Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

BACKGROUND Gastrointestinal neoplasm(GN)significantly impact the global cancer burden and mortality,necessitating early detection and treatment.Understanding the evolution and current state of research in this field is vital.AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research,focusing on key contributors,institutions,and thematic evolution.METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the"bibliometrix"R package,VOSviewer,and CiteSpace.The analysis focused on the distribution of publications,contributions by institutions and countries,and trends in keywords.The methods included data synthesis,network analysis,and visualization of international collaboration networks.RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration.It highlights the United States'critical role in advancing this field,with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute.The last five years,substantial advancements have been made,representing nearly 45%of the examined literature.Publication rates have dramatically increased,from 20 articles in 2002 to 112 in 2022,reflecting intensified research efforts.This study underscores a growing trend toward interdisciplinary and international collaboration,with the Journal of Clinical Oncology standing out as a key publication outlet.This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks.CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis.This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy,ultimately enhancing worldwide patient care.

Intertwined depressive and cognitive trajectories and the risk of dementia and death in older adults:a competing risk analysis

Background Depressive symptoms and cognitive impairment often interact,rendering their associations controversial.To date,their joint trajectories and associations with dementia and death remain underexplored.Aims To explore the interactions between depressive symptoms and cognitive function,their developmental trajectories and the associations with all-cause dementia,Alzheimer’s disease(AD)and all-cause death in older adults.Methods Data were from the Health and Retirement Study.Depressive symptoms and cognitive function were measured using the 8-item Centre for Epidemiologic Studies Depression Scale and the Telephone Interview of Cognitive Status,respectively.All-cause dementia and AD were defined by self-reported or proxy-reported physician diagnoses.All-cause death was determined by interviews.The restricted cubic spline,group-based trajectory modelling and subdistribution hazard regression were used.Results Significant interactions between depressive symptoms and cognitive function in 2010 in their association with new-onset all-cause dementia and AD from 2010 to 2020 were found,especially in women(p for interaction<0.05).Independent trajectory analysis showed that emerging or high(vs no)depressive trajectories and poor or rapidly decreased cognitive trajectories(vs very good)from 1996 to 2010 were at significantly higher risk of subsequent all-cause dementia,AD and all-cause death.15 joint trajectories of depressive symptoms and cognitive function from 1996 to 2010 were determined,where rapidly decreased cognitive function was more common in those with no depressive symptoms.Compared with older adults with the trajectory of no depressive symptoms and very good cognitive function,those with the trajectory of no depressive symptoms but rapidly decreased cognitive function were much more likely to develop new-onset all-cause dementia and death,with subdistribution hazard ratios(95%confidence intervals)of 4.47(2.99 to 6.67)and 1.84(1.43 to 2.36),especially in women.Conclusions To effectively mitigate the risk of dementia and death,it is crucial to acknowledge the importance of preventing cognitive decline in older adults without depressive symptoms,particularly in women.

Action of circulating and infiltrating B cells in the immune microenvironment of colorectal cancer by single-cell sequencing analysis

BACKGROUND The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer(CRC),one of the most prevalent malignancies worldwide.In this study,multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC.By revealing the heterogeneity and functional differences of B cells in cancer immunity,we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies.AIM To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC,explore the potential driving mechanism of B cell development,analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules,and search for possible regulatory pathways to promote the anti-tumor effects of B cells.METHODS A total of 69 paracancer(normal),tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database(https://portal.gdc.cancer.gov/).After the immune cells were sorted by multicolor flow cytometry,the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform,and the data were analyzed using bioinformatics tools such as Seurat.The differences in the number and function of B cell infiltration between tumor and normal tissue,the interaction between B cell subsets and T cells and myeloid cell subsets,and the transcription factor regulatory network of B cell subsets were explored and analyzed.RESULTS Compared with normal tissue,the infiltrating number of CD20+B cell subsets in tumor tissue increased significantly.Among them,germinal center B cells(GCB)played the most prominent role,with positive clone expansion and heavy chain mutation level increasing,and the trend of differentiation into memory B cells increased.However,the number of plasma cells in the tumor microenvironment decreased significantly,and the plasma cells secreting IgA antibodies decreased most obviously.In addition,compared with the immune microenvironment of normal tissues,GCB cells in tumor tissues became more closely connected with other immune cells such as T cells,and communication molecules that positively regulate immune function were significantly enriched.CONCLUSION The role of GCB in CRC tumor microenvironment is greatly enhanced,and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level.Meanwhile,GCB has enhanced its association with immune cells in the microenvironment,which plays a positive anti-tumor effect.

