High Correlation Research Articles

Prediction of FeO content in sintered ore based on ICEEMDAN and CNN-BiLSTM-AM

FeO content of sintered ore is an important reference index for measuring the performance of sintered ore. It significantly impacts the ironmaking process, iron quality, and energy consumption. Aiming at the current problem of delayed and poor accuracy of sintered ore FeO content detection results, this article proposes a hybrid network model that incorporates improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM) for FeO content prediction. First, the FeO content time series were decomposed using ICEEMDAN to obtain sub-layers with different frequencies. Then, the features with higher correlation with FeO content were selected by feature selection as model inputs, followed by predicting the decomposition sequences of FeO content using CNN-BiLSTM-AM for the feature-selected variables, respectively. Finally, all predicted sublayer predictions were reconstructed into the final prediction by summation. The proposed model effectively captures the essence of the sequence through the ICEEMDAN algorithm, extracts deep features from FeO content data using CNN, captures contextual information through BiLSTM, and enhances feature extraction capability with AM. The experimental results show that the collaboration of CNN, BiLSTM, and AM effectively enhances the modelling capability of the model and significantly improves the prediction accuracy. Additionally, the ICEEMDAN decomposition algorithm is employed to enhance the prediction performance further, offering advantages over other decomposition techniques. The MAE, MAPE, RMSE, RRMSE, and R² of the new ICEEMDAN-CNN-BiLSTM-AM (ICBA) model are 0.0751, 0.846%, 0.0937, 1.0500%, and 0.9646, respectively, demonstrating a significant improvement in prediction accuracy and outperforming the relevant comparison models.

Automated analysis of spoken language differentiates multiple system atrophy from Parkinson’s disease

Background and objectivesPatients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson’s disease (PD) frequently display speech and language abnormalities. We explore the diagnostic potential of automated linguistic analysis of natural spontaneous speech to differentiate MSA and PD.MethodsSpontaneous speech of 39 participants with MSA compared to 39 drug-naive PD and 39 healthy controls matched for age and sex was transcribed and linguistically annotated using automatic speech recognition and natural language processing. A quantitative analysis was performed using 6 lexical and syntactic and 2 acoustic features. Results were compared with human-controlled analysis to assess the robustness of the approach. Diagnostic accuracy was evaluated using sensitivity analysis.ResultsDespite similar disease duration, linguistic abnormalities were generally more severe in MSA than in PD, leading to high diagnostic accuracy with an area under the curve of 0.81. Compared to controls, MSA showed decreased grammatical component usage, more repetitive phrases, shorter sentences, reduced sentence development, slower articulation rate, and increased duration of pauses, whereas PD had only shorter sentences, reduced sentence development, and longer pauses. Only slower articulation rate was distinctive for MSA while unchanged for PD relative to controls. The highest correlation was found between bulbar/pseudobulbar clinical score and sentence length (r = −0.49, p = 0.002). Despite the relatively high severity of dysarthria in MSA, a strong agreement between manually and automatically computed results was achieved.DiscussionAutomated linguistic analysis may offer an objective, cost-effective, and widely applicable biomarker to differentiate synucleinopathies with similar clinical manifestations.

Estimation of Actual Evapotranspiration and Crop Coefficient to Improve Water Use Efficiency of Sesamum (Sesamum indicum L.) in Kharif Season Using Digital Weighing Lysimeter

The study was conducted on Estimation of Actual Evapotranspiration and Crop Coefficient to Improve Water Use Efficiency of Sesamum (Sesamum indicum L.) in Kharif Season Using Digital Weighing Lysimeter,” was conducted during the 2023-24 Kharif season with the aim of enhancing sesamum water use efficiency. For estimation of actual evapotranspiration and crop coefficient following are the objectives were to calculate and compare reference evapotranspiration (ETr) using various methods, measure crop evapotranspiration (ETc) using a digital weighing lysimeter, estimate crop coefficient (Kc) value. ETr was calculated using six different methods: Penman-Monteith, Modified Penman-Monteith, FAO Radiation, Priestly-Taylor, Hargreaves-Samani, and Pan Evaporation. The result obtained from that is the seasonal ETr was found 328.53, 395.47, 369.37, 430.26, 416.04, 372.90 mm respectively. While calculating or estimating reference evapotranspiration it is observed that The Modified Penman Monteith method had the highest correlation (R²=0.98) with the standard Penman-Monteith method, while the FAO Radiation and Pan Evaporation methods also performed well. crop Evapotranspiration (ETc) of Sesamum crop, measured using a digital weighing lysimeter, ranged from 0.61 to 5.65 mm/day, with a total of 244.94 mm for the season and the Crop coefficients (Kc) values were calculated by ratio of crop Evapotranspiration (ETc) with Reference Evapotranspiration (ETr), from that polynomial regression models were developed to estimate daily Kc values based on the crop growth ratio (t/T). The daily Kc values determined by using the best fit polynomial equations were averaged over initial, development, mid-season and end-season stage, the Kc values developed by using different methods i.e (Penman-Monteith method) are 0.58, 0.77, 1.00, and 0.36; Kc values (Modified Penman Monteith method) were 0.48, 0.64, 0.84 and 0.32; Kc values (FAO Radiation method) were 0.61, 0.73, 0.91 and 0.33; Kc values (Priestly Taylor method) were 0.26, 0.61, 0.98 and 0.33; Kc values (Hargreaves Samani method) were 0.36, 0.58, 0.92 and 0.28; and Kc values (Pan Evaporation method) were 0.48, 0.65, 0.93 and 0.32, respectively. On an average Kc determined by Penman Monteith method, Modified Penman Monteith method, FAO Radiation method and Pan Evaporation method overestimated the Kc by 26.17, 7.00, 21.92 and 9.38 per cent, respectively compared to FAO-56 values. However, Priestly-Taylor method and Hargraves-Samani method underestimated Kc by 5.55 and 5.78 per cent, respectively.

