Latent Sources Research Articles

Triple oxygen isotope reveals insolation-forced tropical moisture cycles.

Tropical oceans are the main global water vapor and latent heat sources, but their responses to radiative forcing remain unclear. Here, we investigate oceanic moisture dynamics of the western tropical Pacific (WTP) over the past 210,000 years through an approach of planktonic foraminiferal triple oxygen isotope (Δ'17O). The Δ'17O record is dominated by the precession cycles (~23,000 years), with lower values reflecting higher humidity in concert with higher Northern Hemisphere summer insolation. Our empirical and modeling results, combined with other geological archives, suggest that the enhanced moisture convergence over the WTP largely intensifies changes in the meridional and zonal hydrological cycles, affecting rainfall patterns in East Asia and northern South America. We propose that the insolation-driven WTP moisture dynamics play a pivotal role in regulating tropical hydroclimate.

Assessment of water quality and source apportionment of pollution in a tropical river in eastern India: A study utilizing multivariate statistical tools and the APCS-MLR receptor model.

The Mundeswari River, an ecologically distressed river in eastern India, has been subjected to water quality deterioration largely due to anthropogenic activities in its vicinity. This study aimed to comprehensively evaluate the current state of pollution in the river and assess the appropriateness of river water for irrigation, given its extensive use for agricultural purposes. Monthly water quality monitoring was undertaken at four distinct sampling sites (SP1-SP4) over a two-year period (2020-2022), considering seventeen water quality parameters. This research employed principal component analysis/factor analysis (PCA/FA) and absolute principal component score-multiple linear regression (APCS-MLR) receptor modelling. These methodologies were used to discern and quantify potential sources of pollution influencing the water quality of the Mundeswari River. The study revealed that the water quality of the Mundeswari River was most degraded during the pre-monsoon season. Among the four sampling sites, SP3 exhibited the highest level of pollution with mean biochemical oxygen demand (BOD) and chemical oxygen demand (COD) values of 5.36mg/L and 44.72mg/L, respectively. According to the one-way analysis of variance (ANOVA), there was considerable spatial and seasonal disparities (P < 0.05) in most water quality parameters. The PCA/FA extracted four latent pollution sources, accounting for 81.5% of the total variance. The primary factors influencing the quality of river water are natural weathering processes, discharge of domestic effluent and waste, and agricultural runoff. The APCS-MLR receptor model further revealed that agricultural drainage factors and the discharge of domestic effluent and waste had a greater impact on the Mundeswari River. The investigation concluded that the mean values of all indicators for irrigation suitability were below the defined threshold limits, indicating that the water of the studied river appears suitable for irrigation. The outcomes of this study may significantly contribute to the formulation of sustainable strategies for the ecological rejuvenation of the Mundeswari River.

Integrated sources model: A new space-learning model for heterogeneous multi-view data reduction, visualization, and clustering

In machine learning, multi-view data involve multiple distinct sets of attributes (&amp;ldquo;views&amp;rdquo;) for a common set of observations; when each view has the same attributes considered in different contexts, the data are said to contain multiple views of homogeneous format, which can be conceptualized as a tensor. In this article, we describe a novel approach for integrating multiple views of heterogeneous format into a common latent space using a workflow that involves non-negative matrix and tensor factorization (NMF/NTF). This approach, which we refer to as the integrated sources model (ISM), consists of two main steps: Embedding and analysis. In the embedding step, the views are transformed into matrices with common non-negative components. In the analysis step, the transformed views are combined into a tensor and decomposed using NTF. We also present a variant of ISM; the integrated latent sources model (ILSM), which offers significant advantages over ISM in terms of computational power and in cases where the views are highly unbalanced with regard to the number of attributes per view. Noteworthy, ISM can be extended to process multi-omic and multi-view datasets even in the presence of missing views. We provide a proof-of-concept analysis using five examples, including the UCI Digits (the University of California Irvine Pen-Based Recognition of Handwritten Digits) dataset, a public cell-type gene signatures dataset, and a multi-omic single-cell dataset. These examples demonstrate that, in most cases, multi-view clustering is better achieved with ISM or its variant ILSM than with other latent space approaches. We also show how the non-negativity and sparsity of the ISM model components enable straightforward interpretations, in contrast to other approaches that involve latent factors of mixed signs. Finally, we present potential applications to single-cell multi-omics and spatial mapping, including spatial imaging, spatial transcriptomics, and computational biology, which are currently under evaluation. ISM relies on state-of-the-art algorithms invoked through a simple workflow implemented in Python.

