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Article ; Online: Flexible Ring Sensor Array and Machine Learning Model for the Early Blood Leakage Detection during Dialysis

Ping-Tzan Huang / Chia-Hung Lin / Chien-Ming Li

Processes, Vol 10, Iss 2197, p

2022 Volume 2197

Abstract: Severe blood leakage resulting from the detachment of dialysis tubing is often difficult to detect by nurses in busy clinics. This paper presents a flexible blood leakage detection system featuring a ring-light sensor array with an operating wavelength ... ...

Abstract	Severe blood leakage resulting from the detachment of dialysis tubing is often difficult to detect by nurses in busy clinics. This paper presents a flexible blood leakage detection system featuring a ring-light sensor array with an operating wavelength of 500–700 nm, which is held in place by the gauze covering the dialysis puncture site. A ring-light sensor is connected to a bidirectional hetero-associative memory network, which interprets detected changes in signal strength, the output signal of which is transmitted via WiFi to a server at the nursing station where a machine learning algorithm determines whether blood leakage has occurred. The compact design of this early warning system greatly enhances the comfort and mobility of patients undergoing dialysis. The efficacy of the proposed system was demonstrated in experiments involving artificial blood.
Keywords	hemodialysis ; flexible ring sensor array ; machine learning ; bidirectional hetero-associative memory network ; embedded system ; Chemical technology ; TP1-1185 ; Chemistry ; QD1-999
Subject code	629
Language	English
Publishing date	2022-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Optimal Phase Balancing in Electricity Distribution Feeders Using Mixed-Integer Linear Programming

Chia-Hung Lin / Te-Tien Ku / Chung-Sheng Li / Chao-Shun Chen

Sustainability, Vol 15, Iss 4473, p

2023 Volume 4473

Abstract: A mixed-integer linear programming (MILP) model that includes reductions in neutral current, feeder energy-loss cost, customer interruption cost, and labor cost is developed to derive the optimal phase-swapping strategy to enhance the phase balancing of ... ...

Abstract	A mixed-integer linear programming (MILP) model that includes reductions in neutral current, feeder energy-loss cost, customer interruption cost, and labor cost is developed to derive the optimal phase-swapping strategy to enhance the phase balancing of distribution feeders. The neutral current of the distribution feeder is reduced by the phase-swapping strategy so that the tripping of the low-energy overcurrent relay can be prevented and customer-service interruption costs and the labor cost to execute the phase-swapping works can be justified by the energy-loss reduction obtained. The novelty of the study is its derivation of the phase-swapping strategy using mixed-integer linear programming to solve the problem of the unbalance of the distribution feeders. A Taipower distribution feeder is used to derive the phase-swapping strategy to demonstrate the proposed MILP model for phase balancing. The comparison of the phase currents and neutral current before phase-swapping reveals that the three-phase balance was not only significantly improved, but that the voltage unbalance was also decreased dramatically using the proposed phase-swapping strategy.
Keywords	mixed-integer linear programming ; outage management system ; phasing unbalance index ; voltage unbalance factor ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Feedback Regulation of O -GlcNAc Transferase through Translation Control to Maintain Intracellular O -GlcNAc Homeostasis

Chia-Hung Lin / Chen-Chung Liao / Mei-Yu Chen / Teh-Ying Chou

International Journal of Molecular Sciences, Vol 22, Iss 3463, p

2021 Volume 3463

Abstract: Protein O -GlcNAcylation is a dynamic post-translational modification involving the attachment of N -acetylglucosamine (GlcNAc) to the hydroxyl groups of Ser/Thr residues on numerous nucleocytoplasmic proteins. Two enzymes are responsible for O -GlcNAc ... ...

