Total Variation Research Articles

Segmentation of binary sequence via minimizing least square error with total variation regularization

Segmentation of binary sequence via minimizing least square error with total variation regularization

Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations

Sunflower is an important agricultural crop valued for its high oil yield, versatility in culinary and industrial applications and adaptability to diverse environments. Eight advanced sunflower genotypes were tested in a randomized complete block design (RCBD) with three replications at six locations over the 2018 and 2019 seasons. The study aimed to evaluate the effects of environmental and genotypic variations using MANOVA, PCA, and correlation analysis to discover trait patterns and relationships. The MANOVA results revealed highly significant effects of genotype, environment, and their interaction on the 11 dependent variables (p < 0.001). The four principal components account for 74.23% of the total variation, with key traits such as seed yield per hectare, oil yield per hectare, days to maturity, plant height, and grain filling period significantly contributing to the overall variability. Oil yield per hectare and seed yield per hectare exhibited a very strong association (0.974). Days to maturity (DM) and grain filling period (GFP) showed a strong correlation (0.666), suggesting that longer grain filling periods may enhance both maturity and yield. Additionally, plant height (PH) and seed yield per hectare (YELDK) had a moderate correlation (0.491). Breeding programs should target traits with strong correlations to boost sunflower productivity and adaptability. Future research should prioritize selecting genotypes that perform well across diverse environments, focusing on seed yield, oil yield, and traits such as maturity and grain filling period. Additionally, breeding should incorporate disease resistance and optimize days to flowering to develop more robust and productive sunflower varieties.

Multiobjective optimization guided by image quality index for limited-angle CT image reconstruction.

Due to the incomplete projection data collected by limited-angle computed tomography (CT), severe artifacts are present in the reconstructed image. Classical regularization methods such as total variation (TV) minimization, ℓ0 minimization, are unable to suppress artifacts at the edges perfectly. Most existing regularization methods are single-objective optimization approaches, stemming from scalarization methods for multiobjective optimization problems (MOP). To further suppress the artifacts and effectively preserve the edge structures of the reconstructed image. This study presents a multiobjective optimization model incorporates both data fidelity term and ℓ0-norm of the image gradient as objective functions. It employs an iterative approach different from traditional scalarization methods, using the maximization of structural similarity (SSIM) values to guide optimization rather than minimizing the objective function.The iterative method involves two steps, firstly, simultaneous algebraic reconstruction technique (SART) optimizes the data fidelity term using SSIM and the Simulated Annealing (SA) algorithm for guidance. The degradation solution is accepted in the form of probability, and guided image filtering (GIF) is introduced to further preserve the image edge when the degradation solution is rejected. Secondly, the result from the first step is integrated into the second objective function as a constraint, we use ℓ0 minimization to optimize ℓ0-norm of the image gradient, and the SSIM, SA algorithm and GIF are introduced to guide optimization process by improving SSIM value like the first step. With visual inspection, the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and SSIM values indicate that our approach outperforms other traditional methods. The experiments demonstrate the effectiveness of our method and its superiority over other classical methods in artifact suppression and edge detail restoration.

Genetic diversity of populations of East Siberian ende

Based on nucleotide polymorphism of cpDNA intergenic spacers psbA-trnH, trnL-trnF, and trnS-trnG, we studied the genetic diversity and population structure of a mountain species Oxytropis vasskovskyi Jurtz., endemic to East Siberia. The populations were characterized by medium and high haplotype (0.524–1.000) and low nucleotide (0.0003–0.0008) diversity. Variability distribution analysis showed a moderate level of diff erentiation; the interpopulation component accounted for about 44% of the total genetic variation in O. vasskovskyi. We observed demographic expansion for three of fi ve O. vasskovskyi populations. The haplotypes of the fi ve studied populations form a single complex in the genealogical network, which is probably due to their common origin, relatively recent divergence and incomplete lineage sorting, as well as the existing gene exchange between populations. Thus, in four of the fi ve studied populations, a low level of nucleotide diversity and the absence of nucleotide substitutions were determined, which indicates the need to use certain measures to protect all known habitats, conduct regular monitoring of their state and search for new locations of the species.

Assessing genotypic diversity by multivariate analysis and predicting useful selection ranges using decision tree in soybean (Glycine max L.)

