Brain network functional connectivity changes in long illness duration chronic schizophrenia.

Introduction: Chronic schizophrenia has a course of 5 years or more and has a widespread abnormalities in brain functional connectivity. This study aimed to find characteristic functional and structural changes in a long illness duration chronic schizophrenia (10 years or more). Methods: Thirty-six patients with a long illness duration chronic schizophrenia and 38 healthy controls were analyzed by independent component analysis of brain network functional connectivity. Correlation analysis with clinical duration was performed on six resting state networks: auditory network, default mode network, dorsal attention network, fronto-parietal network, somatomotor network, and visual network. Results: The differences in the resting state network between the two groups revealed that patients exhibited enhanced inter-network connections between default mode network and multiple brain networks, while the inter-network connections between somatomotor network, default mode network and visual network were reduced. In patients, functional connectivity of Cuneus_L was negatively correlated with illness duration. Furthermore, receiver operating characteristic curve of functional connectivity showed that changes in Thalamus_L, Rectus_L, Frontal_Mid_R, and Cerebelum_9_L may indicate a longer illness duration chronic schizophrenia. Discussion: In our study, we also confirmed that the course of disease is significantly associated with specific brain regions, and the changes in specific brain regions may indicate that chronic schizophrenia has a course of 10 years or more.

Evaluation of the Antioxidant Activity and Phenolic Composition of Different Monofloral and Polyfloral Brazilian Honey Extracts.

This study aimed to evaluate the chemical composition and antioxidant activity of phenolic extracts from monofloral and polyfloral honey samples obtained from different Brazilian regions. The chemical composition (total content of phenolic compounds and flavonoids) of extracts were measured by using colorimetric assays and analyzed by high performance liquid chromatographic (HPLC-DAD). The antioxidant activity was evaluated by chemical and biochemical assays (reducing power assay, 1,1-diphenyl-2-picrylhydrazyl (DPPH•) and 2,2-azino-bis(3-ethylbenzothiazoline-6-sulphonic) acid (ABTS•+) scavenger assays. It was also investigated the ability of extracts in attenuate lipid peroxidation induced by Fe2+ in phospholipids. The obtained results clearly demonstrated that the botanical origin and geographical region of honey collection influenced the chemical composition and antioxidant activity of extracts. Furthermore, the samples were constituted by phenolic acids and flavonoids, which p-coumaric acid was predominant among the compounds identified. All samples were able to scavenge free radicals and inhibit lipid peroxidation, and good correlations were obtained between the flavonoid content and honey color. In conclusion, the obtained extracts were constituted by antioxidant compounds, which reinforce the usage of honey in human diets, and demonstrated that the region of honey collection strong influenced in the chemical composition and, consequently, its biological effect.

A machine learning-assisted fluorescent sensor array utilizing silver nanoclusters for coffee discrimination.

Coffee is a globally consumed commodity of substantial commercial significance. In this study, we constructed a fluorescent sensor array based on two types of polymer templated silver nanoclusters (AgNCs) for the detection of organic acids and coffees. The nanoclusters exhibited different interactions with organic acids and generated unique fluorescence response patterns. By employing principal component analysis (PCA) and random forest (RF) algorithms, the sensor array exhibited good qualitative and quantitative capabilities for organic acids. Then the sensor array was used to distinguish coffees with different processing methods or roast degrees and the recognition accuracy achieved 100%. It could also successfully identify 40 coffee samples from 12 geographical origins. Moreover, it demonstrated another satisfactory performance for the classification of pure coffee samples with their binary and ternary mixtures or other beverages. In summary, we present a novel method for detecting and identifying multiple coffees, which has considerable potential in applications such as quality control and identification of fake blended coffees.

Adaptive EMG decomposition in dynamic conditions based on online learning metrics with tunable hyperparameters.