Brain Transcriptome Analysis Reveals Metabolic Changes Adapting to Hyperhaline or Hypohaline Environments in Spotted Scat(Scatophagus argus)

The fish brain is crucial for adjusting to environmental changes.Metabolic changes play a vital role in the adaptation to salinity change in aquatic animals.However,few studies have evaluated the responses of the fish brain to salinity changes.To evaluate the response to various salinities,spotted scat(Scatophagus argus)was cultured in water with salinity levels of 5(low salinity:LS),25(control group:Ctrl),and 35(high salinity group:HS)for 22 days.The brain transcriptome was analyzed.In total,1698 differentially expressed genes(DEGs)were identified between the HS and Ctrl groups,and 841 DEGs were identified between the LS and Ctrl groups.KEGG analysis showed that the DEGs in the HS vs.Ctrl comparison were involved in steroid biosynthesis,terpenoid backbone biosynthesis,fatty acid biosynthesis,ascorbate and aldarate metabolism,other types of O-glycan biosynthesis,and fatty acid metabolism.Glyoxylate and dicarboxylate metabolism,one carbon pool by folate,steroid biosynthesis,and cysteine and methionine metabolism were significantly enriched in the LS vs.Ctrl comparison.Additionally,the genes related to metabolism(acc,fas,hmgcr,hmgcs1,mvd,soat1,nsdhl,sqle,cel,fdft1,dnmt3a and mtr)were significantly up-regulated in the HS vs.Ctrl comparison.The genes related to metabolism(lipa,sqle,acc,fas,bhmt,mpst,dnmt3a,mtr,hao2,LOC111225351 and hmgcs1)were significantly up-regulated,while hmgcr and soat1 were significantly down-regulated in the LS vs.Ctrl compparison.These results suggest that salinity stress affects signaling pathways and genes’expressions involved in metabolic processes in the brain,and the differences in metabolism play an important role in adaptation to hyperhaline or hypohaline environments in spotted scat.This research provides a comprehensive overview of transcriptional changes in the brain under hyperhaline or hypohaline conditions,which is helpful to understand the mechanisms underlying salinity adaptation in euryhaline fishes.

Contribution of GIS to Soil Landscape Mapping by Multi-Criteria Analysis Using Weighting: The Case of the Square Degrees of M’Bahiakro (Centre) and Daloa (Centre-West) in Ivory Coast

As part of the drive to improve coffee and cocoa production in Ivory Coast, studies are carried out to identify soils that are favourable for these crops. It is therefore necessary to orientate soil investigations based on reliable criteria that best discriminate soil cover. With this in mind, this study is being carried out to help improve survey methods by mapping soil landscapes. It uses GIS and weighted multicriteria analysis. To do this, satellite images were processed and the geological map of the square degrees of M’Bahiakro and Daloa was reclassified. The results show that relief is the main factor in soil landscape differentiation, with respective weights of 0.58 and 0.67 for the forest and pre-forest zones. In contrast, the weight of geological formation in soil landscape differentiation remains low (0.05 for the forest zone and 0.07 for the pre-forest zone). The criteria used on the base of aggregation sum methods have made it possible to formulate soil landscape mapping prediction functions according to agro-ecological environments in the humid intertropical zone. This is essential for the orientation of soil survey work. Nevertheless, other comparative methods, such as the coding mapping method, could provide elements for discussion to validate the models.

Biochemistry and transcriptome analysis reveal condensed tannins alleviate liver injury induced by regulating cholesterol metabolism pathway

Free cholesterol has been considered to be a critical risk factor of nonalcoholic fatty liver disease(NAFLD).It remains unknown whether dietary intake of condensed tannins(CTs)have distinguishable effects to alleviate liver damage caused by a high cholesterol diet.Male C57BL/6 mice were fed a high cholesterol diet for 6 weeks,and given CTs treatment at a dosage of 200 mg/(kg·day)at the same time.The results indicated that compared with mice fed a normal diet,a high cholesterol diet group resulted in significant weight loss,dysregulation of lipid metabolism in blood and liver,and oxidative stress in the liver,but CTs treatment dramatically reversed these negative effects.Hematoxylin and eosin(H&E)staining and frozen section observation manifested that CTs treatment could effectively reduce the deposition of liver cholesterol and tissue necrosis caused by high cholesterol intake.CTs alleviated liver injury mainly by regulating the expression of related genes in cholesterol metabolism pathway and AMPK phosphorylation.Our results confirmed that CTs have remarkable cholesterol lowering and anti-liver injury effects in vivo.