Predicting symptom severity in PSTVd-infected tomato plants using the PSTVd genome sequence.

Viroids, one of the smallest known infectious agents, induce symptoms of varying severity, ranging from latent to severe, based on the combination of viroid isolates and host plant species. Because viroids are transmissible between plant species, asymptomatic viroid-infected plants may serve as latent sources of infection for other species that could exhibit severe symptoms, occasionally leading to agricultural and economic losses. Therefore, predicting the symptoms induced by viroids in host plants without biological experiments could remarkably enhance control measures against viroid damage. Here, we developed an algorithm using unsupervised machine learning to predict the severity of disease symptoms caused by viroids (e.g., potato spindle tuber viroid; PSTVd) in host plants (e.g., tomato). This algorithm, mimicking the RNA silencing mechanism thought to be linked to viroid pathogenicity, requires only the genome sequences of the viroids and host plants. It involves three steps: alignment of synthetic short sequences of the viroids to the host plant genome, calculation of the alignment coverage, and clustering of the viroids based on coverage using UMAP and DBSCAN. Validation through inoculation experiments confirmed the effectiveness of the algorithm in predicting the severity of disease symptoms induced by viroids. As the algorithm only requires the genome sequence data, it may be applied to any viroid and plant combination. These findings underscore a correlation between viroid pathogenicity and the genome sequences of viroid isolates and host plants, potentially aiding in the prevention of viroid outbreaks and the breeding of viroid-resistant crops.

Unveiling Hidden Sources of Dynamic Functional Connectome through a Novel Regularized Blind Source Separation Approach

Abstract The investigation of the brain’s functional connectome and its dynamic changes can provide valuable insights into brain organization and its reconfiguration. However, the analysis of dynamic functional connectivity (dFC) using functional magnetic resonance imaging (fMRI) faces major challenges, including the high dimensionality of brain networks, unknown latent sources underlying observed dFC, and the large number of brain connections that increase the risk of spurious findings. In this paper, we propose a new regularized blind source separation (BSS) method called dyna-LOCUS to address these challenges. dyna-LOCUS decomposes observed dFC measures to reveal latent source connectivity traits and their dynamic temporal expression profiles. By utilizing low-rank factorization and novel regularizations, dyna-LOCUS achieves efficient and reliable mapping of connectivity traits underlying the dynamic brain functional connectome, characterizes temporal changes of the connectivity traits that contribute to the reconfiguration in the observed dFC, and generates parsimonious and interpretable results in identifying whole-brain dFC states. We introduce a highly efficient iterative Node-Rotation algorithm that solves the non-convex optimization problem for learning dyna-LOCUS. Simulation studies demonstrate the advantages of our proposed method. Application of dyna-LOCUS to the Philadelphia Neurodevelopmental Cohort (PNC) study unveils latent connectivity traits and key brain connections and regions driving each of these neural circuits, reveals temporal expressions and interactions of these connectivity traits, and generates new findings regarding gender differences in the neurodevelopment of an executive function-related connectivity trait.

Flavonoids From the Aerial Parts of Sophora tonkinensis and Their Potential Anti-Inflammatory Activities.

Four undescribed prenylated flavonoids, sophoratones A-D (1-4), and 17 known flavonoids, were obtained from the aerial parts of Sophora tonkinensis. Their structures with absolute configurations were elucidated by detailed interpretation of NMR spectroscopy, mass spectrometry, and ECD calculations. Meanwhile, the ability of these compounds to inhibit the release of nitric oxide (NO) by a lipopolysaccharide induced mouse in RAW 264.7 cells was assayed. The results indicated that some compounds exhibited clear inhibitory effects, with IC50 ranging from 19.91±1.08 to 35.72±2.92 μM. These results suggest that prenylated flavonoids from the aerial parts of S. tonkinensis could potentially be used as a latent source of anti-inflammatory agents.