A comprehensive analysis to reveal the underlying molecular mechanisms of natural killer cell in thyroid carcinoma based on single-cell RNA sequencing data

BackgroundThyroid carcinoma (THCA) is the most common cancer of the endocrine system. Natural killer (NK) cell play an important role in tumor immune surveillance. The aim of this study was to explore the possible molecular mechanisms involved in NK cell in THCA to help the management and treatment of the disease.MethodsAll data were downloaded from public databases. Candidate hub genes associated with NK cell in THCA were identified by limma, WGCNA and singleR packages. Functional enrichment analysis was performed on the candidate hub genes. Hub genes associated with NK cell were identified by Pearson correlation analysis. The mRNA-miRNA-lncRNA and transcription factors (TF) networks were constructed and the drug was predicted.ResultsThe infiltration level of NK cell in THCA tissues was higher than that in paracancerous tissues. KEGG functional enrichment analysis only obtained two signaling pathways, thyroid hormone synthesis and mineral absorption. CTSC, FN1, SLC34A2 and TMSB4X identified by Pearson correlation analysis were considered as the hub genes. Receiver operating characteristic analysis suggested that hub genes may be potential diagnostic biomarkers. In mRNA-miRNA-lncRNA network, FN1 had the highest correlation with IQCH-AS1, and IQCH-AS1 was also correlated with hsa-miR-543. In addition, FN1 and RUNX1 were also found to have the highest correlation in TF network. Finally, NK cell-related drugs belinostat and vorinostat were identified based on ASGARD.ConclusionThe identification of important signaling pathways, molecules and drugs provides potential research directions for further research in THCA and contributes to the development of diagnostic and therapeutic approaches for this disease.

Integrated of Hyperspectral Imaging and Machine Learning Algorithms for Nondestructive Detection of Therapeutic Properties of Plants.

The approaches used to determine the medicinal properties of the plants are often destructive, labor-intensive, time-consuming, and expensive, making it impossible to analyze their quality analysis online. Performance of hyperspectral imaging (HSI) integrated with intelligent techniques to overcome these problems was investigated in this research. For this purpose, three classification methods-support vector machine, random forest (RF), and extreme gradient boosting-were studied for the classification of plants in three classes of medicinal, edible, and ornamental for the organs of leaf, stem, flower, and root. The medicinal effects of the plant organs were determined by measuring different biochemical properties of the organs. The spectral reflectance of the samples was used to train and test the classification methods in which output targets were the plant types. The results showed that amounts of the biochemical factors except oil content of the medicinal plants were higher than the other types of plants. Further, the biochemical factors of flowers and leaves were higher than the other organs indicating that the most therapeutic effect of the plants is through the flowers and leaves. Using HSI, a similar spectral trend was appeared in each organ, whereas it was different among the organs. Using the RF as the best method (precision and accuracy were higher than 0.95), the lowest misclassification rates were related to the stem and leaf datasets, indicating that these two organs were most suitable to classify the plants aromatically. The most misclassifications of the organs were occurred between medicinal and edible plants related to the spectra having higher correlations with flavonoid, phenol, and antioxidant compounds. Overall, the misclassification rates were negligible, and thus, the methods developed in this study can be used online in postharvest processes of the medicinal plants.