Iron and ferritin effects on intensive care unit mortality:A metaanalysis

BACKGROUND The effect of serum iron or ferritin parameters on mortality among critically ill patients is not well characterized.AIM To determine the association between serum iron or ferritin parameters and mortality among critically ill patients.METHODS Web of Science,Embase,PubMed,and Cochrane Library databases were searched for studies on serum iron or ferritin parameters and mortality among critically ill patients.Two reviewers independently assessed,selected,and abstracted data from studies reporting on serum iron or ferritin parameters and mortality among critically ill patients.Data on serum iron or ferritin levels,mortality,and demographics were extracted.RESULTS Nineteen studies comprising 125490 patients were eligible for inclusion.We observed a slight negative effect of serum ferritin on mortality in the United States population[relative risk(RR)1.002;95%CI:1.002-1.004].In patients with sepsis,serum iron had a significant negative effect on mortality(RR=1.567;95%CI:1.208-1.925).CONCLUSION This systematic review presents evidence of a negative correlation between serum iron levels and mortality among patients with sepsis.Furthermore,it reveals a minor yet adverse impact of serum ferritin on mortality among the United States population.

Particle Discontinuous Deformation Analysis of Static and Dynamic Crack Propagation in Brittle Material

Crack propagation in brittle material is not only crucial for structural safety evaluation,but also has a wideranging impact on material design,damage assessment,resource extraction,and scientific research.A thorough investigation into the behavior of crack propagation contributes to a better understanding and control of the properties of brittle materials,thereby enhancing the reliability and safety of both materials and structures.As an implicit discrete elementmethod,the Discontinuous Deformation Analysis(DDA)has gained significant attention for its developments and applications in recent years.Among these developments,the particle DDA equipped with the bonded particle model is a powerful tool for predicting the whole process of material from continuity to failure.The primary objective of this research is to develop and utilize the particle DDAtomodel and understand the complex behavior of cracks in brittle materials under both static and dynamic loadings.The particle DDA is applied to several classical crack propagation problems,including the crack branching,compact tensile test,Kalthoff impact experiment,and tensile test of a rectangular plate with a hole.The evolutions of cracks under various stress or geometrical conditions are carefully investigated.The simulated results are compared with the experiments and other numerical results.It is found that the crack propagation patterns,including crack branching and the formation of secondary cracks,can be well reproduced.The results show that the particle DDA is a qualified method for crack propagation problems,providing valuable insights into the fracture mechanism of brittle materials.

Effect of FeSi additive in dual-chamber sample cup on thermal analysis characteristic values and vermiculating rate of compacted graphite iron

Thermal analysis plays a key role in the online inspection of molten iron quality.Different solidification process of molten iron can be reflected by thermal analysis curves,and silicon is one of important elements affecting the solidification of molten iron.In this study,FeSi75 was added in one chamber of the dual-chamber sample cup,and the influences of FeSi75 additive on the characteristic values of thermal analysis curves and vermiculating rate were investigated.The results show that with the increase of FeSi75,the start temperature of austenite formation TALfirstly decreases and then increases,but the start temperature of eutectic growth TSEF,the lowest eutectic temperature TEU,temperature at maximum eutectic reaction rate TEM,and highest eutectic temperature TERkeep always an increase.The temperature at final solidification point TEShas little change.The FeSi75 additive has different influences on the vermiculating rate of molten iron with different vermiculation,and the vermiculating rate increases for lower vermiculation molten iron while decreases for higher one.According to the thermal analysis curves obtained by a dual-chamber sample cup with 0.30wt.%FeSi75 additive in one chamber,the vermiculating rate of molten iron can be evaluated by comparing the characteristic values of these curves.The time differenceΔtERcorresponding to the highest eutectic temperature TERhas a closer relationship with the vermiculating rate,and a parabolic regression curve between the time differenceΔtERand vermiculating rateηhas been obtained within the range of 65%to 95%,which is suitable for the qualified melt.