Accurate measurement of sulfhydryls and TCEP-releasable sulfhydryls in the liquid phase of wine that contribute to ‘reductive’ aromas using LC-MS/MS

Volatile sulfur compounds (VSCs) are important aroma and flavour characters in food and beverage products. The identification and quantification of these extremely reactive and volatile compounds pose analytical challenges which demand selective and sensitive methods. In this study, a novel quantification method was developed to analyse sulfhydryls as well as the total pool of sulfhydryls which can be released after tris(2-carboxyethyl)phosphine (TCEP) addition from disulfides, polysulfides, metal-bound and other yet to be identified sources naturally present in wine. The majority of methods for VSC quantification analyse VSCs in wine headspace, whereas this method measures sulfhydryls and TCEP-releasable sulfhydryl species, which likely include free and metal-bound sulfhydryl forms, in the liquid phase of wine using UHPLC-MS/MS. Sulfhydryls were derivatised with N-(2-ferroceneethyl) maleimide (FEM), subsequently, followed by differential labelling of sulfhydryls released after TCEP addition with ferrocenecarboxylic acid-(2-maleimidoyl)ethylamide (FMEA). Analysis of commercial wines revealed the presence of hydrogen sulfide, methanethiol, ethanethiol, and 2-mercaptoethanol at aroma-active concentrations. Significant positive correlations were found between MeSH and CH3–S-R TCEP-releasable species, and significant positive correlations were found between EtSH and CH3–CH2–S-R TCEP-releasable species. This method provides important information on sulfhydryls, and may also provide insights into a wine's risk of developing ‘reductive’ faults post-bottling from latent sources.

Total Synthesis of the Allenic Macrolide (+)-Archangiumide.

Archangiumide is the first known macrolide natural product comprising an endocyclic allene. For the ring strain that this linear substructure might entail, it was planned to unveil the allene at a very late stage of the projected total synthesis; in actual fact, this was achieved as the last step of the longest linear sequence by using an otherwise globally deprotected substrate. This unconventional timing was made possible by a gold catalyzed rearrangement of a macrocyclic propargyl benzyl ether derivative that uses a -PMB group as latent hydride source to unveil the signature cycloallene; the protecting group therefore gains a strategic role beyond its mere safeguarding function. Although the gold catalyzed reaction per se is stereoablative, the macrocyclic frame of the target was found to impose high selectivity and a stereoconvergent character on the transformation. The required substrate was formed by ring closing alkyne metathesis (RCAM) with the aid of a new air-stable molybdenum alkylidyne catalyst.

Exosomal noncoding RNAs as noninvasive biomarkers in bladder cancer: a diagnostic meta-analysis

BackgroundIn view of discordance consisting in different reports, a meta-analysis was conducted to comprehensively evaluate the diagnostic efficacy of exosomal noncoding RNAs (ncRNAs) in blood and urine in the detection of bladder cancer.MethodsEligible studies were acquired by systematic retrieval through PubMed, Cochrane Library, and Embase. The pooled diagnostic efficacy was appraised by reckoning the area under the summary receiver operating characteristic (SROC) curve. The latent sources of heterogeneity were probed by subgroup analyses and meta-regression. STATA 12.0, Meta-DiSc 1.4, and RevMan 5.3 were applied to carry out all statistical analyses and plots.ResultsA total of 46 studies from 15 articles comprising 2622 controls and 3015 bladder cancer patients were included in our meta-analysis. Exosomal ncRNAs in blood and urine represented relatively satisfactory diagnostic efficacy in detecting bladder cancer, with a pooled sensitivity of 0.75, a specificity of 0.79, and an area under the SROC curve (AUC) of 0.84. Exosomal microRNAs (miRNAs) exhibited better diagnostic value with a pooled AUC of 0.91 than that of exosomal long noncoding RNAs (lncRNAs). To some extent, the heterogeneity among studies was induced by exosomal ncRNA types (miRNA or lncRNA), exosomal ncRNA profiling (single- or multiple-ncRNA), sample size, specimen types, and ethnicity.ConclusionExosomal ncRNAs in blood and urine may play a vital role in diagnosing bladder cancer as prospective noninvasive biomarkers; nonetheless, their clinical performance needs to be confirmed by further massive proactive researches.

An agent-based modeling approach for lung fibrosis in response to COVID-19.