OBJECTIVE: Robustness to non-stationary conditions is essential to develop stable and accurate wearable neural interfaces. APPROACH: We propose a novel adaptive electromyography (EMG) decomposition algorithm that builds on blind source separation methods by leveraging the Kullback-Liebler divergence and kurtosis of the signals as metrics for online learning. The proposed approach provides a theoretical framework to tune the adaptation hyperparameters and compensate for non-stationarities in the mixing matrix, such as due to dynamic contractions, and to identify the underlying motor neuron (MN) discharges. The adaptation is performed in real-time (~22 ms of computational time per 100-ms batches). MAIN RESULTS: The proposed adaptation algorithm significantly improved all decomposition performance metrics with respect to the absence of adaptation in a wide range of motion of the wrist (80°). The rate of agreement, sensitivity, and precision were ≥ 90% in ≥ 80% of the cases in both simulated and experimentally recorded data, according to a two- source validation approach. SIGNIFICANCE: The findings demonstrate the feasibility of accurately decoding MN discharges in real-time during dynamic contractions from wearable systems mounted at the wrist and forearm. Moreover, the study proposes an experimental validation method for EMG decomposition in dynamic tasks.

OpenHands: An Open-Source Statistical Shape Model of the Finger Bones.

This paper presents statistical shape models of the four fingers of the hand, with an emphasis on anatomic analysis of the proximal and distal interphalangeal joints. A multi-body statistical shape modelling pipeline was implemented on an exemplar training dataset of computed tomography (CT) scans of 10 right hands (5F:5M, 27-37 years, free from disease or injury) imaged at 0.3 mm resolution, segmented, meshed and aligned. Model generated included pose neutralisation to remove joint angle variation during imaging. Repositioning was successful; no joint flexion variation was observed in the resulting model. The first principal component (PC) of morphological variation represented phalanx size in all fingers. Subsequent PCs showed variation in position along the palmar-dorsal axis, and bone breadth: length ratio. Finally, the models were interrogated to provide gross measures of bone lengths and joint spaces. These models have been published for open use to support wider community efforts in hand biomechanical analysis, providing bony anatomy descriptions whilst preserving the security of the underlying imaging data and privacy of the participants. The model describes a small, homogeneous population, and assumptions cannot be made about how it represents individuals outside the training dataset. However, it supplements anthropometric datasets with additional shape information, and may be useful for investigating factors such as joint morphology and design of hand-interfacing devices and products. The model has been shared as an open-source repository ( https://github.com/abel-research/OpenHands ), and we encourage the community to use and contribute to it.

Identification of water transitions using a combination of moisture sorption characteristics and dielectric properties of different parts of jasmine rice (Oryza sativa).

BACKGROUND: In this work, water transition points (first transition: monolayer-multilayer water; and second transition: multilayer-free and solvent water) of different parts of jasmine rice including white rice, brown rice and bran were identified through the integration of sorption isotherm and dielectric properties data. Desorption isotherm data were fitted to four established models to select the optimal model for describing the sorption behaviors. Then, dielectric properties such as dielectric constant (ε') and dielectric loss factor (ε″) were measured across various moisture content levels within the frequency range of 200-20 000 MHz. RESULTS: A type III isotherm was observed for all samples and the Peleg model was the best fit with the experimental data. Monolayer moisture content of the samples, estimated using the GAB model, ranged from 3.25% to 4.17% dry basis. For dielectric properties, frequency and moisture dependencies were evident for all sample types. Moreover, the sorption isotherm models effectively described the relationship between water activity (aw) and dielectric properties as reflected by their goodness of fit, and their strong correlation through principal component analysis and Pearson's correlation results. CONCLUSION: The first water transition occurs at aw values of 0.11, 0.12, and 0.22, while the second transition appears at aw values of 0.9, 0.9 and 0.75-0.85 for white rice, brown rice and bran, respectively. This knowledge will be useful for food processors, providing insights into the optimization of food processing and storage conditions to extend food products' shelf life. © 2024 Society of Chemical Industry.

Spatio-temporal modeling of lake's ecosystem and dynamism in response to changing environment: a case study of L. Ol Bolossat in Kenya.