Accelerating CO2 Outgassing in the Equatorial Pacific from Satellite Remote Sensing

The equatorial Pacific serves as the world’s largest oceanic source of CO2. The contrasting ocean environment in the eastern (i.e., upwelling) and western (i.e., warm pool) regions makes it difficult to fully characterize its CO2 dynamics with limited in situ observations. In this study, we addressed this challenge using monthly surface partial pressure of CO2 (pCO2sw) and air-sea CO2 fluxes (FCO2) data products reconstructed from satellite and reanalysis data at a spatial resolution of 1° × 1° in the period of 1982–2021. We found that during the very strong El Niño events (1997/1998, 2015/2016), both pCO2sw and FCO2 showed a significant decrease of 41–58 μatm and 0.5–0.8 mol·m−2·yr−1 in the eastern equatorial Pacific, yet they remained at normal levels in the western equatorial Pacific. In contrast, during the very strong La Niña events (1999/2000, 2007/2008, and 2010/2011), both pCO2sw and FCO2 showed a strong increase of 40–48 μatm and 1.0–1.4 mol·m−2·yr−1 in the western equatorial Pacific, yet with little change in the eastern equatorial Pacific. In the past 40 years, pCO2sw in the eastern equatorial Pacific was increasing at a higher rate (2.32–2.51 μatm·yr−1) than that in the western equatorial Pacific (1.75 μatm·yr−1), resulting in an accelerating CO2 outgassing (at a rate of 0.03 mol·m−2·yr−2) in the eastern equatorial Pacific. We comprehensively analyzed the potential effects of different factors, such as sea surface temperature, sea surface wind speed, and ΔpCO2 in driving CO2 fluxes in the equatorial Pacific, and found that ΔpCO2 had the highest correlation (R ≥ 0.80, at p ≤ 0.05), highlighting the importance of accurate estimates of pCO2sw from satellites. Further studies are needed to constrain the retrieval accuracy of pCO2sw in the equatorial Pacific from satellite remote sensing.

Evaluation of external and radiological landmark methods for optimizing ultrasound-guided right internal jugular venous catheterization depth in cardiac surgery

The formula-based estimation of the right internal jugular venous (IJV) catheterization depth can be inaccurate when using ultrasound guidance. External landmark-based and radiological landmark-based methods have been proposed as alternatives to estimate the insertion depth. This study aimed to evaluate these methods using transesophageal echocardiography (TEE)-guided insertion depth as the reference. Ninety-seven adult cardiac surgery patients underwent real-time ultrasound-guided right IJV catheterization, with placement at the superior vena cava-right atrium (SVC-RA) junction under TEE guidance. The primary outcome compared the accuracy of external-landmark and radiological-landmark methods in estimating optimal catheter position (2 cm above to 1 cm below the SVC-RA junction), while secondary outcomes assessed their reliability and agreement with TEE. The external-landmark method proved more accurate than the radiological-landmark method (91.8% vs. 68%, p < 0.001) and had a higher correlation with TEE (r = 0.83 vs. 0.67). Bland–Altman analysis showed a mean difference of 0.08 cm between external-landmark and TEE methods (limits of agreement: -1.75 to 1.90 cm) and 0.43 cm for radiological-landmark (limits of agreement: -2.63 to 3.49 cm). Overall, the external-landmark method is a reliable and simple technique for estimating right IJV catheter depth and is more useful in cardiac surgery compared to the radiological-landmark method.Trial registration Thai Clinical Trials Registry (TCTR20200410012). Date registered April 9, 2020.

A novel fabrication of graphitic carbon nitride/chitosan composite modified with thiosemicarbazide for the effective static and dynamic adsorption of Pb2+ from aqueous media.

In this work, graphitic carbon nitride (g-C3N4) prepared by thermal treatment, graphitic carbon nitride/chitosan (GCS), and graphitic carbon nitride/chitosan embedded thiosemicarbazide (TGCS) were developed as an effective solid adsorbent. The fabricated adsorbents were characterized by nitrogen adsorption, ATR-FTIR, TGA, XRD, ζ potential, SEM, and TEM, where TGCS composite had a higher surface area (536.79 m2/g), total pore volume (0.4152 cm3 /g), average pore size (3.09 nm), and pHPZC (6.4). TGCS revealed a Langmuir adsorption capacity of 329.61 mg/g for Pb2+ at an adsorbent dosage of 1.5 g/L, pH 5, 45 min of shaking, and 23 °C. The experimental results were applied well by pseudo-second-order kinetic and Langmuir isotherm. Through the first five adsorption-desorption cycles, only 12.2, 6.2, and 5.1 % decline in efficiency were noted for g-C3N4, GCS, and TGCS, respectively. Column adsorption capacity (qo, mg/g) was determined to be 132.24 mg/g at bed height 3 cm, flow rate of 80 mL/min, and 80 mg/L as initial Pb2+ concentration. Based on the lower reduced chi-square values (ꭓ2 × 10-4, 3.8810-12.9000) and the higher correlation coefficients (R2, 0.9917-0.9979), the Yoon-Nelson and Thomas models suggested an acceptable match for the breakthrough curve. Our findings suggested that TGCS had great adsorption capacity, strong selectivity, and quick kinetics, indicating its potential for water treatment applications.