The severity of the COVID-19 pandemic has created an emerging need to investigate the long-term effects of infection on patients. Many individuals are at risk of suffering pulmonary fibrosis due to the pathogenesis of lung injury and impairment in the healing mechanism. Fibroblasts are the central mediators of extracellular matrix (ECM) deposition during tissue regeneration, regulated by anti-inflammatory cytokines including transforming growth factor beta (TGF-β). The TGF-β-dependent accumulation of fibroblasts at the damaged site and excess fibrillar collagen deposition lead to fibrosis. We developed an open-source, multiscale tissue simulator to investigate the role of TGF-β sources in the progression of lung fibrosis after SARS-CoV-2 exposure, intracellular viral replication, infection of epithelial cells, and host immune response. Using the model, we predicted the dynamics of fibroblasts, TGF-β, and collagen deposition for 15 days post-infection in virtual lung tissue. Our results showed variation in collagen area fractions between 2% and 40% depending on the spatial behavior of the sources (stationary or mobile), the rate of activation of TGF-β, and the duration of TGF-β sources. We identified M2 macrophages as primary contributors to higher collagen area fraction. Our simulation results also predicted fibrotic outcomes even with lower collagen area fraction when spatially-localized latent TGF-β sources were active for longer times. We validated our model by comparing simulated dynamics for TGF-β, collagen area fraction, and macrophage cell population with independent experimental data from mouse models. Our results showed that partial removal of TGF-β sources changed the fibrotic patterns; in the presence of persistent TGF-β sources, partial removal of TGF-β from the ECM significantly increased collagen area fraction due to maintenance of chemotactic gradients driving fibroblast movement. The computational findings are consistent with independent experimental and clinical observations of collagen area fractions and cell population dynamics not used in developing the model. These critical insights into the activity of TGF-β sources may find applications in the current clinical trials targeting TGF-β for the resolution of lung fibrosis.

'Trapped re-entry' as source of acute focal atrial arrhythmias.

Diseased atria are characterized by functional and structural heterogeneities, adding to abnormal impulse generation and propagation. These heterogeneities are thought to lie at the origin of fractionated electrograms recorded during sinus rhythm (SR) in atrial fibrillation (AF) patients and are assumed to be involved in the onset and perpetuation (e.g. by re-entry) of this disorder. The underlying mechanisms, however, remain incompletely understood. Here, we tested whether regions of dense fibrosis could create an electrically isolated conduction pathway (EICP) in which re-entry could be established via ectopy and local block to become 'trapped'. We also investigated whether this could generate local fractionated electrograms and whether the re-entrant wave could 'escape' and cause a global tachyarrhythmia due to dynamic changes at a connecting isthmus. To precisely control and explore the geometrical properties of EICPs, we used light-gated depolarizing ion channels and patterned illumination for creating specific non-conducting regions in silico and in vitro. Insight from these studies was used for complementary investigations in virtual human atria with localized fibrosis. We demonstrated that a re-entrant tachyarrhythmia can exist locally within an EICP with SR prevailing in the surrounding tissue and identified conditions under which re-entry could escape from the EICP, thereby converting a local latent arrhythmic source into an active driver with global impact on the heart. In a realistic three-dimensional model of human atria, unipolar epicardial pseudo-electrograms showed fractionation at the site of 'trapped re-entry' in coexistence with regular SR electrograms elsewhere in the atria. Upon escape of the re-entrant wave, acute arrhythmia onset was observed. Trapped re-entry as a latent source of arrhythmogenesis can explain the sudden onset of focal arrhythmias, which are able to transgress into AF. Our study might help to improve the effectiveness of ablation of aberrant cardiac electrical signals in clinical practice.

