Blind Source Separation Using Two-Dimensional Nonnegative Matrix Factorization In Biomedical Field

Blind Source Separation (BSS) refers to the statistical technique of separating a mixture of underlying source signals.BSS denotes as a phenomena and separation on mixed heart-lung sound is one of its example.The challenge of this research is to separate the separate lung sound and heart sound from mixed heart-lung sound.A clear lung sound for diagnosis purpose able to be obtained after separating the mixed heart-lung sound.In biomedical field,lung information is precious due to it has been provided for respiratory diagnosis.However,the interference of heart sound towards lung sound will generate ambiguity and it will lead to drop down the accuracy of diagnosis.Thus,a clean lung sound is needed to increases the accuracy of diagnosis.One of the ways for non-invasive respiratory diagnosis for obtaining lung information is through extracting lung sound from mixed heart-lung sound by using Two-Dimensional Nonnegative Matrix Factorization (NMF2D) algorithm.This method is based on cocktail party effect in which it refers to human brain able to selectively listen to target among a cacophony of conversations and background noise and this considered as a difficult task to machine.Therefore, duplication on cocktail party effect into machine is used to separate the mixed heart-lung sound.This research presents a novel approach NMF2D algorithm in which a suitable model for signal mixture that accommodated the reverberations and nonlinearity of the signals.The objectives of this research are focusing on investigating the useful signal analysis algorithms,defining a new technique of signal separability,designing and developing novel methods for BSS. In order to process estimation results,cost function such as β-divergence and α-divergence is integrated with NMF2D.Provisions of experiment are convolutive mixed signal is sampled and real recording using under single channel,Time-Frequency (TF) domain is computed by using Short Time Fourier Transform (STFT) respectively.Performance evaluation is done in term of Signal-to-Distortion Ratio (SDR). Theoretically,β and α is parameters that used to vary the NMF2D algorithm in order to yield high SDR value. Experimentally,for the simulation results,the highest SDR value for β-divergence NMF2D is SDR = 16.69dB at β = 0.8 and n = 100.For α-divergence NMF2D,the highest SDR value is SDR = 17.85dB at α = 1.5 and n = 100.Additional of sparseness constraints toward β-divergence NMF2D and α-divergence NMF2D lead to even higher SDR value.There are SDR = 17.06dB for sparse β-divergence NMF2D at λ = 2.5 and SDR = 17.99dB for sparse α-divergence NMF2D at λ = 5. This represents sparseness constraints yield to decrease ambiguity and provide uniqueness to the model.In comparison in between β-divergence,α-divergence,sparse β-divergence and sparse α-divergence NMF2D,it found that SDR value of sparse α-divergence NMF2D is the best decomposition method among all divergences.This can be concluded that sparse α-divergence NMF2D is more applicable in separating real data recording

What are the attractiveness aspects that influence customer loyalty to homestays? A study in Taiwan

The purpose of this study is to investigate the direct relationship between perceived attractiveness aspects and customer loyalty. The perceived attractiveness aspects are operationalized into five dimensions, namely surroundings of the building and features; service quality; homestay facilities; homestay operation and management; and homestay geist and community co-prosperity. The hypotheses are postulated and tested using a sample of 566respondents that were homestay customers in Taiwan The data used in this study was collected via self-administered questionnaires The study employs the structural equation modeling (SEM) technique to test the validity of the proposed hypotheses via Smart-PLS software. The results show that only four out of five sub-hypotheses are supported. The conclusion of this study provides theoretical implications and practical implications, as well as suggestions for future studies either in Malaysia or Taiwan

Alpha-divergence two-dimensional nonnegative matrix factorization for biomedical blind source separation

An alpha-divergence two-dimensional nonnegative matrix factorization (NMF2D) for biomedical signal separation is presented. NMF2D is a popular approach for retrieving low-rank approximations of nonnegative data such as image pixel, audio signal, data mining, pattern recognition and so on. In this paper, we concentrate on biomedical signal separation by using NMF2D with alpha-divergence family which decomposes a mixture into two-dimensional convolution factor matrices that represent temporal code and the spectral basis. The proposed iterative estimation algorithm (alpha-divergence algorithm) is initialized with random values, and it updated using multiplicative update rules until the values converge. Simulation experiments were carried out by comparing the original and estimated signal in term of signal-to-distortion ratio (SDR). The performances have been evaluated by including and excluding the sparseness constraint which sparseness is favored by penalizing nonzero gains. As a result, the proposed algorithm improved the iteration speed and sparseness constraints produce slight improvement of SDR

Circulating Histones Are Major Mediators of Multiple Organ Dysfunction Syndrome in Acute Critical Illnesses.

