reliable for the investigation of DEP cellular mobility. : This study developed a powerful platform to simultaneously measure the DEP-induced trajectories of numerous cells, and to investigate in a robust, efficient, and accurate manner the DEP properties at both the single cell and cell ensemble level. [ABSTRACT FROM AUTHOR]