ODCS: On-Demand Hierarchical Consistent Synchronization Approach for the IoT

An IoT data system is a time constraint in which some data needs to be serviced on or before its deadline. Distributed processing is one of the most latent sources in such systems and is considered a vital design concern. Many sources of delay in the IoT can affect the availability of data from different resources, which may cause missing data deadlines, resulting in a catastrophic effect. In fact, such systems are inherently distributed in nature and use distributed processing. The distributed processing permits different nodes to obtain the information from remote sites, which may take a long time to access the required data. Therefore, it is considered one of the most latent sources in such systems, which is considered a vital design concern. The typical recommended solution for this problem is to commit distributed transactions locally. Therefore, replication techniques are used to enhance the availability of data and consequently avoid the resulting latency. However, the use of local processing raises inconsistent periods. Therefore, this study proposes a new synchronization framework to minimize periods of temporal inconsistency. It permits several connected nodes to synchronize the shared data on demand concurrently without any need to use distributed synchronization, which consumes the system resource and raises its delay cost. The proposed framework aims to enhance the timely response of IoT real-time systems by minimizing the temporal inconsistency periods. The results indicate that the synchronization framework can be completed within a reasonable time period. They also depict improved consistency by minimizing the temporal inconsistency duration and increasing the chance of meeting critical time requirements.

A PM10 chemically characterized nation-wide dataset for Italy. Geographical influence on urban air pollution and source apportionment

Urban textures of the Italian cities are peculiarly shaped by the local geography generating similarities among cities placed in different regions but comparable topographical districts. This suggested the following scientific question: can different topographies generate significant differences on the PM10 chemical composition at Italian urban sites that share similar geography despite being in different regions? To investigate whether such communalities can be found and are applicable at Country-scale, we propose here a novel methodological approach. A dataset comprising season-averages of PM10 mass concentration and chemical composition data was built, covering the decade 2005–2016 and referring to urban sites only (21 cities). Statistical analyses, estimation of missing data, identification of latent clusters and source apportionment modeling by Positive Matrix Factorization (PMF) were performed on this unique dataset. The first original result is the demonstration that a dataset with atypical time resolution can be successfully exploited as an input matrix for PMF obtaining Country-scale representative chemical profiles, whose physical consistency has been assessed by different tests of modeling performance.Secondly, this dataset can be considered a reference repository of season averages of chemical species over the Italian territory and the chemical profiles obtained by PMF for urban Italian agglomerations could contribute to emission repositories.These findings indicate that our approach is powerful, and it could be further employed with datasets typically available in the air pollution monitoring networks.

Wind of change

The value of diaconia is difficult to measure, its immaterial assets not easily grasped. In this article, I contribute to the area in analysing the perspective of 22 deacons on what is most important in their job and what could potentially be of greatest value if there were no restrictions of money and other resources. Data were collected in the midst of the Corona crisis in 2021 in the Porvoo diocese in the Evangelical Lutheran Church of Finland. The timing of data collection therefore coincides with a unique chance to rethink ways of doing things. International research shows that the religious heritage of diaconia has changed from merely supporting people in need to also advocating marginalized groups and building bridges to other actors. This intangible heritage is sensitive to changes in society, whereby latent sources of value creation may be traced. Key contributions are identifying new areas of development of this religious heritage and assessing the value creation taking place – now and in a potential future. For this, understanding the changed role of diaconia in society and in the church are central.

A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis.

Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data's true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the "shared" subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs.

LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY.

Network-oriented research has been increasingly popular in many scientific areas. In neuroscience research, imaging-based network connectivity measures have become the key for understanding brain organizations, potentially serving as individual neural fingerprints. There are major challenges in analyzing connectivity matrices, including the high dimensionality of brain networks, unknown latent sources underlying the observed connectivity, and the large number of brain connections leading to spurious findings. In this paper we propose a novel blind source separation method with low-rank structure and uniform sparsity (LOCUS) as a fully data-driven decomposition method for network measures. Compared with the existing method that vectorizes connectivity matrices ignoring brain network topology, LOCUS achieves more efficient and accurate source separation for connectivity matrices using low-rank structure. We propose a novel angle-based uniform sparsity regularization that demonstrates better performance than the existing sparsity controls for low-rank tensor methods. We propose a highly efficient iterative node-rotation algorithm that exploits the block multiconvexity of the objective function to solve the nonconvex optimization problem for learning LOCUS. We illustrate the advantage of LOCUS through extensive simulation studies. Application of LOCUS to Philadelphia Neurodevelopmental Cohort neuroimaging study reveals biologically insightful connectivity traits which are not found using the existing method.