Development and Psychometric Validation of the Cyber-Self Scale (CSS) in Saudi Arabia.

Measuring adolescents' and youths' perception of their Cyber-Self can enhance the understanding of how digital technology influences identity formation. While psychological literature offers numerous measures of the self, there is a notable lack of studies addressing the measurement of the Cyber-Self. This study aims to evaluate the reliability, factorial- and criterion-related validity, and measurement invariance of the Cyber-Self Scale (CSS) across age and gender among the youth and adolescents in Saudi Arabia. The Cyber Relationship Motives (CRM) and E-Emotional Questionnaire (EEQ) were administered to students at Umm Al-Qura University (N = 335), aged 17-31 years (39.7% male, 60.3% female; mean (M) = 21.75, standard deviation (SD) = 2.17). The results indicated significant positive correlations between the sub-components of the CRM and EEQ. One item was selected based on two criteria: the highest correlation with other items and the highest correlation with the general factor. A total of 12 items were identified as the final form of the CSS, which demonstrated acceptable internal consistency for both male and female participants. Confirmatory factor analysis (CFA) revealed that the CSS model fit the data well, with all 12 items meeting the fit criteria for chi-square and root mean square error of approximation (RMSEA). The Arabic version of the CSS is sufficiently reliable and valid for use among Arabic-speaking adolescents and youth. Further research is recommended to examine its measurement invariance over extended periods.

Optimum Nitrogen and Phosphorus Combination Improved Yield and Nutrient Use Efficiency of Sorghum in Saline Soil.

Two-year experiments were conducted to assess the responses of yield and nutrient use efficiency of sorghum to nitrogen and phosphorus under saline soils. Three nitrogen rates (0, 180, and 360 N kg ha-1) and three phosphorus rates (0, 60, and 120 P2O5 kg ha-1) were used in this study. Our results showed that nitrogen and phosphorus supply increased SPAD (leaf greenness, 5.0-29.1%), NSC (non-structural carbohydrates, 14.0-41.2%), nutrient accumulation (nitrogen: 14.1-50.0%, phosphorus: 11.8-41.5%, potassium: 13.7-28.2%), biomass (fresh: 10.8-29.3%, dry: 5.0-22.8%), yield (16.6-30.7%), and harvest index (2.0-9.8%) of sorghum at the maturity stage grown in saline soils but significantly decreased nutrient use efficiency. Combined application showed better performance on these attributes than sole nitrogen and sole phosphorus. The highest seed yield (5919 kg ha-1) was recorded at 180 N kg ha-1 and 60 P2O5 kg ha-1 treatment, while the largest value of dry biomass (18,401 kg ha-1) was obtained at 360 N kg ha-1 and 120 P2O5 kg ha-1 treatment. The Pearson analysis showed that seed yield had the highest correlation with aerial potassium accumulation, while dry biomass had the highest correlation with NSC as well as aerial nitrogen accumulation. A treatment of 180 N kg ha-1 and 60 P2O5 kg ha-1 was recommended to be used in sorghum production in saline soils based on harvest index and nutrient use efficiency.

Cortex Dictamni induces retinitis pigmentosa in zebrafish by inhibiting pde6a post-transcriptional activity via mmu-mir-6240-p3_2.

Cortex Dictamni (CD) is the dried root skin of Dictamnus dasycarpus Turcz, widely used in the field of traditional Chinese medicine. Recent adverse reactions to CD limited the clinical application in combination with other traditional Chinese medicines. To investigate the retinitis pigmentosa (RP) effects of CD using the zebrafish model and elucidate the underlying molecular mechanism of CD-induced RP in zebrafish. The 3-dpf zebrafish larvae were divided into the control group and the CD group. RNA sequencing was conducted, followed by qRT-PCR to validate the expression of miRNAs and mRNAs. The dual luciferase reporter assay confirmed the interaction between mmu-mir-6240-p3_2 and pde6a. HT22 cells were transfected, treated with CD, and then evaluated for migration, invasion, malondialdehyde levels, superoxide dismutase activity, and acetylcholine activity. The results showed 6228 differentially expressed genes and 66 miRNAs differentially expressed in zebrafish exposed to CD. The person correlation coefficient results showed that mmu-mir-6240-p3_2 had the highest correlation coefficient with pde6a, indicating a negative regulatory relationship. Furthermore, the results of dual luciferase reporter gene further showed that pde6a gene was the direct target of mmu-mir-6240-p3_2. Cell experiment results showed that inhibiting mir-6240-p3_2 can upregulate pde6a expression, alleviate HT22cell injury, and reverse the inhibition of cell migration and invasion induced by CD. CD induces RP in zebrafish by inhibiting pde6a post-transcriptional activity via mmu-mir-6240-p3_2. These findings have important implications for understanding the potential side effects of CD and for developing safer therapeutic strategies involving traditional Chinese medicines.