Lakes' ecosystems are vulnerable to environmental dynamisms prompted by natural processes and anthropogenic activities happening in catchment areas. The present study aimed at modeling the response of Lake Ol Bolossat ecosystem in Kenya to changing environment between 1992 to 2022 and its future scenario in 2030. The study used temperature, stream power index, rainfall, land use land cover, normalized difference vegetation index, slope, and topographic wetness index as datasets. A GIS-ensemble modeling approach coupling the analytical hierarchical process and principal component analysis was used to simulate the lake's extents between 1992 and 2022. Cellular Automata-Markov chain analysis was used to predict the lake extent in 2030. The results revealed that between 1992 and 2002, the lake extent shrunk by about 18%; between 2002 and 2012, the lake extent increased by about 13.58%; and between 2012 and 2022, the lake expanded by about 26%. The spatial-temporal changes exhibited that the lake has been changing haphazardly depending on prevailing climatic conditions and anthropogenic activities. The comparison between the simulated and predicted lake extents in 2022 produced Kno, Klocation, KlocationStrata, K standard, and average index values of 0.80, 0.81, 1.0, 0.74, and 0.84, respectively, which ascertained good performance of generated prediction probability matrices. The predicted results exhibited there would be an increase in lake extent by about 13% by the year 2030. The research findings provide baseline information which would assist in protecting and conserving the Lake Ol Bolossat ecosystem which is very crucial in promoting tourism activities and provision of water for domestic and commercial use in the region.

Evaluation of potential human health risks arising from nitrate and fluoride in the groundwater of Aurangabad, Bihar using GIS and chemometric analysis.

This study employed the groundwater pollution index to assess the appropriateness of groundwater for human consumption. Additionally, the hazard index was utilized to evaluate the potential non-carcinogenic risks associated with fluoride and nitrate exposure among children, women, and men in the study region. A total of 103 samples were collected from the Aurangabad district of Bihar. The analyzed samples were assessed using several physicochemical parameters. Major cations in the groundwater are Ca2+ > Mg2+ and major anions are HCO3- > Cl- > SO42- > NO3- > F- > PO43-. Around 17% of the collected groundwater samples surpassed the allowable BIS concentration limits for Nitrate, while approximately 11% surpassed the allowed limits for fluoride concentration. Principal component analysis was utilized for its efficacy and efficiency in the analytical procedure. Four principal components were recovered that explained 69.06% of the total variance. The Hazard Quotient (HQ) of nitrate varies between 0.03-1.74, 0.02-1.47, and 0.03-1.99 for females, males, and children, respectively. The HQ of fluoride varies between 0.04-1.59, 0.04-1.34, and 0.05-1.82 for females, males, and children, respectively. The central part of the district was at high risk according to the spatial distribution maps of the total hazard index (THI). Noncarcinogenic risks due to THI are 47%, 37%, and 28% for children, females, and males, respectively. According to the human health risk assessment, children are more prone to getting affected by polluted water than adults. The groundwater pollution index (GPI) value ranges from 0.46 to 2.27 in the study area. Seventy-five percent of the samples fell under minor pollution and only one fell under high pollution. The spatial distribution of GPI in the research area shows that the central region is highly affected, which means that this water is unsuitable for drinking purposes.

A bayesian spatio-temporal dynamic analysis of food security in Africa.

Exploring the factors influencing Food Security and Nutrition (FSN) and understanding its dynamics is crucial for planning and management. This understanding plays a pivotal role in supporting Africa's food security efforts to achieve various Sustainable Development Goals (SDGs). Utilizing Principal Component Analysis (PCA) on data from the FAO website, spanning from 2000 to 2019, informative components are derived for dynamic spatio-temporal modeling of Africa's FSN Given the dynamic and evolving nature of the factors impacting FSN, despite numerous efforts to understand and mitigate food insecurity, existing models often fail to capture this dynamic nature. This study employs a Bayesian dynamic spatio-temporal approach to explore the interconnected dynamics of food security and its components in Africa. The results reveal a consistent pattern of elevated FSN levels, showcasing notable stability in the initial and middle-to-late stages, followed by a significant acceleration in the late stage of the study period. The Democratic Republic of Congo and Ethiopia exhibited particularly noteworthy high levels of FSN dynamicity. In particular, child care factors and undernourishment factors showed significant dynamicity on FSN. This insight suggests establishing regional task forces or forums for coordinated responses to FSN challenges based on dynamicity patterns to prevent or mitigate the impact of potential food security crises.

Volatile Compounds for Discrimination between Beef, Pork, and Their Admixture Using Solid-Phase-Microextraction-Gas Chromatography-Mass Spectrometry (SPME-GC-MS) and Chemometrics Analysis.

This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat.

Paddy insect identification using deep features with lion optimization algorithm.