Abstract	Protein O -GlcNAcylation is a dynamic post-translational modification involving the attachment of N -acetylglucosamine (GlcNAc) to the hydroxyl groups of Ser/Thr residues on numerous nucleocytoplasmic proteins. Two enzymes are responsible for O -GlcNAc cycling on substrate proteins: O -GlcNAc transferase (OGT) catalyzes the addition while O -GlcNAcase (OGA) helps the removal of GlcNAc. O -GlcNAcylation modifies protein functions; therefore, dysregulation of O -GlcNAcylation affects cell physiology and contributes to pathogenesis. To maintain homeostasis of cellular O -GlcNAcylation, there exists feedback regulation of OGT and OGA expression responding to fluctuations of O -GlcNAc levels; yet, little is known about the molecular mechanisms involved. In this study, we investigated the O -GlcNAc-feedback regulation of OGT and OGA expression in lung cancer cells. Results suggest that, upon alterations in O -GlcNAcylation, the regulation of OGA expression occurs at the mRNA level and likely involves epigenetic mechanisms, while modulation of OGT expression is through translation control. Further analyses revealed that the eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1) contributes to the downregulation of OGT induced by hyper- O -GlcNAcylation; the S5A/S6A O -GlcNAcylation-site mutant of 4E-BP1 cannot support this regulation, suggesting an important role of O -GlcNAcylation. The results provide additional insight into the molecular mechanisms through which cells may fine-tune intracellular O -GlcNAc levels to maintain homeostasis.
Keywords	epigenetics ; eukaryotic translation initiation factor 4E-binding protein 1 (EIF4EBP1) ; histone deacetylase (HDAC) ; O -GlcNAcase (OGA) ; O -GlcNAcylation ; O -linked N -acetylglucosamine ( O -GlcNAc) ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Language	English
Publishing date	2021-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Effectiveness of a Problem-Solving Program in Improving Problem-Solving Ability and Glycemic Control for Diabetics with Hypoglycemia

Fei-Ling Wu / Chia-Hung Lin / Chia-Ling Lin / Jyuhn-Huarng Juang

International Journal of Environmental Research and Public Health, Vol 18, Iss 9559, p

2021 Volume 9559

Abstract: The purpose of this study was to evaluate the effects of a hypoglycemia problem-solving program (HPSP) on problem-solving ability and glycemic control in diabetics with hypoglycemia. This was a prospective, quasi-experimental study with two groups, using ...

Abstract	The purpose of this study was to evaluate the effects of a hypoglycemia problem-solving program (HPSP) on problem-solving ability and glycemic control in diabetics with hypoglycemia. This was a prospective, quasi-experimental study with two groups, using a pre- and post-repeated measures design. A total of 71 diabetic patients with hypoglycemia were purposively assigned to an experimental group ( n = 34) and a control group ( n = 37). The experimental group participated in an 8-week HPSP, and each weekly session lasted approximately 90 min, while the control group received usual care. Participants were assessed at baseline, 1, 3, and 6 months after intervention care. In the experimental group, 6 months after the HPSP intervention, HbA1c was superior to that before the intervention. In both groups, the score obtained using the hypoglycemia problem-solving scale (HPSS) was low before the intervention. In the experimental group, HPSS tracking improved at all stages after the intervention compared to before the intervention. In the control group, the HPSS score improved slightly in the first month and sixth months after usual care. There were significant differences between and within groups in HbA1c levels and HPSS score over time. The intervention based on the HPSP effectively improves HbA1c level and hypoglycemia problem-solving ability in patients with hypoglycemia.
Keywords	diabetes ; problem-solving ; hypoglycemia ; self-management ; glycated hemoglobin ; Medicine ; R
Subject code	796
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Inflow and outflow stenoses screening on biophysical experimental arteriovenous graft using big spectral data and bidirectional associative memory machine learning model

Chia-Hung Lin / Wei-Ling Chen / Chung-Dann Kan

IET Cyber-Physical Systems (2019)

2019

Abstract: Long-term repeating traumatic puncture is required for dialysis therapy, which results in frequent thrombosis and graduate vascular access stenosis, such as inflow or outflow stenosis and coexistence of both. An arteriovenous graft has a higher patency ... ...