Amide proton transfer-weighted (APTw) imaging and derived quantitative metrics in evaluating gliomas: Improved performance compared to magnetization transfer ratio asymmetry (MTRasym).

Isocitrate dehydrogenase (IDH) status, glioma subtypes and tumor proliferation are important for glioma evaluation. We comprehensively compare the diagnostic performance of amide proton transfer-weighted (APTw) MRI and its related metrics in glioma diagnosis, in the context of the latest classification. Totally 110 patients with adult-type diffuse gliomas underwent APTw imaging. The magnetization transfer ratio asymmetry (MTRasym), magnetization transfer ratio normalized by reference signal (MTRnormref), and spillover-corrected magnetization transfer ratio yielding Rex (MTRRex), and metrics based on Lorentzian fitting (Fit-amide, Fit-MTRnormref, and Fit-MTRRex) were calculated. Group differences were compared between IDH genotypes, and among three glioma subtypes. The diagnostic performances were assessed using the receiver operating characteristic (ROC) analysis and compared. The correlations with Ki-67 expression were also analyzed. All APTw-related metrics exhibited significantly higher values in IDH-wildtype gliomas than in IDH-mutant gliomas (all p < 0.001). Fit-MTRnormref had the best area under the curve (AUC) of 0.858. All APTw-related metrics in glioblastomas were significantly higher than oligodendrogliomas (all p < 0.01) and astrocytomas (all p < 0.001). No metrics had significant difference between oligodendrogliomas and astrocytomas. The highest AUCs was 0.870 for Fit-MTRnormref in distinguishing astrocytomas from glioblastomas, and 0.867 for Fit-MTRRex in distinguishing oligodendrogliomas from glioblastomas. Besides, Fit-MTRnormref had the highest correlation coefficient with Ki-67 expression of 0.578. APTw-related metrics can effectively evaluate glioma IDH status, tumor subtypes and proliferation. The combination of Lorentzian fitting and the reference signal normalization could further improve the diagnostic performance, and perform better than MTRasym.

Development and Validation of a Simplified Financial Toxicity Screening Tool for Use in Clinical Practice.

Cancer-related financial toxicity occurs frequently and is a key driver of inequities in access to care and disparities in treatment outcomes. Current practices to screen for financial toxicity are inconsistent because of the lack of a validated and clinically integrated screening tool. This analysis aimed to create and assess an abbreviated version of the validated Comprehensive Score for Financial Toxicity (COST) tool, a measure of financial toxicity used for research purposes, which could easily be added into often-lengthy clinical screening workflows. At an urban comprehensive cancer center with suburban satellite locations, a financial toxicity screening quality improvement project was conducted from June 2022 to August 2023 as part of routine clinical care: 57,526 longitudinal COST surveys were completed by 38,249 patients with cancer. An iterative algorithm selected the items with highest correlation with the total score. Using a separate validation data set, positive and negative predictive values (PPV and NPV, respectively) of the abbreviated tool (two-item) were assessed against the full COST score, with varying risk thresholds. Inclusion of two COST questions (Q3: "I worry about the financial problems I will have in the future as a result of my illness or treatment"; Q6: "I am satisfied with my current financial situation") yielded a score that had a correlation of 0.922 with the full instrument score. For the two-item scale, PPV ranged from 74% to 91%, and NPV ranged from 91% to 98% when compared with the full COST tool. This analysis of a large data set finds that a simplified COST tool has high predictive value when compared with the full validated measure. An abbreviated COST measure of two questions is suitable for implementation into clinical screening workflows.

Validation and adaptation of the Serbian adaptation of the Marijuana Effect Expectancies Questionnaire among adolescents

Background/Aim. Expectancies about the effects of cannabis have been related to the onset and frequency of its consumption. One of the most used instruments to measure cannabis effect expectancies is the Marijuana Effect Expectancy Questionnaire (MEEQ). The aim of this study was to determine the psychometric properties of the Serbian adaptation of the MEEQ among secondary school pupils. Methods. The retrospective study included 1,642 secondary school pupils (52.3% female) from the South Backa District, Vojvodina, Serbia. In addition to the MEEQ, the Cannabis Use Intention Questionnaire (CUIQ) was also used, as well as a self-report measure of cannabis use. Results. The original six-factor MEEQ model showed the best fit indices with the following factors: Cognitive and Behavioral Impairment, Relaxation and Tension Reduction, Social and Sexual Facilitation, Perceptual and Cognitive Enhancement, Global Negative Effects, and Craving and Physical Effects. All MEEQ scales showed positive and expected correlations with the CUIQ scales, among which the highest correlation was with the Attitudes toward Consumption scale. Correlations with self-report cannabis use were significant for all MEEQ scales, except for Cognitive and Behavioral Impairment. Conclusion. The results suggested good psychometric properties of the Serbian adaptation of the MEEQ. The results also confirmed the originally proposed factor structure, good reliabilities of the scales? scores based on internal consistency, as well as convergent and criterion validity.