Visualization analysis of research hotspots and trends on gastrointestinal tumor organoids

BACKGROUND Gastrointestinal tumor organoids serve as an effective model for simulating cancer in vitro and have been applied in basic biology and preclinical research.Despite over a decade of development and increasing research achievements in this field,a systematic and comprehensive analysis of the research hotspots and future trends is lacking.AIM To address this problem by employing bibliometric tools to explore the publication years,countries/regions,institutions,journals,authors,keywords,and references in this field.METHODS The literature was collected from Web of Science databases.CiteSpace-6.2R4,a widely used bibliometric analysis software package,was used for institutional analysis and reference burst analysis.VOSviewer 1.6.19 was used for journal cocitation analysis,author co-authorship and co-citation analysis.The‘online platform for bibliometric analysis(https://bibliometric.com/app)’was used to assess the total number of publications and the cooperation relationships between countries.Finally,we employed the bibliometric R software package(version R.4.3.1)in R-studio,for a comprehensive scientific analysis of the literature.RESULTS Our analysis included a total of 1466 publications,revealing a significant yearly increase in articles on the study of gastrointestinal tumor organoids.The United States(n=393)and Helmholtz Association(n=93)have emerged as the leading countries and institutions,respectively,in this field,with Hans Clevers and Toshiro Sato being the most contributing authors.The most influential journal in this field is Gastroenterology.The most impactful reference is"Long term expansion of epithelial organs from human colon,adenoma,adenocarcinoma,and Barrett's epithelium".Keywords analysis and citation burst analysis indicate that precision medicine,disease modeling,drug development and screening,and regenerative medicine are the most cutting-edge directions.These focal points were further detailed based on the literature.CONCLUSION This bibliometric study offers an objective and quantitative analysis of the research in this field,which can be considered as an important guide for next scientific research.

A novel box-counting method for quantitative fractal analysis of threedimensional pore characteristics in sandstone

Fractal theory offers a powerful tool for the precise description and quantification of the complex pore structures in reservoir rocks,crucial for understanding the storage and migration characteristics of media within these rocks.Faced with the challenge of calculating the three-dimensional fractal dimensions of rock porosity,this study proposes an innovative computational process that directly calculates the three-dimensional fractal dimensions from a geometric perspective.By employing a composite denoising approach that integrates Fourier transform(FT)and wavelet transform(WT),coupled with multimodal pore extraction techniques such as threshold segmentation,top-hat transformation,and membrane enhancement,we successfully crafted accurate digital rock models.The improved box-counting method was then applied to analyze the voxel data of these digital rocks,accurately calculating the fractal dimensions of the rock pore distribution.Further numerical simulations of permeability experiments were conducted to explore the physical correlations between the rock pore fractal dimensions,porosity,and absolute permeability.The results reveal that rocks with higher fractal dimensions exhibit more complex pore connectivity pathways and a wider,more uneven pore distribution,suggesting that the ideal rock samples should possess lower fractal dimensions and higher effective porosity rates to achieve optimal fluid transmission properties.The methodology and conclusions of this study provide new tools and insights for the quantitative analysis of complex pores in rocks and contribute to the exploration of the fractal transport properties of media within rocks.

Auto-parametric resonance of a continuous-beam-bridge model under two-point periodic excitation:an experimental investigation and stability analysis

The auto-parametric resonance of a continuous-beam bridge model subjected to a two-point periodic excitation is experimentally and numerically investigated in this study.An auto-parametric resonance experiment of the test model is conducted to observe and measure the auto-parametric resonance of a continuous beam under a two-point excitation on columns.The parametric vibration equation is established for the test model using the finite-element method.The auto-parametric resonance stability of the structure is analyzed by using Newmark's method and the energy-growth exponent method.The effects of the phase difference of the two-point excitation on the stability boundaries of auto-parametric resonance are studied for the test model.Compared with the experiment,the numerical instability predictions of auto-parametric resonance are consistent with the test phenomena,and the numerical stability boundaries of auto-parametric resonance agree with the experimental ones.For a continuous beam bridge,when the ratio of multipoint excitation frequency(applied to the columns)to natural frequency of the continuous girder is approximately equal to 2,the continuous beam may undergo a strong auto-parametric resonance.Combined with the present experiment and analysis,a hypothesis of Volgograd Bridge's serpentine vibration is discussed.

Cookie Baking Process Optimization and Quality Analysis Based on Food 3D Printing

In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.