The existence of sufficient genetic variability and knowing the suitable selection criterion ranges are indispensable requirements of breeding studies. This study aimed to explore genetic diversity in ten F2 populations (heterozygous–heterogeneous), five parents, and three commercial check cultivars by multivariate methods and to predict useful selection ranges using a decision tree model for further breeding studies. Hybridizations were made in 2019, F1 populations were grown, and F2 seeds were obtained in 2020. Genotypes were grown according to an Augmented Randomized Complete-Block Design in 2021 at Aydın Adnan Menderes University Faculty of Agriculture. Results revealed considerable genetic diversity and transgressive segregation in key characteristics across the F2 populations. Seed yields of F2 plants are higher than those of the parents. The seed protein and oil content of the F2 plants were not different from the parents and commercial checks. Higher phenotypic than genotypic variance indicated a high influence of environment on the inheritance of all studied characteristics. The highest broad heritability was observed in the number of shattered pods per plant (94.81 %) and the lowest in infertile pods per plant (2.16 %). Seed yield showed a significant and positive genotypic correlation with the number of fertile pods per plant (0.61**) and a significant negative association with the number of fertile branches per plant (−0.53**). Principal component analysis (PCA) of 14 quantitative characteristics showed that soybean genotypes separated by the first four components contributed 71.12 % of total genetic variation, with important yield-attributing characteristics present in PC1 and PC3, while important seed quality characteristics were noted in PC2. The decision tree model indicated that genotypes with high yield and optimum quality could be obtained by selecting the number of fertile pods 87–119 per plant and a hundred-seed weight greater than 14.55 g.

Unveiling the Wing Shape Variation in Northern Altiplano Ecosystems: The Example of the Butterfly Phulia nymphula Using Geometric Morphometrics

The Andean Altiplano, characterized by its extreme climatic conditions and high levels of biodiversity, provides a unique environment for studying ecological and evolutionary adaptations in insect morphology. Butterflies, due their large wing surface compared to body surface, and wide distribution among a geographical area given the flight capabilities provided by their wings, constitute a good biological model to study morphological adaptations following extreme weathers. This study focuses on Phulia nymphula, a butterfly species widely distributed in the Andes, to evaluate wing shape variation across six localities in the Northern Chilean Altiplano. The geometric morphometrics analysis of 77 specimens from six locations from the Chilean Altiplano (Caquena, Sorapata Lake, Chungará, Casiri Macho Lake, Surire Salt Flat, and Visviri) revealed significant differences in wing shape among populations. According to the presented results, variations are likely influenced by local environmental conditions and selective pressures, suggesting specific adaptations to the microhabitats of the Altiplano. The first three principal components represented 60.92% of the total wing shape variation. The detected morphological differences indicate adaptive divergence among populations, reflecting evolutionary responses to the extreme and fragmented conditions of the Altiplano. This study gives insights into the understanding of how high-altitude species can diversify and adapt through morphological variation, providing evidence of ecological and evolutionary processes shaping biodiversity in extreme environments.

A dictionary learning based unsupervised neural network for single image compressed sensing

In the field of Compressed Sensing (CS), the sparse representation of signals and the advancement of reconstruction algorithms are two critical challenges. However, conventional CS algorithms often fail to sufficiently exploit the structured sparsity present in images and suffer from poor reconstruction quality. Most deep learning-based CS methods are typically trained on large-scale datasets. Obtaining a sufficient number of training sets is challenging in many practical applications and there may be no training sets available at all in some cases. In this paper, a novel deep Dictionary Learning (DL) based unsupervised neural network for single image CS (dubbed DL-CSNet) is proposed. It is an effective trainless neural network that consists of three components and their corresponding loss functions: 1) a DL layer that consists of multi-layer perceptron (MLP) and convolution neural networks (CNN) for latent sparse features extraction with the L1-norm sparsity loss function; 2) an image smoothing layer with the Total Variation (TV) like image smoothing loss function; and 3) a CS acquisition layer for image compression, with the Mean Square Error (MSE) loss function between the original image compression and the reconstructed image compression. In particular, the proposed DL-CSNet is a lightweight and fast model that does not require datasets for training and exhibits a fast convergence speed, making it suitable for deployment in resource-constrained environments. Experiments have demonstrated that the proposed DL-CSNet achieves superior performance compared to traditional CS methods and other unsupervised state-of-the-art deep learning-based CS methods.