Defective Mitophagy Impairs Response to Inflammatory Activation of Macrophage-Like Cells.

The role of mitophagy in atherosclerosis has been extensively studied during the last few years. It was shown that mitophagy is involved in the regulation of macrophages, which are important players as immune cells in atherosclerosis development. In this study, we investigated the relationship between mitophagy and response to inflammatory stimulation of macrophage-like cells. Six cybrid cell lines with normal mitophagy, that is, increasing in response to stimulation, and 7 lines with defective mitophagy not responding to stimulation were obtained. The objective of the study was to compare the nature of the inflammatory response in normal and defective mitophagy in order to elucidate the role of mitophagy defects in inflammation. We used cytoplasmic hybrids (cybrids) as cellular models, created using mitochondrial DNA from different atherosclerosis patients. Mitophagy was stimulated by carbonyl cyanide m-chlorophenyl hydrazone (CCCP) and assessed as the degree of colocalization of mitochondria with lysosomes using confocal microscopy. Western blotting methods were used for the determination of proteins involved in the exact mechanism of mitophagy. Experiments with stimulation of mitophagy show a high correlation between these two approaches (microscopy and blotting). The pro-inflammatory response of cybrids was stimulated with bacterial lipopolysaccharide (LPS). The extent of the inflammatory response was assessed by the secretion of cytokines CCL2, IL8, IL6, IL1β, and TNF measured by ELISA. Basal level of secretion of cytokines CCL2, IL8 and TNF was 1.5-2 times higher in cultures of cybrids with defective mitophagy compared to cells with normal mitophagy. This suggests a persistently elevated inflammatory response in cells with defective mitophagy, even in the absence of an inflammatory stimulus. Such cells in the tissue will constantly recruit other immune cells, which is characteristic of macrophages derived from monocytes circulating in the blood of patients with atherosclerosis. We observed significant differences in the degree and type of response to inflammatory activation in cybrids with defective mitophagy. These differences were not so much quantitative as they were dramatically qualitative. Compared with cells with normal mitophagy, in cells with defective mitophagy, the relative (to basal) secretion of IL8, IL6 and IL1b increased after the second LPS activation. This indicates a possible lack of tolerance to inflammatory activation in cells with defective mitophagy, since typically, re-activation reveals a smaller pro-inflammatory cytokine response, allowing the inflammatory process to resolve. In cells with normal mitophagy, exactly this normal (tolerant) inflammatory reaction was observed. Data on the involvement of mitophagy, including defective mitophagy, in disturbances of the inflammatory response in sepsis, viral infections, autoimmune diseases and other pathologies have previously been reported. In this work, we studied the role of defective mitophagy in non-infectious chronic inflammatory diseases using the example of atherosclerosis. We showed a dramatic disruption of the inflammatory response associated with defective mitophagy. Compared with cybrids with normal mitophagy, in cybrids with defective mitophagy, the secretion of all studied cytokines changed significantly both quantitatively and qualitatively. In particular, the secretion of 3 of 5 cytokines demonstrated an intolerant inflammatory response manifested by increased secretion after repeated inflammatory stimulation. Such an intolerant reaction likely indicates a significant disruption of the pro-inflammatory response of macrophages, which can contribute to the chronification of inflammation. Elucidating the mechanisms of chronification of inflammation is extremely important for the search for fundamentally new pharmacological targets and the development of drugs for the prevention and treatment of chronic inflammatory diseases, including atherosclerosis and diseases characteristic of inflammation. Such diseases account for up to 80% of morbidity and mortality.

A new sea ice concentration retrieval algorithm from thermal infrared imagery

ABSTRACT Sea ice concentration (SIC) can be retrieved from thermal infrared (TIR) imagery due to the distinctive thermal properties of ice and water. Nevertheless, existing TIR-based SIC algorithms rely on surface temperature data, which often introduces additional errors. To address this issue, we have developed a new TIR ice concentration algorithm (TIRIA) that directly utilizes TIR brightness temperatures. TIRIA considers factors such as seawater salinity, observation angle and their impacts on seawater brightness temperature. TIRIA and a traditional algorithm, the MODIS potential open water algorithm (MPA), are applied to MODIS TIR imagery. Results are evaluated with near-infrared (NIR) SICs and compared with passive microwave (PM) SICs. Overall, TIRIA outperforms MPA, exhibiting a smaller root mean square error (RMSE) (14.01% compared to 17.63%) and higher correlation coefficient (0.89 compared to 0.81). Both TIRIA and MPA tend to underestimate SIC in high SICs while overestimating it in low SICs. Due to its more accurate identification of water, TIRIA significantly mitigates the overestimation in low SICs. Compared to PM-based SICs, both TIR-based SICs exhibit overall overestimations, with better consistency between TIRIA and PM-based SICs. TIRIA, being independent of surface temperature products and theoretically applicable to any TIR data, showcases great potential for future application.