Pests are a significant challenge in paddy cultivation, resulting in a global loss of approximately 20 % of rice yield. Early detection of paddy insects can help to save these potential losses. Several ways have been suggested for identifying and categorizing insects in paddy fields, employing a range of advanced, noninvasive, and portable technologies. However, none of these systems have successfully incorporated feature optimization techniques with Deep Learning and Machine Learning. Hence, the current research provided a framework utilizing these techniques to detect and categorize images of paddy insects promptly. Initially, the suggested research will gather the image dataset and categorize it into two groups: one without paddy insects and the other with paddy insects. Furthermore, various pre-processing techniques, such as augmentation and image filtering, will be applied to enhance the quality of the dataset and eliminate any unwanted noise. To determine and analyze the deep characteristics of an image, the suggested architecture will incorporate 5 pre-trained Convolutional Neural Network models. Following that, feature selection techniques, including Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and an optimization algorithm called Lion Optimization, were utilized in order to further reduce the redundant number of features that were collected for the study. Subsequently, the process of identifying the paddy insects will be carried out by employing 7 ML algorithms. Finally, a set of experimental data analysis has been conducted to achieve the objectives, and the proposed approach demonstrates that the extracted feature vectors of ResNet50 with Logistic Regression and PCA have achieved the highest accuracy, precisely 99.28 %. However, the present idea will significantly impact how paddy insects are diagnosed in the field.

IMU Data-Driven and PCA-Based Approach to Establish Quantifiable and Practically Applicable Measures for V2 Technique Elements in Cross-Country Skiing.

Quantifying movement coordination in cross-country (XC) skiing, specifically the technique with its elemental forms, is challenging. Particularly, this applies when trying to establish a bidirectional transfer between scientific theory and practical experts' knowledge as expressed, for example, in ski instruction curricula. The objective of this study was to translate 14 curricula-informed distinct elements of the V2 ski-skating technique (horizontal and vertical posture, lateral tilt, head position, upper body rotation, arm swing, shoulder abduction, elbow flexion, hand and leg distance, plantar flexion, ski set-down, leg push-off, and gliding phase) into plausible, valid and applicable measures to make the technique training process more quantifiable and scientifically grounded. Inertial measurement unit (IMU) data of 10 highly experienced XC skiers who demonstrated the technique elements by two extreme forms each (e.g., anterior versus posterior positioning for the horizontal posture) were recorded. Element-specific principal component analyses (PCAs)-driven by the variance produced by the technique extremes-resulted in movement components that express quantifiable measures of the underlying technique elements. Ten measures were found to be sensitive in distinguishing between the inputted extreme variations using statistical parametric mapping (SPM), whereas for four elements the SPM did not detect differences (lateral tilt, plantar flexion, ski set-down, and leg push-off). Applicability of the established technique measures was determined based on quantifying individual techniques through them. The study introduces a novel approach to quantitatively assess V2 ski-skating technique, which might help to enhance technique feedback and bridge the communication gap that often exists between practitioners and scientists.

Combined analysis of the metabolome and transcriptome reveals the metabolic characteristics and candidate genes involved in alkaloid metabolism in Heuchera micrantha Douglas ex Lindl.

BACKGROUND: Alkaloids, important secondary metabolites produced by plants, play a crucial role in responding to environmental stress. Heuchera micrantha, a well-known plant used in landscaping, has the ability to purify air, and absorb toxic and radioactive substances, showing strong environmental adaptability. However, there is still limited understanding of the accumulation characteristics and metabolic mechanism of alkaloids in H. micrantha. RESULTS: In this study, four distinct varieties of H. micrantha were used to investigate the accumulation and metabolic traits of alkaloids in its leaves. We conducted a combined analysis of the plant's metabolome and transcriptome. Our analysis identified 44 alkaloids metabolites in the leaves of the four H. micrantha varieties, with 26 showing different levels of accumulation among the groups. The HT and JQ varieties exhibited higher accumulation of differential alkaloid metabolites compared to YH and HY. We annotated the differential alkaloid metabolites to 22 metabolic pathways, including several alkaloid metabolism. Transcriptome data revealed 5064 differentially expressed genes involved in these metabolic pathways. Multivariate analysis showed that four key metabolites (N-hydroxytryptamine, L-tyramine, tryptamine, and 2-phenylethylamine) and three candidate genes (Cluster-15488.116815, Cluster-15488.146268, and Cluster-15488.173297) that merit further investigation. CONCLUSIONS: This study provided preliminarily insight into the molecular mechanism of the biosynthesis of alkaloids in H. micrantha. However, further analysis is required to elucidate the specific regulatory mechanisms of the candidate gene involved in the synthesis of key alkaloid metabolites. In summary, our findings provide important information about how alkaloid metabolites build up and the metabolic pathways involved in H. micrantha varieties. This gives us a good starting point for future research on the regulation mechanism, and development, and utilization of alkaloids in H. micrantha.