A Bayesian multi-model inference methodology for imprecise momentindependent global sensitivity analysis of rock structures

Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

Superconvergence of Direct Discontinuous Galerkin Methods:Eigen-structure Analysis Based on Fourier Approach

This paper investigates superconvergence properties of the direct discontinuous Galerkin(DDG)method with interface corrections and the symmetric DDG method for diffusion equations.We apply the Fourier analysis technique to symbolically compute eigenvalues and eigenvectors of the amplification matrices for both DDG methods with different coefficient settings in the numerical fluxes.Based on the eigen-structure analysis,we carry out error estimates of the DDG solutions,which can be decomposed into three parts:(i)dissipation errors of the physically relevant eigenvalue,which grow linearly with the time and are of order 2k for P^(k)(k=2,3)approximations;(ii)projection error from a special projection of the exact solution,which is decreasing over the time and is related to the eigenvector corresponding to the physically relevant eigenvalue;(iii)dissipative errors of non-physically relevant eigenvalues,which decay exponentially with respect to the spatial mesh sizeΔx.We observe that the errors are sensitive to the choice of the numerical flux coefficient for even degree P^(2)approximations,but are not for odd degree P^(3)approximations.Numerical experiments are provided to verify the theoretical results.

Integrated multi-scale approach combining global homogenization and local refinement for multi-field analysis of high-temperature superconducting composite magnets

Second-generation high-temperature superconducting(HTS)conductors,specifically rare earth-barium-copper-oxide(REBCO)coated conductor(CC)tapes,are promising candidates for high-energy and high-field superconducting applications.With respect to epoxy-impregnated REBCO composite magnets that comprise multilayer components,the thermomechanical characteristics of each component differ considerably under extremely low temperatures and strong electromagnetic fields.Traditional numerical models include homogenized orthotropic models,which simplify overall field calculation but miss detailed multi-physics aspects,and full refinement(FR)ones that are thorough but computationally demanding.Herein,we propose an extended multi-scale approach for analyzing the multi-field characteristics of an epoxy-impregnated composite magnet assembled by HTS pancake coils.This approach combines a global homogenization(GH)scheme based on the homogenized electromagnetic T-A model,a method for solving Maxwell's equations for superconducting materials based on the current vector potential T and the magnetic field vector potential A,and a homogenized orthotropic thermoelastic model to assess the electromagnetic and thermoelastic properties at the macroscopic scale.We then identify“dangerous regions”at the macroscopic scale and obtain finer details using a local refinement(LR)scheme to capture the responses of each component material in the HTS composite tapes at the mesoscopic scale.The results of the present GH-LR multi-scale approach agree well with those of the FR scheme and the experimental data in the literature,indicating that the present approach is accurate and efficient.The proposed GH-LR multi-scale approach can serve as a valuable tool for evaluating the risk of failure in large-scale HTS composite magnets.

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.

An analysis of agarwood trade patterns,historical perspectives,and species identification challenges:repercussions for importing nations

Background:Agarwood,primarily derived from the Aquilaria and Gyrinops genera,holds significant economic importance.However,there is a lack of comprehensive investigations providing guidance to importing nations regarding cultivation quantities and expected yields of Agarwood from distinct species.This study aims to address this gap by exploring the historical context and trade evolution of Agarwood,highlighting its global importance,and the challenges associated with securing accurate species information.Method:On-site visits to Agarwood cultivation sites were conducted to gain a nuanced understanding of Aquilaria species and their cultivation requirements.Additionally,a thorough analysis of global export and import data for Agarwood products over the last decade was undertaken.Results:China Mainland emerged as the leading exporter of Agarwood,averaging an annual export value of USD 1 million.India’s substantial exports challenge the prevailing notion of limited Agarwood production within its borders.Hong Kong and Singapore are pivotal distribution hubs,while Hong Kong and Taipei feature prominently as import destinations.Our analysis uncovers anomalies in the representation of Agarwood producers from 2001 to 2008,suggesting potential misclassification of Aquilaria Agarwood as Gyrinops in global export information.These findings underscore the urgency of investigating classification and reporting practices in the Agarwood trade.Furthermore,A.filaria emerges as a notable source,while A.malaccensis is decline in prominence.Conclusion:This study provides crucial insights for policymakers,stakeholders,and industry players seeking to make informed decisions in the Agarwood trade landscape.The results highlight the need for accurate species identification,classification,and reporting practices to ensure sustainable cultivation and trade of Agarwood.

Correction: Efficacy of activating blood and resolving stasis therapy for IgA nephropathy: a systematic review and meta-analysis

Medical Data Mining published an article entitled Efficacy of activating blood and resolving stasis therapy for IgA nephropathy:a systematic review and meta-analysis on 16 January 2024.The author confirmed this article’s proof on 15 January 2024 without any questions.But on 15 April 2024,the authors contacted the editorial office to state that the name of the corresponding author is mis-spelled as“Han-Qing Zang”.Hence the mis-spelled name“Han-Qing Zang”is corrected as“Han-Qing Zhang”.“Han-Qing Zhang”is the third author of this article.