OBJECTIVES:Multiple organ dysfunction syndrome is characterized by simultaneous multiple organ failure, which is the leading cause of death in acute critically ill patients. However, what mediates multiple organ dysfunction syndrome is not fully understood. The discovery of toxic effects by extracellular histones on different individual organs strongly suggests their involvement in multiple organ dysfunction syndrome. In this study, we investigate whether circulating histones are major mediators of multiple organ dysfunction syndrome in acute critical illnesses. DESIGN:Combination of retrospective clinical studies and animal models with intervention. SETTING:ICU in a tertiary hospital and research laboratories. PATIENTS:Four hundred and twenty ICU patients, including sepsis (140), severe trauma (63), severe pancreatitis (89), and other admission diagnoses (128). LABORATORY INVESTIGATION:Cells from major organs are treated with calf thymus histones or histone-containing sera. Animal models for sepsis, trauma, and acute pancreatitis are treated with antihistone reagents. INTERVENTION:Antihistone reagents in in vitro, ex vivo, and animal models. MEASUREMENT AND MAIN RESULTS:Retrospective analysis of a prospectively recruited ICU cohort demonstrated a strong correlation between circulating histones and organ injury markers and Sequential Organ Failure Assessment scores. Ex vivo experiments showed that patient sera containing high histone levels were toxic to cultured cells from different origins, suggesting their universal toxicity to multiple organs. Animal models of sepsis, trauma, and pancreatitis further demonstrated a temporal correlation between histone levels and disease severity and multiple organ injury. Importantly, antihistone reagents, that is, antihistone single-chain variable fragment and nonanticoagulant heparin, could dramatically reduce multiple organ injury, particularly of the heart and lungs, and improve survival in mouse models. CONCLUSIONS:High levels of circulating histones are major mediators of multiple organ dysfunction syndrome. Our results indicate that monitoring upon ICU admission could inform on disease severity and developing antihistone therapy holds great potential of reducing multiple organ dysfunction syndrome and improving survival of critically ill patients

Image operator learning coupled with CNN classification and its application to staff line removal

Many image transformations can be modeled by image operators that are characterized by pixel-wise local functions defined on a finite support window. In image operator learning, these functions are estimated from training data using machine learning techniques. Input size is usually a critical issue when using learning algorithms, and it limits the size of practicable windows. We propose the use of convolutional neural networks (CNNs) to overcome this limitation. The problem of removing staff-lines in music score images is chosen to evaluate the effects of window and convolutional mask sizes on the learned image operator performance. Results show that the CNN based solution outperforms previous ones obtained using conventional learning algorithms or heuristic algorithms, indicating the potential of CNNs as base classifiers in image operator learning. The implementations will be made available on the TRIOSlib project site.Comment: To appear in ICDAR 201

Incubation period and serial interval of mpox for the 2022 global outbreak compared to historical (pre-2022) estimates

Better understanding changes to the transmission dynamics of mpox requires comparing recent estimates of key epidemiological parameters, the incubation period and serial interval, with historical data. We derived historical and contrasted them with pooled estimates from the 2022 outbreak. Our findings show the pooled mean infection to-onset incubation period for the 2022 outbreak was 8.1 days compared to 8.2 days historically, indicating the incubation periods remained relatively consistent over time, despite a shift in the major mode of transmission. However, the onset-to-onset serial interval was estimated at 8.7 days using 2022 data, compared to 14.2 days using historical data. Although the reason for this shortening of the serial interval is unclear, it may be due to increased public health interventions and/or a shift in the mode of transmission. Recognizing such temporal shifts is essential for informed response strategies, and public health measures remain crucial for controlling mpox and similar future outbreaks

A Novel Assay for Neutrophil Extracellular Trap Formation Independently Predicts Disseminated Intravascular Coagulation and Mortality in Critically Ill Patients

Rationale: Neutrophil extracellular traps (NETs) are important in the host defense against infection, but they also promote intravascular coagulation and multiorgan failure in animal models. Their clinical significance remains unclear, and available assays for patient care lack specificity and reliability. Objectives: To establish a novel assay and test its clinical significance. Methods: A prospective cohort of 341 consecutive adult ICU patients was recruited. The NET-forming capacity of ICU admission blood samples was semiquantified by directly incubating patient plasma with isolated neutrophils ex vivo. The association of NET-forming capacity with Sequential Organ Failure Assessment scores, disseminated intravascular coagulation, and 28-day mortality was analyzed and compared with available NET assays. Measurements and Main Results: Using the novel assay, we could stratify ICU patients into four groups with absent (22.0%), mild (49.9%), moderate (14.4%), and strong (13.8%) NET formation, respectively. Strong NET formation was predominantly found in sepsis (P < 0.0001). Adjusted by Acute Physiology and Chronic Health Evaluation II score, multivariate regression showed that the degree of NET formation could independently predict disseminated intravascular coagulation and mortality, whereas other NET assays (e.g., cell-free DNA, myeloperoxidase, and myeloperoxidase–DNA complexes) could not. IL-8 concentrations were found to be strongly associated with NET formation, and inhibiting IL-8 significantly attenuated NETosis. Mitogen-activated protein kinase activation by IL-8 has been identified as a major pathway of NET formation in patients. Conclusions: This assay directly measures the NET-forming capacity in patient plasma. This could guide clinical management and enable identification of NET-inducing factors in individual patients for targeted treatment and personalized ICU medicine