Comparative analysis of patient-reported outcomes in total knee arthroplasty and total hip arthroplasty: adjusting for demographic influences.

Total knee arthroplasty (TKA) has consistently demonstrated lower patient satisfaction compared to total hip arthroplasty (THA). However, prior investigations failed to account for the patients' demographic characteristics. This study aimed to conduct a comparative analysis of patient-reported outcomes between TKA and THA while adjusting for patient background. A total of 326 primary TKAs and 259THAs conducted at a single center were assessed using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores preoperatively and at 3months, 1year, and 2years postoperatively. Notably, TKA patients exhibited advanced age and higher body mass index (BMI) than their THA counterparts. To mitigate the impact of these differences, we employed propensity score-matched data, adjusting for background characteristics such as age, gender, BMI, and diagnosis. THA consistently demonstrated significantly superior WOMAC total, pain, and stiffness scores compared to TKA at 3months, 1year, and 2years postoperatively. Nevertheless, no statistically significant disparity in WOMAC physical function scores was observed between the two groups at 3months and 1year postoperatively in the matched data (3months, p = 0.131; 1year, p = 0.269). In contrast to earlier findings, our analysis of propensity score-matched data revealed no significant differences in WOMAC physical function scores between the TKA and THA groups at 3months and 1year postoperatively. The distinctive background factors observed in patients undergoing TKA and THA, notably advanced age and higher BMI, coupled with the delayed improvement timeline in TKA's WOMAC scores compared to that of THA, have the potential to impact patient-reported outcomes. Consequently, clinicians should be mindful of the potential impact of patient background on variations in patient-reported outcome measures following total joint arthroplasty.

Performance Evaluation of Linseed (<i>Linum usitatissimum</i> L.) Varieties in Buno Bedele Zone, South Western Oromia

Linseed (<i>Linum usitatissimum</i> L.) is one of the most important oil crops of Ethiopia and it is considered as the main food crop and the least expensive source of oil for the farmers in many highlands of the country. Even though numerous improved linseed varieties have been released in Ethiopia, the mean seed yield per unit area of the crop remains low due to poor access to improved varieties. To improve the crop’s production and productivity, testing the performance of improved linseed varieties in potential agro ecologies where the varieties have never been grown before is an essential activity. A study was carried out with the objective of testing the performance of improved linseed varieties and their genetic variability for seed yield and related traits in Bedele, Dabo Hana and Didesa districts of Buno Bedele zone, south western Oromia, Ethiopia. This experiment was carried out using twelve linseed varieties were used for investigation (Bekoji-14, Kassa-2, Welen, Bekoji, Kuma, Yadano, Furtu, Bakalcha, Dibane, Horesoba & Jitu) and one local check were sown in randomized complete block design with three replications during the 2022/2023 and 2023/2024 main cropping seasons. Six (6) quantitative morphological traits were collected and analyzed by using R-software accordingly. Combined analysis of data from the three locations revealed that there is significant difference among varieties for days to flowering, days to maturity and grain yield, but non-significant for plant height (cm), number of primary branches per plant and number of capsules per plant. Significant effect of location was observed in plant height, number of primary branch per plant, number of capsule per plant and grain yield however non-significant in days to flowering and days to maturity. The interaction of Variety X location was significant for days to flowering, days to maturity, plant height and grain yield, however non-significant for number of primary branch per plant and number of capsule per plant. The maximum seed yield was recorded in varieties Kuma (1588.9 kgha<sup>-1</sup>) followed by Beokoji-14 (1476.7 kgha<sup>-1</sup>) and the lowest yield (978.9 kg/ha-1) was obtained from local check. The combine AMMI analysis for seed yield across environments revealed significantly affected by environments that hold 26.6% of the total variation. The genotype and genotype by environmental interaction were significant and accounted for 12.40% and 19.42% respectively. Principal component 1 and 2 accounted for 10.78 % and 6.13% of the GEI respectively with a total of 16.91% variation. In general, Kuma and Bekoji-14 varieties were identified as the best varieties for yielding ability, stability and recommended in the area and with similar agro-ecologies.