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews.

This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.

Quantitative analysis of solid dosage forms of Atenolol by Raman spectroscopy.

ObjectiveTo develop a Raman spectroscopy-based analytical model for quantification of solid dosage forms of active pharmaceutical ingredient (API) of Atenolol.Significance:For the quantitative analysis of pharmaceutical drugs, Raman Spectroscopy is a reliable and fast detection method. As part of this study, Raman Spectroscopy is explored for the quantitative analysis of different concentrations of Atenolol.MethodsVarious solid-dosage forms of Atenolol were prepared by mixing API with excipients to form different solid-dosage formulations of Atenolol. Multivariate data analysis techniques such as Principal Component Analysis (PCA) and Partial least square regression (PLSR) were used for the qualitative and quantitative analysis, respectively.ResultsAs the concentration of the drug increased in formulation, the peak intensities of the distinctive Raman spectral characteristics associated with the API (Atenolol) gradually increased. Raman spectral data sets were classified using PCA due to their distinctive spectral characteristics. Additionally, a prediction model was built using PLSR analysis to assess the quantitative relationship between various API (Atenolol) concentrations and spectral features. With a goodness of fit value of 0.99, the root mean square errors of calibration (RMSEC) and prediction (RMSEP) were determined to be 1.0036 mg and 2.83 mg, respectively. The API content in the blind/unknown Atenolol formulation was determined as well using the PLSR model.ConclusionBased on these results, Raman spectroscopy may be used to quickly and accurately analyze pharmaceutical samples and for their quantitative determination.

ToF-SIMS evaluation of PEG-related mass peaks and applications in PEG detection in cosmetic products.

Polyethylene glycols (PEGs) are used in industrial, medical, health care, and personal care applications. The cycling and disposal of synthetic polymers like PEGs pose significant environmental concerns. Detecting and monitoring PEGs in the real world calls for immediate attention. This study unveils the efficacy of time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a reliable approach for precise analysis and identification of reference PEGs and PEGs used in cosmetic products. By comparing SIMS spectra, we show remarkable sensitivity in pinpointing distinctive ion peaks inherent to various PEG compounds. Moreover, the employment of principal component analysis effectively discriminates compositions among different samples. Notably, the application of SIMS two-dimensional image analysis visually portrays the spatial distribution of various PEGs as reference materials. The same is observed in authentic cosmetic products. The application of ToF-SIMS underscores its potential in distinguishing PEGs within intricate environmental context. ToF-SIMS provides an effective solution to studying emerging environmental challenges, offering straightforward sample preparation and superior detection of synthetic organics in mass spectral analysis. These features show that SIMS can serve as a promising alternative for evaluation and assessment of PEGs in terms of the source, emission, and transport of anthropogenic organics.

Relationship between amide ratio assessed by Fourier-transform infrared spectroscopy: A biomarker candidate for polycythemia vera disease.

The study utilized Fourier transform infrared (FTIR) spectroscopy coupled with chemometrics to investigate protein composition and structural changes in the blood serum of patients with polycythemia vera (PV). Principal component analysis (PCA) revealed distinct biochemical properties, highlighting elevated absorbance of phospholipids, amides, and lipids in PV patients compared to healthy controls. Ratios of amide I/amide II and amide I/amide III indicated alterations in protein structures. Support vector machine analysis and receiver operating characteristic curves identified amide I as a crucial predictor of PV, achieving 100% accuracy, sensitivity, and specificity, while amide III showed a lower predictive value (70%). PCA analysis demonstrated effective differentiation between PV patients and controls, with key wavenumbers including amide II, amide I, and CH lipid vibrations. These findings underscore the potential of FTIR spectroscopy for diagnosing and monitoring PV.