The effect of item pool size and stopping rule on bias and error parameters in computerized adaptive testing

This study aims to examine the effects of item pool size and stopping rules on bias and error parameters in computerized adaptive testing (CAT). Simulated data were generated and analyzed for item pools of 200 and 2000 items, fixed-length and error stopping rules, and eight different combinations of Maximum Likelihood (ML) and Expected a Posteriori (EAP) methods for ability estimation. For each sub-problem, the findings are listed in a general table and a collection of graphs. In the light of the findings, the error and bias parameters obtained with the ML skill estimation method with fixed-length stopping rule in both small and large item pools were found to be lower than the EAP method. Similarly, in both small and large item pools, the error and bias parameters obtained with the ML ability estimation method in which the error stopping rule was applied were found to be lower than the EAP method. While the number of items used in the analyses where the error stopping rule was applied was approximately six in both ability estimation methods for the small item pool, approximately five items were used with the EAP method and approximately 21 items were used with the ML method in the large item pool. The application with the lowest RMSE and bias values and the highest correlation between actual and predicted θ values was the application related to the sixth sub-problem in which a large item pool was used, a fixed-length stopping rule was used, and ability estimation was performed with the ML method. The results of the study provide clues for the development of testing strategies in CAT applications and provide guidance for more effective assessment processes in the field of education.

Validation of CRU TS v4.08, ERA5-Land, IMERG v07B, and MSWEP v2.8 Precipitation Estimates Against Observed Values over Pakistan

Global precipitation products (GPPs) are vital in weather forecasting, efficient water management, and monitoring floods and droughts. However, the precision of these datasets varies considerably across different climatic regions and topographic conditions. Therefore, the accuracy assessment of the precipitation dataset is crucial at the local scale before its application. The current study initially compared the performance of recently modified and upgraded precipitation datasets, including Climate Research Unit Time-Series (CRU TS v4.08), fifth-generation ERA5-Land (ERA-5), Integrated Multi-satellite Retrievals for GPM (IMERG) final run (IMERG v07B), and Multi-Source Weighted-Ensemble Precipitation (MSWEP v2.8), against ground observations on the provincial basis across Pakistan from 2003 to 2020. Later, the study area was categorized into four regions based on the elevation to observe the impact of elevation gradients on GPPs’ skills. The monthly and seasonal precipitation estimations of each product were validated against in situ observations using statistical matrices, including the correlation coefficient (CC), root mean square error (RMSE), percent of bias (PBias), and Kling–Gupta efficiency (KGE). The results reveal that IMERG7 consistently outperformed across all the provinces, with the highest CC and lowest RMSE values. Meanwhile, the KGE (0.69) and PBias (−0.65%) elucidated, comparatively, the best performance of MSWEP2.8 in Sindh province. Additionally, all the datasets demonstrated their best agreement with the reference data toward the southern part (0–500 m elevation) of Pakistan, while their performance notably declined in the northern high-elevation glaciated mountain regions (above 3000 m elevation), with considerable overestimations. The superior performance of IMERG7 in all the elevation-based regions was also revealed in the current study. According to the monthly and seasonal scale evaluation, all the precipitation products except ERA-5 showed good precipitation estimation ability on a monthly scale, followed by the winter season, pre-monsoon season, and monsoon season, while during the post-monsoon season, all the datasets showed weak agreement with the observed data. Overall, IMERG7 exhibited comparatively superior performance, followed by MSWEP2.8 at a monthly scale, winter season, and pre-monsoon season, while MSWEP2.8 outperformed during the monsoon season. CRU TS showed a moderate association with the ground observations, whereas ERA-5 performed poorly across all the time scales. In the current scenario, this study recommends IMERG7 and MSWEP2.8 for hydrological and climate studies in this region. Additionally, this study emphasizes the need for further research and experiments to minimize bias in high-elevation regions at different time scales to make GPPs more reliable for future studies.

Influence of plant growth promoting haloalkaliphilic bacteria on mitigation salt and alkali stresses in pistachio seedlings (Pistacia vera L.)