Abstract	Long-term repeating traumatic puncture is required for dialysis therapy, which results in frequent thrombosis and graduate vascular access stenosis, such as inflow or outflow stenosis and coexistence of both. An arteriovenous graft has a higher patency rate than an arteriovenous fistula. This study intends to use the dual-channel auscultation-based non-invasive method to screen inflow and outflow stenoses. Frequency analysis is used to decompose phonoangiography (PAG) signals to frequency features using the different data length of acoustic data. Burg autoregressive method is employed to extract the key frequency parameters from sufficient spectral data, including characteristic frequencies and distinct peaks of power spectral densities (PSDs). In big data processing, PSDs and the degree of stenosis (DOS) have been validated to show a positive correlation with sufficient big spectral data. An intelligent machine learning model, bidirectional hetero-associative memory network (BHAMN), is carried out to identify the level of DOS at the inflow site, the mid-site, or the outflow site of a vascular access. The experimental results will indicate that the proposed intelligent machine learning model has higher hit rates.
Keywords	diseases ; bioacoustics ; blood vessels ; learning (artificial intelligence) ; patient treatment ; content-addressable storage ; autoregressive processes ; medical signal processing ; spectral analysis ; haemodynamics ; acoustic data ; burg autoregressive method ; key frequency parameters ; characteristic frequencies ; power spectral densities ; big data processing ; intelligent machine learning model ; bidirectional hetero-associative memory network ; inflow site ; outflow site ; outflow stenoses ; biophysical experimental arteriovenous graft ; bidirectional associative memory machine ; long-term repeating traumatic puncture ; dialysis therapy ; frequent thrombosis ; graduate vascular access stenosis ; arteriovenous fistula ; dual-channel auscultation-based noninvasive method ; frequency analysis ; phonoangiography signals ; frequency features ; data length ; patency rate ; big spectral data ; Computer engineering. Computer hardware ; TK7885-7895 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	612
Language	English
Publishing date	2019-06-01T00:00:00Z
Publisher	Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Transition of Living Arrangement and Cognitive Impairment Status among Chinese Older Adults

Yen-Han Lee / Chia-Hung Lin / Jia-Ren Chang / Ching-Ti Liu / Mack Shelley / Yen-Chang Chang

Medicina, Vol 57, Iss 961, p

Are They Associated?

2021 Volume 961

Abstract: Background and Objectives: Living arrangement is a crucial factor for older adults’ health. It is even more critical for Chinese older adults due to the tradition of filial piety. With the aging of China’s population, the prevalence of cognitive ... ...

Abstract	Background and Objectives: Living arrangement is a crucial factor for older adults’ health. It is even more critical for Chinese older adults due to the tradition of filial piety. With the aging of China’s population, the prevalence of cognitive impairment among older adults has increased. This study examines the association between living arrangement transition and cognitive function among Chinese older adults. Materials and Methods: Using three waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS; 2008–2009, 2011–2012, and 2014), we analyzed data for older adults (age ≥ 65) who lived with other household members and reported good cognitive function or mild cognitive impairment when they participated in the survey. Multistate Cox regression was employed to study changes in cognitive function. Results: Older adults who transitioned to living alone had lower risk of cognitive impairment (hazard ratio (HR) = 0.66, 95% CI: 0.52, 0.83; p < 0.01), compared with those who continued to live with other household members. Moving into an institution was also not associated with cognitive impairment. Conclusions: With older adults’ transition to living alone, public health practitioners or social workers might educate them on the benefits of such a living arrangement for cognitive function.
Keywords	living arrangement ; cognitive function ; older adults ; China ; multistate survival analysis ; Medicine (General) ; R5-920
Subject code	120
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Towards Resilient Access Equality for 6G Serverless p-LEO Satellite Networks

Shih-Chun, Lin / Chia-Hung, Lin / C., Chu Liang / Shao-Yu, Lien

2022

Abstract: Low earth orbit (LEO) mega-constellations, integrating government space systems and commercial practices, have emerged as enabling technologies for the sixth generation (6G) networks due to their good merits of global coverage and ubiquitous services for ...