Ecotoxicological risk assessment of triclosan, an emerging pollutant in a riverine and estuarine ecosystems: A comparative study.

Triclosan (TCS), an antibacterial biocide, pervades water and sediment matrices globally, posing a threat to aquatic life. In densely populated cities like Mumbai, rivers and coastal bodies demand baseline TCS data for ecotoxicological assessment due to the excessive use of personal care products comprising TCS. This pioneering study compares spatiotemporal TCS variations and risks in freshwater and marine ecosystems employing multivariate analysis of physicochemical parameters. Over five months (January to May 2022), Mithi River exhibited higher TCS concentrations (water: 1.68 µg/L, sediment: 3.19 µg/kg) than Versova Creek (water: 0.49 µg/L, sediment: 0.69 µg/kg). Principal component analysis revealed positive correlations between TCS and physicochemical parameters. High-risk quotients (>1) underscore TCS threats in both water bodies. This study furnishes crucial baseline data, emphasizing the need for effective treatment plans for TCS in effluent waters released into the adjacent aquatic systems.

Direct Readout of Homo- vs Heterochiral Ligand Shell of Quantum Dots.

The chiroptical activity of various semiconductor inorganic nanocrystalline materials has typically been tested using circular dichroism or circularly polarized luminescence. Herein, we report on a high-throughput screening method for identifying and differentiating chiroptically active quantum-sized ZnO crystals using Raman spectroscopy combined with principal component analysis. ZnO quantum dots (QDs) coated by structurally diverse homo- and heterochiral aminoalcoholate ligands (cis- and trans-1-amino-2-indanolate, 2-amino-1-phenylethanolate, and diphenyl-2-pyrrolidinemethanolate) were prepared using the one-pot self-supporting organometallic procedure and then extensively studied toward the identification of specific Raman fingerprints and spectral variations. The direct comparison between the spectra demonstrates that it is very difficult to make definite recognition and identification between QDs coated with enantiomers based only on the differences in the respective Raman bands' position shifts and their intensities. However, the applied approach involving the principal component analysis performed on the Raman spectra allows the simultaneous differentiation and identification of the studied QDs. The first and second principal components explain 98, 97, 97, and 87% of the variability among the studied families of QDs and demonstrate the possibility of using the presented method as a qualitative assay. Thus, the reported multivariate approach paves the way for simultaneous differentiation and identification of chirotopically active semiconductor nanocrystals.

Low-rank prior-based Fast-RPCA for clutter filtering and noise suppression in non-contrast ultrasound microvascular imaging.

Accurate and real-time separation of blood signal from clutter and noise signals is a critical step in clinical non-contrast ultrasound microvascular imaging. Despite the widespread adoption of singular value decomposition (SVD) and robust principal component analysis (RPCA) for clutter filtering and noise suppression, the SVD's sensitivity to threshold selection, along with the RPCA's limitations in undersampling conditions and heavy computational burden often result in suboptimal performance in complex clinical applications. To address those challenges, this study presents a novel low-rank prior-based fast RPCA (LP-fRPCA) approach to enhance the adaptability and robustness of clutter filtering and noise suppression with reduced computational cost. A low-rank prior constraint is integrated into the non-convex RPCA model to achieve a robust and efficient approximation of clutter subspace, while an accelerated alternating projection iterative algorithm is developed to improve convergence speed and computational efficiency. The performance of the LP-fRPCA method was evaluated against SVD with a tissue/blood threshold (SVD1), SVD with both tissue/blood and blood/noise thresholds (SVD2), and the classical RPCA based on the alternating direction method of multipliers algorithm through phantom and in vivo non-contrast experiments on rabbit kidneys. In the slow flow phantom experiment of 0.2 mm/s, LP-fRPCA achieved an average increase in contrast ratio (CR) of 10.68 dB, 9.37 dB, and 8.66 dB compared to SVD1, SVD2, and RPCA, respectively. In the in vivo rabbit kidney experiment, the power Doppler results demonstrate that the LP-fRPCA method achieved a superior balance in the trade-off between insufficient clutter filtering and excessive suppression of blood flow. Additionally, LP-fRPCA significantly reduced the runtime of RPCA by up to 94-fold. Consequently, the LP-fRPCA method promises to be a potential tool for clinical non-contrast ultrasound microvascular imaging.