Poisson noise removal based on non-convex hybrid regularizers

The presence of TV regularizer always induces an unsatisfactory staircase effect. To overcome the staircase while better sustaining edge information, this work proposes a novel model for Poisson noise removal. The model is based on non-convex mixed regularizers, which involves introducing a non-convex penalty into a composition of the total variation and the higher-order total variation. The iterative reweighted l1 algorithm was used to convert the non-convex model into a convex one. The classic alternating direction method of multipliers was then employed to obtain approximate solutions of the model. When applying this model to degraded images contaminated by Poisson noise of medium to high intensity, its performance in noise suppression was tested. The regularizer was compared with others in the terms of the visual effect of the picture, time cost, and several commonly accepted quantitative indicators for evaluation, such as peak signal-to-noise ratio, feature similarity index and structural similarity index. Numerical experiments showed that the present model not only eliminates block artifacts but also retains sharp edges.

Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN.

Single-particle analysis by Cryo-electron microscopy (CryoEM) provides direct access to the conformation of each macromolecule. However, the image's signal-to-noise ratio is low, and some form of classification is usually performed at the image processing level to allow structural modeling. Classical classification methods imply the existence of a discrete number of structural conformations. However, new heterogeneity algorithms introduce a novel reconstruction paradigm, where every state is represented by a lower number of particles, potentially just one, allowing the estimation of conformational landscapes representing the different structural states a biomolecule explores. In this work, we present a novel deep learning-based method called HetSIREN. HetSIREN can fully reconstruct or refine a CryoEM volume in real space based on the structural information summarized in a conformational latent space. The unique characteristics that set HetSIREN apart start with the definition of the approach as a real space-based only method, a fact that allows spatially focused analysis, but also the introduction of a novel network architecture specifically designed to make use of meta-sinusoidal activations, with proven high analytics capacities. Continuing with innovations, HetSIREN can also refine the pose parameters of the images at the same time that it conditions the network with prior information/constraints on the maps, such as Total Variation and denoising, ultimately yielding cleaner volumes with high-quality structural features. Finally, but very importantly, HetSIREN addresses one of the most confusing issues in heterogeneity analysis, as it is the fact that real structural heterogeneity estimation is entangled with pose estimation (and to a lesser extent with CTF estimation), in this way, HetSIREN introduces a novel encoding architecture able to decouple pose and CTF information from the conformational landscape, resulting in more accurate and interpretable conformational latent spaces. We present results on computer-simulated data, public data from EMPIAR, and data from experimental systems currently being studied in our laboratories. An important finding is the sensitivity of the structure and dynamics of the SARS-CoV-2 Spike protein on the storage temperature.

Impact of Triage Nurse Training on Accuracy and Efficiency: A Systematic Review-Based Study

Background Triage in the emergency department (ED) is a crucial procedure that establishes the urgency of patient care based on symptoms at presentation. Although the efficacy of triage nurse training programs varies, their goal is to increase the precision and efficiency of their decision-making. Aim With a focus on research done between 2019 and 2024, the systematic review attempts to evaluate the effect of training programs on the accuracy and efficiency of triage nurses in emergency departments. Method We conducted a thorough search of the PubMed, EMBASE, and Web of Science databases to find pertinent research. Studies assessing training treatments for triage nurses and reporting accuracy and efficiency-related results were included in the inclusion criteria. Both the quality assessment and data extraction were carried out separately by two reviewers. Result Ten studies that used a range of triage training techniques and covered a variety of geographic areas satisfied the inclusion criteria. Research findings consistently showed that, despite variations in study designs and outcome measures, training programs increased triage accuracy and efficiency. Interventions included clinical decision support system integration, training based on simulation, and conventional classroom-based instruction. Conclusion By increasing decision-making efficiency and accuracy in patient evaluation, training programs greatly improve the performance of triage nurses in emergency departments. To improve comparability and support evidence-based policies, future research must standardize training methodologies and outcome measurements.