Abstract	Low earth orbit (LEO) mega-constellations, integrating government space systems and commercial practices, have emerged as enabling technologies for the sixth generation (6G) networks due to their good merits of global coverage and ubiquitous services for military and civilian use cases. However, convergent LEO-based satellite networking infrastructures still lack leveraging the synergy of space and terrestrial systems. This paper, therefore, extends conventional serverless cloud platforms with serverless edge learning architectures for 6G proliferated LEO (p-LEO) satellite ecosystems and provides a new distributed training design from a networking perspective. The proposed design dynamically orchestrates communications and computation functionalities and resources among heterogeneous physical units to efficiently fulfill multi-agent deep reinforcement learning for service-level agreements. Innovative ecosystem enhancements, including ultrabroadband access, anti-jammed transmissions, resilient networking, and related open challenges, are also investigated for end-to-end connectivity, communications, and learning performance. Comment: Submitted for possible publication to the IEEE Communication magazine
Keywords	Computer Science - Networking and Internet Architecture
Subject code	303
Publishing date	2022-05-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Assessment of inflow and outflow stenoses using big spectral data and radial-based colour relation analysis on in vitro arteriovenous graft biophysical experimental model

Wei-Ling Chen / Chung-Dann Kan / Chia-Hung Lin

IET Cyber-Physical Systems (2017)

2017

Abstract: Dialysis vascular accesses are critical for dialysis therapy, but they frequently suffer from stenotic complications. Higher patency rates and thrombosis rates are a concern to nephrology nurses and patients. These complications are complex events, ... ...

Abstract	Dialysis vascular accesses are critical for dialysis therapy, but they frequently suffer from stenotic complications. Higher patency rates and thrombosis rates are a concern to nephrology nurses and patients. These complications are complex events, including inflow stenosis, outflow stenosis, and coexistence of both. Therefore, a biophysical experimental model is employed to mimic the various combinations of stenoses and dialysis circulation circuits on a virtual adult hand. Considering the suggested signal preprocessing specifications, auscultation method and frequency analysis technique are used to extract the key frequency components from sufficient big spectral data. Key frequency components, depending on the degree of stenosis (DOS) (positive correlation), are validated using multiple regression models with multiple explanatory variables and response variables. A new machine learning method, radial-based colour relation analysis, is employed to identify the level of DOS at the inflow and outflow sites. In contrast to the multiple linear regression and traditional machine learning method, the experimental results indicated that the proposed screening model had higher accuracy (hit rate), true-positive rate, and true-negative rate in clinical indication.
Keywords	Big Data ; patient treatment ; medical signal processing ; learning (artificial intelligence) ; diseases ; clinical indication ; machine learning method ; DOS ; degree of stenosis ; frequency analysis technique ; auscultation method ; signal preprocessing specifications ; virtual adult hand ; dialysis circulation circuits ; biophysical experimental model ; nephrology nurses ; thrombosis rates ; patency rates ; stenotic complications ; dialysis therapy ; dialysis vascular accesses ; in vitro arteriovenous graft biophysical experimental model ; radial-based colour relation analysis ; big spectral data ; outflow stenoses ; inflow stenoses ; Computer engineering. Computer hardware ; TK7885-7895 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	310
Language	English
Publishing date	2017-02-01T00:00:00Z
Publisher	Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Regimen comprising GLP-1 receptor agonist and basal insulin can decrease the effect of food on glycemic variability compared to a pre-mixed insulin regimen

Yi-Hsuan Lin / Chia-Hung Lin / Yu-Yao Huang / Hsin-Yun Chen / An-Shun Tai / Shih-Chen Fu / Sheng-Hwu Hsieh / Jui-Hung Sun / Szu-Tah Chen / Sheng-Hsuan Lin

European Journal of Medical Research, Vol 27, Iss 1, Pp 1-

2022 Volume 10

Abstract: Abstract Background Increasing evidence suggests that glucagon-like peptide 1 (GLP-1) receptor agonists (RA) can stabilize glycemic variability (GV) and interfere with eating behavior. This study compared the impact of insulin, GLP-1 RA, and dietary ... ...