Numerical Simulation of Ice Melting Using the Finite Volume Method

The Aim of this paper is to investigate numerically the simulation of ice melting in one and two dimension using the cell-centered finite volume method. The mathematical model is based on the heat conduction equation associated with a fixed grid, latent heat source approach. The fully implicit time scheme is selected to represent the time discretization. The ice conductivity is chosento be the value of the approximated conductivity at the interface between adjacent ice and water control volumes. The predicted temperature distribution, percentage melt fraction, interface location and its velocity is compared with those obtained from the exact analytical solution. A good agreement is obtained when comparing the numerical results of one dimensional temperaturedistribution with the analytical results.

Prevalence of sarcopenia among patients with hepatocellular carcinoma: A systematic review and meta‑analysis.

Sarcopenia is a common condition in patients with hepatocellular carcinoma (HCC). Sarcopenia affects the prognosis of patients with HCC and reduces their quality of life. However, to date, there has been no systematic review and meta-analysis to assess the prevalence of sarcopenia in patients with HCC, to the best of our knowledge. PubMed, Embase, Web of Science and the Cochrane Library were comprehensively screened for relevant literature published from March 2001 to June 2022. A random effect analysis was conducted to pool the incidence rates for each study. Subgroup and meta-regression analyses were used to investigate the latent sources of heterogeneities. The Newcastle-Ottawa Scale was used to estimate the quality of the included studies. The I2 statistic was used to evaluate heterogeneity between studies. In total, 48 studies encompassing 8,959 patients were included in the meta-analysis. The results of the present meta-analysis showed that nearly half (42%) of the patients with HCC had sarcopenia (95% CI, 0.36-0.48). The morbidity of sarcopenia in studies with a high proportion of males (45%) was higher compared with the morbidity observed in studies with a lower proportion of males (37%). In addition, the incidence rate in younger patients (46%) was found to be higher compared with the incidence rate in older patients (39%). In conclusion, the findings in the present systematic review revealed that a large number of patients with HCC suffer from sarcopenia, indicating the necessity of developing screening and intervention measures to improve the outcome in these patients.

Predicting latent source-specific PM2.5 pollution from regional sources at unmonitored sites by Bayesian spatial multivariate receptor modeling

Fine particulate matter (PM2.5) has been a pollutant of main interest globally for more than two decades, owing to its well-known adverse health effects. For developing effective management strategies for PM2.5, it is vital to identify its major sources and quantify how much they contribute to ambient PM2.5 concentrations. With the expanded monitoring efforts established during recent decades in Korea, speciated PM2.5 data needed for source apportionment of PM2.5 are now available for multiple sites (cities). However, many cities in Korea still do not have any speciated PM2.5 monitoring station, although quantification of source contributions for those cities is in great need. While there have been many PM2.5 source apportionment studies throughout the world for several decades based on monitoring data collected from receptor site(s), none of those receptor-oriented studies could predict unobserved source contributions at unmonitored sites. This study predicts source contributions of PM2.5 at unmonitored locations using a recently developed novel spatial multivariate receptor modeling (BSMRM) approach, which incorporates spatial correlation in data into modeling and estimation for spatial prediction of latent source contributions. The validity of BSMRM results is also assessed based on the data from a test site (city), not used in model development and estimation.

Flavonoids from the Roots of Sophora flavescens and Their Potential Anti-Inflammatory and Antiproliferative Activities

The phytochemical investigation of the roots of the traditional Chinese medicinal plant Sophora flavescens led to the isolation of two novel prenylflavonoids with an unusual cyclohexyl substituent instead of the common aromatic ring B, named 4',4'-dimethoxy-sophvein (17) and sophvein-4'-one (18), and 34 known compounds (1-16, 19-36). The structures of these chemical compounds were determined by spectroscopic techniques, including 1D-, 2D-NMR, and HRESIMS data. Furthermore, evaluations of nitric oxide (NO) production inhibitory activity against lipopolysaccharide (LPS)-treated RAW264.7 cells indicated that some compounds exhibited obvious inhibition effects, with IC50 ranged from 4.6 ± 1.1 to 14.4 ± 0.4 μM. Moreover, additional research demonstrated that some compounds inhibited the growth of HepG2 cells, with an IC50 ranging from 0.46 ± 0.1 to 48.6 ± 0.8 μM. These results suggest that flavonoid derivatives from the roots of S. flavescens can be used as a latent source of antiproliferative or anti-inflammatory agents.