Development of a total variation diminishing (TVD) sea ice transport scheme and its application in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids

Abstract. As the demand for increased resolution and complexity in unstructured sea ice models is growing, higher demands are also placed on the sea ice transport scheme. In this study, we couple the Semi-implicit Cross-scale Hydro-science Integrated System Model (SCHISM, v5.11) with Icepack (v1.3.4), the column physics package of the Los Alamos sea ice model (CICE); a key step is to implement a total variation diminishing (TVD) transport scheme for the multi-class sea ice module in the coupled model. Compared with the second-order upwind scheme and the finite-element flux-corrected transport (FEM-FCT) scheme, the TVD transport scheme is overall superior when evaluated based on conservation, accuracy, efficiency (even with very high resolution), and strict monotonicity. Although it is slightly weaker than FEM-FCT in terms of accuracy alone, the TVD scheme still outperforms the other two schemes in comprehensive performance. The new coupled model outperforms the existing single-class ice model of SCHISM in the case of Lake Superior. For the Arctic Ocean case, it successfully reproduces the long-term changes in the sea ice extent, sea ice boundary, concentration observations from satellites, and thickness from in situ measurement.

Understanding the variability of the proximal femoral canal: A computational modeling study.

Statistical shape modeling (SSM) offers the potential to describe the morphological differences in similar shapes using a compact number of variables. Its application in orthopedics is rapidly growing. In this study, an SSM of the intramedullary canal of the proximal femur was built, with the aim to better understanding the complexity of its shape which may, in turn, enhance the preoperative planning of total hip arthroplasty (THA). This includes the prediction of the prosthetic femoral version (PFV) which is known to be highly variable amongst patients who have undergone THA. The model was built on three dimensional (3D) models of 64 femoral canals which were generated from pelvic computed tomography images including the proximal femur in the field of view. Principal component analysis (PCA) was performed on the mean shape derived from the model and each segmented canal. Five prominent modes of variations representing approximately 84% of the total 3D variations in the population of shapes were found to capture variability in size, proximal torsion, intramedullary femoral anteversion, varus/valgus orientation, and distal femoral shaft twist/torsion, respectively. It was established that the intramedullary femoral canal is highly variable in its size, shape, and orientation between different subjects. PCA-driven SSM is beneficial for identifying patterns and extracting valuable features of the femoral canal.

Blind deblurring of astronomical images using a SCGTV-based single-frame method

Atmospheric turbulence often distorts space target imaging, leading to degraded image quality. To address the issue of image quality degradation, various hardware and software approaches have been proposed, including adaptive optics, lucky imaging, and blind deconvolution. Traditional astronomical image deblurring algorithms rely on information from multiple frames, requiring extensive processing time and computational resources. This study introduces the saturation-corrected graph total variation (SCGTV) method to address low signal-to-noise ratio and pixel saturation in single-frame astronomical image deblurring. This method effectively minimizes the influence of atmospheric turbulence and noise by leveraging a reweighted graph total variation prior. Saturation correction and dark channel information are integrated in SCGTV to enhance resolution and reduce artifacts. The SCGTV method is well-designed for astronomical scenarios, successfully deblurring real-world astronomical images and demonstrating superior performance on thirty simulated dataset compared to other algorithms. Quantitative evaluations on artificially blurred datasets demonstrate that SCGTV outperforms existing methods. These enhancements provide significant benefits for astronomical observation and analysis.

Different organic composts application in dryland Mollisol:Residual effect and soil CO2 emission.