Pistachio (Pistacia vera L.) tolerates salinity stress, but its yield decreases with increasing salinity. The present study was conducted to investigate the effect of Khorasan Razavi (Iran) native haloalkaliphile bacterial strains on the vegetative growth, biochemical parameters, and nutrient concentration of pistachio seedlings under orchard conditions. The concentration of ammonia production, tri-indole acetic acid, and ACC deaminase production were measured for Virgibacillus marismortui (61.89, 17.59 mg l-1, and 203.59 µM l-1), and Alkalibacillus haloalkaliphilus (61.55, 36.04 mg l-1 and 512.97 µM l-1), respectively. Most of the vegetative traits decreased up to 12 dS m-1 salinity and then they increased with increasing salinity to 16 dS m-1. The Akbari cultivar was superior in all studied traits except the root-to-shoot ratio. The inoculation with AlkaliBacillus Haloalkalophilus had a stronger impact on nutrient concentrations, while the mixed bacteria treatment had a greater effect on vegetative and biochemical parameters. The effect of bacteria inoculation on a sensitive cultivar (Daneshmandi) was higher in 16 dS m-1. The AlkaliBacillus Haloalkalophilus increased phosphorus, potassium, and boron to 5, 10, and 12.1 mg kg-1 while decreasing sodium to 69 mg kg-1 and Na+/K+ ratio to 0.0028 compared to the mixed bacteria treatment. The highest correlation was found between root volume and stem length (r=0.639) and diameter (r=0.629), indicating the impact of bacteria inoculation on root growth and water relations in saline and sodic conditions. Stem length and diameter growth in young pistachio seedlings are crucial for grafting and economic productivity, so two bacterial treatment mixtures are recommended. 16 dS m-1), two pistachio cultivars (Daneshmandi and se production were measured for Virgibacillus marismortui (61.89, 17.59 mg l-1 and 203.59 µM l-1), and Alkalibacillus haloalkaliphilus (61.55, 36.04 mg l-1 and 512.97 µM l-1), respectively. Results showed that most of the vegetative traits decreased up to 12 dS m-1 salinity and then they increased with increasing salinity to 16 dS m-1. The Akbari cultivar was superior in all studied traits except the root-to-shoot ratio. The inoculation with AlkaliBacillus Haloalkalophilus had a stronger impact on nutrient concentrations, while the mixed bacteria treatment had a greater effect on vegetative and biochemical parameters. The effect of bacteria inoculation on a sensitive cultivar (Daneshmandi) was higher in 16 dS m-1. The AlkaliBacillus Haloalkalophilus increased phosphorous (5 mg kg-1), potassium (10 mg kg-1), and boron (12.1 mg kg-1) concentration and decreased sodium (69 mg kg-1) and Na+/K+ ratio (0.0028) than the mixed bacteria treatment. Inoculation with AlkaliBacillus Haloalkalophilus, two mixed of strain and VirgiBacillus Marismorti treatments caused a 15.1%, 13.8%, and 7.6% reduction in Na+/K+ ratio compared to the control. AlkaliBacillus Haloalkalophilus had a greater effect on reducing the concentration of toxic elements (Na+ and Cl-) and the ratio of sodium to potassium. The highest correlation coefficient was observed between root volume and stem length (r = 0.639) and diameter (r = 0.629), indicating the impact of bacteria inoculation on root growth and water relations in saline and sodic conditions. Due to the importance of the stem length and diameter increase in the early stages of the pistachio seedlings’ growth to grafting and achieve economic productivity, the two mixtures of bacteria treatment were recommended as the maximum stem length, diameter, and leaf area with 55.2, 10.8 and 18.5 cm2, respectively.

VME-EFD : A novel framework to eliminate the Electrooculogram artifact from single-channel EEGs

The diagnosis of neurological disorders often involves analyzing EEG data, which can be contaminated by artifacts from eye movements or blinking (EOG). To improve the accuracy of EEG-based analysis, we propose a novel framework, VME-EFD, which combines Variational Mode Extraction (VME) and Empirical Fourier Decomposition (EFD) for effective EOG artifact removal. In this approach, the EEG signal is first decomposed by VME into two segments: the desired EEG signal and the EOG artifact. The EOG component is further processed by EFD, where decomposition levels are analyzed based on energy and skewness. The level with the highest energy and skewness, corresponding to the artifact, is discarded, while the remaining levels are reintegrated with the desired EEG. Simulations on both synthetic and real EEG datasets demonstrate that VME-EFD outperforms existing methods, with lower RRMSE (0.1358 versus 0.1557, 0.1823, 0.2079, 0.2748), lower ΔPSD in theαband (0.10±0.01 and 0.17±0.04 versus 0.89±0.91 and 0.22±0.19, 1.32±0.23 and 1.10±0.07, 2.86±1.30 and 1.19±0.07, 3.96±0.56 and 2.42±2.48), and higher correlation coefficient (CC: 0.9732 versus 0.9695, 0.9514, 0.8994, 0.8730). The framework effectively removes EOG artifacts and preserves critical EEG features, particularly in theαband, making it highly suitable for brain-computer interface (BCI) applications.