Abstract	Abstract Background Increasing evidence suggests that glucagon-like peptide 1 (GLP-1) receptor agonists (RA) can stabilize glycemic variability (GV) and interfere with eating behavior. This study compared the impact of insulin, GLP-1 RA, and dietary components on GV using professional continuous glucose monitoring (CGM). Methods Patients with type 2 diabetes underwent CGM before and after switching from a twice-daily pre-mixed insulin treatment regimen to a GLP-1 RA (liraglutide) plus basal insulin regimen. The dietary components were recorded and analyzed by a certified dietitian. The interactions between the medical regimen, GV indices, and nutrient components were analyzed. Results Sixteen patients with type 2 diabetes were enrolled in this study. No significant differences in the diet components and total calorie intake between the two regimens were found. Under the pre-mixed insulin regimen, for increase in carbohydrate intake ratio, mean amplitude of glucose excursion (MAGE) and standard deviation (SD) increased; in contrast, under the new regimen, for increase in fat intake ratio, MAGE and SD decreased, while when the protein intake ratio increased, the coefficient of variation (CV) decreased. The impact of the food intake ratio on GV indices disappeared under the GLP-1 RA regimen. After switching to the GLP-1 RA regimen, the median MAGE, SD, and CV values decreased significantly. However, the significant difference in GV between the two regimens decreased during the daytime. Conclusion A GLP-1 RA plus basal insulin regimen can stabilize GV better than a regimen of twice-daily pre-mixed insulin, especially in the daytime, and can diminish the effect of food components on GV.
Keywords	Continuous glucose monitoring ; Glucose variability ; Pre-mixed insulin ; GLP-1 receptor agonist ; Diet ; Medicine ; R
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network

Pi-Yun Chen / Xuan-Hao Zhang / Jian-Xing Wu / Ching-Chou Pai / Jin-Chyr Hsu / Chia-Hung Lin / Neng-Sheng Pai

Applied Sciences, Vol 12, Iss 4079, p

2022 Volume 4079

Abstract: Mammography is a first-line imaging examination approach used for early breast tumor screening. Computational techniques based on deep-learning methods, such as convolutional neural network (CNN), are routinely used as classifiers for rapid automatic ... ...

Abstract	Mammography is a first-line imaging examination approach used for early breast tumor screening. Computational techniques based on deep-learning methods, such as convolutional neural network (CNN), are routinely used as classifiers for rapid automatic breast tumor screening in mammography examination. Classifying multiple feature maps on two-dimensional (2D) digital images, a multilayer CNN has multiple convolutional-pooling layers and fully connected networks, which can increase the screening accuracy and reduce the error rate. However, this multilayer architecture presents some limitations, such as high computational complexity, large-scale training dataset requirements, and poor suitability for real-time clinical applications. Hence, this study designs an optimal multilayer architecture for a CNN-based classifier for automatic breast tumor screening, consisting of three convolutional layers, two pooling layers, a flattening layer, and a classification layer. In the first convolutional layer, the proposed classifier performs the fractional-order convolutional process to enhance the image and remove unwanted noise for obtaining the desired object’s edges; in the second and third convolutional-pooling layers, two kernel convolutional and pooling operations are used to ensure the continuous enhancement and sharpening of the feature patterns for further extracting of the desired features at different scales and different levels. Moreover, there is a reduction of the dimensions of the feature patterns. In the classification layer, a multilayer network with an adaptive moment estimation algorithm is used to refine a classifier’s network parameters for mammography classification by separating tumor-free feature patterns from tumor feature patterns. Images can be selected from a curated breast imaging subset of a digital database for screening mammography (CBIS-DDSM), and K-fold cross-validations are performed. The experimental results indicate promising performance for automatic breast tumor screening in terms of ...
Keywords	convolutional neural network (CNN) ; fractional-order cconvolutional operation ; adaptive moment estimation algorithm ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2022-04-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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