Organic compost application plays an important role in improving the fertility of Mollisol. However, the effects of different organic composts on carbon sequestration varies greatly and its internal mechanism are unclear. We conducted a field experiment to explore the residual proportion of different organic composts and their effects on carbon emissions in dryland Mollisol in Northeast China. There were a total of seven treatments, including chemical fertilizer control (SNF), organic composts from cattle excreta (CRH), sheep excreta (SHP), chicken excreta (CKN), residue after corn starch production (BCS), residue with crop straws (HRS) and mushroom residue (WMC). We monitored annual soil CO2 flux by static chamber method, as well as the changes of environmental factors and soil dissolved carbon and nitrogen. The regulatory mechanism of organic component characteristics on carbon residual porprotion of organic composts were examined by neural network analysis. The results showed that compared with the SNF treatment, soil dissolved organic carbon (DOC) and extractable organic nitrogen increased by 26.3%-103.5% and 21.4%-150.0%, respectively. The aromaticity of soil DOC was significantly reduced. Heterotrophic respiration flux was mainly affected by soil temperature and DOC content, while its temperature sensitivity was significantly reduced in the CKN treatment. Annual accumulation of heterotrophic respiration increased from 203 g·C·m-2 of the control to 234-334 g·C·m-2 under treatments with organic composts applications, with the CKN and HRS treatments showing the strongest impact. The annual carbon residual proportion of different organic composts in Mollisol was in an order of CRH (91.2%)> WMC (82.9%)> BCS (82.6%)> SHP (78.1%)> CKN (70.2%)> HRS (69.3%). Hemicellulose content and C/N of organic composts were the key factors, which explained 58.8% and 32.9% of the total variations of carbon residual proportion. Organic compost from cattle excreta had higher residual proportion due to lower C/N, hemicellulose content and soluble polyphenol content, and thus did not significantly affect Mollisol heterotrophic respiration. Therefore, the application of organic compost from cattle excreta was more efficient to improve organic carbon in dryland Mollisol.

Genotype x environment interaction and yield stability of soybean (Glycine max l.) genotypes in multi-environment trials (METs) in Nigeria

Genotype × environment interaction (GEI) poses a critical challenge to plant breeders by complicating the identification of stable variety (ies) for performance across diverse environments. GGE biplot and AMMI analyses have been identified as the most effective and appropriate statistical techniques for identifying stable and high-performing genotypes across diverse environments. The objective of this study was to identify widely adapted and high-yielding soybean genotypes from Multi-Locational Trials (MLTs) using GGE and AMMI biplot analyses. Fifteen IITA-bred elite soybean lines and three standard checks were evaluated for stability of performance in a 3 × 6 alpha lattice design with three replications across seven locations in Nigeria. Significant (p < 0.001) differences were detected among genotypes, environments, and GEI for grain yield, which ranged between 979.8 kg ha−1 and 3645 kg ha−1 with a mean of 2324 kg ha−1. To assess the stability of genotypes, analyses were conducted using the general linear method, GGE, and the Additive Main Effect and Multiplicative Interaction (AMMI) approach, as well as WAAS and ASV rank indices. In the GGE biplot model, the first two principal components accounted for 67.4 % of the total variation, while in the AMMI model, the first two Interaction Principal Component Axes (IPCA1 and IPCA2) explained 73.20 % and 11.40 % of the variation attributed to genotype by environment interaction, respectively. GGE biplot identified G10 and G16 as the most stable and productive genotypes, while WAASB index revealed G16, G10, G9, G4 and G2 as the most adaptive, stable and productive genotypes across locations, and ASV identified G9, G13, G4, G14 and G10 as the most stable and productive.Consequently, genotypes G2, G4, G9, G10 and G16 displayed outstanding and stable grain yield performance across the test locations and are, therefore, recommended for release as new soybean varieties suitable for cultivation in the respective mega environment where they performed best. More importantly, the two genotypes are recommended for recycling as sources of high-yield and yield stability genes, and as parental lines for high-yield and stable performance for future breeding and genomic selection.

Color, proximate composition, bioactive compounds and antinutrient profiling of rose

Rose (Rosa sp.) is one of the most important ornamentals which is commercialize for its aesthetic values, essential oils, cosmetic, perfume, pharmaceuticals and food industries in the world. It has wide range of variations that is mostly distinguished by petal color differences which is interlinked with the phytochemicals, secondary metabolites and antinutrient properties. Here, we explored the color, bioactive compounds and antinutritional profiling and their association to sort out the most promising rose genotypes. For this purpose, we employed both quantitative and qualitative evaluation by colorimetric, spectrophotometric and visual analyses following standard protocols. The experiment was laid out in randomized complete block design (RCBD) with three replications where ten rose genotypes labelled R1, R2, R3, R4, R5, R6, R7, R8, R9 and R10 were used as plant materials. Results revealed in quantitative assessment, the maximum value of lightness, and the luminosity indicating a brightening of rose petals close to a yellow color from rose accessions R4, and R10, respectively which is further confirmed with the visually observed color of the respective rose petals. Proximate composition analyses showed that the highest amount of carotenoid and β-carotene was found in R10 rose genotype, anthocyanin and betacyanin in R7. Among the bioactive compounds, maximum tocopherol, phenolic and flavonoid content was recorded in R8, R6 and R3 while R1 showed the highest free radical scavenging potentiality with the lowest IC50 (82.60 µg/mL FW) compared to the others. Meanwhile, the enormous variation was observed among the studied rose genotypes regarding the antinutrient contents of tannin, alkaloid, saponin and phytate whereas some other antinutrient like steroids, coumarines, quinones, anthraquinone and phlobatanin were also figured out with their presence or absence following qualitative visualization strategies. Furthermore, according to the Principal Component Analysis (PCA), correlation matrix and cluster analysis, the ten rose genotypes were grouped into three clusters where, cluster-I composed of R3, R4, R5, R8, cluster-II: R9, R10 and cluster-III: R1, R2, R6, R7 where the rose genotypes under cluster III and cluster II were mostly contributed in the total variations by the studied variables. Therefore, the rose genotypes R9, R10 and R1, R2, R6, R7 might be potential valuable resources of bioactive compounds for utilization in cosmetics, food coloration, and drugs synthesis which have considerable health impact.

Adverse Cardiovascular Effects of Nicotine Delivered by Chronic Electronic Cigarettes or Standard Cigarettes Captured by Cardiovascular Intrinsic Frequencies.

Electronic cigarettes have gained popularity as a nicotine delivery system, which has been recommended by some as an aid to help people quit traditional smoking. The potential long-term effects of vaping on the cardiovascular system, as well as how their effects compare with those from standard cigarettes, are not well understood. The intrinsic frequency (IF) method is a systems approach for analysis of left ventricle and arterial function. Recent clinical studies have demonstrated the diagnostic and prognostic value of IF. Here, we aim to determine whether the novel IF metrics derived from carotid pressure waveforms can detect effects of nicotine (delivered by chronic exposure to electronic cigarette vapor or traditional cigarette smoke) on the cardiovascular system. One hundred seventeen healthy adult male and female rats were exposed to purified air (control), electronic cigarette vapor without nicotine, electronic cigarette vapor with nicotine, and traditional nicotine-rich cigarette smoke, after which hemodynamics were comprehensively evaluated. IF metrics were computed from invasive carotid pressure waveforms. Standard cigarettes significantly increased the first IF (indicating left ventricle contractile dysfunction). Electronic cigarettes with nicotine significantly reduced the second IF (indicating adverse effects on vascular function). No significant difference was seen in the IF metrics between controls and electronic cigarettes without nicotine. Exposure to electronic cigarettes with nicotine significantly increased the total IF variation (suggesting adverse effects on left ventricle-arterial coupling and its optimal state), when compared with electronic cigarettes without nicotine. Our IF results suggest that nicotine-containing electronic cigarettes adversely affect vascular function and left ventricle-arterial coupling, whereas standard cigarettes have an adverse effect on left ventricle function.

Blind non-linear spectral unmixing with spatial coherence for hyper and multispectral images

Multi and hyperspectral images have become invaluable sources of information, revolutionizing various fields such as remote sensing, environmental monitoring, agriculture and medicine. In this expansive domain, the multi-linear mixing model (MMM) is a versatile tool to analyze spatial and spectral domains by effectively bridging the gap between linear and non-linear interactions of light and matter. This paper introduces an upgraded methodology that integrates the versatility of MMM in non-linear spectral unmixing, while leveraging spatial coherence (SC) enhancement through total variation theory to mitigate noise effects in the abundance maps. Referred to as non-linear extended blind end-member and abundance extraction with SC (NEBEAE-SC), the proposed methodology relies on constrained quadratic optimization, cyclic coordinate descent algorithm, and the split Bregman formulation. The validation of NEBEAE-SC involved rigorous testing on various hyperspectral datasets, including a synthetic image, remote sensing scenarios, and two biomedical applications. Specifically, our biomedical applications are focused on classification tasks, the first addressing hyperspectral images of in-vivo brain tissue, and the second involving multispectral images of ex-vivo human placenta. Our results demonstrate an improvement in the abundance estimation by NEBEAE-SC compared to similar algorithms in the state-of-the-art by offering a robust tool for non-linear spectral unmixing in diverse application domains.